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journal.pcbi.1002785

Universal Pacemaker of Genome Evolution

Genome-wide analysis of distances between orthologous genes in pairs of organisms from a broad range of taxa belonging to all three domains of life ( bacteria , archaea and eukaryotes ) revealed striking similarity between the distributions of these distances ., All these distributions are approximately lognormal , span a range of three to four order of magnitude and are nearly identical in shape , up to a scaling factor 1–3 ., Although many different explanations are possible of this remarkable conservation of evolutionary rate distribution across the entire spectrum of life , the simplest underlying model is that all genes evolve at approximately constant rates relative to each other , i . e . the changes in the gene-specific rates of evolution are strongly correlated genome-wide ., This general model of evolution can be denoted Universal PaceMaker ( UPM ) of genome evolution: all genes in evolving genomes , in each evolving lineage , change their evolutionary rate ( approximately ) in unison although the pacemakers of different lineages need not to be synchronized ., The existence of UPM is compatible with the considerable amount of available data on fast-evolving and slow-evolving organismal lineages , primarily different groups of mammals 4 , 5 ., Conceivably , lineage-specific accelerations and decelerations of evolution can be caused by changes in the effective population size , and such rate changes are indeed expected to equally affect all genes in evolving genomes ., The evolutionary rate has also been linked with other biological features of animals that are collectively denoted life history 5 ., For instance , a genome-wide comparison of the evolutionary rates in the human and mouse lineages has shown that the number of fixed mutations per unit time is about twofold greater in rodents than it is in primates , with the implication that a lineage-specific , genome-wide change of evolutionary rate occurred after the separation of these lineages 6 ., In the same vein , a genome-wide analysis of ratios between the evolutionary rates of orthologous genes in triplets of related bacterial , archaeal and mammalian species revealed near constancy of these ratios , with only a small percentage of gene-specific deviations that were attributed to functional diversification of individual genes 7 ., A systematic study of densely populated phylogenetic trees for 44 mammalian genes has demonstrated clade-specific slowdown of evolution occurring independently in several orders including primates and whales 8 ., Multiple studies of mitochondrial DNA evolution that used extensive samples from numerous taxa also detected consistent lineage-specific rates that differed by as much as an order of magnitude between animal taxa 9 , 10 ., However , in other analyses , striking differences between lineages in the relative rates of evolution of different genes have been discovered , casting doubt on the universality of lineage-specific rates , leading to the idea of ‘erratic evolution’ 11 , 12 ., The plausibility of the UPM notwithstanding , the genome-wide correlations between the evolutionary rates of individual genes also could be explained within the concept of molecular clock which is one of the central tenets of molecular evolution ., In 1962 Zuckerkandl and Pauling discovered that the number of differences between homologous proteins is roughly proportional to the divergence time separating the corresponding species 13 , 14 ., This phenomenon became known as Molecular Clock ( MC ) and has been validated by multiple independent observations 15–18 ., The MC is the basis of molecular dating whereby the age of an evolutionary event , usually the split between lineages ( such as for example humans and chimpanzee ) , is estimated from the sequence divergence using calibration with dates known from fossil record 19–22 ., From the phylogenetic point of view , when genes evolve along a rooted tree under the MC , branch lengths are proportional to the time between speciation ( or duplication ) events and the distances from each internal tree node to all descendant leaves are the same ( ultrametric tree ) up to the precision of the estimation ( the latter being determined by sampling error which is inevitable in comparison of finite-length sequences ) ., Over the 50 years that elapsed since the seminal finding of Zuckerkandl and Pauling , the MC has been shown to be substantially overdispersed , i . e . the differences between the root to tip distances in many or most subtrees of a given tree usually greatly exceed the expectation from sampling error , under the assumption of a Poisson mutational process 23–26 ., Notably , the overdispersion of the MC has been shown to be lineage-specific: the MC in lineages with large effective population sizes is overdispersed to a greater extent than the MC in lineages with small populations implying that deviations from the MC are controlled by selection 27 ., The demonstration of the overdispersion of the MC inspired the relaxed MC model which is a compromise between an unconstrained tree with arbitrary branch lengths and an MC tree 28 , 29 ., Under the relaxed MC , the evolutionary rate is allowed to change from branch to branch but this change is presumed to be gradual so that related lineages evolve at similar rates ., The relaxed MC model underlies most of the modern methods of molecular dating ., The strict MC implies that all orthologous genes present in a group of organisms and sharing the same evolutionary history evolve in a fully coherent manner even if at different rates ., Indeed , if the divergence between gene sequences is solely determined by the divergence time and gene-specific evolution rate , phylogenetic trees reconstructed from different genes will have the same topology and nearly identical branch lengths up to a scaling factor which is equal to the relative evolution rate ., Under the MC model , the differences between the corresponding branch lengths in different gene trees are due solely to the sampling error which arises from stochastic factors and is expected to be uncorrelated between trees ., The relaxed MC model allows greater , non-random deviations in the lengths of corresponding branches but to our knowledge , the possibility that these evolution rate changes are correlated between genes has not been explicitly considered ., The MC implies the constancy of gene-specific relative evolution rates , with deviations caused by overdispersion ., However , the inverse is not true: the deviations of the absolute evolution rates from the clock could be arbitrarily high ( hence no MC ) but , if they apply to all genes in the genome to the same degree , the relative evolutionary rates would remain approximately the same throughout the entire course of evolution and in all lineages ., In other words , the conservation of the evolutionary rate distribution follows from a model of evolution that is more general and less constrained than the MC , namely the UPM model ., Here we sought to determine which of the two models of gene evolution , the MC and or the UPM , better fits the empirical data ., To this end , we performed comparative analysis of phylogenetic trees for a genome-wide set of prokaryotic gene families and compared the goodness of fit for the two models ., The results show that the UPM model is a better fit than the MC model for the evolution of prokaryotes ., These findings are compatible with the previously observed accelerations and decelerations of evolution in individual evolving lineages ., However , we show that synchronous , genome-wide change of evolutionary rates is a universal trend of genome evolution that appears to pervade the entire history of life ., Our data set consisted of the “forest” of phylogenetic trees reconstructed for 6901 orthologous gene families representing 41 archaeal and 59 bacterial genomes 30 ( see Supporting Text S1 ) ., Although horizontal gene transfer is widespread in the evolution of prokaryotes 31 , 32 , the tree-like statistical trend is detectable in the genome-wide data set and moreover dominates the evolution of ( nearly ) ubiquitous gene families 30 , 33 ., We encapsulate this trend in a rooted supertree ( ST ) that reflects the prevalent vertical descent in the evolution of archaea and bacteria ( see Supporting Text S1 ) ., Each individual original gene tree ( GT ) is compared to the ST and reduced to the maximum agreement subtree ( MAST ) , i . e . the largest set of leaves whose phylogeny fits the ST topology ., Removal of discordant nodes and edges leads to collapse of several edges of the original GT into a single edge ( Figure 1 ) ; then , the length of the newly created GT edge is the sum of the original contributing GT edges ., Likewise , when a GT is mapped to the ST , several adjacent ST edges could correspond to a single edge in the reduced GT , forming a composite edge ., Under both the MC and the UPM models , we assume that the lengths of the ST edges determine the expected lengths of the corresponding GT edges ., For the MC model , edge lengths correspond to time intervals between speciation events , the ST is strictly ultrametric , and gene-specific evolutionary rates are measured in substitutions per site per time unit ., Under the UPM model , edge lengths represent arbitrarily defined “ticks” of the universal pacemaker ( internal time ) , and gene-specific evolutionary rates are measured in substitutions per site per pacemaker unit of internal time ., Formally:where li , k is the length of the i-th edge of the k-th GT , tj is length of the j-th ( possibly composite ) ST edge corresponding to the i-th edge of the k-th GT , rk is the gene-specific evolution rate , and εi , k is the multiplicative error factor for the given edge ., We further assume that the error is random , independent for branches both within and between GTs , and comes from a lognormal distribution with the mean of 1 and an arbitrary variance , translating to a model with an additive normally distributed deviation in the logarithmic scale ., Because the distributions of evolutionary rates tend to follow symmetric bell-shaped curves in log scale 3 , 34 , the assumption of a multiplicative , log-normally distributed deviation seems natural ., First , we seek to find the set of ST edge lengths t and gene rates r that provides the best fit to the entire set of GTs ., Under the assumption of a normally distributed deviation , the likelihood function for the set of GTs given t and r iswhere n is the total number of edges in the set of GTs and E2 is the sum of squares of deviations between the expected and observed edge lengths in the logarithmic scale:where the summation for i is done over the edges of a given GT and the summation for k is done over all GTs ( see Supporting Text S2 ) ., Thus , finding the maximum likelihood solution for {t , r} is equivalent to finding the minimum of E2 ., For the MC model , the ST edge lengths t are constrained by the ultrametricity requirement , whereas for the UPM model , ST edge lengths are unconstrained ., For the analyzed set of 100 genomes , there is a choice of several possible ST topologies , produced using different methods ( see Methods and Supporting Figure S1 ) ., We mapped all original GTs onto each of these STs and obtained reduced GTs that corresponded to the respective MASTs ., The GTs that yielded MASTs with fewer than 10 leaves were discarded ., The ST topology derived from the concatenated alignments of ribosomal proteins provided the maximum total number of leaves in the resulting set of reduced GTs and accordingly was chosen for further analysis ., Altogether , we obtained 2294 reduced GTs with MAST size greater or equal to 10 species including 44 , 889 leaves and 82 , 896 edges ., This set of trees was fit to an ultrametricity-constrained ST ( MC model ) and an unconstrained ST ( UPM model ) ( Table 1 , see Supporting Text S3 for details ) ., We then compared the MC and UPM models in terms of the goodness of fit to the data ., Obviously , the residual sum of squares is lower for the UPM model because it involves independent optimization of all 198 ST edge lengths , whereas under the MC model the edge lengths are subject to 99 ultrametricity constraints ., To account for the difference in the numbers of degrees of freedom , we employed the Akaike Information Criterion ( AIC ) and the Bayesian Information Criterion ( BIC ) to compare the MC and UPM models ., Under the assumption of normally distributed deviations:andwhere E2MC and E2UPM are the residual sums of squares for the MC and UPM models , respectively , n is the total number of GT edges and Δd is the difference in the number of parameters optimized in the process of fitting ( in our case Δd\u200a=\u200a−99 ) ., Because lower AIC values correspond to better quality of fit , negative ΔAIC would indicate preference for the MC model whereas a positive ΔAIC would indicate support for the UPM model ., The relative likelihood weight of the suboptimal model can be estimated as 1/exp ( |ΔAIC|/2 ) ., The same calculations were repeated for smaller , more conservative subsets of gene families with MAST>20 and MAST>30 and also using BIC to compare the fit to the UPM and MC models ( Table 1 ) ., Overall , the results presented in Table 1 reveal overwhelming support of the UPM model over the MC model ., The only exception is the ΔBIC value for MAST>30 that weakly supports the MC model ., This outcome is predictable given the much larger number of parameters in the UPM model , the small number of trees in this subset and the heavier penalty that BIC imposes on parameter-rich models 35 ., Thus , the results show that the evolutionary rates tend to change synchronously for the majority ( if not all ) of the genes in evolving genomes although the rate of the UPM relative to the astronomical time differs for different lineages ., The results of this analysis show that the apparent genome-wide constancy of the relative rates of gene evolution across vast spans of lifes history ( Figure 2A ) is not a trivial consequence of MC but at least in part results from a distinct , fundamental evolutionary phenomenon , the UPM ( Figure 2B ) ., The difference between the UPM and MC models is highly significant but small in magnitude ., Root mean square deviation ( r . m . s . d . ) of GT edges from the expectations derived from UMP ST is large ( a factor of 2 . 45 ) and only slightly less that the r . m . s . d for the MC ST ( a factor of 2 . 48 ) ., Thus , similar to MC , the UPM appears to be substantially overdispersed ., To assess the robustness of the finding that UPM fits the GTs better than MC , we isolated the contributions of individual trees to the E2MC and E2UPM ( E2MC , k and E2UPM , k respectively ) , took 1000 bootstrap samples of the set of GTs and computed ΔAIC values for each sample ., All 1000 ΔAIC values obtained for the resampled sets were positive ( in the range of 1511 to 2147 ) , providing 100% support to the superiority of the UPM model and ensuring that this result is consistent for the majority of the GTs and is not determined by a small number of strongly biased trees ( see Supporting Text S3 and Supporting Figure S2 for details ) ., The distribution of the E2MC , k/E2UPM , k ratios ( Figure 3 ) shows a strong bias toward values greater than unity ( 73% of the GTs ) , supporting the robustness of this result ., The E2MC , k/E2UPM , k ratio characterizes the degree to which the k-th GT favors the UPM model ., Linear model analysis shows that this value is significantly and independently influenced by the average goodness of fit to the ST ( p-value ≪0 . 001; Figure 4 ) , the fraction of the original GT leaves remaining in the MAST with ST ( p-value ≪0 . 001; Supporting Figure S3 ) and the number of the original GT leaves ( p-value ≪0 . 001; Supporting Figure S3 ) ., Thus , the GTs that retain a greater number of leaves in the MAST , fit the ST better and are wider distributed among prokaryotes , typically show the strongest preference for the UPM model over the MC model ., These three factors together explain ∼9% of the variance in ln ( E2MC , k/E2UPM , k ) ., Neither the relative evolution rate nor the functional class of the gene significantly impact the degree of preference of UPM over MC ( see Supporting Text S3 and Supporting Figure S3 for details ) ., Interpreting these findings in terms closer to biology , widely-distributed genes that are subject to relatively little horizontal transfer or sporadic changes of evolution rate that reduce the fit to ST appear to make the greatest contribution to the UPM ., These observations imply that the UPM is indeed a fundamental feature of genome evolution , at least in prokaryotes ., The distribution of estimated relative evolution rates ( Figure 5 ) spans values within a range slightly greater than an order of magnitude ( 0 . 26 to 4 . 58 ) ., This range is considerably more narrow than the range of rates measured over short evolutionary distances 3 , 34 ., Accelerations and decelerations of the UPM are likely to average out over long intervals of evolution , reducing the observed differences between genes ., A logical extension of the UPM is a Multiple PaceMakers ( MPM ) whereby a number of uncorrelated pacemakers ‘guide’ their own sets of trees ., In the extreme case , the number of PMs is equal to the number of GTs so that the individual GTs would be completely uncorrelated ., We sought to explore this case in order to determine how well such a degenerate MPM ( dMPM ) model fits the data compared to the UPM and MC ., Formally , under the basic assumptions of this work , the log likelihood of dMPM is infinite because the E2 value is estimated as the sum of squared differences between the observed and the expected edge lengths ., Under dMPM , each edge is equal to its own expectation sothat E2\u200a=\u200a0 ., However , this logic assumes that the tree edge length is measured precisely and is not subject to any error , whereas the E2 value is dominated by deviations of individual GTs from the universal standard ( MC or UPM ) ., This assumption is obviously unrealistic , so to assess the likelihood of the dMPM , one needs to introduce the edge length estimate error explicitly ., To obtain the lower limit on the E2 value induced by the inherent sampling fluctuations , one should note that the sum of the lengths of the 49 , 981 edges in 967 trees ( MAST size ≥20 ) is 13 , 018 . 5 ( substitutions per site ) , on average 0 . 26 per edge ., With the typical prokaryotic protein length being ∼200 amino acids 36 , this translates into the average of ∼52 substitutions per tree branch ., Assuming that substitutions are generated by a Poisson-type random process , one expects the standard deviation of approximately and the “mean” error of the observed value on the order of ( 52+ ) /52\u200a=\u200a1 . 14 or 0 . 13 log units per branch ., Multiplying the square of this value by 49 , 981 edges , we obtain the E2 value estimate of 843 . 0 , much lower than 35065 . 0 for UPM ., It should be noted that the use of the average gene length and the average number of substitutions per branch comprises the ‘best-case scenario’ because variations in both would necessarily introduce larger deviations which would increase the E2 value ., To calculate the ΔAIC value , one needs to obtain the difference in the degrees of freedom between the UPM and dMPM models ., The UPM model uses the estimates of 198 individual edge lengths in one UPM tree plus 967 GT rates; the dMPM model requires 967±198 edge length estimates and no GT rates , yielding Δd\u200a=\u200a−190 , 301 ., Plugging these values into the equation for ΔAIC , one gets the difference of −194 , 269 in the UPM-dMPM comparison ., Thus , the dMPM model is less likely than the UPM model by 83 , 370 orders of magnitude , an obvious indication that the assumption of completely uncorrelated rate changes does not fit the data ., More specifically , the data would support no more than 476 pacemakers for 967 GTs under ideal conditions ( each GT follows its PM perfectly , so the E2 value remains to be solely determined by sampling fluctuations ) ., Thus , the actual number of distinct pacemakers is expected to be much lower ., The results of the genome-wide comparison of phylogenetic trees of prokaryote genes described here show that the UPM model fits the data substantially better than the MC model ., These findings have no bearing on the validity of the MC but show that a more general conservation principle ( the UPM ) is sufficient to explain the observed correlations between gene-specific evolutionary rates ., It seems a natural possibility that UPM is instigated by shifts in population dynamics of evolving lineages , with changes affecting all genes in the same direction and to a similar degree ., In principle , UPM reflects the well-known phenomenon of lineage-specific acceleration-deceleration of evolution ., However , to our knowledge , the previous studies on this phenomenon have focused primarily on mammals and to a lesser extent other vertebrates 4 , 5 ., Here we show that the UPM can explain the correlations between the evolutionary rates of prokaryote genes on the whole genome scale and over time intervals that span effectively the entire history of life on earth ., The discovery of the UPM opens up several areas of further inquiry ., We show here that an unconstrained model of evolution ( dMPM ) does not fit the data but it remains to be determined whether or not distinct pacemakers govern the evolution of different classes of genes ., The biological connotations of the UPM are of major interest ., Mapping UPM shifts to specific stages of the evolution of life , changes in the life style and population structure of organisms as well as to the geological record could become an important direction of future research ., Three distinct supertrees ( STs ) were tested for the purpose of representing the vertical inheritance trend in the analyzed set of GTs ., The first supertree ( ST1 ) was from 30 ( originally computed using the CLANN program 37; the second supertree ( ST2 ) was computed using the quartet supertree method 38 for all species quartets in the complete set of GTs the third supertree ( ST3 ) was derived from a tree of concatenated sequences of ( nearly ) universal ribosomal proteins 39 ., Maximum Agreement Subtrees ( MAST ) between the supertree ( ST ) and any given gene tree ( GT ) were computed using the agree program of the PAUP* package 40 ., The set of MASTs with the analyzed GTs was computed for each of these STs , yielding a total of 43 , 068 MAST leaves for ST1 , 43 , 411 MAST leaves for ST2 and 44 , 889 MAST leaves for ST3 ( MAST ≥10 for each ST ) ., Accordingly , ST3 was used for all further analyses as the topology that best represented the entire set of GTs ., To perform the LS optimization of the ST edge lengths and the GT relative evolution rates , we used the function fmin_slsqp ( ) that is part of the scipy . optimize package of Python which minimizes a function using sequential least squares programming ., The function also adopts a set of constraints that are necessary for the calculation ., In both the MC and the UPM models , both the ST edges and the GT rates were constrained to positive values ., For the UPM model , the distances from a node to any leaf in a subtree under that node were set equal for all subtrees ., It can be shown by induction that this constraint implies an ultrametric tree ., Thus , we have a constraint for every internal node; in a rooted binary tree with m leaves , there are m−1 such nodes ., Consider a rooted supertree ( ST ) with a fixed topology ., The ST encompasses a set of edges e defined by the ST topology and a set of unknown edge lengths t ., Consider a set of unrooted GTs reduced to MAST with the given ST . Each GT encompasses a set of edges with known edge lengths and an unknown gene-specific evolution rate ( bk , lk and rk for the k-th GT , respectively ) ., Each edge of each GT uniquely maps to an ST path ej , that is a subset of adjacent edges in the ST ( bk , i≡ej where ej⊆e for the i-th edge of the k-th GT ) ., Let be the length of the path ej ., We assume that the length of the i-th edge of the k-th GT is related to the length of the corresponding ST path ej:where εi , k is the multiplicative deviation factor for the given edge ., We further assume that the deviation is random , independent for branches both within and between GTs , and comes from a lognormal distribution with the mean of 1 and an arbitrary variance , translating to a model with an additive normally distributed deviation in the logarithmic scale ( i . e . ln εi , k∼N ( 0 , σ2 ) ) ., Given t and r , the expectation for the logarithm of the length of the i-th edge of the k-th GT is:and the likelihood of observing the length li , k is:where E2i , k\u200a= ( ln li , k−ln tj−ln rk ) 2 ., For all observed edge lengths in all GTs ( l ) , the likelihood function isIn the logarithmic scale:where n is the total number of GT edges ( ) ., Designating the residual sum of squares and substituting the estimate for σ2for large n , we obtain:Because n is constant for a given data set , finding the maximum of L ( l | t , r ) is equivalent to finding the minimum of E2 ., Least Squares ( LS ) is called linear if the residuals are linear for all unknowns ., Linear LS can be represented in a matrix format which has a closed form solution ( given that the columns of the matrix are linearly independent ) ., However , our formulation requires taking logs over sums of unknowns in the case where a GT edge corresponds to a path in ST ( ) ., Then , the problem becomes non-linear with respect to LS and can be solved only using numerical algorithms where the solution is obtained by iteratively refining the parameter values ., This approach requires supplying initial values for the parameters ., The goodness of the initial value estimation is critical for the convergence time of the iterative method and the risk of being trapped in local maximum points ., We employed the following strategy for determining the initial values: For each ST edge , we computed the mean value of the sum over all GT edges that uniquely correspond to the given edge ., Therefore , if we assign one gene a specific rate value ( e . g . the length of some edge ) , we obtain initial rate values for all genes ., It can be easily shown that , if there are no errors in rates ( i . e . σ2\u200a=\u200a0 ) , the above procedure yields the accurate ( ML ) values for all unknowns .

Introduction, Results/Discussion, Methods

A fundamental observation of comparative genomics is that the distribution of evolution rates across the complete sets of orthologous genes in pairs of related genomes remains virtually unchanged throughout the evolution of life , from bacteria to mammals ., The most straightforward explanation for the conservation of this distribution appears to be that the relative evolution rates of all genes remain nearly constant , or in other words , that evolutionary rates of different genes are strongly correlated within each evolving genome ., This correlation could be explained by a model that we denoted Universal PaceMaker ( UPM ) of genome evolution ., The UPM model posits that the rate of evolution changes synchronously across genome-wide sets of genes in all evolving lineages ., Alternatively , however , the correlation between the evolutionary rates of genes could be a simple consequence of molecular clock ( MC ) ., We sought to differentiate between the MC and UPM models by fitting thousands of phylogenetic trees for bacterial and archaeal genes to supertrees that reflect the dominant trend of vertical descent in the evolution of archaea and bacteria and that were constrained according to the two models ., The goodness of fit for the UPM model was better than the fit for the MC model , with overwhelming statistical significance , although similarly to the MC , the UPM is strongly overdispersed ., Thus , the results of this analysis reveal a universal , genome-wide pacemaker of evolution that could have been in operation throughout the history of life .

A central concept of evolution is Molecular Clock according to which each gene evolves at a characteristic , near constant rate ., Numerous studies support the Molecular Clock hypothesis in principle but also show that the clock is indeed very approximate ., Genome-wide comparative analysis of phylogenetic trees described here reveals a distinct , more general feature of genome evolution that we called Universal Pacemaker ., Under this model , when the rate of evolution changes , the change occurs synchronously in many if not all genes in the evolving genome ., In other words , the relative rates of gene evolution remain constant across long evolutionary spans: if a gene is slow relative to the rest of the genes in the given lineage , it is always slow , and if it evolves fast , it is always fast ., We show here that the Universal Pacemaker model fits the available data much better than the traditional Molecular Clock model ., These findings are compatible with the previously observed accelerations and decelerations of evolution in individual lineages but we show that synchronous , genome-wide change of evolutionary rates is a global feature of genome evolution that appears to pervade the entire history of life .

biology, computational biology, evolutionary biology

journal.pgen.1002921

New Susceptibility Loci Associated with Kidney Disease in Type 1 Diabetes

Diabetic kidney disease , or diabetic nephropathy ( DN ) , is the leading cause of end-stage renal disease ( ESRD ) worldwide 1 ., It affects approximately 30% of patients with long-standing type 1 and type 2 diabetes 2 , 3 , and confers added risks of cardiovascular disease and mortality ., DN is a progressive disorder that is characterized by proteinuria ( abnormal loss of protein from the blood compartment into the urine ) and gradual loss of kidney function ., Early in its course , the kidneys are hypertrophic , and glomerular filtration is increased ., However , with progression over several years , proteinuria and decline in kidney function set in , and may result in fibrosis and terminal kidney failure , necessitating costly renal replacement therapies , such as dialysis and renal transplantation ., While current treatments that decrease proteinuria will moderately abate DN progression , recent studies show that even with delivery of optimal care , high risks of cardiovascular disease , ESRD and mortality persist 4 , 5 ., Therefore , discovery of genetic factors that influence development and susceptibility to DN is a critical step towards the identification of novel pathophysiologic mechanisms that may be targeted for interventions to improve the adverse clinical outcomes in diabetic patients ., Whereas the degree of glycemia plays a pivotal role in DN , a subset of individuals with poorly controlled type 1 diabetes ( T1D ) do not develop DN ., Furthermore , strong familial aggregation supports genetic susceptibility to DN ., The sibling risk of DN has been estimated to be 2 . 3-fold 6 ., While prior studies of individuals with T1D have reported on the possible existence of genetic associations for DN , results have been inconclusive ., In GENIE , we leveraged three existing collections for T1D nephropathy ( All Ireland Warren 3 Genetics of Kidneys in Diabetes UK Collection UK-ROI , Finnish Diabetic Nephropathy Study FinnDiane , and Genetics of Kidneys in Diabetes US Study GoKinD US ) comprising 6 , 691 individuals to perform the most comprehensive and well powered DN susceptibility genome-wide association study ( GWAS ) and meta-analysis to date , with the aim to identify genetic markers associated with DN by meta-analyzing independent GWAS , imputed to HapMap CEU II ( Table 1 , Figure 1 ) ., As a result , we here present two new loci associated with ESRD and a locus suggestively associated with DN ., The primary phenotype of interest was DN , defined by the presence of persistent macroalbuminuria or ESRD in individuals aged over 18 who had T1D for at least 10-year duration ., Controls were defined as individuals with T1D for at least 15 years but without any clinical evidence of kidney disease ( see Methods for more detailed definitions ) ., Meta-analysis of the DN results from each cohort resulted in five independent signals with P<10−5 ( Table S1 , Figure S1A ) ., In a parallel analysis of ESRD versus non-ESRD ( n cases\u200a=\u200a1 , 399 , n controls\u200a=\u200a5 , 253; referred to as “ESRD” analysis throughout the manuscript , unless otherwise stated ) , SNP rs7583877 on chromosome 2q11 . 2-q12 achieved genome-wide significance ( P\u200a=\u200a4 . 8×10−9 ) , primarily driven by FinnDiane and the UK-ROI samples , along with six other independent signals reaching P<10−5 ( Figure 2A , Table S1 , Figure S1C ) ., We invited investigators responsible for available collections with similar phenotypes to participate in the secondary genotyping phase of the top ranked SNPs ( n\u200a=\u200a41 including proxies , representing 24 independent signals ) from the initial meta-analysis ., Nine independent cohorts contributed 5 , 873 individuals with comparable phenotypic inclusion criteria ( Table S2 ) ., After the combined meta-analysis of the first and second phase cohorts , the association of the intronic SNP rs7583877 in AFF3 with ESRD retained genome-wide significance ( odds ratio OR\u200a=\u200a1 . 29 , 95% confidence interval CI: 1 . 18–1 . 40 , P\u200a=\u200a1 . 2×10−8; Figure 3A ) , with the bulk of the association evidence still provided by the FinnDiane and UK-ROI cohorts ., The population attributable risk PAR for the causal variant underlying the observed association at rs7583877 was estimated to be 3 . 5%–10 . 5% ., AFF3 belongs to the AFF ( AF4/FMR2 ) family and encodes a transcriptional activator , with DNA-binding activity , initially found to be fused with MLL in some acute lymphoblastic leukemia patients 7 , 8 ., Recent evidence points to a role for AFF3 as an RNA-binding protein , with overexpression affecting organization of nuclear speckles and splice machinery integrity 9 ., Variants near AFF3 have been associated with acute lymphoblastic leukemia 10 , rheumatoid arthritis 11 , 12 and recently T1D 13 , 14 ., Another locus between the RGMA ( RGM domain family , member A ) and MCTP2 ( multiple C2 domains , transmembrane, 2 ) genes on chromosome 15q26 also reached genome-wide significance for association with ESRD ( rs12437854 , OR 1 . 80 , 95% CI: 1 . 48–2 . 17 , P\u200a=\u200a2 . 0×10−9; Table 2 , Figure 3B ) ., PAR estimates for this locus varied from 0 . 5% to 4 . 1% ., For the primary DN phenotype , an intronic SNP in the ERBB4 gene demonstrated consistent protective effects in the replication samples and was the top associated SNP identified from the combined discovery and second stage analysis; however , this did not reach genome-wide statistical significance ( rs7588550 , OR 0 . 66 , 95% CI: 0 . 56–0 . 77 , P\u200a=\u200a2 . 1×10−7 , PAR 28 . 3%–32 . 5% for removal of the major risk allele; Table 2 , Figure 3C ) ., ERBB4 encodes an epidermal growth factor receptor subfamily member , and has been implicated in cardiac , mammary gland and neural development 15 , 16 ., Mutations in ERBB4 have previously been reported in cancer 17 ., Several studies using Madin-Darby canine kidney ( MDCK ) cells and conditional ERBB4 overexpression/knock-out mice , suggest a crucial role for ERBB4 in renal development and tubulogenesis 18 , 19 ., It is possible that our observed signal is in linkage disequilibrium with an untyped SNP , or exerts functional effects over an extended genomic region ., To explore a putative biological signature we identified , for the top three SNPs , all genes within a 2 Mb window ( 1 Mb upstream and downstream ) ., Gene ontology analysis revealed no significant enrichment of biological terms or pathways within this subset of flanking genes ( Table S3 ) ., We determined whether any of these genes were differentially expressed in microarray data derived from tubulointerstitial ( n\u200a=\u200a49 ) or glomerular ( n\u200a=\u200a70 ) human early DN renal biopsy material versus pre-transplant renal biopsies from living kidney donors ( n\u200a=\u200a32 ) 20 ., Around rs7583877 ( AFF3 ) , we noted upregulation of LIPT1 and TXNDC9 , while TSGA10 was downregulated in both tubulointerstitial and glomerular enriched kidney biopsies ( Figure 2 and Table S4 ) ., NPAS2 , which flanks rs7583877 ( AFF3 ) , and FAM174B and CHD2 , which flank rs12437854 ( 15q26 ) , were downregulated in glomerular enriched biopsies of DN patients versus control , but remained unchanged in tubulointerstitial biopsies ( Figure 2 and Table S4 ) ., NPAS2 ( neuronal PAS domain protein 2 ) , has been implicated in circadian rhythms in the distal nephron segments , acting as a regulator of kidney function 21 ., Interestingly , mutations in chromodomain helicase DNA binding protein 2 ( CHD2 ) , encoding a chromatin-remodeling enzyme , result in impaired glomerular function in mice 22 ., Furthermore , at the rs7588550 ( ERBB4 ) locus expression of ERBB4 was down , and SPAG16 upregulated in tubulointerstitial enriched kidney biopsy tissue of DN versus control subjects ( Figure 2 and Table S4 ) ., We also examined whether any of the top three SNPs modulated expression of neighboring genes in cis in a dataset of glomerular and tubulointerstitial kidney biopsies of Pima Indians with type 2 diabetes and DN who had been genotyped on the Affymetrix 6 . 0 array 23 ., In Pima Indians , no adequate proxies ( haplotype-based D′≥0 . 8 ) for the Affymetrix 6 . 0 SNPs that were strongly correlated with GWAS findings ( r2≥0 . 8 ) could be found for rs12437854 , and expression of AFF3 was below detectable thresholds in this dataset; however , two SNPs in the same intron of ERBB4 as rs7588550 ( rs17418640 and rs17418814 ) were associated with genotype-specific expression of ERBB4 in tubulointerstitial but not in glomerular tissue in the Pima cohort ( P<0 . 05; Figure S2 ) ., Follow-up work is required to investigate the DN associated and eQTL signals in this ERBB4 intron ., To explore the potential functional role of these ERBB4 SNPs , we looked for other genes whose expression is correlated with that of ERBB4 ., A total of 388 ERBB4-correlated genes were found in the Pima population ( Benjamini-Hochberg Q-value<0 . 1 ) ., Pathway analysis of these genes indicates coexpression of ERBB4 with collagen-related genes , which have been implicated in renal fibrosis 24 , 25 ( Genomatix Pathway System; Table S5 ) ., Because the low expression level of AFF3 limited exploration of this gene using expression data , we pursued additional functional experiments in an in vitro model of renal fibrosis , namely human tubular epithelia exposed to transforming growth factor-β1 ( TGF-β1 ) ., Low-level basal expression of the AFF3 mouse homologue ( LAF4 ) has been reported in kidney tubules during embryonic development 26 suggesting proximal renal tubule epithelial cells may be suitable for detection and functional interrogation of AFF3 ., TGF-β1 is implicated in the development of diabetic glomerulosclerosis , and there is recent appreciation of its role as a key driver of tubulointerstitial fibrosis ., TGF-β1 induces epithelial cell de-differentiation into a more mesenchymal-like phenotype , characterized by a switch in predominant cadherins from E-cadherin ( epithelial ) to N-cadherin ( mesenchymal ) , and increased vimentin , α-smooth muscle actin , connective tissue growth factor ( CTGF ) and Jagged 1 27 , 28 ., TGF-β1-mediated loss of E-cadherin in renal epithelia , is believed to be mediated through loss of miR-192 expression 29 ., We and others have previously shown that Jagged 1 , a ligand for multiple Notch receptors , is up-regulated in human diabetic kidney disease 30 , 31 , with the Notch signaling pathway implicated in driving renal fibrosis 32 , 33 ., CTGF is a member of the CCN protein family , with biological roles in differentiation and tissue repair ., CTGF is induced by TGF-β1 and enhances expression of multiple extracellular matrix proteins observed in DN , including collagens and fibronectin , and CTGF expression is elevated in the glomeruli of STZ ( streptozotocin ) - treated rats , an in vivo model of T1D 34 ., Basal AFF3 expression was detectable in HK-2 cells , and expression levels were upregulated upon stimulation with TGF-β1 ( 5 ng/ml; 48 h ) , as measured at protein and RNA level ( Figure 4A–4B ) ., Inhibition of AFF3 by siRNA attenuated the expression of TGF-β1-driven markers of fibrosis - CTGF and N-cadherin ( Figure 4C–4E ) ., Taken together , these data suggest that AFF3 may play a role in TGF-β1-induced fibrotic responses of renal epithelial cells ., Traditionally , DN has been viewed as a continuous trait with onset at microalbuminuria , progression to macroalbuminuria , loss of GFR , and culmination in ESRD ., Recent studies have called this paradigm into question , suggesting that the syndrome may perhaps be composed of varying phenotypes 35 , 36 ., Association of rs7583877 ( AFF3 ) and rs12437854 ( RGMA – MCTP2 ) with the different stages of DN was tested on a time-to-event analysis of relevant endpoints using longitudinal data for participants in the FinnDiane discovery collection ., Consistent with our case-control GWAS analyses , the strongest association for rs7583877 was observed for the time from T1D diagnosis to development of ESRD ( hazard ratio HR 1 . 33 , 95% CI: 1 . 18–1 . 49 , P\u200a=\u200a1 . 9×10−6 ) , but also the time from T1D diagnosis to development of macroalbuminuria ( HR 1 . 15 , 95% CI: 1 . 04–1 . 27 , P\u200a=\u200a0 . 006 ) and the time from macroalbuminuria to ESRD ( HR 1 . 16 , 95% CI: 1 . 01–1 . 36 , P\u200a=\u200a0 . 04 ) reached nominal significance ., Similarly , rs12437854 was associated with time from T1D diagnosis to development of macroalbuminuria ( HR 1 . 31 , 95% CI: 1 . 03–1 . 67 , P\u200a=\u200a0 . 03 ) and ESRD ( HR 1 . 35 , 95% CI: 1 . 02–1 . 77 , P\u200a=\u200a0 . 03 ) ( Text S1 , Table S6 , Figure S3 ) ., When we studied these SNPs and their association with various DN-related phenotypes in the case-control setting of the discovery cohorts , similar observations were made supporting the role of these SNPs in the development of ESRD: Whereas we found evidence of association between rs7583877 ( AFF3 ) and all the examined phenotypes with ESRD as the case definition , only moderate association was observed for the DN phenotype ( OR\u200a=\u200a1 . 14 , P\u200a=\u200a0 . 002 ) and no association when patients with macroalbuminuria were compared to controls with normoalbuminuria ( OR\u200a=\u200a1 . 00 , P\u200a=\u200a0 . 95 ) ., rs12437854 ( RGMA – MCTP2 ) had the strongest association with the original ESRD phenotype ( controls defined as all non-ESRD subjects ) and with the ESRD vs . normoalbuminuria phenotype , and moderate association with the DN phenotype and comparison of ESRD vs . macroalbuminuric patients ( Table S7 ) ., An alternative explanation for our ESRD findings may be that the associated variants in AFF3 gene and on chromosome 15q26 might be markers of survival ., Mortality rates are extremely high in patients with kidney disease and macroalbuminuria , with at least 25% of macroalbuminuric patients dying before they reach ESRD 37 ., Thus , the selection of patients with ESRD may be biased towards selection of severe kidney disease survival ., To address this question , we used the time until death as the final end point in the longitudinal analysis ., Neither of the loci associated with ESRD was also associated with mortality ( Text S1 , Table S6 , Figure S3 ) , suggesting that these loci are associated with ESRD per se ., To explore whether these SNPs contribute to DN via related intermediate phenotypes , such as adiposity , fasting lipid levels , or blood pressure we performed in silico searching of publicly available GWAS datasets for our top SNPs 38–41 ., We found nominal , directionally consistent associations of rs12437854 with fasting glucose ( P\u200a=\u200a0 . 03 ) 42 and of rs7583877 with waist-hip ratio ( P\u200a=\u200a0 . 04 ) 43 ( Table S8 ) ., We also considered if previously published T1D and CKD SNP associations were associated with DN or ESRD in our GWAS meta analyses ., Eight of 80 SNPs at T1D-associated loci showed nominal significance with DN or ESRD ( including three at AFF3 that are in weak LD r2 0 . 030–0 . 046 in CEU with the SNPs described here ) , while no CKD SNPs were nominally significant ( Table S9 ) 44–47 ., The lack of association with DN for CKD-associated SNPs suggests that the genetic risk factors for DN may differ from the genetic risk factors for CKD in a nondiabetic population ., Finally , to generate further biological hypotheses based on our GWAS results , we employed MAGENTA 48 gene set enrichment analysis software integrating Gene Ontology ( GO ) terms , KEGG and Ingenuity pathways and PANTHER database entries ( Table S10 ) ., In the analysis of DN as a case phenotype , enriched gene sets included “sugar binding” ( P\u200a=\u200a0 . 0006 ) , “double stranded DNA binding” ( P\u200a=\u200a0 . 001 ) and “nucleic acid binding” ( P\u200a=\u200a0 . 004 ) ., In the analysis of ESRD significantly enriched gene sets ( P<0 . 01 ) included an enrichment of terms associated with DNA binding , including “sequence-specific DNA binding” ( P\u200a=\u200a0 . 003 ) , “positive regulation of transcription” ( P\u200a=\u200a0 . 003 ) , and “homeobox transcription factor” ( P\u200a=\u200a0 . 004 ) ., Taken together , the principal biological signal found within GWAS data suggests an enrichment of transcriptional regulators ., In this largest meta-analysis to date of DN from individuals with T1D , we found two genome-wide significant associations with ESRD ., Variants in AFF3 have been shown to be associated with juvenile idiopathic rheumatoid arthritis , Graves disease , celiac disease and T1D , indicating this may be a pan-autoimmune disease gene ., It is possible that the AFF3 signal represents an association with T1D and/or is a false positive finding , as it was not seen in the follow-up cohorts ., However , we note the following:, 1 ) both FinnDiane and UK-ROI yielded very similar association results ,, 2 ) the number of ESRD cases in the replication cohorts is small ( n\u200a=\u200a363 ) , indicating that statistical power to replicate the original association is limiting ,, 3 ) the association result in the second stage , while non-significant , trends in a consistent direction ( OR 1 . 11 ) ,, 4 ) after evaluating >12 , 000 individuals the AFF3 signal remained genome-wide significant ( P\u200a=\u200a1 . 2×10−8 ) , and, 5 ) we have provided supportive functional evidence that suggests AFF3 may be a relevant contributor to renal disease ., Although survival bias is a possibility in the analyses of ESRD , longitudinal analysis revealed the association of the AFF3 and chromosome 15q26 loci with renal end-points and not with death ., Experimental models provide independent evidence of AFF3 involvement in renal fibrosis and support an association of this locus with a renal phenotype ., Importantly , despite our large sample size , we did not achieve genome-wide statistical significance for DN using a combined proteinuria/ESRD phenotype , suggesting that this phenotype may have been too heterogeneous to detect significant associations with a sample of this size ., For example , lifelong glycemic control , a known risk factor for DN , is not well captured in most existing cohorts ., Nevertheless , this study is the largest , well powered GWAS on DN to date ., We demonstrated a suggestive signal of association at ERBB4 that is supported by experimental data showing haplotype specific mRNA expression in DN biopsies ., Our findings reinforce the need for additional studies of patients with T1D and a homogeneous renal phenotype , in whom additional GWAS , fine-mapping and sequencing to uncover rare variants could be performed ., Integration of our findings with ongoing GWAS in both type 1 and type 2 diabetes DN may also lead to discovery of additional genetic determinants of DN ., The traditional phenotypic definition of DN for individuals with type 2 diabetes may be even more challenging for genetic studies given the heterogeneity of vascular complications and differential renal diagnoses ., Several larger-scale GWAS have now been conducted for renal phenotypes 49–56 , however in most cases the true disease-causing variant and functional impact for specific phenotypes remains to be established ., Encouraging reports include the association of uromodulin with CKD 57 , MYH9/APOL1 with non-diabetic ESRD 58 , 59 , and PLA2R1 with membranous nephropathy , where anti-PLA2R antibodies appear to predict activity of the disease as well as response to therapy 60 ., Our findings point to two transcriptional networks centered around AFF3 and ERBB4 that may be operational in the pathogenesis of kidney disease in diabetes ., All human research was approved by the relevant institutional review boards , and conducted according to the Declaration of Helsinki ., We implemented a two stage analysis , in which a GWAS was performed using a set of three discovery cohorts in the GENIE consortium , and top signals for the DN and ESRD analyses were analyzed further in the second phase in a set of nine independent cohorts ( described below ) with 5 , 873 patients in total ., The patient numbers in the individual studies are given in Table S11 ., Additional details are provided in the online material Text S1 ., Inclusion criteria included white individuals with T1D , diagnosed before 31 years of age , whose parents and grandparents were born in the UK and Ireland ., The case group comprised 903 individuals with persistent proteinuria ( >500 mg/24 h ) developing more than 10 years after the diagnosis of diabetes , hypertension ( >135/85 mmHg and/or treatment with antihypertensive medication ) , and retinopathy; ESRD ( 27 . 2% ) was defined as individuals requiring renal replacement therapy or having received a kidney transplant ., Absence of DN was defined as persistent normal urine albumin excretion rate ( AER; 2 out of 3 urine albumin to creatinine ratio ACR measurements <20 µg of albumin/mg of creatinine ) despite duration of T1D for at least 15 years , while not taking an antihypertensive medication , and having no history of treatment with ACE inhibitors; 1 , 001 individuals formed the control group ., After exclusion of patients with low quality DNA samples , 914 DN/ESRD cases and 956 controls remained for the GWAS ., The FinnDiane study is a Finnish cohort of more than 4 , 800 adult ethnic Finns with T1D , recruited from across Finland , diagnosed prior to age 35 and insulin treatment begun within 1 year ., This study comprises 1 , 721 patients with normal AER , 516 with microalbuminuria , 733 with macroalbuminuria and 682 with ESRD ., The disease status was defined by urine AER or urine ACR in at least two out of three consecutive urine collections at local centers: Microalbuminuria was defined as AER≥20<200 µg min−1 or ≥30<300 mg/24 h or an ACR of 2 . 5–25 mg mmol−1 for men and 3 . 5–35 mg mmol−1 for women in overnight , 24-hour or spot urine collections , respectively ., Similarly , the limit for macroalbuminuria was AER≥200 µg min−1 or ≥300 mg/24 h or ACR≥25 mg mmol−1 for men and ≥35 mg mmol−1 for women ., ESRD was defined as ongoing dialysis treatment or transplanted kidney ., Control patients with normal AER were required to have T1D duration of at least 15 years ., 558 of these patients were included from an independent Finnish cohort collected by the National Institute of Health and Welfare ., These patients met the FinnDiane diagnosis and selection criteria , and were analyzed together with the FinnDiane cohort ., The GoKinD US study consists of a DN case-control cohort of individuals diagnosed with T1D prior to 31 years of age who began insulin treatment within 1 year of T1D diagnosis ., Controls were 18–59 years of age , with T1D for at least 15 years but without DN , n\u200a=\u200a889 ., DN definition includes individuals with ESRD , dialysis or kidney transplant and persistent macroalbuminuria ( at least 2 out of 3 tests positive for albuminuria by dipstick ≥1+ , or ACR>300 µg albumin/mg of urine creatinine ) ., Cases were defined as people 18–54 years of age , with T1D for at least 10 years and DN , n\u200a=\u200a903 ., Individuals recruited to the control group employed the same inclusion criteria as UK-ROI ., Individuals were recruited at two study centers , George Washington University ( GWU ) and the Joslin Diabetes Centre ( JDC ) using differing methods of ascertainment and recruitment 64 ., Analysis of the GoKinD US cohort was limited to individuals whose primary ethnicity was Caucasian ., DNA was sought from worldwide case-control collections of individuals with T1D and known renal status ., A total of 5 , 873 individuals from nine independent collections were genotyped or imputed for the top-ranked SNPs ( n\u200a=\u200a41 including 17 proxies ) , with the exception of the DCCT/EDIC cohort where GWAS data was imputed ., All the patients included in the phase two analysis were adults of European descent and had T1D diagnosed before 35 years of age ., Controls with normal AER had duration of T1D at least 15 years , and cases with DN had minimum T1D duration of 10 years ., If a collection included patients with microalbuminuria , they were excluded from the primary analysis of DN , but included as controls in the analysis of ESRD versus non-ESRD ., The main clinical characteristics of all the replication cohorts are shown in the Table S2 and the cohorts are described in Text S1 ., The primary phenotype of interest was DN , defined as individuals aged over 18 , with T1D for at least 10 years and diabetic kidney disease ., DN includes ESRD or persistent macroalbuminuria as defined in the cohort descriptions above ., Controls were defined as individuals with T1D for at least 15 years but without any clinical evidence of kidney disease ., Individuals with microalbuminuria were excluded from the primary DN analysis in all cohorts ., Disease status definitions were consistent across all the study cohorts ., Details of clinical characteristics for each cohort are defined in Table 1 and Table S2 ., We evaluated a second phenotype to gain further insights into the genetic basis of the most severe form of DN ( leading to ESRD ) , and compared ESRD cases to all those without ESRD ., This phenotype is referred to as the “ESRD” or “ESRD vs . non-ESRD” phenotype throughout the manuscript ., We also considered individuals with ESRD compared to T1D controls with no clinical evidence of DN ., Results for this comparison are given in the online supporting material ( Tables S1 , S6 , S7 , S9 , S10 ) , where this contrast is called “ESRD vs . normoalbuminuria” or “ESRD vs . normo” ., DNA from individuals in the UK-ROI collection were genotyped using the Omni1-Quad array ( Illumina , San Diego , CA , USA ) while FinnDiane samples employed Illuminas BeadArray 610-Quad array ., Samples in UK-ROI and FinnDiane were excluded if they had insufficient DNA quality , quantity or poor genotype concordance with previous genotypes during the fingerprint evaluation stage ., Existing genotype data for the GoKinD US genotype data was downloaded from dbGAP ( phs000018 . v2 . p1 , retrieved June 2010 ) , containing updated genotype data from Affymetrix 500 K set ( Affymetrix , Santa Clara , CA , USA ) ., Samples for UK-ROI and FinnDiane were excluded for insufficient DNA quality , quantity or poor genotype concordance with previous genotypes during a fingerprint evaluation stage ., In the UK-ROI sample , 1 , 830 unique case ( n\u200a=\u200a872 ) and control ( n\u200a=\u200a958 ) individuals were submitted for genotyping on the Omni1-Quad ., For FinnDiane , 3 , 651 individuals ( cases , n\u200a=\u200a1 , 934; controls n\u200a=\u200a1 , 721 ) were submitted for genotyping on the 610-Quad ., For all three discovery datasets ( UK-ROI , FinnDiane , GoKinD US ) , uniform and extensive genotype quality control procedures were applied: SNPs were filtered for those with call rates greater than 90% , minor allele frequency ( MAF ) exceeding 1% , and concordance with Hardy Weinberg Equilibrium ( HWE , P<10−7 ) ., Sample filters included individual call rates greater than 95% , no extreme heterozygosity and cryptic relatedness as determined using identity by descent ( first degree relatives , estimated identity by descent >0 . 4 ) , and admixture assessment using principal components ( plotted with HapMap reference panel , Figure S4 ) ., Additional quality control measures included test of missing by haplotype ( P<10−8 ) , missing by phenotype ( P>10−8 ) and plate effects ( P<10−7 ) ., These quality control steps were performed using PLINK 65 with custom Perl and R analysis scripts ., Known copy number variation and mitochondrial SNPs were excluded from analyses ., Detailed results of each QC step are reported in Table S12 for each study population ., A HapMap control sample was included on all genotyping plates for UK-ROI; average call rate was 99 . 9% with HapMap concordance equaling 99 . 7% ., The average sample call rate was 99 . 5% in UK-ROI with sample heterozygosity 22 . 1% ., Concordance with internal control for FinnDiane was 99 . 996% with an average sample call rate of 99 . 8% ., Principal Component Analysis ( PCA ) was performed separately for each of the three studies with the EIGENSTRAT program 66 in order to detect genetic outliers and to adjust the analyses for population structure ., Genetic outliers were defined as more than six standard deviations away from the center of distribution along any of the ten first principal components and the procedure was repeated until no outliers were detected ., After filtering , PCA were calculated for each study cohort combined with unrelated individuals from three original HapMap populations ( www . hapmap . org ) , and plotted to identify additional admixed individuals ., The first ten principal components were employed to adjust the association analysis for any residual population structure from the cleaned datasets ., In total , directly genotyped results for 823 cases and 903 controls in 791 , 687 SNPs passed QC procedure in UK-ROI ., Similarly , 549 , 530 SNPs with average genotyping rate of 99 . 9% passed the QC filters in 1 , 319 cases , 1 , 591 controls and 460 individuals with microalbuminuria for FinnDiane ., 360 , 899 SNPs in 774 cases and 821 controls for GoKinD US passed quality control and were included in the analysis ., Imputation was performed after the quality control employing MACH 1 . 0 software ( http://www . sph . umich . edu/csg/abecasis/MACH ) with HapMap phase II CEU population as a reference , resulting in ∼2 . 4 million SNPs for each cohort ., The cross-over and error rates were estimated with 50 iteration rounds in roughly 300 randomly selected samples ., The imputation was run with the greedy algorithm and the maximum likelihood method in order to obtain expected allele dosages rather than integer allele counts ., SNPs with low imputation quality ( r2<0 . 6 ) are not reported ., PLINK v1 . 07 67 was employed to conduct association tests for the allele dosage data with logistic regression adjusted for sex , age , the duration of diabetes and the ten first components of the study specific principal component analysis ., UK-ROI and GoKinD US were adjusted for study center , but in the primary DN phenotype the two GoKinD US centers; GWU and JDC , were analyzed separately ., Results from individual studies were adjusted for study specific genomic inflation factor and then combined by fixed effect meta-analysis model using METAL 68 , to estimate the combined effect sizes and significances from beta values and standard error ., Regional association plots were generated using hg18 in LocusZoom 69 ., Quantile-Quantile plots were generated to evaluate the number and magnitude of observed associations compared with those expected under the null hypothesis ( Figure S1 ) ., All SNPs observed with P<10−5 were selected for further analysis ., These SNPs were reviewed and a top SNP ( with a proxy ) was selected for each independent signal ( SNPs more than 500 kb distant or LD r2<0 . 3 in HapMap II CEU ) using the LD-based clumping procedure implemented in PLINK ., De novo genotyping was performed for all phase two cohorts except for DCCT/EDIC using identical designs of Sequenom IPLEX assays ( Sequenom Inc , San Diego , US ) ., The DCCT/EDIC samples were imputed from their GWAS results that had undergone their respective quality control procedure ., The statistical analysis was similar to the discovery cohorts with the difference that the models were not adjusted for principal components ., All results were then combined by meta-analysis using METAL software as previously described ., Time to event analyses were performed on longitudinal data from the FinnDiane discovery cohort using Kaplan-Meier and Cox proportional hazards regression with the aim to evaluate the genetic association of rs7583877 and rs12437854 with time from the diagnosis of T1D to the onset of the following end points: microalbuminuria , macroalbuminuria or ESRD ., Additionally , we analyzed time from onset of macroalbuminuria to development of ESRD ., The most recent kidney status data were utilized for each patient ., We also examined if the two main association loci , rs7583877 and rs12437

Introduction, Results/Discussion, Methods

Diabetic kidney disease , or diabetic nephropathy ( DN ) , is a major complication of diabetes and the leading cause of end-stage renal disease ( ESRD ) that requires dialysis treatment or kidney transplantation ., In addition to the decrease in the quality of life , DN accounts for a large proportion of the excess mortality associated with type 1 diabetes ( T1D ) ., Whereas the degree of glycemia plays a pivotal role in DN , a subset of individuals with poorly controlled T1D do not develop DN ., Furthermore , strong familial aggregation supports genetic susceptibility to DN ., However , the genes and the molecular mechanisms behind the disease remain poorly understood , and current therapeutic strategies rarely result in reversal of DN ., In the GEnetics of Nephropathy: an International Effort ( GENIE ) consortium , we have undertaken a meta-analysis of genome-wide association studies ( GWAS ) of T1D DN comprising ∼2 . 4 million single nucleotide polymorphisms ( SNPs ) imputed in 6 , 691 individuals ., After additional genotyping of 41 top ranked SNPs representing 24 independent signals in 5 , 873 individuals , combined meta-analysis revealed association of two SNPs with ESRD: rs7583877 in the AFF3 gene ( P\u200a=\u200a1 . 2×10−8 ) and an intergenic SNP on chromosome 15q26 between the genes RGMA and MCTP2 , rs12437854 ( P\u200a=\u200a2 . 0×10−9 ) ., Functional data suggest that AFF3 influences renal tubule fibrosis via the transforming growth factor-beta ( TGF-β1 ) pathway ., The strongest association with DN as a primary phenotype was seen for an intronic SNP in the ERBB4 gene ( rs7588550 , P\u200a=\u200a2 . 1×10−7 ) , a gene with type 2 diabetes DN differential expression and in the same intron as a variant with cis-eQTL expression of ERBB4 ., All these detected associations represent new signals in the pathogenesis of DN .

The global prevalence of diabetes has reached epidemic proportions , constituting a major health care problem worldwide ., Diabetic kidney disease , or diabetic nephropathy ( DN ) —the major long term microvascular complication of diabetes—is associated with excess mortality among patients with type 1 diabetes ., Even though DN has been shown to cluster in families , the underlying genetic and molecular pathways remain poorly defined ., We have undertaken the largest genome-wide association study and meta-analysis to date on DN and on its most severe form of kidney disease , end-stage renal disease ( ESRD ) ., We identified new loci significantly associated with diabetic ESRD: AFF3 and an intergenic locus on chromosome 15q26 residing between RGMA and MCTP2 ., Our functional analyses suggest that AFF3 influences renal tubule fibrosis , a pathological hallmark of severe DN ., Another locus in ERBB4 was suggestively associated with DN and resides in the same intronic region as a variant affecting the expression of ERBB4 ., Subsequent pathway analysis of the genes co-expressed with ERBB4 indicated involvement of fibrosis .

genome-wide association studies, medicine, rna interference, chronic kidney disease, diabetic endocrinology, gene expression, endocrinology, diabetes and endocrinology, biology, genetics, diabetes mellitus type 1, genomics, genetics of disease, nephrology, genetics and genomics, human genetics

journal.pcbi.1002017

Dynamic Phenotypic Clustering in Noisy Ecosystems

Species abundances and their variation over time are quantities of fundamental importance in any ecosystem: understanding the forces that shape them is a key part of central problems in ecology , ranging from conceptual questions about the role of neutral processes 1 , 2 to practical issues in biodiversity conservation 3 ., One major driver of changes in species abundances is environmental influences which vary across time and space , such as the weather 4–6 ., A classic example of an ecological phenomenon caused by such environmental noise is the Moran effect , the tendency for a shared fluctuating environment to synchronize the variations in abundance among species and across space 7–10 ., This effect has now been studied in systems with colored noise 11–13 and species dispersal 14 , and in small food webs 15–19 ., The synchronizing effect of noise , however , is opposed by negative interactions between species ( e . g . through resource competition or predation ) which cause compensatory dynamics: when the abundance of one species increases , the abundance of others tend to decrease , creating out-of-step variations 20 ., Although significant progress has been made towards quantifying the total impact of each of these factors 21–23 , it remains unknown how the tension between them influences the dynamics in natural ecosystems ., In such systems , many phenotypically distinct species are embedded in a tangled web of direct and indirect interactions that make it hard to predict the effect of even simple disturbances 24–26 , and non-trivial collective effects could play a significant role ., For instance , even in the absence of noise species interactions can lead to static , clumped patterns across phenotype space 27 , providing a possible explanation for the widely observed tendency for species in a given ecosystem to cluster around a few preferred body sizes 28 , 29 ., Such phenotypic patterns could be ubiquitous but have received relatively little attention 30 ., The idea that the interplay between environmental noise and inter-species interactions could lead to non-trivial effects is supported by both theoretical and empirical studies of ecosystem dynamics ., Even single- or few-species ecological models exhibit a range of complex behaviors , including bifurcations and chaos 31 , strong amplification of environmental noise 32–34 , noise-induced oscillations 35 , 36 , and pattern formation driven by demographic fluctuations 37 ., Empirical observations in nature and laboratory experiments have similarly revealed complex dynamics , including chaotic behavior 38 , 39 , environmental noise and density-dependence intermingling in determining single species abundances 9 , and cases where synchrony in the abundance of a single species across landscapes propagates down a food-web 40 ., In this article , we show that environmental noise can indeed lead to robust , dynamic patterns in phenotype space ., We introduce a simple model of the combined effect of noise and competition in an ecosystem with many species differing in their reliance on growth rate and efficiency , respectively , for survival ., To focus on dynamically emerging patterns rather than on pre-imposed niche differences , we use a minimalist patch-model framework in which all species compete for a single resource and undergo periodic , global dispersal between the patches ., Each species is entirely defined simply by its rate of growth and its efficiency in turning resources into offspring ., We start by considering the model behavior in a fixed environment , showing that it allows many species to coexist stably ., We then introduce external environmental noise and show that it gives rise to systematic and robust alternating patterns of species-species correlations which are accompanied by the formation of dynamic clusters of abundant species in phenotype space ., Finally , we show that these patterns directly reflect a balance between the tendency of noise to synchronize different species and the tendency of competitive interactions to create abundance-differences ., Our patch model is similar to both the theoretical model proposed by Wilson 41 and to ( the metapopulation version of ) the experimental yeast system of MacLean and Gudelj 42 ., The specific formulation was inspired by the rich microbial communities found in soil ( which exhibit many of the same broad ecological patterns as macroscopic species 43 ) , but its basic features – patchiness , repeated environmental disturbances , and the presence of a range of different phenotypic strategies – are shared by many ecosystems ., In this sense , for instance , our model is similar to a model of competition between grasses analyzed by Tilman 44 , 45 ., Hence , we believe that our conclusions will also be relevant to many macroscopic ecosystems ., A key feature of the soil environment , as experienced by microbes , is its granular nature , with dividing cells typically found in separated pockets in the soil matrix 46 ., These communities are not static: cells are constantly dispersed by weather and fresh resources are added and washed away continuously ., Our model describes an ecosystem of N species competing for a single resource on multiple patches containing a fixed amount of the resource ( Figure 1 ) ., The dynamics consists of repeated , two-phase cycles of local reproduction of individuals on their patches until the resource is depleted , followed by global dispersal to fresh patches ( representing periodic environmental influence due to e . g . rainwater ) ., The appearance of full nutrient patches can represent either the dispersal to existing but hitherto unoccupied locations or the addition of new resource by the environmental disturbance ( e . g . deposited by water flow ) ., Each species is described by two basic metabolic parameters , growth rate and efficiency 47 , allowing us to consider the behavior of many species spread along continuous phenotype axes ., Since efficiency would not confer an advantage unless resource availability is what limits growth , the model assumes that dispersal happens only after all resources have been exhausted ., This assumption applies whenever disturbances are rare compared to the typical rates of growth , either because the dispersal events are intrinsically spaced out or because the resources are so finely divided that they only support short bursts of growth ., An example of the first case is ecosystems where dispersal represents a yearly occurrence ( e . g . for seeding plants ) , while the second case is likely to apply to e . g . microbes feeding off scattered organic matter in soil or the ocean ( ‘marine snow’ 48 ) ., For simplicity , we assumed that all nutrient patches are identical and always contain the same amount of resource at the beginning of a cycle ., We also worked in the limit of infinitely many patches and hence infinitely large populations , allowing us to consider the impact of environmental noise on species abundance without complications due to demographic stochasticity ., Growth cycle number t starts with a global seeding pool in which the abundance per patch of each species is given by the vector n ( t ) = ( n1 ( t ) , n2 ( t ) , … , nN ( t ) ) ., From this pool , a fraction α of individuals randomly gets seeded onto a new collection of patches , while the remaining fraction , ( 1−α ) , of the cells is washed out of the system ., We assumed α is very small so that the probability that a patch receives a total of m1 individuals of species 1 , m2 of species 2 etc . is a product of Poisson probabilities: ( 1 ) where m\u200a= ( m1 , m2 , … , mN ) ., The two traits characterizing each species are: ( 1 ) growth rate , μ – the rate of exponential reproduction on a nutrient patch while resources are available , and ( 2 ) efficiency in turning nutrients into offspring , Y – the number of offspring that can be produced by a single individual if it consumes all the resource on a patch ., After seeding , each individual of species k starts replicating at rate μk while consuming the shared resource on its patch at a rate of 1/Yk units per offspring ., Growth on a given patch stops when the resource on that patch is depleted ., The time at which this happens ( T ) is a function of the initial abundance of each species on the patch , as well as of their growth rates and efficiencies , i . e . T\u200a=\u200aT ( m;μ , Y ) , where the vectors μ and Y represent the growth and efficiency parameters for all species , respectively ( see Methods ) ., The final abundance of species k , averaged across all patches with this seeding , is then simply ( 2 ) Since the interval between dispersal events is assumed to be longer than all growth-times , only the final abundances matter ., The new average per-patch abundances , n ( t+1 ) , after all growth has stopped is found by averaging these final abundance over all possible seeding configurations: ( 3 ) where f ( m ) = ( f1 ( m ) , f2 ( m ) , … , fN ( m ) ) ., Equation 3 is the fundamental dynamical equation for the per-patch abundances at the end of growth phase ., It expresses the fact that final species abundances in one cycle determine the abundances in the next by setting the probabilities of the various possible initial seedings ., Details of the model and simulations are given in the Methods section ., We note that dispersal and the availability of new resources are assumed to be linked ., Such linkage is natural if both are driven by the same external factor ( e . g . rainfall dispersing bacterial cells and depositing new resources ) or if one of them is driving the other ., For instance , dispersal can effectively generate new resources if empty patches with new resources are always available and are simply being invaded by dispersal ., While models of competition for a single resource typically lead to competitive exclusion – a single species comes to dominate and drives all others extinct 49 , 50 – division into patches can allow many species to coexist 45 , 51 ., Indeed , numerical simulations of our model for fixed α showed that many species can be stably maintained ( Figure 2 ) , and it can be argued explicitly that arbitrarily many species can coexist if the amount of resource on each patch is very large ( see Methods ) ., The stabilizing mechanism that makes coexistence possible can be understood as a frequency-dependent selection during the growth-phase ., When the total population density fluctuates up , patches are more likely to be seeded with more species , which intensifies competition and promotes selection for fast growth ., If fast-growing species are also less efficient , their increased frequency drives the total population density back down ., Conversely , when the population density is decreased , species have a higher probability of growing on patches with few or no competitors ., This allows high-efficiency species to grow to high densities even if they are growing slowly , leading to an increase in the overall population ., These growth-phase selection pressures – favoring speed ( μ ) and yield ( Y ) , respectively– are examples of R- and K-selection 52 , and can also be interpreted in terms of different levels of selection introduced by the division of the population into isolated groups 53 ., The frequency-dependent fitness can lead to stable , steady-state solutions ( fixpoints ) , n* , of Equation 3 such that n ( t+1 ) =\u200an ( t ) =\u200an*: species abundances relax back to their steady state values following small perturbations ( Figure 2 ) ., For such stabilization to work , however , constraints must prevent species from optimizing both growth and efficiency simultaneously and hence form a ‘super-species’ that will drive all other species extinct 50 ., Cost-benefit reasoning suggests that such trade-offs will indeed generically be present , e . g . high efficiency will typically require more extensive metabolic machinery and hence divert energy away from cellular reproduction 54 , and plants must divide their resources between e . g . root and seeds 55 ., Such trade-offs have indeed been found empirically in a number of contexts 55–58 , and trade-offs between the rate and efficiency of resource utilization has been shown to allow two distinct strains of yeast to coexist 42 ., As our focus is on the dynamics of the ecosystem rather than its assembly through evolution , we will assume the existence of appropriate μ-Y trade-offs which allow community coexistence ., Because of the stabilizing mechanism , trade-offs do not uniquely fix μ and Y for each species; instead , a range of different values are possible ( each leading to different steady state abundances ) , albeit the range of parameters choices narrows as two species become very similar ( Supplementary Figures S1 and S2 ) ., To have an unbiased baseline , we chose sets of parameters that lead to equal species abundance at steady state , i . e . nk*\u200a=\u200an0 for all species k ., Given n0 , μ , and α , we can numerically solve the fixpoint equation n ( t+1 ) =\u200an ( t ) for the species efficiencies Y using Equations 1 and 3 – see Figure 2A ., We introduced shared environmental noise through fluctuations in the dispersal dilution factor α which represents the strength of the environmental disturbance and affects all species in each step ., Specifically , we drew an independent , random α-value in each cycle ( white noise ) from a fixed log-normal distribution ., This choice is convenient for keeping the expectation value of the long-term dilution factor fixed as we changed the noise intensity , but our conclusions do not depends on the exact distribution ( see Methods ) ., The environmental noise was strongly amplified: a 15% variation in α around the mean causes both the total abundance and that of individual species to fluctuate over several orders of magnitude ( Figure 3A ) ., Individual species exhibited short ‘bursts’ of high abundance and occasionally maintained a relatively high abundance over long periods ., No single species permanently gained the upper hand – instead , there was a constant , slow turnover of species , reminiscent of that observed in plankton communities 59 ., But while the fluctuations in the abundance of any single species are erratic , the competitive interactions acted to create a striking coherent pattern in the relative fluctuations of different species ., At any typical time , the most abundant species formed clusters in phenotype space , separated by ‘valleys’ of low-abundance species ( Figure 3B and Supplementary Figures S3 , S4 , and S5 ) ., Due to the turnover of dominant species , the number , and height of clusters changed over time , but the peak-and-valley pattern itself was robust ., Furthermore , peaks tended to have approximately the same width in phenotype space ., This clustered pattern remained when averaging over many cycles , albeit with a smaller amplitude ( Figure 3B , bottom panel ) , and also appeared across replica systems started at different random configurations ., Increasing the noise intensity has little impact on the typical size of the clusters , but naturally leads to larger abundance differences ., At very high noise levels , non-linear effects – presumably related to the stabilizing mechanism discussed above – stabilizes rare species at low densities , leading to clusters separated by very distinct valleys ( Supplementary Figure S6 ) ., Extinction of species can occur at very high noise levels , but was never observed at the noise strengths discussed in this paper ., To understand how the phenotypic clusters are formed , we looked at the pair-wise correlation between species abundances in simulations of the complete model and constrained versions of it ( data series of 105 cycles ) ., When plotted as a function of the phenotypic difference between them , the correlation between two species in the complete model alternates between positive and negative values ( Figure 4A , purple ) , reflecting the clustering we observed in Figure 3B ( since ‘peak-species’ move in synchrony with one another , but out of step with ‘valley-species’ ) ., To separate the contribution of noise and species interaction to this oscillatory pattern , we repeated the simulation with the exact same noise ( same series of α-values ) while artificially fixing the abundance of either all but one , or all but two species , to their steady state values ., These two types of simulations maintain the properties of the steady state while singling out the contribution of the noise itself and the pair-wise interactions combined with noise , respectively ., For the single-species version , we simulated each species separately ( N simulation runs ) and computed pair-wise correlations between the different simulations; for the pairs , we simulated all pairs ( N2 simulations ) and computed the correlation of every pair of species within the corresponding simulation ., We found that when each single species fluctuates independently , the full dynamics is determined by the noise and all species remain strongly positively correlated with each other regardless of how different they are ( Figure 4A , black; no interactions – see also Supplementary Figure S5 ) ., Allowing pairs of species to fluctuate keeps similar species positively correlated , but causes species which are sufficiently phenotypically different become anti-correlated ( Figure 4A , green; pair-wise interactions ) ., Hence , one- or two-species dynamics lead to the standard behaviors – Moran effect and compensatory dynamics , respectively ., The latter effect is also visible in the response to an instantaneous increase in the abundance of a single species: the abundances of the other species drop ( Supplementary Figure S7 ) ., The combination of noise and pair-wise interactions account correctly for the positive correlation between close species and for the negative correlation with some distant species , as seen in the complete model ., However , pair-wise interactions alone are not sufficient for explaining the alternating patterns of multiple peaks of positive and negative correlations: this is a collective phenomenon requiring the interaction of many species ., It only appears as we increase the number species allowed to fluctuate ( Supplementary Figure S8 ) ., The mechanism behind the species clustering in phenotype space can be understood as a dynamic balance between the smoothing ( synchronizing ) effect of noise and the roughening effects of interactions ., When the system is perturbed by a change in the dilution parameter α , all the species change their abundances by similar amounts and in the same direction , generating a relatively smooth ( uniform ) change in the abundance profile across phenotype space ., As shown above , if the species do not interact with each other they will move up and down in almost perfect lockstep and hence maintain a flat uniform profile ( equal abundances ) ., But if the species do in fact all compete , moving in lockstep means that every species experiences either increased or decreased competition from all the others after a perturbation and hence quickly gets pushed back to the fixpoint ., If , for instance , all species simultaneously become more abundant , the resulting shortage of food will quickly decimate each one of them ., Now suppose instead that the system is in a state where some species are above their fixpoint abundances and others below it – i . e . have an abundance profile that oscillates up and down ., In that case , each species experiences a combination of less competition from species that are below their normal abundance and more competition from over-abundant species ., These competitive differences partially cancel each other out , leading to a decreased pull on the abundance of each species and hence a slower relaxation back to the steady state ., The more rugged the profile , the slower the relaxation: if similar species can have very different abundances , they can better cancel out each others effects ., We conclude that noise tends to generate smooth abundance profiles across phenotype space but , conversely , that the most stable profiles are the very jagged ones ., We therefore expect that the typical abundance profile we observe is one that is neither completely flat nor maximally jagged , but instead changes smoothly between high and low abundances i . e . exhibits clusters of abundant species ., This heuristic argument can be tested rigorously by considering a simplified version of our model ( Figure 4B ) ., By expanding Equation 3 around the fixpoint n* and keeping only the leading ( linear ) terms , we obtain a good approximation for weak noise ( see Methods ) ., The interactions between species are now described by a single N×N matrix J , and the eigenvectors of this matrix describe N independent deformations of the abundance profile around the steady state ., These basic deformations can be sorted by their smoothness in phenotype space and are ordered accordingly on the x-axis in Figure 4B – three example profiles are illustrated in the bottom panels ., The presence of both positive and negative elements in all but the first deformation is a direct reflection of compensatory dynamics: they involve some species growing more abundant while others become rarer ., For each deformation , we calculated its propensity to be generated by noise ( Figure 4B , squares ) , and the time it takes for it to decay back to the flat steady state ( Figure 4B , triangles ) – see Methods for details ., The results confirm the argument above: the two properties change in opposite directions as the profiles become more jagged ., The environmental noise tends to generate smooth deformations , but the jagged deformations are much more long-lived ., Statistically , the typical profile will therefore be one showing smooth peaks a few species wide ( Figure 4B , red line peaking at middle smoothness ) ., Changing the noise intensity multiplies the amplitude of each deformation with the same constant and so does not affect the typical cluster size ( see Methods ) ., This analysis agrees excellently with what we observe in our simulations: persistent clustering , with clusters having the same typical size even though the exact abundance profile is constantly changing due to the stochastic noise ( compare Figure 3C and the middle of the bottom panels in Figure 4B ) ., The amplitude distribution ( red line in Figure 4B ) also agrees well with simulations ( Supplementary Figure S9 ) ., The linear analysis also reveals the origin of the strong noise amplification: Although the parameters were not chosen to bring this about , the system is very close to instability , with the most jagged abundance deformation taking τ∼107 cycles to decay back to the fixpoint ( for the parameters used in Figures 3 and 4 ) ., By the same token , a permanent shift in α ( a press perturbation ) will lead to significant shift in the stead-state abundances; the stabilizing mechanism discussed above acts only on changes in the abundances themselves ( see also Supplementary Figure S10 ) ., Our results show that the interplay between environmental noise and species interactions can induce robust patterns of alternating correlations between species abundances , leading to dynamic clustering of abundance in phenotype space ., We demonstrated that the fundamental basis for this pattern is the dynamic balance between synchrony caused by noise ( Moran effect ) and the compensatory dynamics caused by the species interactions ., Environmental noise is thus not merely a randomizing or synchronizing force , but can actively create ecological patterns that do not directly reflect fixed external factors like niches ., These are collective phenomena requiring the presence of many species , suggesting that few-species ecological models may miss entire classes of dynamic behavior that could be important in natural ecosystems ., By pointing to environmental noise as an important structuring factor in ecosystems , these results could cast new light on a number of empirical observations ., For instance , metabolic theory suggests that body mass M is linked to maximal growth rate through the scaling relation 60 , so the clusters we observe across different growth rates could be directly reflected in cluster in the space of body mass ., And indeed , body size cluster have been found to be dynamic in several cases , with the location of the clusters and their number changing over time 61–63 ., Our model provides a simple mechanism for such itinerant clusters and at the same time offers a way to reconcile metabolic theory , which suggest the existence of single optimal body size , with the empirical observation that species rarely cluster at a single optimum 29 ., Dynamic phenotypic clustering also implies that even species which are all direct competitors can arrange themselves into distinct sub-groups whose abundances fluctuate in synchrony for long periods of time ( Figure 4A ) ., This lends support to the suggestion that the apparent lack of strong negative correlations between species found in large-scale empirical studies 64–66 could be due to obscuring effects rather than the actual absence of negative interactions 67 ., The formation of phenotypic clusters bears some resemblance to the classical concept of limiting similarity: the idea that competition puts a limit on how similar the phenotypes of coexisting species can be , and hence implying that two neighboring species must have a finite stretch of unoccupied phenotype space between them 68 ., The sensitivity to environmental fluctuation in our model means that at a permanent shift in α could drive some species extinct and thus effectively lead to a new , larger phenotypic separation of neighboring species ., Conversely , for Lotka-Volterra models it has been shown that a very small perturbation in the parameters can shift the system from allowing the coexistence of arbitrarily similar species to requiring a finite phenotypic difference 69 ., If environmental fluctuations drive such an ecosystem back and forth between these two regimes fast enough to keep many species from going extinct , the result could be bands coexisting species similar to the clusters we observe ., As with all ecological modeling , we have made a number of simplifying assumptions ., Firstly , we have ignored spatial structure beyond that provided by the division into patches ., Secondly , we have worked in the limit of an infinite population size and hence neglected demographic noise ( neutral ecological drift ) ., Finally , we have assumed a pre-existing trade-off between efficiency and growth rate ., The question of how such tradeoffs can evolve and how they affect ecosystem stability is complicated 70–73 , and it would be interesting to understand it in the framework of our model ., Indeed , the noise-induced clusters describe here could themselves play a role in speciation and the maintenance of genetic diversity 74–76 ., Our model assumes that all patches contain the same amount of resource and deviations from this assumption are beyond the scope of this mode ., However , we expect that if the resource amount on each patch was drawn independently from a fixed distribution in each round , the noise would simply average out and the model would converge to a steady state of coexistence similarly to that observed in our model ., A slightly different natural variation would be to consider noise that affects the average amount of resources available on each patch rather than the dilution factor ., A change in the amount of resource per patch is equivalent to a uniform rescaling of all efficiencies ( see Methods ) and therefore , like a change in dilution , will generically shift the balance between fast and slow species ., We would therefore expect such fluctuations to cause qualitatively the same effects as we observe ., Another possible variation of our model is to allow dispersal to occur before growth has finished on all patches ., This would lower the advantage conferred by higher efficiency , so coexistence would require a steeper trade-off between growth-rate and efficiency ., Indeed , in the limit of dispersal time much shorter than growth time , the model simply converges to exponential growth in a well- mixed environment; the efficiency becomes irrelevant and the fastest species takes over the population ., The appearance of dynamic phenotypic clusters in such a minimal simplified model suggest that species clustering in phenotype space could be a generic property of ecologies with many interacting species subject to noise ., Indeed , the underlying mechanism is quite general and other noisy systems involving many interacting parts , e . g . neuronal or molecular networks , might exhibit similar effects ., This mechanism could also work independently along several axes to create clusters in multi-dimensional phenotype spaces which could be seen as temporary ecological guilds 77 ., Indeed , general metabolic theory suggests that body mass linked to many other ecological quantities by similar simple scaling relations 78 so if the clustering in the space of growth-rates transfer to body masses , as we argued above , it should also be reflected in patterns along still other phenotypic axes ., It will be interesting to see whether such noise-induced abundance patterns can be directly observed in natural or laboratory-based experimental ecosystems , particularly microbial ones 79 ., The full model is defined by Equations 1–3 ., To compute the final abundances for a given initial seeding , we first find the growth-time ( T ) given the available amount of resource , ( R ) ., Since all species grow freely , the number of offspring ( not counting the original ancestor ) of a single individual of species k at a time t is exp ( μkt ) −1 , and each new offspring removes 1/Yk units of resources ., Starting from mk individuals , the total amount of resources consumed by the population of species k on a given patch is thus mk ( exp ( μkt ) −1 ) /Yk ., Hence , T is the solution to the equation ., ( 4 ) This equation defines a growth time T for every initial configuration m , given a set of growth rates μ and efficiencies Y . Changing the value of R is equivalent to scaling all the Y-values by a common factor , so we set R\u200a=\u200a1 for convenience ( this is the choice used in this paper ) ., In that case , Y is simply the per-patch number of offspring produced by a single seeded individual in the absence of competitors ., We assumed that the environmental disturbances arrive at intervals longer than the time needed for even the slowest species to grow to saturation , i . e . the time between disturbances is longer than the largest T-value ., Hence , the resources will always be completely exhausted on every patch and the time it took for this to happen ( which varies depending on the seeding of the given patch ) plays no further role ., The final abundances for a given seeding averaged over all patches with this seeding , f ( m ) , are now given by Equation 2 ., Using the average is consistent since we work with an infinite population; however , for a finite population , the stochastic growth differences between individual patches starting with the same seeding could change the results ., With the exception of the rather trivial case N\u200a=\u200a1 , we cannot analytically solve Equation 4 , so we used numerical solutions for the simulations ., Similarly , for N>1 we cannot analytically do the sum in Equation 3 since it depends on quantities than can only be found numerically ., We therefore approximated it by summing over a finite number of seedings , imposing the condition that the combined probability of all neglected configurations was less than 10−7 ( evaluated at the fixpoint ) ., The resulting finite sum was over all seedings that involved at most M seeded individuals in total , where M was picked to satisfy the probability-condition ., All simulations program were written in MATLAB and run on the Harvard Medical School supercomputing cluster

Introduction, Results, Discussion, Methods

In natural ecosystems , hundreds of species typically share the same environment and are connected by a dense network of interactions such as predation or competition for resources ., Much is known about how fixed ecological niches can determine species abundances in such systems , but far less attention has been paid to patterns of abundances in randomly varying environments ., Here , we study this question in a simple model of competition between many species in a patchy ecosystem with randomly fluctuating environmental conditions ., Paradoxically , we find that introducing noise can actually induce ordered patterns of abundance-fluctuations , leading to a distinct periodic variation in the correlations between species as a function of the phenotypic distance between them; here , difference in growth rate ., This is further accompanied by the formation of discrete , dynamic clusters of abundant species along this otherwise continuous phenotypic axis ., These ordered patterns depend on the collective behavior of many species; they disappear when only individual or pairs of species are considered in isolation ., We show that they arise from a balance between the tendency of shared environmental noise to synchronize species abundances and the tendency for competition among species to make them fluctuate out of step ., Our results demonstrate that in highly interconnected ecosystems , noise can act as an ordering force , dynamically generating ecological patterns even in environments lacking explicit niches .

In natural ecosystems , hundreds of species with different characteristics typically live side by side , some competing for the same foods and some preying on others ., A central question in ecology is how the abundance of a given species in such an ecosystem depends on its particular characteristics ( its phenotype ) ., Clearly , fixed environments can favor certain phenotypes ( thick fur in a cold climate ) , but what happens when environmental conditions fluctuate randomly as e . g . the weather does ?, We investigated this question using a simple mathematical model of an ecosystem with many competing species ., We found that , paradoxically , randomness in the environment can lead to the appearance of ordered clusters of abundant species with similar phenotypes , with the species adopting intermediate phenotypes being much less abundant ( a mountains-and-valleys pattern ) ., The clusters move around so that different phenotypes are favored at different times ., We found that these effects arise from the tension between the tendency of noise to level out difference in abundances and the tendency of competition to create larger abundance differences .

ecology, ecosystems, population modeling, population dynamics, theoretical ecology, biology, population ecology, population biology, microbial ecology

journal.pgen.1002512

EMF1 and PRC2 Cooperate to Repress Key Regulators of Arabidopsis Development

Polycomb group ( PcG ) proteins are epigenetic repressors implicated in various developmental and cellular processes 1 , 2 ., PcG proteins function in multi-subunit protein complexes: Polycomb Repressor Complex 1 ( PRC1 ) and PRC2 3 , the core components of which are conserved from Drosophila to humans ., PRC2 marks the target gene by trimethylating histone H3 at lysine 27 ( H3K27me3 ) through the E ( z ) SET domain 4 , 5 , 6 , 7 , 8 ., PRC1 , which binds the H3K27me3 methyl marks and docks on nucleosomes modified by PRC2 , inhibits transcription and blocks remodeling of the target nucleosomes , resulting in gene silencing 9 , 10 , 11 ., Genome-wide studies confirmed co-localization of PRC1 and PRC2 on target genes ., However , there are also genomic sites bound by one , but not the other , PRC 12 and transcriptional networks differentially regulated by PRC1 and PRC2 13 ., PcG action is counteracted by Trithorax Group ( trxG ) protein complexes 14 ., Together , PcG and trxG complexes maintain repressive and active states of chromatin , respectively 14 ., Protein-protein interaction and gel filtration studies have identified three Arabidopsis PRC2-like complexes 15 , 16 , 17 ., Two components , FERTILIZATION INDEPENDENT ENDOSPERM ( FIE ) 18 , and MULTICOPY SUPPRESSOR OF IRA1 ( MSI1 ) 19 , are present in all three putative PRC2s 17 ., Small gene families of homologs of Drosophila Su ( z ) 12 , i . e . , EMBRYONIC FLOWER2 ( EMF2 ) 20 , FERTILIZATION INDEPENDENT SEED2 ( FIS2 ) and VERNALIZATION2 ( VRN2 ) 21 , and of E ( z ) , i . e . , MEDEA ( MEA ) 22 , CURLY LEAF ( CLF ) 23 , and SWINGER ( SWN ) 15 , generate variation in Arabidopsis complex composition for targeted PRC2 regulation of multiple pathways ., The EMF2/FIS2/VRN2 homologs have diverse , and sometimes redundant , roles 24 , 25 , 26 ., The VRN2-containing PRC2 , VRN2-PRC2 , is required for vernalization-induced flowering through the repression of FLOWERING LOCUS C ( FLC ) 21 ., Impairments in FIS2-PRC2 function cause endosperm over-proliferation and seed abortion 26 ., Impairments in the EMF2-PRC2 do not affect seed development , but the plants have a shortened vegetative phase or skip it altogether 18 , 20 , 23 , 27 ., Hence , EMF2-PRC2 is considered responsible for vegetative development ., EMF1 , another Arabidopsis gene required for vegetative development , encodes a plant-specific protein containing sequence motifs found in transcriptional regulators 28 ., EMF1 mutant plants and plants impaired in components of EMF2-PRC2 have similar phenotypes ., Weak emf1 mutants are emf2-like , while strong emf1 mutants have a more severe phenotype than emf2 and the transgenic lines impaired in FIE 18 , 27 , 29 , 30 ., Tissue-specific removal of EMF1 activity from leaf primordia allows vegetative growth , but leads to early flowering plants with curly leaves similar to clf mutants 31 ., The early flowering phenotype of plants impaired in EMF1 or EMF2-PRC2 components was attributed to the ectopic expression of flower organ identity or flower MADS box genes such as AGAMOUS ( AG ) , APETALA1 ( AP1 ) , AP3 and PISTILATA ( PI ) 32 , ., However , these plants have pleiotropic phenotypes and the expression of many genes other than the flower MADS box genes is affected 33 , 34 , 35 ., This suggests that EMF1 and EMF2-PRC2 regulate additional developmental processes ., EMF1 interacts with AG , PI , and AP3 chromatin and displays characteristics similar to the Drosophila PRC1 component , Posterior sex combs ( Psc ) 36 ., It is also required for Arabidopsis RING-finger protein-mediated Histone 2A lysine 119 ( H2AK119 ) ubiquitination 37 ., Mammalian PRC1 contains the RING-finger proteins from an E3 ubiquitin ligase complex that monoubiquitinates H2AK119 38 ., Functional characterization of Arabidopsis RING-finger proteins provided biochemical , molecular , and biological evidence that they have a PRC1 role in maintaining differentiated cell fates 37 , 39 , 40 ., Another Arabidopsis PRC1-like component , the LIKE HETEROCHROMATIN PROTEIN1 ( LHP1 ) , recognizes H3K27me3 and interacts with many H3K27 trimethylated target genes 41 , 42 ., The RING-finger proteins interact with both LHP1 and EMF1; and EMF1 is required for the H2AK119 ubiquitination activity of the RING-finger proteins 37 ., However , EMF1 also interacts with the PRC2 component , MSI1 , in vitro 36 as well as with multiple other proteins 43 ., The role of EMF1 in the PcG mechanism remains unclear ., To better understand the full impact of EMF1 on Arabidopsis growth and development and the mechanisms of EMF1-mediated gene repression , we performed genome-wide mapping of EMF1 binding and analyzed the H3K27me3 and expression patterns of EMF1 target genes in emf1 and PRC2 mutants ., Our results demonstrate direct epigenetic regulation of key genes controlling developmental programs and specifying cell differentiation processes via their interaction with EMF1 ., Based on the requirement of EMF1 for H3K27me3 and H2AK119 ubiquitination on different target genes , we discuss the roles of EMF1 in the PcG mechanism and propose a novel role for EMF1– acting as a linker between the two PcG complexes for genes that depend on EMF1 for both histone modifications ., We have previously shown that EMF1 regulates the flower MADS box genes AG , AP3 , and PI via direct interaction with their chromatin 34 , 36 ., The large number of mis-regulated genes in emf1 mutants 33 , 34 indicates that EMF1 regulates many other genes directly or indirectly ., To identify all EMF1 target genes in Arabidopsis seedlings , we performed Chromatin Immunoprecipitation ( ChIP ) followed by microarray analysis ( ChIP-chip ) , using a transgenic Arabidopsis with a functional transgene – EMF1 tagged with 3FLAG and expressed under its own promoter ( EMF1::EMF1-3FLAG ) that can rescue emf1 mutants 36 ., A high-resolution genome-wide map of EMF1 binding sites in Arabidopsis seedlings was generated by affinity purifying 3FLAG tagged EMF1-bound chromatin and hybridizing the associated DNA to customized NimbleGen High Density 2 tiling microarrays ( HD2 , 2 . 1M array ) representing the entire Arabidopsis genome of 28 , 244 genes without gaps ., Utilizing the ChIPOTLe peak finding algorithm we identified 8 , 541 binding sites ( p<10−6 ) distributed throughout all 5 chromosomes , enriched in the euchromatic regions and underrepresented in the pericentromeric region ( Figure 1A; Figure S1A ) ., 6 , 317 of the EMF1 binding sites are located in the transcribed region of the annotated sequences ( −200 bp to the 3′ end ) of 5 , 533 genes ., The remaining sites are in intergenic regions ( Figure 1B; Table S1 ) ., The 5 , 533 include AG , AP3 and PI ( Figure 1C ) , the known EMF1 target genes that are up-regulated in emf1 mutants , as well as 7 other flower MADS box genes and CRABS CLAW ( CRC ) ( Figure S1B ) ., This is consistent with EMF1 repression of the flower organ program in Arabidopsis seedlings ., Other EMF1 target genes identified by ChIP-PCR by Kim et al . , 34 , namely , LONG VEGETATIVE1 ( LOV1 ) , FLC , and ABSCISIC ACID INSENSITIVE3 ( ABI3 ) , are EMF1 binding genes in our study ., As negative controls , FLOWERING LOCUS T ( FT ) and PHERES1 ( PHE1 ) , which did not interact with EMF1 in ChIP-PCR experiments , are not enriched with EMF1 binding sites ( Figure 1C ) ., We confirmed the ChIP-chip results by ChIP-PCR on an additional 9 randomly selected genes with various enrichment level of EMF1 binding ( Figure S2 ) ., Thus binding sites identified by ChIP-chip likely represent in vivo EMF1-target genes interaction ., Because of the functional similarity between EMF1 and PRC2 , we compared the EMF1 binding pattern and the H3K27me3 modification profile across the whole Arabidopsis seedling genome ., To minimize variability due to sample and microarray differences , we mapped EMF1 binding targets , determined the H3K27me3 profile , and measured mRNA levels ( see below ) with the same NimbleGen HD2 arrays ., The ChIPOTle peak finding program identified 11 , 067 H3K27me3 enriched peaks ( p<10−35 ) , which correspond to 7 , 751 genes that showed 85% overlap with an earlier study ( Table S2; 42 ) ., As reported previously , H3K27me3 peaks tend to be broad , often covering the entire transcriptional unit ( Figure 2A and 2B; Figure S1B ) , hence we used a very strict statistical cutoff for peak identification ., Globally , EMF1 binding and H3K27me3 modification profiles are well correlated ( Figure 2A ) ., Both are found throughout euchromatin regions and are underrepresented in the centromeres of all 5 chromosomes ., At the genic level , the EMF1 binding pattern resembles the H3K27me3 profile , covering the transcription unit with the strongest signal around the transcriptional start site ( TSS , Figure 1C ) ., The EMF1 signal gradually declines towards the 3′ end in some genes and does not extend as far into the 3′ non coding region as H3K27me3 modification does , see , for example , SEEDSTICK ( STK ) , ARGONAUTE5 ( AGO5 ) , AP1 , and SEPALATA1 ( SEP1 ) ( Figure 2B; Figure S1B ) ., To better understand the relationship between EMF1 and PRC2 , we mapped the H3K27me3 sites in emf1 , emf2 , and fie mutant plants ., Because FIE is required during seed development and fie mutants are embryo-lethal , we used a transgenic line that expresses FIE only during the seed development stage to recover homozygous fie seedlings 18 ., Relative to two-week old WT , plants impaired in each of these three genes have no petioles and rosette leaves , a short hypocotyl , and oval shaped cotyledons ., emf2 and plants impaired in FIE are similar in phenotype ., The emf1 allele used in this study , emf1-2 , is a strong allele with a more severe phenotype than emf2 ( 32; Figure 2C ) ., Among the 7 , 751 genes marked by H3K27me3 in WT , 44% show reduced H3K27me3 in emf1 mutants , 54% in emf2 , and 84% in fie ( Figure 2A , 2B , and 2D ) ., This 84% H3K27me3 reduction is consistent with an earlier study 30 , in which a 75% loss in a different FIE-impaired transgenic plant was reported ., The loss of H3K27me3 in fie mutant seedlings indicates that H3K27me3 requires a functional PRC2 complex ., The moderate decline of H3K27me3 in emf2 could be due to partial replacement of EMF2 function by its homolog , VRN2 15 , 16 ., The partial requirement for EMF1 shows that H3K27me3 is less dependent on EMF1 than on PRC2 , indicating a site-specific EMF1-dependent H3K27 trimethylation ., Nevertheless , 75% of the genes with reduced H3K27me3 in emf1 have reduced H3K27me3 in emf2 and fie ( Figure 2E ) , indicating that trimethylation on these genes requires coordinated action by EMF1 and PRC2 ., Because peak calling necessarily involves arbitrary cutoffs , we supplemented the ChIPOTLe analysis that generated the H3K27me3 peaks by an unsupervised k-means clustering algorithm ( k\u200a=\u200a2 , Figure 3A , left panel ) ., The 28 , 244 Arabidopsis genes were aligned at the annotated TSS , the average H3K27me3 signal calculated in each 100 bp bin across the 6 kb region surrounding the TSS , and the data sorted into two clusters , high and low H3K27 trimethylation ., High enrichment level of H3K27me3 in the transcribed , relative to the 5′ untranscribed , region is clearly seen in the highly trimethylated gene cluster ( Figure 3A , left panel ) ., We then arranged EMF1 binding strength to match the H3K27me3 sorting order ( Figure 3A , right panel ) , and found that genes in the cluster of high H3K27me3 exhibit high enrichment level of EMF1 binding , while the cluster with low H3K27me3 genes show low enrichment level of EMF1 binding ., We then arranged H3K27me3 levels in the three mutants according to the high and low H3K27me3 clusters ( Figure 3B ) ., The H3K27me3 level is most drastically reduced in fie , less in emf2 and emf1 , consistent with the ChIPOTLe analysis shown in Figure 2B ., Since the high H3K27me3 cluster of genes shows the most distinct pattern ( Figure 3A and 3B ) , we plotting the average H3K27me3 signal and the EMF1 binding pattern of this cluster of genes across the 6 kb region surrounding the TSS in WT and in the 3 mutants ( Figure 3C ) ., The promoter regions of this highly trimethylated cluster of genes show minimal H3K27me3 modification , while it is highly enriched in the transcribed region ., H3K27me3 enrichment is highest around the TSS , then declines slightly but is maintained throughout the 3 kb of the transcribed region ., As expected , H3K27me3 enrichment is reduced in all three mutants , nearly absent in fie and partially lost in emf2 and emf1 ., Despite the reduction in the mutants , the H3K27me3 pattern across the gene remains remarkably similar to WT ., The EMF1 binding pattern of these highly methylated genes is similar to their H3K27me3 modification pattern in that EMF1 binds primarily the chromatin of the transcribed , rather than the promoter , region ., However , EMF1 binding in this cluster of highly methylated genes shows a precipitous drop from the peak of binding at the TSS in the 3′ direction: the major binding is within 1 kb of the TSS ( Figure 3C ) ., Results from the k-means clustering algorithm and the ChIPOTle method are consistent ., We then used a Perl implementation of the ChIPOTle method to identify the EMF1-bound genes that are trimethylated and found 58% ( 3230 ) of the 5 , 533 EMF1-bound genes exhibit H3K27me3 peaks , called EMF1_K27 genes ( p\u200a=\u200a6×10−184; Fishers exact test , see gene list in Table S2 ) ., Our subsequent analysis focused on the EMF1_K27 genes , highly trimethylated on H3K27 and enriched for EMF1 binding ., Gene ontology ( GO ) analysis of the 3230 EMF1_K27 genes revealed that EMF1 and H3K27me3 co-localize at a remarkably high number of genes involved in transcription factor activity , developmental pathways , and microRNA ( miRNA ) gene silencing ( Table 1; Table S3 ) ., Relative to the whole genome , there is a 2 . 5–5 fold enrichment in the genes belonging to the categories of transcription factor activity , miRNA regulation , and genes involved in leaf , vascular , root , meristem , and flower development ., EMF1 binds preferentially ( p<0 . 05 ) genes involved in biotic and abiotic stresses and in the biosynthesis of , and response to , the major plant hormones: abscisic acid ( ABA ) , auxin , brassinosteroids ( BR ) , cytokinins ( CK ) , ethylene , gibberellic acid ( GA ) , jasmonic acid ( JA ) and salicylic acid ( SA ) , and genes involved in biotic and abiotic stresses ., We next examined the annotated genes with known developmental functions ( Figure 4; Table S4 ) , beginning with the genes required for flower and seed development that are up-regulated in emf1 mutants 33 , 34 ., We found that EMF1 binds a subset of these H3K27me3 modified genes ( Table S4 ) ., For example , EMF1 binds 3 of the 4 major seed regulated genes marked by H3K27me3 , namely , FUSCA3 ( FUS3 ) , ABA INSENSITIVE3 ( ABI3 ) and 2 LEAFY COTYLEDON2 ( LEC2 ) 44 , as well as , a fraction of the downstream seed maturation genes that are trimethylated , e . g . , the LATE EMBRYO ABUNDANT ( LEA ) , OLEOSIN ( OLEO ) , and LIPID TRANSFER PROTEIN ( LTP ) , and seed storage protein genes ( Table S4 ) ., It is worth noting that some genes in the same families are bound by EMF1 but are not marked with H3K27me3 ( Table S4 ) ., EMF1 silences the flower developmental program by interacting with and repressing all known flower organ identity genes and other genes specifying flower organ development , e . g . , CRC , SUPERMAN ( SUP ) , and PETAL LOSS ( PTL , 45 , 46; Figure 4 ) ., Flower organ identity genes are all type II MADS box genes 47 ., We found that EMF1 preferentially interacts with type II MADS box genes ., EMF1 does not interact with the Type I MADS box genes that are important for female gametophyte and early seed development , e . g . , PHE1 ( AGL37 ) , PHE2 ( AGL38 ) , AGL23 , and AGL61 48 , 49 , although they are H3K27 trimethylated in Arabidopsis seedlings ( Table 2; Table S4 ) ., Vegetative development requires not only the repression of the seed and flower programs but also dynamic activation and repression of genes to specify cell fates in the meristems and to dictate organized cell growth and differentiation ., Our study of seedling chromatin showed that EMF1 binds H3K27me3 marked genes that specify cell fates in shoot and root apices and control leaf polarity , e . g . , SHOOT MERISTEMLESS ( STM ) , CLAVATA3 ( CLV3 ) , and WUSHEL ( WUS ) ( Figure 4 ) ., Shoot meristem and leaf primordia in the shoot apex are separated by the expression of the boundary-specific genes encoding the NAC domain transcription factors , NO APICAL MERISTEM ( NAM ) and CUP SHAPED COTYLEDONE ( CUC ) 50 , 51 , 52 , which are negatively regulated by the TEOSINTE BRANCHED1 , CYCLOIDEA , and PCF ( TCP ) genes ., NAM , CUC2 , and CUC3 are all trimethylated and bound by EMF1 ( Figure 4 ) ., EMF1 interacts with 9 of the 10 H3K27 trimethylated TCP genes ., TCP14 affects internode length and leaf shape 53 ., EMF1 interaction with TCP genes that affect diverse aspects of Arabidopsis shoot growth and architecture is consistent with the pleiotropic effect of EMF1 impairment on Arabidopsis shoot development that includes petiole-less cotyledons , short hypocotyl and short inflorescence stem , due to limited cell elongation in emf1 mutants 31 ., Hormones mediate growth and differentiation after germination ., H3K27me3 marks a full spectrum of genes involved in indole-3-acetic acid synthesis , transport and signaling 54 , most of them are EMF1-bound ( Figure 4; Table S4 ) ., EMF1 also interacts with many other hormone genes marked with H3K27me3 , e . g . , CYTOKININ OXIDASE ( CKKX ) , GA OXIDASE , and genes involved in JA , BR , and ethylene synthesis and response ( Table S4 ) ., Temporal and spatial regulation of these EMF1_K27 genes is critical for normal shoot and root architecture and growth patterns ., MicroRNA ( miRNA ) regulation of target genes controls various aspects of developmental transitions 55 ., The juvenile to adult transition of the vegetative shoot is coordinated by the antagonistic activities of miR156 and miR172 , through their opposite expression pattern and the antagonistic function of their target genes 56 ., The miR319-TCP and miR164-CUC miRNA-target nodes are involved in regulated cell proliferation during leaf morphogenesis 55 ., EMF1 interacts with about 50% of the miRNA genes marked by H3K27me3 ( Table S4 ) ., The AGONOUTE ( AGO ) genes mediate gene silencing through small RNA-directed RNA cleavage and translational repression 57 ., EMF1 interacts with all H3K27 trimethylated AGO genes , including AGO10/ZIWILLE ( ZLL ) ( Table S4; Figure 4 ) , which acts in the siRNA and miRNA pathways and is essential for multiple developmental processes in plants 57 ., Thus EMF1 may mediate juvenile and adult growth , as well as , lateral organ enlargement through the regulation of AGO and miRNA genes ., In summary , to promote vegetative development and to regulate cell differentiation during shoot and root organogenesis , EMF1 binds genes required for other developmental phases and genes specifying cell identities ., These are primarily genes trimethylated by EMF2-PRC2 on their H3K27 ., We examined H3K27me3 of the EMF1_K27 genes in emf1 mutants and found two groups of genes ., Group I genes–the EMF1-dependent H3K27me3 genes – comprising 57% of the EMF1_K27 genes ( 1845/3230 ) , are not H3K27me3 enriched in emf1 mutants ., Group II genes– EMF1-independent H3K27me3 genes– comprising 43% of EMF1_K27 genes are trimethylated in emf1 mutants ( Figure 5A; Table S2 ) ., To determine whether the H3K27me3 of EMF1-bound genes is mediated by PRC2 , we examined trimethylation in fie and emf2 mutants ., Most EMF1-bound genes showed reduced methylation in fie –96% of Group I and 76% of Group II genes ( Figure 5A ) ., Therefore , both Group I and Group II genes are indeed methylated by PRC2 ., 83% of Group I and 23% of the Group II genes showed reduced methylation in emf2 ., Methylation may be less affected in emf2 than in fie because of EMF2 and VRN2 redundancy , while FIE participates in both EMF2- and VRN2-PRC2 ., EMF1 targets many chromatin protein genes marked by H3K27me3 in WT seedlings , including FIS2 , VRN2 , MEA , ULTRAPETALA1 ( ULT1 ) , and ULT2 64 ., ULT1 is a component of trxG , the complex that antagonizes PcG action ., EMF1 binding apparently represses ULT1 , as its transcription is up-regulated in emf1 mutants ( Table S6 ) ., EMF1 does not bind the chromatin of EMF2 or the PRC1-like components , LHP1 , AtBMI1A , AtBMI1B , AtRING1A , and AtRING1B ( Table S2; 41 , 65 ) , which are required during postembryonic development , as is EMF1 ., EMF1 does bind AtRING1C , an imprinted gene expressed in the endosperm 66 ., To investigate the epigenetic regulation of EMF1 , we examined EMF1 interaction with itself ., Interestingly , EMF1 binds its own chromatin strongly ., Figure 6 shows EMF1 enrichment of the transcribed region of the EMF1 gene ( p<10−20 ) ., This high level of EMF1 enrichment on EMF1 chromatin is accompanied by H3K27 trimethylation in WT , which is reduced in emf1 mutants , thus placing EMF1 in the category of Group I genes ., Furthermore , EMF1 is up-regulated in emf1 mutants ( Figure 6 ) , providing evidence of EMF1 autoregulation ., EMF1 transcript level is also elevated in emf2 and fie mutants , indicating its repression via a PcG-mediated mechanism ., In addition to the EMF1_K27 genes , we investigated the 2303 EMF1-bound but not trimethylated ( EMF1_no_K27 ) genes in WT seedlings to find out their functional categories and whether they are regulated by EMF1 and PRC2 ( Table S2 ) ., GO analysis showed that the fraction of genes involved in transcription and developmental processes and genes encoding transcription factors is lower in the EMF1_no_K27 than the EMF1_K27 genes , while genes involved in cellular organization and biogenesis , cytosol , and chloroplast are over-represented in the EMF1_no_K27 genes ( Table S5 ) ., The EMF1_no_K27 genes tend to be actively transcribed genes with high RNA levels ., Their average transcript score is more than 4 times that of the EMF1_K27 genes –1 . 83 for the EMF1_no_K27 , relative to 0 . 42 for the EMF1_K27 , genes ., Analysis of NimbleGen transcriptome data showed about 14% of the EMF1_no_K27 genes is up-regulated and 7% down-regulated in emf1 mutants ., A high percentage of these genes are similarly up- and down-regulated in the emf2 and fie mutants , indicating a coordinated regulation of these genes by EMF1 and PRC2 ( Figure S4A ) ., We have previously shown that many photosynthesis genes that encode chlorophyll a/b binding proteins and photosystem I and II proteins are down-regulated in emf1 and emf2 mutants 33 , 34 ., Seventy two percent of these genes are EMF1-bound 34 , which are all EMF1_no_K27 genes and many are coordinately down-regulated in all three mutants ( Table S2; Figure S4A and S4B ) ., These results suggest that EMF1 activates their expression in the absence of H3K27me3 ., Indeed , PRC1 in fly and vertebrate are in some cases recruited to the target genes independent of PRC2 or H3K27me3 67 , 68 ., Alternatively , despite EMF1-binding , deregulation of these genes in emf1 , emf2 , and fie mutants are a consequence of severe phenotypic aberrations in response to loss of these central regulators of development ., Our genome-wide study provided new lines of evidence that support EMF1 acting via the PcG mechanism ., First , EMF1 interacts mostly with euchromatic sites located on all 5 chromosomes , a pattern similar to H3K27 trimethylation ., Second , on the genic level , the EMF1 binding pattern mimics that of the H3K27me3 in binding the transcribed , not the promoter , region with the peak binding activity at the 5′ TSS ., Third , EMF1 represses the seed and flower development genes and cell fate determination genes that are also modified by H3K27me3 ., Fourth , H3K27 trimethylation on EMF1-bound genes is mostly dependent on PRC2 and gene expression is coordinately regulated by EMF1 and PRC2 ., These findings demonstrate that , for genes that are highly enriched for EMF1 binding and H3K27me3 , EMF1 functions in the PcG mechanism ., We investigated H3K27me3 dependency on EMF1 binding and found two groups of genes ., Group I genes are richer in transcription factors and their repression is more dependent on EMF1 and PRC2 than Group II genes ., Most importantly , Group I genes are dependent on EMF1 for H3K27me3 modification , while Group II genes are not ., For Group I genes , which require EMF1 for K27me3 , EMF1 may act prior to , or as a member of , PRC2 to trimethylate H3K27 ., For Group II genes that do not require EMF1 for H3K27me3 , EMF1 may have a PRC1 function , or may be unrelated to PcG action ., Since many Group II genes require PRC2 for H3K27me3 , EMF1 is likely to act via the PcG mechanism , functioning downstream of H3K27trimethylation , as does PRC1 ., The characteristics of the four Arabidopsis RING-finger proteins , AtRING1A , AtRING1B , AtBMI1A , and AtBMI1B , are consistent with their functioning like the mammalian PRC1 uibquitin ligase , which monoubiquitinates H2AK119 8 , 37 ., EMF1 interacts with these proteins , and is required for these RING-finger proteins monoubiquitination of H2AK119 ( H2Aub ) , thus implicating EMF1 in PRC1 activity ., The RING-finger proteins also interact with CLF 40 , the PRC2 H3K27 trimethylase , and with LHP1 37 , 41 , 70 ., The EMF1 binding pattern is similar to that of H2Aub in mouse embryonic fibroblast cells 71 in that both EMF1 binding and H2Aub localization are enriched in the 1 kb 5′ coding region ., It is proposed that H2A ubiquitination interferes with early transcript elongation 67 ., EMF1 preferential localization in the 5′ coding region is consistent with its involvement in PRC1s role in blocking transcription elongation by preventing RNA polymerase movement through the compacted nucleosomes 67 ., However , EMF1 appears to partner with these RING-finger proteins only on a select group of target genes ., Most notably , the signature EMF1 targets , the flower organ identity genes AG and AP3 , are not regulated by the 4 Arabidopsis RING-finger proteins ., However , the class I KNOX ( KNOX1 ) genes , including STM , KNAT1 , KNAT2 , and KNAT6 , as well as WUS , and the seed regulator , FUS3 , are negatively regulated by both EMF1 and the RING-finger proteins 37 , 40 ., EMF1 is bound to all these genes in Arabidopsis seedlings ., Their ectopic expression in loss-of-function mutants suggests that these genes are direct target genes of the RING-finger proteins ., Interestingly , their H3K27me3 shows varying degrees of dependence on EMF1 ., KNOX1 and WUS are Group I genes: H3K27 trimethylation depends on EMF1 ., FUS3 belongs to Group II: EMF1-independent H3K27me3 ( Table S2 ) ., EMF1 may act on Group II genes such as FUS3 by assisting the PRC1 activity of the RING-finger protein-LHP1 complex following H3K27 trimethylation by PRC2 ., For Group I genes such as STM , EMF1 may participate in each PcG complex separately or may act like a linker protein that assists PRC2 in spreading H3K27me3 , while helping PRC1 monoubiquitinate H2A ., EMF1 interaction with MSI1 36 and with the RING-finger proteins 37 is consistent with its involvement in both PRC2 and PRC1 activities ., CLF interacts with AtRING1A/1B in yeast 2-hybrid assays 40 ., Our results , together with this finding indicate a close association of PRC2 and PRC1 in Arabidopsis ., This might be indicative of evolutionary divergence of PcG mechanisms ., In Drosophila PRC2 and PRC1 are separate functions ., Our study indicates that in Arabidopsis PcG proteins can also participate in closely linked PRC2-PRC1 function ., EMF1 and the PRC2 proteins have a different evolutionary history 72 , 73 ., The PRC2 ancestral sequences existed prior to the divergence of the animals and plants ., During plant evolution , gene duplication generated alternate PRC2 components that diversified to control different functions ., EMF1 is a plant-specific gene with homologous sequences found only in higher plants ., It might have functioned first as a general transcriptional regulator for genes involved in development and basic cellular and biochemical activities ., Coupling EMF1 with H3K27 trimethylation could have led to an enhanced targeting of genes in development ., The repression of flower development , which effectively lengthens the vegetative phase , coupled with elaborating plant architecture through the regulation of hormone and signaling genes , may have been instrumental in the evolution of organisms with multiple developmental phases and diverse signaling processes ., This is suggested by the progressive increase in the representation of genes involved in transcription and developmental processes from the H3K27me3 modified genes to the EMF1_K27 to Group I genes ( Figure 5C; Table S5 ) ., The fact that EMF1_K27 genes are highly enriched with H3K27me3 and EMF1 binding suggests an emphasis on this epigenetic mechanism through robust retention of repressive chromatin during cell differentiation ., Similarly , in mammalian cells , some genes are controlled by PRC1 , independent of PRC2 , and others are coordinately controlled by PRC1 with PRC2 13 ., The vast majority of developmental regulator genes are bound by both PRC1 and PRC2 , while genes bound by only one PRC are enriched for the membrane proteins 12 ., The similarity of mutant phenotypes suggests that EMF1 acts primarily with EMF2-PRC2 to mediate developmental processes in Arabidopsis ., EMF1 also acts together with AtBMI1A/1B and AtRING1A/1B to regulate genes maintaining cell identity ., This means that EMF1 should not silence the EMF2-PRC2 or the 4 RING-finger protein genes ., Indeed , EMF1 does not target CLF , EMF2 , SWN , or the RING-finger protein genes ., EMF1 does not interact with VRN2 either , which has similar , ubiquitous expression patterns as EMF1 and EMF2 ., EMF1 interacts with the chromatin of FIS2 and MEA , components of FIS2-PRC2 , consistent with their inactivity after germination ., So far , no up-regulation of these two genes has been detected in the absence of EMF1 ., Thus , EMF1 binding may not be the sole factor responsible for their repression , or their expression may require activators that are absent after germination ., EMF1 coordinates only with EMF2-PRC2 to regulate PcG target genes ., Neither EMF1 nor EMF2–PRC2 regulate the Type I MADS box genes involved in female gametophyte and endosperm development ( Table 2 ) , including PHE1 and PHE2 , whose maternal inheritance is mediated by FIS2-PRC2 74 ., PHE1 and PHE2 do not interact with EMF1 and are not normally expressed post-germination ., Their repression is not likely dependent on EMF1 or EMF2–PRC2 , for they are not ectopically expressed in emf1 and emf2 mutants , even though they are trimethylated on H3K27 ( Figure S3 ) ., This is consistent with a close association of EMF1 with EMF2-PRC2 and its lack of involvement in FIS2-PRC2 mediated epigenetic repression ., ULT1 interacts with ARABIDOPSIS TRITHORAX 1 ( ATX1 ) , thus is considered a component of the Arabidopsis trxG that acts to antagonize PcG action , as evidenced by ult1 mutants rescuing the clf mutant phenotype 75 ., ULT1 and ULT2 , a homolog of ULT1 , are EMF1_K27 genes ( Table 2 ) , and considered to be anti-repressors of PcG genes ., ULT1 is up-regulated in emf1 and emf2 ( Table S6 ) , and both ULT1 and ULT2 are up-regulated in fie 30 ., The temporal and spatial differentiation of ULT1 and ULT2 expression patterns is likely to involve EMF1 , but its role in the fine tuning of the repressor and anti-repressor balance in regulating gene expression remains to be characterized ., Similarly , EMF1 autoregulation must be a dynamic process in order to modulate its epigenetic regulatory activities at a cellular level ., Indeed , although EMF1 transcripts and proteins have been found in all tissues and organs

Introduction, Results, Discussion, Materials and Methods

EMBRYONIC FLOWER1 ( EMF1 ) is a plant-specific gene crucial to Arabidopsis vegetative development ., Loss of function mutants in the EMF1 gene mimic the phenotype caused by mutations in Polycomb Group protein ( PcG ) genes , which encode epigenetic repressors that regulate many aspects of eukaryotic development ., In Arabidopsis , Polycomb Repressor Complex 2 ( PRC2 ) , made of PcG proteins , catalyzes trimethylation of lysine 27 on histone H3 ( H3K27me3 ) and PRC1-like proteins catalyze H2AK119 ubiquitination ., Despite functional similarity to PcG proteins , EMF1 lacks sequence homology with known PcG proteins; thus , its role in the PcG mechanism is unclear ., To study the EMF1 functions and its mechanism of action , we performed genome-wide mapping of EMF1 binding and H3K27me3 modification sites in Arabidopsis seedlings ., The EMF1 binding pattern is similar to that of H3K27me3 modification on the chromosomal and genic level ., ChIPOTLe peak finding and clustering analyses both show that the highly trimethylated genes also have high enrichment levels of EMF1 binding , termed EMF1_K27 genes ., EMF1 interacts with regulatory genes , which are silenced to allow vegetative growth , and with genes specifying cell fates during growth and differentiation ., H3K27me3 marks not only these genes but also some genes that are involved in endosperm development and maternal effects ., Transcriptome analysis , coupled with the H3K27me3 pattern , of EMF1_K27 genes in emf1 and PRC2 mutants showed that EMF1 represses gene activities via diverse mechanisms and plays a novel role in the PcG mechanism .

Polycomb group ( PcG ) proteins are epigenetic repressors maintaining developmental states in eukaryotic organisms ., Plant PcG proteins are expected to be general epigenetic repressors; however , their overall impact on growth and differentiation and their mechanism of repression are still unclear ., Here we identified several thousand target genes of the EMBRYONIC FLOWER 1 ( EMF1 ) protein , which shares no sequence homology with known PcG proteins ., EMF1 regulates developmental phase transitions as well as specifies cell fates during vegetative development ., Trimethylation of histone 3 lysine 27 ( H3K27me3 ) and ubiqutination of lysine 119 of histone H2A are carried out by different PcG protein complexes ., EMF1 is required for both histone modifications on genes specifying stem cell fate in plants , thus revealing a novel role of EMF1 in linking the PcG protein complexes ., Our results have important implications for the evolution of PcG regulatory mechanisms .

biology

journal.pcbi.1000309

Mechanism of Action of Cyclophilin A Explored by Metadynamics Simulations

Proline trans/cis isomerization takes part in several fundamental biological processes , including protein folding 1–3 , immune response 3 , 4 , ion channel gating 5 and cellular signalling 3 , 4 , 6 ., The process , which is also associated to the development of a variety of diseases including HIV-1 infection 7 , 8 , carcinogenesis 6 and Alzheimers 9 , is catalyzed by prolyl isomerase enzymes 1 , 10 , 11 ., The best characterized isomerization in vivo and in vitro occurs in the uncoating and recruitment of the human HIV-1 capsid ( CA ) protein in the virions 7 , and it is catalyzed by cyclophilin A isomerase ( CypA , 12 ) ., At the structural level , CypA features α-helices flanking a beta-barrel , while CA is made of several α-helices connected by loops ( Figure 1 ) ., In the X-ray structure of the complex 13 , the targeted proline-containing backbone unit ( G89-P90 ) , is accommodated in a hydrophobic pocket of CypA ( residues F60 , F113 , L122 , and H126 in Figure 1 ) ., Although the backbone unit is in trans conformation in the X-ray structure 13 , NMR studies have shown that 45% of conformers are cis for CA-CypAR55A in aqueous solution 12 ., The rather significant population of cis conformers could arise not only by the replacement of R55 with Ala , but also by crystal packing forces , different temperature conditions ( 100 K and 298 K for the X-ray and NMR experiments , respectively ) , along with different hydration and ionic strength in the two experiments ., Free energy calculations further support the hypothesis that CypA promotes a significant population of cis conformation 14 , 15 ., Thus , the cis population is likely to increase substantially from water – where it is ∼10% 12 – to the complex in aqueous solution ., In vitro kinetic measurements ( Table, 1 ) show that CypA decreases the isomerization free energy barrier ( ΔG‡cis→trans , ΔF‡cis→trans ) of modified substrate fragments in solution by ∼7 kcal/mol 16 , 17 ., Free energy calculations , based either on classical 15 or quantum-mechanical/molecular mechanics ( QM/MM ) 14 , of the 6 aminoacids long substrate fragment point to a similar trend ( Table 1 ) ., In addition , they suggest that H-bonding between the backbone unit of the targeted glycine-proline peptidyl bond and that of N102@CypA 14 , 15 , 18 , as well as van der Waals interactions between substrate and the CypA hydrophobic pocket ( F60 , F113 , L122 , and H126 ) stabilize the transition state ( TS ) 14 , 15 , 18 ., These hypotheses are consistent with the decrease of kcat/Km associated with the polymorphism of cyclophilins in 102 position ( N to T , S , H and R , Table S1 ) and in the F60A , F113A and H126Q CypA mutants 19 , 20 ( Table S1 ) ., NMR studies , along with computations , have further suggested that the motions of several CypA residues ( including R55 and N102 ) are linked to the enzymatic activity 15 , 21–23 ., Free energy calculations characterized a network of protein vibrations in CypA that are associated with its isomerase activity: flexible loops on the surface are connected to the active site by a network of hydrogen bonds 15 , 22 , 23 ., In spite of the great progress in describing the catalytic process , key mechanistic issues need to be addressed ., Kinetic studies 17 suggested that the overall process involves trans and cis forms in solution and in complex with the protein ., However , the studies so far consider mostly TS stabilization ., Most importantly , the current proposed mechanism cannot explain a plethora of molecular biology data ., These studies show that residues not involved in TS stabilization in the proposed mechanisms are important for the function , as their mutations cause a decrease of kcat/Km ., Indeed , kcat/Km passes from 1 . 6 107 M−1 s−1 in the wild type to 1 . 6 104 M−1 s−1 by mutating the fully conserved R55 residue with Ala 20 ., Although this mutation was proposed originally to destroy an H-bond stabilizing uniquely the TS 13 , 24 , such H-bond was subsequently ruled out in computational works 14 , 15 , 18 and so far no functional role has been ascribed to R55 ., In addition , the H54Q mutation , along with the I57V polymorphism , causes a decrease of kcat/Km , although these residues are not involved in TS stabilization ( Table S1 ) ., Here we use molecular simulations to address these issues ., We calculate the free energy associated with the isomerisation of the 6 aminoacids long ( /HAGPIA/ ) substrate fragment in water and in complex with CypA ., The free energy is calculated as a function of the two reaction coordinates ζ and ψ ( defined in Figure 2 ) , which have been suggested to describe best the energetics of the process 25 , as well as other pairs of different coordinates to cross-check our results ., We use here metadynamics 26 ( in its bias exchange extension 27 ) , which has already been employed to predict the energetics of protein/peptide interactions 28 ., The potential used is an effective one ( specifically the AMBER99 force field 29 ) ., This choice allows a very efficient sampling because of its relatively small computational cost ., In spite of its limitations , 30 this force field is expected to be relatively accurate to describe equilibrium conformations , which is a key aspect of our problem ., The accuracy of the force field for minima and transition states is assessed by comparing our results with first principle quantum chemistry calculations ., Based on this comparison , we find that the force-field biases on the energetics of the minima are negligible , whereas their influence on the barriers is more significant ., Therefore , here the free energy differences between minima are used to predict the relative populations of the equilibrium conformations , whilst the calculated barriers are only compared at the qualitative level to discriminate the most likely cis/trans isomerisation path ., The enzyme turns out to dramatically stabilize the populations of one specific cis conformer relative to the trans ones , which instead are by far the most stable in aqueous solution ., In addition , it lowers the free energy barrier of a specific , one–way isomerization from trans to cis ., These findings allow us to propose a mechanistic hypothesis for the isomerization process that is consistent with all the available experimental data ., The initial structural models of /HAGPIA/ in the free state ( PEPT-WAT ) and in complex with CypA ( PEPT-CypA ) were obtained from the CA N-terminal domain/CypA X-ray structure at 2 . 0 Å resolution ( PDBID:1M9C ) 13 ., The X-ray data were collected at 100 K and slightly basic pH ( 8 . 0 ) 13 ., The peptide and the protein were considered in their zwitterionic form ., The protonation states of all histidine residues were determined by pKa calculations based on the H++ server 31 , 32 and by visual inspection of the hydrogen bond network ., All His residues were protonated at Nδ1 ., The result for PEPT-WAT is consistent with the NMR study on CA N-terminal domain at pH 7 33 ., PEPT-WAT and PEPT-CypA were solvated in a 32 Å×37 Å×38 Å and 57 Å×80 Å 60 Å boxes , containing 1 , 423 water molecules ( for a total of 4 , 349 atoms ) and 8 , 233 water molecules and a Cl− ion ( for a total of 27 , 283 atoms ) , respectively ., The ion was added to neutralize the system ., The Amber99 all atom force field 29 was used for the protein and the chlorine atom; the TIP3P model 34 was used for water ., After minimization up to a convergence of 10−4 kcal/mol ( conjugate gradient algorithm ) , the system was equilibrated for 1 ns ( time step of 1 fs ) in an isothermal-isobaric ensemble ( 1 atm , 310 K ) with the Langevin barostat 35 ( the oscillation period and the decay coefficient were set to 200 fs and 100 fs , respectively ) and thermostat 36 ( coupling coefficient 1 ps−1 ) ., We use the Particle Mesh Ewald scheme 37 with 12 Å cutoff and 0 . 75 Å-spaced Fourier grid; we assume a dielectric constant of 1 ., Van der Walls interaction cutoff was set to 12 Å ., Minimization and equilibration were performed with the NAMD 2 . 6 program suite 38 ., Free energies were calculated using the metadynamics method in its bias exchange variant 26 , 27 ., This approach consists in a continuous addition of a history dependent potential energy that enforces the dynamics to explore conformations that were not previously visited ., Briefly , the forces acting on each atom , or centroid , are corrected by a history dependent contribution , obtained as the derivative of the history dependent potential energy Vt with respect to the atom coordinate ., Vt is given by the sum of a set of Gaussian functions centered on the values st of each chosen collective variable ( CV ) s as time t: ( 1 ) The time interval between the addition of two Gaussian functions τ , as well as the Gaussian height w and Gaussian width δ , are tuned to optimize the ratio between accuracy and computational cost ( Table S2 ) ., Eventually , after exploring all conformations defined within the CV space , the probability distribution of Gaussians becomes flat , and the free energy profile does not change any more ., At this stage , the free energy surface can be easily reconstructed as the opposite of the sum of all Gaussians ., Here we use the bias exchange variant of this method 27 , in which several trajectories with different history dependent potentials ( for instance two CVs a and b ) , , are run in parallel ., At specific time intervals ( of the order of few ps ) , are swapped with a probability evaluated by the standard Metropolis scheme 39 ., ( 2 ) where kB is the Boltzmann constant and T is the temperature ., This method was shown to provide an efficient sampling of the conformational space 27 ., The free energies associated with cis/trans isomerization of the G3P4 bond in PEPT-WAT and PEPT-CypA were calculated as a function of pairs of CVs in the canonical ensemble ., The free energies were first calculated as a function of the ζ and ψ dihedral angles ( Figure 2 and Table S2 ) , because these angles have been shown to be essential to describe the isomerisation process 25 ., The resulting free energy plot was used to define the minima and the transition state regions ( R hereafter ) ., Briefly , we performed a cluster analysis of all the structures 40 and evaluated their population using an umbrella sampling-like approach 41; minima and TSs are characterized by several clusters and only one cluster , respectively ( See Text S1 for details ) ., Free energy barriers were then calculated as differences between the TS cluster and the lowest cluster in the basin of the minimum ., The sampling of TSs was higher in PEPT-WAT than in PEPT-CypA , as shown by the few number of structures clusterized in TSs R ( i . e . less than 10 structures ) ., To improve sampling in PEPT-CypA , we performed additional 4 ns-long MD simulations in which the ζ and ψ angles were harmonically restrained at the values of the TSs ., Restraint center and their associated force constants were fitted so as to keep the entire MD within the region of interest ., To test the robustness of our insights on the enzyme mechanism obtained by the above free energy profiles , we compared our results with free energy plots as a function of other pairs of CVs ., These are:, ( i ) the ζ angle and the proline nitrogen ( P4N ) pyramidalization p that might have a role in peptidyl prolyl cis/trans rotation 25 ., p is defined as the distance between P4N and the centre of a plane determined by three atoms belonging to G3 and P4 ( Figure 3 and Table 2 ) ., ( ii ) The ζ and P4N H-bond coordination number ( s1 ) that has been shown to be important for in vacuo prolyl isomerization 25: ( 3 ) where rP4N-j is the distance between P4N and the atom j ( excluding solvent molecules ) while r0 , n , m are parameters chosen to obtain s1∼1 , when the P4N forms one H-bond ( Table S2 ) ., s1 increases with the number of H-bonds formed by P4N with H-bond acceptors of the peptide ( in water ) or of the peptide and protein in the complex ., ( iii ) p and s1 , to assess the correlation between the pyramidalization and H-bonding of the P4N atom ., Finally , we calculated the free energy profiles as a function of selected pairs specific for PEPT-WAT or for PEPT-CypA ., For PEPT-WAT , these are the following:, ( i ) the ζ and P4N coordination number with water solvent molecules ( s2 ) ., s2 is defined as s1 except that j runs over the water oxygen atoms ., This pair tests whether water is able to catalyze prolyl isomerization by forming an H-bond with P4N ., ( ii ) p and s2 , to evaluate if there is a correlation between P4N pyramidalization and its H-bonding to water molecules ., For PEPT-CypA , we introduced CVs that relate the substrate to the enzyme:, i ) hydrophobic coordination numbers s3 , s4 and s5 ,, i . e ., quantities which depend only on non-polar carbon atoms ., s3 , s4 and s5 describe the hydrophobic interaction between the peptide and the protein: ( 4 ) where rij is the distance between atoms i and, j . i runs over the residues involved in the rotating prolyl bond of the peptide ( G3P4 ) for s3 , and the C and N terminal of the peptide ( H1 , A2 , I5 and A6 ) for s4; j runs over any non-polar carbon of CypA belonging to residues located at 4 Å or less from the peptide ( Table S2 ) for both s3 and s4 ., The parameters n and m were chosen so as to distinguish conformations with and without hydrophobic interactions ( Table S2 ) ., This was done by running 1 ns MD test simulations with different n and m parameters ., s5 describes a subset of such hydrophobic interactions , which play a particularly important role during the isomerisation , as suggested by NMR studies 21 ., These are the interactions of L98 and S99 in CypA and C terminal of the peptide ( I5 , A6 ) ., Thus , for s5 , i runs over I5 and A6 , while j runs over the atoms of L98 and S99 ., Finally , we introduced the reaction coordinate s6: ( 5 ) where j runs over all the H-bond donors/acceptors of the peptide or of CypA that were found within 3Å from R55 N atoms in 1 ns MD: those turned out to belong to residues Q63 and N149; rR55Ns-j is the distance from any N atom of R55 and any atom j; r0 , n and m are chosen as in s1 ( Table S2 ) ., This reaction coordinate increases with the number of H-bonds formed with R55 and hence measures the ability of this residue to form H-bonds with the peptide or CypA residues ( organizing its active site ) : this has been indicated as crucial for the catalysis 14 ., The free energy is calculated as a function of, ( i ) ζ , s3;, ( ii ) ζ , s4;, ( iii ) ζ , s5 and, ( iv ) ζ , s6 to analyze hydrophobic ( s3 , s4 , s5 ) and hydrophilic ( s6 ) interactions along with the cis ( ζ∼0° ) ↔trans ( ζ∼±180° ) interconversion ., All metadynamics calculations ( 17 ns for PEPT-WAT and 40 ns for PEPT-CypA ) were performed at the physiological temperature of 310 K . The temperature was controlled by a Nosè-Hoover thermostat 42 , with coupling time constant of 0 . 05 ps ., Electrostatic interactions were assessed using the particle mesh Ewald schemes 37 with 34 wave vectors in each dimension and fourth-order cubic interpolation ., Van der Waals interactions were evaluated as specified for the equilibration phase ., The time-step was 1 fs ., The Gaussians were added with a frequency of 2 GHz and they had a height of 2 . 5 kJ/mol , the width ( δ ) for each CV is reported in Table S2 ., The exchange trial frequency was 400 MHz ., All bonds were constrained with the LINCS algorithm 43 ., We removed the protein centre of mass translation every 10 steps of molecular dynamics ., The calculations were performed with a locally modified version of Gromacs 3 . 3 . 1 27 , 44 , 45 ., The number of hydrogen bonds ( NHB ) was calculated in TS and minima R , supposing a Boltzmann distribution within the cluster , as assumed in 46 for enthalpy calculations: ( 6 ) where R is the region identifying the minima or the TS ( See Figure S1 ) , Ni is the number of structures belonging to cluster i ( see Figure S1 ) , NHB, ( i ) is the number of H-bonds and F, ( i ) is the free energy for cluster, i . The H-bond between the acceptor P4N and donors D is assumed to exist if the distance ( D-P4N ) ≤3 . 2 Å and the angle ( P4N…D-H ) ≤70° ., The populations of puckered conformations were evaluated in terms of χ2 ( dihedral angle Cα-Cβ-Cγ-Cδ in Figure 2 ) : up-puckering is defined when χ2>10° , planar puckering when -10°<χ2<10° , down puckering when χ2<−10° 47 ., The population of puckered conformations Pχ2 ( C ) is defined as: ( 7 ) where C\u200a= ( up , down or planar puckering ) and i is a cluster belonging to this state ., The free energy associated with each conformation C is estimated as ., The interface coordination number ( IC ) in PEPT-CypA is defined as , where i is the index of the carbon atoms and j runs over the carbon atoms of CypA hydrophobic residues within 4 Å of G3 and P4 residues ., The internal energy/entropy contributions are evaluated for the restrained MD simulations of each minima and TS ., The internal energy contribution to the free energy is calculated as the potential energy averaged over all conformations of the cluster 46 , while the entropic contribution is obtained by the standard thermodynamic relation: ., Principal component analysis ( PCA ) 48 , 49 was used to identify large-scale collective fluctuations in minima and TSs restrained dynamics ., This analysis was performed on Cα of PEPT-CypA complex , using Gromacs 3 . 3 . 1 44 , 45 The statistical error of the free energy for minima and TSs are estimated as the largest difference between the F ( ζ ) values calculated from different 2D free energy profiles ( for more details see text S1 ) ., The statistical error on the enthalpy of minima and TSs was estimated similarly ., The accuracy of the force-field used ( Amber 29 ) was established by a comparison with quantum chemical results on a model system ., We compared the cis↔trans isomerization potential energy of a N-acetyl proline methylamide using the Amber force field 29 and DFT calculations at the B3LYP/6-31G ( d ) 50–53 level of theory ( see text S1 for more details on these calculations ) ., Classical calculations were performed with Gromacs 3 . 3 . 1 44 , 45 ., DFT calculations were performed with Gaussian98 54 ., The free energy profile plot shows that the trans0 conformer , in which ζ∼±180° and ψ∼0° , is the absolute minimum ( Figure 4 and Table 2 ) ., The second trans minimum is trans180 ( ζ∼±180° and ψ∼±180° ) , whose free energy is higher by 3 kcal/mol ( Table 2 , Table 3 ) , possibly because the P4N forms a H-bond to the amide group of the adjacent residue only in trans0 ( Table S3 , see Figure 2 and Figure 1 for atom labelling ) ., However , the number of waters around the peptide is larger for trans180 than for trans0 , since the former has a more extended structure ( Figure 4 ) ., This water-peptide interaction may not stabilize enough trans180 against trans0 to overcome the stabilization of the latter by the P4N…I5N intramolecular H-bond ., The free energies of the two cis conformers , ( cis0: ζ∼0° and ψ∼0° and cis180: ζ∼0° and ψ∼180° ) are 3 and 5 kcal/mol higher than trans0 , respectively ., The stabilization of trans relative to cis has been ascribed to steric clashes in the trans conformations ( see , e . g . 10 ) , and is affected by intramolecular interactions ., In our case , in trans A6 H-binds to H1 , A2 and G3 with higher persistence than cis ( Table S3 ) ., This is expected to stabilize further the trans conformation ., The reason why cis0 is more stable than cis180 is again the presence of the P4N…I5N H-bond only in the first conformation ., As in the case of trans conformers , there are more waters around the peptide in the cis180 than in the cis0 conformation , but water-peptide interactions do not stabilize significantly the cis180 conformation compared to the intramolecular H-bond interaction that stabilize cis0 ., The difference in hydration between trans180 and cis180 is reproduced between trans0 and cis0 ., A direct comparison of our calculation with classical and QM/MM umbrella sampling studies based on the reaction coordinates ω 15 and τ 14 , respectively , is not possible , because these calculations do not distinguish between cis and trans conformations at ψ\u200a=\u200a0° and ψ\u200a=\u200a180° ., The transX→cisX and cisX→transX ( X\u200a=\u200a0° , 180° ) isomerization free energy barriers range between 4–18 and 11–15 kcal/mol , respectively ( Table 3 ) ., These values are similar to those obtained by force-field based umbrella sampling calculations performed on the ω variable ( Table 1 ) 15 ., However , early quantum gas phase calculations showed that , at variance with the ζ angle , the ω angle alone does not describe properly the proximity to a saddle point conformation 25 ., The water shell does not change along the transX↔TS↔cisX pathways ( Figure 4 ) ., This is consistent with the suggested poor solvent reorganization during isomerisation in peptides of similar size 55 , 56 ., However , the solvent could play an important role for the isomerization of peptides smaller than that considered here ., The isomerisation is enthalpy-driven ( Figure S2 ) similarly to that found experimentally on different proline containing peptides 52 , 53 ., Furthermore , proline puckering populations are fully consistent with statistical distributions across peptidyl prolyl bonds in the Brookhaven Protein Databank 47 ( see Text S1 and Figure S3 ) ., The lowest pathway is the cis0→TS1→trans0 pathway with an associated barrier of 11 kcal/mol ( Figure 4 ) , also shown by in vacuum DFT potential energy calculations of N-acetylproline methylamide isomerization ( see Text S1 and Table S4 ) ., This value is of the same order of the experimental values for smaller peptides ( ∼4 residues , Table 1 ) ., The stabilization of TS1 ( as opposed to TS2-TS4 ) may be caused , at least in part , by the larger persistency of the P4N-I5N H-bond ( Table S3 and Table S5 ) ., The formation of this H-bond , not only for TS1 but also for TS3 , has been previously predicted 25 ., P4N instead does not interact with the solvent ( See Text S1 ) ., Obviously , the cis0→trans0 pathway is favored over the reverse one , since trans0 is the most stable state ., The isomerizations involving states with ψ∼±180° are higher in free energy because the P4N-I5N intramolecular H-bond is not present in these conformation ( Figure 4 ) ., Again , a comparison with previous free energy calculations in aqueous solution is not possible since previous studies did not discriminate between trans/cis0 and trans/cis180 ., A similar picture emerges from calculations of the free energy as a function of other CV pairs ( Table 2 ) ., These include the P4N pyramidalization , the key H-bond between P4N and the H-bond donors of the peptide ( Figure 3 ) , and P4N hydration ( See Methods , Text S1 and Figure S4 ) ., We conclude that trans0 is the most populated state ( Figure 4 and Table 2 ) and that the fastest kinetic process produces this isomer , starting from the most populated cis isomer ( cis0 ) ., The presence of the protein alters dramatically the population of the four minima in the F\u200a=\u200aF ( ζ , ψ ) plot ( Figure 5 ) ., The global minimum , and therefore the most populated state , is not a trans configuration: it is cis180 ( representative structure in dataset S4 ) ., Cis stabilization has been already found by AMBER-based and QM/MM free energy calculations as a function of ω 15 and τ 14 ( τ dihedral angle – defined as C ( i-1 ) -O ( i-1 ) -Cδ, ( i ) -Cα ( i-1 ) – is similar to ζ – Cα ( i-1 ) -O ( i-1 ) -C, ( i ) δ-Cα ( i-1 ) –used in this work ) ., Trans0 ( representative structure in dataset S1 ) and trans180 ( representative structure in Dataset S2 ) are 1 kcal/mol higher in free energy than cis180; cis0 ( representative structure in Dataset S3 ) is scarcely populated ( Figure 5 and Table 2 ) ., Cis180 features highly persistent H-bonds between N102@CypA…G3 ( O/N ) @PEPT ( Table 4 ) as well as hydrophobic interactions between G3P4@PEPT and N102 , Q63@CypA , A101@CypA , H126@CypA , F113@CypA , M61@CypA , F60@CypA , L122@CypA ( Table 5 ) ., Similar hydrophobic interactions stabilize trans180 , but less persistently ., Moreover , this conformation is stabilized by the R55@CypA…P4O@PEPT hydrophilic interaction ., As for cis180 , F60@CypA stabilizes also trans0 ( Table 5 ) ., The other CypA hydrophobic residues that stabilize the conformations at ψ∼±180° do not interact significantly in trans0 ( Table 5 ) ., At variance with any other minimum , the active site residues I57@CypA and W121@CypA stabilize solely trans0 , i . e . the most stable conformation in water ., The importance of W121 in the trans conformation was already reported in free energy calculations 15 ., N102 stabilizes cis0 , as cis180 , forming an H-bond to P4O ., Several residues of CypA form hydrophobic interactions with cis0 ., Most of these residues stabilize also other minima ( Q63 , A101 , F113 ) ., However , H54@CypA and A103@CypA stabilize only cis0 , the minimum with the highest free energy ., The lowest free energy barrier is associated with the trans180→TS2→cis180 ( counterclockwise ) path ( Tables 2 and 3 ) ., This is also the only isomerization pathway catalyzed by the enzyme relative to PEPT-WAT ( Tables 2 and 3 ) ., Preferential lowering of the counterclockwise N-terminal rotational free energy barrier is consistent with previous classic MD and free energy calculations 15 , 57 , 58 ., Moreover , as previously noticed 58 , N-terminal residue H1@PEPT is exposed to the solvent , while the C-terminal part is anchored to CypA active site ( Table S6 ) ., TS2 ( representative structure in Dataset S6 ) is stabilized by strong N102N…G3O@PEPT and N102O@CypA…G3N H-bonds ( see Table 4 ) and by persistent hydrophobic interactions between the peptide and Q63 , A101 , H126 , F113 , F60 on CypA ( see Table 5 ) ., The enzyme does not decrease the barrier of the reverse pathway ( cis180→TS2→trans180 ) , as CypA stabilizes similarly cis180 and TS2 ( the latter is a bit more stabilized given the higher persistence of hydrophobic and hydrophilic interactions ) ., In addition , the H-bonds and hydrophobic contacts stabilize TS3 ( representative structure in Dataset S7 ) and TS4 ( representative structure in Dataset S8 ) along with their connected minima ( Table 4 , Table 2 , and Figure 5 ) ., We further notice that the H-bonds in TS3 are the same as in TS2 , although less persistent ( Table 4 ) ., TS1 ( representative structure in Dataset S5 ) is not stabilized by H-bonds and interacts weakly with CypA hydrophobic residues ( in particular , F60 , L122 and W121 , Table 4 ) ., Therefore the peptide in this conformation does not form a tight complex with the protein and it is exposed to the solvent ( Figure 5 ) ., Thus , this isomerization pathway ( trans0↔TS1↔cis0 ) is not too dissimilar from that in water and , indeed , the barriers for the two processes are practically identical ( Table 2 ) ., In each minimum and TS we identify large collective motions of the PEPT-CypA complex using PCA ., We observe that significant modes involve almost the same residues as the motions found in Ref ., 15 ( see Figure S6 ) ., As for the peptide in water , we used other CV pairs ( in addition to ζ , ψ ) to describe the cis/trans isomerization ., These include the pyramidalyzation as well as the number of H-bonds formed by P4N and residues of the peptide or of CypA ., In addition , we included coordinates to take into account, ( i ) the hydrophobic interaction between the peptide and the enzyme , and, ( ii ) the interactions of R55 with both the peptide and the CypA active site ( for a discussion of the other CV pairs see Figure S5 , Text S1 , and Table S7 ) ., All these calculations provide a consistent picture that leads us to conclude that the enzyme catalyzes only one pathway , the one from the most populated trans conformation , trans180 , to the most stable minimum , cis180 ., In water , trans0 is by far the most populated specie ( Table 2 ) ., The same result for the potential energy has been obtained with DFT ( see Text S1 ) gas phase calculations on N-acetylproline methylamide ., The protein environment changes dramatically the populations of the proline conformers , stabilizing cis with respect to trans , as previously reported by CypA-SUC-Ala-Phe-Pro-Phe-pNA complex 17 and CypAR55A-CA 12 ., However , our study provides a quantitative estimate of the populations of conformers ( Table 2 ) : in presence of the enzyme , three conformers ( trans0 , trans180 and cis180 ) are significantly populated ., The most populated one corresponds to cis180 , that is exactly the most unfavorable state in aqueous environment ., This finding has never been reported in literature ., CypA accelerates only one interconversion , namely the one from trans180 to TS2 to cis180 ( Table 3 ) , as already suggested by previous calculations 15 ) : our calculated free energy barrier decreases by 4 kcal/mol with respect to the peptide in water ., Indeed , as well as in the other pathways , almost no effect is found on the reverse pathway ( cis180→TS2→trans180 , 1 kcal/mol , i . e . within the error of the calculations ) , again in agreement with Refs ., 14 , 15 ( Table 3 ) ., The trans180→TS2→cis180 is also associated with the lowest free energy barrier ., We conclude that this pathway is the most likely in the enzyme , although we cannot establish with high accuracy the free energy barrier of the enzymatic reaction ., Based on our calculation , we propose the following mechanism ( Figure 6 ) : Our mechanism provides a first rationale for mutational data which have not been explained so far ., The decreased kcat/Km values ( Table S1 ) of the W121A mutant 20 and the I57V variant 19 cannot be explained in terms of loss of TS stabilization ., In our mechanism , these two mutations are likely to affect substrate stabilization in the trans0 conformation , reducing CypA ability to capture the substrate ., In addition , the dramatic decrease of enzyme efficiency in the R55A mutant ( 0 . 10% residual activity 20 ) , whose effect on TS stabilization is controversial , may be , at least in part , a consequence of the reduced population of trans180 , the reactant of the CypA catalyzed pathway ( trans180→TS2→cis180 ) ., Next , we analyze the effect of several mutations and polymorphism , so far ascribed only to TS stabilization 14 , 15 , 18 , as also found here , that might also have an impact on ground state populations ., Indeed , reduced kcat/Km values on enzymes where N102 exchanges to T , H and R residues 19 may be not only due to destabilization of the TS , but also of the cis180 conformation ., Similarly , some CypA mutations also decrease kcat/Km:, ( i ) F60A 20 may destabilize TS2 , trans0 and cis180;, ( ii ) F113A and H126Q 20 may affect all the species along the trans180→TS2→cis180 pathway ( Table S1 ) ., cis0 , proposed to be the most probable final step in our mechanism based on H54Q mutant , is stabilized exclusively by interactions of H54 and A103 with G3P4 ., We conclude that our mechanism is consistent with all the mutational and polymorphism data and provides a structural basis for most of them .

Introduction, Methods, Results, Discussion

Trans/cis prolyl isomerisation is involved in several biological processes , including the development of numerous diseases ., In the HIV-1 capsid protein ( CA ) , such a process takes place in the uncoating and recruitment of the virion and is catalyzed by cyclophilin A ( CypA ) ., Here , we use metadynamics simulations to investigate the isomerization of CAs model substrate HAGPIA in water and in its target protein CypA ., Our results allow us to propose a novel mechanistic hypothesis , which is finally consistent with all of the available molecular biology data .

Peptidyl prolyl isomerases are ubiquitous enzymes whose actions are crucial in several biological processes , such as , for instance , in cellular signalling and in the onset of several diseases , e . g . , HIV infection ., Therefore , these isomerases are promising targets for the design of new drugs ., For this purpose , we need to understand their molecular mechanism of action ., One of the most characterized peptidyl prolyl isomerases is cyclophilin A . Previous studies characterized the roles of several protein regions in isomerase function ., However , there are still experimentally identified important portions of the protein whose specific actions in the mechanism are still not known ., Here , we address this problem by an extensive computational study of cyclophilin A and a substrate peptide that is part of the HIV-1 capside protein ., We present a novel four-step mechanism of the whole enzymatic process , which is consistent with all of the available experimental data ., Moreover , these steps can be used as targets for the development of drugs , e . g . , for HIV-1 infection .

computational biology/molecular dynamics, biophysics/biocatalysis

journal.pgen.1005154

Genome-Wide Negative Feedback Drives Transgenerational DNA Methylation Dynamics in Arabidopsis

Epigenetic variation of gene expression is mediated by chromatin marks , such as modifications of histones and DNA ., Importantly , these marks and associated gene expression patterns can be inherited over multiple generations in both animals and plants 1 , 2 ., Transgenerational epigenetic inheritance , especially the one associated with DNA methylation , is widespread in plants , and that could have a significant impact on evolution 3–5 ., The long-term dynamics of DNA methylation has recently been examined genome-wide at single base resolution in the flowering plant Arabidopsis 6 , 7; by analysing repeatedly self-pollinated wild type Arabidopsis plants , heritable gain and loss of DNA methylation have been detected , although their frequencies are generally low ., A complementary approach to uncover the background mechanisms controlling long-term DNA methylation dynamics is to examine the effects of impaired DNA methylation pattern over multiple generations ., Factors controlling genomic DNA methylation have been studied extensively in Arabidopsis; and many of these factors constitute positive feedback loops to stabilize epigenetic states ., Cytosine methylation in the context of dinucleotide CG is maintained by maintenance methyltransferase MET1 8 , 9 , while cytosine methylation at non-CG site is mediated by chromomethylases ( CMTs ) 10 , 11 ., The CMTs are recruited to chromatin by methylation of histone H3 lysine 9 ( H3K9me ) , and the H3K9 methylase KYP/SUH4 is also recruited to chromatin with non-CG methylation , generating a self-reinforcing positive feedback loop 11–14 ., Both H3K9me and non-CG methylation are silent heterochromatin marks normally found in repeats and transposable elements ( TEs ) ; and these marks are rarely detectable in transcribed genes ., Exclusion of these marks from transcribed genes depends on the H3K9 demethylase IBM1 ( Increase in BONSAI Methylation 1 ) 13 , 15 ., IBM1 removes H3K9me from transcribed genes , generating another positive feedback loop to stabilize active states 13 ., In addition , a positive feedback loop is also found in a process called RNA-directed DNA methylation ( RdDM ) ., RdDM is a de novo DNA methylation process triggered by double-strand RNA; and factors involved in this process have been extensively studied 16–20 ., The final step of RdDM is DNA methylation of both CG and non-CG sites by the de novo DNA methyltransferase DRM2 ( Domains Rearranged Methylase 2 ) , with the RNAi machinery and small interfering RNA ( siRNA ) functioning as upstream factors ., Interestingly , production of siRNA also depends on DRM2 21 , 22 , suggesting another positive feedback that stabilizes the silent state ., Genome-wide DNA methylation profiles have been determined in mutants of these and other factors controlling DNA methylation 11 , 23 , 24 , although information for the transgenerational effects of these mutations is limited ., Among the Arabidopsis mutants affecting genomic DNA methylation , ddm1 ( decrease in DNA methylation 1 ) is one of the mutations with the strongest effects ., Mutant plants show drastic reduction of DNA methylation at both CG and non-CG sites in heterochromatic repeats and TEs 25 , 26 ., The DDM1 gene encodes a chromatin remodeling factor , which is necessary for DNA methylation in heterochromatic sequences 10 , 27 ., Mutation in its mammalian ortholog Lsh induces loss of DNA methylation , suggesting conserved functions across the animal and plant kingdoms 28 , 29 ., A striking feature of the Arabidopsis ddm1 mutant is the progressive accumulation of the developmental defects; initial generations of the ddm1 mutant grow relatively normally , but many types of developmental abnormalities arise after multiple rounds of self-pollinations 30 , 31 ., Some of the abnormalities are due to DNA sequence changes , such as insertion mutations of de-repressed endogenous TEs 32–34 or a rearrangement of repeats 35 , but others are due to epigenetic changes in gene expression , which correlate with changes in DNA methylation pattern at the affected loci 36 , 37 ., Here we analyze the transgenerational effects of the ddm1 mutation genome-wide , by comparing DNA methylation of the ddm1 mutants before and after the repeated self-pollinations ., This analysis revealed ectopic accumulation of non-CG methylation at hundreds of loci; and unexpectedly , this hypermethylation could only be seen after repeated self-pollinations ., Furthermore , when ddm1-derived chromosomes with disrupted heterochromatin were introduced into a DDM1 wild type background , de novo accumulation of non-CG methylation was induced in trans ., These results lead us to propose a model in which loss of heterochromatin is progressively compensated for through a negative feedback mechanism that leads to heterochromatin redistribution across the genome ., To understand the changes in DNA methylation patterns during self-pollinations of ddm1 mutant genome-wide , we compared DNA methylation before and after the self-pollination of the mutant ., We examined DNA methylation in four individuals of ddm1 homozygous mutants segregated in progeny of a heterozygote ( hereafter called 1G for the 1st Generation ) and also four lines of ddm1 plants independently self-pollinated eight times ( hereafter called 9G ) ( S1 Fig ) ., In 1G , the ddm1 mutation already induced reduction of DNA methylation in heterochromatic regions 10 , 25 , 26 ., Methylation in repetitive sequences , such as transposable elements ( TEs ) ( Fig 1D–1F ) , was much more severely affected than that in low copy sequences , such as genes ( Fig 1A–1C ) ., The reduction was found for both CG sites ( Fig 1A and 1D ) and non-CG sites ., In non-CG sites , both CHG sites ( Fig 1B and 1E ) and CHH sites ( Fig 1C and 1F ) were affected ( H can be A , T , or C ) ., When we compared average DNA methylation of 9G to 1G , two features were noted for both genes and TEs: further decrease of CG methylation and an increased methylation at non-CG sites ( Fig 1 ) ., Although the ddm1 mutation immediately induces a drastic loss of DNA methylation in repeats , further reduction of methylation in later generations has been reported for a few CG sites 30 ., Our genome-wide analysis revealed that many loci behave in a similar manner ( Fig 2A ) ., The progressive reduction of DNA methylation can have significant phenotypic effects; for example , the promoter of the imprinted gene FWA remains methylated in the 1G ddm1 but the methylation is lost stochastically in 9G ddm1 ( Fig 2B ) , generating heritable epialleles that cause late-flowering phenotype 31 , 36 , 38 ., The progressive reduction is seen genome-wide for both genes and TEs ( Fig 1A and 1D ) ., To compare the features of the regions hypomethylated immediately or gradually , we defined differentially methylated regions ( DMRs; details in Materials and Methods ) ., The regions ddm1 affects immediately ( 1G-WT DMRs ) were enriched in dimethylation of histone H3 lysine 9 ( H3K9me2 ) ( Fig 2D left and 2E ) ., H3K9me2 is a mark of silent heterochromatin , and these results are consistent with previous reports 10 , 26 ., In marked contrast , however , regions affected slowly ( 9G-specific DMRs ) have much lower level of H3K9me2 in wild type ( Fig 2D middle ) ., DDM1 gene function is necessary for CG methylation in heterochromatin , but in the long-term DDM1 also has significant effects on CG methylation in less heterochromatic regions including gene bodies ( Fig 2C ) ., More counter-intuitively , our genome-wide analysis revealed a large number of genes and TEs ectopically hypermethylated at non-CG sites in the self-pollinated ddm1 lines ( Figs 3A , 3B , 4A and 5A–5E ) ., The regions CHG hypermethylated also showed hypermethylation at CHH sites ( Figs 3D , 5A–5D , and S6A Fig ) ., In addition , although genic CG methylation tends to decrease progressively from 1G to 9G on average ( Figs 1 and 2 ) , non-CG hypermethylated regions show an increase in CG methylation ( Fig 3D ) ., The CG and non-CG hypermethylation was found reproducibly at specific loci ( S8 Fig ) ., The affected loci include BONSAI and other sequences we have reported previously 37 , 39 , but the majority of the affected loci could only be detected by whole-genome bisulfite sequencing ( WGBS ) , because that can detect increased non-CG methylation with high sensitivity even at loci already CG methylated ., In addition to genes , a large number of TEs showed increase in non-CG methylation ( Figs 3A , 3B , 4E , and S9–S11 Figs ) ., A very unexpected feature revealed by WGBS is that non-CG hypermethylation of genes is almost undetectable in the first generation of ddm1 but is specifically and reproducibly seen in the repeatedly self-pollinated ddm1 lines ., In Fig 3A and 3B , many black dots can be seen along the vertical axis in the panels for 9G but not for 1G ., The non-CG hypermethylation of genes is not a simple extension of the effect seen in the first generation ., This feature can only be detected in later generations ( Fig 3C ) ., In order to further understand the transgenerational dynamics , we examined four independently self-pollinated 2G ddm1 plants ., If the hypermethylation proceeds equally at each self-pollination , the increase from 1G to 2G would be 1/8 or more of the increase from 1G to 9G , provided that the methylation level should saturate at specific level ( the methylation level can not exceed 100% ) ., Interestingly , although hypermethylation proceeded in 2G , the difference between 1G and 2G was much less than 1/8 of that between 1G and 9G , suggesting that the increase is slow initially but accelerated in later generations ( S12 and S13 Figs ) ., How is this non-CG hypermethylation induced ?, Our genome-wide bisulfite analyses revealed that the genes non-CG hypermethylated in the self-pollinated ddm1 tend to have low levels of non-CG methylation already in wild type plants ( Fig 3D ) , suggesting that preexisting small heterochromatin domains may function as seed for further heterochromatin formation ., Interestingly , distribution of H3K9me2 around the DMR is asymmetric; it is enriched in the 3’ of the DMRs ( S14 Fig ) ., We have previously shown that the BONSAI gene is flanked by insertion of a heterochromatic LINE in the 3’ region 37 ( Fig 4A and S13A Fig ) ., The BONSAI hypermethylation in ddm1 is induced in a strain with the LINE insertion but not found in a strain without the LINE insertion 37 ., The DNA methylation spreads from the 3’ LINE to the BONSAI region during repeated self-pollination of ddm1 mutants 37 ., Spread of non-CG methylation from 3’ to 5’ regions was also noted at other loci ( Fig 5A–5D ) ., When the methylation level differs among the four 9G ddm1 plants , plants with stronger signals tended to show relative centroid positions more upstream than plants with weaker signals , suggesting that the signal spreads from 3’ to 5’ ( Fig 5F ) ., These observations suggest that common mechanisms may operate in BONSAI and many , even if not all , affected loci ., We have previously shown that the de novo non-CG methylation in the self-pollinated ddm1 does not require components of the RdDM machinery , such as RDR2 , DCL3 , and DRM2 39 ., On the other hand , the non-CG methylase CMT3 and H3K9 methylase KYP are necessary for the de novo methylation , suggesting that the ectopic methylation occurs by mechanisms mediated by the heterochromatin marks H3K9me and non-CG methylation 39 ., Indeed , the non-CG hypermethylation at the BONSAI locus is associated with ectopic H3K9me ( Fig 4B ) ., The self-reinforcing loop of non-CG methylase and H3K9 methylase activities could be the basis for the acceleration of hypermethylation as the generation proceeds ( S13B Fig ) ., As the two processes enhance each other , the positive feedback would accelerate the spread of the heterochromatin in later generations 12 , 13 ., Increased non-CG methylation has been reported in mutants of the CG methyltransferase gene MET1 40–42 , which results at least in part from a reduction of full-length IBM1 transcript 43 ., The IBM1 gene encodes a demethylase for histone H3K9; and mutation in this gene induces accumulation of H3K9me2 and non-CG methylation in gene bodies ., Interestingly , developmental phenotypes of the ibm1 mutation also become progressively stronger during self-pollinations 15 ., We compared the regions of non-CG hypermethylation in the ibm1 and self-pollinated ddm1 ., Although an overlap can be detected , the majority of the DMRs in ddm1 mutants before and after the self-pollinations were distinct from the DMRs of ibm1 mutants ( Fig 6B and S16 Fig ) ., Just as progressive loss of CG methylation in the ddm1 mutant , ibm1 mutant shows progressive accumulation of non-CG methylation in later generations ( Fig 6A , S15 and S16 Figs ) ., This is consistent with a recent report 44 and likely accounts for the progressive developmental defects in the ibm1 mutant ., We examined DNA methylation patterns of the genes and TEs hypermethylated in the self-pollinated ddm1 lines ( Fig 6C ) ., Compared to the ibm1 mutant , the peak in the ddm1 was shifted toward 3’ end ., Interestingly , the shift of the peak in the hypermethylation was also found for CG methylation ( S5D Fig ) ., Although CG methylation of gene body in wild type peaks around the center ( S5C Fig ) , increase of genic CG methylation in 9G ddm1 was not proportional to the methylation of wild type; instead , the increase of CG methylation was shifted toward 3’ regions ( S5D Fig ) ., Together with the observation that CHG-hypermethylated genes tend to show CG-hypermethylation ( Fig 3D ) , these results suggest a link between the ectopic CG methylation and non-CG methylation , as we discussed previously 39 ., The bias of the hypermethylation signal toward the 3’ region in 9G ddm1 is especially evident in the hypermethylated TEs; the peak was often located outside of the transcription unit for both CHG and CHH methylations ( Fig 6C , bottom half ) ., When different families of TEs are compared , the peak in the downstream region was especially evident in the GYPSY-like LTR retrotransposons ( S10 Fig ) ., Generally , these TEs lost DNA methylation in 1G ddm1 , but regained methylation during the self-pollinations ( S5A and S9–S11 Figs ) ., The ddm1 mutation can induce increased DNA methylation at hundreds of genes and TEs ., The hypermethylation can be a direct consequence of impaired DDM1 function , or alternatively , an indirect effect of disruption of heterochromatin in the mutants ., To test these possibilities , we examined the effect of chromosomes introduced from ddm1 into wild type DDM1 background ., Chromosomes losing DNA methylation in the ddm1 mutants remain unmethylated even after introduction into wild type DDM1 background 25 , 45 ., We examined DNA methylation data of epigenetic recombinant inbred lines ( epiRILs ) 46 ., In the epiRILs , a ddm1 mutant plant was crossed to wild type plant twice to segregate DDM1/DDM1 lines with around one quarter of chromosome segments derived from ddm1 ., Although remethylation can be induced in regions associated with small RNA , hundreds of DMRs remain unmethylated in the wild type DDM1 background 46 , 47 ., Each of these segregating lines have been self-pollinated seven times , which makes most of the genomic regions fixed in ddm1-derived haplotype or wild-type derived haplotype 46 ., We examined if the loci exhibiting hypermethylation in the self-pollinated ddm1 lines also showed hypermethylation in some of the epiRILs ., We utilized DNA methylation data for the 123 epiRILs , which are based on immunoprecipitation ( IP ) of genomic DNA by anti-methylcytosine antibody ., As the context of methylation cannot be distinguished , we examined seven loci that show increased methylation in 9G ddm1 but a relatively low level of methylation at CG sites in wild-type ., In six out of the seven loci examined , we could detect hypermethylation in multiple epiRILs , suggesting that the hypermethylation can be induced or maintained in the DDM1 background ( Figs 7A , 7C , 7E and S17 Fig ) ., In all of them , the hypermethylation showed a strong positive correlation with the amount of disrupted heterochromatin in each of these lines ( Fig 7 , S17 Fig and S1 Table ) , suggesting that the hypermethylation was induced or maintained in the background of disrupted heterochromatin in other genomic regions ., The hypermethylation could be induced de novo or alternatively maintained from the parental ddm1 ., The parental ddm1 plant originally used for making epiRILs was already self-pollinated three times ( 4G ) and that plant also show low level of ectopic methylation at some loci ( S17 Fig ) , which may have the potential to be maintained in DDM1 background 37 ., Very importantly , however , the hypermethylation was found even in chromosome segments originated from wild type DDM1 ( Figs 7B , 7D , 7F and S18–S23 Figs ) , demonstrating that the hypermethylation could be induced de novo after the initial crosses and subsequent repeated self-pollinations in the background of functional DDM1 ., In order to confirm and extend this observation , we used WGBS for an epiRIL with genome-wide reduction of heterochromatic DNA methylation ., The epiRIL98 , which contains large amount of chromosomes with reduced DNA methylation , showed CHG hypermethylation in many genes ( Fig 8A ) , which include BONSAI gene ( S24A Fig ) and genes with body methylation ( S24B–S24C Fig ) ., In the CHG hypermethylated genes , the CHG methylation level was generally much higher than that of the parental 4G ddm1 plant ( Fig 8B ) , suggesting that the hypermethylation was amplified or induced de novo in the background of functional DDM1 ., A large number of CHG hypermethylated genes were found in chromosome regions of wild type haplotype ( Fig 8C and S25 Fig ) , again suggesting that they can be induced de novo ., In control epiRILs with much lower levels of disrupted chromatin , the hypermethylation was undetectable , confirming that the disrupted heterochromatin was responsible ( Fig 8A ) ., Taken together , these results indicate that the hypermethylation can be induced de novo by trans-acting effects of disrupted heterochromatin ., Here we report short- and long-term effects of the ddm1 mutation ., The mutation immediately induces a drastic loss of DNA methylation in heterochromatic regions in the first generation when it becomes homozygous ., In later generations , the ddm1 mutation reproducibly induces ectopic accumulation of DNA methylation in hundreds of genes and TEs ., This work and previous work 39 suggest that the ectopic methylation occurs by spread of heterochromatin marks mediated by the non-CG methylase CMT3 and H3K9 methylase KYP ., Interestingly , this effect was slow in the initial generations but accelerated in later generations , suggesting strong positive cooperativity for the heterochromatin accumulation ., That could be explained by the self-reinforcing positive feedback of H3K9me and non-CG methylation 12 , 13 ., In addition to the local positive feedback , global negative feedback seems important for the DNA methylation dynamics ., The ectopic DNA methylation seems to reflect negative feedback of disrupted heterochromatin in other genomic regions , because the ectopic methylation could also be induced in DDM1 wild type background when the genome contains large amount of chromosomal segments with disrupted heterochromatin ( Figs 7 and 8 ) ., How does the negative feedback work ?, One possible explanation is that disruption of heterochromatin in the ddm1 mutant results in release of heterochromatin-forming factors such as CMTs and H3K9 methylases , which then become available in other regions ., As these factors are normally recruited to heterochromatin , disruption of a large proportion of heterochromatin in the genome would result in increased level of these factors in released conditions , which would induce spread of heterochromatin into normally euchromatic regions and its amplification by the self-reinforcing loop of H3K9me and non-CG methylation ( Fig 9 ) ., In the model we proposed , global reduction of heterochromatin induces ectopic non-CG methylation ( Fig 9 ) ., That would account for the correlation between the global reduction of methylation and ectopic methylation in epiRILs ( Fig 7A , 7C , 7E and S17 Fig ) ., An alternative mechanism would be that ddm1 induces change in a specific locus , such as transcriptional de-repression or repression of a specific gene , and the change is inherited in the DDM1 wild type background and induces the ectopic methylation ., For example , ROS1 gene expression is reduced in mutants with reduced DNA methylaiton 48 , which would lead to hypermethylation at specific loci ., However , although ROS1 gene expression is reduced in ddm1 , it is expressed almost normally in epiRIL98 , which show strong non-CG hypermethylation ( S26A Fig ) ., In addition , DMRs hypermethylated in 9G ddm1 and ros1-dml2-dml3 triple mutant do not overlap much , further suggesting that the hypermethylation in 9G ddm1 is not due to reduced ROS1 expression ( S26B Fig ) ., More generally , we could not find a locus consistently derived from ddm1 parent in all of the plants showing the high level of ectopic hypermethylation in the six loci ( S18–S23 Figs ) ., Although we cannot exclude the possibility that two or more specific loci redundantly mediate the ectopic methylation , a more parsimonious explanation derived from available data would be that the trans-interaction is mediated by global homeostasis ., The de novo methylation in the epiRILs might also be related to mechanisms such as paramutation 49 , 50 , or transchromosomal methylation ( TCM ) 51 ., In these phenomena , methylated sequences induce methylation in related sequences ., However , the ectopic hypermethylation in the epiRILs is generally much higher than that of the parental ddm1 ( Fig 8B ) , suggesting that even if paramutation-like or TCM-like mechanisms are involved , the effect should be much amplified during self-pollinations of epiRILs; and the degree of the amplification correlates with global disruption of heterochromatin ( Fig 7 and S17 Fig ) , which is due to the ddm1-derived chromosomes ., This trans-acting negative feedback could also be understood as a hypersensitive reaction to the challenge by active and proliferating TEs ., Our genome-wide analyses revealed that many of the TEs can be targets of the negative feedback ( Fig 3A and 3B and S9–S11 Figs ) ., Active TEs often keep parts of heterochromatin , which can function as seeds of the self-reinforcing heterochromatin formation ., An increase in non-CG methylation is also seen in mutants of the histone demethylase gene IBM1 ., However , targets of IBM1 are generally euchromatic and they do not overlap much with regions hypermethylated in the self-pollinated ddm1 lines ( Fig 6B and S16 Fig ) ., An increase in non-CG methylation is also found in the maintenance CG methylase gene MET1 40–42 ., As a mechanism for the met1-induced increase in non-CG methylation , loss of IBM1 function is suggested , as IBM1 transcripts become truncated in the met1 mutant 43 ., On the other hand , it has been reported that the main targets of the met1-induced accumulation of H3K9me2 are genes with H3K27me3 , another modification for silent chromatin 52 ., The negative feedback of heterochromatin marks comparable to that seen in the self-pollinated ddm1 lines may also operate in met1 mutants ., In our analyses , although regions affected by met1 , ibm1 , and self-pollinated ddm1 all differ , significant overlaps are noted ( S27 Fig ) ., For these mutants , the local triggers for heterochromatin accumulation appear to be distinct , despite the possible overlap in the downstream mechanisms , including the self-reinforcing loop of non-CG methylation and H3K9me ., Heterochromatin homeostasis mechanisms analogous to those we have uncovered in Arabidopsis may also be operating in other eukaryotes ., Mice with a disruption of its DDM1 homolog Lsh show global reduction of genomic DNA methylation , but interestingly it is also associated with increased DNA methylation at specific regions 29 ., In human cancer , hypomethylation of repeats and TEs is often associated with local hypermethylation of genes , such as tumor suppressor genes 53 , 54 ., In Drosophila , an increase in the amount of heterochromatic Y chromosome can results in a release of silencing at multiple loci in trans 55 , suggesting a negative feedback similar to that discussed here ., Furthermore , Drosophila modifiers of position effect variegation often function in dosage-dependent manners 56 , 57 , consistent with the pathway proposed in Fig 9 ., Positive feedback loops would stabilize and enhance silent and active states 12 , 13 , 21 , 58 , but they carry the risk of going out of control to excess ., A global negative feedback mechanism , together with the local positive feedback , would ensure a robust and balanced chromatin differentiation within the genome , as has been discussed for pattern formation during development 59 , 60 ., In the context of evolution in plants , a large variation in the amount of repetitive sequences is often noted between related species or even within a species 61–63 ., On such occasions , fine-tuning of the amount of trans-acting heterochromatin factors would be especially important , as an imbalance would not only immediately affect gene expression level but also influence the epigenotype in a transgenerational manner ., Isolation of the ddm1-1 and ibm1-4 mutants has been described previously 15 , 25 ., Self-pollination of ddm1 lines was described previously 30 ., In order to remove heritable effects of the ddm1 mutation , the original ddm1 mutant was backcrossed six times in the heterozygous state ., The heterozygous plants were propagated by self-pollination ., 1G ddm1 mutant plants were selected from self-pollinated progeny of the heterozygote ., 9G ddm1 plants were generated by independently self-pollinating different ddm1 segregants eight times ( S1 Fig ) ., Generation of epiRILs has been described previously 64 ., The annotations of genes and TEs are based on The Arabidopsis Information Resource ( http://www . arabidopsis . org/ ) ., TAIR8 was used for analyzing ChIP chip data ( Fig 2E ) , TEG ( TE gene ) data , and epiRILs data ., TAIR10 was used for other analyses ., The details of the annotation of TEGs were described in a document in TAIR web ( ftp://ftp . arabidopsis . org/home/tair/Genes/TAIR8_genome_release/Readme-transposons ) ., For the 1G and 9G ddm1 plants and their controls , genomic DNA was isolated from rosette leaves using the Illustra Nucleon Phytopure genomic DNA extraction kit , and genome-wide bisulfite sequencing was performed as described previously 65 ., Raw sequence data were deposited in the DDBJ ( DNA Data Bank of Japan ) Sequence Read Archive ( DRA; accession nos . DRA002545 , DRA002546 , DRA002548 , DRA002549 , DRA002551 , DRA002554 , DRA002555 , DRA003018 , DRA003019 and DRA003020 ) ., The adaptor sequences were clipped out using the FASTX-toolkit ( http://hannonlab . cshl . edu/fastx_toolkit/ ) ., Reads were trimmed to 90 nucleotide length ( 45 nucleotide for the data obtained from GEO—GSE39901 ) and mapped to reference genomes ( Release 10 of the Arabidopsis Information Resources ) using the Bowtie alignment algorithm 66 with the following parameters , -X 500-e 90-l 20-n 1 ., Only uniquely mapped reads were used ., Clonal reads were removed except one with the best quality ., Any read with three consecutive methylated CHH sites were eliminated ., The level of methylation of cytosine in a genomic region was calculated using the ratio of the number of methylated cytosine to that of total cytosine ., For the three epiRILs and two parental lines , whole-genome bisulfite sequencing was described previously 46 and the data are in GEO ( GSE62206 ) ., DMRs ( differentially methylated regions ) were defined by comparing the methylation level of 100-bp windows throughout the genome between two genotypes ., The windows with at least 20 sequenced cytosines were used for the comparison ., The level of methylation was calculated using the weighed methylation level of each genotype 67 ., The windows were selected as DMRs when difference of methylation level was 0 . 5 or more at CG site or 0 . 3 or more at CHG sites ., For defining contiguous DMR ( conDMR ) , multiple DMRs were merged if they were adjacent to each other or there was only one gap of the 100-bp window ., The centroid of cytosine methylation in conDMR was calculated using the relative position within that region weighed by methylation level of each cytosine ., In Fig 5F , we used conDMR of 500 bp or longer and overlapping with genes ., Each contiguous DMR was aligned according to the orientation of the corresponding gene ., The correlation coefficient between the level and the relative centroid position of DNA methylation was calculated among the four 9G ddm1 plants in each conDMR ., To plot DNA methylation patterns over genes or TEGs in ddm1 mutants , #1 samples of each genotype ( Figs 2A , 3A and 3B ) in 1G ddm1 and 9G ddm1 were used ., To draw the heatmap of methylation of cytosine , cluster 3 . 0 68 and Java Treeview 69 were used ., 15-day-old seedlings were fixed with formaldehyde and ChIP was performed as described previously 70 , using antibody against H3K9me1 ( CMA316 ) and H3K9me2 ( CMA307 ) 71 ., To assure the equal amount of chromatin in each line , input DNA were quantified by quantitative PCR using TaKaRa Dice_Real Time System TP800 and ACT7 primers ., Then , input DNA and each sample were diluted according to the estimated input DNA concentrations ., Input DNA , mock ( without antibody ) , and ChIP samples were analyzed by PCR ., The PCR conditions were as follows: pre-incubation for 2 min at 94°C , 27 cycles at 94°C for 30 sec , 58°C for 20 sec , 72°C for 45 sec and a final extension at 72°C for 4 min ., Primers used for the ChIP are listed in S2 Table ., In addition to the BONSAI locus , we examined six loci with CHG methylation increased more than 0 . 3 from 1G ddm1 to 9G ddm1 ., Three of them were selected for relatively high level of ectopic CHH methylation ( H1 , H2 , H3 ) and three with relatively low CHH methylation ( L1 , L2 , L3 ) ., The increase of CHH methylation from 1G ddm1 to 9G ddm1 is more than 0 . 2 for the three H loci , and it is less than 0 . 02 for the three L loci ., The lengths of amplicons for the six loci are between 250 bp and 300 bp ., ChIP-seq data of various histone modifications 72 in GEO ( GSE28398 ) were used for our analysis ., The coordinates were remapped onto TAIR10 annotation using a script in TAIR 73 ., Enrichment of histone modification in a DMR was calculated by the density of ChIP-seq reads , and normalized by the mean and the standard deviation of the density of reads in 100 , 000 windows randomly chosen across the genome ., The MeDIP-chip data of 123 epigenetic recombinant inbred lines ( epiRILs ) , ddm1 and WT are in GEO ( GSE37284 ) ., The regions that were methylated ( M ) in WT and unmethylated ( U ) in ddm1 were selected as targets of ddm1 mutation using the values for HMM ( hidden Markov model ) status ( M ( methylated ) or I ( Intermediate ) or U ( Unmethylated ) ) 46 ., Global hypo-methylation index of an epiRIL was calculated as the genome-wide average of the values for HMM status of probes on the chip ( M = 0 , I = 0 . 5 , U = 1 ) in the target regions of ddm1 mutation ., The data of inference of inherited haplotypes were shown in the previous study 46 ., Following are names of lines numbered 1–6 in Fig 7 and S18–S23 Figs ., ( Fig 7A and 7B and S18 Fig ) epiRIL208 epiRIL122 epiRIL98 epiRIL232 epiRIL70 epiRIL114; ( Fig 7C and 7D and S19 Fig ) epiRIL122 epiRIL208 epiRIL114 epiRIL258 epiRIL438 epiRIL508; ( Fig 7E and 7F and S20 Fig ) epiRIL208 epiRIL98 epiRIL438 epiRIL508 epiRIL122 epiRIL114; ( S21 Fig ) epiRIL208 epiRIL73 epiRIL71 epiRIL394 epiRIL98 epiRIL438; ( S22 Fig ) epiRIL508

Introduction, Results, Discussion, Materials and Methods

Epigenetic variations of phenotypes , especially those associated with DNA methylation , are often inherited over multiple generations in plants ., The active and inactive chromatin states are heritable and can be maintained or even be amplified by positive feedback in a transgenerational manner ., However , mechanisms controlling the transgenerational DNA methylation dynamics are largely unknown ., As an approach to understand the transgenerational dynamics , we examined long-term effect of impaired DNA methylation in Arabidopsis mutants of the chromatin remodeler gene DDM1 ( Decrease in DNA Methylation 1 ) through whole genome DNA methylation sequencing ., The ddm1 mutation induces a drastic decrease in DNA methylation of transposable elements ( TEs ) and repeats in the initial generation , while also inducing ectopic DNA methylation at hundreds of loci ., Unexpectedly , this ectopic methylation can only be seen after repeated self-pollination ., The ectopic cytosine methylation is found primarily in the non-CG context and starts from 3’ regions within transcription units and spreads upstream ., Remarkably , when chromosomes with reduced DNA methylation were introduced from a ddm1 mutant into a DDM1 wild-type background , the ddm1-derived chromosomes also induced analogous de novo accumulation of DNA methylation in trans ., These results lead us to propose a model to explain the transgenerational DNA methylation redistribution by genome-wide negative feedback ., The global negative feedback , together with local positive feedback , would ensure robust and balanced differentiation of chromatin states within the genome .

DNA methylation is important for controlling activity of transposable elements and genes ., An intriguing feature of DNA methylation in plants is that its pattern can be inherited over multiple generations at high fidelity in a Mendelian manner ., However , mechanisms controlling the trans-generational DNA methylation dynamics are largely unknown ., Arabidopsis mutants of a chromatin remodeler gene DDM1 ( Decrease in DNA Methylation 1 ) show drastic reduction of DNA methylation in transposons and repeats , and also show progressive changes in developmental phenotypes during propagation through self-pollination ., We now show using whole genome DNA methylation sequencing that upon repeated selfing , the ddm1 mutation induces an ectopic accumulation of DNA methylation at hundreds of loci ., Remarkably , even in the wild type background , the analogous de novo increase of DNA methylation can be induced in trans by chromosomes with reduced DNA methylation ., Collectively , our findings support a model to explain the transgenerational DNA methylation redistribution by genome-wide negative feedback , which should be important for balanced differentiation of DNA methylation states within the genome .

journal.pntd.0004211

Socio-economic and Climate Factors Associated with Dengue Fever Spatial Heterogeneity: A Worked Example in New Caledonia

Dengue fever is the most important mosquito-borne viral disease , with an estimated 50 million people being infected each year and 2 . 5 billion people living in areas at risk of dengue worldwide 1 ., The true burden of clinically apparent dengue could be twice as high , and the total burden of dengue fever infections could reach 390 million people when including asymptomatic cases 2 ., Whereas only nine countries were affected by dengue epidemics in the 1970s , more than a hundred countries are now reporting dengue outbreaks on a regular basis , making dengue fever the most rapidly spreading mosquito-borne viral disease in the world 1 , 2 ., This rapid global spatial spread over the past 40 years probably results from recent socio-economic changes such as global population growth and uncontrolled urbanisation ., Lack of effective mosquito control in endemic areas , increased international air traffic or decay in public health infra-structure in developing countries are also important factors that could explain the rapid regional spread of the disease 3–6 ., However , in a given country where there are sufficient numbers of susceptible hosts , these factors need to be associated with suitable climate conditions before dengue fever can establish , since it is transmitted by mosquito species whose life cycle is influenced by temperature , humidity and rainfall 7–9 ., Indeed , several studies have pointed out that the current geographic distribution of dengue fever or its vector worldwide could be predicted accurately based on climate variables using either statistical models 10 or deterministic models 11–13 ., Other studies have pointed out that climate change could have profound consequences on the epidemiology of dengue fever , because increased temperature and rainfall could facilitate viral transmission and could lead to the geographic expansion of the mosquito species responsible for its transmission 11 , 14–17 ., The complex interplay and relative importance of each factor in the occurrence and spread of dengue fever epidemics might differ from one country to another , depending on the specific climate conditions , cultural and socio-economic environment the virus circulates in 18 ., Identifying the factors limiting dengue fever spatial spread at a national level could help understanding the worldwide pattern of dengue disease , could help predicting its future spatial distribution , and could provide national decisions-makers with useful information regarding the appropriate control measures to be implemented ., Most studies trying to identify dengue risk factors spatially were performed at a city scale or a local scale ( < 80 km ) 19–34 ., Among these studies , some have identified risk factors for the presence of Aedes mosquito species , such as socio-economic factors 29 , 30 , proximity to specific plantations 28 , 32 , proximity of potential breeding sites 22 , 28 , 29 , 32 , or human behaviour 30 , 32 ., Some studies highlighted the importance of human movement 23 , 26 , 31 , 33 or population immunity 34 in shaping the spatial transmission of dengue fever at small spatial scales ., High dengue incidence rates have also been found in neighbourhoods with low social income 20 , 26 , difficult access to piped water 21 , 26 , or no implementation of mosquito protection measures 21 , 27 ., Spatial analyses at a country or territorial scale ( > 200 km ) are scarce ., Some of these studies focused on the spatio-temporal dynamics of the disease only 35–37 and proposed hypotheses about the underlying processes , but did not include analyses of potential explicative factors ., To our knowledge , there are only five studies to date identifying and quantifying spatial risk factors for dengue at a “national” scale > ~200 km and < ~1000 km ., Four studies identified temperature as having a major influence on the spatial distribution of locally acquired dengue cases 38–41 , the last one did not assess the role of climate factors 42 ., The role of other factors in the spatial distribution of dengue cases varied from one place to another ., For example , in Australia ( Queensland ) 38 and Taiwan 40 , rainfall seemed to play a minor role whereas in Brazil , rainfall played a major role 39 ., In Taiwan 40 and Argentina 41 , urbanization level was a key factor in dengue fever spatial distribution , and in one province of Thailand 42 , the main factor identified was the proximity to major urban centres ., No association was found with socio-economic covariates in Argentina 41 or Australia 38 ., New Caledonia , where the present study takes place , has a unique situation: it is a developed insular territory located in the inter-tropical area of the South Pacific where the access to high quality data and the lack of terrestrial borders with other countries make it a natural laboratory to study dengue dynamics ., A gross average of ten imported cases is detected each year by the Public Health authorities ., However , large dengue epidemics develop only every three to five years , sometimes causing the circulation of the same serotype during two consecutive years 43 ., A recent study analysing the temporal relationship between dengue epidemic occurrence and climate variables at an inter-annual scale showed that the development of an epidemic in New Caledonia needs precise climate conditions relying on both temperature and relative humidity 43 ., The objectives of the present study were, i ) to characterise the spatial distribution of dengue cases in New Caledonia once an epidemic spreads over the territory;, ii ) to determine which of possible covariates are shaping the observed distribution;, iii ) to explore the potential spatial distribution of dengue cases under future climate projections ., We present a complete methodology , from data collection , data transformation , variable selection , and application to future climate projections ., We address a number of methodological issues such as spatial autocorrelation , correlation between explanatory variables , or potential non-linearity between epidemiological data and explanatory covariates ., New Caledonia is a French territory , located in the Pacific Ocean 1 , 500 km East of Australia ., It is divided into 33 communes covering 18 , 576 km2 ., Out of the 245 , 344 inhabitants ( 2009 ) , around 58% ( 147 , 365 people ) live in Noumea , the main city , and its surroundings ., The rest of the population is scattered in small towns of about 2 , 000 people , or live in rural areas , including traditional Melanesian settlements locally called “tribes” ( Fig 1 ) ., Although the average population density outside Noumea is very low ( 5 . 3 inhabitants per km2 ) , local densities can be high as people gather in small settlements ., New Caledonia is located at the limit of the tropical zone between latitudes 19° and 23° South ., The East coast and the West coast are separated by a mountain range culminating at 1629 m ., Climate is heterogeneous: the East coast and the southern tip of the main island get more rain than the West coast , as mountains provide a vertical lift to the warm and humid air brought by trade winds ., Average rainfall range from 800 mm/year in some western weather stations to 3200 mm/year in the East ., Temperature can drop below 10°C during the cool season on clear nights and sometimes rise above 35°C due to the influence of tropical air masses 45 ., From an oversimplified point of view , there are three population groups , having different cultural and social habits: Melanesian people , people of French descent who migrated two hundred years ago , and people from various origins who migrated recently ., Although the three groups are spatially partially mixed , Melanesian people live mostly on the East coast , whereas the second group live mostly on the West coast and the third group live mainly in Noumea ., In New Caledonia , dengue represents a major public health problem with large epidemics affecting the territory every three to five years and involving a succession of all four serotypes 43 , 46–48 ., Co-circulation of different dengue virus serotypes ( DENV1-4 ) during major epidemics is rare , and has been observed only once ( 2009 ) ., Before 2003 , vector control measures consisted in systematic chemical control of adult mosquitoes covering large areas during the warm season , independently from the occurrence of dengue cases ., Since 2003 , systematic spreading of adulticide has been stopped , and vector control measures include continuous large communication and prevention campaigns fostering source reduction aimed at all citizens , as well as focal chemical control of adult mosquitoes 100 m around declared cases within 24 h of notification ., Public Health infrastructure is reliable , and the surveillance system for dengue fever has been efficient for many years ., All people have access to medical care , even though people living in remote areas might have more difficulties to reach local health centres ., As described below in the results section , temperature is a key factor determining dengue spatial variability over New Caledonia ., We thus decided to explore the evolution of dengue average annual incidence rates during epidemics under changing climate conditions ( considering all others variables as remaining constant ) , by applying the best explicative multivariable model with inputs from maps of temperature for the future ( see methods/data/climate covariates: assessing the trends of future mean temperature in New Caledonia ) ., Because the use of kernels in non–linear SVM models impairs predictions outside the observed range of explanatory variables , we built a linear approximation of the best SVM model on present observed data ., The linear approximation consists of a simple linear model linking the two best explanatory variables to observed dengue age-standardised average ( across epidemic years ) annual incidence rates as the response variable ., Normality and homoscedasticity of residuals were confirmed by the Shapiro-Wilks test and the Bartletts test respectively 67 ., To evaluate the error in incidence rates predictions due to the inter-GCM variability of mean temperature increase projections , we calculated , for each time-period and each scenario , the average annual incidence rates during epidemics as predicted by each GCM , and then calculated a standard deviation of predicted annual incidence rates across the different models ., Fig 3 shows that once an epidemic spreads over the territory , dengue cases are distributed heterogeneously ., Mean annual age-standardised incidence rates across epidemic years range from 22 to 375 cases per 10 , 000 people per year , with a mean across communes of 168 cases per 10 , 000 people per year and a standard deviation across communes of 83 cases per 10 , 000 people per year ., On average the East coast is more affected than the West coast ., We can also see that the North-eastern corner of New Caledonia is heavily affected , with dengue incidence rates two to three times higher than in the rest of the territory ., By definition , the average across epidemic years of age-standardised annual incidence rates reflects mainly the spatial pattern of severe epidemics , i . e . epidemics of years 1995 , 1998 , 2003 and 2009 ., During years 1995 , 1998 and 2003 , the North-eastern corner was the most affected ., During the 2009 epidemic , the most affected communes were Voh and Koné , on the West coast ( see Fig 3 for the location of these communes ) , but the North Eastern corner was still severely affected 72 ., The semi-variograms of dengue incidence rates did not reveal any significant spatial autocorrelation , whether they were calculated for each epidemic year separately or on the average incidence rates across years ., This suggests that the local spread of dengue viruses around a case imported in a commune do not exceed the mean radius of a commune in New Caledonia , e . g . approximately 13 kilometres ., Hence , we did not incorporate any spatial structure into the subsequent models ., Table 1 shows Pearsons correlation coefficients ( rho ) between each explanatory variable and dengue age-standardised annual incidence rates averaged across epidemic years ., Dengue is spatially positively correlated with variables related to temperature and precipitation , but is negatively correlated with variables reflecting mean thermal range or extreme thermal conditions ( see Isothermality , Temp range or the number of days when maximum temperature exceeds 32°C in January , February and March in Table 1 ) ., This suggests that , in a given commune , marked temporal variations of temperature is a factor limiting viral circulation ., Based on the linear dependence measure of correlation , dengue is also more strongly associated with temperature than with precipitation ., Socio-economic variables are highly spatially correlated to dengue average ( across epidemic years ) annual incidence rates ., Variables reflecting peoples way of life ( e . g . place of birth ) , local human density ( e . g . mean number of people per household , percentage of premises under 40 m2 ) , or human movement are more correlated with dengue average ( across epidemic years ) annual incidence rates than variables related to the housing type ( e . g . premises with inside toilets ) ( absolute value of rho up to 0 . 75 for the former and 0 . 58 for the latter ) ., In particular , the fact that the place where people were born is spatially significantly associated with dengue fever incidence rates ( correlation coefficient around 0 . 5 for people born in New Caledonia and– 0 . 5 for people born elsewhere ) whereas the type of premise is not ( absolute correlation coefficient lower than 0 . 3 for variables describing access to water or electricity ) suggests that individual behaviours have a stronger influence on incidence rates than local housing conditions ., Fig 4 shows the PCA results ., For clarity reasons , we only show the results of PCA performed on the variables most spatially correlated with dengue average ( across epidemic years ) annual incidence rates , with an absolute Pearson correlation coefficient over 0 . 6 for socio-economic variables , and over 0 . 4 for climate variables ( these thresholds were selected after verifying that they did not modify the variable pre-selection results ) ., PCA of climate variables ( Fig 4A ) shows that in New Caledonia , temperature is the factor accounting for most of the spatial climatic variability among communes ., Temperature is highly correlated with the first PCA axis which represents 68% of the total climatic variance ., Temperature and rainfall are not spatially correlated at the commune level ., In each group of temperature or rainfall variables , the variables most spatially correlated with dengue average ( across epidemic years ) annual incidence rates were the average mean temperature ( Mean temp ) and the mean daily rainfall during the wettest quarter of the year ( Wettest quarter ) ( see Table 1 ) ., In addition to these two variables , we decided to keep a third variable , the average daily rainfall , for further statistical modelling , as this variable is more easily available in other countries or climate model simulations ., Fig 4B shows that the spatial variability of socio-economic factors mainly reflects the spatial distribution of people with different cultural habits ., Communes where a high proportion of inhabitants live in a tribal way , in small premises , with few means of transportation and a high percentage of unemployment are opposed to communes where many people live a western way of life , in permanent buildings , using air conditioning and getting around using cars ., Even though the number of people per premise seems to be correlated with the proportion of people living in tribes , we kept this variable as it stands out of the cluster of variables representing the way of life ., We thus decided to keep the percentage of unemployed people and the mean number of inhabitants per housing as representative of socio-economic factors for further statistical modelling ., Table 2 shows the RMSE of the optimised models built on all possible combinations of one , two or three of the five selected explanatory variables ( Mean temperature , daily rainfall averaged over the wettest quarter , average daily rainfall , number of people per household and fraction of unemployed people ) ., When looking at univariable non-linear SVM models , the best variable explaining the spatial heterogeneity of dengue average ( across epidemic years ) annual incidence rates is the percentage of unemployed people per commune ., The second most important explanatory variable is the mean temperature ., Rainfall is the least explanatory variable of those selected for multivariable regression modelling ., Moreover , the RMSE of models based on observed rainfall almost equal the initial standard deviation ( across the territory ) of dengue average annual incidence rates , which means that rainfall are poor predictors of dengue average annual incidence rates during epidemic years ., The relationship between dengue average annual incidence rates and each of the explanatory variables is linear , except for the fraction of unemployed people ( S4 Fig ) ., When looking at the spatial structure of dengue average annual incidence rates predicted by SVM models based only on one of the selected variables ( S5 Fig ) , we see that temperature captures mainly the South to North gradient of increasing incidence rates ( S5B Fig ) whereas socio-economic variables captures the spatial heterogeneity between the West coast and the East coast ( S5E and S5F Fig ) ., Temperature seems to have no influence in communes located below 21°S ( S5B Fig ) ., All models based on two explanatory variables and including at least one variable related to rainfall ( best RMSE of ~58 cases per 10 , 000 people per year ) performed worse than the best univariable model ( RMSE of ~53 cases per 10 , 000 people per year ) ., This suggests that in New Caledonia , rainfall has little influence on the spatial variability of dengue viral circulation at the commune level ., Models combining two explanatory variables ( excluding rainfall ) performed better than models based on only one variable ., The addition of a third explanatory variable did not improve significantly model performances ., Hence we focused our attention on models combining two explanatory variables ., The best explicative model is a model predicting increasing average annual incidence rates during epidemics in communes where the mean temperature and the mean number of people per premise increase ( see Fig 5 ) ., The influence of these two variables on the spatial structure of dengue incidence rates is close to linear as shown by almost parallel contour lines on Fig 5A ., This model accurately predicts the sharp mean increase in incidence in the three communes of the North East of New Caledonia ( Hienghène , Ouégoa and Pouébo ) ., The maximal error of the model is observed for Farino ( West coast ) , which is the only commune where all inhabitants live at an altitude higher than 200 m above sea level ., Fig 6 shows the spatial structure of observed average ( across epidemic years ) annual incidence rates ( Fig 6A ) and of average annual incidence rates as predicted by the best SVM model based on two explanatory variables ( Fig 6B ) ., This model captures the observed spatial heterogeneity in average annual incidence rates between the East coast and the West coast , as well as the sharp increase in the three communes of the North East ., Table 3 shows , for both climate change scenarios , the average increase of mean temperature for the two selected 20-year periods compared to the 1980–1999 historical simulations ., All models predict that the mean temperature will increase over time , with projections being more pessimistic for RCP 8 . 5 simulations ., The CMIP5-AR4 inter-model variability in temperature increase is presented in Table 3 and S3 Fig . According to the RCP 8 . 5 scenario , temperature could increase by more than 3°C by the end of the next century , with a standard deviation across models of only 0 . 6°C , showing the strong coherency in different model projections ., Fig 6 shows a comparison of the average ( across epidemic years ) annual dengue incidence rates predicted by the SVM model ( panel 6B ) or the linear model ( panel 6C ) ., The SVM model performs slightly better than the linear one: the correlation coefficient between observed and predicted incidence rates are 0 . 89 ( SVM ) and 0 . 85 ( linear ) , and the RMSE are 42 and 43 cases/10 , 000 people/year respectively for the SVM and the linear model ., The low RMSE of the linear model ( ~43 cases per 10 , 000 people per year ) shows that the linear model based on the two best explanatory variables is suitable ., The Shapiro-Wilks and the Bartletts test confirmed the normality and homoscedasticity of residuals ., Fig 6D and 6E show the potential future spatial distribution of dengue incidence rates during epidemics according to the RCP 4 . 5 and RCP 8 . 5 emission scenarios ., By the end of the century , dengue incidence rates during epidemic years could reach a maximum of 378 cases per 10 , 000 people per year in the most affected commune under the RCP 4 . 5 scenario ( Fig 6D ) , and 454 cases per 10 , 000 people per year in the most affected commune under the RCP 8 . 5 scenario ( Fig 6E ) ., Under the RCP 8 . 5 scenario , communes at low risk now might experience a sharp increase in dengue incidence rates during epidemic years from 64 to more than 200 cases per 10 , 000 people per year ., According to RCP 8 . 5 climate projections , the average ( across communes ) dengue mean annual incidence rates during epidemic years could raise by 29 cases per 10 , 000 people per year for the 2010–2029 period , and by 149 cases per 10 , 000 people per year for the 2080–2099 period , almost doubling dengue burden in New Caledonia by the end of the century ( Table 3 ) ., The spatial association found between temperature and dengue incidence rates during epidemics in New Caledonia can be explained by the influence of temperature on the life cycle of the mosquito transmitting the virus in New Caledonia , Aedes aegypti ., High temperatures increase the productivity of the breeding sites through an acceleration of the metabolism of the mosquito , and a faster development of the micro-organisms the larvae feed on , resulting in a higher vector density even with the same number of breeding sites 7 , 73–75 ., High temperatures also speed up the extrinsic incubation period 7 , 76 , 77 , with the effect that an increased proportion of females Ae ., aegypti can reach the infectious stage before dying ., Finally , warmer temperatures accelerate the mosquito gonotrophic cycle , and make females Ae ., aegypti more aggressive 7 , 74 , 78–80 , increasing the biting rate and the frequency of potential transmission of viral particles to susceptible hosts ., Regarding the effect of increasing temperatures on the mortality of Ae ., aegypti adult mosquitoes , a review of 50 field mark-release-recapture studies has shown that in the field , unless temperatures become extreme ( over 35°C or less than 5°C ) , temperature has little effect on daily mortality rate 81 , highlighting the central importance of the length of the extrinsic incubation period in the ability of adult mosquitoes to transmit dengue viruses ., In Noumea , the main city , precise climate variables and important thresholds values have been identified as necessary conditions to trigger an epidemic ( e . g . number of days when maximal temperature exceeds 32°C in January/February/March , and number of days when maximal relative humidity exceeds 95% during January 43 ) ., At the scale of the entire territory , we found that the spatial distribution of dengue cases during epidemic years is strongly influenced by the average mean temperature ., These results suggest that temperature has a major role in dengue dynamics in an insular territory characterised by climate seasonality ., However , we did not find a strong association between the spatial distribution of dengue cases during epidemics and average rainfall or with the number of days when maximal temperature exceeds 32°C ., The variables influencing either the triggering of an epidemic 43 or its spatial distribution are not the same ., Our findings highlight the complexity of studying and understanding dengue dynamics , the importance of well separating the two epidemiological processes of epidemic triggering in a susceptible population , and its intensity once it has started by clearly defining the modelling target ( incidence rates for epidemic intensity , or dummy variables for epidemic triggering ) , and the importance of well defining the scale of study ( temporal evolution , or spatial distribution ) ., The positive association found between the mean temperature and dengue incidence rates is consistent with the one found in previous studies having analysed the spatial distribution of dengue cases at spatial scale > 200 km 38–42 ., In these studies as well as in ours , all regions were located between 10° and 25° of latitude , at the fringe of the tropical area , except Argentina , where the region studied extends to 35° South ., In the 10° ˗ 25° latitudinal band , annual mean temperature lies in a range of temperature where the life cycle of the mosquito is very sensitive to temperature changes 7 ., Some Aedes species , including Ae ., aegypti , are able to breed in very small amounts of water , e . g . snails’ shells ., Rainfall can play a role in dengue transmission cycle by filling up potential breeding sites 7 , thus influencing the vector density ., Rainfall also increases the relative humidity , which extends the mosquitoes’ lifespan and therefore the likelihood of those who had an infectious blood meal to reach the infectious stage ., However , our study suggests that in New Caledonia , there is no strong association between rainfall and the spatial distribution of cases during epidemics ., A plausible explanation can be the multi-factorial nature of dengue fever , and the relative influence each factor plays on dengue dynamics: despite suitable rainfall conditions , dengue might not circulate well if other factors are limiting dengue viral circulation , such as some human behaviour influencing the contact between vector and host ., This aspect has been highlighted very clearly in the United States 27 ., Some studies have found that the effect of rainfall on vector density can be modulated by human activities such as water storage practices 82 ., However , in New Caledonia , we are not aware of specific practices to store water that could explain the lack of association between rainfall and virus circulation intensity ., Another potential explanation could be that in dry areas , breeding sites are filled up by other non-climatic mechanisms , such as automatic irrigation or plant watering ., Worldwide , the spatial association between rainfall and the spatial distribution of dengue cases at a “national” scale ( > 200 km ) is not as clear as the one for temperature: one spatial study did not find any association between dengue incidence rates and rainfall 40 , whereas two others did 38 , 39 ., Other factors influencing dengue transmission ( e . g . anthropogenic factors influencing the availability of filled breeding sites ) and not included in the different studies might blur the rainfall signal ., It would be interesting to perform the same kind of multi-factorial spatial analysis in areas of epidemic or endemic transmission located closer to the equator , where the mean temperature is higher , to see what climatic factors impact the spatial distribution of cases ., This kind of study could help understand better the complex interplay between the different factors ( climate , socio-economic , immunologic , viral , entomologic… ) associated with dengue fever transmission ., Regarding the link between socio-economic variables and dengue incidence rates during epidemics , a limitation of this study is the absence of historical time series of socio-economic variables ., We then had to assume that the data retrieved from the 2009 census is representative of the mean socio-economic spatial pattern over the epidemic years of the 1995–2012 period ., As there has been no major historical event leading to population migration in New Caledonia during this time period and as socio-economic variables represent mainly people’s way of life , we think this assumption is realistic ., Our results are consistent with previous studies that have pointed out the importance of socio-economic factors on the spatial distribution of dengue cases , whatever the spatial scale studied: national ( >200 km ) 39–42 or local ( <10 km ) 20 , 26 , 29 , 83 ., The spatial association between the percentage of unemployed people and dengue in New Caledonia cannot be interpreted in terms of lack of economic activity only , as shown by the PCA on socio-economic factors ., This variable we selected as input for the models is highly correlated with other variables reflecting the way of life , socio-economic and cultural differences existing in New Caledonia , which are in turn highly correlated to housing type ., Therefore , at this spatial scale in New Caledonia , it is difficult to statistically differentiate the role played by human behaviour , human activity or housing type in dengue fever transmission ., However , those three factors influence the contact rate between viraemic patients or susceptible hosts on one hand , and mosquitoes on the other hand ., This highlights the importance of limiting the contact between humans and vectors and should lead local authorities to strengthen communication campaigns about personal protection measures towards populations at risk ., Regarding the spatial association found between the fraction of unemployed people ( i . e . people’s way of life ) and dengue incidence rates during epidemics in New Caledonia , it is interesting to point out that on the East coast , a larger fraction of inhabitants are Melanesian people living in tribes , whereas on the West coast , the majority of people are people from French descent having a western way of life ., It would be interesting to perform sociologic studies to precisely identify which human behaviour leads to an increased risk of catching dengue fever ., Such information would be useful to define communication messages towards at risk populations ., The spatial association between the number of people per household and dengue incidence rates can be explained by the short flight range of Ae ., aegypti mosquitoes ., These mosquitoes are often captured in the very house where they emerged or in the neighbouring houses , flying an average of 40 to 80 m during their life 84–87 ., Hence , dengue outbreaks involving Ae ., aegypti as the main vector are known to be highly spatially focal , with dengue cases usually clustering within 200 m to 800 m of each other 23 , 33 , 34 , 88–94 ., Our results suggest that , in New Caledonia , dengue cases probably cluster within houses ., Sick people should protect themselves until they are no longer vireamic to avoid human to mosquito transmission , and people living around a case should protect themselves to avoid getting infected while infectious mosquitoes are still active in the neighbourhood ., Taking such individual actions could reduce the intensity of dengue transmission and reduce dengue burden over the territory ., This message could be strengthened in the recommendations given by the authorities ., The results about climate change must be interpreted keeping in mind that they represent a climate risk only , and that the spatial association between dengue incidence rates during epidemics and temperature might change over time depending on socio-demographic changes , or changes in dengue control strategy ., Assuming all other factors remain constant in time , our results suggest that Pub

Introduction, Material and Methods, Results, Discussion

Understanding the factors underlying the spatio-temporal distribution of infectious diseases provides useful information regarding their prevention and control ., Dengue fever spatio-temporal patterns result from complex interactions between the virus , the host , and the vector ., These interactions can be influenced by environmental conditions ., Our objectives were to analyse dengue fever spatial distribution over New Caledonia during epidemic years , to identify some of the main underlying factors , and to predict the spatial evolution of dengue fever under changing climatic conditions , at the 2100 horizon ., We used principal component analysis and support vector machines to analyse and model the influence of climate and socio-economic variables on the mean spatial distribution of 24 , 272 dengue cases reported from 1995 to 2012 in thirty-three communes of New Caledonia ., We then modelled and estimated the future evolution of dengue incidence rates using a regional downscaling of future climate projections ., The spatial distribution of dengue fever cases is highly heterogeneous ., The variables most associated with this observed heterogeneity are the mean temperature , the mean number of people per premise , and the mean percentage of unemployed people , a variable highly correlated with peoples way of life ., Rainfall does not seem to play an important role in the spatial distribution of dengue cases during epidemics ., By the end of the 21st century , if temperature increases by approximately 3°C , mean incidence rates during epidemics could double ., In New Caledonia , a subtropical insular environment , both temperature and socio-economic conditions are influencing the spatial spread of dengue fever ., Extension of this study to other countries worldwide should improve the knowledge about climate influence on dengue burden and about the complex interplay between different factors ., This study presents a methodology that can be used as a step by step guide to model dengue spatial heterogeneity in other countries .

Dengue fever is the most important viral arthropod-borne disease worldwide and its geographical expansion during the past decades has been of growing concern for scientists and public health authorities because of its heavy sanitary burden and economic impacts ., In the absence of an effective vaccine , control is currently limited to vector-control measures ., In this context , understanding the sociologic , entomologic and environmental factors underlying dengue dynamics is essential and can provide public health authorities with sound information about control measures to be implemented ., In this study , we analyse socio-economic , climatic and epidemiological data to understand the impact of the studied factors on the spatial distribution of dengue cases during epidemic years in New Caledonia , a French island located in the South Pacific ., We identify at risk areas , and find that temperature and people’s way of life are key factors determining the level of viral circulation in New Caledonia ., Hence , communication campaigns fostering individual protection measures against mosquito bites could help reduce dengue burden in New Caledonia ., Using projections of temperature under different scenarios of climate change , we find that dengue incidence rates during epidemics could double by the end of the century , with areas at low risk of dengue fever being highly affected in the future .

journal.pntd.0002968

Experimental Infection of the Pig with Mycobacterium ulcerans: A Novel Model for Studying the Pathogenesis of Buruli Ulcer Disease

Buruli ulcer ( BU ) , caused by infection with Mycobacterium ulcerans , is a human disease of the skin primarily affecting subcutaneous fat tissue and leading to ulceration of the overlying dermal and epidermal layers 1 , 2 ., The disease is reported from countries worldwide but has its highest prevalence in West Africa 3 ., Natural reservoirs of M . ulcerans as well as the mode ( s ) of transmission are not clearly identified 3 , 4 ., While for a long time wide surgical excision was the only treatment option for BU , since 2004 the World Health Organization ( WHO ) recommends antibiotic therapy with rifampicin and streptomycin for 8 weeks 5 ., This change in standard treatment has reduced recurrence rates to less than 2% 6–9 ., M . ulcerans produces the polyketide exotoxin mycolactone that is responsible for the necrotizing nature of BU 10 ., Three distinct non-ulcerative stages of the disease are described: subcutaneous , painless and movable nodules or papules , oedema and plaques ., All three stages may progress to ulceration once the destruction of the subcutis results in collapse of the overlying epidermis and dermis 11 ., Ulcerative BU lesions have been histopathologically well described through the analysis of excised tissue from surgically treated patients ., Coagulative necrosis , fat cell ghosts and epidermal hyperplasia together with the presence of extracellular clusters of acid fast bacilli ( AFB ) in the absence of major inflammatory infiltrates in central parts of the lesions are considered hallmarks of the disease and can also be used for histopathological diagnosis 12 , 13 ., However , early , pre-ulcerative stages have been described less frequently , because in particular in the African BU endemic regions patients are rarely reporting at treatment centres during early stages of the disease ., Furthermore , with the replacement of surgical treatment by chemotherapy , tissue samples are not easily available any longer ., Therefore , a suitable experimental animal infection model is required to contribute to the understanding of early host-pathogen interactions and pathogenesis in BU ., A range of animal species have been reported of being naturally infected with M . ulcerans and of developing ulcerative lesions ., These include koalas , possums , cats , dogs and horses 14–21 ., Except for possums which appear to be unusually susceptible to the disease , these animal infections seem to occur only sporadically 22 ., Experimental M . ulcerans infections have been performed with amphibians , armadillos , rats , mice , guinea pigs and monkeys , with a mouse foot pad model being most widely used for studying the efficacy of prophylactic and therapeutic interventions 23–29 ., Here we propose the pig ( Sus scrofa ) as experimental M . ulcerans infection model , since pigs are closely related to humans in terms of many aspects of anatomy and physiology 30 , 31 ., The pig is widely used as a model in dermatological studies because pig skin , in contrast to rodent skin , has striking similarities to human skin 32 ., Not only the thickness of the epidermis and the dermis are comparable to human skin 33 , but also the presence of a subcutaneous fat cell layer is favouring the pig model over the mouse foot pad model commonly used for analysing BU pathogenesis ., Furthermore , the porcine immune system reflects the human immune system in many aspects better than the murine immune system does 34 , 35 ., For all these reasons we explored here the potential of the pig to serve as model for human M . ulcerans infection ., All animal experiments described here were approved by the Animal Welfare Committee of the Canton of Berne under licence number BE50/11 , and conducted in compliance with the Swiss animal protection law and with other national and international guidelines ., The M . ulcerans strain used in this study was isolated in 2010 from a swab taken from the undermined edges of the ulcerative lesion of a Cameroonian BU patient 4 ., Five passages of the strain after isolation were done in Bac/T medium ( Biomerieux ) at 30°C ., For preparation of the inoculum , bacteria were cultivated in Bac/T medium for 6 weeks , recovered by centrifugation and diluted in sterile phosphate-buffered saline ( PBS ) to 375 mg/ml wet weight corresponding to 2×108 CFU/ml as determined by plating serial dilutions on 7H9 agar plates ., From this stock solution suspension serial dilutions in PBS were prepared for infection ., Specific pathogen-free 2-month-old pigs ( Large White ) from the in-house breeding unit of the Institute of Virology and Immunology ( IVI ) were kept under BSL3 conditions one week prior and during the time of experimental infection ., Animals were checked once daily for macroscopic signs of infection , had ad libitum access to water and were fed daily with complete pelleted food ., Pigs were infected on both flanks at four to six infection sites with 100 µl of M . ulcerans suspension , containing 2×107 , 2×106 , 2×105 , 2×104 or 2×103 CFU ., Injection areas were wiped with 70% ethanol and bacterial suspensions injected subcutaneously with a 26G needle ., Individual infection sites were encircled with a black marker and the labelling renewed at least once a week ., Animals were euthanized at 2 . 5 weeks or 6 . 5 weeks post-infection and tissue samples taken as described below ., In addition , the effect of mycolactone was studied directly by injecting 5 µg or 0 . 5 µg of synthetic mycolactone A/B 36 and analysing tissue specimens taken 2 . 5 weeks later ., Pigs were euthanized by intravenous injection of pentobarbital ( 150 mg/kg bodyweight ) and subsequent exsanguination ., Skin tissue at infection sites was extensively excised with a scalpel and scissors , including all layers of the skin down to the fascia , and samples were immediately transferred to 10% neutral-buffered Formalin solution ( approx . 4% formaldehyde ) ., After fixation samples were transferred to 70% ethanol for storage and transport , dehydrated and embedded into paraffin ., 5 µm thin sections were cut , deparaffinised , rehydrated and directly stained with Haematoxylin/Eosin ( HE ) or Ziehl-Neelsen/Methylene blue ( ZN ) according to WHO standard protocols 11 ., Stained sections were mounted with Eukitt mounting medium ( Fluka ) ., Pictures were taken with a Leica DM2500B microscope or with an Aperio scanner ., In order to assess early effects of the subcutaneous experimental infection of pigs with doses of 2×103 to 2×107 M . ulcerans CFU , injection sites were closely monitored for macroscopic changes of the skin ., At 2 . 5 weeks after injection of the bacteria , first changes in colouration and thickness of the skin became apparent at the sites inoculated with the highest inoculation doses , 2×107 and 2×106 CFU ( Fig . 1 , B1 ) ., Like nodular BU lesions in humans , these early lesions were elevated , movable , firm and palpable ., When these skin areas were excised 2 . 5 weeks and 6 . 5 weeks after infection and vertically cut in half after fixation in formalin , roundish yellow structures reflecting coagulative necrosis in the dermis became macroscopically apparent ( Fig . 1 , B2 ) ., A belt with reddish colour , reflecting infiltrating cells and bleeding into the skin , was observed around the necrotic core ., While these structures were larger at sites inoculated with a dose of 2×107 CFU than at sites inoculated with 2×106 CFU , the general architecture observed with both inoculation doses was similar ., At sites inoculated with <2×105 CFU , no macroscopically visible alterations of the skin were found 2 . 5 weeks after infection ( Fig . 1 , A1 and A2 ) ., At 6 . 5 weeks after experimental infection , sites injected with the highest inoculation dose had either enlarged to an indurated plaque ( Fig . 1 E1 ) or ulcerated ( Fig . 1 , D1 ) ., At sites injected with 2×106 CFU , nodular lesions were observed that were flatter and less palpable compared to those detected 2 . 5 weeks after infection ( Fig . 1 , C1 ) ., These lesions were macroscopically clearly visible in cross sections through the tissue ( Fig . 1 , C2 ) ., Nodular and ulcerative lesions exhibited greyish/reddish colour changes in the dermis and subcutis ( Fig . 1 , C2 and D2 ) ., The plaque lesion developing after injection with 2×107 CFU appeared as long cord-like structure with a centre made of yellowish necrotic slough , surrounded by several layers differing in colouration ( Fig . 1 , E2 ) ., Microcopically , infiltrating immune cells were found 2 . 5 weeks after infection at all sites inoculated with ≥2×104 CFU ( Fig . 2 , A1 , B1 , C1 and D1 ) ., As expected from the macroscopically observed signs , the most pronounced histopathological alterations were associated with the two highest inoculation doses ( 2×107 and 2×106 CFU ) ., The non-ulcerative lesions that developed between the dermis and the underlying muscle tissue displaced the fat layer ( Fig . 2 , A1 and B1 ) and caused the macroscopically visible elevation of the skin ( Fig . 1 , B1 ) ., Microscopically , a necrotic core surrounded by large numbers of infiltrating cells and interspersed with fat cell ghosts was observed ( Fig . 2 , A2 and B2 ) ., At sites infected with 2×105 CFU , no necrotic core structures but some fat cell ghosts and accumulations of infiltrating cells were found ( Fig . 2 , C1 and C2 ) ., The infection with 2×104 CFU caused a small accumulation of infiltrating cells ( Fig . 2 , D1 and D2 ) and no signs of infection and/or inflammation were observed at sites inoculated with the lowest dose ( 2×103 CFU ) ., At 6 . 5 weeks after experimental infection , histopathological changes were only found at sites that had been injected with 2×107 or 2×106 CFU ., In contrast , the skin appeared macro- and microscopically healthy following infection with lower doses of M . ulcerans , exhibiting intact epidermis and fat cells , undistorted collagen fibre networks and no marked inflammatory infiltration ( Fig . 3 , D1 and D2 ) ., Where the infection focus had started to ulcerate , strong infiltration towards the destroyed epidermis was observed ( Fig . 3 , A1 and A2 ) ., No AFB were found in this region , indicative for loss of the necrotic core with the major burden of AFB through the ulceration ( Fig . 3 , A2 , Fig . 4 , B1 and B2 ) ., Small clusters of AFB were found at deeper sites in the tissue , lateral to the ulceration site ( Fig . 4 , B3–B5 ) ., Infiltration and destruction of collagen fibres extended into the lower part of the dermis and the upper part of the subcutis , reaching far beyond the area where the epidermis was destroyed ( Fig . 3 , A1 ) , indicating the formation of undermined edges ( Fig . 3 , A2 , dotted line ) ., The overall architecture of the plaque lesion that had developed resembled the nodular stages seen 2 . 5 weeks after infection , i . e . a necrotic centre was surrounded by layers of infiltrating cells ( Fig . 3 , B1 ) ., While large clumps of extracellular AFB were found in the necrotic core after injection of 2×107 CFU ( Fig . 3 , B2 ) , AFB were less abundant and bacterial clumps smaller when 2×106 CFU were used for infection ( Fig . 3 , C2 ) ., Fig . 4A depicts the complex architecture of a plaque lesion ( 2×107 CFU dose ) with several distinct belts of infiltrating cells surrounding a central necrotic core which contained huge clusters of AFB but was completely devoid of infiltration ( Fig . 4A , Ring 1 , A1 and A2 ) ., In the surrounding ring 2 , AFB were scarce and had mostly a beaded appearance ., In addition to these single AFB , small globi-like clusters of AFB were found , along with Methylene blue stained remains of infiltrating cells ( Fig . 4A , Ring 2 , A3 and A4 ) ., Ring 3 contained mostly small infiltrating cells that appeared intact , and some acid-fast bacterial debris ( Fig . 4A , Ring 3 , A5 and A6 ) ., The outermost layer that could be distinguished did not contain AFB and was mainly built by macrophages and lymphocytes ( Fig . 4A , Ring 4 , A7 ) ., Hence , the number and integrity of AFB decreased from the centre to the periphery of the lesion , whereas the integrity of the cellular infiltration showed an opposite trend , most likely reflecting levels of the cytotoxic macrolide mycolactone decreasing from centre to periphery ., All key features of BU pathology in humans were also found in the experimentally infected pig skin ., Already 2 . 5 weeks after infection , coagulative necrosis ( Fig . 5 , A1 ) , fat cell ghosts ( Fig . 5 , A2 ) and extracellular clusters of AFB ( Fig . 5 , A3 and A4 ) were detected ., Slight epidermal hyperplasia was already observed at 2 . 5 weeks and became more pronounced 6 . 5 weeks after infection ( Fig . 5 , A5–A7 ) ., At this time , typical histopathological hallmarks of more advanced human BU lesions also emerged in the infected pig skin , namely formation of granulomas ( Fig . 5 , A8 ) and presence of giant cells ( Fig . 5 , A9 ) ., Not only experimental infection with M . ulcerans led to these typical alterations in the skin , but also the injection of synthetic mycolactone A/B ( Fig . 5B ) ., Besides the general histopathological changes , another similarity to findings in human BU 37 was observed: the formation of satellite infection foci adjacent to the primary lesion ., A striking example for this is depicted in Fig . 4B where two satellite foci with small clusters of AFB in a necrotic core were found peripheral to the ulcerated main infection focus ( Fig . 4B , Region 2 ) ., Likewise in the plaque lesion depicted in Fig . 4A , clusters of AFB were found near the main infection focus ( Fig . 4A , Ring 5 , A8 ) ., Detailed studies on the early pathogenesis of BU in an animal model closely mimicking human BU would be very important for a better understanding of host-pathogen interactions and the relative importance of different effector functions of the innate and adaptive immune system against M . ulcerans ., Here we explored the potential of the pig to serve as model for human M . ulcerans infection ., After having infected pigs subcutaneously with high doses ( 2×106 or 2×107 CFU ) of M . ulcerans bacteria , we observed the development of different forms of BU lesions ( nodules , plaques and ulcers ) ., Macroscopic and histopathological changes closely mirrored human BU ., Challenge with lower doses ( 2×103 to 2×105 CFU ) resulted in limited tissue destruction and/or infiltration 2 . 5 weeks after infection , which resolved spontaneously until week 6 . 5 ., Likewise , the dose of bacteria transmitted may be of critical importance for the outcome of a natural M . ulcerans infection in humans ., Sero-epidemiological analyses in human populations living in BU endemic areas have indicated that exposure to M . ulcerans often leads to self-resolving , non-symptomatic infections , as indicated by development of M . ulcerans specific antibody responses 38 , 39 ., While macrophages and other immune cells might be able to eliminate smaller numbers of scattered M . ulcerans cells , microcolonies of a critical size may develop a protective cloud of mycolactone around them ., If the local concentration of the macrolide cytotoxin exceeds a certain level , infiltrating cells may be killed before they can reach the bacteria ., This leads to the characteristic picture of clusters of extracellular AFB located primarily in the necrotic core of advanced lesions , which is devoid of living infiltrating immune cells , but contains debris of early inflammatory infiltrates 40 , 41 ., In our study , we observed round elevations of the skin already 2 . 5 weeks after infection ., These alterations were firm , movable and clearly palpable and hence displayed the characteristic features of human BU nodules 11 ., Microscopic investigation of the infected skin sites revealed that most histopathological hallmarks of BU had already developed during the first 2 . 5 weeks of infection if 2×106 or 2×107 CFU of M . ulcerans was used ., The experimentally induced nodules exhibited a necrotic core containing extracellular AFB surrounded by infiltrating cells and fat cell ghosts ., Subcutaneous injection of the bacteria led to the formation of an infection focus in the lower dermis and subcutis , where it is also typically found in human BU 13 ., In ulcerative human BU lesions AFB are typically focally distributed and not evenly dispersed in the affected tissue 37 , 42 ., Ulceration leads to the shedding of necrotic tissue containing masses of AFB ., Therefore the bacterial burden is usually higher in non-ulcerative lesions than in ulcers , where the majority of the remaining AFB reside in the undermined edges of the ulcers ., Our histopathological analyses showed , that like in human BU disease 37 , satellite lesions may develop near the primary lesion ., These may emerge from globi-like accumulations of AFB originating from bacteria that were internalized and transported to distant sites by phagocytic cells ., Globi-like accumulations are also found in human BU 43 and in experimentally infected mice 44–46 ., Again , these microcolonies may have to reach a critical size to be able to develop a protective cloud of mycolactone around them ., The emergence of only small numbers of newly established microcolonies may explain why borders of advanced ulcers often appear to be very heterogeneous with respect to disease activity with some regions displaying progressive tissue destruction and others showing spontaneous healing tendencies ., At 6 . 5 weeks after infection with 2×106 or 2×107 CFU of M . ulcerans , lesions that were still closed comprised a necrotic centre containing clumps of AFB surrounded by well stratified belts of infiltrating cells ., Similarly , lesions consisting of a necrotic core surrounded by an inner belt of CD14 positive monocytes/macrophages and a more external belt of CD3 positive T-cells have been described in human BU 47 ., The integrity and number of bacteria was decreasing to the outer rim of the lesion ., In contrast , the density and integrity of the cellular infiltrates decreased towards the necrotic core ., In the pig model first macroscopic signs of infection ( nodules ) developed relatively fast after injection of a high number of bacteria ., For human BU disease in Uganda and Southern Australia incubation periods of 4–13 weeks and 5–38 weeks have been estimated , respectively 48 ., However , incubation periods as short as 2–3 weeks have also been described 49 ., Despite extensive analyses we did not find bacteria in the tissue with low inoculation doses at the 6 . 5 week time point ., Therefore we assume that also at later time points lesions would not develop with these low infection doses ., It is possible that pigs are more resistant to M . ulcerans infection than humans ., Consequently , the size of the inoculum to achieve productive experimental infection may be higher for pigs than for the natural infection of humans ., This high experimental inoculation dose may have led to fast progression of the disease ., In conclusion , our findings indicate that the pig is a very good animal model to study many aspects of M . ulcerans infection ., Pig skin represents a much closer model for human skin than murine foot pads , ears or tails with respect to physiology , structure and abundance of fat tissue 50 ., In addition , the immune system of the pig resembles the human system more closely than that of the mouse 34 ., In particular the development of new therapeutic and prophylactic interventions might benefit from the porcine M . ulcerans infection model .

Introduction, Materials and Methods, Results, Discussion

Buruli ulcer ( BU ) is a slowly progressing , necrotising disease of the skin caused by infection with Mycobacterium ulcerans ., Non-ulcerative manifestations are nodules , plaques and oedema , which may progress to ulceration of large parts of the skin ., Histopathologically , BU is characterized by coagulative necrosis , fat cell ghosts , epidermal hyperplasia , clusters of extracellular acid fast bacilli ( AFB ) in the subcutaneous tissue and lack of major inflammatory infiltration ., The mode of transmission of BU is not clear and there is only limited information on the early pathogenesis of the disease available ., For evaluating the potential of the pig as experimental infection model for BU , we infected pigs subcutaneously with different doses of M . ulcerans ., The infected skin sites were excised 2 . 5 or 6 . 5 weeks after infection and processed for histopathological analysis ., With doses of 2×107 and 2×106 colony forming units ( CFU ) we observed the development of nodular lesions that subsequently progressed to ulcerative or plaque-like lesions ., At lower inoculation doses signs of infection found after 2 . 5 weeks had spontaneously resolved at 6 . 5 weeks ., The observed macroscopic and histopathological changes closely resembled those found in M . ulcerans disease in humans ., Our results demonstrate that the pig can be infected with M . ulcerans ., Productive infection leads to the development of lesions that closely resemble human BU lesions ., The pig infection model therefore has great potential for studying the early pathogenesis of BU and for the development of new therapeutic and prophylactic interventions .

Buruli ulcer caused by Mycobacterium ulcerans infection is a necrotizing disease of the skin and the underlying subcutaneous tissue ., Since the skin of pigs ( Sus scrofa ) has striking structural and physiological similarities with human skin , we investigated whether it is possible to develop an experimental M . ulcerans infection model by subcutaneous injection of the mycobacteria into pig skin ., Injection of 2×106 or 2×107 colony forming units of M . ulcerans led to the development of lesions that were both macroscopically and microscopically very similar to human Buruli ulcer lesions ., In particular for the characterization of the pathogenesis of Buruli ulcer and of immune defence mechanisms against M . ulcerans , the pig model appears to be superior to the mouse foot pad model commonly used for the evaluation of the efficacy of chemotherapeutic regimens .

mycobacterium ulcerans, bacterial diseases, infectious diseases, buruli ulcer, medicine and health sciences, anatomical pathology, histopathology, animal models of infection, pathology and laboratory medicine, animal studies, animal models of disease, neglected tropical diseases, biology and life sciences, tropical diseases, actinobacteria, bacteria, organisms, research and analysis methods

journal.pcbi.1007010

Revealing evolutionary constraints on proteins through sequence analysis

Proteins play crucial roles in all cellular processes , acting as enzymes , motors , receptors , regulators , and more ., The function of a protein is encoded in its amino-acid sequence ., In evolution , random mutations affect the sequence , while natural selection acts at the level of function , however our ability to predict a protein’s function directly from its sequence has been very limited ., Recently , the explosion of available sequences has inspired new data-driven approaches to uncover the principles of protein operation ., At the root of these new approaches is the observation that amino-acid residues which possess related functional roles often evolve in a correlated way ., In particular , analyses of large alignments of protein sequences have identified “sectors” of collectively correlated amino acids 1–6 , which has enabled successful design of new functional sequences 3 ., Sectors are spatially contiguous in the protein structure , and in the case of multiple sectors , each one may be associated with a distinct role 4 , 7 ., What is the origin of these sectors , and can we identify them from sequence data in a principled way ?, To address these questions , we developed a general physical model that naturally gives rise to sectors ., Specifically , motivated by the observation that many protein properties reflect additive contributions from individual amino acids 8–10 , we consider any additive trait subject to natural selection ., As a concrete example , we study a simple elastic-network model that quantifies the energetic cost of protein deformations 11 , which we show to be an additive trait ., We then demonstrate that selection acting on any such additive trait automatically yields collective correlation modes in sequence data ., We show that the main signature of the selection process lies in the small-eigenvalue modes of the covariance matrix of the selected sequences , but we find that some signatures also exist in the widely-studied large-eigenvalue modes ., Finally , we demonstrate a principled method to identify sectors and to quantify mutational effects from sequence data alone ., We focus on selection on an additive scalar trait, T ( α → ) = ∑ l = 1 L Δ l ( α l ) , ( 1 ), where α → = ( α 1 , … , α L ) is the amino-acid sequence considered , L is its length , and Δl ( αl ) is the mutational effect on the trait T of a mutation to amino acid αl at site l ., Mutational effects can be measured with respect to a reference sequence α → 0 , satisfying Δ l ( α l 0 ) = 0 for all l ., Eq 1 is very general as it amounts to saying that , to lowest order , mutations have an additive effect on the trait T , which can be any relevant physical property of the protein , say its binding affinity , catalytic activity , or thermal stability 12 ., System-specific details are encoded by the single-site mutational effects Δl ( αl ) , which can be measured experimentally ., The assumption of additivity is experimentally validated in many cases ., For instance , protein thermal stability , measured through folding free energy , is approximately additive 8 , 13 ., Importantly , we allow selection to act on a phenotype that is a nonlinear function of T . Permitting a phenotypic nonlinearity on top of our additive trait model is motivated by the fact that actual phenotype data from recent high-throughput mutagenesis experiments were accurately modeled via a nonlinear mapping of an underlying additive trait 10 ., Protein sectors are usually defined operationally as collective modes of correlations in amino-acid sequences ., However , the general sequence-function relation in Eq 1 suggests an operational definition of a functional protein sector , namely as the set of sites with dominant mutational effects on a trait under selection ., Selection can take multiple forms ., To be concrete , we first consider a simple model of selection , assuming a favored value T* of the trait T , and using a Gaussian selection window ., We subsequently show that the conclusions obtained within this simple model are robust to different forms of selection ., Our Gaussian selection model amounts to selecting sequences according to the following Boltzmann distribution:, P ( α → ) = exp ( w ( α → ) ) ∑ α → exp ( w ( α → ) ) , ( 2 ), where the fitness w ( α → ) of a sequence is given by, w ( α → ) = - κ 2 ( T ( α → ) - T * ) 2 = - κ 2 ( ∑ l = 1 L Δ l ( α l ) - T * ) 2 ., ( 3 ), The selection strength κ sets the width of the selection window ., Such selection for intermediate values of a trait can be realistic , e . g . for protein stability 8 ., However , the form of selection can vary , for example selection can be for a nonlinear transform of a trait to be above a certain threshold 10 , and several relevant selection variants are investigated below ., Crucially , while the trait is additive ( Eq 1 ) , the fact that fitness ( Eq 3 ) and selection ( Eq 2 ) are nonlinear functions of the trait leads to coupling between mutations ., This phenomenon is known as global 10 , 14 or nonspecific 9 epistasis , and its relevance has been shown in evolution experiments 14 , over and above contributions from specific epistasis 9 , 15 ., The focus of this paper is on global epistasis , and we do not include specific epistasis ., Studying the interplay of these two types of epistasis will be an interesting future direction ., For our elastic model of the PDZ domain , the distribution of the additive trait δE for random sequences is shown in Fig 1, ( d ) ., We use the selection process introduced in Eqs 2 and 3 to limit sequences to a narrower distribution of δEs , corresponding , e . g . , to a preferred ligand-binding affinity ., The fitness of a binary sequence S → , a particular case of Eq 3 , reads:, w ( S → ) = - κ 2 ( ∑ l = 1 L Δ l S l - δ E * ) 2 ., ( 6 ), Here , the selection strength κ sets the width of the selection window , and δE* is its center ., For all selections , we take κ = 10 / ( ∑ lΔ l 2 ) , so that the width of the selection window scales with that of the unselected distribution ., We have confirmed that our conclusions are robust to varying selection strength , provided κ ∑ lΔ l 2 ≫ 1 ( see Fig . 3 in S1 Appendix ) ., Although mutations have additive effects on the trait δE , the nonlinearities involved in fitness and selection give rise to correlations among sites ., For instance , if δE* = 0 and if Δl < 0 for all l , as in Fig 1 , a mutation at a site with large |Δl| will decrease the likelihood of additional mutations at all other sites with large |Δl| ., Previous approaches to identifying sectors from real protein sequences have relied on modified forms of Principal Component Analysis ( PCA ) ., So we begin by asking: can PCA identify sectors in our physical model ?, PCA corresponds to diagonalizing the covariance matrix C of sequences: it identifies the principal components ( eigenvectors ) ν → ( j ) associated with progressively smaller variances ( eigenvalues ) λ ( j ) ., We introduce 〈⋅〉* to denote ensemble averages over the selectively weighted sequences , reserving 〈⋅〉 for averages over the unselected ensemble ., The mutant fraction at site l in the selected ensemble is 〈 S l 〉 * = ∑ S → S l P ( S → ) , and the covariance matrix C reads, C l l ′ = 〈 ( S l - 〈 S l 〉 * ) · ( S l ′ - 〈 S l ′ 〉 * ) 〉 * ., ( 7 ) To test the ability of PCA to identify a functional sector , we employed the selection window shown in orange in Fig 1, ( d ) ., The resulting eigenvalues are shown in Fig 1, ( e ) ., One sees outliers ., In particular , why is the last eigenvalue so low ?, Due to the narrow selection window , according to Eq 6 the highly-weighted sequences satisfy ∑ lS l Δ l = S → · Δ → ≈ δ E * ., This means that in the L-dimensional sequence space , the data points for the highly-weighted sequences lie in a narrow region around a plane perpendicular to Δ → ( Fig 1 ( g ) ) ., Hence , the data has exceptionally small variance in this direction , leading to a particularly small eigenvalue of C . Moreover , the corresponding last principal component ν → ( L ) points in the direction with the smallest variance and is consequently parallel to Δ → ( Fig 1, ( f ) ) ., Formally , in Eq 6 , Δ → appears in a quadratic coupling term where it plays the part of a repulsive pattern in a generalized Hopfield model 34 , 35: alone , such a term would penalize sequences aligned with Δ → ., But here , Δ → also appears in a term linear in S → and as a result Eq 6 penalizes sequences that fail to have the selected projection onto Δ → ., In this example , the last principal component accurately recovers the functional sector corresponding to the largest elements of the mutational-effect vector Δ → ., More generally , to quantify the recovery of Δ → by a given vector ν → , we introduce, Recovery = ∑ l | ν l Δ l | ∑ l ν l 2 ∑ l Δ l 2 , ( 8 ), which is nonnegative , has a random expectation of ( 2 / π L ) ∑ l | Δ l | / ∑ lΔ l 2 for L ≫ 1 ( S1 Appendix ) , and saturates at 1 ( including the case of parallel vectors ) ., For our test case , Fig 1 ( h ) shows Recovery for all principal components ., The last one features the highest Recovery , almost 1 , confirming that it carries substantial information about Δ → ., The second-to-last principal component and the first two also provide a value of Recovery substantially above random expectation ., Outlier eigenvalues arise from the sector , and accordingly , we find that the number of modes with high Recovery often corresponds to the number of sites with strong mutational effects ., A more formal analysis of this effect will be an interesting topic for further study ., In our model , Δ → is fundamentally a direction of small variance ., So why do the first principal components also carry information about Δ → ?, Qualitatively , when variance is decreased in one direction due to a repulsive pattern Δ → , variance tends to increase in orthogonal directions involving the same sites ., To illustrate this effect , let L = 3 and Δ → = ( - 1 , 1 , 0 ) , and consider the sequences S → satisfying Δ → · S → = 0 ( namely ( 0 , 0 , 0 ) ; ( 1 , 1 , 0 ) ; ( 0 , 0 , 1 ) ; ( 1 , 1 , 1 ) ) ., The last principal component is Δ → , with zero variance , and the first principal component is ( 1 , 1 , 0 ) : Recovery is 1 for both of them ., This selection conserves the trace of the covariance matrix ( i . e . the total variance ) , so that decreasing the variance along Δ → = ( - 1 , 1 , 0 ) necessarily increases it along ( 1 , 1 , 0 ) ., This simple example provides an intuitive understanding of why the large-eigenvalue modes of the covariance matrix also carry information about Δ → ., It is worth remarking that Eq 6 is a particular case of a general fitness function with one- and two-body terms ( known as fields and couplings in Ising or Potts models in physics ) ., Here , the values of these one- and two-body terms are constrained by their expressions in terms of Δ → ., In practice , several traits might be selected simultaneously ( see below ) , yielding more independent terms among the fields and couplings ., More generally , such one- and two-body descriptions have been very successfully employed via Direct Coupling Analysis ( DCA ) to identify strongly coupled residues that are in contact within a folded protein 36–38 , to investigate folding 39 , and to predict fitness 33 , 40–45 and conformational changes 46 , 47 , as well as protein-protein interactions 48 , 49 ., A complete model of protein covariation in nature should necessarily incorporate both the collective modes described here and the strongly coupled residue pairs which are the focus of DCA ., An important concern is whether the last principal component is robust to small and/or noisy datasets ., Indeed , other directions of small variance can appear in the data ., As a second example , we applied a different selection window , centered in the tail of the distribution of δEs from our elastic model of the PDZ domain ( Fig 2, ( a ) , inset ) ., This biased selection generates strong conservation , 〈Sl〉* ≈ 1 , for some sites with significant mutational effects ., Extreme conservation at one site now dictates the last principal component , and disrupts PCA-based recovery of Δ → ( Fig 2, ( a ) and 2, ( b ) ) ., To overcome this difficulty , we developed a more robust approach that relies on inverting the covariance matrix ., Previously , the inverse covariance matrix was successfully employed in Direct Coupling Analysis ( DCA ) to identify strongly coupled residues that are in contact within a folded protein 36–38 ., The fitness in our model ( Eq 6 ) involves one and two-body interaction terms , and constitutes a particular case of the DCA Hamiltonian ( S1 Appendix ) ., A small-coupling approximation 37 , 38 , 50 , 51 ( S1 Appendix ) gives, C l l ′ - 1 ≈ ( 1 - δ l l ′ ) κ Δ l Δ l ′ + δ l l ′ ( 1 P l + 1 1 - P l ) , ( 9 ), where Pl denotes the probability that site l is mutated ., Since we are interested in extracting Δ → , we can simply set to zero the diagonal elements of C−1 , which are dominated by conservation effects , to obtain a new matrix, C ˜ l l ′ - 1 ≈ ( 1 - δ l l ′ ) κ Δ l Δ l ′ ., ( 10 ), The first eigenvector of C ˜ - 1 ( associated with its largest eigenvalue ) should accurately report Δ → since , except for its zero diagonal , C ˜ - 1 is proportional to the outer product Δ → ⊗ Δ → ., We call this approach the Inverse Covariance Off-Diagonal ( ICOD ) method ., As shown in Fig 2, ( d ) and 2, ( e ) , ICOD overcomes the difficulty experienced by PCA for biased selection , while performing equally well as PCA for unbiased selection ( Fig . 2 in S1 Appendix ) ., Removing the diagonal elements of C−1 before diagonalizing is crucial: otherwise , the first eigenvector of C−1 is the same as the last eigenvector of C and suffers from the same shortcomings for strong conservation ., Here too , eigenvectors associated to both small and large eigenvalues contain information about Δ → ( Fig 2, ( b ) and 2, ( d ) ) ., An important challenge in sector analysis is distinguishing multiple , independently evolving sectors 4 , 7 , 52 ., We can readily generalize our fitness function ( Eqs 3 and 6 ) to allow for selection on multiple additive traits:, w ( S → ) = - ∑ i = 1 N κ i 2 ( ∑ l = 1 L Δ i , l S l - T i * ) 2 , ( 11 ), where N is the number of distinct additive traits T i ( S → ) = ∑ lΔ i , l S l under selection , Δ → i is the vector of mutational effects on trait Ti , κi is the strength of selection on this trait , and T i * is the associated selection bias ., For example , Δ → 1 might measure how mutations change a protein’s binding affinity , while Δ → 2 might be related to its thermal stability , etc ., In Fig . 5 in S1 Appendix , we consider selection on two distinct additive traits , using synthetically-generated random mutational-effect vectors Δ → 1 and Δ → 2 ( S1 Appendix ) ., Note that these mutational effects are thus unrelated to our toy model of protein elastic deformations: as stated above , our approach holds for any additive trait under selection ., ICOD then yields two large outlier eigenvalues of the modified inverse covariance matrix C ˜ - 1 ., The associated eigenvectors accurately recover both Δ → 1 and Δ → 2 , after a final step of Independent Component Analysis ( ICA ) 7 , 53 , 54 that successfully disentangles the contributions coming from the two constraints ( see S1 Appendix ) ., We further tested the performance of ICOD by systematically varying the selection bias , both for our toy model of PDZ elastic deformations and for more general synthetically-generated random mutational-effect vectors ( Fig 3 ) ., ICOD achieves high Recovery of these various mutational-effect vectors for both single and double selection over a broad range of selection biases T* , albeit performance falls off in the limit of extreme bias ., How does ICOD compare with other approaches to identifying sectors ?, We compared the performance of ICOD with Statistical Coupling Analysis ( SCA ) , the original PCA-based method 4 , 7 ., In SCA , the covariance matrix C is reweighted by a site-specific conservation factor ϕl , the absolute value is taken , C ˜ l l ′ ( SCA ) = | ϕ l C l l ′ ϕ l ′ | , and sectors are identified from the leading eigenvectors of C ˜ ( SCA ) ., We therefore tested the ability of the first eigenvector of C ˜ ( SCA ) to recover Δ → for a single selection ., We found that the square root of the elements of the first SCA eigenvector can provide high Recovery of Δ → ( Fig 3 , and Figs . 13 , 14 in S1 Appendix ) ., However , the performance of SCA relies on conservation through ϕl , and it has been shown that residue conservation actually dominates sector identification by SCA in certain proteins 52 ., Consequently , for unbiased selection , SCA breaks down ( Fig 3, ( a ) , dashed curves ) and cannot identify sector sites ( Fig . 17 in S1 Appendix ) ., ICOD does not suffer from such shortcomings , and performs well over a large range of selection biases ., Note that in SCA , only the top eigenvectors of C ˜ ( SCA ) convey information about sectors ( Figs . 13 , 15 in S1 Appendix ) ., We also compared ICOD with another PCA-based approach 34 , which employs an inference method specific to the generalized Hopfield model , and should thus be well adapted to identifying sectors within our physical model ( Eq 6 ) ., Overall , this specialized approach performs similarly to ICOD , being slightly better for very localized sectors , but less robust than ICOD for strong selective biases and small datasets ( S1 Appendix ) ., Exactly as for PCA and ICOD , within this method , the top Recovery is obtained for the bottom eigenvector of the ( modified ) covariance matrix , consistent with Δ → in our model being a repulsive pattern 34 , but large Recoveries are also obtained for the top eigenvectors ( Fig . 18 in S1 Appendix ) ., To assess the robustness of functional sectors to selections different from the simple Gaussian selection window of Eqs 2 and 3 , we selected sequences with an additive trait T above a threshold Tt , and varied this threshold ., For instance , a fluorescent protein might be selected to be fluorescent enough , which could be modeled by requiring that ( a nonlinear transform of ) an additive trait be sufficiently large 10 ., As shown in Fig 4 , the corresponding sectors are identified by ICOD as well as those resulting from our initial Gaussian selection window ., In Fig 4, ( d ) , we show the performance of both ICOD and SCA at recovering sectors arising from selection with a threshold ., Consistent with previous results ( see Fig 3 ) , we find that ICOD is more robust than SCA to extreme selections ., We also successfully applied ICOD to other forms of selection: Fig . 8 in S1 Appendix shows the case of a quartic fitness function replacing the initial quadratic one ( Eq 3 ) in the Boltzmann distribution ( Eq 2 ) and Fig . 9 in S1 Appendix shows the case of a rectangular selection window ( S1 Appendix ) ., These results demonstrate the robustness of functional sectors , and of ICOD , to different plausible forms of selection ., So far , we have considered binary sequences , with only one type of mutation with respect to the reference state ., In the S1 Appendix , we demonstrate that our formalism , including the ICOD method , extends to mutations among q different states ., The case q = 21 , which includes the 20 different amino-acid types plus the alignment gap is the relevant one for real proteins ., The single-site mutational effects Δl are then replaced by state-specific mutational effects Δl ( αl ) with αl ∈ {1 , … , 21} ( see Eq 1 ) ., Fig . 10 in S1 Appendix shows that the generalized version of ICOD performs very well on synthetic data generated for the case q = 21 ., We further demonstrate that sector identification is robust to gauge changes ( reference changes ) and to the use of pseudocounts ( S1 Appendix ) ., While the main purpose of this article is to propose an operational definition of functional protein sectors and to understand how they can arise , an interesting next question will be to investigate what ICOD can teach us about real data ., As a first step in this direction , we applied ICOD to a multiple sequence alignment of PDZ domains ., In this analysis , we employed a complete description with q = 21 , but we compressed the ICOD-modified inverse matrix using the Frobenius norm to focus on overall ( and not residue-specific ) mutational effects ( see S1 Appendix for details ) ., As shown in Fig 5, ( a ) and 5, ( b ) , both ICOD and SCA identify one strong outlying large eigenvalue , thus confirming that PDZ has only one sector 6 ., Recall that due to the inversion step , the largest eigenvalue in ICOD is related to the mode with smallest variance , whose importance was demonstrated above ., Furthermore , as seen in Fig 5, ( c ) and 5, ( d ) , both methods correctly predict the majority of residues found experimentally to have important mutational effects on ligand binding to the PDZ domain shown in Fig 1, ( a ) 6 ., For instance , over the 20 top sites identified by ICOD ( resp . SCA ) , we find that 85% ( resp . 75% ) of them are also among the 20 experimentally most important sites ., Note that for SCA , we recover the result from Ref ., 6 ., The performance of ICOD is robust to varying the cutoff for removal of sites with a large proportion of gaps ( see Fig . 21 in S1 Appendix ) , but notably less robust than SCA to pseudocount variation ( see Fig . 22 in S1 Appendix ) ., Importantly , both ICOD and SCA perform much better than random expectation , which is 29% ., Hence , both of these methods can be useful to identify functionally important sites ., The slightly greater robustness of SCA to pseudocounts on this particular dataset ( see Fig . 22 in S1 Appendix ) might come from the fact that many of the experimentally-identified functionally important sites in the PDZ domain are strongly conserved 52 , which makes the conservation reweighting step in SCA advantageous ., Since residue conservation alone is able to predict most of the experimentally important PDZ sites 52 , we also compared conservation to SCA and ICOD: ranking sites by conservation ( employing the conservation score of Ref . 7 , see S1 Appendix ) indeed identifies 70% of the top 20 experimentally-determined sites with important mutational effects ., Interestingly , ICOD scores are slightly more strongly correlated with conservation than SCA scores are correlated with conservation ( see Fig . 23 in S1 Appendix ) , despite the fact that conservation is explicitly used in SCA and not in ICOD ., Overall , this preliminary application to real data highlights the ability of ICOD to identify functionally related amino acids in a principled way that only relies on covariance ., We emphasize that the main goal of this paper is to provide insight into the possible physical origins of sectors , and into the statistical signatures of these physical sectors in sequence data ., A more extensive application of ICOD and related methods to real sequence data will be the subject of future work ., We have demonstrated how sectors of collectively correlated amino acids can arise from evolutionary constraints on functional properties of proteins ., Our model is very general , as it only relies on the functional property under any of various forms of selection being described by an underlying additive trait , which has proven to be valid in many relevant situations 8–10 , 13 ., We showed that the primary signature of functional selection acting on sequences lies in the small-eigenvalue modes of the covariance matrix ., In contrast , sectors are usually identified from the large-eigenvalue modes of the SCA matrix 4 , 7 ., This is not in contradiction with our results because , as we showed , signatures of our functional sectors are often also found in large-eigenvalue modes of the covariance matrix ., Besides , the construction of the SCA matrix from the covariance matrix involves reweighting by conservation and taking an absolute value or a norm 4 , 7 , which can substantially modify its eigenvectors , eigenvalues , and their order ., Conservation is certainly important in real proteins , especially in the presence of phylogeny; indeed , the SCA matrix , which includes both conservation and covariance , was recently found to capture well experimentally-measured epistasis with respect to the free energy of PDZ ligand binding 55 ., However , the fundamental link we propose between functional sectors and small-eigenvalue modes of the covariance matrix is important , since large-eigenvalue modes of the covariance matrix also contain confounding information about subfamily-specific residues 56 and phylogeny 57 , and consistently , some sectors identified by SCA have been found to reflect evolutionary history rather than function 4 ., Interestingly , the small-eigenvalue modes are also the ones that contain most information about structural contacts in real proteins 35 ., Hence , our results help explain previously observed correlations between sectors and contacts , e . g . the fact that contacts are overrepresented within a sector but not across sectors 58 ., We introduced a principled method to detect functional sectors from sequence data , based on the primary signature of these sectors in the small-eigenvalue modes of the covariance matrix ., We further demonstrated the robustness of our approach to the existence of multiple traits simultaneously under selection , to various forms of selection , and to data-specific questions such as reference choices and pseudocounts ., Importantly , our modeling approach allowed us to focus on functional selection alone , in the absence of historical contingency and of specific structural constraints , thus yielding insights complementary to purely data-driven methods ., The collective modes investigated here are just one source of residue-residue correlations ., Next , it will be interesting to study the intriguing interplay between functional sectors , phylogeny , and contacts , and to apply our methods to multiple protein families ., Our results shed light on an aspect of the protein sequence-function relationship and open new directions in protein sequence analysis , with implications in synthetic biology , building toward function-driven protein design .

Introduction, Model and methods, Results, Discussion

Statistical analysis of alignments of large numbers of protein sequences has revealed “sectors” of collectively coevolving amino acids in several protein families ., Here , we show that selection acting on any functional property of a protein , represented by an additive trait , can give rise to such a sector ., As an illustration of a selected trait , we consider the elastic energy of an important conformational change within an elastic network model , and we show that selection acting on this energy leads to correlations among residues ., For this concrete example and more generally , we demonstrate that the main signature of functional sectors lies in the small-eigenvalue modes of the covariance matrix of the selected sequences ., However , secondary signatures of these functional sectors also exist in the extensively-studied large-eigenvalue modes ., Our simple , general model leads us to propose a principled method to identify functional sectors , along with the magnitudes of mutational effects , from sequence data ., We further demonstrate the robustness of these functional sectors to various forms of selection , and the robustness of our approach to the identification of multiple selected traits .

Proteins play crucial parts in all cellular processes , and their functions are encoded in their amino-acid sequences ., Recently , statistical analyses of protein sequence alignments have demonstrated the existence of “sectors” of collectively correlated amino acids ., What is the origin of these sectors ?, Here , we propose a simple underlying origin of protein sectors: they can arise from selection acting on any collective protein property ., We find that the main signature of these functional sectors lies in the low-eigenvalue modes of the covariance matrix of the selected sequences ., A better understanding of protein sectors will make it possible to discern collective protein properties directly from sequences , as well as to design new functional sequences , with far-reaching applications in synthetic biology .

classical mechanics, statistics, random variables, covariance, amino acid sequence analysis, multivariate analysis, mathematics, algebra, damage mechanics, research and analysis methods, sequence analysis, sequence alignment, bioinformatics, mathematical and statistical techniques, deformation, principal component analysis, statistical methods, probability theory, physics, eigenvectors, linear algebra, natural selection, database and informatics methods, biology and life sciences, physical sciences, evolutionary biology, eigenvalues, evolutionary processes

journal.pgen.1001008

Elevated Oxidative Membrane Damage Associated with Genetic Modifiers of Lyst-Mutant Phenotypes

LYST is a large cytoplasmic protein that influences several traits relevant to human health and disease 1 ., Mutations in the encoding gene , LYST , can cause Chediak-Higashi syndrome , a rare , autosomal recessive disorder characterized by variable degrees of oculocutaneous albinism , immunodeficiency , prolonged bleeding time , and progressive neurologic dysfunction 2 , 3 ., Lyst-mutant mice also exhibit ocular defects resembling exfoliation syndrome 4 , a common disease that is characterized by iris defects , fibrillar accumulations , and aberrantly dispersed pigment throughout the anterior chamber of the eye 5 ., As fibrillar material and dispersed pigment accumulate in the outflow structures of the eye , intraocular pressure can become elevated and a secondary form of glaucoma often ensues ., The extent to which Chediak-Higashi syndrome and exfoliation syndrome resemble each other at a mechanistic level remains to be determined , but both disease states clearly share important links to LYST ., Since the time the Lyst gene was initially discovered 2 , 6 , a cellular framework for understanding LYST function has only partially emerged ., LYST is present in most tissues 7 and loss-of-function mutations lead to the enlargement of lysosome-related organelles including lysosomes , melanosomes , and platelet-dense bodies 8 ., In this enlarged state , the organelles often fail to undergo normal movements 9–12 , and exhibit altered protein components consistent with defective protein trafficking 13–16 as well as impaired lysosomal exocytosis leading to defects in plasma membrane repair 11 ., LYST contains relatively few motifs with definitive function , thus providing limited insight into how LYST protein might contribute to these defects ., Domains present in LYST include several ARM/HEAT repeats located near the amino terminus , a perilipin domain , a BEACH domain , and seven WD40 repeats located near the carboxy terminus 1 ., Multiple protein-protein interactions involving LYST have been identified , including interactions with HGS , YWHAB ( commonly referred to as 14-3-3 ) , and CSNK2B 17 ., Collectively , these studies suggest that LYST organizes protein-complexes important to lysosome-related organelles , perhaps through interactions with membrane domains ., Here , a genetic approach for expanding knowledge of Lyst function is undertaken ., The goal of these experiments is to identify genetic modifiers of Lyst-mediated phenotypes in mice ., C57BL/6J mice homozygous for the beige-J mutation of the Lyst gene ( B6-Lystbg-J ) exhibit a unique iris phenotype characterized by iris stromal atrophy , pigment dispersion , dark iris color , and altered morphology of the iris pigment epithelium 4 , ., Because the iris is easily assayed , we reasoned that these iris phenotypes could form a convenient basis for genetic screens of Lyst-dependent modifiers ., Two approaches are taken , one candidate-based and another based on manipulation of genetic background ., Both experimental approaches implicate oxidative stress as contributing to the mechanism of disease ., Testing this hypothesis directly , we found that Lyst mutation leads specifically to an accumulation of lipid hydroperoxides ., Likely a consequence of impaired lysosomal exocytosis and a resulting failure in plasma-membrane repair , these findings implicate oxidative membrane damage as a pathological component of Lyst-mutant phenotypes ., Previously , adult B6-Lystbg-J mice were shown to have an iris disease involving pigment dispersion and a distinct transillumination defect 4 , 18 ., To determine whether these phenotypes are the consequence of altered development or an early-onset degenerative disease , iris phenotypes of B6-Lystbg-J and C57BL/6J control mice were compared throughout postnatal development ( Figure 1 ) ., While the iris of C57BL/6J mice remained relatively constant with age ( Figure 1A–1L ) , the iris of B6-Lystbg-Jmice followed a degenerative course ( Figure 1M–1U; additional time points provided in Figure S1 ) ., At 17 days of age , when mice became just large enough to examine with an ophthalmic slit-lamp , the iris of B6-Lystbg-J mice appeared relatively normal , by both slit-lamp examination ( Figure 1M and 1P ) and histologic analysis ( Figure 1S ) ., At 60 days of age , slit-lamp analyses indicated early signs of iris disease characterized by a dark and granular-appearing iris ( Figure 1N ) ., At this age B6-Lystbg-J mice also exhibited minor concentric iris transillumination defects ( Figure 1Q ) , which have previously been shown to correlate with altered morphology of the iris pigment epithelium 4 ., Histologic analysis confirmed both that the iris stroma was atrophied and that the morphology of the iris pigment epithelium was altered ( Figure 1T ) ., By 100–135 days of age , these changes had become more striking ( Figure 1O , 1R , and 1U ) , with the most notable change being that the iris transillumination defects were more pronounced ., Collectively , these results indicate that iris disease in B6-Lystbg-J mice is the consequence of an early-onset degenerative process ., Having established this , we next set out to identify genetic modifiers of these Lyst-mutant phenotypes that might shed light on the underlying molecular mechanisms ., As in the case of B6-Lystbg-J mice , DBA/2J mice also develop a degenerative iris disease involving iris stromal atrophy and iris transillumination defects 19 , 20 ., The iris disease of DBA/2J mice is caused by digenic interaction of two genes encoding proteins found within melanosomes , Tyrp1 and Gpnmb 21 , and can be rescued by mutations that decrease pigment production 21 , 22 ., To test whether pigment production is also important to the iris disease of B6-Lystbg-J mice , genetic epistasis experiments were performed ., Albino B6 . Tyrc-2J mice were intercrossed with B6-Lystbg-J mice to generate mice homozygous for both mutations on a uniform C57BL/6J genetic background ( B6 . Tyrc-2J Lystbg-J ) ., The rationale for this experiment was that if pigment production contributes to Lyst-mutant phenotypes , B6 . Tyrc-2J Lystbg-J mutant irides lacking pigment production should exhibit suppressed phenotypes ., Cohorts of B6 . Tyrc-2J Lystbg-J mutant mice were generated and analyzed ( Figure 2 ) ., The Tyrc-2J mutation rescued all observable Lyst-mediated iris phenotypes , with B6 . Tyrc-2J Lystbg-J eyes indistinguishable from control B6 . Tyrc-2J eyes ( B6 . Tyrc-2J Lystbg-J , n\u200a=\u200a20 eyes at 2–5 months , 12 eyes at 9–11 months , 72 eyes at 12–19 months; B6 . Tyrc-2J , n\u200a=\u200a30 eyes at 2–5 months , 8 eyes at 9–11 months , 34 eyes at 12–19 months ) ., The iris stroma of B6 . Tyrc-2J and B6 . Tyrc-2J Lystbg-J eyes were free of stromal atrophy ( Figure 2A and 2B ) , with no accumulations of macrophages or debris in the anterior chamber ( Figure 2C and 2D ) ., All eyes exhibited transillumination defects typical of albino mouse eyes , with no indication of the concentric transillumination defect characteristic of Lyst-mutant mice ( Figure 2E and 2F ) ., Rescue was confirmed by histologic analysis of the iris ( Figure 2G and 2H; additional time points in Figure S2 ) ., Together , these results identified Tyr as a genetic suppressor of Lyst-mutant iris phenotypes , and indicated that melanin production contributes to the pathological events leading to iris disease in B6-Lystbg-J mice ., To complement the candidate-driven search for potential Lyst modifiers , a genetic background-driven approach was also undertaken by creating and analyzing a congenic strain of DBA/2J mice containing the Lystbg-J mutation ( D2 . Lystbg-J ) ., The rationale for this experiment was that Tyrp1 mutation , Gpnmb mutation , or other factors from the DBA/2J genetic background might affect Lyst-mutant iris phenotypes ., After 10 generations of backcrossing , D2 . Lystbg-J mice homozygous for the Lystbg-J mutation were generated and assayed for relevant iris phenotypes ( Figure 3 ) ., The Lystbg-J mutation caused a lightening of the DBA/2J coat color ( Figure S3 ) ., At all ages examined , the DBA/2J background enhanced Lystbg-J ocular phenotypes ( n\u200a=\u200a30 eyes of D2 . Lystbg-J mice 1–7 months of age ) ., At ages when DBA/2J mice with wild-type Lyst alleles exhibited only mild indices of iris abnormalities ( Figure 3A , 3D , and 3G ) , D2 . Lystbg-J mice exhibited severe disease ( Figure 3B , 3E , and 3H ) that was enhanced over that in B6-Lystbg-J mice ( Figure 3C , 3F , and 3I ) ., In D2 . Lystbg-J irides , the extent of iris stromal atrophy and iris transillumination defects was notably worsened , and resulted in large accumulations of pigment within the inferior irideocorneal angle ., These results indicate that the DBA/2J genetic background enhances iris phenotypes of Lystbg-J mice ., The identity of the DBA/2J modifier was subsequently shown to be located within a small region of mouse chromosome 4 and is likely the Tyrp1b mutation ., DBA/2J mice have a known mutation in the Tyrp1 gene 23 , which similar to the Lystbg-J mutation , also causes iris stromal atrophy 19 , 21 ., To directly test whether Tyrp1 genotype influences Lyst phenotypes in mice , a wild-type Tyrp1 allele was crossed onto the D2 . Lystbg-J genetic background by intercrosses with the previously described D2 . Tyrp1B6GpnmbB6 congenic strain of mice 24 ., Irides of DBA/2J mice with differing Lyst and Tyrp1 genotypes were subsequently compared ( Figure 4 ) ., As described above , the Lystbg-J mutation results in a subtle , but readily detectable , pattern of iris transillumination defects on the C57BL/6J genetic background ( Figure 4A ) , a phenotype that is greatly enhanced on the DBA/2J genetic background ( Figure 4B ) ., Among 39 ( D2 . Lystbg-J X D2 . Tyrp1B6GpnmbB6 ) F2 progeny examined , a total of 11 mice exhibited transillumination defects of two severities ., Four mice homozygous for the Tyrp1b mutation , but with at least 1 wild-type Lyst allele , exhibited mild transillumination defects ( Figure 4C ) ., Seven mice homozygous for the Lystbg-J mutation , but with at least 1 wild-type Tyrp1 allele , exhibited moderate transillumination defects ( Figure 4D ) ., The severity of transillumination defects for DBA/2J mice with wild-type Tyrp1 were greatly reduced in comparison to those in D2 . Lystbg-J mice ( compare Figure 4D to Figure 4B ) ., Gpnmb genotype , which was also segregating in these crosses , had no discernable influence ., Quantification based on analysis of the amount of red light present in images of these eyes ( Figure S4 ) led to the same conclusion , transillumination defects in DBA/2J mice with wild-type Tyrp1 were significantly reduced in comparison to those in D2 . Lystbg-J mice ( P<0 . 001 , Students two-tailed t-test ) ., These results map a DBA/2J-derived modifier of Lyst to a small ( approximately 14–36 cM , ref 22 ) congenic interval that encompasses the Tyrp1 gene ., Because the Tyrp1b mutation is the only known mutation within this interval in DBA/2J mice , Tyrp1b is likely to be the causative modifier ., The TYRP1 protein is often affiliated with an enzymatic activity as a 5 , 6-dihydroxyindole-2-carboxylic acid ( DHICA ) oxidase that is active in melanin synthesis 25 ., However , TYRP1 has also been reported to have a catalase function , and as such could also broadly influence cellular reactions to oxidative stress 26 ., The findings of both the candidate-driven and genetic background-driven approaches suggested that Lyst influences oxidative stress associated with melanin synthesis ., To independently test this hypothesis , lipid hydroperoxide and protein oxidation levels were measured from iris lysates of 2–3 month-old mice ( Figure 5 ) ., All contexts of Lyst mutation resulted in significantly higher lipid hydroperoxide levels compared to strain-matched controls ( Figure 5A ) ., Lyst genotype , genetic background , and the interaction between Lyst genotype and genetic background all significantly influenced lipid hydroperoxide levels ( P<0 . 001 in all comparisons , two-way ANOVA ) ., In contrast , while genetic background significantly influenced protein carbonylation levels ( P\u200a=\u200a0 . 003 , two-way ANOVA ) , Lyst genotype did not ( Figure 5B ) ., Indices of accumulated oxidative lipid damage were also observed from immunohistochemical analysis of 4-HNE localization ( Figure S5 ) ., Thus , the Lystbg-J mutation specifically altered the accumulation of oxidative damage in the membrane compartment ., Importantly , the elevation in lipid hydroperoxide levels observed in the context of different genetic backgrounds mirrored the extent of disease sensitivity ( B6 . Tyrc-2J Lystbg-J<B6-Lystbg-J<D2 . Lystbg-J ) ., This correlation is consistent with the previous finding that Lyst mutation impairs lysosomal exocytosis , which is important for plasma membrane repair 11 , and supports the notion that oxidative membrane damage contributes to the pathology of Lyst-mutant phenotypes ., Oxidative membrane damage resulting from aberrant LYST function could have particularly important ramifications for the neurodegenerative component of Chediak-Higashi syndrome 1 , 27 ., Elevated levels of oxidized lipids have been observed in several neurodegenerative diseases 28 ., It is possible that the same process responsible for rapid degeneration of the iris might , over a longer time frame , contribute to damage in cells that are challenged by other forms of oxidative stress , for example in aging neurons ., Supporting this , extensively aged D2 . Lystbg-J mice spontaneously developed a severe tremor indicative of a neurodegenerative phenotype , whereas B6-Lystbg-J mice did not ( Video S1; n\u200a=\u200a5 mice per strain , 17–20 months in age ) ., Further histologic analysis indicated that these D2 . Lystbg-J mice exhibited Purkinje-cell degeneration ( Figure 6 ) ., Although the D2 . Lystbg-J mouse cerebellum was normal in overall size and lobule morphology ( Figure 6A and 6B ) , it consistently contained focal areas lacking Purkinje cells ( Figure 6C–6F; n\u200a=\u200a5 mice per strain , 17–20 months in age ) ., Analysis of sections from the spinal cord and sciatic nerve failed to show any degenerative pathology , suggesting limited , if any , lower motor neuron or peripheral nerve involvement ( Figure S6 ) ., These findings indicate that the DBA/2J genetic background also uncovered a Lyst-mediated phenotype in the CNS , causing a tremor likely mediated by Purkinje-cell degeneration ., Because of the requirement for extensive aging , it is not yet known whether the DBA/2J-derived modifier ( s ) of this neurodegenerative phenotype and the degenerative iris disease are identical ., However , given that Tyrp1 is expressed in the brain ( Figure S7 ) and is thought to exhibit catalase activity 26 , this seems likely ., In order to test whether the observed Purkinje-cell degeneration also involves oxidative damage to the cell membrane , lipid hydroperoxide levels were measured in cerebellar lysates of B6-Lystbg-J and D2 . Lystbg-J mice ( n\u200a=\u200a4 mice per strain , 17–20 months in age ) ., An average 25% elevation in lipid hydroperoxides was observed in cerebella of D2 . Lystbg-J mice compared to B6-Lystbg-J mice , but the trend was not statistically significant ( P\u200a=\u200a0 . 20 , two-way ANOVA ) ., Although no histologic defects were apparent in the cerebral cortex or brain stem ( data not shown ) , lipid hydroperoxides in the cortex were elevated by an average of 14% ( P<0 . 001 , two-way ANOVA ) , and levels in the brain stem by 51% ( P\u200a=\u200a0 . 04 , two-way ANOVA ) ., Despite the limited statistical power of these results , they suggest that , as in the iris , the sensitivity of Lyst-mutant neuronal phenotypes in the CNS may involve elevated lipid hydroperoxide levels ., Here we have extended knowledge of Lyst-mediated phenotypes through studies of Lyst genetic modifiers ., Taking advantage of iris phenotypes as a convenient assay , two genetic contexts with important modifying influences were identified ., Albinism completely rescued Lyst-mutant iris phenotypes , and the DBA/2J genetic background enhanced them ., Both results implicate melanosomes in progression of disease associated with Lyst mutation ., Because melanin production occurring in melanosomes is a potent source of reactive oxygen species , the iris of all three strains was tested for indices of oxidative stress by measuring levels of protein and lipid oxidation ., These experiments demonstrated that in pigmented cells , Lyst mutation specifically results in oxidative damage to lipid membranes , which correlates with the overall phenotypic severity of iris phenotypes observed among the enhancer and suppressor strains ( B6 . Tyrc-2J Lystbg-J < B6-Lystbg-J < D2 . Lystbg-J ) ., Thus , these experiments with Lyst genetic modifiers suggest that one mechanism contributing to Lyst-mutant phenotypes is oxidative membrane damage ., B6-Lystbg-J mice have previously been described to exhibit multiple features of Chediak-Higashi syndrome 1 , as well as an iris disease recapitulating aspects of exfoliation syndrome 4 , 18 ., In mice , both disease associations are characterized by changes to pigmented tissues , including coat color and iris morphology ., From a mechanistic perspective , these results are directly relevant to the pathophysiology of Lyst-mutant defects in melanosomes ., Eumelanin production occurring in melanosomes is known to be a potent source of oxidative stress 29 , 30 ., The mechanisms that protect melanosomes and pigment-producing cells from this insult are not well understood ., Our current findings support the hypothesis that Lyst influences these events by modulating the repair of oxidatively damaged membranes ., Exocytosis of intracellular vesicles plays an important role in plasma membrane repair 31 , and experiments with cultured cells have previously demonstrated that Lyst mutations cause defects in lysosomal exocytosis and plasma membrane repair 11 ., The oxidative membrane damage observed in the iris may well represent an accumulation caused by deficient repair ., Thus , other defenses against oxidative damage to lipids are presumably overcome , leading to elevated levels of oxidatively damaged membranes and , ultimately , cellular demise 32-34 ., The identification of Tyrp1 as a likely modifier of Lyst-mutant phenotypes challenges common notions of TYRP1 function ., TYRP1 is typically ascribed to function as a melanocyte-specific protein involved in melanin synthesis with DHICA oxidase activity ., However , human TYRP1 appears to lack DHICA oxidase activity 35 , indicating that this activity is not evolutionarily conserved ., Furthermore , Tyrp1 is not exclusively expressed in only pigment producing cells where DHICA is found ., Based on our results and data provided in online databases such as the Allen Institute for Brain Sciences Mouse Brain Atlas 36 , Tyrp1 is also expressed in the brain ., An alternative function for TYRP1 that is consistent with our current findings is to provide catalase activity 26 ., A function for TYRP1 as a catalase that influences reactive oxygen species would be consistent with the observation that the Tyrp1b mutation is associated with elevated oxidative stress , and would provide a rational explanation for its ability to enhance Lyst-mediated oxidative membrane damage ., However , in considering potential links between Tyrp1 and Lyst , it is important to point out a caveat of our current experiments ., The D2-derived modifier has formally been mapped only to a congenic interval containing Tyrp1 ., Given that the Tyrp1b and Lystbg-J mutations independently cause similar phenotypes in the iris , it is highly likely that Tyrp1 is the causative modifier , yet it remains possible that an as yet unknown modifier exists in close proximity to this gene ., Experiments testing this directly are underway ., Our current findings have important implications with respect to Chediak-Higashi syndrome ., A defining component of this syndrome is progressive neurologic dysfunction 1 ., Although bone-marrow transplantation can correct the immunological aspects of Chediak-Higashi syndrome and significantly extend lifespan , this treatment does not correct the neurologic aspects of the disease 37 , 38 ., A deeper understanding of LYST-mediated neurodegenerative phenotypes is critical for the eventual development of improved therapies for this condition ., In the current analysis , a change in genetic background has uncovered a neurodegenerative phenotype involving the loss of Purkinje cells in mice with the widely utilized Lystbg-J mutation ., Our preliminary experiments suggest that , as in the case of the iris , the neuronal phenotype may involve an accumulation of oxidatively damaged membranes ., Due to the large size of the neuronal cell and its expansive plasma membrane 39 , neurons are likely to be in need of continuous membrane repair , and especially sensitive to defects in this process ., D2 . Lystbg-J mice represent a new resource for further dissecting these mechanisms , and for testing various anti-oxidant therapies for potential benefit in mouse models of Chediak-Higashi syndrome ., Our findings also have important implications with respect to ophthalmic disease ., The ocular phenotypes of B6-Lystbg-J mice , particularly the iris transillumination defects , resemble those seen in exfoliation syndrome 4 ., Several studies implicate oxidative stress as contributory to exfoliation syndrome 5 , including the observation that aqueous humor from exfoliation syndrome patients has decreased levels of catalase activity 40 ., The results presented here suggest that such changes are likely to be pathological ., Furthermore , LYST , and other genes influencing oxidative stress , are suggested as candidates worthy of consideration for contributing to hereditary forms of exfoliation syndrome which is likely to also be strongly influenced by genetic modifiers 41 ., Despite the existence of many Lyst alleles in mice , the resource that this allelic series represents has only begun to be utilized in assigning genotype-phenotype correlations ., The bg-J mutation utilized here results from a 3-bp deletion predicted to remove a single isoleucine from the WD40 domain of the LYST protein 4 ., Previous western blot analysis of cultured fibroblasts homozygous for the bg-J mutation failed to detect LYST protein 42 , suggesting that the mutation may represent a null allele ., However , this experiment has not been performed on tissues isolated directly from the mouse , nor have genetic complementation tests with a definitive null ( such as a deletion or targeted mutation ) been performed , leaving uncertainty regarding classification of the bg-J allele ., Neurodegenerative phenotypes have previously been described for only one other allele ( LystIng3618 ) 43 , which like the bg-J mutation , also disrupts the LYST WD40 domain ., To our knowledge , iris phenotypes have not yet been assessed in any Lyst mutant strains other than those described here ., Thus , it is not yet clear whether the iris and neuronal phenotypes described here will pertain to all Lyst alleles or might be specific to just a sub-class of mutations , though this is an issue that is addressable and worthy of follow-up ., In addition to mutations in mice , a variety of mutations relevant to LYST have been identified in other model organisms ., One example is the Drosophila BEACH family member , blue cheese ( bchs ) ., Like LYST , the Bchs protein is predicted to be a large ( 400 kDa ) protein containing a BEACH domain followed by a series of WD40 repeats near the C-terminus ., Unlike LYST , Bchs also contains a PI ( 3 ) P-binding FYVE domain ., Mutations in bchs result in reduced adult life span and age-related neuronal degeneration 44 ., The bchs gene exhibits genetic interactions with genes involved in lysosomal transport and is therefore thought to encode a scaffolding protein involved in vesicle transport 45 ., In motor neurons from bchs mutants , anterograde transport of endolysosomal vesicles toward synaptic termini is particularly affected , leading to a hypothesis that a degradative function of endolysosomal compartments at the neuromuscular junction is important in preventing neuron degeneration 46 ., With respect to our current findings with Lyst mutant mice , these observations demonstrate that lysosomes undoubtedly make several contributions important to neuronal survival and point to the opportunity afforded by experiments with model organisms to study these events ., A direct Lyst ortholog exists in Drosophila ( CG11814 ) , but mutant phenotypes associated with this gene have not yet been described ., In the future , it will be interesting to examine the extent to which bchs and CG11814 mutant phenotypes resemble each other and what additional insights might be gained by genetic studies of these genes ., In conclusion , we have performed both candidate-driven and genetic background-driven experiments to identify Lyst modifiers ., A priori , the expectation would have been that modifiers of Lyst would logically be related to organelle biogenesis ., Instead , it seems that at the level of the whole animal , oxidative damage to membranes is a highly relevant event ., In our ongoing work , we intend to further test the links between Tyrp1 and Lyst-mediated ophthalmic disease , and to dissect the neurodegenerative disease uncovered in D2 . Lystbg-J mice ., C57BL/6J , B6-Lystbg-J/J ( abbreviated throughout as B6-Lystbg-J ) , DBA/2J , and B6 ( Cg ) -Tyrc-2J/J ( abbreviated throughout as B6 . Tyrc-2J ) mice were obtained from The Jackson Laboratory , Bar Harbor , Maine ., D2 . Tyrp1B6GpnmbB6 mice 24 were kindly provided by Dr . Simon John of The Jackson Laboratory and subsequently bred at The University of Iowa ., Unless otherwise noted , all experiments with B6-Lystbg-J mice utilized mice homozygous for the bg-J mutation ., All mice utilized were housed and bred at the University of Iowa Research Animal Facility ., Mice were maintained on a 4% fat NIH 31 diet provided ad libitum and were housed in cages containing dry bedding ( Cellu-dri; Shepherd Specialty Papers , Kalamazoo , MI ) ., The environment was kept at 21°C with a 12-h light:12-h dark cycle ., All animals were treated in accordance with the Association for Research in Vision and Ophthalmology Statement for the Use of Animals in Ophthalmic and Vision Research ., All experimental protocols were approved by the Animal Care and Use Committee of The University of Iowa ., Anterior chamber phenotypes were assayed using a slit-lamp ( SL-D7; Topcon , Tokyo , Japan ) and photodocumented using a digital camera ( D100; Nikon , Tokyo , Japan ) ., All ocular exams utilized conscious mice ., Based on previous observations of Lyst-mutant mice 4 , several traits uniformly present in adult B6-Lystbg-J mice were followed for potential phenotypic modification ., For assessment of anterior chamber phenotypes , a beam of light was shone at an angle across the eye , and the anterior chamber was examined for iris stromal atrophy , pigment dispersion , and dark iris appearance ., For assessment of iris transillumination defects , a small beam of light was shone directly through the undilated pupil of the mouse and the iris was examined for the ability of reflected light to pass through diseased or depigmented areas of the iris ., All photographs of like kind were taken with identical camera settings and prepared with identical image software processing ., Unless otherwise noted , all slit-lamp images were collected at 25× magnification , cropped , and reduced in size ., Severity of iris transillumination defects was quantified by measuring the R-value from RGB formatted digital images ., Digital images of iris transillumination defects from left and right eyes of 4 mice per genotype were analyzed using Adobe Photoshop software ( Adobe Systems Inc . , San Jose , CA ) ., From 2 images per eye , 2 circular sampling windows of equivalent size , each covering approximately 5% of the measurable area of the iris , were uniformly placed ( 1 superior and 1 inferior ) on the temporal halves of each iris image using the Elliptical Marquee tool ., RGB values for the sample areas were averaged using the Average Blur Filter and R-values measured with the Eyedropper tool ., In total , each genotype of mice involved analysis of 32 sample areas whose R-values were utilized in statistical analysis ., Samples from different tissues were processed as explained below , and imaged using a light microscope ( BX52; Olympus , Tokyo , Japan ) equipped with a digital camera ( DP72; Olympus , Tokyo , Japan ) ., Eyes were fixed in 2 . 5% gluteraldehyde in 0 . 1 M Na cacodylate for 16 hours , and post fixed with 1% osmium tetroxide in 0 . 1 M Na cacodylate buffer at room temperature for 1 hour ., A series of acetone dehydrations were performed followed by infiltration with Embed-812/DDSA/NMA/DMP-30 for 24 hours ., 0 . 5-µm sections were cut ( EM UC6 ultramicrotome; Leica , Wetzler , Germany ) , and stained with 1% toluidine blue ., Cerebella were cut down the midline , yielding 2 hemispheres ., The left cerebellar halves were fixed overnight at 4°C in 4% paraformaldehyde in 1X PBS ( pH 7 . 4 ) , and embedded in paraffin ( Tissue Prep Paraffin Beads T565; Fisher , Pittsburgh , PA , USA ) ., Mid-sagittal 5-µm sections were cut ( Microm HM 355; Thermo Fisher , Waltham , MA , USA ) and stained with hematoxylin-eosin ( H&E ) ., Sciatic nerves were removed from the left hindlimb and fixed at 4°C in 2 . 5% osmotically-balanced glutaraldehyde in 0 . 1 M Na cacodylate buffer ( pH 7 . 4 ) for at least 24 hours ., Following rinses with cacodylate buffer , nerves were post fixed with 1% osmium tetroxide in 0 . 1 M Na cacodylate buffer at room temperature for 1 hour ., Dehydration was then carried out through a series of 40-minute incubations in 25% , 50% , 75% , 90% , and 100% graded ethanol ., Nerves were infiltrated overnight at room temperature , with 33% , 66% , and 100% resin ( Low Viscosity Spurr Epoxy Resin; Ted Pella , Redding , CA ) in propylene oxide ., Specimens were embedded in resin , and 1-µm cross sections were cut ( EM UC6; Leica , Wetzler , Germany ) and stained with toluidine blue ., Dissected spinal columns were fixed in Bouins fixative for >1 week ., Following rinses with 70% ethanol , 3-mm cross sections were cut from the cervical , thoracic , and lumbar regions of each column ., The 3 cross sections from each column were embedded in paraffin ( Tissue Prep Paraffin Beads T565; Fisher , Pittsburgh , PA ) , and 5-µm cross sections were cut ( Microm HM 355; Thermo Fisher , Waltham , MA , USA ) and stained with H&E ., The Lystbg-J mutation results from a 3-bp deletion predicted to remove a single isoleucine from the WD40 domain of the LYST protein 4 ., Lyst genotype was assessed by PCR amplifying a fragment of genomic DNA that flanks the causative 3-bp bg-J deletion 4 and assessing product lengths ., To generate B6 ( Cg ) -Tyrc-2J Lystbg-J mice ( abbreviated throughout as B6 . Tyrc-2J Lystbg-J ) , B6 . Tyrc-2J mice were bred to B6-Lystbg-J mice , and each region was bred to homozygosity ., The Tyrc-2J allele is a spontaneously arising missense mutation that also influences splicing of the tyrosinase pre-mRNA , ultimately resulting in complete absence of the tyrosinase protein 47 ., Tyr genotype was inferred from coat color ., To generate congenic mice with the Lystbg-J mutation on a DBA/2J genetic background ( D2 . B6-Lystbg-J/Andm; abbreviated throughout as D2 . Lystbg-J ) , B6-Lystbg-J mice were reiteratively bred to DBA/2J mice and each successive generation genotyped to select breeders heterozygous for the Lystbg-J mutation ., This process was continued for 10 generations of backcrossing ., At the 10th generation , the mice were intercrossed and the Lystbg-J mutation was bred to homozygosity ., Congenic mice were genotyped with the closely linked D13Mit17 marker and confirmed by genotyping of the causative 3 bp bg-J deletion ., G

Introduction, Results, Discussion, Materials and Methods

LYST is a large cytosolic protein that influences the biogenesis of lysosome-related organelles , and mutation of the encoding gene , LYST , can cause Chediak-Higashi syndrome ., Recently , Lyst-mutant mice were recognized to also exhibit an iris disease resembling exfoliation syndrome , a common cause of glaucoma in humans ., Here , Lyst-mutant iris phenotypes were used in a search for genes that influence Lyst pathways ., In a candidate gene–driven approach , albino Lyst-mutant mice homozygous for a mutation in Tyr , whose product is key to melanin synthesis within melanosomes , exhibited complete rescue of Lyst-mutant iris phenotypes ., In a genetic background–driven approach using a DBA/2J strain of congenic mice , an interval containing Tyrp1 enhanced Lyst-dependent iris phenotypes ., Thus , both experimental approaches implicated the melanosome , an organelle that is a potential source of oxidative stress , as contributing to the disease phenotype ., Confirming an association with oxidative damage , Lyst mutation resulted in genetic context–sensitive changes in iris lipid hydroperoxide levels , being lowest in albino and highest in DBA/2J mice ., Surprisingly , the DBA/2J genetic background also exposed a late-onset neurodegenerative phenotype involving cerebellar Purkinje-cell degeneration ., These results identify an association between oxidative damage to lipid membranes and the severity of Lyst-mutant phenotypes , revealing a new mechanism that contributes to pathophysiology involving LYST .

LYST is a poorly understood protein involved in hereditary disease ., Mutations in the encoding gene cause Chediak-Higashi syndrome , a rare lethal disease affecting multiple tissues of the body ., Mutations in Lyst also recapitulate features of exfoliation syndrome , a common disease affecting the anterior chamber of the eye ., Unfortunately , the Lyst gene is quite large , rendering it difficult to study by many molecular and cellular approaches ., Here , we use a genetic approach in mice to identify additional genetic pathways which might modify , or prevent , the ill consequences associated with Lyst mutation ., Our experiments demonstrate that Lyst mutation results in elevated levels of oxidative damage to lipid membranes ., These results identify a previously unrecognized consequence of Lyst mutation and a modifiable pathway of potential clinical relevance in humans ., Ultimately , knowledge of these events will contribute to the design of new therapeutic strategies allowing a similar alleviation of disease in humans .

genetics and genomics/animal genetics, cell biology/membranes and sorting, genetics and genomics/disease models, ophthalmology/inherited eye disorders, ophthalmology/glaucoma

journal.ppat.1000466

Genomic Analyses of the Microsporidian Nosema ceranae, an Emergent Pathogen of Honey Bees

Honey bees , Apis mellifera , face diverse parasite and pathogen challenges against which they direct both individual and societal defenses 1 ., Severe honey bee colony losses have occurred in the past several years in the United States , Asia , and Europe ., Some of these losses have been attributed to Colony Collapse Disorder ( CCD ) , a sporadic event defined by high local colony mortality , the rapid depopulation of colonies , and the lack of known disease symptoms 2 ., While causes of CCD are not yet known , and are likely to be multifactorial , increased pathogen loads in declining bees suggest a role for disease One candidate disease agent is the microsporidian Nosema ceranae , a species that has sharply increased its range in recent years 3 ., Microsporidia are a highly derived lineage of fungi that parasitize a diverse assemblage of animals 4 ., N . ceranae was first described from colonies of the Asian honey bee , Apis cerana , that were sympatric with A . mellifera colonies in China ., Fries et al . 5 suggested that a host switch from A . ceranae to A . mellifera occurred relatively recently ., Currently , N . ceranae is the predominant microsporidian parasite of bees in North America 6 and Europe 3 ., N . ceranae is an obligate intracellular parasite of adult honey bees ., Ingested spores invade the gut epithelium immediately after germination Intracellular meronts eventually lead to mesospores that can invade neighboring cells after host-cell lysis ., Ultimately , hardier exospores are passed into the gut and excreted , at which point these exospores are infective to additional hosts ., While congener N . apis appears to restrict its life cycle to the gut wall , N . ceranae was recently shown to invade other tissues 7 ., Health impacts of Nosema infection on honey bees include a decreased ability to acquire nutrients from the environment and ultimately a shortened lifespan 8 ., At the colony level , Nosema infection can lead to poor colony growth and poor winter survivorship ., Nevertheless , N . ceranae is widespread in both healthy and declining honey bee colonies and its overall contribution to honey bee losses is debatable 9 , 10 , 11 ., Genetic studies of N . ceranae and its infected host have been hindered by a lack of genetic data ., Prior to this study , microsporidian sequence data were most extensive for the mammalian pathogen Encephalitozoon cuniculi , complemented by genome or EST surveys of several other human and insect pathogens 4 , 12 , 13 , 14 and a recent draft annotation of the mammalian pathogen Enterocytozoon bieneusi 15 ., Public sequences for N . ceranae were limited to ribosomal RNA loci ., We therefore chose pyrosequencing to rapidly and cost-effectively characterize the N . ceranae genome , simultaneously illuminating the ecology and evolution of this parasite while enabling focused studies of virulence mechanisms and population dynamics ., A genomic approach also leverages existing microsporidian and fungal genome sequence , advancing through comparative analysis our understanding of how microsporidian genome architecture and regulation has evolved ., Microsporidia are remarkable in having small genomes that overlap prokaryotes in size , a propensity for overlapping genes and transcripts , few introns , and predicted gene complements less than half that found for yeast 16 , 17 , 18 ., Microsporidian cells are also simplified at the organellar level and lack mitochondria , instead containing a genome-less organelle , the mitosome , that appears incapable of oxidative phosphorylation but may function in iron-sulfur biochemistry 19 , 20 ., Biochemical studies and sequence analyses have identified novel features of carbon metabolism and a dependency on host ATP , but much of their metabolism remains unclear 17 , 19 and major metabolic pathways can differ substantially among species 15 ., Here we analyze a draft genome assembly for N . ceranae , present a gene set of 2 , 614 putative proteins that can now be used to uncover salient aspects of Nosema pathology , and describe gene families and ontological groups that are distinct relative to other sequenced fungi ., We provide formatted annotations for viewing with the Gbrowse genome viewer 21 , which we hope will aid future studies of this economically important pathogen and of microsporidia in general ., Honey bees infected with N . ceranae were collected from the USDA-ARS Bee Research Laboratory apiaries , Beltsville , MD ., Alimentary tracts of these bees were removed and crushed in sterile water and filtered through a Corning ( Lowell , MA ) Netwell insert ( 24 mm diameter , 74 µm mesh size ) to remove tissue debris ., The filtered suspension was centrifuged at 3 , 000×g for 5 minutes and the supernatant discarded ., The re-suspended pellet was further purified on a discontinuous Percoll ( Sigma-Aldrich , St . Louis , MO ) gradient consisting of 5 ml each of 25% , 50% , 75% and 100% Percoll solution ., The spore suspension was overlaid onto the gradient and centrifuged at 8 , 000×g for 10 minutes at 4°C ., The supernatant was discarded and the spore pellet was washed by centrifugation and suspension in distilled sterile water ., Approximately 106 N . ceranae spores were suspended in 500 µl CTAB buffer ( 100 mM Tris-HCl , pH 8 . 0; 20 mM EDTA , pH 8 . 0; 1 . 4 M sodium chloride; 2% cetyltrimethylammonium bromide , w/v; 0 . 2% 2-mercaptoethanol ) and broken by adding 500 µg of glass beads ( 425–600 µm , Sigma-Aldrich , St . Louis , MO ) into the tube and disrupting the mixture at maximum speed for 2–3 minutes using a FastPrep Cell Disrupter ( Qbiogene , Carlsbad , CA ) ., The mixture was then incubated with proteinase K ( 200 µg/ml ) for five hours at 55°C ., Genomic DNA was extracted in an equal volume of phenol/chloroform/isoamyl alcohol ( 25∶24∶1 ) twice , followed by a single extraction in chloroform ., The purified DNA was precipitated with isopropanol , washed in 70% ethanol , and dissolved in 50 µl sterile water ., The concentration and purity of the DNA were determined by spectrophotometric absorption at 260 nm , and ratios of absorption at 260 nm and 280 nm ., Extracted DNA was pooled , sheared , and processed using in-house protocols at 454 Life Sciences ( Branford , CT ) ., The template was then amplified by two separate runs of 32 emulsion-PCR reactions each , with each reaction comprised of templates containing 454-linker sequence attached to 600 , 000 sepharose beads 22 ., Successful amplifications were sequenced using GS FLX picotiter plates and reads were trimmed of low-quality sequence before assembly with the Celera Assembler package CABOG 23 ., Gene predictions were merged from three distinct sources ., We first used the Glimmer package 24 , which is designed for predicting exons of prokaryote and small eukaryote genomes , using a hidden Markov model to evaluate the protein-coding potential of ORFs ., The model was initially trained on ORFs identified by Glimmers longorf program , and then run with the following parameters: a minimum length of 90 codons , a maximum overlap of 50 bp , and a threshold score of 30 ., ORFs that contained a high proportion of tandem sequence repeats were ignored ., Secondly , we identified all additional ORFs not predicted to be protein-coding by Glimmer that were BLASTX 25 matches to GenBank fungal proteins ( at a lax expectation threshold of 1 . 0E-5 ) ., Finally , all remaining ORFs were searched with the HMMER program ( http://hmmer . janelia . org ) for Pfam-annotated protein domains 26 using an expectation threshold of 1 . 0×10E-1 ., In 58 cases , adjacent ORFs matching different parts of the same GenBank protein or Pfam domain could be joined by hypothesizing a single-base frameshift error in the assembly ., Our annotations span the start and stop codons of these conjoined ORFs and indicate the approximate site of the frameshift with the ambiguity characters N and X , respectively , in the nucleotide and protein sequence ., tRNA genes were predicted with the program ARAGORN 27 ., Ribosomal genes were identified by BLASTN searches and alignments with existing Nosema ribosomal sequence in GenBank and the SILVA ribosomal database 28 ., Nucleotide composition of protein-coding genes was investigated with the program INCA2 . 0 29 ., We identified probable one-to-one orthologs among these three genomes using reciprocal best BLASTP matches , with the additional requirements that the best match have an expectation ≤1 . 0E-10 and 103 lower than the second best match ( identical protein predictions in E . cuniculi were considered equivalent ) ., Best-fit homologs in yeast , as determined by BLASTP with a minimum expectation of 1 . 0E-10 , were used to annotate N . ceranae genes with GO Slim ontologies 30 ., Signal peptides were predicted using the SignalP 3 . 0 program 31 and transmembrane domains were predicted with TMHMM 2 . 0 32 ., Assignments to conserved positions in metabolic and regulatory pathways were based on the KEGG annotation resource 33 , assisted by the Blast2Go program 34 ., Repetitive elements were identified by searching against Repbase 35 , by the pattern searching algorithm REPuter 36 , and by intragenomic BLASTN analyses ., Sequence information and annotations are posted in Genbank ( www . ncbi . nlm . nih . gov ) under Genome Project ID 32973 ., High-quality reads from two 454 GS FLX sequencing runs contributed 275 . 8 MB for assembly ., The assembly was complicated by an extreme AT bias , frequent homopolymer runs ( which are prone to sequencing error ) , and numerous repetitive elements ( see below ) ., Sixty-one independent assemblies were evaluated by systematically increasing the error parameter from zero to 6% in 0 . 1% increments ., The final assembly used an error rate of 3 . 5% because this maximized both the N50 of contig size and the length of the longest contig ., To search for potential mis-assembly , we compared this version to other assemblies using MUMmer 37 ., We identified two contigs that likely contained collapsed repeats and replaced these with alternative versions assembled with a stricter error parameter ., Other parameters of the assembly remained at their default CABOG settings ., Sequencing and assembly statistics are summarized in Table, 1 . Accidental incorporation of non-target DNA sequence into genome assemblies is a ubiquitous hazard even with stringent sample preparation ., We therefore used BLAST , depth of coverage , and G+C content as criteria to help identify potential contamination , but found no evidence of sequence derived from the host genome ( A . mellifera ) , the sympatric congener N . apis , or another common fungal pathogen of bees , Ascosphaera apis ., However , we did find evidence for low-level contamination by an unknown ascomycete fungus , indicated by generally short , low-coverage , high-GC contigs with consistently stronger BLASTX matches to Ascomycota than to Microsporidia ., We therefore removed all contigs with less than five-fold coverage and a G+C content of 0 . 5 or greater ( see Fig . S1 ) , as well as any contig that matched ascomycete ribosomal or mitochondrial sequence ., After purging these suspect contigs and removing all contigs less than 500 bp in length , there remained 5465 contigs that totaled 7 . 86 MB of DNA ., The N50 contig size of the pruned assembly was 2 . 9 kb ( i . e . , half of the total assembly , or 3 . 93 MB , was in contigs greater than 2 . 9 kb ) ., The mean sequence coverage of contigs was 24 . 2× ., Using the GigaBayes suite of programs 38 , 39 , we estimated the frequency of simple polymorphisms ( indel or nucleotide , P≥0 . 90 per site ) on the 100 longest contigs to be 1 . 0 per kilobase ., Genomic G+C content of the final contig set was low compared with E . cuniculi , 26% vs . 47% , but typical of other surveyed microsporidia ., Genomic contigs of Enterocytozoon bieneusi in GenBank have a G+C content of 24% , and Williams et al . 13 reported genomic G+C contents of Brachiola algerae and Edhazardia aedis to be 24% and 25% , respectively ., Although several factors potentially associated with microbial base composition have been investigated , such as ambient temperature , mutation bias , and selection on genome replication rates , the causes of compositional bias remain unclear ( see , for example , 40 and references cited therein ) ., Because genome assemblies may not accurately represent true genome size , due to such factors as redundancy at contig ends or collapsed repeats , we applied the method of Carlton et al . 41 to estimate genome size from sequence coverage , excluding repeats ., We first classified all 22-mers occurring in the read sequence not more than 40 times as the unique portion of the genome ., Using this filter , the average coverage was 26 . 6× and 28 . 2× for regions of at least 1 kb and 10 kb in length , respectively ., The total length of the N . ceranae reads is 261 . 0 MB after filtering reads with G+C content higher than 50% ., With these values , the total genome size could be as high as 9 . 8 MB ., However , this G+C filter may be overly permissive; increasing the filter stringency to 35% G+C reduces the genome size estimate to 8 . 6 MB ., An additional consideration is that , at the estimated level of coverage , we expect the entire genome to be sequenced with few singletons or small contigs ., Yet 30 . 0 MB of read sequence assembled into contigs with 10 or fewer reads , including 5 . 5 MB of single-read contigs ., These small contigs are likely to be from reads with relatively high sequencing error ., If so , this would boost the average coverage of the assembly by 3×–3 . 5× and reduce the genome size to as low as 7 . 7 MB ., Our attempts to measure the genome size empirically with pulse-field gel electrophoresis did not adequately resolve N . ceranae chromosomes ., However , this technique in other Nosema species has yielded genome size estimates of 7 . 4–15 MB 42 ., Thus , while our computational estimate is in reasonable agreement with current genome size estimates for the genus , an unknown but potentially significant portion of the genome may be unrepresented in this assembly and the absence of particular sequences should not be considered definitive ., The genome sequence of E . cuniculi revealed an unusual distribution of sequence repeats , characterized by a lack of known transposable elements , a paucity of simple repeats , and an abundance of near-perfect segmental duplications of 0 . 5–10 kb in length ., Pulse-field gel electrophoretic studies have identified gross variation in the size of homologous chromosomes among and within isolates of E . cuniculi 43 and the microsporidian Paranosema grylli 44 , indicating that large segmental duplications are potentially important sources of intraspecific variation ., The origins and gene content of such duplications are therefore of particular interest ., While the present assembly limits our ability to describe larger segmental duplications in N . ceranae , we were able to investigate sequence repetition in the genome by searching for microsatellite motifs and by using REPuter 36 to detect complex repeats ., All eight dinucleotide repeats found were ‘AT’ repeats , ranging from a perfect 9-unit repeat to an imperfect ( 3 mismatches ) 21-unit repeat ., There were six AAT repeats greater than 6 units in length and four ATC repeats ., We confined our search for complex repeats to those contigs greater than 1 , 200 bp in length , so as to identify repeats likely to be dispersed in the genome rather than confined to the most poorly assembled fragments ., REPuter identified a total of 4 , 731 sequence pairs with at most three mismatches that ranged from 70 bp ( the minimum threshold for detection ) up to 312 bp in length with a median of 85 bp ., Repeats were over-represented on smaller contigs , even within the analyzed set of relatively long contigs , indicating that they had affected assembly success ., BLASTN analyses of the REPuter-identified repeats against the N . ceranae genome revealed a novel dispersed repeat with a conserved core domain approximately 700 bp in length ( Fig . S2 ) ., The boundaries of the element are not completely clear because the conserved domain often occurs as tandem copies , there are two or more subtypes of the element based on multiple sequence alignments , and , as expected , copies are most abundant on short contigs and near contig ends ., Using an E-value cutoff of 1 . 0E-5 , we identified one or more matches on 250 contigs ., No conserved coding potential was evident for these elements , nor did we detect any homology with sequences in GenBank or Repbase ., Surprisingly , this element contains a candidate polII promoter that is well conserved and generally scores between 0 . 90 and 1 . 00 ( the maximum value ) when submitted to a neural network prediction tool 45 ., Whether this promoter-like motif is functional and , if so , whether it produces a coding or noncoding transcript remain to be seen ., However , it is clear from BLAST searches that this promoter sequence is not associated with any of our predicted genes ( see below ) , nor could we identify it in E . cuniculi or yeast ., We identified 2 , 614 putative protein-coding genes , with reference names , coordinates , and annotation features provided in Text S1 ., Gene models were not required to have a start methionine to allow for gene predictions truncated at ends of contigs and ( rarely ) the possibility of non-canonical start codons or frameshifts in the assembly ., In addition to BLAST-hit annotations , Text S1 also lists Pfam protein domains as well as signal peptide and transmembrane motifs ., Texts S2 and S3 , respectively , contain GFF-formatted data and a configuration file for viewing our annotations with the Gbrowse viewer 21 ., An example of these annotations viewed in GBrowse is shown in Fig . S3 ., The number of protein-coding genes we have predicted for N . ceranae lies in between the 1 , 996 Refseq proteins given by GenBank for the sequenced E . cuniculi genome and the 3 , 804 predicted for E . bieneusi from sequence representing only two-thirds of the estimated genome content ., The density of genes on the 100 largest N . ceranae contigs averaged 0 . 60 genes/kb ( 64 . 8% coding sequence ) ., This is a lower proportion of coding sequence than found in E . cuniculi and Antonospora locustae ( 0 . 94 and 0 . 97 genes/kb , respectively 4 ) , but comparable to some other microsporidia 13 ., However , gene density declines considerably with contig size ( Fig . S4 ) , consistent with a preponderance of repetitive elements ( described above and to follow ) or other noncoding sequence in these regions ., We found forty-six contigs containing sequences that matched N . ceranae ribosomal sequence at an expectation of E<1 . 0E-10 , but no contig contained a complete ribosomal locus ., Assembly of Sanger-sequenced Enterocytozoon bieneusi genomic DNA resulted in a similar fracturing of ribosomal sequence 15 ., It seems likely , then , that N . ceranae ribosomal loci contain abundant polymorphism and/or error-prone sequences that are recalcitrant to our assembly parameters ., Polymorphism among rRNA loci has been reported for N . bombi 46 whereas the sub-telomeric location of E . cuniculi ribosomal loci 16 suggests potential a association with repetitive sequence ., We found 65 tRNA genes with 44 distinct anticodons ( Fig . S5 ) , sufficient to match all codons with third-position wobble ., Five tRNA gene predictions contain introns , and a putative tRNA intron-endonuclease ( NcORF-01478 ) was also identified ., Aminoacyl-tRNA synthetases were found for all twenty standard amino acids , as well as two enzymes involved in selenoamino acid metabolism ( NcORF-00234 and NcORF-00337 ) ., tRNA genes are particularly abundant for the common amino-acid leucine ( 11 genes ) , yet there was no overall correlation between the frequency of an amino-acid in predicted proteins and the number of tRNAs for that amino-acid ( not shown ) ., In comparison , there are only 46 tRNA genes in E . cuniculi , yet they also match 44 distinct anticodons ., E . cuniculi contains a tRNA matching the codon ‘CCC’ that was not found in N . ceranae , whereas N . ceranae contains a tRNA with the anti-codon TCA that is not found in E . cuniculi ., tRNAs of this latter type match the stop codon TGA and are assumed to be charged with the nonstandard amino-acid selenocysteine 47 ., This tRNA was also predicted by tRNAscan-SE 48 , and alignment of the raw reads revealed no evidence of sequence ambiguity in the anticodon loop ., Elongation factors for selenocysteine incorporation are uncharacterized outside of mammals and bacteria ( reviewed in 49 ) ., We did not find a homolog of the SelU family 50 , the most broadly distributed selenoprotein family across eukaryotes ., Of the six selenoproteins reported by the SelenoDB database 49 to be present in yeast , we found only two clear homologs in N . ceranae , NcORF-01193 and NcORF-01194 , both of which are glutathione peroxidases ., These genes do not appear to be selenoproteins in N . ceranae because the predicted stop codons are not TGA and no downstream coding potential is evident ., We identified six genes with predicted short introns as well as two spliceosomal proteins , the U1- and U6-associated proteins ( NcORF-00067 and NcORF-01581 ) ., While Katinka et al . 16 inferred eleven ribosomal protein genes with introns in E . cuniculi , only five of the N . ceranae orthologs also contained an intron ., The sixth N . ceranae gene containing an intron encodes the S4 ribosomal protein , which lacks an intron in E . cuniculi ., The intronic sequences show fairly strong conservation within and between species ( Fig . 1 ) , suggesting selection for efficient recognition by the spliceosomal machinery ., Only one non-ribosomal , protein-coding gene of E . cuniculi has been predicted to contain introns ( gi|107906965 , a phosphatidyltransferase; 16 ) ., Alignment with the N . ceranae and yeast orthologs ( not shown ) indicates that while the N . ceranae gene ( NcORF-02116 ) may have a single intron in a position similar to the second of two predicted E . cuniculi introns , it can be read through giving an equally plausible translation ., Given the lack of other known introns in non-ribosomal proteins , it seems more parsimonious to conclude that the E . cuniculi sequence contains an assembly error or nonsense mutation ., This interpretation is consistent with analysis of the more-distantly related E . bieneusi 15 , in which no introns were found in ribosomal proteins and potential introns identified in non-ribosomal proteins could be read through without detriment to their alignment with E . cuniculi homologs ., Thus , spliceosomal introns in microsporidia appear to be both dispensable and , when present , confined to a particular ontological group , although confirmation of this hypothesis will require analysis of full-length cDNAs ., Patterns of transcript initiation and termination vary dramatically among microsporidians , and common eukaryotic regulatory motifs appear to have been obscured by genome compaction 14 , 51 ., To characterize the 5′ context of N . ceranae coding sequences , we analyzed a sample of 280 genes with one-to-one orthologs in E . cuniculi and yeast that align well at the 5′ end ( to maximize our confidence in the predicted start methionine ) ., Plotting the frequency of the yeast TATA box motif , TATAATAAT 52 , in the 200-bp region upstream of the start codon shows a pronounced peak in motifs that begin near the −27 position , relative to their frequency in random sequence of the same base composition ( Fig . S6 ) ., The proportion of upstream sequences ( 12 . 5% ) with a TATA motif beginning within the window −25 to −32 is three-fold greater than the randomly generated sample ( 4 . 1% ) ., These data suggest that TATA-like promoters , which occur in about 20% of yeast genes 52 , are also important components of N . ceranae gene regulation ., In comparison , an unspecified AT-rich transcription initiation sequence was identified within 120 bases of E . cuniculi ORFs 16 ., Phylogenetic footprinting and/or direct experiment may enable a more sensitive model of microsporidian promoters ., In addition to analyzing TATA motifs , we searched for novel 5′ motifs by applying MEME 53 to a slightly expanded version of the reference set described above ( n\u200a=\u200a292 ) , by narrowing the search region to 60 positions upstream of the start codon ., MEME identified a motif containing a cytosine triplet that is highly over-represented ( E\u200a=\u200a1 . 9E-258 ) , and these motifs occur predominantly within 15 bp of the start codon ., The motif is further characterized by a thymine homopolymer just upstream of the cytosine triplet ., A sequence logo and an alignment of representative sequences are shown in panel A of Fig ., 2 . We then used MEME to identify the single highest-scoring motif in the equivalent regions of E . cuniculi ( panel B of Fig . 2 ) ., A shorter but otherwise similar motif was identified , consisting of a cytosine triplet bracketed by a purine and a pyrimidine , as is the case in N . ceranae ., However , the expectation for a motif of this length and composition in the E . cuniculi sample was 9 . 2E+7 , i . e . it is statistically invisible in the absence of the corroborating N . ceranae motif ., Because the cytosine triplet is the most conserved component of the two motifs , we investigated the distribution of cytosine triplets nearest the start codon ., We compared these distributions in both genomes relative to random sequence of the same base composition ., The distributions of cytosine triplets were highly concordant between the two genomes , and much more frequent than expected by chance between the −10 and −3 positions , as shown in Fig ., 3 . Considering the high AT-content of the N . ceranae genome , the preponderance of AT-rich synonymous codon use , particularly relative to E . cuniculi , is unsurprising ( Fig . S7 ) ., More remarkable is that the G+C content of N . ceranae genes ( 27% ) is only minimally higher than the genome average of 26% , primarily because third position G+C is substantially lower ( 18% ) than the intergenic average of 23% ( Fig . 4 ) ., E . bieneusi genes downloaded from GenBank ( January 2009 ) show an even greater bias toward A+T in the third position ( 25% genic and 12% third position G+C ) , suggesting that this may be a common feature of AT-rich microsporidia ., However , few N . ceranae genes showed significant codon-usage bias after controlling for length and nucleotide composition ( Fig . S8 ) , and the most codon-biased genes in N . ceranae appear unrelated by homology or function to the most biased genes in the other microsporidian samples ., Divergence in nucleotide composition between N . ceranae and E . cuniculi has also impacted nonsynonymous sites , as it has in other organisms 54 , 55 ., Pooling the proteomes of the two species reveals differences in amino-acid use ( Fig . S9 , panel A ) that are consistent with conservative substitutions driven by mutation pressure ., For example , E . cuniculi encodes more arginine and less lysine than does N . ceranae , suggesting conservative replacement of lysine codons AAA and AAG with arginine codons AGA and AGG ., Other notable differences in N . ceranae relative to E . cuniculi include higher frequencies of isoleucine , phenylalanine , and tyrosine ( amino-acids coded by AT-rich codons ) and lower frequencies of glycine and alanine , which have GC-rich codons ., The magnitude of these differences are only slightly attenuated when considering only the core set of orthologs both species share with yeast ( panel B of Fig . S9 ) rather their complete proteomes , demonstrating that even ancient proteins participating in conserved cellular processes are impacted by evolutionary change in base composition ., Estimates of orthology based on reciprocal BLAST scores ( Table 2; see Materials and Methods ) indicate that 1 , 252 N . ceranae genes ( 47 . 9% ) are one-to-one orthologs with E . cuniculi proteins ., N . ceranae shares about twice as many genes with E . cuniculi as either shares with Saccharomyces cerevisiae , and we identified only 411 one-to-one orthologs that are conserved among all three genomes , or seven percent of the already streamlined yeast proteome ( 5880 predicted proteins ) ., To identify conserved microsporidian-specific genes that may be relevant to their life history , we searched all N . ceranae – E . cuniculi ortholog pairs by BLASTP against the GenBank nr database and against Pfam ., We identified 11 genes ( Fig . S10 ) that had no Pfam domain significant at E<0 . 5 and no BLAST hit outside the phylum Microsporidia with an expectation below 1 . 0E-5 ., Only one of these conserved proteins ( NcORF-00083 ) is homologous to a known polar tube protein , PTP3 56 ., The polar tube proteins are major structural components of the microsporidian polar filament/polar tube , a defining character of the phylum that enables invasion of the host cell 57 ., Two additional polar tube components have been identified in other species , PTP1 and PTP2 58 ., The genes encoding these proteins are closely linked in E . cuniculi and A . locustae but are divergent at the amino-acid level 59 ., As all three PTPs have signal peptides , we searched our N . ceranae gene predictions for adjacent genes with this motif ., NcORF-01664 and NcORF-01663 encode two such proteins and have similar lengths and amino-acid compositions to PTP1 and PTP2 , respectively , of E . cuniculi and A . locustae ( Fig . 11 ) , and are likely the orthologous genes ., Yet they share only 16 . 7% and 19 . 6% percent identity , respectively , when aligned with their putative E . cuniculi orthologs ., These results further confirm the low sequence conservation among putatively orthologous polar tube proteins 59 , suggesting that they may be evolving rapidly in response to host variation ., We examined the pattern of synteny between N . ceranae genes and E . cuniculi genes on the three longest contigs ( ∼170 kb ) ., Syntenic regions encompassing 2–10 genes are common on these contigs ( Fig . S12 ) , although changes in order and orientation within these regions are also common ., This conservation of synteny appears to be somewhat higher than that reported between E . cuniculi and E . bieneusi 60 , consistent with a much higher level of protein-sequence conservation in general between N . ceranae and E . cuniculi homologs than between either species and E . bieneusi ., To illustrate this , we performed a BLAST search with the set of N . ceranae genes that have one-to-one orthologs in yeast and E . cuniculi against the combined GenBank protein set for E . bieneusi , E . cuniculi , and S . cerevisiae ., Figure S13 shows the distribution of BLAST scores and expectations for the best match between N . ceranae and each of the other three genomes ( only results for N . ceranae proteins with matches in all three genomes are plotted , n\u200a=\u200a234 ) ., The E . cuniculi match was substantially better in almost all cases , whereas the E . bieneusi match was , on average , only modestly better than the S . cerevisiae match ., The smaller proportion of the E . bieneusi genome ( relative to E . cuniculi ) that has been sequenced 15 represents a potential bias in this comparison , because there is a greater likelihood that a higher-scoring E . bieneusi homolog exists that has not been annotated , yet such a bias could not explain the broad and consistent pattern shown in Fig . S13 ., A ribosomal phylogeny of microsporidia places N . ceranae and E . cuniculi closer to each other than to E . bieneusi with 100% bootstrap support , yet all three species are in the same subclade of the five microsporidian subclades identified by Vossbrinck and Debrunner-Vossbrinck 61 ., Thus , our data would seem to support the conclusion of Keeling and Slamovits 62 that the rate of protein-sequence evolution in micros

Introduction, Materials and Methods, Results, Discussion

Recent steep declines in honey bee health have severely impacted the beekeeping industry , presenting new risks for agricultural commodities that depend on insect pollination ., Honey bee declines could reflect increased pressures from parasites and pathogens ., The incidence of the microsporidian pathogen Nosema ceranae has increased significantly in the past decade ., Here we present a draft assembly ( 7 . 86 MB ) of the N . ceranae genome derived from pyrosequence data , including initial gene models and genomic comparisons with other members of this highly derived fungal lineage ., N . ceranae has a strongly AT-biased genome ( 74% A+T ) and a diversity of repetitive elements , complicating the assembly ., Of 2 , 614 predicted protein-coding sequences , we conservatively estimate that 1 , 366 have homologs in the microsporidian Encephalitozoon cuniculi , the most closely related published genome sequence ., We identify genes conserved among microsporidia that lack clear homology outside this group , which are of special interest as potential virulence factors in this group of obligate parasites ., A substantial fraction of the diminutive N . ceranae proteome consists of novel and transposable-element proteins ., For a majority of well-supported gene models , a conserved sense-strand motif can be found within 15 bases upstream of the start codon; a previously uncharacterized version of this motif is also present in E . cuniculi ., These comparisons provide insight into the architecture , regulation , and evolution of microsporidian genomes , and will drive investigations into honey bee–Nosema interactions .

Honey bee colonies are in decline in many parts of the world , in part due to pressures from a diverse assemblage of parasites and pathogens ., The range and prevalence of the microsporidian pathogen Nosema ceranae has increased significantly in the past decade ., Here we describe the N . ceranae genome , presenting genome traits , gene models and regulatory motifs ., N . ceranae has an extremely reduced and AT-biased genome , yet one with substantial numbers of repetitive elements ., We identify novel genes that appear to be conserved among microsporidia but undetected outside this phylum , which are of special interest as potential virulence factors for these obligate pathogens ., A previously unrecognized motif is found upstream of many start codons and likely plays a role in gene regulation across the microsporidia ., These and other comparisons provide insight into the architecture , regulation , and evolution of microsporidian genomes , and provide the first genetic tools for understanding how this pathogen interacts with honey bee hosts .

genetics and genomics/microbial evolution and genomics, evolutionary biology/microbial evolution and genomics, infectious diseases, evolutionary biology/evolutionary and comparative genetics, evolutionary biology/genomics, genetics and genomics/genome projects, ecology/environmental microbiology, genetics and genomics

journal.pgen.1003211

Genome-Wide Analysis Reveals Selection for Important Traits in Domestic Horse Breeds

Since domestication of the horse approximately 5 , 000 years ago 1–3 , selective breeding has been directed mainly toward the use of the horse in agriculture , transportation , and warfare ., Within the past 400 years , the founding of formal breed registries and continued breed specialization has focused more upon preserving and improving traits related to aesthetics and performance ., As a result , most horse breeds today are closed populations with high phenotypic and genetic uniformity of individuals within the breed , but with a great deal of variation among breeds ., High-throughput , whole-genome SNP arrays can now be used to exploit this population structure to identify the effects of selection upon the equine genome ., Once genomic regions targeted by selection are detected , the variants and processes that have contributed to desired phenotypes within breeds and across performance groups can be more readily identified ., Population-based approaches to identify signals of selection using loss of heterozygosity and/or other diversity indices have been successful in several domestic species ., In dogs , these studies have led to the identification of genomic regions implicated in the selection of characteristics such as coat color and texture , body size , skin wrinkling , and disease 4 , 5 , as well as the identification of signals of selection across genes with both known and unknown function 6–9 ., Similar studies in cattle have identified regions of interest that encompass genes with known or potential importance for muscling , feed efficiency , milk production , and reproduction 10–13 , and genomic targets of selection for reproductive traits , coat pigmentation , and lack of horns ( polled ) have been identified in the sheep 14 ., While a considerable number of traits are under selection in the many breeds and performance groups of the horse , the only prior population-based study of selection in the horse utilized microsatellite loci to identify loci of importance to the Thoroughbred with respect to three other breeds 15 ., This report , using an autosomal single nucleotide polymorphism ( SNP ) array and a sample set of 33 different breeds , represents the first large-scale study of how selective pressures have shaped the equine genome ., Within each breed , genotypes in 500 kb windows were evaluated to identify those more divergent from the other breeds in the study than expected , therefore signifying potential genomic targets of selection ., Of the regions of putative selection identified , priority was given to regions with the highest di value within a breed , regions with consecutive windows of significance covering at least 1 Mb , regions shared by breeds with similar phenotype , or regions showing significance near genes with known or suggested functional effect ., Regions chosen for follow-up studies were further investigated by phasing genotypes to discover any extended haplotypes present at high frequency in the breed ( s ) of interest ., Haplotypes were then scanned to identify candidate genes for follow-up study ., This report focuses upon regions of selection that are hypothesized to be involved in coat color , performance , gait ( pattern of locomotion ) and size ., Our results support the use of this technique in the horse to identify novel variants of functional importance and to further elucidate how selection has shaped the equine genome ., A total of 744 horses representing 33 breeds ( average 22 . 5 horses/breed ) genotyped with the Equine SNP50 Beadchip ( Illumina ) were included in the analysis ., Information regarding the breeds , sample sizes and breed-specific phenotypes is found in Table 1 ., Horses were selected to represent a random sample of the breed whenever possible and were chosen to be unrelated to one another at , or more recent to , the grandsire/dam level ., In the case where pedigree information was not available , horses were removed from the analysis so no pair had a genome sharing value of greater than 0 . 3 ( see methods ) ., All horses included in the study genotyped at a rate greater than 0 . 98 ., The FST-based statistic , di 4 , was calculated for autosomal SNPs in 500 kb windows , with a minimum of 4 SNPs per window , and defining the populations by breed ., The di statistic is a summation at each window of pairwise FST values for each breed combination , corrected by the value expected from genome-wide calculations; therefore , a large value of di indicates greater divergence at that 500 kb window than that observed across the genome as a whole ., In total , 23 , 401 SNPs were evaluated within 3 , 229 windows ( 68 . 7% of the autosomes ) , averaging 7 . 25 SNPs per window ( range 4–20 ) ., SNPs from the full data set not included in the analysis of di were largely removed due to failure to meet the minimal SNP density requirement ., The 33 windows within each breed , which fell into the upper 99th percentile of the empirical distribution , were considered putative signatures of selection; these regions in each breed are listed in Table S1 ., The maximum di value per breed ranged from 33 . 8 in the New Forest Pony to 104 . 4 in the Peruvian Paso ., Of the 3 , 229 windows analyzed , 695 ( 2 . 7% ) were significant ( within the upper 99th percentile ) in at least one breed ., The di plots from all breeds in the study are provided in Figure S1 ., Prior work has suggested that coat color was a target of selection early in horse domestication 2 and several breeds continue to be selected for a uniform coat color or color patterns ., Two examples of selection for coat color include the light chestnut coat desired in Belgian draft horses bred in the United States , and the dun coloration characteristic of the Norwegian Fjord ., Chestnut coat color is the result of a recessive mutation of the melanocortin 1 receptor ( MC1R ) 16 ., This MC1R variant represents a gene with known effect that has been shown to be contained within an extended haplotype 17 ., Genotypes for the MC1R locus were available for most horses in 14 of the breeds as reported previously 17 ., In this sample , two breeds , the Morgan and Belgian , were fixed for the missense mutation that results in the base coat color of chestnut ., Due to SNP density , di was not calculated across the MC1R locus itself ., However , the window with the highest di value in the Morgan was a region on ECA3 with two consecutive significant windows ( ECA3:37 , 825 , 015; di\u200a=\u200a67 . 22 ) near the MC1R locus ( ECA3:36 , 259 , 276–36 , 260 , 354 ) ( Figure S1 ) ., Phasing of the genotypes uncovered a 1 . 57 Mb , 12 SNP haplotype spanning MC1R in all Morgan chromosomes ( N\u200a=\u200a80 ) ; this haplotype extended 2 . 55 Mb in 95% of the chromosomes ( Figure 1 ) ., Although the Belgian population had the identical 1 . 57 Mb haplotype across the MC1R locus , it did not have a high-frequency haplotype extending into the regions in which di was calculated ., As the result of the different haplotype lengths in each breed , the di statistic identified the signature of selection around MC1R in the Morgan , but not in the Belgian ( Figure 1 and Figure S1 ) ., The frequency of the mutant MC1R allele in the 12 other breeds for which the MC1R genotype was known ranged from 0 . 19 in the Andalusian to 0 . 88 in the American Saddlebred ( hereafter “Saddlebred” ) ( data not shown ) ., In addition to the Morgan , significant di values adjacent to the MC1R locus were found in the Finnhorse and Saddlebred , which each had a high frequency ( 0 . 85 ) of the same extended haplotype ( 2 . 19 and 2 . 64 Mb , respectively ) across MC1R as that found in the Morgan ( data not shown ) ., Significant di windows upstream and adjacent to the MC1R region were also found in the Andalusian , Exmoor Pony , Fell Pony , Icelandic , North Swedish Horse , and Shire ( Figure S1 ) ; however , these populations did not have a high frequency of the MC1R haplotype consistent with chestnut coat color ., Another instance of detection of a coat color locus was found on ECA8 in the Norwegian Fjord , a breed selected for the dun coat color dilution ., A significant window on ECA8 , centered at 17 . 5 Mb , is in the same region as the genetically mapped locus associated with the dun dilution 18 ., Racing performance is a trait of high economic importance to the equine industry ., Variation in racing aptitudes range from that of the American Quarter Horse ( hereafter “Quarter Horse” ) , which was originally bred to sprint ¼ mile ( 400 m ) , to the opposite extreme of the Arabian and Akhal Teke breeds that compete in endurance races up to and over 100 miles ( 160 . 9 km ) ., Intermediate to the Quarter Horse and endurance horses , the Thoroughbred races distances ranging from 5/8 to 2 miles ( 1–3 . 2 km ) , and the Standardbred competes at a distance of approximately one mile ( 1600 m ) under harness at a trot or pace rather than a gallop ., A large region of putative selection was found on ECA18 in the American Paint Horse ( hereafter “Paint” ) and Quarter Horse populations , which included the highest di values observed in each breed ( Figure 2 ) ., Not only was the highest di value for each breed found in this region , but 9 and 10 of the 11 consecutive windows ( from 60 . 59 to 68 . 84 Mb ) , were significant in the Quarter Horse and Paint , respectively ., A characteristic under strong selection within particular breeds and often a breed-defining trait is the ability to perform alternate gaits , which are characterized by variations in the pattern and timing of footfall ., The standard gaits of the domestic horse and wild equids include the ( flat ) walk , trot , canter , and gallop ( Table 3 ) ., However , instead of the two-beat contralateral gait of the trot , some horses perform the pace , a two-beat ipsilateral gait ., Other natural variations in movement include four-beat ambling gaits characteristic of the Tennessee Walking Horse , Peruvian Paso , Paso Fino , and others , with unique variations in rhythm between breeds ( Table 3 and Table 4 ) ., The window centered at 23 . 2 Mb on ECA23 represented the maximum di value for four breeds: the Icelandic , Peruvian Paso , Standardbred , and Tennessee Walking Horse ( Figure 6 ) ., In the Icelandic , the region of significance encompassed two consecutive , 500 kb windows , while in the Peruvian Paso , significance stretched across six consecutive windows ., The Puerto Rican Paso Fino also had two adjacent , significant windows in this region although neither signified the largest di value within the breed ., This region of ECA23 was chosen for follow-up study due to the high level of significance in these breeds and the fact that all of the above breeds share the phenotype of possessing alternative gaits ., The most significant di value in the Thoroughbred was the first of three consecutive windows of significance on ECA17 ( Figure 2 ) ., Phasing revealed a 2 . 49 Mb , 55 SNP haplotype in the Thoroughbred that was present in 85% of the chromosomes sampled ( Figure 8 ) ., The 2 . 49 Mb haplotype is present but less frequent in the Hanoverian ( 43% ) , Swiss Warmblood ( 36% ) , Quarter Horse ( 34% ) , and Paint ( 24% ) ., Considering all non-Thoroughbreds in the study , the haplotype is found at a frequency of 12 . 1% and is absent in 15 of the 33 breeds studied ., This region ( 20 . 69–23 . 18 Mb ) includes 23 annotated or predicted genes in EquCab2 . 0 , and also includes 2 retrotransposed elements , 3 pseudogenes , and 2 known miRNA ( Table S2 ) ., The Puerto Rican Paso Fino had a significant di peak in this same region of ECA17 , but had an unrelated 270 kb haplotype found in 31 of 40 chromosomes ( Figure 8 ) ., No annotated genes are within the shorter haplotype found in the Puerto Rican Paso Fino ., The second greatest di value in the Thoroughbred was also highly significant in the Quarter Horse; this region on ECA14 contained a 982 kb haplotype shared across the Thoroughbred , Quarter Horse , Paint , and Swiss Warmblood , and includes ten annotated genes ., This haplotype is found at a frequency of 0 . 33 across all 33 breeds and 0 . 25 when considering all but those named above ( data not shown ) ., In other breeds , unique signals were observed on ECA2 in the French Trotter and ECA7 in the Standardbred ., These putative signatures of selection span 10 . 4 and 12 . 9 Mb , respectively , and contain large , extended haplotypes across the regions of significance ., A similar signature is observed in the Standardbred as well as the Tennessee Walking Horse on ECA8 where the minimum value of di is elevated above the baseline across a large region of the chromosome ( Figure 6 ) ., Size is a phenotype easily observed and therefore selectively bred ., As a result of selection , diversity in size occurs in terms of both height and mass ., The extremes of size are found in the Miniature horse , which is often as small as 29 in ( 0 . 74 m ) at the withers ( base of the neck ) and weigh less than 250 lbs ( 113 kg ) , and in the draft breeds that have a wither height of 72 in ( 1 . 83 m ) or more and can weigh over 2000 lbs ( 907 kg ) ., The genetic determination of coat color in horses is largely understood 16 , 18 , 33–39 and allows for a test of the di statistic ., For example , a di value above the threshold on ECA8 in the Norwegian Fjord is in the region to which dun coat color has been linked 18 , supporting known phenotypic selection for dun coloration in this breed ., However , the ability of the di statistic to identify divergence around the known mutation for chestnut coat color in MC1R serves as a more appropriate and convincing demonstration of the utility of the statistic to find extended haplotypes that differ among populations ., At the same time , the chestnut coat color locus , which is assumed to be contained within an extended haplotype described by 17 , also highlights limitations of the statistic ., The most obvious of these limitations is that due to low polymorphic SNP density and parameter settings for the calculation of di , the di value is not calculated for windows covering the entire genome , and in this instance was not calculated across the MC1R locus itself ., While incomplete genome coverage can result in true signatures of selection going undetected , it can be overcome in situations where the signature of selection includes long haplotypes detectable by neighboring di windows ., This appears to be the case for MC1R in the Morgan population , which was fixed for an extended haplotype containing the mutant allele , allowing for detection via di windows neighboring the locus ., However , this was not the case in the Belgians , which were also fixed for the MC1R mutation but did not have a haplotype extending as far as that observed in the Morgan ., If this were not a known mutation , this locus could have gone undetected in the Belgian ., The statistic did , however , detect the MC1R region in the Saddlebred , which had a high proportion ( 0 . 88 ) of the mutant allele , and in the Finnhorse for which MC1R genotype data was not available but which has experienced historic selection for chestnut coat color 40 ., These appear to be examples of true positive signatures of selection across this locus ., The significance of di in the region of MC1R in the Shire and Exmoor breeds , which are not commonly chestnut in color , highlights another limitation of this approach: it is blinded to phenotype ., While one can propose phenotype ( s ) that may be driving the signature of selection based upon known mutations , candidate genes found in the region , and/or by shared phenotypes among breeds sharing the same regions of significance , validation of selection and the identification of causative polymorphisms is dependent upon often labor intensive follow-up studies ., In addition , false positive windows are expected as a result of genetic drift and/or founder effect , which are common phenomena in the development of domestic breeds ., Shire horses are commonly bay , black , or grey , while Exmoor ponies are almost exclusively brown or dark bay; considering these two breeds , it can be hypothesized that the significant di value at the MC1R locus in these breeds , and underlying haplotype , reflect selection against the chestnut coat color , or for alternative coat colors 40 , 41 ., However , without additional work , it cannot be determined if the di signals in the Shire or Exmoor are false positive signals of selection , the result of genetic drift , or if the region may include other variant ( s ) contained within alternative haplotypes that are also under selection ., At the same window near MC1R , the Icelandic and Fell Pony populations had di statistics that were slightly greater than the threshold values ., The frequency of the MC1R haplotype found in the Morgan and Belgian was moderate in each of these breeds ., Significant di values for the Icelandic and Fell Pony could indicate that the locus is under moderate selection in these breeds or could represent false positive signals of selection resulting from the additive nature of the statistic ., A similar phenomenon occurred on ECA23 , the region of significance for gaited breeds ., Because the statistic is additive , the extreme divergence in a few breeds in the study ( e . g . Peruvian Paso , Tennessee Walking Horse ) , yielded an elevated di values across all populations ., As a result , breeds such as the Paint , Quarter Horse , Morgan , and Mongolian , still have a di value falling into the 99th percentile of the empirical distribution but with no extended , high frequency haplotype or other evidence of selection at the locus , likely representing a false positive signal of selection ., The signature of selection and underlying homozygosity in ECA18 seen in the Paint and Quarter Horse samples was profound given the relatively recent derivation of these breeds from a diverse founding stock , short blocks of linkage disequilibrium 17 , and continued admixture with the Thoroughbred ., The Paint and Quarter Horse do however share similar ancestry , experience continued admixture , and as a result have low genetic differentiation ( JLP , unpublished data ) ; those traits , along with shared selective pressures results in similar signals of di in the case of ECA18 as well as across other loci ., The high frequency and size of the extended haplotype on ECA18 suggests extreme selective pressure for the phenotype that is driving this putative signature of selection ., Although the haplotype was long , it was centered upon the MSTN gene ., MSTN was chosen for sequencing because of its function as a negative regulator of muscle development 22–26 , 42 and involvement in muscle fiber type determination 43–45 , coupled with the fact that the Quarter Horse is historically known for its ability to sprint ¼ mile and continues to be selected for heavy muscling ., Also , recent work has suggested that an intronic variant in MSTN is predictive of the best race distance for the Thoroughbred 19 , 20 , 46; specifically , these studies 19 , 20 suggest that horses homozygous for the “C” allele ( position g . 66493737C>T ) are better suited for short distance racing , heterozygotes are more capable middle-distance racers , and homozygotes for the “T” allele have greater stamina for long-distance races ., In addition to predicting optimal racing distance , MSTN has been implicated as important to racing success 47 , 48 and also as having a role in body composition 49 ., Fiber typing results in the Quarter Horse indicate significantly higher Type 2B and lower Type 1 gluteal muscle fiber proportions in the presence of the 5′ SINE insertion or “C” allele at the intron 1 SNP in the MSTN gene; this is the first histological evidence that one or both of these polymorphisms may play a functional role in muscle fiber composition in the horse ., Type 1 and Type 2B muscle fibers differ in that Type 2B fibers are the fastest contracting and largest fibers in cross-sectional area , whereas Type 1 fibers are slower contracting , smaller fibers ., Selection in the Quarter Horse for sprinting ability is hypothesized to favor an increase in Type 2B muscle fibers , allowing for faster and more powerful skeletal muscle contraction ., Evidence of selection in the Quarter Horse for the SINE insertion and/or “C” allele of the intron 1 SNP , which are in high linkage disequilibrium in both the Quarter Horse and the Thoroughbred 19 , is consistent with prior implications of the intronic “C” allele as an indicator that a Thoroughbred race horse is best suited for short distance races while the “T” allele denotes horses better suited for longer distance racing 19 , 20 , 47 ., Although not quantified in this study , it is also possible that an increase in fiber number , in addition to the observed change in fiber proportions , may occur as a result of one or both MSTN mutations as observed in MSTN null mice 50 ., The mechanism by which either MSTN mutation may be acting to alter muscle fiber proportions in horses is not yet understood ., It has been hypothesized that the intron 1 SNP may disrupt a putative transcription factor binding site 19 and a study of a cohort of untrained , young Thoroughbreds showed increased MSTN skeletal muscle mRNA expression from horses homozygous for the “C” allele 51 although in that work the authors do not analyze their results in relation to the SINE insertion ., A hypothesized method for a functional effect of the SINE insertion stems from its position in the promoter of the gene , effectively shifting the position of many promoter elements 227 bp upstream of the transcription start site ., A displacement of the wild-type position of promoter elements , including E box , FoxO and SMAD binding sites , which have been found to be critical for regulation of MSTN promoter activity 52–55 , is hypothesized to down-regulate the expression of the gene ., However , further work is needed to elucidate exactly how the timing and expression of MSTN may change with respect to either polymorphism , and how the SINE and/or the intron 1 SNP are functionally contributing to the observed differences in muscle fiber type proportions ., Finally , although the MSTN variants shown to be associated with muscle fiber type proportions are found most commonly within the extended haplotype putatively under selection , the haplotype derived from the SNP array is not 100% predictive of any of the MSTN variants we assayed; therefore , it is possible that the haplotype is tagging a different variant of selective importance that is also in linkage disequilibrium with the variants studied , or that the ascertainment of the SNPs resulting in common variants being present on the SNP chip does not allow for the detection of subtle variations in haplotype in this region ., In this study , we define a gaited horse as one exhibiting a different footfall pattern than displayed in the ( flat ) walk , trot , canter , or gallop , or a variation in the rhythm of the gait ., Alternative forms of movement ranging from a 2-beat lateral gait ( pace ) , to 4-beat diagonal and lateral ambling gaits are natural in many breeds and are often breed-defining characteristics ., Alternative gaits have been selected due to increased ride comfort and for their associated visual characteristics ., Horses from gaited breeds are judged upon their ability to perform the breed-specific gait and may be penalized for performing gaits not desired by the registry ., The signature of selection on ECA23 was found among all breeds in our sample that have been selected for alternative gaits as a breed-defining characteristic ., The identification of one locus under a strong signal of selection shared across gaited breeds was initially surprising given the diversity of gaits found among breeds and the alternative hypothesis that each type of gait has a distinct evolutionary history ., However , the significant across-breed signal of selection and conserved haplotype on ECA23 common among gaited breeds is compelling evidence that a major locus is involved in the determination of gait ., Only two annotated genes are contained within the portion of this ECA23 haplotype shared across gaited breeds ., Both genes are in the doublesex and mab-3 related transcription factor family ( DMRT2 and DMRT3 ) ., While the primary role of these genes has historically been thought to be in sex differentiation 56 , 57 , recent work has suggested that their role is more far-reaching 58 , 59 ., Concurrent with this study , a variant in DMRT3 has been significantly associated with the ability to pace in Icelandic horses , and also appears to be necessary for horses in other breeds to perform alternate gaits 59 ., The independent identification of this locus , which is contained within the haplotype shared across all gaited breeds ( data not shown ) , supports the data suggesting that this region was targeted by selection for gait and endorses the use of this technique to identify loci under selection in the horse ., The presence of the same ECA23 haplotype in the gaited breeds within the Mangalarga Paulista , Saddlebred , and Morgan is not surprising ., Certain individuals within these breeds have the ability to gait , although alternate gait is not a breed-defining characteristic ., Conversely the “gait” haplotype is not found exclusively in populations that are gaited; this was also true for the DMRT3 mutation that is presumably driving this signature of selection 59 ., For example , the French Trotters and Standardbreds included in the calculation of di do not display alternative gaits beyond the walk , trot , canter , and gallop , but are bred to race at a trot ., In this study population , the “gait” haplotype also segregates ( 54% presence ) in the Finnhorse , which is divergently selected for light draft , riding , or trotting types 40 ., There is evidence that trotting performance is heritable 60 and haplotypic evidence , as well as that reported in 59 , indicates that an effect of this locus on trotting aptitude cannot be ruled out ., Finally , the gaited populations in which this haplotype is found ( Icelandic , Tennessee Walking Horse , Peruvian Paso , Puerto Rican Paso Fino ) have various types of alternative gaits ., Unless there are several variants captured within this haplotype , it appears that this locus does not itself explain the entirety of the variation in gait present in domestic horses ., We therefore hypothesize that gait is a polygenic trait , and while a major locus on ECA23 may be permissive for gaitedness , variations among breeds are determined by modifying loci ., The popularity and economic value of Thoroughbred racehorses have led to extreme interest in identifying genomic variants that can be utilized to predict and/or improve performance ., Several previous studies have focused upon identifying genomic regions and examining candidate genes that may be associated with performance traits in this breed 15 , 61 , 62 ., In this study , the most significant di windows led to the identification of a large , 2 . 49 Mb conserved haplotype on ECA17 present in a large majority of the Thoroughbred chromosomes ., All other breeds in which this haplotype is common are closely related to the Thoroughbred ( JLP , unpublished data ) ., This region of ECA17 was previously implicated as having selective importance in the Thoroughbred 15 , and the syntenic region of the canine genome was noted as a region of selection in several dog breeds 9 ., With long blocks of LD in the Thoroughbred breed , and many annotated and predicted genes , this region represents an area that is of further interest for evaluation via resequencing ., Finally , the significant window on ECA14 in the Thoroughbred shared with the Quarter Horse and Swiss Warmblood contains an extended haplotype found in moderate frequency ( 0 . 32 ) across all breeds and also many annotated genes ., This is another of many regions not investigated further in this study that are promising targets for future exploration ., Size , including both height and mass , is a highly-studied phenotype ., Loci involved in the determination of size have been identified in the dog 4 , 6 , 9 , 63–65 , cattle 66–68 and humans 69–71 , among other species ., In horses , a majority of variation in skeletal measurements in can be explained by one principal component 72 and four loci have been identified that account for a large proportion of variance in size across breeds 73 ., In addition , two significant quantitative trait loci for size have been identified in the Franches-Montagnes 74 ., Although all are known for their size and strength , each draft horse population has its own , unique history ., While the British Isles native Clydesdale and Shire , and mainland European Belgian and Percheron , are all considered heavy draft horses , several breeds with smaller stature but still bred for substance and strength ( light draft ) are included in the dataset ., Therefore , a conserved haplotype across all heavy and light draft breeds , regardless of their origin , is strong evidence that the locus on ECA11 may be involved in the determination of size , as defined by height and/or mass ., Support for this assertion is also found in an alternate conserved haplotype present across the same SNPs in the Miniature horse and Shetland pony and by the recent work of 73 who propose LIM and SH3 protein 1 ( LASP1 ) , on ECA11 as a candidate gene for size ., In addition to utility in the horse , the identification of functional polymorphism ( s ) in this region may lend insight into size variation in other species ., Several genes are frequently proposed as important to the determination of height or mass in mammals; we examined the regions surrounding many of these genes for significant di values , finding evidence of putative selection in three instances: IGF1 , NCAPG , and HGMA2 ., In studies of dog , mice , and humans , insulin-like growth factor 1 ( IGF1 ) has been implicated to be as a major locus in size determination 6 , 64 , 65 , 75–78 ., In our genome scan the light draft breed , Franches-Montagnes , was the only sample with a significant di value in the region encompassing IGF1 ( ECA28 ) ., A common haplotype in the Franches-Montagnes was identified in 68 . 4% of the chromosomes of that population ., This haplotype , however , was found in approximately 17% of the entire sample and in moderate frequencies in breeds that vary in size such as the Miniature , Shetland , and North Swedish Horse ( data not shown ) ., While this could indicate selection for a variant , the sharing of this haplotype with horses of all sizes suggests that either this locus is not the primary determinant of size in the horse , it is a false positive , it is under selection at moderate intensities across breeds , or that there may be one or more polymorphisms within the region found in a variety of haplotypic backgrounds ., The gene NCAPG , or region containing this gene ( often including LCORL ) , has repeatedly been associated with body size in humans and cattle 66–71 , 77 , 79 ., In horses , the region of ECA3 including NCAPG and LCORL was reported to be one of four loci that explain a significant proportion of variance to size in an across-breed study 73 ., Additionally , this locus was recently associated with wither height in the Franches-Montagnes , where it was found to explain over 11 percent of variance in degressed estimated breeding value for the trait 74 ., In our study the Franches-Montanges did not have a notable di value at this window , which is expected , as the segregation of this trait within the breed is what allowed for its detection under a QTL analysis framework ., As in the case of MC1R , the genic region of NCAPG itself is not covered by a di window due to low SNP density in the region; however the most proximal window was significant in the Belgian , which had a fixed haplotype shared with the Clydesdale ., The high conservation of this haplotype across these breeds , as well as its occurrence in two sport horse

Introduction, Results, Discussion, Materials and Methods

Intense selective pressures applied over short evolutionary time have resulted in homogeneity within , but substantial variation among , horse breeds ., Utilizing this population structure , 744 individuals from 33 breeds , and a 54 , 000 SNP genotyping array , breed-specific targets of selection were identified using an FST-based statistic calculated in 500-kb windows across the genome ., A 5 . 5-Mb region of ECA18 , in which the myostatin ( MSTN ) gene was centered , contained the highest signature of selection in both the Paint and Quarter Horse ., Gene sequencing and histological analysis of gluteal muscle biopsies showed a promoter variant and intronic SNP of MSTN were each significantly associated with higher Type 2B and lower Type 1 muscle fiber proportions in the Quarter Horse , demonstrating a functional consequence of selection at this locus ., Signatures of selection on ECA23 in all gaited breeds in the sample led to the identification of a shared , 186-kb haplotype including two doublesex related mab transcription factor genes ( DMRT2 and 3 ) ., The recent identification of a DMRT3 mutation within this haplotype , which appears necessary for the ability to perform alternative gaits , provides further evidence for selection at this locus ., Finally , putative loci for the determination of size were identified in the draft breeds and the Miniature horse on ECA11 , as well as when signatures of selection surrounding candidate genes at other loci were examined ., This work provides further evidence of the importance of MSTN in racing breeds , provides strong evidence for selection upon gait and size , and illustrates the potential for population-based techniques to find genomic regions driving important phenotypes in the modern horse .

A breed of the horse typically consists of individuals sharing very similar aesthetic and performance traits ., However , a great deal of variation in traits exists between breeds ., The range of variation observed among breeds can be illustrated by the size difference between the Miniature horse ( 0 . 74 m and 100 kg ) and draft horse ( 1 . 8 m and 900 kg ) , or by comparing the optimum racing distance of the Quarter Horse ( 1/4 mile ) to that of the Arabian ( 100 miles or more ) ., In this study , we exploited the breed structure of the horse to identify regions of the genome that are significantly different between breeds and therefore may harbor genes and genetic variants targeted by selective breeding ., This work resulted in the identification of variants in the Paint and Quarter Horse significantly associated with altered muscle fiber type proportions favorable for increased sprinting ability ., A strong signature of selection was also identified in breeds that perform alternative gaits , and several genomic regions identified are hypothesized to be involved in the determination of size ., This study has demonstrated the utility of this approach for studying the equine genome and is the first to show a functional consequence of selective breeding in the horse .

animal genetics, genetic mutation, genome evolution, population genetics, mutational hypotheses, gene function, mutation, genetic polymorphism, biology, evolutionary genetics, genetic drift, haplotypes, natural selection, genetics, genomics, evolutionary biology, genomic evolution, genetics and genomics

journal.pcbi.1001091

Synaptic Plasticity and Connectivity Requirements to Produce Stimulus-Pair Specific Responses in Recurrent Networks of Spiking Neurons

Most environmental stimuli , to which an animal must develop an appropriate response , comprise multiple features and sub-features that are common to many other stimuli ., Since these other stimuli could engender an alternative response by the animal , it is essential that an animal is able to recognize specific combinations of stimulus features in order to distinguish and respond effectively to differing stimuli that share many features ., The simplest step in the formation of specific responses to complex stimuli is the ability to combine two inputs and produce a response distinct from either input alone or other input pairings ., Associative learning is necessary for an animal to recognize that at least two previously unrelated objects or events comprise a composite stimulus that requires a specific response 1 , 2 , 3 , 4 , 5 ., Some of the most difficult associative learning processes involve tasks that utilize exclusive-or , XOR , logic ( Figure 1A ) ., Associative learning tasks that employ XOR logic include pair-associative learning 3 , 6 , transitive inference 7 , and biconditional discrimination tasks 8 , 9 , among others ., These tasks vary in design and sensory modality , but they all share one requirement , the development of stimulus-pair selectivity to solve the task ., Rats and monkeys require extensive training 2 , 10 to perform well in such tasks ., The difficulty in XOR tasks ( Figure 1A ) arises from the requirement for an animal to produce a response to stimulus-pairs ( e . g . A+B ) selectively , in a manner that differs from its response to the individual stimuli that comprise them ( e . g . A or B ) ., For example , in biconditional discrimination ( Figure 1A ) 9 , if the animal learns to respond to one member of the stimulus-pair ( e . g . B from A+B ) then while it will respond correctly to stimulus-pair A+B it would respond incorrectly to C+B ., Thus , in biconditional discrimination , as in other tasks based on XOR logic , successful decision-making requires responses selective to stimulus-pairs ( e . g . A+B vs . C+B ) ., The results of our studies based on the biconditional discrimination task can be applied to a number of associative learning tasks that employ XOR logic such as visual association 3 , 6 , transitive inference tasks 11 , and many others 4 ., What remains unclear in these tasks is how the requisite stimulus-pair representations form ., Here we investigate how cells responsive to specific conjunctions of stimuli form , by examining what synaptic plasticity rules can generate stimulus-pair specificity within a randomly connected network of spiking neurons and compare with the likelihood of their initial chance occurrence 12 ., Our work shares some similarities to a previous computational study 13 , which produced associations between individual , temporally separated stimuli in structured networks ., However , our focus is on the general role of network connectivity 12 and synaptic plasticity rules described in vitro , necessary to solve multiple tasks requiring pair-associative learning ., We study the well-known correlation-based mechanism for changing excitatory synaptic strengths , spike-timing-dependent plasticity ( STDP ) 14 , 15 as well as a more recent formulation , triplet STDP 16 ., Triplet STDP is distinguished from standard STDP through its rate dependence – favoring potentiation over depression as overall rate increases ., Standard STDP determines the sign of plasticity from the relative times of each single presynaptic and single postsynaptic spike pair but fails to replicate such rate dependence ., The higher order spike interactions included in triplet STDP fit recent in vitro data better 16 , 17 , 18 , 19 , as well as the observed rate dependence of more classic experiments data 17 , 18 , 20 , 21 , 22 while maintaining standard STDP observations 16 ., Thus , we incorporate a recent computational model of triplet STDP to determine how its affects the network differently from that of standard STDP ., Recent modeling studies of recurrent networks undergoing STDP suggest the plasticity mechanism could be detrimental in the formation of pair-specific responses for two reasons ., First , the competition among inputs to a single cell inherent in STDP 23 could lead single cells to become responsive to a single stimulus , or the complete network to respond to only one stimulus-pair 24 ., Alternatively , plasticity among the excitatory connections can lead to a phenomenon termed attractor accretion in recent work 25 whereby cells associate with multiple stimulus-pairs ., Such over-association would be detrimental when a specific stimulus-pair response is necessary , but has been shown to be useful when generalization is necessary 25 ., Thus , in addition to excitatory plasticity , we model a recently described form of inhibitory plasticity 26 , 27 long-term potentiation of inhibition ( LTPi ) , which produces an increase in strength of inhibitory connections to excitatory cells if the inhibitory cell spikes while the excitatory cell is depolarized but not spiking ., We study how these excitatory and inhibitory plasticity rules operate in conjunction with multiplicative postsynaptic scaling , a mechanism for homeostasis 28 ., We make minimal assumptions regarding network structure by studying networks with random afferent projections and random recurrent connections ., To demonstrate the robustness of learning rules , we study them in a variety of networks , with differing levels of sparseness , excitability and degree of correlation in the connections from input groups that respond to individual stimuli ., We define a measure of pair selectivity at the neuronal level , and measure the distribution of selectivity across cells before and after training ., When comparing multiple networks , we use the mean of the stimulus-pair selectivity across cells ., In order to determine whether or not the information about stimulus-pairs within a given associative network is sufficient to produce a reliable behavioral response , we train a binary winner-takes-all ( WTA ) network , whose inputs are obtained from our associative network ., The WTA network serves as a model for perceptual decision-making 29 , 30 ., Its afferent synapses are modified by a Dopamine ( DA ) reward-based plasticity rule that , in principle , can lead it to produce responses that maximize reward 31 ., We found that in many cases , both standard STDP and triplet STDP produced lower selectivity to stimulus pairs and less reliable decision-making performance than found in the network before learning ., This limitation on the ability of STDP to produce pair-selective cells arose from potentiation of synaptic connections between cells , which were initially selectively responsive to different stimulus pairs , but gained responses to the stimulus pair favored by the connected cell ., We term this undesirable phenomenon of losing selectivity through the gaining of extra responses as ‘over-associativity . ’ Over-associativity was prevented by LTPi , which could produce cross-inhibition ., Networks trained with LTPi alone or in combination with STDP produced reliable decision-making across the largest range of networks tested in this study ., Thus , these results demonstrate a valuable role for this recently discovered form of inhibitory plasticity ., Throughout this paper we describe how learning rules affect stimulus-pair selectivity ., Stimulus-pair selectivity can be plainly stated as how responsive a neurons firing rate is to one stimulus-pair ( e . g . A+B ) over all other stimulus-pairs ( for a formal definition , see the experimental procedures ) ., Any cell responding equally to all four stimulus-pairs is least selective ( giving a measure of 0 ) while any cell responding to a single stimulus-pair is the most selective ( giving a measure of 3 ) ., A concrete example of a single neuron ( Figure 2A , B ) is useful for understanding the selectivity metric ., Initially , the neuron is approximately equally responsive ( as measured by the number of spikes produced ) to each stimulus-pair ( giving a measure of near 0 ) ( Figure 2A ) ; however after training with LTPi and triplet STDP , the neuron becomes selective to only stimulus-pair A+B ( giving a measure of 3 ) , maintaining its initial firing rate in response to the combination A+B , despite the pruning of other stimulus-pair responses ( Figure 2B ) ., Non-linearity is necessary for cells to generate stimulus-pair selectivity greater than one , however selective it is to individual inputs ., For example , a cell responding linearly to inputs only from stimulus “A” would fire at a rate , rA , to stimulus-pairs “A+B” and “A+D” and at a rate of zero to stimulus-pairs “C+B” and “C+D” , producing a stimulus-pair selectivity of 1 ., Such a cell could not help in the task ., Similarly , a cell responding linearly to the individual inputs “A” and “B” , firing at rate rA+rB , to the pair “A+B” , at rate rA , to pair “A+D” , at rate rB , to pair “C+B” and a rate of zero to pair “C+D” would also have stimulus-pair selectivity of one ., Such cells are also unlikely be unhelpful in training an XOR task , since they produce equal numbers of spikes for the two desired responses producing equal drive to the decision-making network ( spikes fired to stimulus-pairs “A+B” and “C+D” equals spikes fired to “C+B” and “C+D” ) ., The excess spikes of a single cell in response to a stimulus-pair are likely to be swamped by noise , unless other cells respond similarly to the same stimulus-pair ., Thus , to assess how well the network as a whole produces pair-selectivity , we measure the distribution of selectivity across all excitatory cells before and after learning ( Figure 2C ) ., We assess network responses by examining how the final distribution compares to the initial distribution ., Figure 2C provides an example of a population that increased its selectivity following training , as seen by the rightward shift in the final overall distribution , along with many cells reaching the maximum selectivity value of 3 ., Hereafter , we use the mean of the distribution across cells as a measure of the networks pair selectivity ( Figure 2D ) ., In the Supplementary Information ( Figure S2 ) we describe how well measures of pair-selectivity correlate with our measure of behavioral performance described in a later section of the results ., In this work , we have demonstrated how local cell-specific rules 14 , 16 , 27 , 34 , 35 affect global network function to produce the stimulus-pair selectivity as a solution to cognitive tasks with the underpinnings of exclusive-or , XOR , logic 36 , 37 ., The qualitative robustness of our results , demonstrated by modeling a broad range of networks and conditions , extends these findings broadly , showing they are not the result of a specific set of hand-tuned parameters ., Our associative network starts as a completely general one , but becomes sculpted via the paired stimuli it receives to maximally respond to those stimulus-pairs ., Combining the unsupervised learning of the associative network with the reward-based learning of connections to the decision-making layer , leads to a system that learns to respond to salient stimuli ( i . e . those that determine reward ) in the environment ., The condition of our network before learning is based on the minimal assumption of random connectivity , yet with appropriate plasticity rules , the functional structure can evolve to allow a fundamental cognitive task to be solved ., It is likely that the base structure of specific areas of the brain – such as the structured connectivity typical of cortex 38 – provides an advantage in solving relevant tasks ., Thus , future investigations can be illuminating of the effect on learning of other structures that more closely resemble cortex for initial connectivity , such as a small-world network 39 , 40 ., We find that homeostasis is essential within our networks , since all the plasticity rules ( including standard STDP ) can be unstable in a sparse , recurrent network ., With homeostasis , we find that firing rates converge to a steady state value , though it is not the same as the goal rate dictated by homeostasis ., That is , when multiple plasticity mechanisms combine simultaneously within a network , the steady state ( i . e . the final stable ) activity pattern differs from that of any single plasticity mechanism acting alone ., One question we investigated is whether sparse or dense activity of cells is beneficial for producing solutions to paired-stimulus association tasks ., The argument for sparse firing runs as follows ., If one input is insufficient to cause a cell to fire , then cells only fire when two of their inputs are active ., If input connections are highly sparse , then given only 4 stimuli are used , the chances of any cell receiving inputs from greater than 2 stimuli become negligible ., Thus , any cell receiving multiple inputs and being able to become active does so when its unique stimulus-pair is present ., Indeed , we did find that as networks became sparser , the number of active cells became lower , but the selectivity of those active cells became higher ., Nevertheless , when measuring decision-making performance , these networks were unreliable , essentially because the downstream neurons received too little input from such sparse firing to overcome random fluctuations from background activity ., Mongillo et al . 13 have shown that when different inputs are non-overlapping , Hebbian plasticity of excitatory synapses alone is sufficient to produce paired associations , even when the stimuli are separated in time ., Such a sparse extreme of no overlap is optimal for producing discrete pools of cells , which respond persistently to single stimuli ., The paired association corresponded to the synaptic connection from one discrete pool to another ., In essence , the initial sparseness led to individual stimulus-specific pools that became homogeneous via intra-pool excitatory plasticity ., These properties are not ideal when one stimulus can be paired with multiple other stimuli with the required response dependent on the particular pairing ( as with XOR logic ) ., Essentially , A-responsive cells cannot be pooled together if stimulus A combined with stimulus B ( e . g . A+B ) requires a different response from stimulus A combined with stimulus D ( e . g . A+D ) ., The need for heterogeneity is more readily satisfied with randomly overlapping inputs ., Recent work by Rigotti et al . 12 suggests that dense activity , found with an input connection probability of ½ , would be optimal for solving tasks that incorporate XOR logic ., Their work with binary neurons operates in a regime where the non-linearity of saturation at maximal activity is as useful as the non-linearity of the firing threshold at zero activity ., In our network , neurons were far from saturation , which is perhaps one reason we did not observe greatest selectivity in these dense networks ., However , if cortical neurons operate at a level where input saturation ( e . g . via NMDA synapses ) is as strong as the firing threshold non-linearity , and if noise fluctuations added to the network by neurons of maximal firing rate are no greater than those at minimal firing rate , then the results based on binary synapses 12 are more relevant than those of our sets of networks ., Since the main non-linearity in the responses of our neurons is their firing threshold , optimal selectivity among firing neurons arises if all cells are silent except for those most responsive to a particular stimulus-pair ., However , such a limit of sparse activity leads to very few selective cells , which fire at very low rates ( mean rate during the stimulus is <3 Hz in the sparsest networks , Figure S1 ) and are insufficient to drive a reliable response in a downstream decision-making network with typical levels of noise ., Thus , denser networks with a greater number of selective cells 12 and higher mean firing rates are beneficial ., The optimal network would be based on a trade-off between the total numbers of selective cells , the mean firing rate of those selective cells and how selective they are to particular paired stimuli ., No two neurons or initial synapses are the same within our networks ., Neurons are individualized by heterogeneity in intrinsic properties ( cellular time constant , leak conductance , firing threshold and refractory time ) , and initial synaptic strengths are drawn from a uniform distribution about a mean ., Moreover , sparse , random connectivity , both of inputs and of recurrent connections , ensures that each neuron responds differently to stimuli ., That randomness in network structure is a beneficial property 12 , 41 for the brain highlights the brains nature , as an adaptive , biological organ ., We incorporated heterogeneity for two reasons ., First , we wanted to more closely approximate biophysical networks and observations of the brain 42 , 43 , 44 , 45 , 46 ., Second , heterogeneity in the network is critical for its development ., Heterogeneity in the connections and cellular properties causes neurons to fire differentially to stimuli ., Correlations in the connectivity lead to correlated activity , which plasticity rules act upon 47 , 48 ., Thus , plasticity can enhance initial diversity of responses to increase the stimulus-pair selectivity of cells ., Diversity of neural responses by initial heterogeneity provides an animal with a framework to solve any cognitive task 12 ., One can ask whether the role of training is simply the learning of an appropriate motor output from a constant internal representation of the stimuli , or whether training enhances neural responses to those stimuli ., In principle , any synaptic plasticity mechanism that increases the initial variability of neural responses should be beneficial in solving XOR-like tasks ., Perhaps the most surprising result was that networks with STDP alone , in nearly all cases , failed to produce reliable decisions – indeed performing worse than untrained random networks ., The sometimes useful role of STDP in attractor concretion 25 reduced the diversity of responses in our associative network , thus diminishing task performance ., We did expect that cross-inhibition – an accentuation of differences in neural responses achieved naturally by LTPi ( Figure 3 ) – could be produced by the combination of Hebbian excitation and a global suppression of activity , through homeostasis ., However , while LTPi succeeded over a range of parameters and networks , triplet STDP only succeeded in a finely tuned subset of these parameters ., This is likely due to an inherent instability when adjusting the recurrent weights within a single set of cells ( the excitatory-to-excitatory connections ) in a Hebbian manner ., In contrast , the changes wrought by LTPi on excitatory cells do not affect the presynaptic activity of inhibitory cells in the networks we consider here , so overall activity levels are more easily stabilized ., While networks modified by LTPi alone had the greatest propensity to generate high selectivity and reliable decisions , LTPi could be added to networks in combination with STDP to increase reliability of decision-making ., Given these findings , such a combination of plasticity mechanisms could provide an organism with the most robust learning method by generating a network with strong selectivity and firing rates ., Further , in networks that produce short-term memory , it is likely that a mechanism such as triplet STDP of excitatory synapses is needed to generate sufficient recurrent excitation 29 , 30 , 49 ., In summary , heterogeneity of neural responses is essential for producing solutions to certain cognitive tasks 12 ., Any plasticity mechanisms that either specifically increase the strongest responses or suppress the weakest responses of cells will enhance any heterogeneity initially present in randomly connected networks and facilitate task performance ., We use leaky integrate-and-fire neurons 50 defined by the leak conductance , gL , synaptic conductances AMPA , NMDA , GABAA , and a refractory conductance ., Further , we define the neurons by a resting potential ( i . e . leak potential ) , reset and threshold potential ., The threshold potential is dynamic in the sense that it is not a hard threshold; rather , it increases to a maximal value and decreases to a base value as the firing rate increases and decreases respectively ., This was added so that at high firing rates the neurons could sustain persistence such as neurons in the decision-making network ., We model NMDAs voltage dependence as described below ., LIF neurons had a mean leak reversal potential of VL\u200a=\u200a−70 mV+/−2 . 5 mV , a fixed membrane time constant of τm\u200a=\u200a10 ms+/−0 . 75 ms and leak conductance of gL\u200a=\u200a35 µS+/−1 µS in the standard low threshold regime , and values of gL\u200a=\u200a40 µS and gL\u200a=\u200a50 µS+/−1 µS in the high threshold regimes ., Excitatory neurons had a firing threshold of Vth\u200a=\u200a−50 mV+/−2 mV , a reset voltage of Vref\u200a=\u200a−60 mV+/−2 mV , and a refractory time constant of τreset\u200a=\u200a2 ms+/− . 25 ms . Inhibitory neurons had a firing threshold of Vth\u200a=\u200a−50 mV+/−2 mV , a reset voltage of Vref\u200a=\u200a−60 mV+/−2 mV , and a refractory time constant of τreset\u200a=\u200a1 ms+/− . 25 ms . Heterogeneity of these parameters was drawn from uniform distribution with the given ranges ., Excitatory LIF neurons had a mean leak reversal potential of VL\u200a=\u200a−70 mV , membrane time constant of τm\u200a=\u200a20 ms , and leak conductance of gL\u200a=\u200a35 µS ., Excitatory neurons had a firing threshold of Vth\u200a=\u200a−48 mV , a reset voltage of Vreset\u200a=\u200a−55 mV , and a refractory time constant of τref\u200a=\u200a2 ms . Inhibitory LIF neurons had a mean leak reversal potential of VL\u200a=\u200a−70 mV , membrane time constant of τm\u200a=\u200a10 ms , and leak conductance of gL\u200a=\u200a30 µS ., Excitatory neurons had a firing threshold of Vth\u200a=\u200a−50 mV , a reset voltage of Vreset\u200a=\u200a−55 mV , and a refractory time constant of τref\u200a=\u200a1 ms . Synaptic currents were modeled by instantaneous steps after a spike followed by an exponential decay described by the equation below 51 ., Recurrent excitatory currents were modeled by AMPA ( EAMPA\u200a=\u200a0 mV , τAMPA\u200a=\u200a2 ms ) and NMDA receptors ( ENMDA\u200a=\u200a0 mV , τNMDA\u200a=\u200a100 ms ) ., Inhibitory currents were modeled by GABAA receptors ( EGABA\u200a=\u200a−70 mV , τGABA\u200a=\u200a10 ms ) ., NMDA receptors were also defined by the voltage term 52: Neurons do not have a hard reset; rather we use a refractory conductance with a dynamic behavior in order to mimic a delayed rectifier potassium current described by the synaptic ODE , with an increase in refractory conductance per spike , δgref\u200a=\u200a0 . 002 µS , refractory time constant τref\u200a=\u200a2 ms , and refractory reversal potential Vref\u200a=\u200a−70 mV ., Neurons do not have a hard spike threshold either that reaches a higher depolarized value with each spike ., This is important for persistent neural activity in our decision-making network ., The max Vth\u200a=\u200a150 mV ., In order to investigate the robustness of each learning rule , we examined their effects on sets of 25 different networks with each set explored across six network regimes ., We examined how the sparseness and correlations of input groups affected both the initial selectivity of a network and how the network responds to each of the synaptic plasticity rules ., Input sparseness is defined via the probability of any input group projecting to any given cell ., As input connection probability increases , sparseness decreases ., We used the following five values for input connection probability: 1/2 , 1/3 , 1/5 , 1/10 and 1/20 ., We produced different degrees of input correlations by altering the number of independently connected input groups of cells per stimulus , using 2 , 4 , 6 , 10 or 20 independent groups ., Each input group produced independent Poisson spike trains with a mean firing rate defined by: =\u200a480 Hz/Number of Input groups ( e . g . 10 input groups of 48 Hz ) ., Correlations weakened progressively as the number of inputs increased due to the increasing number of independent input Poisson spike trains producing the same overall spike rate ., Five levels of input sparseness , combined with five different degrees of input correlations led to 25 variant networks in each regime ., The goal of the present study is to determine how various forms of synaptic plasticity can operate on an initially randomly connected network ( Figure 1B ) to produce the functional responses necessary to solve a cognitive task ., Thus , our initial network possessed no structure in its afferent connections and in its internal recurrent connections ., In the present work we did not alter the random connectivity structure during training , but assessed whether it provided a sufficient substrate for the correlation-based synaptic learning rules to generate functional structure by strengthening and weakening existing synapses ., Random connectivity produced cell-to-cell variability since no two cells receive identical inputs ., Such heterogeneity of the inputs across cells leads to a network of neurons with diverse stimulus responses ., The initial diversity of stimulus responses was typically insufficient to produce the tuned activity needed to solve the behavioral task ( Figure 1A ) , but was essential to provide a basis upon which correlation-based plasticity rules could act differentially ., While random connectivity can be thought of as a minimal assumption , in contrast to the fine-tuning needed by many spiking neuron-based models of cognitive tasks , such randomness also provided sufficient variability in responses that in principle the network could be trained to produce specific responses to any pairs of inputs ., Excitatory-to-excitatory connections are sparse-random with a probability of 10% ., Inhibition is feedforward only , so there are no excitatory-to-inhibitory connections ., Inhibitory-to-Inhibitory connections are all-to-all ., Finally , Inhibitory-to-excitatory synapses connect randomly with a probability of 25% ., Initial synaptic strength is a mean value of W0\u200a=\u200a0 . 05+/−50% uniformly about the mean and scales in strength with size ., These simulations were carried out with an 400 neuron network with an excitatory∶inhibitory ratio of 4∶1 ., We examined one set of networks ( Figure S8 ) with recurrent inhibition where the excitatory cells connect with a probability of 25% to any inhibitory cell with a fixed mean strength W0 ., The decision-making network based on 29 is composed of two excitatory and inhibitory pools of a total 500 neurons with a an excitatory∶inhibitory ratio of 4∶1 and synaptic strength W0\u200a=\u200a0 . 25 ., Connections within each pool are all-to-all ., Cross-inhibition is direct from each inhibitory pool to the opposing excitatory pool , which generates winner-take-all activity so that only one pool is stable in the up state ( active ) ., Network bistability is generated by strong inhibition and self-excitation ., Connections to the decision layer are initially all-to-all from excitatory neurons with a uniform strength of in all trained networks , DW0\u200a=\u200a0 . 075 ., In untrained initial networks , the disparity in firing rates between dense and sparse networks was too large ( Figure S1 ) for a single synaptic strength to effectively drive all networks; thus we used a separate DW0\u200a=\u200a0 . 125 for the sparse networks ( 1/10 , 1/20 ) ., The decision-making network receives a linear ramping input initiating at the start of the cue and continues until the end of the cue where it reaches its maximal value of gurgency\u200a=\u200a5 µS at the end of the cue ., This input is adapted after the “urgency-gating” model 53 , and it ensures that a decision is made each trial ., We model two different types of noise ., First , we model voltage noise by a Gaussian distribution of zero mean with unit variance and amplitude in the associative layer ., Second , we model synaptic conductance noise for the AMPA and GABAA conductance that is drawn from a uniform distribution from zero to 1 with amplitude in the associative layer and amplitude in the decision-making layer ., For all connections , changes in synaptic strength are limited to a maximum of 50% per trial , while across all trials; synaptic strength is bounded between zero and 20W0 , where W0 is the initial mean synaptic strength ., LTPi is modeled after 27: LTPi occurs when an inhibitory cells fires , but the excitatory cell is depolarized and silent ., If the excitatory cell is co-active , then there is no change in the synapse strength ., We refer to this as a veto effect in our model of LTPi ., Any excitatory spike within a window of +/−20 ms for an inhibitory spike will result in a veto ., For each inhibitory spike ( non-vetoed ) the synapse is potentiatiated by idW\u200a=\u200a0 . 005 ., LTPi was reported experimentally as a mechanism for increasing ( but not decreasing ) the strength of inhibitory synapses in cortex 27 ., To compensate for the inability of LTPi to depress synapses , we use multiplicative postsynaptic scaling 28 for homeostasis at the inhibitory-to-excitatory synapses ., We explicitly model the postsynaptic depolarization required by LTPi by defining a voltage threshold that the postsynaptic excitatory cell must be above in order for potentiation to occur ., Because simulation cells do not match experimentally used cells exactly , we explored a wide range of values in Figures S9 , S10 ., In the main body of the paper we used a value of −65 mV , which is 5 mV above the leak reversal ., Finally , we include a hard upper bound of inhibitory synaptic strength , such that those cells most strongly inhibited ( so being less depolarized as well as not spiking ) in practice receive no further potentiation of their inhibitory synapses ., We implement STDP using standard methods 23 , assuming an exponential window for potentiation following a presynaptic spike at time tpre and for depression following a postsynaptic spike at time tpost , so that the change in connection strength , ΔW , follows:Standard STDP produces changes in synaptic weight whose sign depends only on the relative order of spikes , thus only on the relative order and direction of changes in rate , not on the absolute value of the rate ., The LTD amplitude A− was 0 . 80 , and the LTP amplitude A+ was 1 . 20 ., The LTD time constant , τ− , was 25 ms; the LTP time constant , τ+ , was 16 ms . For every spike that updates the synapse the synaptic strength changes by dW\u200a=\u200a0 . 005 ., Triplet STDP was modeled after the rule published by Pfister & Gerstner 2006 16 ., Their model includes triplet terms , so that recent postsynaptic spikes boost the amount of potentiation during a “pre-before-post” pairing , while recent presynaptic spikes boost the amount of depression during a “post-before-pre” pairing ., Specifically when We use the parameters cited from the full model “all-to-all” cortical parameter sets in the paper ., The amplitude terms are doublet LTP , doublet LTD , triplet LTP , and triplet LTD ., The time constants we used are τ2+\u200a=\u200a16 . 68 ms , τ2−\u200a=\u200a33 . 7 ms , τy\u200a=\u200a125 ms , and τx\u200a=\u200a101 ms . These parameters generated an LTD-to-LTP threshold for the postsynaptic cell of 20 Hz , above which uncorrelated Poisson spike trains produce potentiation and below which they produce depression ., For every spike that updates the synapse the synaptic strength changes by dW\u200a=\u200a0 . 005 ., Synapse stability is maintained by multiplicative postsynaptic scaling 28 that is approximate to the following update on a trial-by-trial basis: The change i

Introduction, Results, Discussion, Materials and Methods

Animals must respond selectively to specific combinations of salient environmental stimuli in order to survive in complex environments ., A task with these features , biconditional discrimination , requires responses to select pairs of stimuli that are opposite to responses to those stimuli in another combination ., We investigate the characteristics of synaptic plasticity and network connectivity needed to produce stimulus-pair neural responses within randomly connected model networks of spiking neurons trained in biconditional discrimination ., Using reward-based plasticity for synapses from the random associative network onto a winner-takes-all decision-making network representing perceptual decision-making , we find that reliably correct decision making requires upstream neurons with strong stimulus-pair selectivity ., By chance , selective neurons were present in initial networks; appropriate plasticity mechanisms improved task performance by enhancing the initial diversity of responses ., We find long-term potentiation of inhibition to be the most beneficial plasticity rule by suppressing weak responses to produce reliably correct decisions across an extensive range of networks .

Learning to associate relevant stimuli in our environment is important for survival ., For example , identification of an object , such as an edible fruit , may require us to recognize a unique combination of features – color , shape , size – each of which is present in other , perhaps inedible , objects ., Thus , how the brain associates distinct stimuli to produce specific responses to particular combinations of stimuli is of fundamental importance in neuroscience ., We aim to address this question using computational models of initially non-functional , randomly connected networks of spiking neurons , which are modified by correlation-based learning rules identified experimentally ., Correlation-based learning rules use the spikes of neurons to change connection strength between neurons ., Correlation-based learning rules can enhance stimulus-pair representations that arise naturally in random networks ., Altering the strength of inhibitory-to-excitatory connections alone was the most beneficial change , generating high stimulus-pair selectivity and reliably correct decisions across the widest range of networks ., Surprisingly , changing connections between excitatory cells alone often impaired stimulus-pair selectivity , leading to unreliable decisions ., However , such impairment was ameliorated or reversed by changes in the inhibitory-to-excitatory connections of those networks ., Our findings demonstrate that initial heterogeneity and correlation-based changes of inhibitory synaptic strength can help generate stable network responses to stimulus-pairs .

neuroscience/behavioral neuroscience, neuroscience/neural homeostasis, neuroscience/neurodevelopment, neuroscience/neuronal and glial cell biology, computational biology, neuroscience/theoretical neuroscience

journal.pgen.1006005

The Genomic Basis of Evolutionary Innovation in Pseudomonas aeruginosa

An evolutionary innovation is a new trait that allows organisms to exploit new ecological opportunities ., Some popular examples of innovations include flight , flowers or tetrapod limbs 1 , 2 ., Innovation has been proposed to arise through a wide variety of genetic mechanisms , including: domain shuffling 3 , changes in regulation of gene expression 4 , gene duplication and subsequent neofunctionalization 5 , 6 , horizontal gene transfer 7 , 8 or gene fusion 9 ., Although innovation is usually phenotypically conspicuous , the underlying genetic basis of innovation is often difficult to discern , because the genetic signature of evolutionary innovation erodes as populations and species diverge through time ., One way to circumvent this difficulty is to directly study the evolution of innovation in real time using microbial model systems 10 , 11 ., The large population size and short generation time of microbes allows for rapid evolution under conditions that can be easily replicated ., Samples from evolving populations can be cryogenically preserved in a non-evolving state so that evolved genotypes can be directly compared with their ancestors ., Also , bacteria have compact genomes , making it possible to characterize the functional and genetic basis of adaptation 12 , 13 ., Recent experiments using this approach have provided detailed examples of the evolution of a number of innovations 14–19 , such as novel metabolic traits 15 and ecological specialization 19 ., However , there is a difference between evolving a new trait ( innovation ) and improving an already exiting one ( optimization ) 17 and it remains unclear if evolutionary adaptations that require qualitatively new traits ( innovations ) generally have a different genetic basis than adaptations that require mere fine tuning ( optimization ) of an existing trait ., The objective of this study is to determine the genomic mechanisms underpinning evolutionary innovation and optimization using bacterial metabolism as a model system ., To achieve this goal , we allowed populations of P . aeruginosa founded by a single clone to evolve in Biolog microtiter plates containing culture medium supplemented with 95 unique carbon sources ., Crucially , the ancestral clone produces a clear bimodal pattern of growth on these carbon sources: in some of the carbon sources it grows poorly while in others it grows well ., Carbon sources that support little or no growth above the carbon-free control challenge bacteria to evolve novel metabolic traits ., These carbon sources can therefore be used to study evolutionary innovation ., In contrast , carbon sources that allow the ancestral clone to grow to at least a moderate population density challenge bacteria to improve existing traits ., These carbon sources can be used to study the genetic basis of evolutionary optimization ., Following 140 generations of evolution we identified carbon sources that populations consistently adapted to ., We then isolated clones from populations that evolved in these carbon sources and used whole genome sequencing of more than 80 evolved clones to determine the genetic basis of evolutionary innovation and optimization ., To understand the pleiotropic consequences of innovation we used high-throughput phenotypic assays to measure the fitness of the clones evolved in a single carbon source in the 94 remaining substrates of the Biolog plate ., This experimental strategy has two main benefits ., First , by comparing the mutations and phenotypes observed in clones adapted through innovation and optimization it is possible to test for distinct genomic signatures associated with innovation ., Second , by studying the evolution of multiple novel traits , it is possible to make general conclusions about the genetic basis of innovation ., We first assessed the growth of P . aeruginosa PAO1 in the 95 unique carbon sources provided by Biolog microtiter plates ., Each well on a Biolog plate contains a common inorganic growth medium that is either supplemented with a unique carbon source ( 95 wells ) , or not supplemented and acts as a negative control ( 1 well ) ., The parental PAO1 strain ( ancestral clone hereafter ) showed a clear bimodal pattern of growth in these 95 carbon sources , both in terms of viable cell titre and optical density ( Fig 1A , see Materials and Methods ) ., Some carbon sources supported very low levels of growth that were comparable to the growth observed in the negative control well; selection on these substrates challenges P . aeruginosa to evolve new metabolic traits ., In contrast , other carbon sources supported good levels of growth; selection on these substrates challenges P . aeruginosa to optimize existing metabolic traits ., Although this distinction is intuitive , it is necessary to formally define a threshold between innovation and optimization ., To do so we fitted a mixture distribution to the viable cell titre for the 95 carbon sources ., We used the point where the two distributions intersected to classify the carbon sources in two groups: innovation ( carbon sources that supported poor growth , similar to the carbon-free control ) and optimization ( carbon sources that supported growth to high population density ) ., This classification was also supported by optical density data ( see Materials and Methods ) ., We evolved 4 replicate populations founded by the ancestral clone in each of the 95 carbon sources present in the Biolog microtiter plates by serially propagating cultures on 4 replicate Biolog plates for 30 daily serial transfers , which corresponds to approximately 140 generations of bacterial growth ., At the end of the evolution experiment , we tested for adaptation on each of the 95 carbon sources by comparing the growth rate of the 4 replicate populations that had evolved on each carbon source to the growth rate of the ancestral clone on the same carbon source ., We used growth rates to assess adaptation because they provide a higher resolution than viable cell titre , which can allow for the detection of small differences in the rate of adaptation across substrates ., We note , however , that growth rate and viable cell titre measures strongly correlate ( r = 0 . 887 , P < 0 . 001 ) ., Given that evolutionary innovation involves the origin of novel phenotypes , whereas optimization involves the refinement of existing phenotypes , optimization should evolve more readily than innovation ., Consistent with this expectation , the proportion of populations that evolved increased growth rate was significantly lower on carbon sources that challenged bacteria to innovate as opposed to optimize existing traits ( 51 . 50% vs . 63 . 89% , P = 0 . 01 , One-tailed Fishers exact test ) ., Moreover , the fraction of carbon sources where all 4 replicate populations evolved increased growth rate was almost 50% lower on carbon sources that challenged bacteria with evolutionary innovation as opposed to optimization ( Fig 1B; P = 0 . 042 , One-tailed Fishers exact test ) ., To understand the genetic basis of adaptation , we sequenced the genome of 4 independently evolved clones from carbon sources where all 4 replicate populations evolved increased growth rates ., Our rationale for this sequencing strategy is as follows ., Parallel increases in growth rate suggest that selection was very effective on these substrates , increasing the probability that clones from these substrates carry potential beneficial mutations ., Second , by sequencing multiple clones that evolved on the same substrate it is possible to identify genes that show parallel molecular evolution ., Parallelism is common in bacterial populations , and it provides a simple way to identify genes that contribute to adaptation 19–22 ., Specifically , we sequenced the genomes of 84 clones from carbon sources that challenged bacteria to both innovate ( 8 carbon sources , 32 clones ) and optimize existing traits ( 13 carbon source , 52 clones ) ., The ancestral clone produces a clearly bimodal distribution of growth on these 21 carbon sources , with an approximately 10-fold difference in mean viable cell density between carbon sources where innovation as opposed to just optimization occurred ( S1 Fig ) ., We identified 143 unique mutations in the genomes of the 84 sequenced clones , amounting to a mere 1 . 70 mutations per clone on average ( S1 Data ) ., These were all mutations that accumulated in the course of the experiment and that were not present in the ancestral clone ., Most of the mutations that we identified were SNPs ( 74% ) , but we also detected short indels ( 8% ) , large deletions ( 12% ) , and duplications ( 4% ) ., The proportions of these types of mutations did not differ between clones that had adapted through innovation and optimization ( S2 Fig and S1 Table; P = 0 . 213 , Pearson’s X2 test ) ., Although populations of P . aeruginosa sometimes evolve elevated mutation rates during cystic fibrosis infections 19 and during long-term selection experiments 23 , 24 , we did not find any hypermutator strains with mutations in genes involved in DNA replication and repair , such as the methyl-directed mismatch repair pathway ., Several lines of evidence suggest that most of the SNPs that we detected were beneficial mutations ., First , the vast majority ( 97/106 ) of point mutations we detected were non-synonymous ( S1 Table ) ., We only detected three synonymous mutations and two affected a gene where parallel synonymous evolution occurred , suggesting that these were beneficial synonymous mutations 25 ., Thus , our estimate of the rate of substitution of non-synonymous mutations to putatively neutral synonymous mutations is 97/1 ., Second , the number of mutations per clone was approximately 40% higher in clones that had to adapt through innovation ( 2 . 1 mutations per clone ) as opposed to clones that adapted through optimization ( 1 . 53 mutations per clone ) ( S3 Fig; P = 0 . 034 , two-sample one-tailed Kolmogorov-Smirnov test ) ., Given that the number of generations of evolution was highly similar across carbon sources ( see Materials and Methods ) , this difference in the number of genetic changes is consistent with the idea that populations that had to adapt through innovation were exposed to stronger selection ., This difference is particularly striking , given that populations that had to adapt through innovation were associated with a small population size , which should reduce the rate of fixation of beneficial mutations ., Finally , parallel molecular evolution was very common: 65 . 73% of the mutations occurred in genes that were mutated in more than one clone ( S1 Data ) , which is significantly greater than the amount of parallel evolution expected due to chance alone ( permutation test , P < 0 . 001 ) ., Gene-level parallel evolution tended to occur between replicate clones that evolved on the same carbon source , and genes that were only mutated in clones from an individual carbon source accounted for 75% of the parallelism that we observed ., Interestingly we found parallel evolution in all 4 replicate clones that evolved on 5 carbon sources ( L-alanyl-glycine , glycyl-l-glutamic acid , L-serine , D , L- α glycerol phosphate , and glycerol ) , involving 24 mutations ., In every case , parallel evolution on these substrates involved transcriptional regulators ., Recent work in the closely related bacterium P . fluorescens suggests that parallel evolution by mutations in transcriptional regulators is common because it provides an efficient mechanism to translate genetic variation into phenotypic variation 26 ., This may explain why parallel regulatory evolution was very common on some substrates ., Rigorous test of this idea is outside the scope of this paper and it would require further experimental work , as in 26 ., We also observed higher-order parallel evolution involving different genes that act in the same operon ., Parallelism by definition becomes more common as the scale at which it is measured increases; for example , parallelism is necessarily more common when it is measured at the level of genes than at the level of nucleotides ., However , it is difficult to objectively measure parallelism above the level of the gene at a genome-wide scale , especially given the large number of genes of unknown function in the P . aeruginosa genome , and we therefore , focused our analysis of parallelism at the level of genes ., Like many free-living bacteria , the genome of P . aeruginosa is made up almost entirely of protein coding sequences ( 89 . 4% coding DNA ) ., Because innovation involves the origin of novel phenotypes , it is reasonable to expect that innovation should be associated with more radical changes to proteins ., The vast majority of mutations that we observed were non-synonymous substitutions in protein coding regions ( S1 Table ) , but the relative frequency of radical amino acid substitutions did not differ significantly ( Z-test; P = 0 . 84 ) between evolutionary innovation ( n = 21; 53 . 8% ) and optimization ( n = 28; 56 . 8% ) ., Short insertions and deletions ( indels ) that introduce frameshifts can also produce radical changes to proteins ., However , we only found 6 indels that introduced frameshifts , making it impossible to test for a difference in the frequency of indels observed under innovation ( n = 4 ) and optimization ( n = 2 ) ., In summary , innovation and optimization did not leave distinct signatures on the structure of proteins in our experiments ., To gain further insights into the mechanistic basis of adaptation , we compared the functional roles of genes carrying mutations in clones evolved through innovation and optimization ., Changes in the regulation of gene expression have been proposed to play an important role in evolutionary innovation 16 , 27 , 28 ., Mutations in regulatory genes were common , and in many cases these mutations could be clearly linked to metabolic traits that were under selection ( S2 Table ) ., For example , adaptation to L-serine repeatedly evolved by non-synonymous mutations in a transcription factor ( PA2449 ) that regulates the expression of genes involved in serine metabolism 29; similarly , acquiring the ability to metabolize L-Alanyl-Glycine repeatedly evolved by mutations in pdsR , a repressor of a di-peptide and amino acid transport operon ., We found that the proportion of mutations in genes involved in transcription was greater in clones from populations that had to adapt through innovation as opposed to optimization , supporting the idea that altered gene expression is an important feature of innovation ( Fig 2 , S3 Table; P = 0 . 036 , One-tailed Fisher’s Exact Test ) ., Intergenic mutations also have the potential to change gene expression , for example by altering transcription factor binding sites 16 ., For example , evolution in both L-aspartic acid and L-glutamic acid resulted in parallel substitutions in the promoter region of a P . aeruginosa homolog ( PA5479 ) of a Bacillus subtilis L-aspartate and L-glutamate transporter 30 ., Similarly , one clone evolved in D-Serine and one clone evolved in Glycerol have , respectively , a SNP upstream D-Amino acid dehydrogenase ( PA5304 ) and a SNP upstream a glyceraldehyde-3-phosphate dehydrogenase ( PA2323 ) ., However , intergenic sequences make up only 10 . 6% of the P . aeruginosa genome , suggesting limited potential for adaptation by regulatory mutations in non-coding sequences ., Consistent with this idea , we detected only a very small number of intergenic mutations in clones evolved through innovation ( n = 4 ) and optimization ( n = 6 ) , making it impossible to rigorously test the role of intergenic mutations in innovation ., It is difficult to make a priori predictions regarding associations between other functional categories of genes and innovation , but we found that innovation was also preferentially associated with mutations in metabolic genes ( Fig 2 , S3 Table; P<0 . 01 , One-tailed Fisher’s exact test ) , whereas optimization was associated with mutations in genes involved in cell processes and signalling ( Fig 2; P<0 . 01 , One-tailed Fisher’s exact test ) ., Recent work in experimental evolution has focused on understanding the detailed molecular mechanisms by which individual beneficial mutations increase fitness ( e . g . :15 , 18 , 25 , 31 , 32 ) , and this work has made an important contribution to a broader functional synthesis in evolutionary biology 33 ., We found that 46% of all mutations occurred in genes that were only mutated on a single carbon source and 83 . 6% of the mutated genes were only mutated in one carbon source , suggesting that substrate-specific adaptation was a key driver of evolution in this experiment ., In many cases , mutations in these genes can be putatively linked to the metabolism of the carbon source that populations evolved on , and this was particularly the case for genes involved in transcription and metabolism and among clones that had to adapt through innovation ( S2 Table ) ., At the same time , we also found mutations in a small fraction of genes ( 16 . 4% ) across multiple carbon sources ., As an extreme example we found a gene ( PA1561 ) involved in aerotaxis , mutated 16 times across 10 substrates , suggesting that mutations in this gene represent a general adaptation to Biolog plates ., Unfortunately , it is impossible to precisely measure the substrate specificity of the mutations that we detected without carrying allelic replacement experiments to generate strains carrying single mutations ., In S2 Table we provide a list of the mutations that occurred on each carbon source and their putative role ., However , rigorously determining the biochemical basis of the fitness advantages conferred by individual mutations is outside the scope of this article , as our goal is to understand the genetic mechanisms of evolutionary innovation , and not the biochemical basis of novel metabolic pathways ., Moreover , achieving a detailed functional understanding of adaptation in this system would be incredibly challenging given the diversity of selective pressures that we imposed and the diversity of mutations that we observed ., Gene duplication is a major source of evolutionary innovation 6 , 34 , and some elegant studies show that it can facilitate adaptation in bacterial populations 35 , 36 ., We detected six cases of de novo gene duplication ., Every case involved parallel duplications , suggesting that duplication was adaptive ., Strikingly , all four clones that adapted through innovation on glycyl-L-glutamic acid evolved independent duplications of a 5 . 6 Kb region that contains an operon ( PA4496-PA4500 ) involved in di-peptide and amino acid transport 37 ( S4A Fig ) ., Using information on the frequency of SNPs in the sequenced clones , we were able to re-construct the evolutionary history of these duplications ., Adaptation to glycyl-L-glutamic acid evolved via a repeatable two-step process ., The first is a missense or nonsense mutation in the repressor of the operon , psdR ( PA4499 ) ., The second is a tandem duplication of the operon , most likely as a result of homologous recombination between the flanking sequences of the operon ( S4B Fig ) ., The inactivation of the repressor plus the duplication of the operon probably results in increased expression of this operon ., We were able to infer the chronology of this adaptation because all reads supported the novel mutations in the pdsR gene , as we would expect if duplication followed mutation ., This multi-step process of potentiating mutations that alter the regulation of an operon , followed by adaptive gene amplification , is very similar to a previously described mechanism for the evolution of citrate utilization in Escherichia coli 15 ., We also found large ( > 300 genes , >5% of genome ) duplications in two of the four clones that adapted through optimization on hydroxyl-L-proline ., These duplications overlapped in a large region comprising most of their genes ( 262 genes , ≈288 Kb ) ., This overlap suggests that the duplications were adaptive , but their large scale makes it difficult to infer exactly why ., Overall , the limited incidence of duplication in clones that adapted through either innovation ( n = 4 ) or optimization ( n = 2 ) suggests that de novo duplication is not frequently involved in metabolic innovation ., This result is consistent with recent work showing that de novo duplication makes only a minor contribution to adaptation to gene loss in E . coli 16 and yeast 38 ., In addition to the origin of novel duplicate genes , the divergence of already existing duplicate genes in the P . aeruginosa genome can also play a key role in evolutionary innovation 39 ., To test its importance in our experiment , we classified P . aeruginosa genes into duplicates and singletons using a clustering method based on Blast similarity searches ., Sequence similarity in bacterial genomes can arise as a consequence of gene duplication of existing genes in the genome , which produces paralogs that are similar as a result of shared ancestry ., Alternatively , bacteria can acquire new genes that are similar to existing genes in the genome by horizontal gene transfer ., In practice it is very difficult to distinguish between these two mechanisms for the origin of novel genes , and Lerat and colleagues have proposed the term synologs to describe homologous genes in bacterial genomes 40 ., We found that clones that adapted through innovation acquired more mutations in existing duplicate genes than expected due to chance alone based on the frequency of duplicate genes in the P . aeruginosa genome ( Fig 3 , P<0 . 01 , Pearson’s X2 test ) ., In contrast , the frequency of mutations in duplicate genes in clones that adapted through optimization was indistinguishable from the frequency of duplicates in the P . aeruginosa genome ( S4 Table; P>0 . 05 , Pearson’s X2 test ) ., We repeated this analysis using a broad range of similarity cut-offs to identify duplicate genes ( see Materials and Methods ) ., Our results remained robust , we consistently detected an enrichment of mutations in duplicate genes in clones that adapted through innovation , irrespective of the cut-offs used to identify duplicates ( S5 Fig and S4 Table ) ., This result suggests that the divergence of existing duplicates plays an important role in the ability to evolve novel metabolic phenotypes ., We re-emphasize , however , that this analysis does not distinguish between duplicate genes that arose due to horizontal gene transfer and spontaneous duplication ., What constrains the evolution of metabolic innovations that could allow P . aeruginosa to expand its ecological niche ?, One possible answer is that fitness costs associated with novel metabolic traits may impose a trade-off that limits metabolic innovation 41–43 ., To test this hypothesis , we measured the growth of 2 of the sequenced clones from each carbon source across the 94 alternative carbon sources present on a Biolog plate , and we compared it to the growth of the ancestral clone on each carbon source ., We did a total of 4750 growth assays ( S6 Fig ) and we established conservative criteria to infer positive or negative pleiotropy ., Because our evolved clones carried only a small number of beneficial mutations ( 1 . 70 mutations per clone on average ) , we can be confident that altered growth on alternative carbon sources reflects the pleiotropic side-effects of beneficial mutations ., However , we cannot entirely rule out the possibility that some clones carried conditionally neutral mutations that spread by hitch-hiking with beneficial mutations , but the scarce number of synonymous mutations suggests that conditionally neutral mutations are infrequent ., Adaptation was associated with pleiotropic costs , because evolved clones showed reduced growth on an average of 14 . 76 carbon sources that could be used by the ancestral clone ., However , the pleiotropic cost of innovation was 70% greater than the pleiotropic cost of optimization , which is consistent with the idea that pleiotropy constrains innovation ( Fig 4 , S5 Table; P<0 . 01 , Pearson’s X2 test ) ., The precise mechanistic causes of negative pleiotropy are difficult to determine 44 without measuring the effects of the individual mutations that contributed to adaptation in our system ., However , the association between evolutionary innovation and mutations in regulatory and metabolic genes suggests that mutations in both of these categories of genes are likely candidates to explain negative pleiotropy ., While these observations show that both innovation and optimization have costs , not all pleiotropy may be negative ., Surprisingly , we found that positive pleiotropy—where an evolved population shows increased growth on one or more alternative carbon sources—was just as common as negative pleiotropy ., The frequency of positive pleiotropy did not differ between clones that adapted through innovation and optimization ( Fig 4 , S5 Table; P = 0 . 61 , Pearsons X2 test ) ., Clones that adapted through innovation were enriched in mutations in duplicated genes and paid higher pleiotropic effects than clones that adapted through optimization ., This observation is counter-intuitive , because we would expect that mutations in existing gene duplicates should be associated with low pleiotropic costs , given that the other copy of the duplicate may provide functional backup for the mutated copy ., To explore this counter-intuitive observation further , we compared the pleiotropic costs expressed by clones carrying mutations in duplicates genes that have close or distant homologs in the P . aeruginosa genome ., This analysis is motivated by the assumption that functional redundancy between duplicate genes decays as they diverge from each other ., Interestingly , we found that clones carrying mutations in genes that have close homologs have a lower pleiotropic cost than clones without mutations in genes with close homologs ( Table 1 , S6 Table; P<0 . 01 , Pearsons X2 test ) ., In contrast , we see the opposite pattern in distant homologs: The pleiotropic cost of clones with mutations in genes with distant homologs is higher than that of clones without mutations in distant homologs ( Table 1 , S6 Table; P<0 . 01 , Pearsons X2 test ) ., Collectively , these results support the idea that redundancy between duplicates minimizes the cost of innovation ., Microbiologists have known for a long time that bacteria can evolve novel metabolic traits in the laboratory 45 , and we have taken advantage of the experimental tractability of microbial metabolism to study evolutionary innovation at a broad scale using high-throughput experimental methods coupled to whole genome re-sequencing ., This approach provides the opportunity to study the generality of evolutionary outcomes under a range of selective conditions 16 , 38 ., Using this approach , we have shown that there are significant differences in the genomic basis of metabolic innovation and optimization in P . aeruginosa ., Opportunistic pathogens , such as P . aeruginosa , encounter a novel niche when they establish long-term infections in human hosts , and altered metabolism plays a role in evolutionary transition to specialization on a pathogenic lifestyle 46 ., Understanding the causes of evolutionary innovation may , therefore , contribute to our ability to predict the evolution of host-specialization in pathogenic bacteria ., At a functional level , we found that both innovation and optimization are predominantly driven by substitutions in proteins , which is hardly surprising given that the genome of P . aeruginosa is made up of 90% coding DNA ., Interestingly , innovation and optimization leave similar signatures in proteins , and we did not find any evidence of an excess of radical substitutions associated with innovation ., In contrast , we found profound changes in the functional roles of genes that contributed to innovation and optimization ., Specifically , we found that innovation is associated with mutations in transcription regulators and metabolic genes ., Changes in the expression of existing metabolic pathways that have a basal or underground ability to metabolize novel compounds and changes in the structure of metabolic enzymes that increase their activity towards novel substrates could be involved in the origin of innovations ., Importantly , previous studies have provided detailed examples of how both of these mechanisms can lead to evolutionary innovation in bacteria 15 , 45 , 47–49 ., One of the main results of our study is that mutations in pre-existing duplicate genes in the P . aeruginosa genome play an important role in metabolic innovation , but not optimization ., It is important to recall that we identified duplicate genes based on sequence similarity , and not necessarily common ancestry ., Importantly , this method does not distinguish between duplicates that arise via spontaneous duplication ( paralogs ) and horizontal gene transfer , but irrespective of the origins of the duplicates , duplication is expected to result in genetic and functional redundancy 50 ., Why are duplicates so important for innovation ?, Our results show that evolving new metabolic traits is associated with pleiotropic costs ., This is not surprising given that innovation is associated with mutations in genes involved in transcription and metabolism ., Trade-offs between evolving novel metabolic pathways and maintain existing ones may therefore constrain innovation ., How can this obstacle be overcome ?, Carrying duplicate genes produces redundancy , and this redundancy can potentiate innovation through neo-functionalization 4–6 , 51 ., The presence of an extra gene copy with functional overlap increases mutational robustness and this increase facilitates the exploration of novel gene functions while the other copy maintains its ancestral function 52–54 ., The importance of gene duplication for mutational robustness is still debated 54–60 ., Our results support its importance ., We find that mutations in genes that have a close homolog in the genome tend to be associated with lower costs than mutations in duplicate genes that have distant homologs ., Therefore , our experiments provide evidences of a link between duplication , robustness , and evolvability in P . aeruginosa ., In contrast to eukaryotes , most new genes in γ-proteobacteria , including P . aeruginosa , are acquired by horizontal gene transfer , and not by gene duplication 40 ., For example , we identified approximately 10% of genes in the P . aeruginosa genome as being pre-existing duplicates , whereas Lerat and colleagues 40 estimated that only about 1% of genes in γ-proteobacteria genomes arise by duplication ., This discrepancy suggests that pre-existing horizontally acquired genes are likely to have played a key role in evolutionary innovation in our experiment ., Horizontal gene transfer has mainly been viewed as an important source of evolutionary innovation by providing bacteria with access to a very wide pool of genes that confer novel and important phenotypes , such as antibiotic resistance in pathogenic bacteria 8 , 61 , 62 ., Our results suggest that the horizontal acquisition of functionally redundant genes may also play a key role in evolutionary innovation by providing bacteria with increased genetic robustness to mutations that generate novel phenotypes ., Although redundant duplicate genes provide a genetic substrate for innovation , it is well established that acquiring new genes as a result of horizontal gene transfer or gene duplication , carries a fitness cost in bacteria 31 , 36 , 63–66 ., Owing to this cost , newly acquired genes tend to be lost from bacterial populations unless gene acquisition , per se , is beneficial 6 , 36 or because addiction genes , such as toxin-antitoxin systems , select against the loss of acquired genes 67 ., Indeed , fitness costs may explain why we observed so few instances of de novo duplication ., Selection against newly acquired redundant genes in the short term m

Introduction, Results, Discussion, Materials and Methods

Novel traits play a key role in evolution , but their origins remain poorly understood ., Here we address this problem by using experimental evolution to study bacterial innovation in real time ., We allowed 380 populations of Pseudomonas aeruginosa to adapt to 95 different carbon sources that challenged bacteria with either evolving novel metabolic traits or optimizing existing traits ., Whole genome sequencing of more than 80 clones revealed profound differences in the genetic basis of innovation and optimization ., Innovation was associated with the rapid acquisition of mutations in genes involved in transcription and metabolism ., Mutations in pre-existing duplicate genes in the P . aeruginosa genome were common during innovation , but not optimization ., These duplicate genes may have been acquired by P . aeruginosa due to either spontaneous gene amplification or horizontal gene transfer ., High throughput phenotype assays revealed that novelty was associated with increased pleiotropic costs that are likely to constrain innovation ., However , mutations in duplicate genes with close homologs in the P . aeruginosa genome were associated with low pleiotropic costs compared to mutations in duplicate genes with distant homologs in the P . aeruginosa genome , suggesting that functional redundancy between duplicates facilitates innovation by buffering pleiotropic costs .

Novel traits play a key role in evolution by providing organisms with access to new ecological niches ., Novelty is often conspicuous at a phenotypic level , but it is difficult to determine its underlying genetic basis ., To address this problem , we have studied how the bacterium P . aeruginosa evolves novel metabolic traits , such as the ability to degrade new sugars , in real-time ., After 30 days of evolution we sequenced the genomes of bacteria that have evolved novel metabolic traits ., We found that mutations mainly affected genes involved in transcription and metabolism ., Our main finding is that novelty tends to evolve by mutations in pre-existing duplicated genes in the P . aeruginosa genome ., Duplication drives novelty because genetic redundancy provided by duplication allows bacteria to evolve new metabolic functions without compromising existing functions ., These findings suggest that past duplication events might be important for future innovations .

bacteriology, organismal evolution, medicine and health sciences, pathology and laboratory medicine, genome evolution, pathogens, microbiology, cloning, pseudomonas aeruginosa, optimization, mutation, mathematics, microbial evolution, molecular biology techniques, evolutionary adaptation, bacteria, bacterial pathogens, research and analysis methods, pseudomonas, genomics, medical microbiology, microbial pathogens, molecular evolution, molecular biology, evolutionary genetics, point mutation, genetics, biology and life sciences, physical sciences, computational biology, evolutionary biology, bacterial evolution, evolutionary processes, organisms

journal.pntd.0002882

A High Resolution Case Study of a Patient with Recurrent Plasmodium vivax Infections Shows That Relapses Were Caused by Meiotic Siblings

Plasmodium vivax is the most widespread of the human malaria parasite species with 2 . 85 billion people living in areas at risk for P . vivax infection 1 ., Worldwide there are approximately 100 million cases of P . vivax annually , and the severity of P . vivax infection is increasingly recognized as more cases of death and drug resistance are reported 2 ., The predominant biological mechanism that accounts for the increased range of P . vivax is the ability of these parasites to persist as dormant liver stages known as hypnozoites ., This unique parasite stage is metabolically inactive and can remain dormant for months to years before reemerging to cause clinical disease 3 , 4 ., Asymptomatic hypnozoite carriers therefore represent a major impediment to malaria elimination efforts ., Despite the large burden of P . vivax malaria throughout the world , little is definitively known about hypnozoite reactivation ., Recent reports in the literature have shown that relapses occurring in patients with a low number of hypnozoites in their liver are usually clonal and the relapse parasites are genetically homologous to the parasites from the initial infection 5–7 ., In contrast most relapse infections in endemic settings , where patients harbor hypnozoites from multiple infectious mosquito bites , are polyclonal infections caused by parasites genetically heterologous to the initial infection 8–10 ., The heterologous infections do share some alleles suggesting the parasites share a common ancestor 8 but the polyclonal nature and higher allelic diversity 11 of these infections along with the limited number of genetic markers used in previous studies make it difficult to assess the specific genetic relationship ., The complex dynamics of P . vivax relapse infections prevents using genotyping methods to classify recurrent malaria episodes in the field as relapse infections caused by hypnozoites , as recrudescent infections caused by a failure to clear the initial infection , or as reinfections ., The inability to distinguish between the three causes of recurrent malaria infection in endemic areas prevents accurate estimates of hypnozoite prevalence and inhibits the ability to study this parasite stage directly ., Furthermore , the inability to distinguish relapse infections from reinfections prevents drug efficacy trials in endemic countries , impeding the development of the next generation of anti-hypnozoite drugs and hindering a thorough understanding of resistance to primaquine , the only currently licensed drug able to clear hypnozoites and achieve a radical cure ., Due to these confounding aspects of relapse infection , studies of travelers who move into an endemic region , contract malaria once , and then leave could be particularly informative ., Here we report the analysis of whole genome sequencing data from sequential recurrent parasite infections obtained from a patient who had a P . vivax malaria episode shortly after arriving in Canada from Sudan ( where the infection occurred ) , and subsequently experienced two relapses , 3 months and 33 months after the first episode , despite treatment with the recommended drug regimen ., Analysis of single nucleotide variants ( SNVs ) identified in the three recurrent malaria infections demonstrate that while the first recurrent infection was polyclonal , the two subsequent infections , which can be definitively categorized as relapse infections , were clonal ., In addition , comparison of the SNVs identified in the three infections demonstrate that the parasites isolated from the patient are most likely meiotic siblings and are the result of a single sexual cross in the mosquito vector ., The protocol used to collect human blood samples for this work was approved by the Health Research Ethics Board of the University of Alberta and written informed consent was obtained from the patient ., The consent form states in English that blood samples collected from the patient may be used to genetically characterize the parasites and that samples may be shared with other researchers for scientific purposes ., P . vivax DNA used in this study was isolated from a symptomatic patient who was blood smear positive for P . vivax malaria in Alberta , Canada as previously described 12 ., The patient was a 38-year-old male originally from Eritrea ., In December 2008 he spent nearly one month in Sudan where , according to patient history , he contracted malaria for the first time in his life ., The patient was treated with chloroquine but not primaquine ., After treatment , he relocated to Canada in mid-January 2009 and experienced his first recurrent episode ( EAC01 ) within two weeks of arrival ., He was treated with chloroquine ( 600 mg base immediately , 300 mg base at 6 , 24 , and 48 h ) and primaquine ( 30 mg daily P . O . for 14 d ) as standard clinical care ., The patient recovered clinically and was blood smear negative for malaria on day 16 ., Subsequently , the patient experienced two relapse episodes within 3 months ( EAC02 ) and 33 months ( EAC03 ) of his initial recurrent infection in Canada ., During EAC02 , the patient was treated with the same regimen of chloroquine as before but was given an extended 28 d dose of primaquine ( 30 mg P . O . daily ) and was blood smear negative after two days ., During the final episode of this study , the patient was treated with chloroquine for three days and received standard primaquine ( 14 d 30 mg P . O . daily ) and was again blood smear negative after two days and recovered clinically ., At each episode , EDTA-preserved blood was collected from the patient and submitted to the Provincial Laboratory for Public Health ( Edmonton , Canada ) for routine confirmation of malaria by real-time PCR ., Blood from the first two episodes was stored at −20°C prior to use for sequencing ., Whole blood from the third episode was centrifuged to pellet red blood cells and stored at −80°C ., Parasite DNA was extracted from 40 µL of whole blood using the PSS GC12 instrument ( Precision System Science Co . Ltd . ) with the DNA 200 protocol and kits ( E2003 ) ., DNA was eluted into a 100 µL volume ., Genotyping was based on sequence repeats in the microsatellites 1 . 501 , 3 . 502 , 3 . 27 , and MS16 , as well as the genes msp1F3 , msp3α , msp4 and msp5 using the primers and protocol as previously described 13 ., All molecular markers were amplified by nested or semi-nested PCR using 3 µL of extracted DNA as a template in the first amplification step and 1 µL of the first PCR product for the second amplification ., The PCR reaction was performed in a final volume of 20 µL containing 1X PCR buffer ( Qiagen ) , 2 mM of MgCl2 ( Qiagen ) , 200 µM of each dNTP ( Takara Bio ) , 0 . 25 µM of each primer , and 1 . 5 units of HotStar Taq DNA polymerase ( Qiagen ) ., The cycling program was as follows: 5 min at 95°C followed by 30 cycles of denaturation for 1 min at 95°C , annealing for 1 min at 56°C–62°C ( depending on the marker analyzed ) , and elongation for 1 min at 72°C with a final elongation for 5 min at 72°C ., PCR was performed in a Thermal Cycler 2720 ( Applied Biosystems ) ., Amplification was confirmed in a 2% agarose gel and PCR products were stored at 4°C in the dark ., The product size was resolved by capillary electrophoresis in an ABI Prism 3100 Genetic Analyzer ( Perkin Elmer Applied Biosystems ) , using GS500 LIZ as the internal size standard and the microsatellite settings ., The results were analyzed using GeneMapper software ( version 3 . 5; Applied Biosystems ) ., All electropherograms were inspected visually and peaks above a cut off of 300 relative fluorescent units ( RFU ) were considered true amplification products ., Based on the repeat length , alleles were grouped into 3-bp bins for MS16 , msp1F3 , msp3α , msp4 and msp5 , 4-bp bins for 3 . 27 , 7-bp bins for 1 . 501 or 8-bp bins for 3 . 502 ., Multiple alleles per locus were scored if minor peaks were >33% of the height of the predominant allele present for each locus ., For samples EAC01 , EAC02 , and EAC03 , bulk genomic DNA was isolated from frozen whole blood samples using the DNeasy Blood and Tissue kit ( Qiagen ) as per the manufacturers instructions ., A Taqman qPCR assay for P . vivax b-tubulin ( PVX_094635 ) was used to assess the P . vivax DNA quantity in the bulk gDNA isolated from the patient blood sample ( Primer 1: CGAAAGGAAGCAGAAGGATG and Primer 2: GGGGAGGGGAATACTGAAAA with a Hydrolysis Probe of CAGGTAGTGGTATGGGAACCTTGCTGA ) ., The qPCR reaction was conducted using Applied Biosystems Taqman 2× Genotyping Master Mix ( Life Technologies ) , 20 ng bulk genomic DNA , 900 nM of each primer , and 250 nm of the fluorescent hydrolysis probe ., Reactions were carried out on an Applied Biosystems StepOne Plus ( Life Technologies ) using the manufacturers standard protocol ., A 12-point standard curve was made from Sal1 reference DNA originally obtained from the CDC by serially diluting 20 ng of Sal1 gDNA 1∶2 for a theoretical lower limit of quantitation of 0 . 02% P . vivax DNA ., Total P vivax DNA was calculated by comparing the Ct value of the sample to the 12-point standard curve of Sal1 reference DNA ., Whole genome capture ( WGC ) of the initial infection and the relapse samples was performed as previously described 14 ., Briefly , Illumina TruSeq v . 3-style Y-adaptors ( CCACTCATGCAGGTGAGCGTC*T and /Phos/GACGCTCACCTATGTCTCCCT ) were ligated onto Sal1 reference genomic DNA that had been sheared to 200 bp using an S-series Covaris Adaptive Focused Acoustic machine ( Covaris ) ., The T7 promoter sequence ( bold ) was added into the standard Illumina amplification primers ( TTCTAATACGACTCACTATAGGGAGACATAGGTGAGCCTC and CCACTCATGCAGGTGAGCGTCT ) used to amplify the ligated products ., To create the whole genome baits , the resulting library was used in an in vitro transcription reaction following the manufacturers protocol ( Ambion MEGAshortscript T7 Kit , Life Technologies ) with the exception that biotin labeled dUTP was used in replacement of the supplied dUTP ., Bulk genomic DNA was carried through the standard Illumina whole genome sequencing ( WGS ) library preparation process using Adaptive Focused Acoustics for shearing ( Covaris ) , end-repair , A-tailing and ligation ( New England Biolabs ) ., Hybridization capture was carried out as previously described 14 , 15 ., Briefly , 750 ng of the whole genome baits were incubated with 500 ng of the bulk genomic DNA-fragment library along with 2 . 5 µg of human Cot-1 DNA , 2 . 5 µg of salmon sperm DNA , 2 . 5 ug of Human genomic DNA , and 1 unit of blocking oligonucleotides complementary to the Illumina TruSeq v . 3 adaptor and incubated for 24 hours at 65°C ., After the hybridization , the captured targets were selected by pulling down the biotinylated probe/target hybrids by using streptavidin-coated magnetic beads ( Dynabeads MyOne Streptavidin T1; Life Technologies ) as previously described 14 ., Whole genome capture samples were sequenced on an Illumina Hi-Seq2000 at the TSRI Next Generation Sequencing Core Facility ., Samples were paired-end sequenced for 101 bp per read and one 7 bp index read using Illumina v . 3 chemistry ., Base calls were made using Illumina RTA ( v . 1 . 12 ) software ., Data for each sample sequenced in this study is available in the NCBI Sequence Read Archive SRA057904 ., Fastq files obtained from sequencing were aligned to the Sal1 reference using BWA ( v . 0 . 5 . 9 ) with soft clipping of bases with quality score 2 and below 16 ., PCR duplicates were next identified and marked using Picard ( v . 1 . 51 ) MarkDuplicates ., Aligned reads were then realigned around indels and areas of high entropy using GATK ( v . 1 . 3+ ) IndelRealigner , and the base quality scores of realigned reads were then recalibrated using GATK TableRecalibration 17 , 18 ., After realignment and recalibration the samples were considered “clean” and ready for use in downstream analysis ., Genome wide coverage and loci covered to a certain percentage were calculated using GATK DepthOfCoverage 18 ., For all GATK DepthOfCoverage analyses the minimum mapping quality ( mmq ) was set to 29 and the minimum base quality ( mbq ) was set to 20 ., SNV discovery was conducted on the ten publicly available P . vivax genomes North Korea I: SRP000316 , Mauritania I: SRP000493 , Brazil I: SRP007883 , India VII: SRP007923 , IQ07: SRP003406 , SA94–SA98: SRA047163 ., Only those reads from each sample that aligned in proper pairs were used in the SNV discovery process ( samtools view –f, 2 ) 19 ., SNVs were identified in each sample individually using GATK UnifiedGenotyper and stringent filters were applied to achieve the highest confidence SNV set possible with GATK VariantFiltration ., The filters used included minimum depth of coverage of 20 , minimum ReadDepthAndAllelicFractionBySample of 1 . 0 , maximum Fishers Exact test for strand bias of 3 . 0 , and maximum HaplotypeScore of 3 . 0 ., Additionally SNVs were required to be biallelic with respect to Sal I and SNVs that were within 50 bp of each other were both excluded ., The resulting 10 high stringency genotype sets were combined into a single set of 55 , 399 high confidence SNVs ., The 55 , 399 high confidence SNVs identified in the SNV discovery process outlined above were then genotyped in all three samples using GATK UnifiedGenotyper ., Those loci with multi-allelic genotypes were used only for analysis of clonality ., The resulting VCF file was annotated using SnpEff v . 3 . 3 ( snpeff . sourceforge . net ) 20 and principal components analysis and all SNV plots were completed with MATLAB v . 7 . 12 . 0 . 635 ( The Mathworks ) ., For the Fws calculation the reference and alternate read depths were extracted from the VCF file and used in Equation 1 where pw and ps are the allelic frequency of the reference allele within the sample and within the population , respectively , and qw and qs are the allelic frequency of the alternate allele within the sample and within the population , respectively 21 ., ( 1 ) Regions of contiguous DNA that were identical between samples were identified and a weighted average block size metric ( HapBlockMet ) was calculated for each pairwise comparison using all identified haplotype blocks according to Equation 2 where d is the length of the block ., ( 2 ) Copy number variants were detected using a novel CNV detection algorithm 22 ., Briefly , after reads were aligned to the reference genome , depth of coverage was normalized for GC bias across the entire genome excluding the apicoplast and mitochondria ., Regions were considered amplified if the average of continuous bases normalized by a Gaussian curve with standard deviation of 50 bases showed a two fold or greater read coverage relative to the rest of the genome ., Genome fold coverages were analyzed in a per region fashion , with each region that had a statistically significantly higher coverage ( p<0 . 05 , normalized for number of regions , two-proportion z-test compared to average ) being called as a copy number variant ., The size of the region was varied , with the first and last base pair positions being considered the boundaries of the CNV , and the region that produced the most significant result was considered to have the true CNV boundaries ., The patient , a 38-year-old male from Northeast Africa , moved to Canada in mid-January 2009 and presented with P . vivax malaria one month after experiencing his first primary infection in Sudan ., During this initial recurrent infection , the patient was treated with the recommended regimen of both chloroquine and primaquine ., After subsequent recovery , the patient presented with P . vivax malaria again three months later and was treated with chloroquine and an extended 28-day course of primaquine ., Thirty months later the patient presented with a third recurrent P . vivax malaria infection and was given a standard dose of chloroquine and primaquine ., Throughout the three malaria infections the patient had not travelled outside of areas in North America known to be non-endemic for malaria , thereby ruling out reinfection 12 ., According to the patients medical history , the first recurrent infection ( EAC01 ) was classified as either a recrudescence caused by a failure to clear parasites from the primary infection in Sudan or a relapse infection caused by reactivation of a dormant hypnozoite ., The second ( EAC02 ) and third ( EAC03 ) recurrent infections were classified as relapse infections since parasites were cleared from the patient after each preceding infection in Canada ., Infected blood samples were collected during each malaria episode and frozen ., Because of the unique history of this patient , the samples were thawed and examined after the third malaria episode ., Although the P . vivax-infected patient blood samples had not been collected using the leukocyte depletion protocol required for efficient direct sequencing analysis of P . vivax samples 23 , 24 , we were able to sequence the three parasite strains using a whole genome capture technique utilizing RNA baits derived from the SalI reference strain of P . vivax 14 using an in-solution hybridization capture procedure 15 , 25 ., Briefly , in this method the P . vivax DNA from a patient sample hybridizes to the biotinylated RNA baits and the DNA/RNA hybrids are then purified using streptavidin beads , resulting in the depletion of most of the contaminating human DNA ., This method enriched the P . vivax DNA , which initially comprised less than 1 . 0% of the total genomic DNA ( gDNA ) in the patient infected whole blood samples , to 20%–40% of total DNA content allowing efficient whole genome sequencing analysis of the parasite DNA ( Table 1 ) ., Enriched P . vivax gDNA was sequenced on an Illumina HiSeq 2000 using 100 base-pair paired end reads and 5 . 1–8 . 5 billion bases were obtained for each parasite strain resulting in genome wide coverage of 35X–118X ( Table 1 ) ., In addition , for all three samples , >88% of the genome could be assigned a confident genotype ( Table 1 ) ., Several types of reads were evident in comparison to the SalI reference sequence ., These include clear homozygous single nucleotide variants such as the one causing the S117N change in the P . vivax dihydrofolate reductase gene ( PVX_089950 ) ( Figure 1A ) , biallelic reads ( Figure 1B ) , such as the “T” in the isoleucine codon and the “C” in the valine codon at amino acid 1478 in the P . vivax multidrug resistance associated protein ( PVX_097025 ) , and multiallelic reads in a noncoding region on chromosome 13 ( Figure 1C ) ., Although the biallelic reads appeared real and , in many cases , involve alleles present in other P . vivax isolates , multiallelic reads , such as that shown in Figure 1C , appeared to be due to alignment errors based on the fact that numerous mismatches are found throughout the read ., These alignment errors were subsequently removed by excluding cases where there were more than 1 SNV in 50 bases ., These misalignments were not used in further analyses ., The single and biallelic reads , their readcount , and their position in the genome are given in Supporting Dataset 1 ., In order to examine the origins of the parasites , we first sought to identify the genetic differences between the three infections by examining their genome sequence ., Using the whole genome sequencing data , we genotyped the three P . vivax infections at 55 , 399 positions ( Table 2 ) ., These loci were selected by analyzing the genome sequences of 10 diverse P . vivax strains from the NCBI Sequence Read Archive and identifying the location of SNVs that differed from the SalI reference sequence ., SNVs included in this set of markers were required in at least one of the 10 sequences to have a minimum coverage of 20 reads and all reads indicating the presence of a single allele ., These markers were spaced , on average , 408 bases apart across the genome and the distribution was similar across all chromosomes ( data not shown ) ., Approximately 44% of the SNVs genotyped were located in coding regions , which constitute 54 . 6% of the genome ( Table 2 ) ., The samples analyzed here were different from the SalI reference genome at 23 , 379 , 20 , 734 , and 20 , 934 of the genotyped loci for EAC01 , EAC02 , and EAC03 , respectively ( Table 2 ) ., Of these variant loci , 19 , 667 , 19 , 623 , and 20 , 110 had five or more reads mapping to the locus in EAC01 , EAC02 , and EAC03 , respectively , and were considered high quality genotype calls ( Table 2 ) ., In order to establish that all the infections were of East African origin , as suggested by the patients medical history , we compared the three infections isolated here with five geographically diverse P . vivax strains for which both sequencing data and geographical data are publicly available using the same 55 , 399 loci 23 , 26 ., Using principal components analysis 27 , we show that our three parasite strains from East Africa are very closely related to one another ( Figure 2 ) ., In addition , the three samples from our patient are most closely related to the West African Mauritania I and India VII strains and are diverged from P . vivax samples from South America and the North Korea I strain ( Figure 2 ) ., Principal components analysis corroborates the patients medical history and offers further proof that he was not reinfected by an unrelated P . vivax strain while residing in Canada ., With the dense set of genetic markers obtained from WGS data we next sought to establish the clonality of the three infections ., For this analysis we used both single and biallelic genotype calls in EAC01 , -02 , and -03 , and calculated the percentage of loci containing more than one allele in this haploid organism as an indicator of clonality ., The parasites in the first blood sample ( EAC01 ) were determined to be polyclonal with 43 . 7% ( 8 , 603 of the 19 , 667 variant loci with 5 or more reads ) possessing more than one allele ( Table 2 ) ., In addition , the Fws value , another indicator of multi-clonal infections which compares the parasite diversity within a single patient to the parasite diversity seen on the population level 21 , 28 , is 0 . 51 ( clonal\u200a=\u200a1 . 0 ) for the initial infection and is suggestive of more than one clone ( Table, 2 ) 28 ., In contrast , both relapses arising from hypnozoites activated 30 months apart ( EAC02 and EAC03 ) appeared to be clonal based upon the same metrics as above ( Table 2 ) , which is consistent with previous reports of relapse infections in non-endemic settings 6 , 29 ., The first relapse ( EAC02 ) had an Fws of 0 . 97 and the second relapse had an Fws of 0 . 94 ( Table 2 ) ., In addition both relapse samples had very few loci with multiple alleles ( 258 ( 1 . 31% of total SNVs ) for EAC02 , 303 ( 1 . 51% of total SNVs ) for EAC03 ) at the variant genotyped loci ( Table 2 ) ., For EAC03 , 69% ( 45 ) of the 303 multi-allelic loci also gave mixed reads in one of the other sequenced samples ( e . g . India VII , IQ07 , Brazil 1 , North Korea or Mauritania ) suggesting these loci are in an area of the P . vivax genome which is problematic to sequence using current short-read technology ., In addition , more than 35% of these multi-allelic loci in EAC02 and EAC03 were clustered in subtelomeric regions and regions encoding internal variable gene families , which comprise only 12% of the P . vivax genome ( Table 2 ) ., Also of note , the distribution of multi-allelic loci was nonrandom with many of the mixed read alleles mapping to one 70 kb fragment on the right arm of chromosome 7 , suggesting that this region might be duplicated in the three East African isolates ., We therefore conclude that these few loci containing multiple alleles in the clonal samples are most likely the result of sequencing/alignment errors to these highly variable sequences , which frequently duplicate and recombine during mitotic growth 30 ., To compare these findings using conventional methods , the three P . vivax infections were subjected to eight-marker RFLP genotyping , which represents the current standard in characterizing P . vivax diversity ., The markers used here consisted of four microsatellites and four genes of the highly variable merozoite surface protein family , all of which have been shown to be variable in previous studies 13 , 31 , 32 ., These regions were amplified using PCR ( see methods ) , and the size of the PCR products were analyzed on an ABI Prism 3100 Genetic Analyzer ., As expected , the first infection ( EAC01 ) showed multiple bands for 6 of the 8 markers , while EAC02 and EAC03 appeared monoclonal , with only a single band for each of the 8 markers for EAC02 , and 7 of the 8 markers for EAC03 , where three bands were observed for the 3 . 27 microsatellite marker ( Table 3 ) ., It seems unlikely that this extra band is informative given that the regions appear perfectly identical in EAC02 and EAC03 in this region by whole genome sequencing ( Figure 3 ) ., It is possible that the extra bands for the 3 . 27 marker are due to either contamination or PCR artifacts resulting from mis-hybridization of the PCR primers for this marker in the East African isolates ., These data suggest that microsatellite genotyping of field isolates , although standard , may lead to inaccurate conclusions about polyclonal infections ., We next sought to determine the genetic relatedness between the recurrent parasite infections using the genotyped markers ., Genotyping at the 55 , 399 loci showed that 4 , 434 ( 32 . 8% ) of the 13 , 536 genotyped positions ( 5 or more reads ) that were variant between the three infections produced different base calls in the two relapse samples ( Supporting Dataset 1 ) , contradicting the microsatellite analysis showing that the two relapses are virtually identical ( Table 3 ) ., This result initially suggested that these two episodes might have come from different infection events ., To investigate further , EAC02 and EAC03 were subsequently subjected to a pairwise comparison in which confidently genotyped markers were plotted as a function of chromosome position ( Figure 3 ) ., Surprisingly , these data indicate that the parasites exhibited a highly non-random pattern in which regions of identity were organized into large blocks with a weighted average size of 715 kb and that the microsatellite markers were located by chance in regions that happened to lack variant SNVs ., As a control , the two relapse samples were also compared in a pairwise manner to the Brazil I strain since this strain is also believed to be derived from a relapse infection ., We observed no evidence that the relapse samples shared large contiguous sequences of DNA with this South American strain ( Figure 3 ) ., We additionally calculated the haplotype block sizes from pairwise comparisons between EAC02 and Brazil I and EAC02 and Mauritania 1 ( the strain most closely related by PCA to the East African samples investigated here ) ., The haplotype block sizes for these comparisons were 5 . 8 kb and 6 . 3 kb , respectively , indicating that EAC02 shared no large contiguous pieces of DNA with these other P . vivax stains ., We next compared the two definitive relapse samples to the first sample ( EAC01 ) at those loci that were unambiguously genotyped in all three infections ., We again found that the strains obtained from our patient over 33 months shared substantial portions of the P . vivax genome and that the regions of identity were organized into contiguous blocks of genomic sequence ( data not shown and Supporting Dataset 1 ) ., These analyses further suggest that parasites from all three infections are highly related , yet genetically distinct , to one another ., Since EAC01 was a polyclonal infection , we sought to determine if it was comprised exclusively of parasites directly related to EAC02 and EAC03 ., If EAC01 only contained parasites that were directly related to the second and third infections , then genotyped loci in EAC01 would not contain more than two alleles ., Of the 10 , 775 loci that contained more than one allele in EAC01 only six loci ( 5 . 6×10−4% ) possessed more than two alleles with high confidence ( greater than 5 reads ) ., These data suggest that the first infection is comprised of two or more parasites directly related to EAC02 and EAC03 ., Additionally , when EAC01 variant mixed-read alleles from chromosome one were sorted by read count ( Table 4 ) one resulting haplotype perfectly matched that of EAC02 and EAC03 ( which are identical on chromosome, 1 ) and the other was completely different ., These data suggest that either there are three clones , one with the chromosome 1 EAC02/03 haplotype and two with the alternative haplotype , or that there are only two different clones present , but that EAC02/03 haplotype clone is less abundant ( Table 3 ) ., Overall these data suggest that EAC02 and EAC03 and the two clones in EAC01 are separated by only a single meiosis ., In the related human parasite , P . falciparum , sexual crosses performed using chimpanzees showed that the average number of bases per 100 recombination events ( one centimorgan\u200a=\u200a10 kb ) is 9 . 6 kb 33 ., Based on the 27 breakpoints shown in Figure 3 over the 26 . 9 Mb P . vivax genome 34 we estimate that the average number of bases per 100 recombinations is a similar 10 kb ., Although no laboratory crosses of P . vivax have been performed , our clones appear to be the progeny of a natural cross ., The malaria parasite undergoes sexual reproduction during the mosquito life cycle stage including recombination between male and female gametes ., Since the infections were most likely caused by “sibling” parasites , we next sought to determine if they had arisen from a single zygote and if there was evidence of reciprocal recombination events , which would indicate a direct genetic relationship between the parasite strains from the recurrent infections ., To accomplish this task we analyzed the whole genome sequencing data in multiple two way comparisons that separated the two clones in EAC01 into two different virtual clones ( EAC01A and EAC01B ) based on read count ( Figure 4 ) ., While it is recognized that this is not ideal because stochastic differences in read count are highly likely , without the parental haplotype a third sample is , nevertheless , necessary to visualize reciprocal events ., This is because although a recombination event can be found that occurred in one sibling but not the other using WGS , reciprocal events would be invisible ., Because of the potential for noise we further filtered the set to include only the highest quality base calls ., Specifically , we used a dataset of 5938 positions that had at least 20 reads across EAC01 , EAC02 , and EAC03 major alleles ., Regions were plotted across the chromosome and colored based on whether they were identical or different ., Comparisons between EAC01A and EAC01B , EAC02 , and EAC03 and a fourth comparison between EAC02 and EAC03 are shown in Figure 4 ., The two clones ( EAC02 and EAC03 ) and the two virtual clones ( EAC01A and EAC01B ) were identical across much of the genome ., As suggested previously , on chromosome 1 , EAC01B , EAC02 and EAC03 were almost identical ( different at 1 of 94 loci genotyped ) , but different from EAC01A at 93 of these loci ., It is not clear that the microsatellite genotyping ( Microsatellite 1 . 501 , Table, 3 ) would have detected the EAC01A virtual clone , but conventional microsatellite genotyping of chromosome 1 showed only 1 product in all three isolates for marker 1 . 501 ., Likewise , there was no evidence of recombination on chromosome 5 ( 183 of 183 loci identical in EAC01B , EAC02 , and EAC03 ) ., Nevertheless , clear evidence of reciprocal recombination events were visible on all other chromosomes with most chromosomes having between 1 and 4 ., These breakpoints were identified as areas of the genome where one clone transitions from sharing a haplotype to not sharing a haplotype ( Figure 4 ) ., It should be noted that breakpoints detected using read counts of the two vir

Introduction, Materials and Methods, Results, Discussion

Plasmodium vivax infects a hundred million people annually and endangers 40% of the worlds population ., Unlike Plasmodium falciparum , P . vivax parasites can persist as a dormant stage in the liver , known as the hypnozoite , and these dormant forms can cause malaria relapses months or years after the initial mosquito bite ., Here we analyze whole genome sequencing data from parasites in the blood of a patient who experienced consecutive P . vivax relapses over 33 months in a non-endemic country ., By analyzing patterns of identity , read coverage , and the presence or absence of minor alleles in the initial polyclonal and subsequent monoclonal infections , we show that the parasites in the three infections are likely meiotic siblings ., We infer that these siblings are descended from a single tetrad-like form that developed in the infecting mosquito midgut shortly after fertilization ., In this natural cross we find the recombination rate for P . vivax to be 10 kb per centimorgan and we further observe areas of disequilibrium surrounding major drug resistance genes ., Our data provide new strategies for studying multiclonal infections , which are common in all types of infectious diseases , and for distinguishing P . vivax relapses from reinfections in malaria endemic regions ., This work provides a theoretical foundation for studies that aim to determine if new or existing drugs can provide a radical cure of P . vivax malaria .

Plasmodium vivax is capable of remaining dormant in the human liver for months to years after an initial infection , creating an asymptomatic human reservoir ., This unique aspect of parasite biology makes eliminating P . vivax distinctly different from P . falciparum elimination , and yet very little is known about this dormant parasite stage ., Lack of knowledge about the dormant liver stage prevents the creation of new drugs and public health interventions directed at P . vivax ., In order to better understand this particular parasite stage , we used whole genome sequencing to analyze three sequential P . vivax infections , two of which could be definitively categorized as having arisen from dormant liver stages ., Our whole genome sequencing data demonstrates that dormant liver stage parasites are closely related yet not , as had previously been postulated , identical ., These data highlight the need for a new paradigm to investigate P . vivax dormant liver stages in order to design the next generation of P . vivax drugs and effective global health interventions .

sequencing techniques, genome sequencing, genetics, biology and life sciences, molecular biology techniques, microbiology, genomics, molecular biology, parasitology

journal.pcbi.1003888

Size Does Matter: An Integrative In Vivo-In Silico Approach for the Treatment of Critical Size Bone Defects

Although bone has a unique restorative capacity , i . e . it has the potential to heal scarlessly , the conditions for spontaneous bone healing are not always present , leading to a delayed union or a non-union ., The orthopedic literature does not specify a universally accepted definition of a fracture non-union 1 , 2 ., The eventual bony union after an atypical long period of healing , in comparison to the normal healing period , is called a delayed union ., The absence of healing during at least three to six months defines a fracture non-union in humans ., Fracture non-unions ( hypertrophic , atrophic or oligotrophic ) are classified based on their radiographic and histological appearance 1 , 3 ., Hypertrophic non-unions are characterized by an abnormal vascularity and abundant callus formation ., They are typically caused by excessive motion at the fracture site , which prevents bony bridging although the essential biological factors are present 1 ., Atrophic non-unions , however , are the result of inadequate biological conditions and typically appear on radiographs as blunted bony ends ., They show little callus formation around the fracture gap , filled with mostly fibrous tissue and little or no evidence of mineral deposition 1 ., Oligotrophic non-unions have some radiographic and biological characteristics of both hypertrophic and atrophic non-unions , i . e . they possess the required biological activity but show little or no callus formation 4 ., Excess motion , a large interfragmentary gap 5 , open fracture 5–7 , the particular bone 8 , location of the trauma within the bone 8 , loss of blood supply 9 , severe periosteal and soft-tissue trauma 6 , 7 are some of the mechanical and biological risk factors for the development of a non-union ., Preexisting patient risk factors such as old age 10 , cachexia and malnutrition 11 , immune compromise 12 , genetic disorders ( e . g . type 1 neurofibromatosis ) , osteoporosis 13 , anticoagulants 14 , anti-inflammatory agents 15 , etc . may also affect the fracture healing outcome but are not the primary causes 16 ., Besides an extensive amount of experimental research , several computational models have also been developed to further unravel the occurrence of fracture non-unions ., For comprehensive reviews on mathematical models of fracture healing , we refer the reader to Geris et al . 17 , Isaksson et al . 18 and Pivonka et al . 19 ., Despite the large amount of ( often phenomenological ) information existing in the literature , additional in vivo , in vitro and in silico research is still required to address the key mechanisms that lead to fracture non-unions , determine the factors predictive of fracture complications and establish the optimal therapeutic strategies for each type of fracture non-union ., In this work we propose an integrative in vivo - in silico approach to investigate the occurrence of oligotrophic and atrophic non-unions as well as to design possible treatment strategies thereof ., The gap size of the domain geometry of a previously published mathematical model has been enlarged in order to study the complex interplay of blood vessel formation , oxygen supply , growth factors and cell proliferation on the final healing outcome in large bone defects ., The simulation results are corroborated by comparison with dedicated experimental data ., Next , the mathematical model is used to explain the underlying mechanisms that lead to the experimental observations as well as design different treatment strategies ., Finally , the potential of the combined in silico - in vivo approach is demonstrated by applying it to the case of BMP-treated fracture healing ., All animal experiments were conducted according to the regulations and with approval of the Animal Ethics Committee of the KU Leuven ., C57BL/6 mice were purchased from the R . Janvier Breeding Center ( France ) ., A segmental defect was created in the right tibia of 14 week-old male mice as described elsewhere 20 ., Briefly , animals were anaesthetized with a ketamine-xylazine mixture ( 100 mg/kg ketamine and 15 mg/kg xylazine ) and the right lower leg was shaved ., A custom-made external fixator , based on the Ilizarov external fixation device , was fixed to the tibia using 27 G steel needles ., Subsequently , the tibia was exposed and a 4 . 5–5 mm mid-diaphyseal segment was excised with a 6 . 5 mm diamond saw disk ( Codema n . v . , Kortrijk , Belgium ) ., A demineralized CopiOs scaffold ( 2 . 5×2 . 5×5 mm3; Zimmer b . v . b . a . , Wemmel , Belgium ) seeded with 1×106 mouse periosteal cells ( passage 4 ) was implanted , the skin was sutured and animals received postoperative analgesia ( buprenorphine , 60 µg/kg body weight ) ., The demineralized CopiOs- scaffold was used to minimize the soft tissue collapse within the critical size defect ., After 3 , 14 or 56 days animals were sacrificed , the tibia was excised and samples were analyzed by μCT and then processed for histology ., Murine periosteum-derived cells ( mPDC ) were isolated from the long bones of 8 week-old male mice as previously described 21 ., In short , the femurs and tibias isolated from 8 week old male C57BL/6J mice were dissected and digested with collagenase-dispase after protecting the epiphyses with low melting point agarose ., After a filtration and washing step , the cells were plated at 1×104 cells per square centimeter and replated when reaching 80–90% confluency ., After isolation , cells were pooled per 2–3 mice and cultured in a humidified incubator at 37°C with 5% CO2 in α-minimal essential medium ( α-MEM ) supplemented with 2 mM glutaMAX-I , 1% penicillin/streptomycin ( 100 units/ml and 100 µg/ml respectively ) and 10% fetal bovine serum ( all from Gibco , Life Technologies , Gent , Belgium ) ., When reaching 80–90% confluency , cells were trypsinized and reseeded at 7500 cells/cm2 ., To deliver BMP2 at the defect site , mPDCs were transduced 72 hours prior to implantation with an adenoviral vector encoding human BMP2 ( a generous gift from Dr . Frank Luyten , KU Leuven , Belgium ) at a multiplicity of infection of 50 ., Bone formation in large bone defects was followed by radiographic images at different time points after surgery using the Skyscan 1076 high resolution in vivo micro-computed tomography ( μCT ) scanner ( Bruker-μCT , Kontich , Belgium ) ., For bone quantification , samples retrieved at day 56 were scanned using the high resolution SkyScan 1172 μCT system ( Bruker-μCT ) at a pixel size of 10 µm with 50 kV tube voltage and 0 . 5 mm aluminum filter ., Projection data was reconstructed using the NRecon software and quantification of mineralized tissue was performed using the CTAn software ( both from Bruker-μCT ) ., Isolated bones were fixed in 2% paraformaldehyde overnight and decalcified in EDTA for 14 days at 4°C prior to dehydration , embedded in paraffin and sectioned at 4 µm ., Histochemical staining with hematoxylin and eosin ( H&E ) and immunohistochemical staining for mouse CD31 is routinely performed in our lab and has been described previously 21 ., Images were taken on a Zeiss Axioplan 2 light microscope using the Zeiss AxioVision software ., Data are presented as means ± standard error of the means ., Data were analyzed by one-way ANOVA using the NCSS statistical software ., Differences were considered statistically significant at p<0 . 05 ., The multiscale computational framework for the mathematical modelling of bone fracture healing and its relation to angiogenesis was established earlier and has been described in detail in 22 ., The framework consists of ( 1 ) a tissue level describing the various key processes of bone fracture healing with 10 continuous variables , ( 2 ) a cellular level representing the developing vasculature with discrete endothelial cells and ( 3 ) an intracellular level that defines the internal dynamics of the Dll4-Notch signaling pathway in every endothelial cell ( Figure 1 ) ., The model accounts for the various key processes that occur during the soft and hard callus phase of bone fracture healing ( see 22 for a more detailed description ) ., While the model described in 22 already partially accounted for the role of oxygen , we have recently extended the model to capture the various effects of oxygen on cellular processes in a much more complete and refined way 23 ., A brief description of the oxygen model is found below and more details are given in Supporting Text S1 ., After the initial inflammation phase ( which is not included in the current mathematical model ) , the fracture callus is filled with a cocktail of granulation matrix , stem cells and growth factors ., In regions where oxygen is abundantly available ( i . e . close to the cortex in the case of normal fracture healing ) , the mesenchymal stem cells will directly differentiate into osteoblasts and form bone through the intramembranous pathway ., In regions where the oxygen tension is lower ( i . e . the central fracture callus in the case of normal fracture healing ) , the mesenchymal stem cells will differentiate to chondrocytes that will form a cartilage template to mechanically stabilize the fracture ., This is followed by endochondral ossification during which blood vessels and osteoblasts are attracted to the central fracture callus , resulting in degradation of the cartilage template and bone formation ., Finally , the newly formed bone is remodeled ( not included in the current mathematical model ) ., At the tissue level , the fracture healing process is described by calculating the spatiotemporal evolution of the density of mesenchymal stem cells ( cm ) , osteoblasts ( cb ) , chondrocytes ( cc ) , fibroblasts ( cf ) , bone ( mb ) , cartilage ( mc ) , fibrous matrix ( mf ) , osteochondrogenic growth factor ( gbc ) , angiogenic growth factor ( gv ) and oxygen ( n ) using 10 non-linear , coupled partial differential equations of the taxis-diffusion-reaction type ., At the cellular level , the evolution of the discrete vasculature is determined by sprouting , vascular growth and anastomosis and is modeled by a lattice-free method ., At the intracellular level , an agent-based model is used to implement the rules that capture the intracellular dynamics of the Dll4-Notch signaling pathway which determines tip cell selection during sprouting angiogenesis ., The oxygen model includes an accurate description of the oxygen dependency of a number of cellular processes , namely osteogenic and chondrogenic differentiation , cell proliferation , cell death , oxygen consumption and the hypoxia-dependent production of an angiogenic growth factor ., The cellular consumption of oxygen was described using a Michaelis-Menten kinetic law where the cell-specific maximal oxygen consumption rate has the following relative cellular order: chondrocytes<MSCs<osteoblasts<fibroblasts ., The oxygen values at which the considered cell-specific oxygen-dependent processes occur at maximal rate or at which their rate changes are based on a rigorous literature screening of the state-of-the-art experimental knowledge ( Figure 2 ) ., More specifically , the relative order of the oxygen dependent processes was determined as accurately as possible since it is crucial to the behavior of the oxygen model ., The complete description of the set of equations , the boundary and initial conditions , the parameter values , implementation details as well as some underlying assumptions and simplifications can be found in Supporting Text S1 as well as in previous publications 22 , 24 , 25 ., The geometrical domain of the fracture callus , as well as the boundary conditions and initial positions of the endothelial cells ( cv ) are shown in Figure 3-B ., Note that the periosteum near the bone ends is considered to be well vascularized such that a muscular contribution to the vasculature ( i . e . the initial position of the endothelial cells ) is unnecessary ., To simulate the bone regeneration process in a large bone defect , the domain was extended over a distance equal to half the gap size of a murine critical sized defect ( 5 mm ) ., The effect of the host environment on the fracture healing process is explored with several combinations of boundary conditions , however in the standard compromised condition the influence of the host environment is neglected thereby representing the worst-case scenario ( Figure 3-B ) ., It has been shown experimentally that the amount of cells and growth factors is significantly reduced in a large fracture gap 26 , 27 ., Therefore , in order to simulate this effect , the initial conditions for the MSCs and osteochondrogenic growth factors were decreased tenfold to 2 . 103 cells/ml and 10 ng/ml respectively in the central callus area ( indicated with dots in Figure 3-B ) ., The initial oxygen tension ( ninit ) in the central callus area is equal to 3 . 7% ., All other model parameters as well as initial and boundary conditions were left unchanged with respect to the normal healing case 23 and can be found in Supporting Text S1 ( Figure 3 ) ., Note that the computational model does not simulate the presence of the demineralized CopiOs scaffold , which was used to minimize the soft tissue collapse within the critical size defect ., Previous results have however shown that the demineralized carrier structure does not contribute nor enhance the bone formation process ., The results of the mathematical model are quantified in terms of tissue fractions , specified for each part of the fracture callus ( i . e . endosteal , periosteal and intercortical ) ., The tissue fractions are calculated by the following procedure: first the spatial images are binarized using tissue-specific thresholds ( 0 means that the tissue is not present , 1 means that the tissue is present in a grid cell ) ., Subsequently , an equal weight is assigned to the different tissues , i . e . if a grid cell contains three tissues , the area of that grid cell is divided by three in the final calculations of the tissue ( area ) fractions 23 ., A qualitatively similar healing progression is predicted by the simulation results as observed experimentally ( Figures 4 and 5 ) ., At early time points a periosteal reaction , characterized by a thickening of the periosteal layer ( Figures 5-B1 , B1′ and C1 ) as well as the presence of a hematoma , a fibrous-like tissue associated with the presence of numerous ( red ) blood cells ( Figures 5-B1 , B1″ ) , are observed at the cortical host bone site , both supporting the initial and boundary conditions that were applied in the multiscale model ( Figure 3-B ) ., In the center of the large bone defect no signs of tissues or infiltration of blood vessels are detected , only scaffold material together with a low cellularity is observed ( Figures 5-B1-center , C1′ ) , corresponding to the predictions of the in silico model ( Figure 4-G ) ., On day 14 , a periosteal endochondral ossification reaction is seen , evidenced by the presence of cartilage ( large round cells staining grey-blue with H&E ) and trabecular-like bone ( dense matrix , staining bright pink with H&E , with the clear presence of embedded osteocytes ) ( Figures 5-B2 , B2′; arrow indicates cartilage ) , while direct bone formation occurs endosteally ( Figures 5-B2 , B2″ ) ., The mathematical model predicts a similar distribution of tissue formation , i . e . direct bone formation near the bony ends and endochondral ossification further away in the fracture callus ( Figure 4-B , C ) ., In the center of the defect only a highly dense fibrous tissue is observed in both the experimental , the scaffold remains stained pink-blue with H&E but lack the presence of embedded cells ( Figure 5-B2-center ) , as well as the mathematical model ( Figure 4-A ) ., In contrast to the experimental model , the mathematical model does not predict any blood vessels in the central callus area ( indicated with dots in Figure 3 ) ., These vessels , however , appear to be small and immature whereas the blood vessels that are associated with the sites of bone formation are large and mature ( compare Figures 5-C2 and C2′ ) ., This discrepancy might be explained by the fact that the mathematical framework only models angiogenesis , i . e . blood vessel growth through the creation of new vessel branches from existing ones , whereas vasculogenesis , i . e . de novo network formation from scattered endothelial cells , is not included here ., Indeed , after bone fracture the hematoma will be filled with blood , containing amongst others endothelial precursor cells , which could explain the small , immature blood vessels observed experimentally ., We would like to stress , however , that this is a first hypothesis that is currently being explored further ., Notice the closure of the bone marrow canal by new bone on day 56 , separating the bone marrow ( right ) from the scaffold region ( left ) ( Figure 5-B3 , B3′ , B3″ ) ., As such , capping of the bone ends has occurred both in the experimental and the mathematical model ( Figure 4-C ) ., The blood vessels in the center are still much smaller compared to those near the edges of the defect ( compare Figure 5-C3 and C3′ ) ., In the center of the defect no signs of bone formation are detected , only fibrous tissue is seen , at this time point associated with a very low cellular content ( Figure 5-B3-center ) ., Also in the mathematical model no additional bone formation is predicted between post fracture day ( PFD ) 60 and 90 , thereby classifying this fracture as a non-union 1 , 2 ., After this qualitative validation of the model predictions with the experimental observations of bone healing in a large defect , the model was used to understand the mechanisms underlying the occurrence of fracture non-unions ., It appears that in the mathematical model , chondrogenic differentiation and cell survival are severely impaired in the central callus area ( indicated with dots in Figure, 3 ) due to the harsh hypoxic conditions ( optimal oxygen tension for chondrogenic differentiation is 3% , minimal oxygen tension for MSC and chondrocyte survival is 0 . 5% , see Figure, 2 ) ( Figure 4-D , F ) ., Consequently , the angiogenic growth factor ( gv ) , which is the major stimulus for vascular growth and as such endochondral ossification , is not produced in the central callus area ( Figure 4-E ) ., As a result , the bone healing stops after capping of the bony ends , resulting in an atrophic non-union ( Figure 4-C ) ., Note that the predicted bone front extents further into the callus than observed in the in vivo model ., This might be due to some limitations of the computational model ., Firstly , in the current model all the progenitor cells can differentiate towards both the chondrogenic and osteogenic lineage , depending on the local growth factor concentrations and oxygen tensions ., In reality , however , it has been shown that the progenitors from the endosteal callus can only differentiate towards the osteogenic lineage , resulting in the absence of cartilage in the endosteal callus 28 ., Progenitor cells from the periosteum do have the capability to differentiate to both lineages , explaining why endochondral ossification mainly occurs in the periosteal callus 28 ., As such , the current simplification of the model leads to an overestimation of the amount and the location of the cartilage matrix , resulting in an overestimation of the predicted bone formation ., Secondly , the current model does not account for changes in callus size and shape during the healing process which may also influence the bone formation process ., After establishing the in silico and in vivo non-union model , the in silico model was further used to explore the influence of the gap size on the healing outcome ( Figure 6 ) ., By increasing the gap size , the bone tissue fraction at PFD 90 is reduced whereas the cartilage fraction remains similar ( close to zero ) and the fibrous tissue fraction is greatly increased ( Figure 6 ) ., Although the bone tissue fraction reaches 84% in a 3 mm defect , there is no cortical bridging which indicates the formation of a non-union ., The simulation therefore predicts that a murine bone defect becomes critical at 3 mm ., In the remaining part of this study we will focus on the bone regeneration process in 5 mm defects , in correspondence with the in vivo set-up described above ., Since for all the different gap sizes explored in Figure 6 , the same set of initial and boundary conditions was employed , the occurrence of fracture non-unions might be attributed to an inadequate vascularization of the central callus region ., More specifically , the ingrowing vasculature which originates from the bony ends , needs to cover a larger distance in larger defects , resulting in a too late vascularization of the central fracture area ( Figure 4-G ) and consequently harsh hypoxic conditions ( Figure 4-F ) ., As was explained above , these hypoxic conditions lead to cell death thereby arresting the production of angiogenic growth factors and ultimately the bone healing process ( Figure 4-C ) ., Clearly , the spatiotemporal patterns of oxygen tension are an important determinant of successful bone repair which prompted us to investigate the complex interplay between oxygen delivery , diffusion and consumption in a critical size defect ( 5 mm ) ., An extensive sensitivity analysis was performed on the parameter values describing the delivery of oxygen ( Gn ) , the diffusion of oxygen ( Dn ) and the oxygen consumption by osteoblasts ( Qb ) , chondrocytes ( Qc ) , MSCs ( Qm ) and fibroblasts ( Qf ) ., Moreover , since experimental evidence has shown that the biological potential ( e . g . the amount of osteoprogenitor cells and growth factors present ) might be greatly reduced in critical size defects 26 , 27 , we also explored the influence of the initial conditions ( cm , init , gbc , init , cf , init , mf , init , ninit ) in the central callus area ( indicated with dots in Figure, 3 ) on the fracture healing outcome ( Table S1 in the supplementary material ) ., The initial position of the endothelial cells ( see Figure S1 in the supplementary material ) , has a small influence on the final bone tissue fraction ( +/−2% ) ., This difference can be attributed to a different spatial filling of the blood vessels in the 2D simulated geometry and is in the same range as the influence of the stochastic component in the description of blood vessel migration on the simulation outcome ( +/−3% ) 24 ., Based on these findings , we consider deviations of more than 2% with respect to 50% of bone tissue fraction to be sufficient to warrant further analysis ., In order to gain more understanding in the complex non-linear dynamics of the oxygen model , the mechanisms underlying these significant deviations were investigated and are discussed in more detail below ., The sensitivity analysis revealed a non-linear influence of the initial amount of MSCs ( cm , init ) on the bone tissue fraction at PFD 90 ., This can be explained by the fact that on the one hand a low initial concentration of MSCs ( cm , init<2 . 104 cells/ml ) reduces the biological potential of the fracture site since less cells can contribute to the bone healing process ., On the other hand , a high initial concentration of MSCs ( cm , init>2 . 105 cells/ml ) will worsen the detrimental hypoxic conditions in the central callus region due to the increased amount of oxygen consumption ., The initial concentration of fibroblasts ( cf , init ) does not show this non-linear behavior ., High initial concentrations of fibroblasts and/or MSCs are detrimental ( cf , init>5 . 105 cells/ml ) since the increased oxygen consumption will lower the average oxygen tension in the central callus area ., Contrary to the MSCs , low initial concentrations of fibroblasts do not seem to have a major influence on the final amount of bone formation ., This is mainly because fibroblasts do not contribute to the biological potential of the hematoma as they cannot differentiate towards the osteogenic or chondrogenic lineage ., The sensitivity analysis also indicates that the amount of osteochondrogenic growth factors present in the fracture hematoma ( gbc , init ) is a critical determinant of the final amount of bone formation ., Indeed , increasing the growth factor concentration results in a significant increase in the amount of bone formation measured after 90 days of healing ., This result can be attributed to an increased chondrogenic differentiation which limits the oxygen consumption since chondrocytes consume less oxygen than MSCs ., As such , the central hypoxic area will be reduced leading to more bone formation ., After the inflammation phase , the fracture callus is filled with granulation tissue ( represented here by mf , init ) ., It appears that a large amount of granulation tissue negatively influences the fracture healing outcome which is due to its inhibitory effect at large matrix densities on the proliferative capacities of MSCs , fibroblasts , chondrocytes and osteoblasts ., Similar to the initial amount of MSCs also the initial oxygen tension ( ninit ) has a non-linear effect on the final amount of bone formation ., Very low oxygen tensions ( ninit<0 . 5% ) lead to a larger hypoxic area and less bone formation whereas oxygen tensions above 4% ( ninit>4% ) hamper the proliferation of chondrocytes , thereby disrupting the cartilage production and consequently the endochondral ossification process ., Interestingly , in the intermediate range of oxygen tensions ( 0 . 5%<ninit<4% ) , lower initial oxygen tensions appear to result in more bone formation ( Table S1 , 0 . 7% versus 3 . 7% oxygen tension of the standard compromised condition ) ., Although intuitively we would expect that these low oxygen tensions would lead to worse hypoxic conditions , model analyses show that the average oxygen tension in the fracture callus remains above 0 . 8% during the entire healing period ( note that the low oxygen tensions of the central callus area are averaged with the high oxygen tensions near the bony ends ) , which is well above the oxygen threshold for chondrocyte and MSC cell death ( i . e . 0 . 5% ) ., As such the oxygen tension is low enough to inhibit extensive proliferation ( as the chondrocytes and MSCs preferentially proliferate at 3% and 4% oxygen tension respectively , Figure, 2 ) and therefore avoiding too much oxygen consumption , but high enough to keep a small amount of remaining stem cells alive ., Moreover , the oxygen consumption is not only reduced due to the smaller amount of consuming cells ., The cellular consumption of oxygen is also oxygen dependent , leading to a lower cellular consumption in low oxygen environments ., It is the combination of these effects that limits the drop of the average oxygen tension , allowing the MSCs to survive and contribute to the bone healing process for a longer period of time ( 40 days for case ninit\u200a=\u200a0 . 7% versus 4 days in the standard condition ) ., A similar reasoning can be made for the case where an initial gradient of oxygen tensions was applied to the central callus region ( ninit , gr\u200a=\u200a0 . 8%/mm*x ) ., In this simulation the oxygen tension varied from 0% in the middle of the callus to 4% at the bony ends ., The low oxygen tensions in the central area supported the maintenance of a small population of MSCs for a longer period of time ( 6 days versus 4 days in the standard condition ) ., This resulted in a larger amount of cartilage and finally bone ., Note that this specific gradient in oxygen tension is less beneficial for the amount of bone formation at PFD 90 than a uniform distribution of 0 . 7% , as in the case of the gradient the oxygen tension in the middle of the callus is too low to sustain cell viability ., Besides investigating the influence of the initial conditions , the sensitivity analysis also focused on the complex interplay between oxygen delivery ( Gn ) , diffusion ( Dn ) and consumption ( Qb , Qc , Qm , Qf ) ., Altering the oxygen delivery ( Gn ) by the vasculature has a large effect on the final amount of bone formation ., Very low values of oxygen delivery increase cell death in the central hypoxic area , resulting in the absence of any bone formation ., Increasing the value of oxygen delivery slightly improves the fracture healing outcome ., Note that , although the bone tissue fraction is 37% in case of Gn\u200a=\u200a22 . 10−12 mol/cell . day and 55% in case of Gn\u200a=\u200a3 . 2 . 10−12 mol/cell . day , the spatial extent of bone ingrowth at PFD 90 is very similar ( results not shown ) ., This is however masked by the increased proliferation and matrix production of fibroblasts who thrive in the well-oxygenated environment created by Gn\u200a=\u200a22 . 10−12 mol/cell . day ., As such , the bone tissue fraction for Gn\u200a=\u200a22 . 10−12 mol/cell . day is reduced with respect to Gn\u200a=\u200a3 . 2 . 10−12 mol/cell . day ., The parameter values of the cell-specific oxygen consumption rates ( Qb , Qc , Qm , Qf ) also influence the outcome of the model significantly ., For all cell types , it is beneficial to reduce the oxygen consumption rates since this will limit the decrease in oxygen tension in the central fracture area and consequently the amount of cell death ., This benefit is greatest for the MSCs and chondrocytes as these cell types mainly populate the central fracture area and contribute to the hypoxic conditions encountered here ., Conversely , the amount of bone formation is greatly reduced when the oxygen consumption rate of the MSCs ( Qm ) or chondrocytes ( Qc ) is increased ., The model outcome is also negatively affected by a high osteoblastic oxygen consumption rate ( Qb ) whereas a high fibroblastic consumption rate ( Qf ) only slightly reduces the final amount of bone ., In the first case , the oxygen tension near the bony ends is reduced , resulting in hampered osteogenic differentiation and limited bone formation ., In the latter case , the fibroblasts reduce the oxygen tension in the entire callus area ( the fibroblasts are initially uniformly distributed in the fracture callus ) but this drop is limited due to the small amount of fibroblasts present ., Interestingly , a similar reasoning does not hold for the MSCs ( although they are also initially uniformly distributed and limited in cell population ) since they mainly grow in the central fracture zone whereas the fibroblasts optimally proliferate in a well-oxygenated environment such as the tissues surrounding the bony ends ., As such , a high oxygen consumption rate of MSCs severely impairs the bone formation process whereas a high oxygen consumption rate of fibroblasts only slightly reduces the amount of bone formed at PFD 90 ., It can be noticed from Table S1 and Figure S2 that the diffusion properties of oxygen have a major impact on the simulation outcome ., Reducing the diffusion coefficient of oxygen impairs the bone formation due to the creation of a larger hypoxic zone ( Figure S2-A , C ) ., Increasing the diffusion coefficient appears to be beneficial although a closer look at these simulation results reveals that the endochondral process is not captured correctly anymore with bone formation largely preceding the ingrowth of new blood vessels ( Figure S2-D , F ) ., Note that also in this case a non-union is formed , since there is no cortical bridging , even though a bone tissue fraction of 89% is reached ( Table S1 ) ., Increasing the diffusion coefficient even further results in a complete absence of bone formation since the resulting oxygen tensions are too low for any cell type to survive ( Figure S2-H ) ( see Supporting Text S3 ) ., In conclusion , we can state that the initial conditions have an important impact on the final amount of bone formation ., They are however not sufficient to result in complete healing of critical size defects due to insufficient vascularization of the central callus area , leading to hypoxic conditions and cell death ., As such , an adequate and timely restoration of the vasculature appears to be an important determinant of the healing outcome ., Insp

Introduction, Materials and Methods, Results, Discussion

Although bone has a unique restorative capacity , i . e . , it has the potential to heal scarlessly , the conditions for spontaneous bone healing are not always present , leading to a delayed union or a non-union ., In this work , we use an integrative in vivo - in silico approach to investigate the occurrence of non-unions , as well as to design possible treatment strategies thereof ., The gap size of the domain geometry of a previously published mathematical model was enlarged in order to study the complex interplay of blood vessel formation , oxygen supply , growth factors and cell proliferation on the final healing outcome in large bone defects ., The multiscale oxygen model was not only able to capture the essential aspects of in vivo non-unions , it also assisted in understanding the underlying mechanisms of action , i . e . , the delayed vascularization of the central callus region resulted in harsh hypoxic conditions , cell death and finally disrupted bone healing ., Inspired by the importance of a timely vascularization , as well as by the limited biological potential of the fracture hematoma , the influence of the host environment on the bone healing process in critical size defects was explored further ., Moreover , dependent on the host environment , several treatment strategies were designed and tested for effectiveness ., A qualitative correspondence between the predicted outcomes of certain treatment strategies and experimental observations was obtained , clearly illustrating the models potential ., In conclusion , the results of this study demonstrate that due to the complex non-linear dynamics of blood vessel formation , oxygen supply , growth factor production and cell proliferation and the interactions thereof with the host environment , an integrative in silico-in vivo approach is a crucial tool to further unravel the occurrence and treatments of challenging critical sized bone defects .

In 5–10% of fracture patients , the bone fractures do not heal in the normal healing period ( delayed healing ) or do not heal at all ( non-union ) ., In order to investigate the causes of impaired healing and design potential treatment strategies , we have used a combined experimental and computational approach ., More specifically , large bone defects were analyzed in mouse models and simulated by a previously published computational model ., After showing that the predictions of the computational model match the observations of the experimental model , we have used the computational model to investigate the underlying mechanisms of action ., In particular , the results indicated that the new blood vessels do not reach the central fracture zone in time due to the large defect size , which leads to insufficient oxygen delivery , increased cell death and disrupted bone healing ., The healing , however , could be rescued by adequate blood vessel ingrowth from the overlying soft tissues ., Moreover , potential treatment strategies were designed based on the influence of these soft tissues ., In conclusion , this study demonstrates the potential of a combined experimental and computational approach to contribute to the understanding of pathological processes like the impaired bone regeneration in large bone defects and design future treatments thereof .

theoretical biology, biology and life sciences, computational biology

journal.pntd.0004695

The Viral Polymerase Inhibitor 7-Deaza-2’-C-Methyladenosine Is a Potent Inhibitor of In Vitro Zika Virus Replication and Delays Disease Progression in a Robust Mouse Infection Model

Zika virus ( ZIKV ) , a mosquito-borne flavivirus , was first isolated from a febrile Rhesus monkey in the Zika Forest in Uganda in 1947 1 ., During the last 5 decades sporadic ZIKV infections of humans were reported in Gabon , Nigeria , Senegal , Malaysia , Cambodia and Micronesia 2 , 3 , 4 , leading to a benign febrile disease called Zika fever ., The latter is characterized by headache , maculopapular rash , fever , arthralgia , malaise , retro-orbital pain and vomiting 5 , 6 ., In 2007 , an epidemic of fever and rash associated with ZIKV infection was reported in Micronesia ., During this outbreak 185 cases of ZIKV infections were confirmed ., The seroprevalence in the affected region was 73% 7 ., During the more recent ZIKV outbreak in French Polynesia FP between October 2013 and February 2014 over 30 , 000 people sought medical care 8 , 9 ., Since then , ZIKV has spread to new areas in the Pacific , including New Caledonia , the Cook Islands , and Chile’s Easter Island 7 , 10 ., As of 2015 ZIKV is causing an epidemic in Central and South America with an increasing number of cases reported particularly in Brazil , Colombia and El Salvador 11–14 , demonstrating that this is a truly emerging human pathogen ., Hundreds of cases of Guillain-Barré syndrome have been reported in the wake of ZIKV infections 15 , 16 , 17 ., As a result of a marked increase in the number of cases of microcephaly among infants born to virus-infected women , Zika has been declared a public health emergency of national importance in Brazil 16 , 17 , 18 ., In addition , an increasing number of travelers returning sick from endemic regions were diagnosed with ZIKV 19–24 ., The Aedes aegypti mosquito , the primary vector for ZIKV transmission , is expanding in all ( sub- ) tropical regions of the world and was recently reported to be present in California , USA 25 ., There is neither a vaccine nor a specific antiviral therapy for the prevention or treatment of infections by ZIKV ., The increasing incidence of Zika fever stresses the need for both preventive and therapeutic measures ., We here report on the establishment of, ( i ) a panel of assays that allow to identify inhibitors of ZIKV replication as well as, ( ii ) a robust animal model of ZIKV infection with brain involvement ., The viral polymerase inhibitor 7-deaza-2’-C-methyladenosine ( 7DMA ) was identified as an inhibitor of in vitro ZIKV replication and was shown to reduce viremia and to delay the time to disease progression in virus-infected mice ., Ribavirin , 1- ( β-d-ribofuranosyl ) -1H-1 , 2 , 4-triazole-3-carboxamide ( Virazole; RBV ) was purchased from ICN Pharmaceuticals ( Costa Mesa , CA , USA ) ., 2’-C-methylcytidine ( 2’CMC ) and 7-deaza-2-C-methyl-D-adenosine ( 7DMA ) were purchased from Carbosynth ( Berkshire , UK ) ., Favipiravir ( 6-fluoro-3-hydroxy-2-pyrazinecarboxamide; T-705 ) and its defluorinated analogue T-1105 ( 3-hydroxypyrazine-2-carboxamide ) were obtained as custom synthesis products from abcr GmbH ( Karlsruhe , Germany ) ., ZIKV ( strain MR766 , passaged five times in the insect cell line C6/36 ) was obtained from the European Virus Archive ( EVA; http://www . european-virus-archive . com/viruses/zika-virus-strain-mr766 ) ., Lyophilized virus was reconstituted in DMEM and virus stocks were generated on C6/36 mosquito cell cultures ( ATCC CRL-1660 ) grown in Leibowitz medium supplemented with 10% fetal calf serum ( FCS ) , 1% non-essential amino acids ( NEAA ) and 20 nM HEPES at 28°C , without CO2 ., At the time ZIKV caused a complete cytopathic effect ( CPE ) d5-d7 post infection; pi the supernatant was harvested and viral titers were determined by endpoint titration on Vero cells ( African Green monkey kidney cells; ECACC ) , Vero E6 cells ( Vero C1008; ATCC CRL-1586 ) and BHK-J21 cells ( baby hamster kidney cells; ATCC CCL-10 ) ., For end point titrations , cells were seeded in a 96-well plate at 5×103 or 104 cells/well in 100 μL assay medium and allowed to adhere overnight ., The next day , 100 μL of ZIKV was added to each well , after which the virus was serially diluted ( 1:2 ) ., Following 5 days of incubation , culture medium was discarded and replaced with ( 3- ( 4 , 5-dimethylthiazol-2-yl ) -5- ( 3-carboxymethoxyphenyl ) -2- ( 4-sulfophenyl ) -2H-tetrazolium; MTS ) and the absorbance was measured at 498 nm following a 1 . 5h-incubation period ., Subsequently , cultures were fixed with ethanol and stained with 1% Giemsa staining solution ( solution of azure B/azure II-eosin/methylene blue 1:12:2 ( w/w/w ) in glycerol/methanol 5:24 ( v/v ) ; total dye content: 0 . 6% ( w/w ) Sigma-Aldrich ) ., The different cell types as well as ZIKV tested negative for mycoplasma ., Vero cells were grown in growth medium , consisting of MEM ( Life Technologies ) supplemented with 10% FCS , 2 mM L-glutamine and 0 . 075% sodium bicarbonate ( Life Technologies ) ., Antiviral assays were performed using the same medium except that 10% FCS was replaced with 2% FCS , referred to as ‘assay medium’ ., Vero cells were seeded at a density of 104 cells/well in a 96-well plate in 100 μL assay medium and allowed to adhere overnight ., To each well 100 μl of culture medium containing 50% cell culture infectious doses ( i . e . , CCID50 ) of ZIKV was added , after which 2-fold serial dilutions of the compounds were added ., Following 5 days of incubation CPE was determined by means of the MTS readout method and by microscopic evaluation of fixed and stained cells ., In parallel , cell cultures were incubated in the presence of compound and absence of virus to evaluate a potential cytotoxic effect ., The 50% effective concentration ( EC50 ) , which is defined as the compound concentration that is required to inhibit virus-induced CPE by 50% , and 50% cytotoxic concentration ( CC50 ) , which is defined as the compound concentration that is required to inhibit the cell growth by 50% , was visually determined ., The Z’ factor was calculated by the following formula 1-3× ( SDCC+SDVC ) / ( ODCC-ODVC ) ; VC , virus control; CC , cell control ., Vero cells were seeded at a density of 5×104 cells/well in 96-well plates in growth medium and allowed to adhere overnight ., Cells were washed 3 times with PBS and incubated with 100 μL CCID50 ( MOI~0 . 2 ) of ZIKV in assay medium for 1 h at 37°C ., Next , cells were washed 3 times with PBS and 2-fold serial dilutions of the compounds were added ., Supernatant was harvested at day 4 pi and stored at -80°C until further use ., The EC50 value , which is defined as the compound concentration that is required to inhibit viral RNA replication by 50% , was determined using logarithmic interpolation ., Vero cells were seeded at a density of 2×105 cells/well in 24-well plate in growth medium and allowed to adhere overnight ., Cells were washed twice with PBS and incubated with ZIKV at an MOI~1 in assay medium for 30 min at 37°C ., After the incubation , cells were washed twice with PBS , after which assay medium was added to the cells ., Cells were harvested at 0 , 4 , 6 , 8 , 10 , 12 , 14 , 16 , 18 , 20 , 22 and 24 hours pi and stored at -80°C until further use ., For the time-of-drug addition studies , cells were seeded and infected as described above and 7DMA ( 178 μM ) or ribavirin ( 209 μM ) was added to the medium at different time points pi ( see above ) ., Cells were harvested at 24 hours pi and stored at -80°C until further use ., Vero cells were cultured in growth medium ., Cells were incubated with ZIKV for 1 h , washed and overlaid with a mixture of 2% ( w/v ) carboxymethylcellullose ( Sigma Aldrich ) and MEM supplemented with 2% FCS , 4 mM L-glutamine and 0 . 15% sodium bicarbonate ., Two-fold serial dilutions of compounds were made in the overlay medium ., Cells were fixed and stained using a 10% v/v formaldehyde solution and a 1% methylene blue solution , respectively ., Infectious virus titer ( PFU/mL ) was determined using the following formula: number of plaques × dilution factor × ( 1/inoculation volume ) ., Vero cells were infected with ZIKV as described for the virus yield reduction assay ., After removal of the virus , 2-fold serial dilution ( starting at 89 μM ) of 7DMA was added to the cells ., At 72 h pi , cells were subsequently fixed with 2% paraformaldehyde in PBS and washed with PBS supplemented with 2% BSA ., Anti-Flavivirus Group Antigen Antibody clone D1-4G2-4-15 ( Millipore ) and goat anti-mouse Alexa Fluor 488 ( Life Technologies ) were used to detect ZIKV antigens in infected cells ., Cell nuclei were stained using DAPI ( 4 , 6-diamidino-2-fenylindool; Life Technologies ) and read out was performed using an ArrayScan XTI High Content Analysis Reader ( Thermo Scientific ) ., The EC50 value , which is defined as the compound concentration that is required to inhibit viral antigen expression by 50% , was determined using logarithmic interpolation ., RNA was isolated from 100–150 μl supernatant using the NucleoSpin RNA virus kit ( Filter Service , Germany ) according to the manufacturer’s protocol ., RNA from infected cells was isolated using the RNeasy minikit ( Qiagen , The Netherlands ) , according to the manufacturer’s protocol , and eluted in 50 μL RNase-free water ., During RT-qPCR the ZIKV NS1 region ( nucleotides 2472–2565 ) was amplified using primers 5’-TGA CTC CCC TCG TAG ACT G-3’ and 3’-CTC TCC TTC CAC TGA TTT CCA C-5’ and a Double-Quenched Probe 5’-6-FAM/AGA TCC CAC /ZEN/AAA TCC CCT CTT CCC/3’IABkFQ/ ( Integrated DNA Technologies , IDT ) ., Viral RNA was quantified using serial dilutions of a standard curve consisting of a synthesized gene block containing 145 bp of ZIKV NS1 ( nucleotides 2456–2603 ) : 5-GGT ACA AGT ACC ATC CTG ACT CCC CTC GTA GAC TGG CAG CAG CCG TTA AGC AAG CTT GGG AAG AGG GGA TTT GTG GGA TCT CCT CTG TTT CTA GAA TGG AAA ACA TAA TGT GGA AAT CAG TGG AAG GAG AGC TCA ATG CAA TCC TAG-3 ( Integrated DNA Technologies ) ., All experiments were performed with approval of and under the guidelines of the Ethical Committee of the University of Leuven P087-2014 ., Virus stock was produced as described earlier and additionally concentrated by ultracentrifugation ., Infectious virus titers ( PFU/ml ) were determined by performing plaque assays on Vero cells ., 129/Sv mice deficient in both interferon ( IFN ) -α/β and IFN-γ receptors ( AG129 mice; male , 8–14 weeks of age ) were inoculated intraperitoneally ( ip; 200 μL ) with different inoculums ranging from 1×101–1×105 PFU/mL of ZIKV ., Mice were observed daily for body weight change and the development of virus-induced disease ., In case of a body weight loss of >20% and/or severe illness , mice were euthanized with pentobarbital ( Nembutal ) ., Blood was collected by cardiac puncture and tissues ( spleen , kidney , liver and brain ) were collected in 2-mL tubes containing 2 . 8 mm zirconium oxide beads ( Precellys/Bertin Technologies ) after transcardial perfusion using PBS ., Subsequently , RLT lysis buffer ( Qiagen ) was added to the Precellys tubes and tissue homogenates were prepared using an automated homogenizer ( Precellys24; Bertin Technologies ) ., Homogenates were cleared by centrifugation and total RNA was extracted from the supernatant using the RNeasy minikit ( Qiagen ) , according to the manufacturer’s protocol ., For serum samples , the NucleoSpin RNA virus kit ( Filter Service ) was used to isolate viral RNA ., Viral copy numbers were quantified by RT-qPCR , as described earlier ., For histological examination , tissues ( harvested at d13-15 pi ) were subsequently fixed in 4% formaldehyde , embedded in paraffin , sectioned , and stained with hematoxylin-eosin , essentially as described before 26 ., Anti-Flavivirus Group Antigen Antibody , clone D1-4G2-4-15 ( Millipore ) was used to detect ZIKV antigens in tissue samples ., Induction of pro-inflammatory cytokines and chemokines was analyzed in 20 μL serum using the mouse cytokine 20-plex antibody bead kit ( ProcartaPlex Mouse Th1/Th2 & Chemokine Panel I EPX200-26090-901 ) , which measures the expression of TNF-α , IFN-γ , IL-6 , IL-18 , CCL2 ( MCP-1 ) , CCL3 ( MIP-1α ) , CCL4 ( MIP-1β ) , CCL5 ( RANTES ) , CCL7 ( MCP-3 ) , CCL11 ( Eotaxin ) , CXCL1 ( GRO-α ) , CXCL2 ( MIP-2 ) , CXCL10 ( IP-10 ) , GM-CSF , IL-1β , IL12p70 , IL-13 , IL-2 , IL-4 , and IL-5 ., Measurements were performed using a Luminex 100 instrument ( Luminex Corp . , Austin , TX , USA ) and were analyzed using a standard curve for each molecule ( ProcartaPlex ) ., Statistical analysis was performed using a one-way ANOVA ., AG129 mice ( male , 8–14 weeks of age ) were treated with either 50 mg/kg/day 7DMA resuspended in 0 . 5% or 0 . 2% sodium carboxymethylcellulose ( CMC‐Na; n = 9 ) or vehicle ( 0 . 5% or 0 . 2% CMC‐Na; n = 9 ) once daily ( QD ) via oral gavage for 10 consecutive days ., Since the bulk-forming agent CMC has dehydrating properties 27 , mice that received the drug ( or vehicle ) formulated with 0 . 5% CMC received ( on days 6–9 ) subcutaneous injections with 200 μL of saline ., One hour after the first treatment , mice were infected via the intraperitoneal route with 200 μL of a 1×104 PFU/ml stock of ZIKV ., Blood was withdrawn from the tails at different days pi ., Viral RNA was extracted from 20 μL of serum using the RNA NucleoSpin RNA virus kit ( Filter Service ) followed by viral RNA quantification by means of RT-qPCR ., Statistical analysis was performed using the Shapiro-Wilk normality test followed by the unpaired , two-tailed t-test in Graph Pad Prism6 ., Inter-group survival was compared using the Log-rank ( Mantel-Cox ) test ., The in vivo efficacy of 7DMA was determined in two independent experimental animal studies ., Evaluation of cytokine induction was performed using the ProcartaPlex Mouse Simplex IP-10 ( CXCL10 ) , TNF-α , IL-6 and IL-18 kits ., In an additional animal study , AG129 mice ( male , 8–14 weeks of age ) were treated with 50 mg/kg/day 7DMA resuspended in 0 . 2% sodium carboxymethylcellulose ( CMC‐Na; n = 6 ) or vehicle ( 0 . 2% CMC‐Na; n = 6 ) once daily ( QD ) via oral gavage for 5 successive days ( starting 2 days prior to infection ) and infected ip with 200 μL of a 1×104 PFU/ml stock of ZIKV ., Animals were euthanized at day 5 pi and testicles were collected and stored until further use ., End point titrations in different cell lines revealed that Vero cells are highly permissive to ZIKV , hence , these cells were selected to establish antiviral assays ., Infection with 100×TCID50 of ZIKV resulted in 100% cytopathic effect 5 days after infection ( S1B Fig ) , as assessed by microscopic evaluation as well as by the MTS readout method ., The Z’ factor ( a measure of statistical effect size to assess the quality of assays to be used for high-throughput screening purposes; 28 ) of the CPE-reduction assay was 0 . 68 based on 64 samples ( from 8 independent experiments ) determined by the MTS readout method ( S1C Fig ) ., The assay is thus sufficiently stringent and reproducible for high throughput screening purposes ( see also S2 Fig ) ., The CPE-reduction assay was next employed to evaluate the potential anti-ZIKV activity of a selection of known ( + ) ssRNA virus inhibitors ( i . e . 2’-C-methylcytidine , 7-deaza-2-C-methyladenosine , ribavirin , T-705 and its analogue T-1105 ) ., All compounds resulted in a selective , dose-dependent inhibitory effect on ZIKV replication ( Table 1 ) ., The antiviral effect of these compounds was confirmed in a virus yield reduction assay , a1 . 7log10 and 3 . 9log10 reduction in viral RNA load at a concentration of 22 μM and ≥45 μM , respectively , was noted ( Table 1 and Fig 1A ) ., Since 7DMA resulted in the largest therapeutic window ( SI > 37; Table 1 ) , the antiviral activity of this compound was therefore next assessed in a plaque reduction assay and in an immunofluorescence assay to detect viral antigens ., The inhibitory effect of the compound in both assays was in line with those of the CPE-reduction and virus yield reduction assay ( Table 1 , Fig 1A ) ., At a concentration of 11 μM , 7DMA almost completely blocked viral antigen expression ( Fig 1B , left panel ) ., 7DMA is , as its 5’-triphosphate metabolite , an inhibitor of viral RNA-dependent RNA polymerases ., Addition of the compound to infected cells could be delayed until ~10 hours pi without much loss of antiviral potency; when first added at a later time point , the antiviral activity was gradually lost ., This is line with the observation that onset of intracellular ZIKV RNA production was determined to occur at 10 to 12 hours pi ( Fig 2 ) ., The reference compound ribavirin ( a triazole nucleoside with multiple proposed modes of action; 29 ) , in contrast , already lost part of the antiviral activity when added at time points later than 4 hours pi ( Fig 2 ) ., Intraperitoneal inoculation of IFN-α/β and IFN-γ receptor knockout mice ( AG129 ) with as low as 200 μL of a 1×101 PFU/ml stock of ZIKV resulted in virus-induced disease ( see below ) and mice had to be euthanized at a MDE ( mean day of euthanasia ) of 18 . 5 days pi ( Fig 3A ) ., Infection with higher inoculums ( 1×102–1×105 PFU/ml; 200 μL ) resulted in a faster progression of the disease ( MDE of 14 days pi ) with the first signs of disease appearing at day 10 pi ., Surprisingly , inoculation of SCID mice with 200 μL of a 1×104 PFU/ml stock of ZIKV resulted in delayed disease; SCID mice had to be euthanized at day 40 . 0 ± 4 . 4 pi , roughly 26 days later than AG129 mice ( S3 Fig ) ., Disease signs in AG129 mice included paralysis of the lower limbs , body weight loss , hunched back and conjunctivitis ., High levels of viral RNA were detected in brain , spleen , liver and kidney from mice that were euthanized at day 13–15 pi ( Fig 3B ) ., Histopathological analysis on tissues collected at day 13–15 pi revealed the accumulation of viral antigens in neurons of both the brain ( Fig 4A ) and the spinal cord ( Fig 4D ) as well as in hepatocytes ( Fig 4E ) ., Acute neutrophilic encephalitis ( Fig 4C ) was observed at the time of onset of virus-induced morbidity ., It was next assessed whether infection with ZIKV resulted in the induction of a panel of 20 cytokines and chemokines at different time points pi ( day 2 , 3 , 4 and 8; Figs 3C and 3D and S4A–S4G ) ., In particular , levels of IFN-γ and IL-18 were increased systematically and significantly during the course of infection ( Fig 3C and 3D ) , whereas levels of IL-6 , CCL2 , CCL5 , CCL7 , CXCL1 , CXCL10 and TNF-α first increased , reaching a peak level at day 3 pi ( CCL2 , CXCL1 , TNF-α; S4A–S4C Fig ) or day 4 pi ( IL-6 , CCL7 , CXCL10; S4D–S4F Fig ) pi and then gradually declined ., Levels of CCL5 subsequently increased at day 2 pi , dropped at day 3 pi , and gradually increased again at day 4 and 8 pi ( S4G Fig ) ., AG129 mice were infected with 200 μL of a 1×104 PFU/ml stock of ZIKV and were treated once daily with 50 mg/kg/day of 7DMA or vehicle via oral gavage ( Fig 5 ) data from the two independent experiments were not pooled since different amounts of CMC ( respectively 0 . 5% and 0 . 2% ) were used for formulation ., Vehicle-treated mice had to be euthanized two weeks after infection MDE of 14 . 0 and 16 . 0 days , respectively ., 7DMA was well tolerated no marked changes in body weight mass , fur , consistency of the stool or behavior during the treatment period and markedly delayed virus-induced disease progression MDE of 23 . 0 in the first study ( p = 0 . 003 as compared to the control ) and 24 . 0 in the second study ( p = 0 . 04 as compared to the control ) ( Fig 5A ) ., 7DMA also reduced the viral RNA load in the serum of infected mice by 0 . 5log10 , 0 . 8log10 , 0 . 9log10 , 0 . 7log10 and 1 . 3log10 , respectively , at day 3 , 5 , 6 , 7 and 8 pi ( Fig 5B ) ., Interestingly , at day 5 pi high levels of viral RNA ( 6 . 4log10 ) were found in the testicles of vehicle-treated mice ( Fig 5C ) ., At day 8 pi ( shortly before the onset of disease in the vehicle controls ) , levels of IFN-γ in the serum were significantly higher in vehicle than in drug-treated mice ( Fig 5D ) ., The rapid geographical spread of ZIKV , particularly in Central and South America poses a serious public health concern given that infection with this virus is less benign than initially thought ., Hundreds of patients have been reported with Guillain-Barré syndrome 16 , 17 ., Most importantly , in Brazil a dramatic upsurge in the number of cases of microcephaly has been noted in children born to mothers infected with ZIKV ., The annual rate of microcephaly in Brazil has increased from 5 . 7 per 100 000 live births in 2014 to 99 . 7 per 100 000 in 2015 16 , 17 , 18 ., There is , hence , an urgent need to develop preventive and counteractive measures against this truly neglected flavivirus member ., We here report on the establishment of, ( i ) in vitro assays that will allow to identify novel inhibitors of ZIKV replication and, ( ii ) a ZIKV infection model in mice in which the potential efficacy of such inhibitors can be assessed ., ZIKV was found to replicate efficiently in Vero cells and to produce full CPE within a couple of days ., The Z’ factor that was calculated for a colorometric ( MTS method ) CPE-based screen indicated that this is a robust assay that is amenable for high-throughput screening purposes ., A plaque reduction , an infectious virus yield and a viral RNA yield reduction assay as well as an immunofluorescent antigen detection assay were established that will allow to validate the in vitro activity of hits identified in CPE-based screenings ., Productive infection of human dermal fibroblasts , epidermal keratinocytes and immature dendritic cells with the ZIKV has recently been reported 30 ., However , Vero cells may be ideally suited for high throughput screening purposes , making these cells most useful to confirm the antiviral activity of interesting inhibitors of viral replication ., We employed the assays that we established to assess the potential anti-ZIKV activity of a number of molecules with reported antiviral activity against other ssRNA viruses ., In particular , the nucleoside analogue 7DMA was identified to inhibit ZIKV replication with a potency that was more or less comparable between the different in vitro assays ., 7DMA was originally developed by Merck Research Laboratories as an inhibitor of hepatitis C virus replication 31 , but was also shown to inhibit the replication of multiple flaviviruses , i . e . dengue virus , yellow fever virus as well as West Nile and tick-borne encephalitis virus with EC50 values ranging between 5 and 15 μM , which is thus comparable to the EC50 values for inhibition of in vitro ZIKV replication 31 , 32 , 33 ., In line with its presumed mechanism of action , i . e . inhibition as its 5’-triphosphate of the viral RNA-dependent RNA polymerase , time-of-drug-addition experiments revealed that the compound acts at a time point that coincides with the onset of intracellular viral RNA replication ., To assess the in vivo efficacy of ZIKV inhibitors , we established a model of ZIKV infection in mice ., AG129 mice proved highly susceptible to ZIKV infections; even an inoculum of ~10 PFU/ml resulted in virus induced-morbidity and mortality ., Although ZIKV-infected SCID mice ( deficient in both T and B lymphocytes ) developed severe disease requiring euthanasia ( paralysis of the lower limbs , body weight loss , hunched back ) , these mice were more resistant to ZIKV infection than AG129 mice ., SCID mice succumbed to infection roughly 26 days later than AG129 mice when inoculated with the same viral inoculum ., Thus , ZIKV infections in mice are mostly controlled by the interferon response rather than by lymphocytes , indicating that the innate immune response to ZIKV is critical ., AG129 mice have been shown to be highly susceptible to infection with other flaviviruses; in particular allowing the development of dengue virus infection models in mice 32 , 34 , 35 ., At the time of virus-induced morbidity and mortality , ZIKV was detected in multiple organs such as kidney , liver and spleen , but also in the brain and spinal cord ., The latter is in line with the observation that infected mice developed acute neutrophilic encephalitis with movement impairment and paralysis of the limbs ., Brain involvement in ZIKV-infected mice may be relevant for brain-related pathologies in some ZIKV-infected humans 16 , 17 ., Interestingly , the virus was also detected at high levels in the testicles of infected mice ., A few cases of sexual transmission of the ZIKV in humans have been reported 36 , 37; the observation that the virus replicates in the testicles in mice may suggest that the virus can also replicate in human testicle tissue thus explaining sexual transmission ., Pro-inflammatory cytokines ( IFN-γ , IL-18 , IL-6 , TNF-α ) and chemokines ( CCL2 , CCL5 , CCL7 , CXCL1 , CXCL10 ) were found to be increased in sera of ZIKV-infected mice , indicating that infection causes systemic inflammation ., In particular IFN-γ and IL-18 were continuously increased during the course of infection; both cytokines could therefore potentially function as predictive markers of disease progression and disease severity in this mouse model ., Whether these cytokines are also upregulated during the acute phase of the infection in humans remains to be studied ., Of note , the fact that ZIKV infection leads to the production of IL-18 suggests that the inflammasome is activated during the course of infection ., Surprisingly , we could detect increased levels of IL-18 , but not of IL-1β , which is also produced upon activation of the inflammasome 38 ., To our knowledge , the observation that the inflammasome could be implicated in ZIKV infection is unprecedented ., Recently , a small study was reported involving 6 ZIKV-infected patients in which during the acute phase 11 cytokines/chemokines were found to be significantly increased , of which 7 were also increased during recovery 39 ., Despite the fact that immunocompromised AG129 mice have an altered cytokine metabolism and were infected with the prototype ZIKV MR766 strain belonging to a different lineage than the one infecting the Latin American patients ( African versus Asian , respectively ) , similarities in cytokine expression were noted between both studies ., IL-6 , CCL5 and CXCL10 were significantly increased in ZIKV-infected patients as well as in the infected mice ., In the ZIKV-infected patients IFN-γ levels , which were markedly increased in ZIKV-infected mice , were also increased during both the acute and the reconvalescent phase of the infection , albeit non-significantly ., Likewise , TNF-α levels , which were increased early in infection in mice , were ( non-significantly ) increased during the acute phase of infection in the patients ., More studies are necessary to assess whether the cytokine profile in these 6 patients is representative for larger groups ., Treatment of ZIKV-infected mice with 7DMA significantly reduced viremia ( between day 3 and 8 post infection ) and delayed virus-induced morbidity and mortality ., The compound was very well tolerated in mice , which is in line with earlier reports 31 ., The reduction in viremia and , hence , the delay of virus-induced disease was relatively modest , which is not surprising given the relatively weak in vitro activity of the compound as compared to , for example , the EC50 values ( sub μM or even nM range ) of most HCV inhibitors ., Most importantly , the use of this compound allowed to validate the ZIKV mouse model to assess the efficacy of ZIKV inhibitors ., Whether 7DMA ( or related analogues ) may have future in the control of ZIKV infections remains to be explored ., AG129 mice have been used as well in the development of DENV vaccines , the DENV AG120 mouse models offer multiple disease parameters to evaluate protection by candidate vaccines 40 ., Hence , the ZIKV mouse model presented here may also serve to study the efficacy of vaccine strategies against the ZIKV ., In conclusion , we here report on a panel of in vitro cellular assays that will allow to run large-scale antiviral screening campaigns against ZIKV and to validate the antiviral activity of hit compounds ., A number of molecules , including the viral polymerase inhibitor 7DMA , were found to inhibit the in vitro replication of ZIKV ., Hence , 7DMA can be used as a reference compound/comparator in future studies ., Moreover , a robust ZIKV mouse infection model was established; 7DMA delayed virus-induced mortality and , hence , validates this model for antiviral studies ., Moreover , the model may be useful to study the efficacy of vaccination strategies against the ZIKV .

Introduction, Materials and Methods, Results, Discussion

Zika virus ( ZIKV ) is an emerging flavivirus typically causing a dengue-like febrile illness , but neurological complications , such as microcephaly in newborns , have potentially been linked to this viral infection ., We established a panel of in vitro assays to allow the identification of ZIKV inhibitors and demonstrate that the viral polymerase inhibitor 7-deaza-2’-C-methyladenosine ( 7DMA ) efficiently inhibits replication ., Infection of AG129 ( IFN-α/β and IFN-γ receptor knock-out ) mice with ZIKV resulted in acute neutrophilic encephalitis with viral antigens accumulating in neurons of the brain and spinal cord ., Additionally , high levels of viral RNA were detected in the spleen , liver and kidney , and levels of IFN-γ and IL-18 were systematically increased in serum of ZIKV-infected mice ., Interestingly , the virus was also detected in testicles of infected mice ., In line with its in vitro anti-ZIKV activity , 7DMA reduced viremia and delayed virus-induced morbidity and mortality in infected mice , which also validates this small animal model to assess the in vivo efficacy of novel ZIKV inhibitors ., Since AG129 mice can generate an antibody response , and have been used in dengue vaccine studies , the model can also be used to assess the efficacy of ZIKV vaccines .

A robust cell-based antiviral assay was developed that allows to screen for and validate novel inhibitors of Zika virus ( ZIKV ) replication ., The viral polymerase inhibitor 7-deaza-2’-C-methyladenosine ( 7DMA ) was identified as a potent ZIKV inhibitor ., A mouse model for ZIKV infections , which was validated for antiviral studies , demonstrated that 7DMA markedly delays virus-induced disease in this model .

vero cells, innate immune system, medicine and health sciences, immune physiology, cytokines, pathology and laboratory medicine, pathogens, animal models of disease, biological cultures, rna extraction, microbiology, immunology, animal models, viruses, developmental biology, model organisms, rna viruses, molecular development, extraction techniques, research and analysis methods, animal models of infection, animal studies, medical microbiology, microbial pathogens, mouse models, viral replication, cell lines, immune system, flaviviruses, virology, viral pathogens, physiology, biology and life sciences, organisms, zika virus

journal.pgen.1007019

Incomplete dominance of deleterious alleles contributes substantially to trait variation and heterosis in maize

Understanding the genetic basis of phenotypic variation is critical to many biological endeavors from human health to conservation and agriculture ., Although most new mutations are likely deleterious 1 , their importance in patterning phenotypic variation is controversial and not well understood 2 ., Empirical work suggests that , although the long-term burden of deleterious variants is relatively insensitive to demography 3 , population bottlenecks and expansion may lead to an increased abundance of deleterious alleles over shorter time scales such as those associated with domestication 4 , postglacial colonization 5 or recent human migration 6 ., Even when the impacts on total load are minimal , demographic change may have important consequences for the contribution of deleterious variants to phenotypic variation 3 , 7–9 ., Together , these considerations point to a potentially important role for deleterious variants in determining patterns of phenotypic variation , especially for traits closely related to fitness ., In addition to its global agricultural importance , maize has long been an important genetic model system 10 and central to debates about the basis of hybrid vigor and the role of deleterious alleles 11 , 12 ., The maize domestication bottleneck has lead to an increased burden of deleterious alleles in maize compared to its wild ancestor teosinte 13 , and rapid expansion following domestication likely lead to an increase in new mutations and stronger purifying selection 4 ., More recently , modern maize breeding has lead to dramatic reductions in effective population size 14 , but inbreeding during the development of modern inbred lines may have decreased load by purging recessive deleterious alleles 15 ., Nonetheless , substantial evidence suggests an abundance of deleterious alleles present in modern germplasm , from the observed maintenance of heterozygosity during the processes of inbreeding 16 , 17 and selection 18 to genome-wide association results that reveal an excess of associations with genes segregating for damaging protein-coding variants 19 ., Modern maize agriculture takes advantage of hybrid maize plants that result from the cross between two parental inbred lines 12 ., These crosses result in a phenomenon known as hybrid vigor or heterosis , in which the hybrid plant shows improved agronomic qualities compared to its parents ., Heterosis cannot be easily predicted from parental phenotype alone , and the genetic underpinnings of heterosis remain largely unknown ., The most straightforward explanation for heterosis has been simple complementation of recessive deleterious alleles that are homozygous in one of the inbred parents 20 , 21 ., While this model is supported by considerable empirical evidence 22 , 23 , it fails in its simplest form to explain a number of observations such as heterosis and inbreeding depression in polyploid plants 11 , 24 , 25 ., Other explanations , such as single-gene heterozygote advantage , clearly may play an important role in some cases 26 , 27 , but mapping studies suggest such models are not easily generalizable 28 ., In this study , we set out to investigate the contribution of deleterious alleles to phenotypic variation and hybrid vigor in maize ., We created a partial diallel population from 12 maize inbred lines which together represent much of the ancestry of present-day commercial U . S . corn hybrids 29 , 30 ., We measured a number of agronomically relevant phenotypes in both parents and hybrids , including flowering time ( days to 50% pollen shed , DTP; days to 50% silking , DTS; anthesis-silking interval , ASI ) , plant size ( plant height , PHT; height of primary ear , EHT ) , grain quality ( test weight which is a measure of grain density , TW ) , and grain yield ( GY ) ., We conducted whole genome sequencing of the parental lines and characterized genome-wide deleterious variants using genomic evolutionary rate profiling ( GERP ) 31 ., We then test models of additivity and dominance for each phenotype using putatively deleterious variants and investigate the relationship between dominance and phenotypic effect size and the long-term fitness consequences of a mutation as measured by GERP ., Finally , we take advantage of a Bayesian genomic selection framework 32 approach to explicitly test the utility of including GERP scores in phenotypic prediction for hybrid traits and heterosis ., We formed a partial diallel population from the F1 progeny of 12 inbred maize lines ( S1 Table , S1 Fig ) ., Field performance of the 66 F1 hybrids and 12 inbred parents were evaluated along with two current commercial check hybrids in Urbana , IL over three years ( 2009-2011 ) in a resolvable incomplete block design with three replicates ., To avoid competition effects , inbreds and hybrids were grown in different blocks within the field ., Plots consisted of four rows ( 5 . 3 m long with row spacing of 0 . 76 m at a plant density of 74 , 000 plants ha−1 ) , with all observations taken from the inside two rows to minimize effects of shading and maturity differences from adjacent plots ., We measured plant height ( PHT , in cm ) , height of primary ear ( EHT , in cm ) , days to 50% pollen shed ( DTP ) , days to 50% silking ( DTS ) , anthesis-silking interval ( ASI , in days ) , grain yield adjusted to 15 . 5% moisture ( GY , in bu/A ) , and test weight ( TW , weight of 1 bushel of grain in pounds ) ., We estimated Best Linear Unbiased Estimates ( BLUEs ) of the genetic effects in ASReml-R ( VSN International ) with the following linear mixed model:, Y i j k l = μ + ς i + δ i j + β k i j + α l + ς i · α l + ε, where Yijkl is the phenotypic value of the lth genotype evaluated in the kth block of the jth replicate within the ith year; μ , the overall mean; ςi , the fixed effect of the ith year; δij , the random effect of the jth replicate nested within the ith year; βkij , the random effect of the kth block nested within the ith year and jth replicate; αl , the fixed genetic effect of the lth individual; ςi · αl , the random interaction effect of the lth individual with the ith year; and ε , the model residuals ., We calculated the broad sense heritability ( H2 ) of traits based on the analysis of all individuals ( inbred parents , hybrid progeny , and checks ) following the equation:, H 2 = V G / ( V G + V G × E / i + V E / ( i × j ) ), where i = 3 ( number of years ) and j = 3 ( number of replicates per year ) ., The BLUE values for each cross can be found in S1 Table; values across all hybrids were relatively normally distributed for all traits ( Shapiro-Wilk normality tests P values >0 . 05 , S1 Fig ) , though some traits were highly correlated ( e . g . Spearman correlation r = 0 . 98 for DTS and DTP , S2 Fig ) ., We estimated mid-parent heterosis ( MPH ) as:, M P H i j = G ^ i j − m e a n ( G ^ i , G ^ j ), where G ^ i j , G ^ i and G ^ j are the BLUE values of the hybrid and its two parents i and j ., Note that for ASI , lower trait values are considered superior ., General combining ability ( GCA ) was estimated following Falconer and Mackay 33 , and the estimated values can be found in S2 Table ., We extracted DNA from the 12 inbred lines following 34 and sheared the DNA on a Covaris ( Woburn , Massachusetts ) for library preparation ., Libraries were prepared using an Illumina paired-end protocol with 180 bp fragments and sequenced using 100 bp paired-end reads on a HiSeq 2000 ., Raw sequencing data are available at NCBI SRA ( PRJNA381642 ) ., We trimmed raw sequence reads for adapter contamination with Scythe ( https://github . com/vsbuffalo/scythe ) and for quality and sequence length ( ≥20 nucleotides ) with Sickle ( https://github . com/najoshi/sickle ) ., We mapped filtered reads to the maize B73 reference genome ( AGPv2 ) with bwa-mem 35 , keeping reads with mapping quality higher than 10 and with a best alignment score higher than the second best one for further analyses ., We called single nucleotide polymorphisms ( SNPs ) using the mpileup function from samtools 36 ., To deal with known issues with paralogy in maize 15 , SNPs were filtered to be heterozygous in fewer than 3 inbred lines , have a mean minor allele depth of at least 4 , have a mean depth across all individuals less than 30 and have missing alleles in fewer than 6 inbred lines ., Data on the total number of SNPs called and the rate of missing data per line are shown in S3 Table ., We estimated the allelic error rate using three independent data sets: for all individuals using 41 , 292 overlapping SNPs from the maize SNP50k bead chip 14; for all individuals using 180 , 313 overlapping SNPs identified through genotyping-by-sequencing ( GBS ) 37; and for B73 and Mo17 using 10 , 426 , 715 SNP from the HapMap2 project 15 ., Alignments and genotypes for each of the 12 inbreds are available at CyVerse ( https://doi . org/10 . 7946/P2WS60 ) ., Because these parents are highly inbred , knowing their homozygous genotype also allows us to know the genotype of the F1 derived from any two of the parents ., To test whether alignment to the B73 reference introduces a bias in relatedness estimation , we computed kinship matrices using both our SNP data as well as genotyping-by-sequencing data ( version AllZeaGBSv2 . 7 downloaded from ( www . panzea . org ) ) obtained from alignments to a set of sequencing reads ascertained from a broad germplasm base 38 ., The two matrices were nearly identical ( Pearson’s correlation coefficient r = 0 . 995 ) , suggesting the degree of relatedness among lines is not sensitive to using B73 as the reference genome ., We used genomic evolutionary rate profiling ( GERP ) 39 estimated from a multi-species whole-genome alignment of 13 plant genomes 40 including Zea mays , Coelorachis tuberculosa , Vossia cuspidata , Sorghum bicolor , Oryza sativa , Setaria italica , Brachypodium distachyon , Hordeum vulgare , Musa acuminata , Populus trichocarpa , Vitis vinifera , Arabidopsis thaliana , and Panicum virgatum; the alignment and estimated GERP scores are available at CyVerse ( https://doi . org/10 . 7946/P2WS60 ) ., We define “GERP-SNPs” as the subset of SNPs with GERP score >0 , and at each SNP we assign the minor allele in the multi-species alignment as the likely deleterious allele ., Finally , we predicted the functional consequences of GERP-SNPs based on genome annotation information obtained from SnpEff 41 ., The multi-species alignment made use of the B73 AGPv3 assembly , and to ensure consistent coordinates , we ported our SNP coordinates from AGPv2 to AGPv3 using the Gramene assembly converter ( http://ensembl . gramene . org/Zea_mays/Tools/AssemblyConverter ? db=core ) ., To compare GERP scores ( for all SNPs with GERP > 0 ) to recombination rate and allele frequencies , we obtained the NAM genetic map 42 from the Panzea website ( http://www . panzea . org/ ) and allele frequencies from the > 1 , 200 maize lines sequenced as part of HapMap3 . 2 43 ., To compare the burden of deleterious alleles in modern inbred lines to landraces , we extracted genotypic data of 23 specially-inbred traditional landrace cultivars ( see 15 for more details ) from HapMap3 . 2 ., For each line , we calculated burden as the count of minor alleles present across all GERP-SNPs divided by the total number of non-missing sites ., We separated sites into fixed ( present in all individuals of a group ) and segregating for landrace and modern maize samples separately ., We estimated the additive and dominant effects of individual GERP-SNPs using a GBLUP model 44 implemented in GVCBLUP 45:, Y i = μ + ∑ j = 1 n X i j α j + ∑ j = 1 n W i j d j + ε, where Yi is the BLUE value of the ith hybrid , μ is the average genotypic value , αj is the allele substitution effect of the jth GERP-SNP , dj is the dominant effect of the jth GERP-SNP , Xij = {2p , 2p − 1 , 2p − 2} , ε is the model residuals , and Wij = {−2p2 , 2p ( 1 − p ) , −2 ( 1 − p ) 2} are the genotype encodings for genotypes A1 A1 , A1 A2 , and A2 A2 in the ith hybrid for the jth GERP-SNP with p of the A1 allele ., The additive and dominance SNP encoding ensures that the effects are independent for a given GERP-SNP ., We extracted additive ( a = α − 2p ( 1 − p ) d ) and dominant ( d ) effects from the GVCBLUP output file ( see supplemantary file of Da et al . , 44 for more details ) ., We first estimated the total variance explained under models of complete additivity ( d = 0 ) or complete dominance ( α = 0 ) ., Then , to assess correlations between SNP effects and GERP scores , we calculated the degree of dominance ( k = d/a ) 46 for SNPs that each explained greater than the genome-wide mean per-SNP variance ( total variance explained divided by total number of GERP-SNPs ) ., Because this approach can lead to very large absolute values of k , we truncated GERP-SNPs with |k = d/a|>2 for all further analyses ., To compare the variance explained by our model to that explained by random SNPs , we used a 2-dimensional sampling approach to create 10 equal-sized datasets of randomly sampled SNPs ( including SNPs with GERP score < = 0 ) matched for allele frequency ( in bins of 10% ) and recombination rate ( in quartiles of cM/Mb ) ., For each dataset we fit the above model separately and estimated SNP effects and phenotypic variance explained by each SNP ., To test the relationship between GERP score and dominance under a simple model of mutation-selection equilibrium , we estimated the selection coefficient s by assuming that yield is a measure of fitness ., We assigned the yield-increasing allele at each GERP-SNP a random dominance value in the range of 0 ≥ k ≥ 1 and calculated its equilibrium allele frequency p under mutation-selection balance using p=μs for values of k > 0 . 98 and p = 2 μ k + 1 for k ≤ 0 . 98 ., We then simulated datasets using binomial sampling to choose SNPs in a sample of size n = 12 inbreds ., We imputed missing data and identified regions of identity by descent ( IBD ) between the 12 inbred lines using the fastIBD method implemented in BEAGLE 47 ., We then defined haplotype blocks as contiguous regions within which there were no IBD break points across all pairwise comparisons of the parental lines ( S3 Fig ) ., Haplotype blocks at least 1 Kb in size were kept for further analyses ., Because there is no recombination in an inbred parent , this allows us to project the diploid genotype of each F1 based on the haplotypes of the two parents ., In the projected diploid genotype of each F1 , haplotype blocks were weighted by the summed GERP scores of all GERP-SNPs ( python script ‘gerpIBD . py’ available at https://github . com/yangjl/zmSNPtools ) ; blocks with no SNPs with positive GERP scores were excluded from further analysis ., For a particular SNP with a GERP score g , the homozygote for the conserved ( major ) allele was assigned a value of 0 , the homozygote for the putatively deleterious allele a value of 2g , and the heterozygote a value of ( 1 + k ) × g , where k is the dominance estimated from the GBLUP model above ., We used the BayesC option from GenSel4 32 for genomic selection model training with 41 , 000 iterations ., We removed the first 1 , 000 iterations as burn-in ., We used the model, Y i = μ + ∑ j = 1 n r j I i j + ε, where Yi is the BLUE value of the ith hybrid , rj is the regression coefficient for the jth haplotype block , and Iij is the sum of GERP scores under an additive , dominance or incomplete dominance model for the ith hybrid in the jth haplotype block ., We used a 5-fold cross-validation method to conduct prediction , dividing the diallel population randomly into training ( 80% ) and validation sets ( 20% ) 100 times ., After model training , we obtained prediction accuracies by comparing the predicted breeding values with the observed BLUE values in the corresponding validation sets ., For comparison , we permuted GERP scores using 50k SNP ( ≈ 100Mb or larger ) windows which were circularly shuffled 10 times to estimate a null conservation score for each IBD block ., We conducted permutations on all GERP-SNPs as well as on a restricted set of GERP-SNPs only in genic regions to control for GERP differences between genic ( N = 221 , 960 ) and intergenic regions ( N = 123 , 216 ) ., We conducted permutation cross-validation experiments using the same training and validation sets ., We estimated the posterior phenotypic variance explained using all of the data to derive correlations between breeding values estimated from the prediction model and observed BLUE values ., Note that the correlation used here is different from the prediction accuracy ( r ) used for the cross-validation experiments , where the latter is defined as the correlation between real and estimated values; the two statistics will converge to the same value when there is no error in SNP/haplotype effect estimation 48 ., Finally , to compare our genomic prediction model to a classical model of general combining ability , we used the following equations:, Y i j = μ + G C A i + G C A j + ε Y i j = μ + G C A i + G C A j + G i j + ε, where Yij is the BLUE value of the hybrid of the ith and jth inbreds , μ is the overall mean , GCAi and GCAj are the general combining abilities of the ith and jth inbreds , Gij is the breeding value of the hybrid of the ith and jth inbreds as estimated by our genomic prediction model , and ε the model residuals ., Sequencing data have been deposited in NCBI SRA ( SRP103329 ) database , and code for all analyses are available in the public GitHub repository ( https://github . com/yangjl/GERP-diallel ) ., We created a partial diallel population from 12 maize inbred lines which together represent much of the ancestry of present-day commercial U . S . corn hybrids ( S1 Table ) 29 , 30 ., We measured a number of agronomically relevant phenotypes in both parents and hybrids , including flowering time ( days to 50% pollen shed , DTP; days to 50% silking , DTS; anthesis-silking interval , ASI ) , plant size ( plant height , PHT; height of primary ear , EHT ) , test weight ( TW; a measure of quality based on grain density ) , and grain yield ( GY ) ., In an agronomic setting GY—a measure of seed production per unit area—is the primary trait selected by breeders and thus analogous to fitness ., Plant height and ear height , both common measures of plant health or viability , were significantly correlated to GY ( S2 Fig ) ., For each genotype we derived best linear unbiased estimators ( BLUEs ) of its phenotype from mixed linear models ( S1 Table ) to control for spatial and environmental variation ( see Methods ) ., We estimated mid-parent heterosis ( MPH , Fig 1a ) for each trait as the percent difference between the hybrid compared to the mean value of its two parents ( see Methods , S1 Table ) ., Consistent with previous work 28 , we find that grain yield ( GY ) showed the highest level of heterosis ( MPH of 182% ± 60% ) ., While flowering time ( DTS and DTP ) is an important adaptive phenotype globally 49 , it showed relatively little heterosis in this study , likely due to the relatively narrow geographic range represented by the parental lines ., We resequenced the 12 inbred parents to an average depth of ≈ 10× , resulting in a filtered set of 13 . 8M SNPs ., Compared to corresponding SNPs identified by previous studies ( see Methods ) , we observed a mean genotypic concordance rate of 99 . 1% ., In order to quantify the deleterious consequences of variants a priori , we made use of Genomic Evolutionary Rate Profiling ( GERP ) 39 scores of the maize genome 50 ., GERP scores provide a quantitative measure of the evolutionary conservation of a site across a phylogeny that allows characterization of the long-term fitness consequences of both coding and noncoding positions in the genome 51 ., Sites with more positive GERP scores are inferred to be under stronger purifying selection , and SNPs observed at such sites are thus inferred to be more deleterious ., At each site with GERP scores > 0 ( hereafter called GERP-SNPs ) , we designated the minor allele from the multispecies alignment as putatively deleterious ., Of the 350k total segregating GERP-SNPs in our parental lines , 14% are detected in coding regions , equally split between synonymous ( N = 64 , 439 ) and non-synonymous ( N = 65 , 376 ) sites ( S4 Table ) ., Each line carries , on average , 139k potential deleterious SNPs , 19k of which are in coding regions ( S5 Table ) ., The reference genome B73 contains only ≈ 1/3 of the deleterious SNPs of the other parents , likely due to reference bias in identifying deleterious variants ., The F1 hybrids of the diallel each contain an average of ≈ 56 , 000 homozygous deleterious SNPs , ranging from 47 , 219 ( PH207 x PHG35 ) to 77 , 210 ( PHG84 x PHZ51 ) ( S6 Table ) ., To compare the burden of deleterious variants between our elite maize lines and traditionally cultivated landraces , we used genotypes from the maize HapMap3 . 2 43 for our diallel parents and 23 specially-inbred landrace lines 15 ( S5 Table ) ., Compared to landraces , the parents of our diallel exhibited a greater burden of fixed ( allele frequency of 1 ) deleterious variants but a much smaller burden of segregating SNPs , resulting in a slightly lower overall proportion of deleterious sites ( mean of 1 . 3M deleterious alleles out of 6 . 5M total sites vs . 0 . 6/3 . 3M; Fig 1b ) ., Population genetic theory predicts that deleterious variants should be at low overall frequencies , and that such variants should be enriched in regions of the genome with extremely low recombination 52 ., Using data from more than 1 , 200 lines in maize HapMap3 . 2 43 , we find that allele frequency of the minor alleles in the multi-species alignment shows a strong negative correlation with GERP score ( Fig 1c ) ., This negative correlation holds using allele frequency derived from our 12 parental lines ( S4 Fig ) , though as expected is less significant given the smaller sample size ., SNPs found in regions of the genome with low recombination also show higher overall GERP scores ( Fig 1d ) , a trend particularly noticeable around centromeres ( S5 Fig ) ., These results match previous empirical findings in maize that deleterious alleles are rare 19 and most abundant in the lowest recombination regions 17 , 40 , 53 , and support the use of GERP scores as a quantitative measure of the long-term fitness effects of an observed variant ., We first investigate the impacts of deleterious variants on phenotype using simple linear regressions ., Across all hybrids , the number of homozygote GERP-SNPs was negatively correlated with grain yield , plant height , and ear-height per se ( see S6 Table for complementation data and S7 Table for correlations with all traits ) ., We next applied a genomic best linear unbiased prediction ( GBLUP ) 44 modeling approach to estimate the effect sizes and variance explained by GERP-SNPs for each of the phenotypes per se across our diallel ( see Methods ) ., GERP-SNPs had larger average effects and explained more phenotypic variance than the same number of randomly sampled SNPs ( including SNPs with GERP score < = 0 ) matched for allele frequency and recombination ( Fig 2a ) ., We found the cumulative proportion of dominance variance explained by GERP-SNPs was higher for traits showing high heterosis ( Spearman correlation P value < 0 . 01 , r = 0 . 9 ) , from ≈ 0 for flowering time traits to as much as 24% for grain yield ( S6 Fig ) ., Distributions of per-SNP dominance k = d a ( see Methods ) across traits were consistent with the cumulative partitioning of variance components ( Fig 2b ) and matched well with expectations from previous studies showing a predominantly additive basis for flowering time 54 and plant height 55 but meaningful contributions of dominance to test weight and grain yield 28 , 30 ., Although our diallel population is relatively small , our estimated values explain as much ( for traits with low dominance variance like flowering time ) or more variance ( for traits with substantial dominance variance like grain yield ) than sets of data with randomly shuffled values of dominance ( n = 10 randomizations of k per trait; S7 Fig ) ., We then evaluated the relationship between GERP score and SNP effect size , dominance , and contribution to phenotypic variance ., We found weak or negligible correlations between effect size and GERP score for flowering time and grain quality , but a strong positive correlation for fitness-related traits ( Fig 2c and 2d ) ., The variance explained by individual SNPs , however , was largely independent of GERP score ( S8 Fig ) , likely due to the observed negative correlation between allele frequency and GERP score ( Fig 1c ) ., Finally , we observed a positive relationship between GERP score and the degree of dominance ( k ) for grain yield ( Fig 2e ) , such that the putatively deleterious allele at SNPs with higher GERP scores are also estimated to be more recessive for their phenotypic effects on grain yield ( larger k for the major allele ) ., We investigated a number of possible caveats to the results presented in Fig 2 ., First , to control for the potential inflation of SNP effect sizes in regions of high linkage disequilibrium , we removed SNPs from regions of the genome in the lowest quartile of recombination ., While some individual correlations changed significance , our overall results appear robust to the removal of low recombination regions ( S9 Fig ) ., Second , we tested the impact of reference bias caused by inclusion of the B73 genome in the multi-species alignment used to estimate GERP scores ., To do so , we removed the 11 hybrids which include as one parent the reference genome line B73 and repeated the above analyses ., Doing so dramatically reduces the size of our dataset , but we nonetheless find significant correlations between complementation and phenotype ( S7 Table ) , that GERP-SNPs explain a greater proportion of overall variation than randomly sampled SNPs ( S10a Fig ) , and that the relative pattern of dominance among traits remains the same ( S10b Fig ) ., While most of the correlations between effect size and GERP score lose significance ( S10c and S10d Fig ) , likely due to the decreased sample size , the positive correlation between dominance and GERP score remains significant even in the absence of B73-derived hybrids ( S10e Fig ) ., Finally , because natural selection will maintain dominant deleterious alleles at lower frequencies than their recessive counterparts , we investigated whether the ascertainment bias against rare alleles present in our small sample would lead to the observed correlation between GERP and dominance ., Simulations of SNPs with random dominance at mutation-selection balance ( see Methods ) , however , failed to find any relationship between dominance and GERP score ( S11 Fig ) , though we caution that the dramatic demographic shifts involved in the recent history of maize 4 make such a simulation approximate at best ., To explicitly test the informativeness of alleles identified a priori as putatively deleterious , we implemented a genomic prediction model that evaluates complementation at the haplotype level by incorporating GERP scores of individual SNPs as weights ( see Methods ) ., We explored the explanatory power with several different models and found that a model which incorporates both GERP scores and dominance ( k ) estimated from our GBLUP model explained a greater amount of the posterior phenotypic variance for most traits per se ( Fig 3a ) and heterosis ( MPH ) ( Fig 3b ) ., A simple additive model showed superior explanatory power for flowering time , however , consistent with previous association mapping results that flowering time traits are predominantly controlled by a large number of additive effect loci 54 ., To explicitly test the utility of incorporating GERP information in prediction models , we compared cross-validation prediction accuracies of the observed GERP scores to those from datasets in which GERP scores were circularly shuffled along the genome ( see Methods ) ., Models incorporating our observed GERP scores out-performed permutations ( Fig 3c and 3d ) , even when considering only SNPs in genes ( S12 Fig ) ., Our model improved prediction accuracy of grain yield by more than 4 . 3% , and improvements were also seen for plant height ( 0 . 8% ) and testing weight ( 3 . 3% ) ., While our model showed no improvement in predicting heterosis for traits showing low levels of heterosis ( Fig 1a ) , including GERP scores significantly improved prediction accuracy for heterosis of grain yield ( by 1% ) ., Finally , our approach also significantly improved model fit for phenotypes of all traits per se as well as heterosis for GY and PHT compared to traditional models of genomic selection that use general combining ability ( see Methods , S2 Table ) calculated directly from the pedigree of the hybrid population 56 ( ANOVA FDR <0 . 01 and difference in AIC < 0 , S8 Table ) ., In this study , we use genomic and phenotypic data from a partial diallel population of maize to show that an incomplete dominance model of deleterious mutation both fits predictions of population genetic theory and explains phenotypic variation for fitness-related phenotypes and hybrid vigor ., We find genome-wide support for hypotheses predicting that more damaging variants are more recessive ., Finally , we show that leveraging evolutionary annotation information in silico enables us to predict grain yield and other traits , including heterosis , with greater accuracy ., Together , these results help reconcile alternative explanations for hybrid vigor and point to the utility of leveraging evolutionary history to facilitate breeding for crop improvement .

Introduction, Materials and methods, Results, Discussion

Deleterious alleles have long been proposed to play an important role in patterning phenotypic variation and are central to commonly held ideas explaining the hybrid vigor observed in the offspring of a cross between two inbred parents ., We test these ideas using evolutionary measures of sequence conservation to ask whether incorporating information about putatively deleterious alleles can inform genomic selection ( GS ) models and improve phenotypic prediction ., We measured a number of agronomic traits in both the inbred parents and hybrids of an elite maize partial diallel population and re-sequenced the parents of the population ., Inbred elite maize lines vary for more than 350 , 000 putatively deleterious sites , but show a lower burden of such sites than a comparable set of traditional landraces ., Our modeling reveals widespread evidence for incomplete dominance at these loci , and supports theoretical models that more damaging variants are usually more recessive ., We identify haplotype blocks using an identity-by-decent ( IBD ) analysis and perform genomic prediction analyses in which we weigh blocks on the basis of complementation for segregating putatively deleterious variants ., Cross-validation results show that incorporating sequence conservation in genomic selection improves prediction accuracy for grain yield and other fitness-related traits as well as heterosis for those traits ., Our results provide empirical support for an important role for incomplete dominance of deleterious alleles in explaining heterosis and demonstrate the utility of incorporating functional annotation in phenotypic prediction and plant breeding .

A key long-term goal of biology is understanding the genetic basis of phenotypic variation ., Although most new mutations are likely disadvantageous , their prevalence and importance in explaining patterns of phenotypic variation is controversial and not well understood ., In this study we combine whole genome-sequencing and field evaluation of a maize mapping population to investigate the contribution of deleterious mutations to phenotype ., We show that a priori prediction of deleterious alleles correlates well with effect sizes for grain yield and that variants predicted to be more damaging are on average more recessive ., We develop a simple model allowing for variation in the heterozygous effects of deleterious mutations and demonstrate its improved ability to predict both phenotypes and hybrid vigor ., Our results help reconcile alternative explanations for hybrid vigor and highlight the use of leveraging evolutionary history to facilitate breeding for crop improvement .

biotechnology, alleles, plant science, model organisms, mathematics, forecasting, statistics (mathematics), experimental organism systems, plant genomics, molecular genetics, plants, research and analysis methods, grasses, mathematical and statistical techniques, maize, plant genetics, molecular biology, genetic loci, inbreeding, heterosis, eukaryota, plant and algal models, phenotypes, heredity, genetics, biology and life sciences, physical sciences, genomics, plant biotechnology, statistical methods, organisms

journal.pcbi.1004144

Predicted Role of NAD Utilization in the Control of Circadian Rhythms during DNA Damage Response

Circadian rhythms are biological oscillations occurring with an approximately 24-hour period affecting many processes ., In mammals , these oscillations are centrally controlled in the brain by the suprachiasmatic nuclei ( SCN ) ., The SCN synchronizes the peripheral circadian clocks that exist in nearly every cell ., Disruption of the circadian clock can lead to higher incidence of certain forms of cancer , and circadian timing can affect both the tolerability and efficacy of cancer therapeutics though the underlying mechanisms for these effects are still not well-understood 1 , 2 ., Mutations of core circadian components in tumors can affect several properties of circadian oscillations , including: changes in amplitude , phase shifts , and period 3 ., Investigation into the molecular components of the circadian clock has revealed much about how these biological rhythms function ., In mammals , the core of the circadian clock is coordinated by four components that operate in a transcription-translation feedback loop ., The positive ( i . e . transcriptional activation ) arm of the circadian clock involves a transactivating heterodimer complex composed of Brain and Muscle Arnt-Like protein-1 ( BMAL1 ) and Circadian Locomotor Output Cycles Kaput ( CLOCK ) that induces the transcription of many genes; the current model and its simplifications are described in the Model section ., Gene expression microarray analyses have shown that as much as 10% of an organisms transcriptome could be under circadian influence with expression exhibiting circadian oscillations; this value depends on experimental conditions and the tissue of origin 4 ., The BMAL1/CLOCK transactivating complex operates on E-box regions of gene promoters ., Additionally , CLOCK is an acetyltransferase involved in chromatin remodeling that is required for the proper operation of the circadian clock 5 ., The negative ( i . e . transcriptional repression ) arm of the circadian clock involves the Cryptochrome ( CRY1 and CRY2 ) and Period ( PER1 , PER2 , and PER3 ) genes that act as inhibitors of the BMAL1/CLOCK transcription factor complex ., CRY/PER heterodimers in the nucleus suppress CLOCK/BMAL1-mediated transcription completing the feedback loop , which then repeats to result in increased transcriptional activity as the levels of CRY/PER complex diminish 6 ., The degradation of CRY/PER levels is partially triggered by CKI-epsilon ( Casein Kinase I-epsilon ) mediated phosphorylation , which marks the PER proteins for proteasomal degradation 7 ., Period ( PER ) proteins have been shown to interact with ATM and CHK2 , two key proteins involved in DNA damage response; the Neurospora ortholog for CHK2 , PRD-4 , has been shown to promote the phosphorylation of the PER protein analogue in Neurospora , FRQ 8 , 9 ., Several studies show the existence of interplay between the pathways regulating circadian rhythms and those regulating DNA damage response ., For example , disruptions to the core components can lead to alterations in DNA damage response pathways through altered expression patterns 10 ., The reverse has also been observed , in that circadian oscillations can be reset by genotoxic stress 11 , 12 ., Rat-1 fibroblasts were subjected to pulses of ionizing radiation resulting primarily in phase advancements of circadian oscillations 11 ., In contrast , other forms of perturbation produce phase advancements and delays , such as in the case of pharmacological perturbation with dexamethasone 13 ., Dexamethasone is a glucocorticoid agonist capable of resetting the circadian phase of asynchronous cells by triggering the expression of PER1 14 ., The molecular basis for the regulation of the circadian clock in the presence of genotoxic stress continues to be explored 11 , 12 ., As our understanding of circadian regulation expands , so do the interconnections with other biological processes ., Several recent studies have shown the circadian clock to be regulated by proteins , such as SIRT1 , involved with DNA damage response and cellular metabolic state through their consumption of nicotinamide adenine dinucleotide ( NAD ) 15 , 16 ., NAD participates in many oxidation-reduction reactions and functions , including ATP production 17 ., Supplies of NAD are under circadian regulation due to circadian oscillation of nicotinamide phosphoribosyltransferase ( NAMPT ) that controls a rate-limiting step in the salvage of NAD 16 , 18 ., In its DNA damage response role , NAD is involved in cell fate decisions through its utilization by PARP1 and SIRT1 , as recently reviewed 19 ., PARP1 is an ADP-ribosyltransferase where the ADP-ribosyl moieties are obtained from the cleavage of NAD ., PARP1 is activated in the presence of DNA strand breaks ( its activity can increase 10–500 fold ) and helps to recruit DNA repair proteins 20 , 21 ., At severe levels of DNA damage , energy depletion due to loss of NAD and ATP may trigger necrosis 20 , 22 ., SIRT1 is an NAD-dependent protein deacetylase that can help inhibit transcription through histone deacetylation ., The acetylation of histones leads to the activation of gene expression by inducing a relaxed chromatin confirmation at gene promoters , which permits the access of DNA transcription proteins 15 ., Histone acetylation is counter-balanced through deacetylation causing a condensed chromatin state and transcriptional silencing ., SIRT1 is involved in DNA damage responses through interaction with several key proteins , such as p53 , where the deacetylation of p53 inhibits p53 and promotes cell survival 23 ., More recently , SIRT1 has been implicated in the regulation of the circadian clock in several ways ., First , SIRT1 destabilizes the interaction between CRY and BMAL1 through the deacetylation of BMAL1; the deacetylation of BMAL1 is counter-balanced at the same position through the acetyltransferase activity of CLOCK 15 , 24 ., Second , SIRT1 has been shown to deacetylate PER destabilizing the protein and promoting its degradation , which may promote transcription during circadian oscillations 25 ., Finally , SIRT1 is recruited to promoters of PER2 and NAMPT and is involved in the chromatin remodeling of the vicinity of each of the two promoters 16 ., The circadian clock has been the subject of several mathematical models that have helped in our understanding of the molecular mechanisms underlying regulation of the circadian clock 26 , 27 ., Our understanding of the NAD circadian regulation dynamics and the molecular mechanism regulating the phase resetting response of the circadian clock upon exposure to genotoxic stress remains incomplete; given the interactions mentioned above , it is possible that NAD utilization may be involved ., We have developed an ordinary differential equation ( ODE ) model that includes the role of NAD in the regulation of SIRT1 ., The current study explores the potential role of NAD depletion in phase resetting of the circadian clock through the activities of the NAD consumers , SIRT1 and PARP1 ., Also , we examine the effect of multiple perturbations on the circadian cycle and how these perturbations may account for this observed behavior of the primarily phase advancement resetting of the circadian clock seen during DNA damage ., We have developed a simple model ( referred to here as the current model ) representing the circadian clock of mammals , which extends a previous model developed by Hong et al . ( referred to here as the Hong 2009 model ) 28 ., As in the Hong 2009 model , we only consider the activity of the PER protein and have subsumed the paralogs of the CRY ( Cryptochrome ) and PER ( Period ) genes into a single species CP in order to simplify the model ., Within the model , PER can exist as a monomer , dimer , or in complex with BMAL1/CLOCK ., BMAL1/CLOCK is inactivated when it exists in a complex with the PER dimer ., Each form of PER contains a phosphorylation term that simulates the phosphorylation that triggers proteasomal degradation 7 ., Fig . 1 shows a wiring diagram for the current model using the Molecular Interaction Map ( MIM ) notation for bioregulatory networks and drawn using PathVisio-MIM 29 , 30 ., Each interaction is labelled and described in Table 1; these descriptions are used to label the reactions in the SBML model file ., The original form of CRY/PER mRNA transcription in the Hong 2009 model used a Hill function , but this is zeroed out in the current model using kms ( kms = 0 ) in Equation 1 ( below ) ., We extend the Hong 2009 model to account for the effects of acetylation on transcription for both PER and NAMPT by using Equation 1 through Equation 15 from Smolen et al . re-worked for the system described in the current model; these equations become the method for describing transcription rather than usage of a Hill function 31 ., Deacetylation of histones results in chromatin compaction and decreased transcription as a result of lowered accessibility of DNA polymerase to these regions of condensed chromatin ., In the case of PER , the first term of Equation 8 accounts for the fractional levels of histone acetylation ., The rate of promoter acetylation is a function of acetylation regulated by the BMAL1/CLOCK ( TF ) complex through CLOCK acetyltransferase activity and inhibited by the effects PER dimer , Equation 13 ., Further , it is known that CLOCK is able to acetylate histones at positions deacetylated by SIRT1 15 ., The rate of histone acetylation is regulated by the basal rate of histone deacetylation and the SIRT1 deacetylation activity simulated as a two substrate Michaelis-Menten reaction that utilizes NAD in the process; the activity of SIRT1 is discussed further below ., Therefore , unlike Smolen et al . , we do not use a single , fixed deacetylation rate 31 ., This is consistent with the work of Nakahata et al . , which showed that peak SIRT1 deacetylation activity coincided with the lowest acetylation levels of histone H3 15 ., This level of single histone acetylation is then used to generate an overall promoter accessibility value , Equation 9 ., Lastly , this promoter accessibility value is multiplied by a maximal rate of transcription to denote the expression of PER , Equation 1 ., The same mechanism is used to denote the expression of NAMPT ., Neither SIRT1 expression nor protein levels are under circadian control , yet its deacetylation activity is regulated in a circadian manner 15 ., Therefore , we do not consider changes to SIRT1 levels and only consider the ability of SIRT1 to utilize NAD to deacetylate three species ( PER , BMAL1/CLK , and acetylated histone ) within the model , thereby affecting circadian rhythms via separate mechanisms ., First , SIRT1 deacetylates PER2 destabilizing the protein and promoting its degradation 25 ., Second , acetylation of BMAL1 promotes the binding of CRY1 to BMAL1 and BMAL1 is a target of SIRT1 deacetylation 32 ., Third , as a histone deacetylase SIRT1 is able to deacetylate lysine residues of histones helping to produce transcriptionally silenced chromatin that exists with a closed chromatin structure 33 ., Two parameters specify the activity of SIRT1 in the model ., The first parameter VSIRT1c regulates the deacetylation of PER ( either monomer , dimer , or in complex with BMAL1/CLOCK ) and the second parameter , VSIRT1d , regulates the histone deacetylation ., The levels of NAD are regulated using a first-order reaction dependent on the availability of NAMPT ., The model includes perturbation inputs from the Hong 2009 model , dexamethosone ( Dex ) and the CHK2 phosphorylation ( kchk2 affecting PER monomer and dimer and kchk2c affecting PER in complex with BMAL1/CLOCK ) ., All simulations were conducted using MATLAB ( http://www . mathworks . com ) ., Copies of our model as a Systems Biology Markup Language ( SBML ) generated using COPASI ( http://www . copasi . org ) are published as supporting information on the PLOS website ( S1 File ) ., The model is a system of 11 equations described above and shown below ., Equation 12 and Equation 13 denote the rate promoter acetylation for the NAMPT and PER promoters , respectively ., Equation 14 denotes the level of inactive complex , while Equation 15 is the total amount of PER that exists in the system ., Equation 1: CRY/PER mRNA Equation 2: BMAL1/CLOCK complex Equation 3: CRY/PER protein monomer Equation 4: CRY/PER protein dimer Equation 5: NAMPT mRNA Equation 6: NAMPT protein Equation 7: Single histone acetylation ( NAMPT promoter ) Equation 8: Single histone acetylation ( CRY/PER promoter ) Equation 9: DNA accessibility value ( CRY/PER promoter ) Equation 10: DNA accessibility value ( NAMPT promoter ) Equation 11: NAD Equation 12: Rate of NP promoter acetylation Equation 13: Rate of CP promoter acetylation Equation 14: Inactive complex ( BMAL1/CLOCK and PER dimer ) Equation 15: Total amount of PER Kinetic parameters used for the current model are described in Table 2; the table also lists the parameter values necessary to reconstitute the Hong 2009 model ., Rate constants were based on previously published circadian models 28 , 31 ., Kinetic parameters unique to the current model were then optimized to generate oscillations in the current work ., Rate constants are in units of h-1 ., The resulting amplitudes have similar orders of magnitude to the original Hong 2009 model ., Initial values used in the current model are described in Table 3; initial values to reconstitute the Hong 2009 model are also listed in Table, 3 . The concentrations of proteins and metabolites are in arbitrary units ( AU ) because these are currently not known for many circadian clock proteins ., Damage was simulated by altering levels of kparp and kchk2 as described in the Results section using the parameters in Table, 4 . The period was calculated by finding the mean of the simulated results and then finding the time points where a selected time point was greater than the mean and the subsequent time point was less than the mean ., For each of the selected time points , the previous time point was subtracted to produce the period value ., The resulting values were then averaged for the final period value; a requirement was imposed that at least seven oscillations were necessary to produce this value otherwise an error value , negative one , was produced ., The period was calculated using the time series for the CRY/PER ( CP ) protein ., Differences in phase were calculated after 19 days ( 19 circadian oscillations ) between the unperturbed and perturbed systems ., The phase shift ( advancement or delay ) was calculated using the difference between oscillation peaks for the two systems ., Treatments were induced at each circadian hour , and the phase response curve was calculated using the time series data for the CRY/PER ( CP ) protein ., Fig . 2A illustrates the oscillatory behavior simulated by the model for the core circadian components using the current parameter set outlined in Table 2 ., The system oscillates with an autonomous period of 23 . 8 hours , which is well within the range seen in circadian oscillations of mice 34 ., The current model simulates a free-running circadian clock without external stimuli or cues ( zeitgebers ) periodically synchronizing the clock and this is the state in which current model results are described ., The model can account for entrainment by varying the Dex as a square-wave increasing the value of Dex to 0 . 125 for 12 hours and decreasing it to 0 for another 12 hours ., Circadian models , such as the one by Leloup and Goldbeter in 2003 , make use of varying PER transcription to simulate the effect of light entrainment ., Dexamethasone with its ability to trigger PER transcription therefore is a suitable substitute for entrainment by light 14 , 35 ., Fig . 2B illustrates the oscillations in the histone acetylation levels for both PER and NAMPT mRNA ., Histone acetylation levels peak at approximately hour 22 in Fig . 2B , helping the relaxation of DNA to permit transcription to be initiated ., The peak levels of PER and NAMPT mRNA are then reached after a lag of ~6 hours ., Experimentally , peaks in the acetylation levels of histones H3 and H4 have been observed 4 and 8 hours in advance of the PER1 and PER2 mRNA peaks 36 ., Acetylated histone and NAD levels oscillate in antiphase , as seen when comparing Fig . 2B and Fig . 2C ., In the context of the model , this is due to a feedback mechanism involving NAD production and SIRT1 activity where NAD levels ( NAD ) rise to their peak measured levels ~5 hours after the peak levels of NAMPT mRNA ( N ) ., This is the time when SIRT1 activity is at its maximum and acetylated histone levels decline to their minimum ~5 hours later ., NAD levels oscillate by approximately 40% during each circadian cycle , as shown in Fig . 2C , in response to oscillations in NAMPT protein levels; NAD levels oscillate in phase with NAMPT levels ., Similar changes in oscillations levels have been seen experimentally 16 , 18 ., This decline in the NAD levels is a product of several SIRT1 deacetylation processes captured by the current model , as well as the basal degradation of NAD levels via processes external to the model ., Fig . 3 shows that the current model retains the phase dynamics present in the Hong 2009 model that are critical in the modeling of circadian systems ., There is a lag of ~3 hours between the peak of PER mRNA and the peak in PER monomer levels; this is similar to experimental results seen for mammalian circadian rhythms 6 ., Peaks in the PER monomer levels then proceed prior to the peak in the PER dimer levels several hours later , and peak levels in the PER dimer are then antiphase to the levels of the transcription factor BMAL1/CLOCK ., The Hong 2009 model possesses an autocatalytic positive feedback loop involving PER that is necessary to sustain oscillations 28 ., This feedback loop requires that differential stabilities exist between PER monomer and PER in complexes , either the dimeric form alone or in the dimeric form complexed with BMAL1/CLOCK ., This mechanism arises from experimental evidence in the Drosophila circadian clock by Kloss et al . wherein PER complexes were shown to be less susceptible to degradation 37 ., The current model exhibits the same autocatalytic requirement with a smaller value for the degradation of the PER dimer ( kcp2d ) than for the degradation of the monomeric PER form ( kcpd ) by two magnitudes of order ., In contrast to the Hong 2009 model which possesses values for the two parameters ( kcpd and kcp2d ) with a smaller difference , in the current model we assume the activity of SIRT1 ( VSIRT1c ) in the destabilization of PER in either monomeric or in complexes to be equivalent , which means that kcpd2d accounts for a smaller portion of the degradation of the PER dimer ., Due to the importance of circadian rhythms in the synchronization of biological processes , circadian oscillations must be robust to minor perturbations and must stably oscillate in the presence of varied parameters resulting from individual variation ., The results of a study of the circadian rhythms of 72 mice from 12 inbred mouse strains showed this robustness of circadian oscillations 34 ., Across the combined strains , the period mean was 23 . 53 ( range 22 . 94 to 23 . 93 ) hours ., We expected a similar robustness in the current model and tested the sensitivity of the model to perturbations of each parameter individually using a method that has been used in computational studies previously 31 , 38 ., Model robustness was tested by increasing and decreasing parameter values individually by 20% and plotting the resulting amplitude changes in PER mRNA ( often used as an experimental proxy in PER luciferase experiments ) against the oscillation periods ., The results of this testing are shown in Fig . 4 , and this testing suggests that the model is robust to parameter perturbations ., Out of the perturbations tested , none of the parameter sets resulted in periods that deviated from 24 hours by more than 3 hours ., A majority of the parameter perturbations clustered near the current model parameter values from Table 2 ( this is shown in red in Fig . 4 ) with only slight increases or decreases of the period and amplitude ., Stress input variables: Dex , kchk2 , kchk2c , and kPARP are set to 0 in the current model parameter set , and therefore , they are not expected to , nor did they , have any effect during the sensitivity testing ., Three parameters resulted in periods less than 23 hours and PER mRNA amplitudes less than 0 . 4 AU ., All three of these parameters affected PER , either mRNA or protein , levels ., Decreases of 20% to PER protein synthesis rate ( kcps ) and PER mRNA synthesis rate ( VM ) , resulted in this behavior , while an increase of 20% to the PER mRNA degradation ( kmd ) also resulted in a similar behavior with a decreased amplitude and period ., A 20% decrease in PER mRNA degradation resulted in the opposite behavior with both an increase in amplitude and a period; as shown in Fig . 4 , this is the only parameter that resulted in periods greater than 26 hours ., Next , phase response curves ( PRCs ) were generated using pulses of dexamethasone ( Dex ) which trigger the transcription of PER to show that the current model is able to produce both Type 1 and Type 0 PRCs as with the Hong 2009 model ., Phase response curves illustrate the relationship between the timing of a perturbation and the effect of the perturbation on a circadian oscillation in the form of a phase shift 39 ., There are two types of PRCs , Type 1 and Type 0 ., The resulting PRC is often dependent on the strength of the perturbation with Type 1 PRCs occurring at lower perturbations than Type 0 ., As shown in Fig . 5B , low values of Dex ( Dex = 0 . 15 ) result in a Type 1 PRC ( shown in Fig . 5A ) whereby there is a continuous transition between phase advancements ( positive values on the PRC ) and delays ( negative values ) in response to the dexamethasone stimulus ., At high values of Dex ( Dex = 20 ) , a Type 0 PRC is produced with a discontinuity between the phase advancements and delays of the system ., We next examined the roles of NAD biosynthesis and SIRT1 activity in the current model given the multiple deacetylation interactions in the model utilizing NAD via SIRT1 activity ., Current literature contains a contradiction as to the effect of SIRT1 inhibition on PER2 mRNA levels ., Nakahata et al . have shown that the inhibition of SIRT1 activity leads to an increased maximal level of PER2 mRNA 15 , 16 ., Asher et al . have shown the reverse—that an inhibition SIRT1 activity results in a decrease in PER2 mRNA levels 25 ., Both increases and decreases may be theoretically possible via SIRT1 activity , since SIRT1 can affect the positive ( i . e . transcriptional activation ) and negative ( i . e . transcriptional repression ) regulation arms of circadian rhythms ., We began to address this apparent contradiction in our simulations by decreasing the rate of NAD biosynthesis ., As shown in Fig . 6 , this result agreed with the Asher et al . experimental results by qualitatively producing a decrease of approximately 12% in CRY/PER mRNA ( M ) levels following a decrease of 75% from the original VNADc parameter value 25 ., We then further investigated this behavior by decreasing SIRT1 activity by reducing VSIRT1c ( non-histone deacetylation activity ) and VSIRT1d ( histone deacetylation activity ) to determine if either of these parameters would result an increase of CRY/PER mRNA levels ., Similar to Asher et al . , a decrease in VSIRT1c results in CRY/PER mRNA level decreases , as shown in Fig . 7 ., Similar to Nakahata et al . , a decrease in VSIRT1d results in an increase of CRY/PER mRNA levels , as shown in Fig . 8 , due to a smaller repressive effect by SIRT1 on transcription 15 , 16 ., Fig . 9 shows the percentage change in maximal levels of CRY/PER mRNA levels over the parameter values that exhibit stable oscillations for the SIRT1-related parameter values ., We find these results to be robust by reducing each of these three parameters to 30% of the original value ( this is near the lower limit where parameter decreases for VSIRT1c , VSIRT1d , and VNADc continue to result in oscillations ) and conducting a sensitivity analysis as described above ., Sensitivity analysis for each of these parameters shows increases in the maximal levels of CRY/PER mRNA consistently for VSIRT1d and decreases for both VSIRT1c and VNADc ., While both VSIRT1c and VSIRT1d parameters contribute to the overall state of the system , the parameters VSIRT1c and VSIRT1d have opposing effects and parameter VSIRT1c has a stronger overall effect within the model ., Next , we examined the effect of DNA damage on circadian rhythms , which has been experimentally studied by Oklejewicz , et al . using Rat-1 fibroblasts 11 ., In the current model we have examined this effect via the two possible mechanisms ., First , the current model allows the examination of DNA damage as simulated by the activation of CHK2 ( kchk2 ) to phosphorylate PER monomer and dimer that triggers their degradation , and the second being sharp decreases in NAD levels on the circadian clock using changes in kPARP to simulate PARP1 activity ., As a major participant in DNA damage response , PARP1 activity becomes greatly increased in response to DNA strand breaks and is recruited to the sites of DNA damage in a matter of minutes 20 ., Since ionizing radiation results primarily in phase advancement , we asked whether perturbations in PARP1 , singly or in combination with CHK2 , could produce similar phase responses , and if so by what mechanism these phase advancements arise ., To compare the phase responses between simulations , we use the ratio of the maximum phase advancement in a PRC to the maximum phase delay in the PRC 28 ., The PRC for the Hong 2009 is described in Fig . 2 of Hong et al . 28 ., For comparison , Table 4 shows these PRC ratio results for both the Hong 2009 model using the current model and re-parameterized ( using the parameters from Table 2 ) and for the current model under various parameter conditions ., Additionally , in Table 4 we provide the fraction of the area under the PRC that is positive; these values are largely consistent with the ratio metric ., With the re-parameterized model , we first perturb the model using the same kchk2 ( kchk2 = 0 . 2 ) from Hong et al . There is a discrepancy in values for the ratio ( 3 . 54 as originally published versus 3 . 0193 here ) , but we believe this may be a by-product of numerical analysis and we use our value as the point of comparison ., Perturbing the current model using the same kchk2 ( kchk2 = 0 . 2 ) value results in a larger positive fraction of the area under the phase response curve ., We next calculated the positive area fraction using only kPARP ( kPARP = 20 ) for a treatment duration of two hours ., This yielded a PRC where the majority of the area was positive , similar to the one observed for the re-implemented Hong 2009 model; 0 . 6235 versus 0 . 8513 , respectively ., We next wondered whether a combination of perturbations would yield a larger positive area fraction ., Using the values kchk2 = 0 . 1 and kparp = 10 , we calculated a positive area fraction slightly greater than the fraction value for the CHK2 perturbation alone in the current model ., This is with a CHK2 value of half the value used for the Hong 2009 re-parameterization ., At kchk2 = 0 . 2 and kparp = 20 , we produce a positive area fraction that is almost completely positive ., These results are robust when we conduct a sensitivity analysis at these high levels of perturbation with parameter changes of 20% ., The model is less robust to changes in parameters that further decrease NAD production; this is expected given the strain on NAD levels due to PARP activity ., The resulting PRC has a near bimodal appearance ., Within the context of the model this effect has a direct relation on the activities of SIRT1 in the model both as an inhibitor of transcription and as a mechanism for the destabilization of PER protein ., This effect of this CHK2 perturbation occurs at a circadian time of 10 hours , shown in Fig . 10 , which is during peak of PER dimer levels ( the dominant form of the repressor in the system ) , shown in Fig . 2A ., This degradation allows mRNA levels of PER and NAMPT to rise in advance of the unperturbed model thereby resulting in a strong phase advancement ., The delays for this CHK2-dependent PRC occur at troughs of PER dimer levels ., This degradation of the PER dimer repressor at this point causes a slight increase in the maximum PER mRNA level relative to the unperturbed model in the subsequent circadian cycle resulting in the delay observed in the CHK2-dependent PRC ., The CHK2-dependent PRC is in contrast to the PARP-dependent PRC , shown in Fig . 10 , at the highest value tested ( kparp = 20 ) ., At this value , a Type 1 PRC is also produced , but whereas the CHK2 perturbation degrades PER dimer levels , the simulated consumption of NAD by PARP removes an inhibitory effect ( the deacetylation of PER leading to its degradation by the activity of SIRT1 ) on this repressor causing an opposite effect; the peak of the PARP-dependent PRC occurs at roughly circadian time 20 hours and its trough at circadian time 10 hours ., This increase in PER dimer levels causes an inactivation of transcription by repressing the activity of BMAL1/CLOCK , which acts as both the transcription factor complex and as the histone acetylatransferase in the model ., Therefore , these two perturbations , NAD depletion and PER degradation , may have different effects depending on the circadian time ., The disparate effects of these two perturbations are seen in Fig . 10; advance-delay ratio and positive area fraction results are listed in Table 4 ., In combinations of the two perturbations , a bimodality in the PRC emerges at larger values of the two perturbations , which is not directly seen experimentally in the observations by Oklejewicz et al . suggesting that if this is a mechanism that exists biologically , then the balance between these two forms of perturbation may be under additional regulation 11 ., Yet , the phase response curves seen experimentally in response to DNA damage are undoubtedly the products of several forms of perturbation each that may have a dominant effect depending on the phase of the system during perturbation ., Here we have developed a simple model that expands on the work of both Hong et al . and Smolen et al . to produce a mathematical model that connects circadian rhythms to DNA damage response and metabolism via the regulation of chromatin remodeling 28 , 31 ., The current model predicts a molecular mechanism through which multiple forms of perturbation , as a result of DNA damage , and multiple post-translational modifications can reproduce the experimentally observed phase response curve as shown in Oklejewicz et al . in Fig . 1 of that publication 11 ., We began with the hypothesis that the activities of SIRT1 and PARP1 in regulating the circadian rhythm could impact on the primarily phase advancement seen in circadian oscillations during the response to genotoxic stress given their known interactions with core circadian clock components ., To investigate this question , we expanded a previous model to account for the activity of SIRT1 in the regulation of transcription and circadian clock components and the activity of PARP1 during DNA damage response ., The model reveals that the regulation of the circadian clock may be wired in a way that integrates multiple forms of post-translational modifications as a mechanism to respond to environmental stress; in the case of acetylation , this post-transcriptional modification is controlled using a circadian feedback mechanism through regulation of NAMPT ., We examined phase response curves resulting from various conditions by using the simulated effects of CHK2 and PARP1 activity ., The results of our in silico study help to confirm the potential for CHK2 involvement i

Introduction, Methods, Results, Discussion

The circadian clock is a set of regulatory steps that oscillate with a period of approximately 24 hours influencing many biological processes ., These oscillations are robust to external stresses , and in the case of genotoxic stress ( i . e . DNA damage ) , the circadian clock responds through phase shifting with primarily phase advancements ., The effect of DNA damage on the circadian clock and the mechanism through which this effect operates remains to be thoroughly investigated ., Here we build an in silico model to examine damage-induced circadian phase shifts by investigating a possible mechanism linking circadian rhythms to metabolism ., The proposed model involves two DNA damage response proteins , SIRT1 and PARP1 , that are each consumers of nicotinamide adenine dinucleotide ( NAD ) , a metabolite involved in oxidation-reduction reactions and in ATP synthesis ., This model builds on two key findings:, 1 ) that SIRT1 ( a protein deacetylase ) is involved in both the positive ( i . e . transcriptional activation ) and negative ( i . e . transcriptional repression ) arms of the circadian regulation and, 2 ) that PARP1 is a major consumer of NAD during the DNA damage response ., In our simulations , we observe that increased PARP1 activity may be able to trigger SIRT1-induced circadian phase advancements by decreasing SIRT1 activity through competition for NAD supplies ., We show how this competitive inhibition may operate through protein acetylation in conjunction with phosphorylation , consistent with reported observations ., These findings suggest a possible mechanism through which multiple perturbations , each dominant during different points of the circadian cycle , may result in the phase advancement of the circadian clock seen during DNA damage .

Many physiological processes are regulated by the circadian clock , and we are continuing to learn about the role of the circadian clock in disease ., Research in recent years has begun to shed light on the feedback mechanisms that exist between circadian regulation and other processes , including metabolism and the response to DNA damage ., A challenge has been to understand the dynamic nature of the protein interactions of these processes , which often involve protein modification as a means of communicating cellular states , such as damaged DNA ., Here we have devised a model that simulates an alteration of the circadian clock that is observed during DNA damage response ., A novel aspect of this model is the inclusion of SIRT1 , a protein that regulates core circadian proteins through modification and helps to repress gene expression ., SIRT1 is dependent on a metabolite regulated by the circadian clock and is depleted during DNA damage ., In conjunction with a second form of protein modification , our results suggest that multiple forms of protein modification may contribute to the experimentally observed alterations to circadian function .

journal.pntd.0006406

Genetic profiling of Mycobacterium bovis strains from slaughtered cattle in Eritrea

Mycobacterium bovis ( M . bovis ) is the causative agent of bovine tuberculosis ( BTB ) , achronic , infectious and contagious disease that also affects other domestic animals as well as humans 1 , 2 ., Although BTB is prevalent in dairy cattle in Eritrea as shown by Omer et al . ( 2001 ) 3 and Ghebremariam et al . ( 2016 ) 4 by skin-test based survey , detection and isolation of the causative agent has never been done ., Routine meat inspection at municipal slaughterhouses is performed for identifying tuberculosis-like lesions ( TBL ) that usually results in either total or partial condemnation of carcasses depending on the level of TBL dissemination , however , confirmatory testing or trace back epidemiological investigations are not conducted in Eritrea . Genotyping is a vital tool for trace back in epidemiological investigations , and according to Biek et al . ( 2012 ) 5 results from WGS alone can provide insight into TB epidemiology even in the absence of detailed contact data ., Despite the usefulness of genotyping , it is rarely used in developing countries , i . e . , in Africa , Asia , and South America 6–9 ., The routine use of such tool in these countries could be instrumental in complementing BTB control strategies ., Spoligotyping and variable number of tandem repeat ( VNTR ) profiling have been used extensively in many countries to document the molecular epidemiology of Mycobacterium tuberculosis complex ( MTBC ) species 7 , 10–14 ., For this reason , the digital MTBC molecular genotypes are predominantly stored in these two forms globally15–18 ., The recent technological advancements in molecular genetics imply that we can now more than ever understand the molecular epidemiology of MTBC at amore granular level ., In the last few years , whole genome sequencing ( WGS ) for typing of pathogens has been explored and yielded important additional information on strain diversity in comparison to the classical DNA typing methods ., Analysis of data from WGS also allows detection of minute differences in genetic diversity and this has contributed retrospectively to outbreak investigations 19–23 ., Significantly , WGS allows for better genomic coverage withsingle nucleotide polymorphisms ( SNP ) profiling than the two classical typing methods 24 , 25 ., WGS has also led to a significant growth in quantitative methodology that allows for a robust estimation of phylogenetic and temporal relationships between samples26 ., All these aspects are essential in enhancing our understanding of local and distant , recent and historical dynamics of BTB 5 , 24 ., Although several reports predict that the use of WGS for genotyping will eclipse the classical MTBC typing tools 27 , this will likely take longer to occurin Africa ., It is therefore important to compare their utility in resource limited settings ., Although such tools have never been used in Eritrea , their use would greatly enhance our understanding of:, a ) the genetic diversity of M . bovis ,, b ) its evolution and, c ) the patterns of spread ( spatial and temporal ) between dairy herds , in the country and region ., Such data ( information ) would be critical for safeguarding and further development of the dairy industry of Eritrea . In the present study , the classical MTBC typing tools ( Spoligotyping and MIRU-VNTR ) as well as WGS were used to gain insight into the spatial and temporal dynamics , genetic diversity and evolution of M . bovis strains circulating in Eritrean dairy cattle ., Furthermore , to infer local and international historical phylogenetic relationships ., Pooled tissue samples ( lungs and pleura , mediastinal , bronchial , deep inguinal and lung lymph nodes ) , were collected from 15 animals that showed TBL in gross pathology , at the Asmara municipal slaughterhouse from March 2014 to May 2015 ., These 15 animals were all those with TBL during the study period ., The animals were slaughtered for meat purpose and processed as part of the normal work of the abattoir ., Approximately 5–10 grams of pooled tissues from each sampled animal were collected in sterile specimen containers , and immediately transported on icepacks to the National Animal and Plant Health Laboratory ( NAPHL ) , Asmara , and stored at -20°C until processing for culture ., Data collected from individual animals ( Table 1 ) included: source of the animal slaughtered , date of slaughter , species , breed , sex , age , pregnancy status ( pregnant/non-pregnant ) , ante mortem clinical signs , post mortem lesions , and type of the tissue samples collected ., In addition , retrospective meat inspection data for the period 2010 to 2015 were retrieved from the logbook of the slaughterhouse ., Samples were processed for M . bovis culture as follows: approximately 5 g of each pooled tissue sample with TBL per animal was cut into small pieces and covered with 100 ml of sterile distilled water in a biohazard cabinet ( Esco Class II BSC; Labotec , SA ) ., The samples were homogenized using an Ultra-Turrax homogenizer at 17500 rpm ( Separation Scientific , SA ) ., Seven millilitres of the homogenate was poured into each of two separate 15 ml falcon tubes , and the remaining homogenate was poured into individual 50 ml centrifuge tubes and stored at -20°C as reference samples ., The samples were decontaminated with 7 ml of 2% HCL ( final concentration of 1% ) and 7 ml of 4% NaOH ( final concentration of 2% ) , respectively , and incubated at room temperature ( 18–25°C ) for 10 min ., After subsequent centrifugation ( HeraeusLabofuge 400 ) of the samples at 3500 rpm for 10 min . , supernatants were poured off and 7 ml of sterile distilled water was added ., After washing , the centrifugation step was repeated and most of the supernatant was poured off ., The pellets were re-suspended in a volume of approximately 1ml using a sterile inoculation loop ., Two loops of each of the pellets were spread evenly onto two Löwenstein-Jensen ( L-J ) media slants supplemented with pyruvate ( National Health Laboratory Service , SA ) and onto one L-J medium slant supplemented with glycerol ( BD Diagnostics ) , and incubated at 37°C for up to ten weeks ., The slants were monitored weekly for mycobacterial growth ., Ziehl-Neelsen staining was conducted andlysate ( DNA ) of acid fast bacteria was subjected to polymerase chain reaction ( PCR ) testing to identify bacteria as MTBCas previously described 28 , 29 ., Subsequently , deletion analysis was performed on the isolates using PCR primers targeting the RD4 ( region of difference-4 ) as previously described for M . bovis identification 30 ., The three features used to distinguish M . bovis clonal complexes were:, a ) they are a derivative of most recent clonal ancestors ( MRCA ) spoligotype, b ) region of difference deletion and, c ) geographic restriction ( Example: African 1 is localized in West Africa ) To obtain the whole genome sequences , DNA of the 14 Eritrea M . bovis isolates was extracted ( dx . doi . org/10 . 17504/protocols . io . nsgdebw ) and sequenced on a MiSeq instrument ( Illumina , San Diego , CA ) using 2x250 paired-end chemistry and the Nextera XT library preparation kit ( Illumina , San Diego , CA ) ., FASTQ files from the instrument were put through the National Veterinary Services Laboratories ( NVSL ) in-house pipeline ( see https://github . com/USDA-VS ) ., Briefly , reads were aligned to the reference genome AF2122/97 , NCBI accession number NC_0002945 , using BWA and Samtools32 , 33 ., A depth of coverage of 80X was targeted ., BAM files were processed using Genome Analysis Toolkit ( GATK ) ’s best practice workflow ., SNPs were called using GATK’s HaplotypeCaller outputting them to variant call files ( VCF ) 34–36 ., Results were filtered using a minimum QUAL score of 150 and AC = 2 ., From the VCFs , SNPs gathered were outputted to three formats: an aligned FASTA file; tab-delimited files sorted by position location and by SNP groups; and a maximum likelihood phylogenetic tree created with RAxML37 ., The tree was built using a GTR-CAT model with input taken as an alignment file containing only informative and validated SNPs ., SNPs were visually validated using Integrative Genomics Viewer ( IGV ) 38 ., Because WGS isolates from this region of the globe are not readily available , databases from three laboratories ( United States Department of Agriculture , Centre de Recercaen Sanitat Animal ( CReSA ) —Institute de Recerca i Technologia Agroalimentáries ( IRTA ) , Spain , and Tuberculosis Research Laboratory , Department of Veterinary Public Health and Preventive Medicine , University of Ibadan , Nigeria ) that are actively sequencing M . bovis isolates were queried and field isolates that were within 150 SNPs of the Eritrea isolates were included in our analysis ., Also included for perspective were widely available reference strains , AN5 , Ravenel , 95–1315 , AF2122/97 , BCG , and BZ-31150 ., “FASTQ files from the isolates sequenced were uploaded into NCBI short read archive ., Accession numbers Bioproject and sample numbers are listed in supplemental S1 Table ., During the period 2010 to 2015 , 78 , 820 cattle were slaughtered and 38 carcasses , originating from Maekel and Debub regions , were totally condemned due to generalized TBLs showing caseous necrosis identified in gross pathology in the lungs , livers , pleura ( chest ) , peritoneum , and lymph nodes ., Besides , fore quarters of three animals , plucks , shoulders , chests and heads of six cattle were partially condemned due to the presence of TBL ( Table 2 ) ., All , except one ( local breed ) , of the condemned carcasses were of the exotic HF breed or their crosses ., Out of the 15 animals sampled from March 2014 to May 2015 , nine originated from Maekel and one from Debub , while the origin of the other five slaughtered animals was unknown due to lack of records ., Detailed gross pathology information on the tissues collected is presented in Table 1 ., During this period 26 , 603 cattle were slaughtered and nine out of the 15 carcasses sampled , were totally condemned due to generalized TBL ., In addition , the entire plucks and shoulders of three animals were partially condemned ( Table 2 ) , and from three other animals , tissues with TBL were collected and the carcasses passed for consumption ., Out of the 15 pooled tissue samples cultured on L-J media slants supplemented with pyruvate , 14 yielded smooth dysgonic growth , suggestive of M . bovis presence ., All the 14 isolates were identified as MTBC ., Subsequent examination by M . bovis specific PCR targeting the RD4 , yielded banding patterns typical of M . bovis with a 268 bp product indicating RD4 deletion ., The dominant spoligotype identified in our study was SB0120 , named BCG-like by Haddad et al . 13 and considered as parental strain for the M . bovis vaccine strain ., It accounted for 64% of the isolates , whereas the other two spoligotypes SB0134 and SB0948 did so for 29% and 7% , respectively ., The first two strains are widely distributed in a number of African countries , namely; Ethiopia , Algeria , Zambia , South Africa 6 , 10 , 16 , 40–43 as well as in Italy , Spain , other European countries and Mexico 13 , 44–49 ., In addition to cattle , SB0120 affects wildlife and humans in Africa and Europe 50–53 ., The third spoligotype ( SB0948 ) has been reported in France , Italy , and in Zambia 13 , 41 , 44 ., The relatively high frequency of the spoligotype SB0120 found in the present study may indicate its predominance in Eritrean dairy cattle , though difficult to conclude with such small sample size ., The second predominant spoligotype ( SB0134 ) appears to have evolved from spoligotype SB0120 by the loss of spacers 4 and 5 in addition to spacers 3 , 9 , 16 , and 39–43 that classify spoligotype SB0120 ., This finding might not be surprising , in view of the past trade relations between Eritrea and Ethiopia , as both SB0120 and SB0134 spoligotypes are also present in Ethiopia ., Besides , these two countries share open borders that consequently allowed the uncontrolled movement of animals as obtained in most African countries ., Therefore , it might be plausible to speculate that these strains of M . bovis are shared between Eritrea and Ethiopia ., On the other hand , it might also be plausible to suggest Italy as a possible source of these strains , on the following grounds:, a ) long historical ties ( 1900 to 1970s ) between Eritrea and Italy existed ,, b ) Italian settlers initiated the establishment of dairying in Eritrea in the 19th century by importing exotic breeds ( Holstein–Friesian ) ,, c ) the M . bovis spoligotypes detected in our study are also wide spread in Italy ., Although the reason for the apparent predominance of the two spoligotypes ( SB0120 and SB0134 ) needs further study , as this may indicate an epidemiological link between different dairy farms/regions in Eritrea , as buying and selling of cows between dairy farms is common in the country54 without following strict sanitary rules . Since not all the slaughtered cattle with TBL had records of their farm of origin , it may also be possible to suspect that some of the slaughteredanimals might have originated from the same farm ., It is noteworthy , however , that based on the WGS data there appears to be at least two introductions of M . bovis into Eritrean dairy cattle , an SB0120 strain and SB0134 strain ., The SB1517 ( Ethiopian strain; Fig 2 ) is an offspring of SB0134 suggesting that the common ancestor of the cluster was SB0134 ., Spoligotype SB0948 was found in only one animal ., It is a descendant of spoligotype SB0120 as it differs by the absence of spacer 1 only , and deviates only by the Mtub21 locus in its VNTR profile from the other members of the SB0120 group ( Fig 1 ) ., Further , the WGS data confirmed that SB0948 is a recent descendant of a sub-cluster of SB0120 isolates ., Though unclear what its relevance is in neighboring Ethiopia , this spoligotype was reported in several countries in Africa and Europe 13 , 41 , 44 , 48 , 55 ., The African and global comparisons of spoligotype profiles ( Fig 3and S1 Fig ) demonstrated the regional and global distribution of the spoligotype and VNTR profiles and their similarities with the Eritrean ones ., These similarities could be attributed to the following two plausible reasons:, a ) inter-regional and global livestock trade ,, b ) colonial livestock and livestock product trade within their then colonies and outside ., Variable number tandem repeat ( VNTR ) profiles are considered appropriate to complement spoligotyping due to their ability to discriminate between M . bovis strains as defined by spoligotyping15 , 55 , 56 ., The three spoligotypes were clustered into six VNTR profiles ( Fig 1 ) ., The diversity seen in the VNTR profiles may suggest that M . bovis has been circulating in the dairy herds of the country for quite a long time with only minor mutations as the BCG-like spoligotype ( SB0120 ) is the predominant one ., Four of the VNTR profiles ( ER-2 , -3 , -4 and 6 ) may have derived from the predominant VNTR profile ER-1 , that corresponds to spoligotype SB0120 ., According to Smith et al . 49 , strains bearing the same spoligotype pattern are assumed to be a set of individuals derived relatively recently by clonal replication from a single ancestral cell ., On the basis of the VNTR profile , both strains , SB0948 and SB0134 , are clustered within the SB0120 group with a loss of only one locus ( Mtub12 ) in the former and two loci ( ETR-B and ETR-E ) , in the latter strain , respectively ., One of the VNTR profiles within the SB0134 strain exhibited two different VNTR alleles ( 3 and 4 tandem repeats ) for locus ETR-E ( Fig 1 ) , suggesting either a mixed infection with two distinct strains or a microevolution in this strain ., The VNTR profiles found in our study showed clonal variants differing at their loci as compared to what was reported in other parts of Africa ( i . e . , Zambia ) ( Fig 1 & Fig 3 ) , though they were all M . bovis strains belonging to SB0120 spoligotype ., This clonal difference ( Fig 1 & Fig 3 ) seen in our study may have been attributed to the absence of active livestock ( dairy cattle ) trade between Eritrea and other parts of Africa ( Zambia ) or due to the different geographical locations and livestock management systems between the countries , that might have dictated the microevolutions ( mutations ) differently ., The possible reason for having the same spoligotype ( SB0120 ) in Eritrea and other African countries ( S1 Fig ) , might be that the source of the cattle for Eritrea and the other countries was Europe , as Europe is the source for the high yielding dairy cows , like the Holstein Friesians , that are imported by most African countries with the aim of improving milk production in their countries in order to realize food security ., The investigation of the 14 M . bovis isolates for clonal complex differentiation revealed that they belonged to none of the complexes identified so far i . e . , African 1 ( RDAf1 ) , African 2 ( RDAf2 ) , European 1 ( RDEu1 ) and European 2 ( RDEu2 ) 15–18 ., The absence of members that belong to clonal complex African 1& 2 in our samples could suggest limited introduction of such strains from the neighboring Eastern and Western African livestock movement routes ., It is noteworthy , that these two strains ( SB0120 and SB0134 ) are present in Ethiopia 16 , although most of the other strains in this country belong to Af2 ., In the current study , little strain diversity is recorded ( Fig 1 ) as compared to studies conducted in other countries with similar agricultural setting like Eritrea6 , 42 ., The WGS results matched the conventional laboratory methods with better resolution ., These data support two separate introductions of M . bovis into Eritrea , with subsequent localized spread ., The common ancestor of these two groups is shared widely with isolates in the USA and Spain , with greater diversity found in Spain suggesting an introduction from Europe ., The presence of a common ancestor in these distantly located countries may be due to the international livestock trade between these countries , geographical proximity and similar livestock production systems ., Example: the origin of the high yielding dairy breed ( Holstein Friesian ) is Europe ., As indicated in the spoligotyping section above , the spoligotype SB0120 , predominant in our study , is also ubiquitous in Europe , especially in France13 , Italy 44 , Portugal45 , and Spain48 , most likely as a result of geographical closeness and trade relations between these countries ., Therefore , our finding may not be a surprise , given the historical establishment of ‘intensive’ dairy farming by the Italian settlers in Eritrea through the importation of high yielding dairy breeds ( Holstein Friesians ) to meet the high demand for milk and dairy products ., The fact that the Eritrean strains are between ( close to ) Spain samples ( Fig 2 ) may suggest two introductions or may be just one introduction; i . e . , from Europe ( Italy ) ., Since we do not have information that shows historical , political or trade ties between Spain and Eritrea , we can speculate that either the strains are circulating in Italy and Spain ., Or that , the Italian settlers during the establishment of dairy farms may have imported the cattle from Spain or other European countries where the same strains of M . bovis might have been circulating ., A classical analogy for this speculation may be rinderpest that was brought to Sub-Saharan Africa by Italian forces in 1889 , with infected cattle they had imported from India , Aden , South Russia to feed their army that had then occupied Massawa ( Eritrea ) 57 ., However , although phylogenetic comparison with Italian M . bovis isolates could not be done in our study , we cannot refute the possibility that these strains originate from Italy or via the above indicated routes from other countries ., The second probable route of introduction for one of the groups of the Eritrean strains , but not for the other , may be Ethiopia considering the long and close historical relationship and uncontrolled livestock movement between these two countries ., The Ethiopian and Eritrean samples have accumulated 8–16 additional SNPs since sharing a common ancestor suggesting a recent common source and regional spread ., But the four Eritrean samples ( strains ) are within 5–6 SNPs from sharing a common ancestor suggesting these isolates have established and spread within Eritrea , though it might be premature to reach into conclusion with such small sample size ., Eritrea , on the other hand , might have introduced this strain to Ethiopia . This is plausible because both intensive dairy farming , established 100 years ago in Eritrea and the first report of BTB ( Pirani , 1929 ) , cited by Omer et . al . 3 , occurred earlier than in Ethiopia where ‘intensive’ dairy farming started in the 1950s ( 1947 ) by importing Friesians and Brown Swiss 58 ., This was followed by the detection of acid fast bacilli in a cow’s milk , in one study , and detection of what was called ‘Mycobacteria tuberculosis bovine type’ seemingly , M . bovis from 18 cattle , in another study , in Eritrea , by Sfroza in 19443 ., The samples collected in this study are not considered representative of all strains possibly circulating in Eritrea ., However , Asmara slaughterhouse , as the country’s biggest facility mostly slaughters exotic cattle breeds from various regions in Eritrea in which previously a high BTB prevalence was reported 4 ., Therefore , the panel of samples still provides a valuable insight in the genetic strain composition from mostly dairy producing regions in Eritrea and a valuable basis for future investigations ., The current study characterized strains of M . bovis in Eritrea and revealed their ( dis ) similarities with the strains generally present in Africa and Europe , as well as potential routes of introduction of M . bovis ., Though the sample size is small , our study provides important information as well as availability of technology for future in-depth molecular studies including more samples from dairy cattle as well as cattle and goats from the traditional livestock sector . This study provides information on the origin of the M . bovis strain in Eritrea , its genetic diversity , evolution and patterns of spread ( spatial and temporal ) between dairy herds ., The information obtained will be instrumental in making informed decisions in future BTB control strategy for Eritrea ., Our study has some limitations ., The samples were collected from one slaughterhouse and were few due to the absence of tissues with TBL during the study period ., The low prevalence of BTB in the traditional livestock raising system 59 where majority of slaughtered animals come from , has limited the possibility of detecting more M . bovis strains from different geographical regions of Eritrea ., Genetic profiling of M . bovis strains is a highly useful approach which can aid in the study and control of the temporal and geographical disease spread in the country and the African continent where BTB is largely uncontrolled . We recommend future studies in Eritrea to include genetic profiling of Italian isolates so as to support or negate our hypothesis with certainty than just live with speculation that the origin of the Eritrean M . bovis strains was Italy ., In future studies in Eritrea , inclusion of more regional slaughterhouses including animal traceability will enable us gain greater insight into the epidemiology of BTB in the country which will allow the M . bovis genotype to be linked to the population from which it was obtained ., We also recommend that simultaneous detection and strain differentiation of M . bovis isolates should become a reality in the routine of human tuberculosis reference laboratories , as well as in the routine meat inspection at municipal slaughterhouses ., Therefore , using the One Health paradigm ( i . e . interdependence between the medical and veterinary fields ) , greater integration between agriculture and health sectors could be an important strategy to control M . bovis in several places in the world where the agent is disseminated between animals and humans .

Introduction, Materials and methods, Results, Discussion

Mycobacterium bovis ( M . bovis ) is the main causative agent for bovine tuberculosis ( BTB ) and can also be the cause of zoonotic tuberculosis in humans ., In view of its zoonotic nature , slaughterhouse surveillance , potentially resulting in total or partial condemnation of the carcasses and organs , is conducted routinely ., Spoligotyping , VNTR profiling , and whole genome sequencing ( WGS ) of M . bovis isolated from tissues with tuberculosis-like lesions collected from 14 cattle at Eritrea’s largest slaughterhouse in the capital Asmara , were conducted . The 14 M . bovis isolates were classified into three different spoligotype patterns ( SB0120 , SB0134 and SB0948 ) and six VNTR profiles ., WGS results matched those of the conventional genotyping methods and further discriminated the six VNTR profiles into 14 strains ., Furthermore , phylogenetic analysis of the M . bovis isolates suggests two independent introductions of BTB into Eritrea possibly evolving from a common ancestral strain in Europe . This molecular study revealed the most important strains of M . bovis in Eritrea and their ( dis ) similarities with the strains generally present in East Africa and Europe , as well as potential routes of introduction of M . bovis ., Though the sample size is small , the current study provides important information as well as platform for future in-depth molecular studies on isolates from both the dairy and the traditional livestock sectors in Eritrea and the region ., This study provides information onthe origin of some of the M . bovis strains in Eritrea , its genetic diversity , evolution and patterns of spread between dairy herds ., Such information is essential in the development and implementation of future BTB control strategy for Eritrea .

The livestock sector plays a major role in poverty and hunger reduction in the vast majority of Africa , as a source of food , cash income , manure , draught power , transportation , savings , insurance and social status ., However , for livestock to play this vital role , the impact of diseases of economic and zoonotic importance need to be reduced ., Bovine tuberculosis , mainly caused by Mycobacterium bovis , is such an infectious disease ., Slaughterhouse ( gross pathology ) surveillance , followed by bacterial culture and genotyping , are options to identify the disease-causing agents , their distribution , and enabling trace back of the sources of infections , in order to prevent their re-introduction and spread ., Unfortunately , genotyping is by far not generally introduced in the continent ., In the present study , tissues with tuberculosis-like lesions were collected from the Asmara municipal slaughterhouse , the largest slaughterhouse in Eritrea , and bacterial culture , classical Mycobacterium tuberculosis complex typing ( Spoligotyping and VNTR profiling ) , as well as whole genome sequencing ( WGS ) were used to gain insight into the spatial and temporal distribution , genetic diversity and evolution of M . bovis strains circulating in Eritrean dairy cattle ., The results revealed ( dis ) similarities of the Eritrean M . bovis strains with the strains generally present in Africa and Europe , potential routes of introduction to Eritrea and genetic diversity of the M . bovis strains ., Future in-depth molecular studies including more samples from dairy cattle as well as cattle and goats from the traditional livestock sector are recommended .

livestock, evolutionary biology, geographical locations, eritrea, molecular genetics, molecular biology techniques, genotyping, bacteria, africa, research and analysis methods, artificial gene amplification and extension, actinobacteria, molecular biology, evolutionary genetics, agriculture, people and places, polymerase chain reaction, genetics, biology and life sciences, europe, organisms, mycobacterium bovis

journal.pgen.1004559

Early Mesozoic Coexistence of Amniotes and Hepadnaviridae

Viruses and their hosts share a rich coevolutionary past that is evidenced by a plethora of viral relics buried within host genomes ., A striking example for this is the human genome where genomic relics of ancient , endogenized viruses constitute ∼8% of its total sequence 1 ., These “fossils” of viruses have been collectively termed endogenous viral elements ( EVEs ) 2 and originate from host germline integration , followed by vertical transmission and subsequent fixation of virus-derived DNA in the genome of the host population 3 , 4 , 5 ., The recent and ongoing availability of numerous genome sequences from non-model organisms 6 , 7 has given rise to the field of paleovirology 8 , the study of anciently integrated viruses , and has yielded the first direct insights into the long-term evolution of certain virus families 2 , 9 , 10 ., The vast majority of EVE copies belongs to the Retroviridae family 1 , 5 of viruses which rely on reverse transcription and obligate host genome integration , however , paleovirology has unearthed genomic fossils of all other major groups of eukaryotic viruses 2 , 5 , 11 ., Whenever an EVE is present at a unique genomic location , it is possible to date the upper and lower age boundary of viral endogenization events by comparison of orthologous EVE insertions among different host species 5 , providing direct evidence for host-virus coexistence ., The Hepadnaviridae are a family of reverse-transcribing dsDNA viruses infecting various species of birds 12 and mammals , including bats 13 , rodents 14 , and primates 15 ., In humans , hepatitis B virus ( HBV ) poses one of the most widespread global health problems that affects more than 2 billion people and leads to >500 , 000 deaths per year 16 ., Despite the availability of a number of primate genome sequences 7 , HBV EVEs are absent or undetectable in these and other mammalian genomes 10 ., In contrast , many bird genomes contain HBV fossils , such as the zebra finch and other songbirds 2 , 10 , 17 , the budgerigar 18 , 19 , and other representatives of Neoaves 10 ., Direct evidence from paleovirology suggests a coexistence of birds and HBVs that spans ∼70 million years ( MY ) of the Mesozoic and Cenozoic Eras , with HBV endogenizations dating from >82 million years ago ( MYA ) to <12 . 1 MYA 10 ., Based on this fossil record , Hepadnavirus evolution might have either been characterized by an ancient coexistence with amniotes 10 or by a more recent bird-mammal host switch 10 , the latter being in line with the paucity of extant host species and lack of mammalian HBV EVEs ., The validity of either hypothesis is largely dependent on the genomic fossil record of HBVs 10 ., The same is also the case for the enigmatic origin of the X gene of mammalian HBVs , as X appears to be absent in ancient avian HBV EVEs 10 , while some extant avian HBVs exhibit an X-like gene 20 ., The X gene is known to be involved in the generation of liver tumors in chronic HBV infection in humans and woodchucks 21 , 22 , 23 , 24 , 25 , 26 , so the elucidation of the evolutionary emergence of the X gene is of broad relevance to biological and medical research on hepatitis B viruses ., Here we report endogenous hepadnaviruses from recently sequenced turtle 27 , 28 , snake 29 , and crocodilian genomes 30 , 31 ., Among these EVEs is a near-complete crocodilian HBV genome from the Late Mesozoic and an Early Mesozoic turtle HBV , providing us with the unprecedented opportunity to study the host range , genome evolution and deep phylogeny of Hepadnaviridae ., We show that genome organization and replication is highly conserved among HBVs with the exception of the presence of the oncogenic X gene , for which we infer an evolutionary scenario of de-novo emergence in the ancestor of mammalian HBVs ., Finally , our hepadnaviral fossil record reveals Mesozoic coexistence of Hepadnaviridae with three of their five major host taxa and supports a scenario of ancient amniote–HBV cospeciation ., We searched the recent saltwater crocodile , gharial , and American alligator draft genome assemblies 30 , 32 using whole viral genomes of the duck HBV ( DHBV; AY494851 ) and the Mesozoic avian eHBV ( eZHBV_C 10 ) , and identified two endogenous crocodilian HBVs ( eCRHBVs; Fig . 1A ) ., Likewise , we screened the genomes of turtles ( painted turtle , softshell turtles , and sea turtle 27 , 28 ) , squamate lepidosaurs ( cobra , boa , python , and anole lizard 29 , 33 , 34 , 35 ) , and mammals ( human , opossum , and platypus 36 , 37 , 38 ) for the presence of eHBVs ., We detected a single locus in turtle genomes , hereafter referred to as endogenous turtle HBV ( eTHBV; Fig . 1A ) , two endogenous snake HBVs in the cobra genome ( eSNHBVs; Fig . 1A ) , but no EVEs in the remaining squamate and mammalian genomes ., Our presence/absence analyses show that all four available cryptodiran turtle genomes plus the sampled pleurodiran ( side-necked ) turtles ( Mesoclemmys and Podocnemis ) exhibit the eTHBV insertion , while it is absent in the orthologous position in crocodilian genomes ( Fig . 1B ) ., This suggests that it is of Triassic origin and was endogenized in the ancestor of Testudines that lived 207 . 0–230 . 7 MYA 39 , 40 ., eCRHBV1 ( Fig . 1C ) is present in all crocodilians except alligators ( i . e . , Longirostres 41 ) and is 63 . 8–102 . 6 MY 42 old , i . e . , of Cretaceous origin ., The second crocodilian EVE ( eCRHBV2 ) is exclusively shared between saltwater and dwarf crocodile; its endogenization thus occurred during the Paleogene in the ancestor of Crocodylidae ( 30 . 7–63 . 8 MYA 42 , ) ., Unfortunately , the snake EVEs remain undated , as none of the cobra eSNHBV loci could be aligned to other squamate genomes for ascertainment of EVE presence/absence ( Fig . 1A ) ., Given the dense fossil record of crocodilians and turtles that provides multiple calibrations for molecular dating of species divergences 39 , 42 , we suggest that the aforementioned dates are robust age estimates of eCRHBV1 , eCRHBV2 , and eTHBV endogenizations ., Furthermore , molecular dating studies using mitochondrial genomes 44 , 45 or nuclear loci 42 , 43 yielded similar results on crocodilian divergence times , and the basal turtle divergence time of 207 MYA 39 , 46 ( i . e . , the Cryptodira–Pleurodira split ) is a nuclear estimate that is well compatible with mitochondrial estimates 47 , 48 and the fossil record 49 ., Annotation assigns these five eHBV insertion sequences no extant protein-coding function in their hosts genomes ( see GenomeBrowser 50; the two crocodilian eHBVs are located within very large introns and the snake eHBV loci are undetectable in the lizard genome , while the turtle eHBV appears to constitute intergenic sequence ) ., In line with this , we identified several frameshifting indels and premature stop codons in all five eHBVs ( S1 Table ) ., Most of these were lineage-specific and found at different locations , indicating that they were not present in the common ancestor where the viral integration occurred ., To determine whether any of the eHBV fragments may still show any sign of having been functional in the past before incurring stops and frameshifts , we performed likelihood ratio tests of the ratio of the rate of non-synonymous to the rate of synonymous substitutions ( ω ) either fixed to 1 or being allowed to vary freely ., As none of these tests provided statistical support for deviation from ω\u200a=\u200a1 ( S2 Table ) , there was thus no evidence for non-neutral evolution of these loci in the sampled genomes ., Similar observations were previously made in selection tests on avian eHBVs where neutrality could not be rejected 17 , which may suggest that none of the currently known HBV EVEs possess an obvious protein-coding function in their host genomes ., The crocodilian and turtle eHBVs GC content is similar to the GC level of the adjacent flanking sequence of the host ( S1 Figure ) , which suggests that they have resided in the host genome for long enough to show a host-like base composition ., Given that we detected no sign of non-neutral evolution of the crocodilian and turtle eHBV loci since their respective endogenization events , another line of evidence for the antiquity of their integration is the level of sequence divergence between orthologous eHBVs ., We therefore calculated distances per eHBV locus ( see Materials and Methods ) and applied neutral substitution rates for crocodilians ( 3 . 9×10−10 substitutions/site/year 30 ) and turtles ( 8 . 43×10−10 substitutions/site/year for Pelodiscus sp . and 4 . 77×10−10 substitutions/site/year for Chelonia mydas 30 ) to determine locus-specific estimates of respective endogenization times ., Consequently , we inferred integration events to have happened 70 . 3 MYA in eCRHBV1 , 20 . 5 MYA in eCRHBV2 , and 179 . 0 or 316 . 3 MYA in eTHBV ., While these dates are compatible with our lower age boundaries of endogenization events derived from eHBV presence/absence patterns ( Fig . 1A ) , we suggest that the distance-based values are less robust estimates , as they rely on a limited number of nucleotides from a single genomic locus and are thus easily prone to biases caused by , for example , variation in substitution rates among lineages ( e . g . , Pelodiscus vs . Chelonia ) or among genomic regions ., Extant avihepadnaviruses ( avian HBVs ) and orthohepadnaviruses ( mammalian HBVs ) have a circular genome organization with overlapping open reading frames ( ORFs ) and a streamlined genome size of about 3 . 0 kb and 3 . 2 kb , respectively 14 ., The crocodilian , snake , and turtle eHBV fragments comprise up to 81% of an Avihepadnavirus genome ( Fig . 2A ) , permitting us to reconstruct large portions of their genome organization ., We detected overlapping regions of the precore/core ( preC/C ) ORF with the polymerase ( pol ) ORF ( eCRHBVs and eTHBV; Fig . 2A ) and of the presurface/surface ( preS/S ) ORF with the pol ORF ( eCRHBVs and eSNHBV1; Fig . 2A ) , which suggests that all known extant and fossil avian , crocodilian , and mammalian HBVs exhibit a highly similar genome organization ., This probably also applies to snake and turtle HBVs , because , while the eSNHBV1 and eTHBV fragments only span ∼14 and ∼21% of an HBV genome , they contain a region of overlapping ORFs ( Fig . 2A ) ., Finally , we used approaches based on similarity searches and alignments , and did not detect any evidence for an X ORF in our non-avian eHBVs ( Fig . 2A ) ., In addition to protein-coding sequences , we detected genomic features related to viral replication ( Fig . 2B–D ) , as the near-complete eCRHBV1 genome comprises the region where avihepadnaviruses and orthohepadnaviruses contain direct repeats ( DR ) and the RNA encapsidation signal ( ε ) ., This region lies within the end of the pol ORF and the start of the preC/C ORF 51 ( Fig . 2A ) , but eCRHBV1 exhibited no significant nucleotide sequence similarity against DR+ε sequences of avian and mammalian HBVs ., Yet , our structural analyses identified a DR motif of 14 nt that is present in identical copies within pol ( DR2 ) and preC/C ( DR1 ) ., We further detected a 54-nt RNA hairpin motif with a priming bulge ( 5′–UUAC–3′ ) identical to the first four RNA nucleotides of the DR motif and reverse complementary to the ( – ) -DNA primer in avian HBVs 51 , suggesting that this is a structure that functionally corresponds to ε of extant HBVs ( Fig . 2B ) ., In avian and mammalian HBVs , ε interacts with the ( – ) -DNA primer that is covalently linked to the conserved tyrosine residue of the terminal protein ( TP ) domain of the Pol protein 51 and establishes encapsidation of viral pregenomic RNA 52 , 53 as well as reverse transcription into viral ( – ) -DNA 53 , 54 ., Despite the lack of sequence similarity between avian and mammalian HBV ε 51 , as well as the putative crocodilian HBV ε ( see S2 Figure ) , hepadnaviral replication appears to require strong structural constraint on ε with regards to stable base-pairing , as well as the presence of a bulge region and an apical loop ( Fig . 2B–D and refs . 19 , 51 , 55 ) ., Only the 4-nt binding sites for the ( – ) -DNA primer within DR and ε exhibit sequence conservation among Hepadnaviridae ( Fig . 2B–D and S2 Figure ) ., Recent paleovirological studies on avian eHBVs suggest that extant avihepadnaviruses and orthohepadnaviruses exhibit relatively shallow branches within phylogenetic trees compared to the deep divergences among eHBVs 5 , 10 ( see also S3C Figure ) ., This suggests a recent divergence of circulating viruses among each of these two HBV lineages , whereas their endogenous avian counterparts appear to be relics of several distantly related , ancient lineages 10 , 18 with avihepadnaviruses being sister clade to one of them 10 ., We reevaluated this by inferring the phylogenetic relationships based on Pol and PreC/C protein sequences of the non-avian eHBVs among Hepadnaviridae ., In addition to full-length avian eHBVs and a dense sampling of extant HBVs , we included reverse-transcribing outgroups such as retroviruses , caulimoviruses , and retrotransposons ., In phylogenetic trees of both Pol and PreC/C ( Fig . 3B–C , S4A–C Figure ) , the avian eHBVs form ancient , unrelated lineages , but with an eZHBV_C+avihepadnaviruses clade in the Pol tree and an eBHBVs+avihepadnaviruses clade in the PreC/C tree ., This reversal in branching order could be explained by interspecific viral recombination events , as have been observed in some extant HBV lineages 56 , 57 , but is more likely due to the very limited amount of phylogenetically informative characters in the short PreC/C protein ., Irrespective of the branching order of avian eHBVs , the two crocodilian eHBVs ( eCRHBV1 and eCRHBV2 ) consistently group together as a third major hepadnaviral lineage , and form the sister group of all avian HBVs and eHBVs , which is supported with high bootstrap values in the Pol tree ( Fig . 3B ) ., This grouping , of course , is largely dependent on the position of the root of the Hepadnaviridae phylogeny ., Our dense ingroup and outgroup sampling yields a Pol tree topology that strongly suggests Orthohepadnaviridae as the first branch among HBVs with respect to the remaining hepadnaviral lineages ., Thus , in relation to avian and mammalian HBVs , the phylogenetic position of crocodilian HBVs reflects the host phylogenetic relationships between birds , crocodilians , and mammals 27 , 30 , 40 ( Fig . 3A ) ., Unfortunately , it is not possible to include eTHBV in this well-resolved Pol tree , as the turtle EVE spans only a small part ( 16 aa ) of the Pol sequence ., Consequently , the phylogenetic affinities of eTHBV are solely inferred from the PreC/C tree , which exhibits a lack of bootstrap support on its backbone , presumably as a consequence of too few phylogenetically informative characters within the PreC/C protein ( 342 aligned aa sites ) ., However , the PreC/C tree does recover eTHBV as sister lineage of crocodilian+bird HBVs , which supports the above-mentioned similarity of the deep phylogenetic relationships among HBVs , as well as among their amniote hosts 27 , 30 , 40 ( Fig . 3A ) ., Finally , with regards to snake eHBV affinities , the short sequences of eSNHBV1 ( 141 aa Pol ) and eSHBV2 ( 57 aa PreC and 123 aa Pol ) hamper a well-supported resolution of the tree backbones , yet there is topological indication for a grouping of eSNHBV1 with avian HBVs+eHBVs ( S4A Figure ) and eSNHBV2 with crocodilian eHBVs ( S4B–C Figure ) ., Annotation suggests that an X or X-like ORF is absent in non-avian eHBVs , while the genomes of orthohepadnaviruses and avihepadnaviruses appear to contain an X and X-like gene , respectively ( Fig . 4A ) ., Even when aligning the translated sequences of eHBVs in the region homologous to the putative X-like ORF of avihepadnaviruses 20 , all eHBVs and even several extant avian HBVs exhibit several internal stop codons at conserved positions ( Fig . 4B ) , suggesting that an X-like ORF never existed in these unrelated HBV lineages ., While it remains unclear whether the putative X-like gene in DHBV has a function 58 , it is interesting to note that the ribonuclease H ( RNH ) domain ( partially overlapping with the X/X-like ORF region ) has a moderate GC content in eHBVs and avihepadnaviruses ( S3 Figure ) , while mammalian HBV genomes exhibit a conserved X gene and a highly elevated GC content of the RNH domain ., Despite both overlapping with the RNH domain of the pol ORF , X and X-like ORFs are found in different reading frames ( Fig . 4A ) ., Considering that the Pol protein sequence is homologous among all HBVs and is encoded in the +1 frame , the fact that X resides in the +2 frame and X-like in the +3 frame counters homologization of the codon and protein sequence encoded in the X and X-like ORFs ., This provides further evidence that the ancestor of Hepadnaviridae lacked an X or X-like gene and that the X protein arose de novo in the Orthohepadnavirus lineage 10 ., The partially overlapping nature of X suggests that it emerged by using an unoccupied reading frame within a pre-existing ORF , a process termed overprinting 59 ., We therefore conducted overprinting analyses ( S3 Table ) using the method described by Pavesi et al . 60 for detecting de-novo ORFs based on their codon usage ., Although the X codon usage shows an expected weaker correlation with the rest of the viral genome than is the case with the other , older overlapping ORFs ( S3 Table ) , subsampling analyses suggest that the overlapping part of the X ORF is too short to derive a statistically significant conclusion ( S5 Figure ) ., In contrast to non-mammalian HBVs where the RNH domain of pol and the start of preC/C overlap , these two ORFs are instead disjoined from each other in mammalian HBVs and together encompass the non-overlapping part of the X ORF ( Fig . 4A ) ., It has been proposed that an ORF overlap can easily be eliminated in connection with a duplication of the particular region 61 , which could well have been the case in the ancestor of orthohepadnaviruses and led to the present genome organization ., This would also explain why , apart from the aforementioned differences , the locations of all other genomic features of this region have remained unchanged throughout HBV evolution , such as the exact location of DR1 , DR2 , and ε within the pol and preC/C ORFs ., To test whether there are still detectable sequence remnants ( i . e . , duplicated amino acid motifs ) of such an ancient segmental duplication , we screened the genomes of all orthohepadnaviruses against themselves as well as each other via translated nucleotide similarity searches and considered only hits that were in the same orientation in the HBV genome ., Only one amino acid motif of considerable length ( i . e . , >9 translated aa on the same strand ) appears to be duplicated ( Fig . 4C ) in the entire Orthohepadnavirus genome with up to 50% sequence similarity between the two copies ., Both potential duplicates reside within the preC/C ORF , one of them at its very beginning and the other near the 5′ end of the pol ORF ., We therefore propose a novel scenario for de-novo emergence of the X ORF in orthohepadnaviruses ( Fig . 4A ) ., This builds on the suggestion by Pavesi et al . 60 that the overlapping part of X emerged de novo via overprinting of the pol RNH domain and is completed by our inference of the origin of the non-overlapping part of the X ORF ., We hypothesize that the non-overlapping part of X arose by duplication of the first two thirds of the preC/C ORF that extended from the preC/C start to the above mentioned amino acid motif ( Fig . 4D ) ., A subsequent deletion of the first half of the first duplicate ( Fig . 4D ) purged the surplus in DR and ε motifs , potentially because it interfered with correct viral replication ., If this coincided with the induction of a frameshift mutation ( Fig . 4D ) within the partial duplicate of preC/C , this shifted the intact downstream preC/C ORF ( +2 frame ) by one nucleotide ( +3 frame ) relative to pol that resides in the +1 frame ., This would have thus prepositioned the +2 frame of the partial preC/C duplicate for overprinting ( Fig . 4D ) , while keeping the intact preC/C ORF unaffected , as it resides in a different reading frame ., Our study , together with a previous study on a Mesozoic eHBV in birds 10 , provides direct evidence for the coexistence of Hepadnaviridae and three of the five major clades of amniotes during the Mesozoic Era , two of which ( i . e . , crocodilians and turtles ) were previously not thought to be candidate hosts of extant HBVs 14 ., The latter is also the case for snakes ., While the cobra eHBVs remain undated , the three datable non-avian eHBVs described herein are ≥30 . 7 MY old , so we assume that these non-avian EVEs constitute snapshots of an ancient but now extinct host-virus association ., This is in line with the paucity of HBV endogenization events in crocodilian , snake , and turtle genomes , in contrast to birds where dozens of these occurred during their long-lasting and ongoing coexistence 2 , ., Furthermore , our non-avian HBV fossils suggest that the minimum age of definite existence of Hepadnaviridae is not >82 MY as suggested in ref ., 10 , but >207 MY and thus reaches far into the Mesozoic Era ., When considering indirect paleovirological evidence such as our phylogenetic analysis grouping mammalian HBVs as sister to crocodilian+avian HBVs ( but in disagreement with the apparent lack of mammalian HBV fossils 10 ) , Hepadnaviridae could be considered as a considerably older family of viruses with the root of all known HBVs at least in the Early Mesozoic or even in the common ancestor of Amniota ., The fact that we identified eHBVs in crocodilian , snake , and turtle genomes implies that Mammalia is the only major lineage of land vertebrates that lacks evidence for the existence of endogenous hepadnaviruses ., Unfortunately , it was not possible to determine whether the cobra eSNHBVs or their flanking sequences are present or absent in other squamate lepidosaurs ( anole lizard 35 , python 34 , and boa 33 ) , which can potentially be explained by the accelerated neutral substitution rate characteristic to this clade 27 , 34 that , together with a very high rate of DNA loss 62 , hampers the detection and comparison of orthologous non-functional genomic loci across this level of species divergence ., Likewise , fast molecular evolution must have led to the scarcity of ancient transposable element ( TE ) insertions and retroviral EVEs in these genomes 62 ., This is not expected in the case of the very slowly evolving genomes of turtles 27 , 28 and crocodilians 30 , 31 , all of which are littered with ancient TEs 63 , and readily explains why Mesozoic eHBVs are still detectable as such in their genomes , even after >200 MY of sequence decay and lack of selective constraint ., The absence of endogenous hepadnaviruses in mammals 10 despite dozens of available genome sequences 7 and a rich diversity of extant , exogenous HBV infections 13 remains puzzling ., Under the scenario of an ancient coexistence/codivergence of amniotes and Hepadnaviridae , mammalian HBVs would have had equal time for recurring , stochastic germline endogenization of viral fragments as avian HBVs had since the speciation of their amniote ancestor ., Also , relative to squamates , mammalian genomes appear to have a much slower rate of DNA loss 62 and a lower substitution rate 27 , suggesting that a fixed HBV endogenization in the germline would have potentially been detectable even after many millions of years ., Although the rate of mammalian sequence evolution is somewhat higher than that for birds 27 , 64 , it is less than that of squamates and therefore less likely to erase the evidence for a fixed HBV endogenization unless it were truly ancient ., We conclude that , while the so far sequenced representatives of major mammalian lineages generally seem to lack eHBVs , it cannot be excluded that the foreseeable sequencing of thousands of additional mammalian genomes 6 might lead to the unearthing of recent , lineage-specific endogenizations of mammalian eHBVs ., Given the evidence that hepadnaviruses coexisted with their amniote hosts at least since the Early Mesozoic , it is striking that the genome organization of HBVs have remained relatively stable over the course of >200 MY , including the patterns of overlapping protein-coding sequences and structures involved in viral replication ., The only major difference among HBV genomes appears to be the presence or absence of an X gene ., Our analyses provide multiple and independent lines of evidence that the common ancestor of Hepadnaviridae did not exhibit a fourth ORF and that the X gene therefore is an evolutionary novelty that arose in the Orthohepadnavirus lineage 10 ., If the expressed X-like protein in duck HBVs is indeed functional 20 , 65 ( note that its function was questioned in ref . 58 ) , then this gene must have emerged de novo within avihepadnaviruses , as its putative ORF region is heavily disrupted by internal in-frame stop codons in all endogenous HBV lineages discovered so far ., For example , in eCRHBV1 and eZHBV_C there are more premature stop codons in the ∼120 codons of the X-like ORF than in the total of >1100 codons of the three remaining ORFs together ( compare Fig . 4B with S1 Table and ref . 10 ) ., Most importantly , it has previously been overlooked that the X and X-like ORFs cannot represent a single , homologous origin of a gene by overprinting because they lie within different frames of the homologous region of the pol ORF that they overlap with ., Any structural 66 or functional 20 similarities between the encoded proteins must have thus evolved independently ., A scenario of X emergence via segmental duplication of preC/C and subsequent overprinting of parts of pol and preC/C ORFs provides a simple explanation to why the DR1 ( nested at the 5′ end of preC/C ) and DR2 ( nested at the 3′ end of pol ) sequences are separated by a few hundred bp of non-overlapping , X-specific sequence in orthohepadnaviruses , while they are only a few dozens of nucleotides apart from each other in non-mammalian HBVs where the X gene is missing and pol+preC/C are overlapping instead ., It is worth noting that Liu et al . 19 recently reported an avian eHBV genome that was endogenized with partially duplicated pol and preC/C ORFs , suggesting that segmental duplications do occur during replication in the virus particle and also seem to be present in the viral DNA genome that resides in the host nucleus ., Finally , the restriction of the presence of X to mammalian HBVs coincides with the notion that chronic HBV infection is associated with HCC development in mammals only , while avian HBVs do not seem to cause HCC in birds 20 ., This further adds to the substantial evidence for an oncogenic effect of the X gene of orthohepadnaviruses 21 , 22 , 23 , 24 , 25 , 26 ., Although the X protein is known to have several indispensable functions in regulation of protein interactions 26 , 67 , 68 , the initial selective advantage during its de-novo emergence remains enigmatic in the light of the otherwise highly stable , streamlined genomes of Hepadnaviridae ., Subsequent to our initial tBLASTx searches 69 ( cutoff e-value 1e–10 ) for sequence similarity between DHBV/eZHBV_C and non-avian amniote genomes , we extracted all resultant BLAST hits ( including >5 kb of sequence per eHBV flank ) for eTHBV , eCRHBV1 , eCRHBV2 , eSNHBV1 , and eSNHBV2 from turtle , snake , and crocodilian genomes ., In the case of genomes that did not yield a tBLASTx hit , we obtained orthologous sequences via BLASTn searches using the aforementioned eHBV flanks ., The sequences of each eHBV locus were aligned using MAFFT ( E-INS-i , version 6 , http://mafft . cbrc . jp/alignment/server/index . html ) 70 , followed by manual realignment ( see S1 Dataset for full sequence alignments ) ., Presence/absence states were ascertained by standards similar to those used for presence/absence of retroposon insertions 71 ., Consequently , the shared presence ( orthology ) of an eHBV is indicated by identity regarding its truncation , orientation , and genomic target site ., eHBV absence corresponds to orthologous sequences flanking an empty eHBV target site ., To complete our turtle and crocodilian sampling , we sequenced orthologous fragments of the eTHBV locus in pleurodiran turtles ( Mesoclemmys dahli , Podocnemis expansa ) and the eCRHBV2 locus in the dwarf crocodile ( Osteolaemus sp . ) using standard methods 71 ., Briefly , PCR reactions ( 5 min at 94°C followed by 35–40 cycles of 30 s at 94°C , 30 s at 45–53°C and 45–60 s at 72°C; final elongation of 10 min at 72°C ) were performed using specific oligonucleotide primers ( see S4 Table ) , followed by direct sequencing ., The sequences were deposited in the European Nucleotide Archive ( accession numbers LK391754-LK391756 ) ., We tested for evidence of non-neutral evolution in eHBV sequences by comparing nested codon models where ω was fixed to 1 or allowed to vary freely in codeml using model 0 on each pair of closely related host species with codon frequency F3X4 72 ., Model fit was assessed using the likelihood ratio test and evidence for non-neutral evolution was defined as rejection of the null model ( ω\u200a=\u200a1 ) ., After removal of premature stop codons and frameshifting indels , we analyzed the non-overlapping and overlapping parts of each ORF separately as coding sites are synonymous in one frame but non-synonymous in others in overlapping ORFs ., This in principle allows us to interpret the results of the codon model for the non-overlapping sequences ., As three of the five non-avian eHBVs are present in orthologous positions in two or more host species , respectively , we estimated nucleotide distances between orthologous sets of sequences ., The best-fit model of nucleotide substitution was chosen using jModeltest 2 73 under the Akaike Information Criterion ( HKY model: -lnL 2610 . 28570 ) and sequences were analyzed in BASEML 72 using the HKY model under a global clock and considering the respective species tree topologies of Fig . 1A ., The calculated node ages ( i . e . , half of the distance between a given pair of sequences that diverged since the root of the species tree ) were 0 . 027 for eCRHBV1 ( 2 , 501 bp ) , 0 . 008 for eCRHBV2 ( 1 , 650 bp ) , and 0 . 151 for eTHBV ( 910 bp ) ., In order to subsequently date eHBV divergences using these distances , we used neutral substitution rates reported by Green et al . 30 ., For crocodilians , they estimated a neutral rate of 3 . 9×10−10 substitutions/site/year based on a whole-genome alignment between saltwater crocodile and American alligator ., In the case of turtles , we used neutral substitution rates based on conserved 4-fold degenerate sites 30 , namely 8 . 43×10−10 substitutions/site/year for Pelodiscus sp ., and 4 . 77×10−10 substitutions/site/year for Chelonia mydas ., We aligned nucleotide sequences of eTHBV , eSNHBV1 , eSNHBV2 , eCRHBV1 , and eCRHBV2 to the whole genomes of DHBV and eZHBV_C 10 ., The resulting alignment was used to localize putative start and stop codon positions for hepadnaviral ORFs , as well as to identify frameshifts ., Nucleotide and amino acid sequences of hepadnaviral protein-coding genes were reconstructed after replacement of premature stop codons with “NNN” in the nucleotide sequences and removal of frameshift mutations ( see S2 Dataset for the near-complete genome of the crocodilian eCRHBV1 ) ., Nucleotides of frameshifting insertions were omitted and frameshifting deletions were accounted for by insertion of “N” residues ., DR sequences were identified in the near-complete eCRHBV1 genome by screening the region around the pol ORF end and the preC/C ORF start for identical direct repeat sequences ., Furthermore , we ana

Introduction, Results, Discussion, Materials and Methods

Hepadnaviridae are double-stranded DNA viruses that infect some species of birds and mammals ., This includes humans , where hepatitis B viruses ( HBVs ) are prevalent pathogens in considerable parts of the global population ., Recently , endogenized sequences of HBVs ( eHBVs ) have been discovered in bird genomes where they constitute direct evidence for the coexistence of these viruses and their hosts from the late Mesozoic until present ., Nevertheless , virtually nothing is known about the ancient host range of this virus family in other animals ., Here we report the first eHBVs from crocodilian , snake , and turtle genomes , including a turtle eHBV that endogenized >207 million years ago ., This genomic “fossil” is >125 million years older than the oldest avian eHBV and provides the first direct evidence that Hepadnaviridae already existed during the Early Mesozoic ., This implies that the Mesozoic fossil record of HBV infection spans three of the five major groups of land vertebrates , namely birds , crocodilians , and turtles ., We show that the deep phylogenetic relationships of HBVs are largely congruent with the deep phylogeny of their amniote hosts , which suggests an ancient amniote–HBV coexistence and codivergence , at least since the Early Mesozoic ., Notably , the organization of overlapping genes as well as the structure of elements involved in viral replication has remained highly conserved among HBVs along that time span , except for the presence of the X gene ., We provide multiple lines of evidence that the tumor-promoting X protein of mammalian HBVs lacks a homolog in all other hepadnaviruses and propose a novel scenario for the emergence of X via segmental duplication and overprinting of pre-existing reading frames in the ancestor of mammalian HBVs ., Our study reveals an unforeseen host range of prehistoric HBVs and provides novel insights into the genome evolution of hepadnaviruses throughout their long-lasting association with amniote hosts .

Viruses are not known to leave physical fossil traces , which makes our understanding of their evolutionary prehistory crucially dependent on the detection of endogenous viruses ., Ancient endogenous viruses , also known as paleoviruses , are relics of viral genomes or fragments thereof that once infiltrated their hosts germline and then remained as molecular “fossils” within the host genome ., The massive genome sequencing of recent years has unearthed vast numbers of paleoviruses from various animal genomes , including the first endogenous hepatitis B viruses ( eHBVs ) in bird genomes ., We screened genomes of land vertebrates ( amniotes ) for the presence of paleoviruses and identified ancient eHBVs in the recently sequenced genomes of crocodilians , snakes , and turtles ., We report an eHBV that is >207 million years old , making it the oldest endogenous virus currently known ., Furthermore , our results provide direct evidence that the Hepadnaviridae virus family infected birds , crocodilians and turtles during the Mesozoic Era , and suggest a long-lasting coexistence of these viruses and their amniote hosts at least since the Early Mesozoic ., We challenge previous views on the origin of the oncogenic X gene and provide an evolutionary explanation as to why only mammalian hepatitis B infection leads to hepatocellular carcinoma .

medicine and health sciences, cancer genetics, genome evolution, evolutionary biology, microbiology, oncogenes, hepatitis b virus, liver diseases, infectious hepatitis, hepatitis, gastroenterology and hepatology, genome analysis, genetic elements, genome annotation, molecular genetics, infectious diseases, medical microbiology, parasitism, microbial pathogens, molecular evolution, hepatitis viruses, molecular biology, community ecology, hepatitis b, ecology, trophic interactions, viral pathogens, genetics, biology and life sciences, species interactions, genomics, mobile genetic elements, viral diseases, computational biology

journal.pcbi.1005926

A theory of how active behavior stabilises neural activity: Neural gain modulation by closed-loop environmental feedback

Neural response are strongly sensitive to behavioural state ., The onset of movement such as running and whisking is coincident with prominent modulations in neural activity in sensory areas 1–3 ., The rodent whisker system has become a key model system within which to investigate these changes 4–6 ., The onset of active whisking in a previously quiet but attentive rodent is correlated with a marked reduction in endogenous synchronous neural activity of neurons in sensory areas; quantified as a reduction in low frequency fluctuations and a decrease in correlations between the membrane potentials of neurons in the barrel cortex 4 ., Furthermore , membrane potential responses to experimentally induced perturbations of the whisker are also reduced by the presence of whisking 6 ., These changes suggest that movement reduces neural gain 7 , 8 in the barrel cortex suppressing neural fluctuations and sensory response ., Several internal pathways have been implicated in this gain regulation including various neuromodulatory pathways 9 , 10 , intracortical feedback modulation by motor areas 11 or they could be directly triggered by changes in sensory input 12 , 13 via thalamo-cortical projections 14 ., Despite this gain reduction , robust responses to sensory input occur during active contact events when the whisker collides with an object placed in the whisk field 5 , 6 ., Thus , a whisking-induced gain reduction cannot by itself account for the difference in sensory responses to whisker perturbations and active contact events without appeal to additional mechanisms 15 ., The reafference principle ( RP ) 16 also does not straightforwardly explain these differences ., The RP explains the amplitude of sensory response by a mismatch between the actual sensory input and its prediction , where the prediction is based on an efference copy ( an internal copy of motor command ) ., But the RP does not explain why sensory responses to whisker perturbations , which are always unpredicted , are suppressed during movement ., Active behaviours are defined by closed-loop feedback interactions between brain/body/environment which are central to motor control and , it has been argued , pivotal to account of perceptual processes 17–19 ., During active whisking reafferent sensory input ( sensory input resulting from ones own actions ) conveys information about proprioceptive sensory feedback of whisking and which informs the subsequent motor control of the vibrissae 20 , 21 ., Repeated cycles of reafferent sensory input followed by motor output constitute a closed-loop feedback interaction between cells in the barrel cortex and the vibrissae 22 ., In this work , we show that in this system closed-loop feedback mediated by whisking vibrissae can:, 1 . Suppress synchronous endogenous neural fluctuations and passive sensory responses ,, 2 . Account for large response to active touch events because of a transient interruption of this feedback ., The results provide a nuanced view of predictive coding where neurons represent predictions errors about consequences of motor actions rather than the difference between the predicted and actual sensory input ., More generally these results strongly support the centrality of closed-loop interaction in perceptual apparatus 17 by suggesting a specific role they play in event detection ., To support a key prediction of this theory we examine how closed-loop interactions in a motor control behaviour impact on neuronal fluctuations ., Specifically , we re-analysed data from a second system , a larval zebrafish behaving in a virtual reality where fictive water flow is simulated by a grating ( striped image ) drifting across the fish retina 23 ., In this set up zebrafish larvae are immobilised with a neuromuscular blocker ., The fishs attempted movements relative to the grating are monitored through motor neuron activity and translated into appropriate modulation of the velocity of the grating 23 ., With data from this setup we show that the presence of closed-loop interactions between neurons and fictive swim speed causes the suppression of synchronous neural fluctuations across the fish brain in a manner analogous with the rodent whisker system ., Further we show that the amount of this suppression for each neuron is correlated with the strength of its involvement in the optomotor signaling ., Together , these results suggest that understanding changes in neural activity across the brain caused by the onset of movement requires the study of closed-loop brain/body/environment interactions beyond open-loop sensory paradigms ., Thus we strongly support the argument that a full understanding of phenomenology of neural circuits during active behaviors requires moving away from the idealisation of the brain as an input/output information processor toward its role as a dynamic control system regulating behaviour 19 ., In moving animals , the brain receives sensory input that originates in the external environment , or exafferent sensory input ( Fig 1A , blue arc ) ., In addition , efferent motor commands ( Fig 1A , green arc ) drive the body and environment and induce reafferent ( self-generated ) sensory input ( Fig 1A , red arc ) 16 , 24 ., To develop an intuition of how closed-loop feedback , mediated by reafferent input , could impact on neural activity we introduce two model conditions ., First , we assume that when an animal is not moving the brain receives only exafferent input , we describe this as an open-loop condition ( Fig 1B , top ) ., Second , when the animal begins to move the brain interacts with the environment coupling motor action and reafferent sensory input , we refer to this as a closed-loop condition ( Fig 1C top ) ., Note: it is likely that some reafferent input is always present but our focus here is on the effect that the onset of a previously absent reafferent sensory pathway could have on neural activity ., We examine these two conditions in a simple idealized model , see 17 for a similar idealisation , where brain variable B ( which describes collective neural activity , e . g . , membrane potential activity ) receive input from , or interacts with , the body and environment ., In the open-loop condition the collective neural activity , Bo ( t ) , is assumed to be described in term of a first-order linear differential equation ,, dBo ( t ) dt=−Bo ( t ) τ+I ( t ) +ξo ( t ) ,, ( 1 ), where ξo is white noise of instantaneous variance σ2 generated inside the brain , t is time , τ is the time constant of the system and I ( t ) is exafferent input ., Essentially , in the absence of input , we represent collective neural activity as a simple leaky integrator system with leak timescale τ driven by endogenous noise ( see Fig 1B , bottom , for traces ) ., Of interest here is the magnitude of fluctuations which can be calculated as the autocorrelation peak ( instantaneous variance ) of variable Bo which is Peako = σ2 τ/2 , and the gain of the response to sensory input ( calculated as the ratio between a static input and an equilibrium response ) , which is simply Gaino = τ ., Thus in this simple system both the gain and the fluctuations are determined by the timescale of the endogenous dynamics ., However , during the closed-loop condition we write the dynamics of the brain variable ,, dBc ( t ) dt=−Bc ( t ) τ+wBc ( t ) +I ( t ) +ξc ( t ) ,, ( 2 ), where we have idealised reafferent input as a simple self-feedback signal with strength w , i . e . , we have assumed this feedback is linear and instantaneous ( we will relax this assumption later ) ., In this condition , the continuous cycles of reafferent input constitute a closed-loop feedback signal to the brain ., The presence of this feedback changes the effective time constant to τeff = τ/ ( 1−wτ ) ., The magnitude of the fluctuations is now characterized by autocorrelation peak Peakc = Peako/ ( 1−wτ ) and the effective gain of the system is Gainc = Gaino/ ( 1−wτ ) ., In particular , if this feedback is negative ( w < 0 ) , it will suppress both fluctuations and the gain of sensory responses , see Fig 1B and 1C ( bottom panels ) ., This very simple model suggests that , in principle , closed-loop feedback mediated by the body/environment could have a direct impact on neural activity ., One way to accentuate sensory responses is described in Fig 1D ., Here the brain initially has low closed-loop gain ( Gainc = τ/ ( 1−wτ ) ) and thus exhibits suppressed fluctuations ., However , if during a sensory event ( Fig 1D , grey bar ) closed-loop feedback is interrupted , e . g . , if whisking is interrupted by contact with an object ( see below ) , then brain will have temporarily high open-loop gain ( Gaino = τ ) ., Thus the combination of a large sensory response and suppressed background fluctuations prior to sensory event can accentuate signal-to-noise ratios ., In the following , we explain how these three conditions can be realized in the rodent whisker system ., In this study we proposed the idea that negative closed-loop sensory feedback during active behavior reduces network gain , which in turn , suppresses synchronous neural fluctuations and modulates sensory responses ., We supported this with modelling and data analysis in the whisker system and in a behaving zebrafish , see summary Fig 7 ., The formal component of our theory , i . e . , that closed-loop sensory feedback can modulate a systems gain , is well documented in dynamical systems theory and control theory 32 , 33 ., This gain control occurs even though the pathways mediating feedback are purely additive ( c . f . Eqs 1 and 2; i . e . , effectively repeated cycles of feedback accumulate over time and produce a multiplicative effect ) ., Thus , a constitutively active closed-loop feedback that mediates action-perception cycles is essential for the form of gain control we propose ., This means that discrete and intermittent involvement of reafferent input does not imply gain modulation ., For example , the classical reafference principle explains neural responses by a one-time detection of the mismatch between an efference copy ( predicted ) and reafferent ( actual ) input 16 ., However , this situation is likely an inaccurate idealization to describe the closed-loop systems studied here ., For example , in the zebrafish system , swim bouts typically occur every 700 ms and this interval closely overlapped with the peak of the estimated sensory feedback interaction ( Fig 6B ) ., Hence , the neural responses in the fish experiment suggest a more dynamic system , where neural activity evoked by many cycles of action and sensation are continuously and mutually interacting ., The idea that closed-loop feedback is central to cognition is not new and has early precedents in behavioral psychology 19 , resonate with a movement in embodied cognitive science 18 , 34 , 35 and has recently been proposed as concrete alternative to input/output conception of perceptual processing 17 , 36 ., Our work shares the view of these proposals and provides a specific example where brain function is contingent on closed-loop interactions between brain/body/environment ., Furthermore , we provided a mathematical model showing why neural dynamics underlying cognitive states cannot be recapitulated even if the sensory input during active behavior is identically repeated , i . e . , a replay condition 37 ., The presence of continuous negative closed-loop sensory feedback during active behavior is fundamental for our theory ., In our rodent study we assumed negative closed-loop sensory feedback was mediated directly by a cortical-whisker circuit ., However , our theory is agnostic to the detail of the neural implementation and several other schemes are possible ( see S2 Appendix ) ., This assumption is consistent with the idea that the barrel cortex comprises a nested set of servo control loops that regulate various aspects of whisker dynamics 22 ., At the level of the whole vibrissa system multiple parallel and nested feedback loops both positive and negative most likely exist 22 ., In zebrafish , the presence of negative feedback during swimming behavior is a priori necessary for optic-flow stabilization behavior because the fish must act in opposition to perceived optic flow in order to minimize horizontal displacement 38 , 39 ., Interestingly , neurons that received strong negative feedback and were substantially stabilized were located in the cerebellum ( Fig 6D ) ., This is consistent with the theoretical viewpoint that the cerebellum is strongly involved in the action-perception cycle 40–42 ., We suggest that closed-loop sensory feedback plays a major role in brain state control ., However , importantly , we do not propose this mechanism is mutually exclusive with other mechanisms , such as thalamo-cortical input 25 or neuromodulation 10 , 43 , 44 because brain state transitions also occur in the absence of sensory feedback e . g . , the onset of running that does not change the visual input 3 , 45 , during sleep 46 , 47 , or by dissection of the sensory nerve 5 , 25 ., Mechanisms underlying brain state transitions are likely to be redundant and occur even in the absence of mechanisms , such as thalamo-cortical input 25 or corollary discharge 26 , albeit involving further delay ( see S1 ) ., Such functional redundancy may help to maintain the stability of brain state 44 , 48 , 49 ., Furthermore , the relative importance of internal and external mechanisms might adaptively change in an experience-dependent manner 50 ., In the whisking model , we proposed that the regulation of cortical gain by closed-loop sensory feedback could explain enhanced active touch ., Specifically , negative sensory feedback during whisking reproduces suppressed fluctuations and reduces responses to passive whisker stimulation ( see Figs 2 and 3 ) ., Moreover , robust neural response to active touch events could be explained by the interruption of this feedback when the whisker is driven into an external object ., These interruptions transiently release the cortex from a low gain state and enhancing sensory responses to salient sensory stimuli ., This mechanism for active touch contrasts with the account of sensory processing suggested by the reafference principle 16 , which postulates that motor efference is discounted from sensory input , allowing animals to sense exafferent signals ( externally caused sensory input ) without being confounded by the consequences of their own motor actions ., In contrast , our theory suggests that the sensory system is insensitive to pure exafference during active sensing 4; see Fig 3 , but sensitive to the interruption of reafference which may allow animals to focus attention on the consequences of their own motor actions ., This idea is supportive of other work that has cast doubt on the role of efference copy during active sensing 51 ., This mechanism is also distinct from the most common form of predictive coding 52 , where neural activity represents the error between the actual and the brain’s prediction of sensory input ., Instead our suggestion could be viewed as a more specific form of predictive coding where neurons represent predictions errors about consequences of motor actions , in this sense it is closer to the idea of active inference 53 , 54 ., While it is straightforward to generalize this sensory mechanism to other tactile systems , its implication for other modalities is less clear ., However , in theory , closed-loop sensory feedback could be interrupted anywhere along the action-perception cycle , thus dynamically regulating neural gain ., The timely interruption of this feedback , possibly related to transient freezing of behavior , could serve as a general mechanism for temporarily accentuating neural responses against a background of reduced noise ., For example , closed-loop sensory feedback could be gated by the frequency of miniature eye movements 55 a hypothesis that complements a previous proposal suggesting such movements are under active closed-loop control 56 ., Furthermore , cerebellum neurons , which are strongly involved in the sensory-motor cycle , could be suppressed in anticipation of salient sensory events by a relevant brain area , such as the reticular formation 31 , 57 ., The importance of using naturalistic sensory stimuli to study and manipulate brain state dynamics is widely demonstrated 58 ., However , an important prediction of our theory ( Fig 4 ) , supported by our experimental findings is that brain dynamics during active sensing cannot be fully recapitulated or re-encoded , even if the same sensory input is precisely recorded and replayed back into a passive brain ., These results provide evidence that brain state during active behaviors can only be accurately understood by a quantitative account of ongoing brain-environment interactions 18 ., To investigate the ‘in principle’ feedback between barrel cortex and whiskers we model a simple cortical circuit that interacts with a single whisker , see Fig 2A ., Our cortical circuit comprises of N excitatory and N inhibitory neurons ( i = 1…N are excitatory and i = N+1 , … , 2N are inhibitory , N = 100 ) modeled as a linear dynamical system by ,, x . i=−xi+∑j=12Nwijxj−ai−wxθθp+ξi+I ,, which is numerically simulated by a Euler forward integration method with time-bin dt = 0 . 5 ms . Hereafter , all time derivatives are taken to represent single-step differences divided by dt ( e . g . x ., ( t ) =x ( t+dt ) −x ( t ) /dt ) , but we omit the ms time unit ., wij is the synaptic strength from neuron j to i , ai is an adaptation current described below , θp is the whisker protraction angle interacting with neurons with weight wxθ = 0 . 002 , I is exafferent input that takes I = 0 . 035 upon whisker stimulation and otherwise zero , and ξi is independent white noise of unit variance added to each neuron ., We interpret xi as both the firing rate and membrane potential , assuming a roughly linear relationship between the two ., Entries in the connectivity matrix are assigned as wij = bijJ + b′ijg for excitatory synapses ( j = 1… , N ) and wij = −b″ijg for inhibitory synapses ( j = N + 1 , … , 2N ) , where bij , b′ij , b″ij are all random binary values that take b0 > 0 with probability p = 0 . 1 and 0 with probability 1 − p , respectively ., The weights are scaled by J=1pN and g=g0√2Np ( 1−p ) , so that dynamics are insensitive to the parameter values of p and N . Note that the eigenvalue spectrum of the connectivity matrix wij is centered around b0 and spread with the radius b0g0 in the limit of large N . Hence , the network is excitation dominated ., The variability of weight values across neurons is controlled by the magnitude b0g0 of the excitatory-inhibitory-balanced component and this variability is controlled by the parameter g0 = 0 . 05 , which reproduces highly synchronized up/down-like fluctuations during the quiet state ., To promote significant network fluctuations observed in the barrel cortex we scale of the connectivity matrix b0 such that the lead eigenvalue of this matrix is close to unity ( ≈ 0 . 975 and the dynamics are close to instability ., We include an adaptation current that gives these fluctuations a low frequency ( ca . 1 Hz ) component modelling up/down-like oscillations 59–61 in the absence of neuron/whisker interactions ., The adaptation current is integrated as, ai˙=−0 . 07ai+0 . 008xi, Over time , the adaptation variable slowly builds upon neural activity and suppresses neurons , resulting in the ca ., 1-Hz oscillation ., Consequently , in the absence of interactions with the whisker , implemented by setting wxθ = 0 , this simple network reproduces the power spectrum and cross-correlogram of neurons in the barrel cortex 5 , 6 , see Fig 2B and 2C ., We model a simple flexible vibrissa as two hinged sections ( with bending angle θh ) connected at the base ( with protraction angle θp to the body ) of unit length which are constrained by simple torsion springs with spring constant k1 and k2 respectively , see Fig 2A ., We assume the whisker is light and frictionless and simulated it by numerically minimising the energy of the system ,, E=k1 ( θp−θeq ) 2+k22θh2 ,, where θeq equilibrium value of the base spring ., Here , only the ratio k1/k2 is important for the results and , without losing generality , we set k1 = 1 ., The central hinge spring has an equilibrium value of zero angular displacement and thus tends to align both sections ., Whisking is driven both by the cortex and a central a pattern generator ( CPG ) 62 ., Specifically , the equilibrium value of the base spring , θeq is set as ,, θeq˙=−0 . 93θeq+wθxN∑i=1Nxi+u ,, where the second term on the right-hand side is the sum of activity in the cortical excitatory population and the third term is the external CPG activity ., Here u is modeled as simple stochastic oscillator , given by, u˙=− . 98u+2πFwhiskv+ξu, v˙=− . 98v−2πFwhisku+ξv ,, where Fwhisk = 10Hz is the frequency of the oscillator and ξu , ξv are independent Gaussian white noise ., wθx = 0 . 085 describes the relative strength of the cortex versus the CPG in driving the whisker variable ., With this parameter , the whisker is mainly driven by the CPG and is only modulated by cortical activity ., In this model , most excitatory neurons respond to whisker retraction and drive whisker protraction ., Adding a separate counterpart population that responds to whisker protraction and drives whisker retraction in a similar manner does not change the model’s behavior ., We simulate a passive deflection of the whisker by a brief injection of input of I = 0 . 035 to the cortical neurons for c . a 25 ms . The magnitude of this input approximately matches the evoked change over the standard deviation of the membrane potential ( ΔVm/σVm ) in response to magnetic whisker deflection during the whisking condition 5 ., Contact events are simulated by simulating a horizontal solid wall is placed above the whisker ( 1 unit length away ) ., To simulate contact with the wall we solve the energy equation subject to the length constraint in the vertical direction ,, sin ( θp ) +sin ( θp−θh ) <1 ., Thus , as the whisker collides with the wall it deforms accordingly , see Fig 2A ., By adjusting the relative stiffness of each torsion spring ( i . e . k2/k1 ) , we can control the degree to which the protraction angle is affected by contact events , e . g . , if the whisker is very flexible , the protraction angle will change continuously , despite contact of the tip ., During contact we also inject an input ( I = 0 . 035 ) to the cortical neurons for the duration of the contact event , but for no longer than 25 ms to simulate contact-detection signal that results from the stereotypical response of pressure sensitive cells in the trigeminal ganglion 27 ., The model was run for 200 s in the closed loop , open-loop , and sustained period of active touch to calculate all quantitative measures ., To quantify the discriminability of whisker contact events we calculated an information theoretic measure of generalized signal-to-noise-ratio ., Specifically , we calculated the Chernoff distance 63–65 between probability distributions , p1 ( x ) and p0 ( x ) , in the presence or absence of a sensory event , respectively ., Specifically , this measure, Ψ ( p1∥p0 ) ≡−min0<λ<1log∫p1λ ( x ) p01−λ ( x ) dx, summarises the detectability of whisker stimulation based on population responses and , unlike a naive calculation of signal-to-noise ratio , is applicable even when p1 ( x ) and p0 ( x ) are very different distributions ., For our model , the probability distribution for each condition is well described by a Gaussian distribution ,, p0/1 ( x ) =|2πC0/1|−1/2exp ( −12 ( x−μ0/1 ) C0/1−1 ( x−μ0/1 ) ) ,, where C0/1 and μ0/1 are covariance matrix and vector of means , respectively , in the presence ( with subscript 1 ) or absence ( with subscript 0 ) of a sensory event ., By substituting this into the expression for Chernoff distance and employing the Gaussian integral identity and expressing the Chernoff distance in terms of C0 , C1 , and μ0 , μ1 , we calculate the covariance and mean between a small number of neurons ( here three ) , randomly selected from the cortical network described above ., We calculate covariance’s across ensembles of 500 networks every 10 ms for a period of 1 s , starting at the onset of the sensory event ., Minimization with respect to λ is computed numerically ., In a transgenic fish expressing the calcium indicator GCaMP2 brain-wide calcium activity was monitored using a two-photon microscope to scan single planes in the brain ., We analyzed the calcium signal ( ΔF/F ) at various sample frequencies ( ca . 2−3 Hz ) across 1908 cells in 32 fish , see 23 and electrical recordings of swim power ., We analyzed data taken from a 6-min recording of 1−6 prominent calcium sources per fish , putative neurons , across 600 trials ., In the first 3 min , the fish performed the closed-loop optomotor behavior ., For the subsequent 3 min , each fish was presented with the stimulus received in the closed-loop stimulus which is a repeat of what the animal experienced in the previous 3 min , the replay condition ., In the original study , the gain ( i . e . , the multiplicative factor between fictive swim power , and the speed of visual feedback ) was alternated between a high and low gain condition every 30 s ., This gain alternating protocol is not relevant to the current study ., To reduce this variability in data , we subtracted the mean activity level in each gain setting in our analysis ( from both brain and behavior variables ) ., Notably , our main results were qualitatively the same , even without such subtraction of the means ., We distinguish variables in the closed loop condition ( Bc and Ec ) and replay condition ( Br and Er ) , see Fig 5B ., Specifically , we assume that the closed-loop dynamics in the frequency domain are described by the following equations ,, Bc ( ω ) =F ( ω ) Ec ( ω ) +RBc ( ω ), ( 4 ), Ec ( ω ) =G ( ω ) Bc ( ω ), where F ( ω ) is an afferent filter describing the interaction from the environment to the brain ( i . e . , the Ec → Bc filter , see Fig 5B dashed blue arrow ) and G ( ω ) is an efferent filter from the brain to the environment ( i . e . , the Bc → Ec filter , see Fig 5B dashed green arrow ) , respectively , and RBc ( ω ) is the residual inputs not accounted of by the filters ., Note: we have assumed that the noise on the environment is negligible , this is a reasonable assumption given that visual flow is directly modulated by motor nerve activity ., Similarly , we also write the replay dynamics in the frequency domain as ,, Br ( ω ) =F ( ω ) Ec ( ω ) +RBr ( ω ), ( 5 ), Er ( ω ) =G ( ω ) Br ( ω ) ., In the replay condition , neurons are driven by the recorded visual stimulus in the closed-loop condition , which is determined by fish’s motor activity in the closed-loop condition Ec ., Note: we have made the assumption that F ( ω ) and G ( ω ) are the same filter in the both conditions ( i . e . , the interactions with the same color in Fig 6A have the same property ) because the sensory and motor circuits in the brain remain the same between the conditions ., We use Eq 5 in the replay condition to fit the linear filters F ( ω ) and G ( ω ) because the computation would be more involved in the closed-loop condition than the replay condition ., We first calculate linear filter F ( Fig 6A , solid blue arrow ) that minimizes the mean square error between the observed variable Br and the convolution F * Ec over time ., Next , we determine G ( t ) by first calculating the residual variability of neural activity in the replay condition that cannot be accounted for by the closed-loop environment , i . e . , RBr ( ω ) =Br ( ω ) −F ( ω ) Ec ( ω ) and subsequently calculating how RBr drives the environment in the replay condition Er , effectively determining the Br → Er interaction ( Fig 6A , solid green arrow ) ., The filters were constrained as a superposition of Laguere functions ., We use Laguere functions up to the order that best satisfied the Akaike Information Criterion 66 ., Almost all filters had an order that was mid-range between 1 and 15 ., The Ec → Br → Er interaction ( Fig 6A solid orange arrow ) is then straightforwardly computed by the convolution of both filters , H ( ω ) = F ( ω ) G ( ω ) ., Based on the assumption that the filters are the same in the two conditions , we assume that self-feedback in the closed-loop condition ( Fig 6A , dashed orange arrow ) is the same as H ( ω ) ., In our investigation , we calculated the ratio of the low frequency power of neural fluctuations between the closed-loop and replay conditions ., We then compare this empirical ratio with the theoretically expected ratio based on the estimated filters ., To derive this theoretically expected ratio , we write the dynamics of neural activity in the closed- and replay conditions in the frequency domain as ,, Closed‑loop:Bc ( ω ) =H ( ω ) Bc ( ω ) +RBc ( ω ) = ( 1−H ( ω ) ) −1RBc ( ω ) Replay:Br ( ω ) =H ( ω ) Bc ( ω ) +RBr ( ω ) ,, where H ( ω ) = F ( ω ) G ( ω ) is the estimated combined filter in the frequency domain and we assume the noise in the closed- and replay conditions have the same power spectrum , i . e . , RBc ( ω ) 2=RBr ( ω ) 2 ., The ratio of the power between each condition is then ,, Bc ( ω ) 2Br ( ω ) 2=1H ( ω ) 2+1−H ( ω ) 2 ., We also investigated the effect of accumulative cycles of feedback on brain dynamics by comparing the full closed-loop effect with a control effect that includes only one-time feedback ., Namely , we can expand the contribution of each cycle in a geometric series as, Bc ( ω ) = ( 1−H ( ω ) ) −1RBc ( ω ) = ( 1+H ( ω ) +H2 ( ω ) +H3 ( ω ) ⋯ ) RBc ( ω ), where the O ( Hn ) term in the above Taylor expansion describes the effect from signal propagation along the feedback loop for n times ., By neglecting the contributions with n>1 , we can write the effect of a single cycle of feedback effect as ,, B1 ( ω ) = ( 1+H ( ω ) ) RBc ( ω ) ., This yields an alternative expression for the ratio of the power between each condition that only includes one-time effect of feedback as ,, B1 ( ω ) 2Br ( ω ) 2=1H ( ω ) 2+1+H ( ω ) −2 ., To further investigate how the effective interaction between the brain and the environment depends on the closed-loop feedback , we compare Ec → Br filter in the replay condition and the Ec → Bc filter in the closed-loop condition naively computed by neglecting closed-loop effects ., Notably , the naïve Ec → Bc filter in the closed-loop condition generally has an acausal component , because the brain Bc and the environment Ec are mutually interacting ( see below ) ., Thus to calculate these filters we use Hermite rather than the Laguere functions to capture the acausal ( t<0 ) side of the filter ., To quantify the difference between these filters , using Eq 4 , we write, Ec ( ω ) = ( 1−H ( ω ) ) −1 ( G ( ω ) RBc ( ω ) ) ,, and thus the naïve Ec → Bc filter in the closed-loop condition is, Bc ( ω ) Ec* ( ω ) Ec ( ω ) Ec* ( ω ) =F ( ω ) + ( Ec ( ω ) RBc* ( ω ) Ec ( ω ) Ec* ( ω ) ) *=F ( ω ) + ( G ( ω ) 1−H ( ω ) ) *|RBc ( ω ) |2|Ec ( ω ) |2, where * describes complex conjugate ., Hence , this filter is different from the corresponding filter F ( ω ) in the replay condition by the second term ., To predict the second term without knowing RBc , we again assume |RBc ( ω ) |2≈|RBr ( ω ) |2 , where the latter spectrum is based on the residual RBr computed in the replay condition .

Introduction, Results, Discussion, Material and methods

During active behaviours like running , swimming , whisking or sniffing , motor actions shape sensory input and sensory percepts guide future motor commands ., Ongoing cycles of sensory and motor processing constitute a closed-loop feedback system which is central to motor control and , it has been argued , for perceptual processes ., This closed-loop feedback is mediated by brainwide neural circuits but how the presence of feedback signals impacts on the dynamics and function of neurons is not well understood ., Here we present a simple theory suggesting that closed-loop feedback between the brain/body/environment can modulate neural gain and , consequently , change endogenous neural fluctuations and responses to sensory input ., We support this theory with modeling and data analysis in two vertebrate systems ., First , in a model of rodent whisking we show that negative feedback mediated by whisking vibrissa can suppress coherent neural fluctuations and neural responses to sensory input in the barrel cortex ., We argue this suppression provides an appealing account of a brain state transition ( a marked change in global brain activity ) coincident with the onset of whisking in rodents ., Moreover , this mechanism suggests a novel signal detection mechanism that selectively accentuates active , rather than passive , whisker touch signals ., This mechanism is consistent with a predictive coding strategy that is sensitive to the consequences of motor actions rather than the difference between the predicted and actual sensory input ., We further support the theory by re-analysing previously published two-photon data recorded in zebrafish larvae performing closed-loop optomotor behaviour in a virtual swim simulator ., We show , as predicted by this theory , that the degree to which each cell contributes in linking sensory and motor signals well explains how much its neural fluctuations are suppressed by closed-loop optomotor behaviour ., More generally we argue that our results demonstrate the dependence of neural fluctuations , across the brain , on closed-loop brain/body/environment interactions strongly supporting the idea that brain function cannot be fully understood through open-loop approaches alone .

Animals actively exploring or interacting with their surroundings must process a cyclical flow of information from the environment through sensory receptors , the central nervous system , the musculoskeletal system and back to the environment ., This closed-loop sensorimotor system is essential for an animals ability to adapt and survive in complex environments ., Importantly , closed loop feedback signals also regulate brainwide neural circuits for behavior ., Specifically , the activity of coherent populations of neurons inform motor behaviours and in turn are influenced by sensory feedback signals mediated by the environment ., We develop a theory that suggests that this feedback can explain the marked changes in large-scale neural dynamics and sensory processing ( together referred to as brain state ) that coincide with the onset of active behaviours ., This feedback may contribute to flexible context dependent neural computations in brain systems .

control theory, medicine and health sciences, fish, swimming, engineering and technology, membrane potential, vertebrates, electrophysiology, social sciences, neuroscience, animals, biological locomotion, motor neurons, control engineering, animal anatomy, systems science, mathematics, zoology, computer and information sciences, animal cells, touch, animal physiology, cellular neuroscience, psychology, eukaryota, cell biology, vibrissae, physiology, neurons, biology and life sciences, cellular types, physical sciences, sensory perception, organisms

journal.ppat.1004381

A New Human 3D-Liver Model Unravels the Role of Galectins in Liver Infection by the Parasite Entamoeba histolytica

The protozoan parasite Entamoeba histolytica is the etiological agent of human amoebiasis ., The parasite has a simple life cycle alternating the contaminating cyst and the vegetative trophozoite form ., Infection occurs upon uptake of cysts with contaminated water or food and their differentiation into trophozoites , which colonize the intestine ., Amoebae may breach the intestinal barrier and disseminate through the portal vein system , mainly to the liver ( approximately 1% of the carriers ) ., Hepatic amoebiasis is characterized by the induction of an inflammatory host response , the invasion of the liver parenchyma and the subsequent formation of abscesses , and leads to 70 , 000 deaths per year 1 ., To study hepatic amoebiasis experimentally , we previously used hamsters ( Mesocricetus auratus ) since these are highly susceptible to E . histolytica infection and develop amoebic liver abscesses in a few days 2 ., With this animal model we have shown that invasion of the liver parenchyma depends on amoebic adhesion to host cells through the activity of the galactose- or N-acetyl-galactosamine-inhibitable ( Gal/GalNAc ) lectin , the main amoebic adhesion 3 ., For instance , trophozoites deficient in signalling through the Gal/GalNAc lectin ( HGL-2 strain ) form small foci close to the endothelium which do not progress to liver abscesses 4 ., We also observed endothelial cell apoptosis in the vicinity of wild-type trophozoites as shortly as 1 h after infection , whereas with HGL-2 trophozoites cell death was delayed by almost 24 h 5 ., However , the step in which HGL-2 trophozoites are blocked is currently not known ., Liver sinusoidal endothelial cells ( LSEC ) and hepatocytes are the major cellular components of the liver , accounting for around 80% of the liver mass 6 , and LSEC constitute the first hepatic barrier during liver invasion ., Little is known about the transmigration of E . histolytica through the hepatic endothelium , and the molecules required for interaction with LSEC ., We have previously shown that cells of a human LSEC line respond in a localized manner to the presence of E . histolytica with changes in the integrin-mediated adhesion signalling , retraction , loss of their cytoskeleton organization and focal adhesion complexes 7 ., These alterations may be relevant for the disease , by facilitating the endothelial barrier crossing or altering the immuno-modulatory function of LSEC ., Further studies aiming to determine the mechanisms of amoebic adhesion to LSEC and endothelium crossing have been limited by the simplicity of two-dimensional ( 2D ) cell culture systems and the difficulty to molecularly handle the high complexity of animal models ., Moreover , animal data should be extrapolated with caution to the human disease since humans being the exclusive natural hosts for E . histolytica and rodents do not reproduce human liver physiology and immunology , in particular regarding human inflammatory diseases 8 ., The development of a human 3D-liver model , consisting of cells grown in 3D-scaffolds and bridging the gap between cell cultures and tissues , would provide a new alternative for the study of human hepatic amoebiasis ., While in the cell biology field the utility and advantages of in vitro tissue-like models are recognized , for infectious disease studies they have been used only rarely 9 10 11 ., Tissue-like models allow the use of primary or immortalized human cells , the control of the non-cellular components of the microenvironment and analysis by advanced imaging techniques 12 13 ., Major advantages of this approach are the reduction of the complexity to a controlled but still physiologically relevant level and the possibility to define the roles of individual components at the molecular level ., The major objective of the present work was to analyse the early stages of hepatic sinusoid invasion by E . histolytica , in a human and physiologically relevant system ., We intended to examine the mechanisms of E . histolytica adhesion to and crossing of the endothelial hepatic barrier , migration to and interaction with hepatocytes , and the induction of the immune response ., To accomplish this objective we elaborated a human 3D-liver model mimicking basic features of the hepatic sinusoid environment ., In vivo , LSEC form monolayers devoid of a basement membrane that are separated from hepatocytes by the Disses space containing loosely organized extracellular matrix ( ECM ) components ., Our 3D-liver model is thus composed of an LSEC layer co-cultivated on top of a hepatocyte layer embedded in a 3D collagen-I ( COL-I ) matrix ., We chose to use cells from established cell lines , since primary cell cultures rapidly lose their hepatic phenotype 14 15 and show higher phenotypic variability ., The LSEC line used was established from non-tumour liver endothelial cell primary cultures , and cells maintain the expression of typical markers 16 ., Huh-7 cells , though hepatoma-derived , are widely used for their phenotypic resemblance to differentiated hepatocytes 17 18 ., By analysing several physiological aspects such as sinusoid architecture , matrix porosity and barrier permeability , the presence of cell-cell and cell-ECM adhesion molecules , the expression of hepatic markers , as well as the secretion of soluble molecules , we demonstrated the value of the 3D-liver model for the study of hepatic invasion by E . histolytica ., Two-photon microscopy was used to monitor interactions of E . histolytica with the 3D-liver model components ., We investigated the organization of the LSEC and hepatocyte layers , as well as trophozoite cytoadhesion and matrix crossing , migration and cytotoxic effects ., The data showed that LSEC efficiently function as an endothelial barrier for the parasite ., Trophozoites impaired in Gal/GalNAc lectin ( the major adhesin ) function showed reduced ability to cross the LSEC barrier , indicating an important role for this protein in the early steps of liver invasion ., The 3D-liver model made possible long-term incubations with E . histolytica under serum-free conditions permitting to determine the secretome ( proteome of soluble factors ) composition and to identify new factors released upon amoebic interaction with the human cells ., The analysis identified for the first time galectin family members participating in amoebic infection ., Galectin has been reported to play a role during the innate immune response to several microbial infections 19 20 ., ELISA confirmed the presence of galectins and further indicated that the hepatic cells respond to amoebic interactions by a temporally and spatially organized pro-inflammatory reaction ( IL-1β , IL-6 , IL-8 , TGF-β1 ) ., Cell adhesion and binding assays revealed the participation of galectin-1 and -3 in amoebic adhesion to cells ., The role of galectins was confirmed by the striking reduction of amoeba adhesion to siRNA-treated LSEC exhibiting reduced galectin-1 levels ., The data reveal a dual role of human galectin-1 and -3 and describe , for the first time , the participation of human galectins in hepatic infection with E . histolytica ., Taken together , the 3D-liver model we established we conclude on the combined action of amoebic Gal/GalNAc lectin and human galectins during the early steps of hepatic amoebiasis ., The human 3D-liver model we established is composed of a monolayer of Huh-7 hepatocytic cells embedded in a 3D COL-I matrix and an LSEC monolayer plated on top of the matrix facing the medium ( Figure 1 ) ., The 3D-liver model architecture organization was evaluated by two-photon microscopy and SHG visualization of the hepatic cells layers and matrix structure ( Figure 1 ) ., The matrix structure in the absence and in the presence of the hepatic cells was compared demonstrating that without cells ( Figure 1A–B ) the COL-I fibres were homogeneously distributed throughout the matrix , which was at least 900 µm high ., In the presence of the cells ( Figure 1D–E ) , the same matrix layer had a mean height of only around 500 µm and its structure changed to a heterogeneous distribution of the COL-I fibres ., The SHG signals of the fibres were more intense and the matrix porosity ( Figure 1C and F ) was significantly smaller in the vicinity of LSEC and hepatocytes , demonstrating that the hepatic cells remodel the matrix architecture of their 3D environment ., To quantify the ability of E . histolytica to cross the LSEC layer and to determine the rate of migration in the 3D matrix towards the hepatocytes we used a 100 µm mean distance between LSEC and hepatocytes enabling the distinction of the compartments ( Figure 1 ) ., This matrix height takes into account the size of trophozoites ranging from 25 to 50 µm and their high motility ( 10 µm/sec ) ., The distance between LSEC and hepatocytes diminished progressively over culture time and the most reproducible 100 µm mean distance was obtained upon three days of culture ., Furthermore , to decipher the individual contributions of LSEC and hepatocytes to the response to amoebae we compared the 3D-liver model with setups lacking either the LSEC or the hepatocyte layer ( schematically represented in Figure 2A ) ., Several characteristics of the 3D-liver model were analysed to determine its physiological relevance ., Cell morphology , growth and the presence of cell-cell and cell-ECM adhesion molecules were monitored over time ., By immunofluorescence microscopy , LSEC and hepatocytes were positive for ICAM-1 and integrin-β1 , and hepatocytes showed a strong E-cadherin surface labelling ( Figure S1 ) ., To evaluate LSEC barrier function , monolayer permeability was determined through the ability of 1 µm diameter fluorescent microspheres to penetrate into the compartments of the 3D-liver model ( Figure S2 ) ., After 3 h of incubation , only 0 . 14% ( mean number ) of the beads added on top of the LSEC layer were found in the matrix beneath ., In the absence of LSEC , the proportion of beads inside the matrix was comparable to the matrix without cells ( around 80% ) and the beads stopped at the position of the hepatocyte layer ., Data show that both the LSEC and hepatocyte layer of the 3D-liver model are efficient barriers for 1 µm particles ., Albumin secretion and the expression of transcripts encoding several hepatocyte-specific functions were compared between the 3D-liver model ( with or without LSEC ) and Huh-7 standard 2D monocultures ( Figure 2 ) ., Albumin release was significantly higher in the 3D-liver model than in 2D cultures in all time points analysed ( Figure 2B ) ., The hepatic markers we chose are either involved in drug metabolism as cytochromes P-450 ( CYP2C19 , CYP3A4 ) and UDP glucuronosyltransferase-1A6 ( UGT1A6 ) or belong to the solute carrier transporter family ( SLC2A1 , SLC2A2 ) ., In addition , we tested expression of hepatocyte nuclear factor-α ( HNF4A ) , a key transcription factor for many hepatic genes ., The expression of most of those genes was maintained or increased between the 3- to 14d-period tested ( Figure 2C and S3 ) ., Moreover , transcript levels for SLS2A1 , SLC2A2 , CYP2C19 and UGT1A6 were higher in the 3D-liver model than in the 2D culture at the 3d time-point ( Figure 2D and S3 ) ., Together the results demonstrate that the 3D-liver model retains hepatic barrier performance and hepatocyte functionality in a more physiological environment than standard 2D cell cultures ., For the experiments described below we used the 3D-liver model 3d after having started its preparation ., The 3D-liver model offers the possibility to characterize the molecules released by the cells into the culture medium on its top , since in this model LSEC have lost the strong serum dependence observed in conventional cell culture conditions , i . e . they are viable , morphologically normal and express adhesion markers ICAM-1 and integrin-β1 for at least 12 h without serum ( data not shown ) ., The compounds released were identified after 3 h in fresh serum-free medium , using liquid chromatography–mass spectrometry ( LC-MS/MS ) analysis ( Table S1 ) ., From the 64 human-specific proteins identified ( Table S2 ) , the 45 proteins known for being released were grouped according to their main functions ( Table 1 ) ., Several components and regulators of the blood coagulation ( 9 proteins ) , the complement cascade and the innate immune response ( 7 proteins ) were detected ., Within the group of 11 plasma transporters , all exclusively or mainly synthesized in hepatic cells , well-known hepatic markers 14 were present , such as serum albumin , α-fetoprotein , apolipoproteins A-I , A-II , B-100 , and E . In addition to COL-I , the ECM components fibronectin , nidogen-1 , and fibrinogens were found , as well as several adhesion molecules ., These results reveal that a variety of hepatic functions are expressed and that the hepatic cells are capable to enrich their micro-environment in the 3D-liver model ., The 3D-liver model was used to study initial steps of liver invasion by E . histolytica ., Virulent ( i . e . inducing amoebic liver abscesses in the hamster ) trophozoites were added to the medium on top of the 3D-liver model ( Figure 3A ) ., The majority of the amoebae adhered to the LSEC layer ., For at least 6 h , all trophozoites localized inside the 3D-liver model were migrating and their mobility indicates that cells were likely alive ., Approximately 20% to 30% of the amoebae have crossed the LSEC barrier ( Figure 3B ) after incubation for 1 . 5 h or 3 h , respectively ., In the absence of hepatocytes , amoebic invasion was significantly lower ( Figure 3C ) , suggesting the existence of attractant molecules secreted by hepatocytes or a difference in a potentially mechanical effect of the remodelled matrix ., In the absence of the LSEC layer ( Figure 3A ) more than 60% of the amoebae crossed the matrix after 3 h ( Figure 3C ) and their migration towards the hepatocytes was significantly increased resulting in a different invasion rate profile ( Figure 3F ) ., Thus , the LSEC monolayer behaves as an efficient barrier for trophozoite invasion ., Virulence-attenuated trophozoites ( unable to produce liver abscesses in the hamster ) adhered to LSEC in the 3D-liver model as efficiently as virulent trophozoites ., However , their capacity to invade is significantly diminished , with less than 5% of the trophozoites crossing the LSEC barrier after 1 . 5 h ( Figure 3D ) and no significant increase after 3 h of interaction ( data not shown ) , showing their reduced ability to efficiently leap over the first barrier of liver infection , i . e . LSEC crossing ., To characterize the morphological changes in the human cell monolayers upon interaction with E . histolytica , two-photon microscopy and SHG were used ., During amoebic interaction with the LSEC , local detachment of individual cells from the COL-I matrix was frequently observed at sites of amoebae crossing the layer ( Figure 3G and H ) but areas of detachment did not obviously extend over time , suggesting the existence of a replacement or repair mechanism ., Trophozoites containing vesicles labelled with the cell tracker used for the hepatic cells were frequently and specifically observed in the vicinity of the LSEC layer ( Figure 3I ) ., Amoebic engulfment of LSEC portions could either originate from the detachment and the subsequent uptake of portions of live LSEC by trogocytosis , 21 , or from phagocytosis of apoptotic bodies or dead cell debris ., Trophozoites rapidly migrated through the matrix in the direction of the hepatocytes ( 150 µm in 1 . 5 h ) ., The percentage of amoebae crossing the hepatocyte layer was low and phagocytic-like structures were not frequent in trophozoites having reached the hepatocytes ., No detachment or formation of gaps was observed during amoebic interaction with the hepatocyte monolayer , even after 6 h of interaction ., However , immunofluorescence experiments with antibodies against E-cadherin , a marker for epithelial tight junctions , revealed a clear reduction of the signal in the presence of E . histolytica ( Figure S4 ) ., This reduction could reveal changes in cell-cell contacts that may ultimately increase the epithelial barrier permeability and facilitate the crossing of the hepatocyte barrier after longer incubation periods ., Adhesion to mammalian cells is a prerequisite for the cytotoxic effects of E . histolytica and depends upon the amoebic Gal/GalNAc lectin 3 ., To examine its role in the penetration of the 3D-liver model , the effect of galactose on trophozoite invasion was first analysed ., Galactose almost completely abolished the ability of virulent trophozoites to cross the LSEC layer ( Figure 3D ) , the proportion of amoebae being about five times lower than for the glucose control ( CTL ) ., Amoebic crossing of the COL-I matrix ( tested in the setup without LSEC ) was also significantly reduced in the presence of galactose ( Figure 3E ) ., To further investigate the participation of Gal/GalNAc lectin , amoebic transfectants ( HGL-2 strain ) were used expressing a dominant-negative form of the lectin 4 ., Compared to control transfectants ( CTL ) , HGL-2 trophozoites presented a significant reduction in the LSEC ( Figure 3D ) and matrix crossing activity ( Figure 3E ) ., The less pronounced effects found with HGL-2 transfectants compared with galactose could be due to the fact that in HGL-2 trophozoites only the intracellular signalling of Gal/GalNAc lectin is blocked , while the extracellular function remains unchanged 5 ., Secretome analysis was performed to identify human proteins released during amoebic hepatic invasion ( Table S1 ) ., Products released into the medium were determined after 3 h interaction with virulent E . histolytica ., Within the 139 human-specific proteins found exclusively in response to amoebae ( Table S2 ) many human cytoskeletal proteins were present that likely originate from dying cells having lost their plasma membrane integrity ., Note that the induction of host cell death is a main feature of E . histolytica infection ., We found 24 known released or surface-associated proteins ( Table 2 ) , comprising further components of the complement cascade ( 3 proteins ) and the blood coagulation system ( 4 proteins ) , and 7 proteins involved in cell/cell or cell/ECM interactions ., Among the 8 proteins with functions in antigen presentation and immune responses , macrophage migration inhibitory factor ( pro-inflammatory cytokine MIF ) , galectin-1 , and galectin-3 binding protein were present ., Galectin-1 is a regulator of a variety of immune responses and inflammation in host–pathogen interactions ., Interestingly , galectin-1 has not been described before in the context of amoebic liver infection ., The identification of the immune-regulatory proteins MIF and galectin-1 in the secreted fraction suggests that cytokines may be released upon E . histolytica interaction with the 3D-liver model , which may not have been discovered in the secretome analysis due to their low abundance and/or degradation during the procedure ., To increase the sensitivity of detection , we next performed ELISA for a selection of cytokines ( IFNγ , IL-1β , IL-6 , IL-8 , TNFα , TGF-β1 ) , growth factors ( acidic FGF , HGF and VEGF ) and in addition , galectin-1 and galectin-3 ., Analysis was carried out for the 3D-model without and with E . histolytica , and the setup without LSEC as control ., Samples were prepared from 3 distinct compartments of the 3D-liver model ( Figure 4A ) ., The first corresponded to the medium on top ( outside ) of the 3D-liver model ( supernatant S1 , as used for the proteome approach ) , the second to the medium inside the 3D-liver model ( supernatant S2 ) , and the third to the matrix- and cell-associated molecules liberated after collagenase treatment of the non-soluble fraction ( S3 ) ., FGF , HGF , IFNγ and TNFα were not found in any fraction tested ., VEGF ( Figure 4B ) and TGF-β1 ( Figure 4C ) were readily detected in the three compartments of all samples , with different quantities ., Without amoebae , IL-8 ( Figure 4D ) was , as expected , easily detected in all fractions , IL-6 ( Figure 5A ) only in low amounts in S1 and S2 of the 3D-liver model , whereas IL-1β ( Figure 5B ) was not found ., Low galectin-1 ( Figure 5C ) and galectin-3 ( Figure 5D ) levels were present in the soluble fractions of the 3D-liver model ., In the presence of amoebae , the VEGF release was unchanged ( Figure 4B ) , but many significant modifications in the cytokine profiles occurred , concerning different compartments ( Figure 5E–F ) and showing distinct kinetics ( Figure 6 ) ., The presence of amoebae significantly increased the amounts of TGF-β1 ( Figure 4C ) in the soluble fractions , IL-8 ( Figure 4D ) augmented in S1 and S3 , and IL-6 ( Figure 5A ) and galectin-1 ( Figure 5C ) in all fractions of the 3D-liver model ., Galectin-3 ( Figure 5D ) was only increased in S1 ., Interestingly , IL1-β ( Figure 5B ) , undetectable in the absence of amoebae , was revealed inside the 3D-liver model ( Figure S2 and S3 ) after 3 h and 6 h ( Figure 6D ) ., Without the LSEC layer , the inflammatory reaction was less pronounced ( Figure 5E–F ) , indicating the substantial participation of LSEC in the establishment of liver immune responses 22 ., Moreover , the significant differences observed suggest that in the 3D-liver model , LSEC contribute to cytokine amounts either by a directional release into the underlying matrix or by modulating release from hepatocytes ( Figure 5E–F for scheme ) ., The data demonstrate that the hepatic cells in the 3D-liver model create a pro-inflammatory environment in response to the presence of E . histolytica ( Figure 5E–F ) and thus initiate an innate immune response regulated in time and space ., Galectin-1 and -3 exist in the extracellular milieu and in association with the surface of different cell types ., Galectin-1 is characteristic of endothelial cells and galectin-3 is mainly present in epithelial cells 20 ., The drastic reduction of amoebic invasion by the presence of galactose ( Figure, 3 ) prompted us to investigate the potential role of galectin-1 and -3 in E . histolytica adhesion to the human cells ., The ability of E . histolytica to bind human galectin-1 and -3 was examined using bacterially expressed human recombinant proteins ., Their binding to trophozoite surfaces after 25 min of incubation was observed in immunofluorescence experiments using anti-galectin antibodies ., The signal intensity was variable in the amoebic population and the labelling was frequently unevenly distributed ( Figure 7A and B ) , suggesting protein clustering ., Binding was quantified using an ELISA-like assay ( Figure 7C and D ) ., Moreover , trophozoites adhere to surfaces covered with galectin-1 or -3 ( Figure 7E ) ., Immunofluorescence experiments with the 3D-liver model revealed the presence of galectin-1 at the LSEC and of galectin-3 at the Huh-7 surface ( Figure 8A , C ) ., To test E . histolytica binding to the cell surface-associated galectins of the hepatic cells , Huh-7 ( Figure 8B ) and LSEC ( Figure 8D ) monolayers were incubated with the trophozoites in the presence or the absence of either the recombinant galectins , or galactose and lactose , both sugars known to inhibit human galectins ( lactose stronger than galactose ) and amoebic lectins as Gal/GalNac ( galactose stronger than lactose ) , and the number of amoebae adhered to the human cells was determined ., Trophozoite adhesion was drastically diminished by the sugars or the recombinant proteins ( Figure 8B and D ) , indicating for the first time the ability of E . histolytica to bind and adhere to human galectin-1 and -3 ., To further analyse the dependence of amoebic adhesion upon human surface galectin , we focussed on LSEC as the first target of amoeba interaction and performed siRNA knock-down experiments for galectin-1 ., Galectin-1 specific siRNA strongly reduced the level of surface galectin-1 in transfected LSEC ( Figure 8E ) and trophozoite adhesion was decreased by around 40% ( Figure 8F ) , demonstrating the participation of surface-associated galectin-1 in E . histolytica adhesion to LSEC ., The potential immuno-modulatory role of galectin-1 and -3 in the 3D-liver model was examined by testing the ability of the recombinant proteins to modify cytokine release ( Figure 9A–D ) ., Bacterially expressed galectin-1 and -3 recombinant proteins ( 1 µg/ml ) were added for 6 h to the medium on top of the 3D-liver model in the absence of amoebae and cytokine amounts quantified by ELISA ( Figure 9A–D ) ., Galectin-3 promoted the release of IL-1β and significantly increased IL-6 and IL-8 levels ., Galectin-1 promoted the IL-6 and TGF-β1 release ., In addition , lactose competition in the presence of E . histolytica ( Figure 9 ) completely abolished the enhanced cytokine release observed ., To exclude that the stimulation was induced by bacterial endotoxin contaminations , we performed a control using galectin-1 purified from human cell lines ( note that galectin-3 was not available ) and obtained a similar capacity to induce the cytokine release ., Overall the data suggest an important immuno-regulatory role for galectin-1 and -3 through the stimulation of the release of pro-inflammatory cytokines , which may be relevant for the induction of the host response during liver invasion by E . histolytica ., Liver abscesses are a fatal feature of infection with E . histolytica ., Host and parasite factors leading to liver infection remain largely unknown ., An important question is how this parasite adheres to and crosses the liver endothelium ., Based on our previous data , we hypothesized in this work that amoebic Gal/GalNAc lectin and surface-bound ( or secreted ) human factors are involved in this key step of liver invasion ., However , the existing experimental systems ( standard cell culture and animals ) did not allow the molecular analyses necessary to test this hypothesis ., We thus established a new human 3D-liver model , reproducing main characteristics of hepatic sinusoids , designed for the study of E . histolytica infection ., It is the first 3D-liver model using cells of human cell lines co-cultured in a 3D COL-I scaffold in the sandwich approach ., The latter was chosen to obtain a hepatic sinusoid-like organization of the cells 12 23 in a 3D architecture , which is more physiologically relevant than strategies like 2D systems 9 or 3D cellular spheroids 11 ., Though our 3D-liver model was built with only a single collagen type , the hepatic cells remodelled the matrix and released further ECM components and adhesive proteins , suggesting that the cells are able to diversify the initially homogenous COL-I matrix ., We showed that hepatocytes maintain several physiological functions beyond the time-point used for our analyses and LSEC express the surface receptors ICAM-1 and integrin-β1 , and exhibit barrier function ., Major advantages of this 3D-liver model are: it is human-relevant ( uses human cells ) ; preserves a physiological context ( mimics the hepatic sinusoid architecture ) , presents a controlled , reproducible in vitro environment ., One appealing perspective is the use of the 3D-liver model for the study of other important parasitic or viral hepatic infections , but specific adaptations to each of the pathogens under investigation will be required ., Notably , the inclusion of other liver-resident ( stellate and Kupffer cells ) and immune ( monocytes , macrophages , NKT cells ) cell types , blood components related to the innate immune response , variations in the oxygen concentration or the application of flow to mimic mechanical forces of the blood stream can be envisaged ., The 3D-liver model here described was used to analyse the initial events occurring upon E . histolytica interactions with hepatic host cells ., We examined human cell responses and parasite abilities to cross the endothelial barrier ., We identified new key elements of amoeba-cell interactions triggering a pro-inflammatory response ( the data are summarized in Figure 10 ) ., For the first time , we have discovered the role of amoebic Gal/GalNAc lectin in the amoebic crossing of the endothelial barrier and a function of human galectins in amoebic adhesion ., Also for the first time , we were able to characterize the spatio-temporal distribution of the components of the pro-inflammatory response against E . histolytica in a hepatic human-relevant system ., The better characterization of the E . histolytica liver inflammatory process in the human host is an important issue for the understanding of the disease ., In fact , neutrophils , macrophages and T cells have been related to the local host immune responses in human amoebic abscesses 24 , but the human host inflammatory response during E . histolytica liver invasion is poorly known ., Though it appears relatively controlled over time ( i . e . restricted to areas surrounding amoebae-containing foci , mainly single abscess of restricted size in humans ) , it likely contributes to amoebic progression , diminished liver function , and clinical complications 25 ., Here we showed that during amoebic invasion of the 3D-liver model several cytokines ( IL-1β , IL-6 , IL-8 as well as galectin-1 ) accumulated in the matrix-associated fraction ., Although cytokine accumulation in ECM has been documented and their retention proposed as an essential mechanism for the establishment of gradients and compartments 26 , little is known on the functions of these ECM-associated molecules ., Nonetheless , ECM-associated galectin-1 is able to trigger T cell death at lower concentrations than the soluble form 27 ., In the context of liver infection , it will be interesting to examine the hypothesis that ECM-associated galectin-1 is able to trigger cell death ., We also demonstrated the binding of soluble galectin-1 and -3 to amoebae and the participation of native ( i . e . cell surface-associated ) galectin-1 and -3 in amoebic adhesion to LSEC and hepatocytes ., Moreover , incubation of the 3D-liver model with recombinant galectin-1 or -3 induced an inflammatory response , similar to the response to the presence of amoebae , though less complete ., From all these data we can suggest a dual role of galectin-1 and -3 during amoebic hepatic infection ., First , cell surface-linked galectin-1 and -3 promote amoebic adhesion to human liver cells , and second , released galectin-1 and -3 induce a pro-inflammatory hepatic response ., It is possible that parasite binding to human cells directly facilitates the galectin release , but more data will be necessary to conclude on this ., Amoebic liver abscess formation based on carbohydrate-sensitive adherence mechanisms is here for the first time suggested at the molecular level , by the discovery of the simultaneous participation of both parasite and human lectins in the invasion of the 3D-liver model ., We do not know if amoebic Gal/GalNAc and human galectins interact in a direct way through sugar residues ., In fact , galectin-1 and -3 are released into the extracellular milieu by a non-classical secretion pathway and thus seem not to be glycosylated in the ER-Golgi trafficking pathway 28 ., Here we found that bacterially expressed galectins ( i . e . not glycosylated ) bind to E . histolytica and that binding was blocked by carbohydrates ( lactose ) , but we did not formally demonstrate that this binding depends on the lectin function of human galectins ., Galectin-1 and -3 are the most ubiquitously expressed and extensively studied members of the galectin family ., Several immuno-regulatory functions have been discovered for galectin-1 and -3 in acute and chronic inflammation 29 ., For instance and of relevance for our findings , up-regulated galectin-3 expression during Toxoplasma gondii hepatic infection seams to exert an important role in innate immunity , including a pro-inflammatory and a dendritic cell regulatory effect 19 ., We conclude that both , the adhesive and the immuno-regulatory role of galectin-1 and -3 we detected are relevant

Introduction, Results, Discussion, Materials and Methods

Investigations of human parasitic diseases depend on the availability of appropriate in vivo animal models and ex vivo experimental systems , and are particularly difficult for pathogens whose exclusive natural hosts are humans , such as Entamoeba histolytica , the protozoan parasite responsible for amoebiasis ., This common infectious human disease affects the intestine and liver ., In the liver sinusoids E . histolytica crosses the endothelium and penetrates into the parenchyma , with the concomitant initiation of inflammatory foci and subsequent abscess formation ., Studying factors responsible for human liver infection is hampered by the complexity of the hepatic environment and by the restrictions inherent to the use of human samples ., Therefore , we built a human 3D-liver in vitro model composed of cultured liver sinusoidal endothelial cells and hepatocytes in a 3D collagen-I matrix sandwich ., We determined the presence of important hepatic markers and demonstrated that the cell layers function as a biological barrier ., E . histolytica invasion was assessed using wild-type strains and amoebae with altered virulence or different adhesive properties ., We showed for the first time the dependence of endothelium crossing upon amoebic Gal/GalNAc lectin ., The 3D-liver model enabled the molecular analysis of human cell responses , suggesting for the first time a crucial role of human galectins in parasite adhesion to the endothelial cells , which was confirmed by siRNA knockdown of galectin-1 ., Levels of several pro-inflammatory cytokines , including galectin-1 and -3 , were highly increased upon contact of E . histolytica with the 3D-liver model ., The presence of galectin-1 and -3 in the extracellular medium stimulated pro-inflammatory cytokine release , suggesting a further role for human galectins in the onset of the hepatic inflammatory response ., These new findings are relevant for a better understanding of human liver infection by E . histolytica .

The study of liver infection is based on animal models , but the animal physiology does not always reflect the reality of the human host ., This is particularly true for pathogens whose exclusive natural hosts are humans , such as Entamoeba histolytica , the protozoan parasite responsible for amoebiasis ., Here , we constructed an experimental human 3D-liver model able to reproduce the first steps of amoebic hepatic infection ( barrier crossing , tissue migration and pro-inflammatory reaction ) ., Using this 3D-liver model we were able to decipher the first stages of hepatic invasion by E . histolytica and to unravel the role played by galectin-1 and galectin-3 during amoebic hepatic adhesion and pro-inflammatory reaction ., Moreover , the model enables analysis usually not possible with in vivo samples , such as the quantification of pro-inflammatory cytokines released inside the tissue microenvironment ., Our 3D-liver model has the potential to bridge the gap between animal models and the reality of the human host for the study of amoebic infection and other infectious diseases of the liver .

biotechnology, cell physiology, cell motility, liver, tissue engineering, immunology, parasitic protozoans, parasitology, infectious disease immunology, protozoans, bioengineering, extracellular space, cell adhesion, entamoeba histolytica, hepatocytes, amoebas, immune response, anatomy, cell biology, clinical immunology, biology and life sciences, intestinal parasites, organisms

journal.pntd.0001501

Using Molecular Data for Epidemiological Inference: Assessing the Prevalence of Trypanosoma brucei rhodesiense in Tsetse in Serengeti, Tanzania

For the vector-borne diseases , pathogen prevalence in a vector population is an indicator of disease risk , and accurate measures of the proportion of vectors carrying infections are needed for, ( i ) guiding allocation of resources or targeting intervention programs 1;, ( ii ) monitoring the success of control interventions 2; and, ( iii ) as parameters in models of disease transmission which are increasingly used to predict disease distribution and persistence , and plan control interventions 3 ., Approaches for detecting parasite prevalence in vector populations , known as xenomonitoring , have until recently usually relied on dissection of insect vectors and visualisation of parasites by microscopy , which is time consuming and reliant on operator skill ., PCR has presented an alternative technique for several parasite-vector systems , e . g . Plasmodium spp 4 , Oncocerca volvulus 5 , 6 , Leishmania spp ., 7 , 8 , and the nematodes which cause lymphatic filariasis , Wuchereria bancrofti , Brugia malaya and Brugia timori 9 , 10 , generally having better ability to differentiate between species of similar morphology , increased sensitivity , and hence requiring smaller sample sizes 4 , 6 , 8 ., Human African trypanosomiasis ( HAT ) is caused in East Africa by Trypanosoma brucei rhodesiense and transmitted by tsetse flies ( Glossina spp ) ., Measuring the prevalence of T . b ., rhodesiense in the tsetse vector is of particular importance as HAT occurs in developing countries where resources for surveillance and disease control are limited 11 and knowledge of human disease risk is important for effective targeting of available resources ., In addition , HAT is characterised by its focal nature , with human cases continuing over long periods of time in specific geographical areas , but the reasons for this persistence are not clear 12 ., The prevalence of infection in tsetse is an important component in understanding transmission dynamics and detecting spatiotemporal trends , which have important implications for disease control ., Assessment of the prevalence of trypanosomes within tsetse populations has traditionally comprised dissection and microscopic examination of the mouthparts , midguts and salivary glands of the fly , relying on the differing development and maturation sites of the trypanosome subgenera to identify trypanosome species 13 ., Trypanosomes found only in the mouthparts are classified as Duttonella or vivax-like , trypanosomes located in the mouthparts and midguts are classified as Nannamonas or congolense-type , and trypanosomes found in the midgut and salivary glands are Trypanozoon or brucei-like ., When trypanosomes are found only in the midgut , the infection is assumed to be immature ., This dissection/microscopy technique has several disadvantages for use in field studies: it is not possible to differentiate below the level of subgenus ( for example T . simiae cannot be differentiated from T . congolense , since they share development sites in the fly ) ; mature and immature infections cannot always be differentiated; and mixed infections cannot be identified or discriminated ., Dissection and trypanosome identification are highly dependent on operator skill , and there exist variations in protocols , with some authors only examining the midgut and salivary glands if trypanosomes are found within the mouthparts 14 , 15 , whilst others examine all the organs 16 , 17 ., A suite of molecular tools has been developed for the trypanosomatids 18 , 19 ., PCR and sequence analysis techniques have served to overcome some of the disadvantages of dissection/microscopy and highlighted new information about tsetse-trypanosome interactions ., PCR primers with high sensitivity and specificity now permit trypanosomes to be reliably identified to species or subspecies level , for example new strains or potentially even species of trypanosome have been identified 20 , 21 , 22 , and human-infective T . b ., rhodesiense and its morphologically-identical subspecies Trypanosoma brucei brucei ( not pathogenic to man ) can be accurately differentiated 23 ., Mixed infections are common , with approximately one third of PCR positive flies carrying more than one trypanosome species 20 , 24 , 25 and up to four trypanosome species identified in individual flies 24 , 25 ., However , when it comes to assessing the prevalence of trypanosome infections in tsetse it is clear that the results generated by dissection/microscopy do not correlate well with data generated by PCR ( for example only 38% 25 to 51% 24 of Nannomonas or T . congolense-like and Duttonella or T . vivax-like infections are classified as the same species by both techniques ) ., For T . brucei sensu lato , with its potential for human infection , this presents a particular problem ., In areas where T . b ., rhodesiense is known to occur in wildlife and livestock hosts , and human cases are reported , the majority of studies of T . brucei s . l . in tsetse by dissection/microscopy show prevalence of zero , even when thousands of flies are examined 16 , 26 ., However when whole tsetse flies have been analysed by PCR surprising amounts of T . brucei s . l . DNA has been found , with 2% of G . palpalis and 18% of G . pallidipes testing positive 27 , 28 ., The discrepancy between dissection/microscopy and PCR highlights the issues of assessing the true prevalence of human infective trypanosomes in tsetse populations , particularly as it is not clear how these measures relate to transmissibility ., Furthermore , it would be useful if a consensus could be reached as to how best to use molecular data , either alone or in combination with results of dissection/microscopy , to generate prevalence measures ., This study presents data from a persistent focus of Rhodesian HAT in the Serengeti National Park ( SNP ) , Tanzania ., Whilst cases of HAT have been reported in this area for over one hundred years 29 , recent cases in both the local population and tourists have renewed public health concerns about the disease 30 , 31 ., With abundant populations of G . swynnertoni and G . pallidipes , and almost 100 000 tourists visiting the SNP each year in addition to resident staff and local populations 32 , understanding and mitigation of human disease risk is a priority ., Previous studies carried out in SNP have relied on dissection/microscopy to determine tsetse prevalence ( Table 1 ) ., Large scale studies in 1970 and 1971 failed to identify any salivary gland infections 16 , 26 but a subsequent pooled rodent inoculation study detected nine out of 11000 G . swynnertoni flies ( 0 . 08% ) infected with T . brucei s . l . 33 ., These findings contrast with results of a more recent study that reported a prevalence of 3 . 0% for T . brucei s . l . in G . swynnertoni 34 and raise questions as to whether the wide variation in detected prevalence reflects real changes in tsetse infection levels and human exposure risk , or reflect methodological differences ., This study assessed the prevalence of T . brucei s . l . and T . b ., rhodesiense in the two main tsetse species in SNP , G . swynnertoni and G . pallidipes , using, ( i ) dissection/microscopy and, ( ii ) PCR analysis of infected midguts and salivary glands ., A third approach was applied to infer the prevalence of T . b ., rhodesiense in tsetse from a mathematical model of disease transmission , to examine whether previously reported low prevalences were consistent with other parameters that have been estimated for this system ., All field work was conducted in SNP , Tanzania , between October and November 2005 and August and October 2006 ., Tsetse sampling was carried out with the Tsetse and Trypanosomiasis Research Institute , Tanga , Tanzania ., Seven sites were randomly selected for tsetse trapping in savannah and open woodland areas , within 1 km of roads and within a 40 km radius of park headquarters at Seronera , where tsetse dissection was conducted ( coordinates UTM 36M, ( i ) 711676 , 9731432;, ( ii ) 706816 , 9733868;, ( iii ) 710747 , 9733536;, ( iv ) 695691 , 9727934;, ( v ) 700825 , 9746320;, ( vi ) 693961 , 9733122;, ( vii ) 695278 , 9741360 ) ., In each study site , three Epsilon traps 35 were installed for between five and eleven days , depending on trap catches ., Each trap was situated at least 200 m from the next , and erected in mottled shade to reduce fly mortality ., When placing traps , areas with fallen trees were avoided and traps were placed so that the entrances were directed towards gaps in vegetation , measures known to maximise fly catches by following the natural patterns of tsetse flight 36 ., The location of each trap was recorded using a handheld global positioning system ( Garmin Ltd , Kansas , USA ) ., Traps were baited with 4-methylphenol ( 1 mg/h ) , 3-n-propylphenol ( 0 . 1 mg/ ) , 1-octen-3-ol ( 0 . 5 mg/h ) and acetone ( 100 mg/h ) 37 and emptied twice daily ., All live non-teneral flies were dissected and labrum , hypopharynx , salivary glands and midgut examined for trypanosomes under 400× magnification 38 ., For each fly , species , sex and the presence or absence of trypanosomes in each organ were recorded ., To prevent contamination between flies and between different parts of each fly , dissection instruments were cleaned in 5% sodium hypochlorite , followed by rinsing in distilled water then phosphate buffered saline between each organ ., Flies carrying trypanosome infections was categorised according to Lloyd and Johnson 13 ., Confidence intervals were calculated using binomial exact 95% limits ., All trypanosome-positive midguts and salivary glands were macerated in phosphate buffered saline and applied to FTA Classic cards ( Whatman , Maidstone , UK ) for further analysis ., A subset of trypanosome-negative midguts was also preserved on FTA cards ., FTA cards were allowed to dry for two hours and stored in foil envelopes with dessicant at ambient temperature prior to processing ., For each sample , one disc of diameter 2 mm was cut out from the FTA card using a Harris Micro Punch™ tool ., Between cutting of the sample discs , 10 punches were taken from clean FTA paper , to prevent contamination between samples ., Discs were washed for two washes of 15 minutes each with FTA purification reagent ( Whatman Biosciences , Cambridge , UK ) , followed by two washes of 15 minutes each with 1X TE buffer ( Sigma Aldrich , Dorset , UK ) ., Each disc was dried at room temperature for 90 minutes , and then used to seed a PCR reaction ., After every seven sample discs , a negative disc was included and the punch tool and mat cleaned , to reduce the risk of contamination between discs , and ensure that any potential contamination would be detected ., No evidence of contamination was seen in the sequence of dissection or PCR results ., TBR primers were used to detect a 177 bp satellite repeat sequence common to T . b ., brucei , T . b ., rhodesiense and T . b ., gambiense 39 ., PCR was carried out in 25 µl reaction volumes containing 16 . 0 mM ( NH4 ) 2SO4 , 67 mM Tris-HCl , 0 . 01% Tween 20 ( NH4 buffer , Bioline Ltd , London , UK ) 1 . 5 mM MgCl2 , 800 µM total dNTPs , 0 . 4 µM of each primer TBR1 and TBR2 , 0 . 7 Units of BioTaq Red DNA polymerase ( Bioline Ltd , London , UK ) and one washed disc ., For samples testing positive for T . brucei s . l . , T . b ., rhodesiense was differentiated from T . b ., brucei by detection of the serum-resistance associated ( SRA ) gene ., Simultaneous amplification of another single copy gene , a phospholipase C ( PLC ) sequence found in T . brucei s . l . , confirmed that there was sufficient T . brucei s . l . material present in the sample to detect the presence of T . b ., rhodesiense 40 ., SRA PLC PCR was carried out in duplicate in a 25 µl reaction volume containing 3 mM MgCl , 1 . 25 µl of Rediload dye ( Invitrogen , Karlsbad , California ) , 1 . 5 Units Hot StarTaq ( Qiagen , Crawley , UK ) , 0 . 2 µM of each primer and one washed disc ., The SRA gives a 669 bp product , with a PLC band at 324 bp ., For all PCRs , one negative control ( water ) and one positive control ( genomic DNA ) were run for every 16 samples , in addition to negative control blank discs ., PCR products were run on a 1 . 5% ( w/v ) agarose gel at 100 V , stained with ethidium bromide and visualised under an ultraviolet transilluminator ., Detection of T . b ., rhodesiense in a tsetse midgut does not indicate a mature infection as only a small proportion of midgut infections will develop to mature infections in the salivary glands ., The following calculation was used to predict the prevalence of mature transmissible T . b ., rhodesiense infections , where Dispos is the proportion of flies with midguts which were positive by dissection/microscopy , PCRpos is the proportion of these which tested positive by PCR , PTbr/Tbb is the proportion of T . brucei s . l . positive flies with sufficient genetic material present ( ie give positive results with PLC PCR ) which test positive for T . b ., rhodesiense ( as determined by SRA PCR ) and Pmat is the proportion of immature T . b ., rhodesiense infections which develop to maturity in the salivary glands , estimated to be 0 . 12 ( CI 0 . 10–0 . 14 ) , 41 , 42: ( 1 ) This calculation relies on three assumptions:, ( i ) that dissection/microscopy is 100% sensitive for detecting trypanosome infections in tsetse midguts , and that all flies carrying T . brucei s . l . will have midgut infectons;, ( ii ) that TBR PCR has 100% sensitivity and specificity for detection of T . brucei s . l . in tsetse midguts;, ( iii ) that SRA PCR has 100% sensitivity and specificity for detection of T . b ., rhodesiense , if the sample is positive on PLC PCR ., The implications of potential assumption violations on the prevalence estimate are addressed in the discussion ., Confidence intervals were calculated by repeat sampling from nested distributions of the data ., Since the value for Pmat was taken from Milligan et al . ( 1995 ) the distribution of the original data was used , where Y is the number of flies with midgut infections and Pmat is the proportion of these which developed mature salivary gland infections ( Y\u200a=\u200a1133 , Pmat\u200a=\u200a0 . 12 ) ., Potential values were generated by sampling from the following nested distributions with 10 000 iterations , and ninety five percent confidence intervals calculated by taking the 2 . 5% and 97 . 5% quantiles of the values obtained: n1∼binom ( N , Dispos ) , n2∼binom ( n1 , PCRpos ) , n3∼binom ( n2 , PTbr/Tbb ) , p1∼binom ( Y , Pmat ) , n4∼binom ( n3 , p1/1133 ) ., Rogers 43 model of vector-borne trypanosome transmission was adapted for one host population ( wildlife , x ) and two vector populations ( G . swynnertoni , y1 and G . pallidipes , y2 ) ., Although occasional cases of human African trypanosomiasis do occur , the rate of human feeding by tsetse is very low 0 . 1% of feeds on blood meal analysis , 16 , so the human population was not included in the model ., The model is described by the following equations: ( 2 ) ( 3 ) ( 4 ) that were simultaneously solved using the lsoda function in the package odesolve in R ( http://www . r-project . org/ ) to give equilibrium conditions for the prevalence of T . b ., rhodesiense in wildlife hosts , G . swynnertoni and G . pallidipes and which could be compared to empirically derived estimates of prevalence ., Parameters were based on those described by Rogers 43 but adjusted to reflect infection in wildlife ( Table 2 ) ., Parameters specific to T . b ., rhodesiense , and to G . swynnertoni and G . pallidipes , were used where possible ., The proportion of tsetse developing salivary gland infection after feeding on an infected cow is 16% for G . morsitans ( closely related to G . swynnertoni ) and 2 . 1% for G . pallidipes 44; however wildlife exhibit a degree of trypanotolerance and generally show low parasitaemia 45 , which reduces the probability that a feeding tsetse will develop infection , also indicated by very low infection rates in tsetse fed on wildlife experimentally 46 , 47 ., A number of wildlife species do not appear to develop infection with T . brucei s . l . , either proving uninfectible in experimental infections eg baboons 46 or rarely observed with natural infection despite being popular hosts for tsetse , eg elephant 16 , 48 , 49 , so the probability that an infected tsetse feeding on a host results in an infection is also lower compared to cattle ., The incubation period of 18 days follows that of Dale et al . 50 for laboratory infections of T . b ., rhodesiense in G . morsitans flies; no specific data were available for G . pallidipes so the same value was used ., Wildlife host parameters have been chosen to represent all wildlife species ., Duration of incubation period and duration of infection are therefore estimated mean values from experimental infections of wildlife 46 , 51 , 52 ., Although age prevalence patterns suggest the development of some immunity to T . brucei s . l . in lions 53 , experimental infections do not indicate a clear immune period in other species 46 ., SNP has high densities of both wildlife 54 and tsetse 34 ., All statistical analyses and model solving were carried out using R 2 . 12 . 1 ( The R Foundation for Statistical Computing , http://www . r-project . org ) ., In total , 6455 tsetse were dissected and examined , comprising 4356 G . swynnertoni ( 2759 females , 1597 males ) and 2099 G . pallidipes ( 1472 females , 627 males ) ., Overall , trypanosomes were observed ( in mouthparts , midgut , or both ) in 9 . 2% of G . swynnertoni ( females 10 . 2% , males 7 . 5% ) , and 3 . 7% of G . pallidipes ( females 3 . 9% , males 3 . 2% ) examined ., No salivary gland infections were observed ., Using the classical trypanosome species identification based on the location of parasites within the fly , the prevalence of T . vivax-like , T . congolense-like and T . brucei-like trypanosomes is shown in Table 3 ., For 5428 flies ( all those sampled in 2006 ) , all midguts where trypanosomes were observed ( n\u200a=\u200a133 ) were analysed by PCR ( Table 4 ) ., No flies were found with salivary gland infections ., The prevalence of flies with trypanosomes in the midgut on dissection/microscopy , which were also midgut PCR positive ( Dispos×PCRpos , assumed to represent T . brucei s . l . immature infections ) was 0 . 83% in G . swynnertoni and 0 . 71% in G . pallidipes ., All midguts that tested positive for T . brucei s . l . were further analysed with SRA PCR , with 10 out of 43 PLC positive and 1 of these SRA positive , therefore the proportion of T . brucei s . l . testing positive for T . b ., rhodesiense was 0 . 1 ., Using the expression in Eq ., 1 , this gives a predicted prevalence of transmissible T . b ., rhodesiense infections of 0 . 010% for G . swynnertoni and 0 . 0085% for G . pallidipes ( Table 4 ) ., The prevalence was also calculated separately by sex and using sex-specific maturation ratios of 0 . 21 for males and 0 . 044 for females 41 ., The predicted prevalence of T . b ., rhodesiense mature infections in G . swynnertoni was 0 . 016% for males ( the number of flies testing positive on dissection/microscopy and PCR out of the total number examined was 11/1448 ) and 0 . 0035% for females ( 20/2289 ) , and in G . pallidipes was 0 . 019% for males ( 5/541 ) and 0 . 0024% for females ( 7/1151 ) ., Midguts from 78 flies with no trypanosomes observed on microscopy were also analysed by PCR ., Of these , 3 . 8% ( n\u200a=\u200a3 ) tested positive for T . brucei s . l . ., None of these tested positive with PLC or SRA ., Assuming equilibrium , the model yielded prevalences of T . b ., rhodesiense of 0 . 0064% in G . swynnertoni and 0 . 00085% for G . pallidipes ., The model predicted the prevalence of T . b ., rhodesiense in wildlife hosts to be 2 . 5% , which is within the range of reported prevalences in wildlife in SNP of 1 . 8% and 4 . 3% 55 , 56 ., The results of all three approaches are presented in Table 5 ., In this study we present data obtained from three different approaches to measuring the prevalence of transmissible T . b ., rhodesiense infections in tsetse populations in Serengeti National Park ., Fundamental difficulties have been identified associated with the detection of trypanosome infections in tsetse , requiring new approaches to move beyond generation of infection prevalence data to make inferences about transmissibility ., The three approaches used in this study confirmed the prevalence of T . b ., rhodesiense in SNP to be very low ., The prevalence of T . brucei s . l . measured by dissection/microscopy was zero , despite confirmation by the other techniques that T . brucei s . l . was circulating in the area , and evidence of infection in wildlife and human hosts , highlighting a common problem with this technique ., The results from PCR analysis of tsetse midguts were used to generate a measure of transmissible infections ., In addition , a mathematical model of disease transmission used to predict the prevalence of transmissible infections based on other parameters for this system , confirmed the low prevalence gained by other approaches was compatible with the prevalence of T . b ., rhodesiense in wildlife hosts reported in SNP ., This study highlights specific challenges in measuring transmissible T . b ., rhodesiense infections in tsetse , which have important implications for assessing this variable , and interpreting temporal and spatial patterns of infection in affected areas of Africa ., These results illustrate the difficulties of dissection/microscopy techniques , which in this study estimated the prevalence of T . brucei s . l . in tsetse populations as zero , despite strong evidence to indicate the presence of infection in tsetse using other techniques , and evidence for circulation of T . b ., rhodesiense in vertebrate hosts in the same area 30 , 31 , 55 ., The low prevalence commonly obtained through dissection/microscopy is often attributed to low diagnostic sensitivity of this technique , and there is evidence that some infections which would be classed as immature by microscopy may actually be transmissible ., For example , inoculation of trypanosomes found in the mouthparts from flies with trypanosomes present in the mouthparts and midgut by dissection did give rise to T . brucei s . l . infections in mice , both in laboratory and field studies 57 , 58 , and PCR of dissection-negative salivary glands revealed additional T . brucei s . l . infected flies in Glossina palpalis palpalis in Cote dIvoire 59 ., Whilst this may play a part in the low prevalence observed , the use of other techniques in this study confirmed the prevalence to be extremely low , and the prevalence of zero by dissection/microscopy in this study is more likely attributed to insufficient sample size than low sensitivity ., With a prevalence of 0 . 01% ( the highest of the estimates in this study ) it would be necessary to examine around 30 000 flies to detect a difference from zero with 95% confidence ., Dissection/microscopy has a number of other disadvantages: it is time consuming and requires skilled technicians , and whilst it does not require substantial investment in technology , this may be outweighed by high staff costs ., Identification of species , mixed infections and immature infections is unreliable , particularly if other trypanosome species are also of interest ., Furthermore dissection/microscopy alone cannot differentiate between T . b ., brucei and T . b ., rhodesiense ., The dissection/microscopy technique was first discussed in detail by Lloyd and Johnson in 1924 as an alternative to cumbersome rodent inoculation studies ., However , Lloyd and Johnson relied principally on morphology of the developmental and infective forms , using the location within the fly only as an additional aid ., It is clear that in areas where the prevalence is very low , dissection is less than ideal ., However , since the majority of historical studies have relied on dissection/microscopy it is important to understand how these data compare to those generated by other techniques if we want to be able to detect temporal trends ., PCR-based techniques have the potential to provide a sensitive and specific tool to identify flies carrying T . b ., rhodesiense ., We found that 30% of microscopy-positive midguts tested positive for T . brucei s . l . by PCR in G . swynnertoni and 41% in G . pallidipes ., It is difficult to compare these directly with other studies as protocols vary widely , but between 7 . 9% and 19% of microscopy-positive midguts have been reported testing positive for T . brucei s . l . in these tsetse species 20 , 21 , 25 ., However , a PCR positive fly does not indicate a transmissible infection , but only indicates the presence of trypanosomal DNA ., Here we have combined PCR data with information on the proportion of immature T . b ., rhodesiense infections which mature to the salivary glands to estimate the prevalence of mature transmissible infections ., The prevalence was within the confidence limits of dissection/microscopy and similar to the predictions of the model ., Prevalence was higher in males than females , reflecting the increased probability of maturation in males 41 ., Although in this study , dissection/microscopy were carried out prior to PCR , the increased likelihood of detecting immature T . brucei s . l . in midguts by PCR means the sample size can be lower for the equivalent precision , reducing field costs and time compared to the substantial sample sizes needed for dissection/microscopy only ., The calculation used to predict the prevalence of mature T . b ., rhodesiense infections by incorporating dissection/microscopy and PCR data relied on assumptions regarding the sensitivity of dissection/microscopy for detecting midgut trypanosome infections , and the diagnostic sensitivity and specificity of TBR and SRA PCRs when used on tsetse midgut samples ., Whilst identification of trypanosomes in the midgut is widely used in the laboratory there is little data available on the sensitivity of this technique in the field ., There is however no evidence to suggest that flies can carry T . brucei s . l . without trypanosomes being present in the midgut ., TBR and SRA PCRs have high specificity 40 , 60 ., Whilst the analytical sensitivity of TBR and SRA PCRs is known ( they are both able to detect 0 . 1 pg of trypanosome genetic material or less , equivalent to one trypanosome 39 , 40 ) , there is no quantitative data on the diagnostic sensitivity when used on tsetse samples ., The diagnostic sensitivity of TBR on blood samples from livestock is 76% 60; however the number of parasites in tsetse midgut samples is several fold higher than the parasitaemia in livestock ( which is often <10 trypanosomes/ml 40 ) hence diagnostic sensitivity is likely to be considerably higher for tsetse samples ., Imperfect test sensitivity and specificity can significantly affect prevalence estimates , particularly when the prevalence is very low 61 ., Ideally the sensitivity and specificity of each technique would have been included in the analysis to produce prevalence estimates and confidence intervals that reflect this information ., The paucity of data to examine these assumptions illustrates the importance of more critical assessment of these techniques , but likely reflects the difficulty of assessing sensitivity and specificity in the absence of a gold standard technique ., In the absence of quantitative data , the most likely violation of the assumptions is that the sensitivity of each technique is not 100% hence the prevalence may have been underestimated ., In this study , 10% T . brucei s . l . infections were identified as T . b ., rhodesiense ., Whilst this is not outside the range of values found in previous studies 62 , a proportion of one third has been more commonly reported 63 ., SRA PCR targets a single copy gene , and therefore requires the presence of a large amount of parasite DNA ., Despite an initial sample size of over 6000 flies , only ten infected midguts had sufficient genetic material present to check for T . b ., rhodesiense , so our estimate of the proportion of T . brucei s . l . which are T . b ., rhodesiense is not very precise ( 10% , CI 0 . 2–44% ) ., Using the value of 33% resulted in a prevalence of T . b ., rhodesiense in G . swynnertoni of 0 . 03% and in G . pallidipes of 0 . 028% ., It is interesting that 3 . 8% of microscopy-negative flies tested positive for T . brucei s . l . by PCR ., Previous authors have found high prevalences of T . brucei s . l . by PCR ( for example 18% 27 ) , and there are potential explanations for this high detection rate ., Flies that test positive on PCR but were microscopy-negative may result from the presence of trypanosomal DNA ( known to be detectable for over 10 days in the absence of live trypanosomes 64 ) or a very small number of trypanosomes for example in a recent blood meal where trypanosomes are not able to establish an infection ., Experimentally it has been established that only around 12–43% of susceptible flies feeding on an infected host will develop an immature infection even in teneral flies 44 , 65 ., In older flies , the majority of trypanosomes ingested will not develop further ., Simple calculations illustrate that if trypanosomal DNA is detectable for 10 days , flies feed every 3 days and 5% of hosts carry T . brucei s . l . , at any one time , up to 17% of flies may have detectable T . brucei s . l . DNA , in the absence of an immature or mature infection ., Given the drawbacks of using other techniques , it is reassuring that a model incorporating independently estimated parameters for this system predicted similar values for the prevalence of T . b ., rhodesiense in tsetse ., Whilst it might seem questionable whether the very low prevalence found by the other techniques is consistent with the reported prevalence of T . b ., rhodesiense in wildlife hosts of 1 . 8–4 . 3% 55 , 56 , a simple equilibrium-based model analysis showed that with T . b ., rhodesiense prevalence in wildlife of 2 . 5% , the prevalence in tsetse remains below 0 . 01% , and consistent with field measures ., For diseases such as HAT where low prevalence raises diagnostic challenges , broad agreement of prevalence estimates using quite different approaches permits a measure of confidence in each ., A constraint to going forwards with making assessments of prevalence is the absence of a gold standard technique for identifying transmissible T . b ., rhodesiense infections in tsetse ., Dissection/microscopy requires prohibitive samples sizes and potentially may not detect all transmissible infections; PCR techniques based on amplification of DNA from midguts rely on assumptions of factors which are known to vary and tests for which the diagnostic performance is poorly defined; models require accurate knowledge of all other parameters in a system and assumptions regarding equilibrium dynamics ., Even rodent inoculation may miss infections as rodents often fail to become infected due to their innate resistance to infection ., However , approaches for the future are likely to rely on PCR based techniques so it is important that reliable and comparable protocols are developed ., Currently , there are many different approaches reported for using PCR data to look at T . brucei s . l . in tsetse populations , including PCR of any organs found infected 25 ( similar to this study although we did not include mouthparts ) , PCR of all organs in the fly if any organ is found infected on dissection/microscopy 59 , 66 and PCR of whole tsetse flies for example 27 , 28 ., This variety of protocols raises two important issues: To interpret data from PCR analysis it is important to be clear what PCR results do or do not represent ., For example , identification of T . brucei s . l . DNA by PCR in

Introduction, Methods, Results, Discussion

Measuring the prevalence of transmissible Trypanosoma brucei rhodesiense in tsetse populations is essential for understanding transmission dynamics , assessing human disease risk and monitoring spatio-temporal trends and the impact of control interventions ., Although an important epidemiological variable , identifying flies which carry transmissible infections is difficult , with challenges including low prevalence , presence of other trypanosome species in the same fly , and concurrent detection of immature non-transmissible infections ., Diagnostic tests to measure the prevalence of T . b ., rhodesiense in tsetse are applied and interpreted inconsistently , and discrepancies between studies suggest this value is not consistently estimated even to within an order of magnitude ., Three approaches were used to estimate the prevalence of transmissible Trypanosoma brucei s . l . and T . b ., rhodesiense in Glossina swynnertoni and G . pallidipes in Serengeti National Park , Tanzania:, ( i ) dissection/microscopy;, ( ii ) PCR on infected tsetse midguts; and, ( iii ) inference from a mathematical model ., Using dissection/microscopy the prevalence of transmissible T . brucei s . l . was 0% ( 95% CI 0–0 . 085 ) for G . swynnertoni and 0% ( 0–0 . 18 ) G . pallidipes; using PCR the prevalence of transmissible T . b ., rhodesiense was 0 . 010% ( 0–0 . 054 ) and 0 . 0089% ( 0–0 . 059 ) respectively , and by model inference 0 . 0064% and 0 . 00085% respectively ., The zero prevalence result by dissection/microscopy ( likely really greater than zero given the results of other approaches ) is not unusual by this technique , often ascribed to poor sensitivity ., The application of additional techniques confirmed the very low prevalence of T . brucei suggesting the zero prevalence result was attributable to insufficient sample size ( despite examination of 6000 tsetse ) ., Given the prohibitively high sample sizes required to obtain meaningful results by dissection/microscopy , PCR-based approaches offer the current best option for assessing trypanosome prevalence in tsetse but inconsistencies in relating PCR results to transmissibility highlight the need for a consensus approach to generate meaningful and comparable data .

Human African trypanosomiasis is a fatal disease that is carried by a tsetse vector ., Assessing the proportion of tsetse which carries human-infective trypanosomes is important in assessing human disease risk and understanding disease transmission dynamics ., However , identifying flies which carry transmissible infections is difficult , due to potential presence of other trypanosome species in the same fly , and concurrent detection of immature infections which are not transmissible ., We used three methods to estimate the proportion of flies carrying human-infective trypanosomes: dissection and microscopic examination of flies to visualise trypanosomes directly in the fly; PCR of fly midguts in which trypanosomes were observed by microscopy; and theoretical analysis using a mathematical model of disease transmission ., All three methods found the prevalence to be extremely low ., Given the low prevalence , dissection/microscopy requires prohibitively large sample sizes and therefore PCR-based approaches are likely to be of most value ., However , interpretation of PCR data is not straightforward; whilst PCR identifies flies carrying pathogen genetic material it does not directly identify flies with transmissible infections ., This study highlights the need for a consensus approach on the analysis and interpretation of PCR data to generate reliable and comparable measures of the proportion of flies which carry transmissible human-infective trypanosomes .

public health and epidemiology, epidemiology, biology, microbiology, population biology

journal.pcbi.0040023

Evolution of Complex Modular Biological Networks

Biological function is an extremely complicated consequence of the action of a large number of different molecules that interact in many different ways ., Elucidating the contribution of each molecule to a particular function would seem hopeless , had evolution not shaped the interaction of molecules in such a way that they participate in functional units , or building blocks , of the organisms function 1–4 ., These building blocks can be called modules , whose interactions , interconnections , and fault-tolerance can be investigated from a higher-level point of view , thus allowing for a synthetic rather than analytic view of biological systems 5 , 6 ., The recognition of modules as discrete entities whose function is separable from those of other modules 7 introduces a critical level of biological organization that enables in silico studies ., Here , we evolve large metabolic networks based on an artificial chemistry of precursors and metabolites , and examine topological and information-theoretical modularity measures in the light of simulated genetic interaction experiments ., Intuitively , modularity must be a consequence of the evolutionary process , because modularity implies the possibility of change with minimal disruption of function 1 , a feature that is directly selected for 3 , 8 ., Yet , if a module is essential , its independence from other modules is irrelevant unless , when disrupted , its function can be restored either by a redundant gene or by an alternative pathway or module ., Furthermore , modularity must affect the evolutionary mechanisms themselves , so that both robustness and evolvability can be optimized simultaneously 1 , 9 , 10 ., A thorough analysis of these concepts requires both an understanding of what constitutes a module in biological systems and tools to recognize modules among groups of genes ., In particular , a systems view of biological function requires that we develop a vocabulary that not only classifies modules according to the role they play within a network of modules and motifs , but also how these modules and their interconnections are changed by evolution , i . e . , how they constitute units of evolution targeted directly by the selection process 4 ., The identification of biological modules is usually based either on functional , evolutionary , or topological criteria ., For example , genes that are co-expressed and/or coregulated can be classified into modules by identifying their common transcription factors 11 , 12 , while genes that are highly connected by edges in a network form clusters that are only weakly connected to other clusters 13 ., From an evolutionary point of view , genes that are inherited together but not with others often form modules 14–16 ., Yet , the concept of modularity is not at all well defined ., For example , the fraction of proteins that constitutes the core of a module and that is inherited together is small 14 , implying that modules are fuzzy but also flexible so that they can be rewired quickly , allowing an organism to adapt to novel circumstances 17 ., Progress in our understanding of the modular nature of biological networks must come from new functional data that allow us to study different groups of genes both together and apart , and compare this data to our topological , information-theoretic , and evolutionary concepts ., A promising set of data is provided by genetic interactions 18 , such as synthetic lethal pairs of genes ( pairs of mutations that show no phenotype on their own but that are lethal when combined ) , or dosage rescue pairs , in which a knockout or mutation of a gene ( in general , a loss of function ) is suppressed by overexpressing another gene ., Such pairs are interesting because they provide a window on cellular robustness and modularity brought about by the conditional expression of genes ., Indeed , the interaction between genes—gene epistasis 19—has been used to successfully identify modules in yeast metabolic genes 20 ., However , often interacting pairs of genes lie in alternate pathways rather than cluster in functional modules , do not interact directly , and thus are expected to straddle modules more often than lie within one 21 ., In silico evolution is a powerful tool if complex networks can be generated that share the pervasive characteristics of biological networks , such as error tolerance , small-world connectivity , and scale-free degree distribution 22 ., If furthermore each node in the network represents a simulated chemical or a protein catalyzing reactions involving these molecules , then it is possible to conduct a detailed functional analysis of the network by simulating knockdown or overexpression experiments ., This functional datum can then be combined with evolutionary and topological information to arrive at a more sharpened concept of modularity that can be tested in vitro when more genetic data become available ., Previous work on the in silico evolution of metabolic 23 , signaling 24 , 25 , biochemical 26 , 27 , regulatory 28 , as well as Boolean 29 , electronic 30 , and neural 30–32 networks has begun to reveal how network properties such as hubness , scaling , mutational robustness as well as short pathway length can emerge in a purely Darwinian setting ., In particular , in silico experiments testing the evolution of modularity both in abstract 33 and in simulated electronic networks 30 suggest that environmental variation is key to a modular organization of function ., In the experiments we describe below , we evolve large metabolic networks of many hundreds of nodes with over a thousand edges for up to 5 , 000 generations from simple networks with only five genes ., These networks are complex—in the sense of information-rich 34 , 35—are topologically interesting , and function within simulated environments with different variability that can be arbitrarily controlled ., We analyze these networks using new tools that allow us to see genetically interacting pairs in the light of different concepts of modules , and compare our results to an application of those tools to the yeast protein–protein interaction network ., Networks evolve to be highly complex , increase in size and develop complex pathways to metabolize the precursors ., Typically , pathways evolve first via duplication and divergence of the existing genes , but later pathways are combined and new pathways emerge by evolving import proteins for precursors that leak into cells and for which catalytic proteins had evolved ., Reaction networks are complicated , involving loops and multiple interconnections ., Evolution shapes our artificial metabolic networks into complex tightly connected pathways that are modular in nature , and that share many of the well-known properties of biological networks , such as scale-free edge distribution , small-world connectivity , and hubness ., We can use these networks to study how established concepts of modularity—such as betweenness centrality clustering and information-theoretic modularity—compare to the rate at which genetically interacting pairs are disrupted by either removing nodes with high BC , or merging nodes that have been assigned to the same information-theoretical cluster ., By evolving networks in different environments that are expected to yield different modularities , we can dissect the impact of genetically interacting pairs on modularity notions ., When we compare the behavior of genetically interacting pairs in our evolved networks to those in the yeast protein–protein interaction network , we find commonalities and some discrepancies ., One of our main findings is that synthetic lethal pairs usually lie within modules , no matter how modules are defined , and that compensatory ( suppressor ) pairs preferentially straddle modules ., We also find that in our metabolic networks , many nodes that are assigned the same module in fact have high betweenness centrality themselves , a property that does not appear to be shared with the yeast protein–protein interaction graph , where random pairs separate faster than compensatory pairs ., A number of differences between the networks can explain these findings ., First , the functional graphs ( Figure 1B ) we use to determine nodes of a network have a different connectivity pattern than protein–protein interaction networks as shown in Figure 3 , and are sparser ., Second , the multi-copy suppressor pairs we use to mark genetic compensation in our metabolic networks are different in nature from the dosage rescue pairs listed in Reguly et al . 18 ., Also , synthetic lethality for metabolic networks refers almost exclusively to functional redundancy , whereas synthetic lethality in yeast can involve complex and indirect interactions ., While in principle we could have restricted the comparison of our evolved networks to only the metabolic component of the yeast interaction network , the number of genetically interacting pairs of genes affecting metabolic genes in Reguly et al . is not sufficient to establish significance ., Experimental work in progress by several groups to obtain a large number of multi-copy suppressor pairs in yeast will change this situation dramatically ., We find no evidence that dynamic environments are required for the evolution of functional modules 30 , 33 ., Rather , it appears that genes segregate into functional modules as long as there are a large number of different ways to achieve functionality ., Indeed , on the contrary , metabolic networks evolved in dynamic environments appear to be less modular ., We can understand this finding by noting that our dynamic environments change randomly by omitting the availability of a random fraction of precursors , as opposed to the modular changes implemented in Ref ., 30 ., To deal with the unpredictability of the environment , our metabolic networks first evolve reactions that produce precursors from other precursors and metabolites ( see Figure S8 ) such that several different genes produce the same precursor from different precursors and metabolites at any point in time ., In that way , the evolved redundancy ensures the presence of any particular precursor ., Because this redundancy creates connections between pathways , the modularity score of such networks is lower ., We also find that networks evolve more slowly in dynamic environments , but they are more robust to environmental fluctuations in return ., Thus , at least for metabolic networks , robustness and modularity do not necessarily go hand-in-hand ., The in silico evolution of functional networks based on artificial genetics and chemistry presents an opportunity to study how complex networks , their structure and organization , evolve over time to cope with environments with varying degrees of predictability ., We believe that such networks can provide a formidable benchmark for experiments with biochemical networks , and allow predictions with hitherto unavailable accuracy ., The type of functional interaction experiments that we performed on our large evolved networks anticipates high-throughput efforts currently under way using temperature-sensitive yeast deletion mutants and their multi-copy suppressors , and suggests that dosage rescue ( or multi-copy suppressor ) pairs of genes represent an appropriate and sensitive tool to study modularity in biological networks ., Molecular interactions occur through proteins that catalyze the reactions between the molecules of our artificial chemistry and transport them in and out of cells ., These proteins are encoded by an artificial genetics using the four “nucleotides” 0 , 1 , 2 , and 3 and determine the rate at which the reactions proceed ., An open reading frame on a chromosome starts with four zeros ( see Table S1 ) , followed by a code indicating the expression level , followed by a tag designating the protein type , followed by the specificity and the affinity ., The specificity is a 12 nucleotide stretch that determines the target molecule or reaction ( e . g . , if the tag is “import” , 123321000000 specifies that molecule 1-2-3=2-1 is transported into the cell ) ., Reactions are specified by mapping the 5 , 020 , 279 legal reactions to the 412 = 16 , 777 , 216 possible 12-mer specificities , in such a manner that any mutation in the specificity region is guaranteed to catalyze a legal reaction ., A proteins affinity is determined by an “active site” that has four domains; one each for the four molecules involved in the reaction A + B → A′ + B′ ., The binding affinity of a transport protein to the specified target is obtained by averaging the affinity of all four domains ., Each domain has twelve entries that are matched to particular molecules ( of maximally twelve atoms ) in the following manner ., First , a molecule is translated into its binary equivalent , for example , 1-2-3=3-2-1 is 01-10-11-11-10-01-00-00-00-00-00-00 ( zeros are used to pad molecules smaller than 12 atoms ) ., The 24 bit domain of the protein P is compared with the binary equivalent of the target molecule M , resulting in an affinity score D ( M , P ) that is highest if the protein domain is precisely complementary to the molecule ., So , for example the perfect domain for molecule 1-2-3=3-2-1 is 10-01-00-00-01-10-11-11-11-11-11-11 ., Numerically , D ( M , P ) is obtained as 1 − S ( M , P ) , where S ( M , P ) is a similarity score, where, is the base-10 translation of the logical bitwise EQUAL of the molecules and proteins ith site ., The base-10 translation of the equivalent of a perfect match ( ‘11 ) is 3 , so that the maximal, is 12 × 32 = 108 , ensuring that 0 ≤ A ( M , P ) ≤ 1 ., The complementarity scheme is chosen to minimize the occurrence of domains of the type 00-00-00–00 , as they would be decoded as start codons ., The maximal genome size in this model is 120 , 000 bits , or 60 , 000 nucleotides , on 2 circular chromosomes ., Genes are allowed to overlap ., Note that because of the absence of recombination , one of the two chromosomes consistently degenerates during evolution so that all of the complexity ends up contained in a single circular genome ., Cells live in a two-dimensional space where precursor molecules are produced at defined locations and diffuse out , so that the concentration of molecule M at distance d from the source , M ( d ) , depends on the concentration at the source via, which is the solution of the diffusion equation with a diffusion coefficient D = 1/2 , at time t = 1 ., Molecule concentrations Mi are updated according to a discretized version of the standard metabolic rate equations 46, for molecules i = 0…607 , where the sum runs over reactions j = 1 to r , and the matrix cij is the connectivity matrix of the network defined as, and vj is the metabolic flux, In Equation 3 ,, is the number of edges leaving molecule l , and we defined the reaction matrix for reaction j, as well as the affinity A ( j ) by, where D ( Mp , Pp ) are the affinities of protein domain Pp to the molecules Mp as defined above ., The fitness of an organism is determined by the amount and complexity of the molecules it can metabolize from the precursors ., The 608 possible molecules of the artificial chemistry are numbered according to their complexity ( length and type of atoms ) :, and the first 53 molecules are arbitrarily termed precursors ., The remaining 555 molecules are metabolites of increasing complexity ( the most complex one being M607 ) ., Each different molecule metabolized by the cell contributes to the total fitness ., If Δ ( Mi ) is the total amount of molecule i synthesized by the cell , the total fitness is calculated using the fitness value of each the molecules Mi , which depends on its index i via, as, In Equation 6 , the product extends only across metabolites that have achieved non-vanishing abundance during a cells lifetime ., Because of the explicit dependence of a cells fitness on the concentration of precursors in the cells vicinity , fitness is context dependent , and in principle depends on the frequency of other cells in a population ., Due to the multiplicative nature of the fitness function , the discovery of new pathways is always beneficial with the same percentage , and the fitness increases exponentially during evolution ., We usually plot the logarithm of the fitness , which is additive ., A Genetic Algorithm 47 is used to evolve circular genomes encoding genes using the nucleotide alphabet 0 , 1 , 2 , 3 ., Mutations are Poisson-random with a mean of one mutation per genome ( and a maximum of six mutations per genome ) ., With a probability of 1/16 per genome , a stretch of 4–512 base pairs is duplicated and inserted directly adjacent to the duplicated stretch ., With the same probability , a stretch of the same size is deleted from the genome ., No recombination takes place between genomes ., The probability for a genome to be replicated is proportional to the fitness calculated in Equation 6 ( Wright-Fisher selection ) ., Organisms must be at least 8 updates old before they can replicate , and they are protected from death during those first 8 updates ., We designed the ancestral genome to have 3 genes on the first 1 , 000 bp chromosome , with the 2nd chromosome of 1 , 000 bps filled with poly-‘3′s in order to be as distant as possible to start codons ., However , it turned out that the third gene has a start codon ( 0000 ) within its specificity domain as well as in the sequence specifying the expression level , both of which give rise to two additional proteins in overlapping reading frames ( see Figure 11 ) ., Those proteins , because they are useless to the organism , quickly disappear within the first tens of generations ., The spaces between the first three genes are filled with random sequence , and the 880 bp genome is padded with 120 poly-‘3′s , to make up the 1 , 000 bp of the ancestral genome as sketched in Figure 11 ., The complexity of an organism can be estimated by the amount of information its genome encodes about the environment within which it thrives 34 , 35 , 48 ., We can estimate the information content I of a sequence s of length L encoding the bases 0 , 1 , 2 , 3 by I = L − H ( s ) , where the entropy of the sequence H ( s ) is approximated by the sum of the per-site entropies, , with a per-site entropy, In Equation 7 , the pi are the probabilities to find base i at position x , which can be obtained from an alignment of genomes in mutation-selection balance ., For small populations and long genomes , this balance is not achieved , and the substitution probabilities pi must be estimated using the fitness effect of each substitution wi according to the implicit equation 49, where, is the mean fitness of the possible alleles at that position and μ is the mutation rate per site ., We obtain the fitness wi of each allele at each position by constructing the genotype and evaluating the fitness of the cell it gives rise to in the appropriate environment ., ( Mutations that appear to be beneficial are counted as wild-type fitness . ), Using the four values wi , the probabilities pi can be obtained by iterating Equation 8 10 , 000 times or until the variance of all pi drops below 10−12 ., To assign a modularity score to our networks , we use the information bottleneck method 50 , as applied to biological networks by Ziv et al . 40 ., Briefly , the method assigns clusters to the nodes of a network described by a random variable X using an assignment random variable Z and a relevance variable Y ( the bottleneck ) by maximizing both the simplicity of the description ( maximizing the mutual entropy between the graph and its description I ( X : Z ) ) and its relevance or fidelity ( maximizing I ( Y : Z ) ) ., This is achieved via a hard clustering method that starts with a description Z with one fewer nodes than X , then calculates the conditional probability p ( z | y ) from a diffusion process and selects those nodes of X to merge in the description Z that result in the highest I ( Y : Z ) ., This process iterates until all the nodes have been joined and the size of Z is one ., This procedure results in a list of nodes ( from highest cluster probability to lowest ) that can be used to study how synthetic lethal and knockdown suppressor pairs are merged as an alternative to the topological clustering via betweenness centrality ., A modularity score for each network is obtained as the area under the information curve obtained by plotting the normalized quantities I ( Z : X ) /H ( X ) and I ( Z : Y ) /I ( X:Y ) against each other 40 ., Because random graphs give rise to an information curve with area 0 . 5 , any modularity score above 0 . 5 signals a modular organization of the network ., To obtain the modularity score in Figure 6 , we averaged the modularity score of the largest , second largest , etc . connected components of the network μi weighted by their relative size ., Thus , if the ith largest connected component of the network of size N is ni , then the average modularity score is ( note that ni ≥ 5 is required as the modularity of smaller networks cannot be obtained ) The average distance D of each node to any other defines the average geodesic distance of a graph, where n is the total number of nodes , d ( i , j ) is the shortest path distance between i and j , and m is the total number of edges ., We measure the robustness of evolved networks with respect to node deletions and to changes in the precursor concentrations ., Even though these perturbations are unrelated prima facie , there is evidence that mutational robustness and robustness to noise are correlated 28 ., We measure mutational robustness by removing n random nodes and determining the ( scaled ) fitness of the remaining graph, , where, is the mean of 1 , 000 independent fitness measurements of a network where n random nodes have been removed ., The fitness decreases exponentially as long as less than 30% of the nodes are removed , suggesting a ( “knock-out” ) robustness parameter ρKO defined via, Environmental robustness is determined by evaluating the fitness of an organism as more and more of the 53 precursor molecules are removed ., Fitness declines exponentially with the number of deleted nodes or chemicals removed , and robustness can be quantified by the slope of the decrease of log fitness , defining ρENV in a similar manner ., The betweenness centrality of a node in a network topology measures how many shortest paths go through that node ., If bi is the ratio of the number of shortest paths between a pair of nodes in the network that pass through node i and the total number of shortest paths between those two nodes , then the unscaled betweenness of node i is, , and the ( scaled ) betweenness centrality is 45, where n is the number of nodes in the network ., The betweenness centrality is positive and always less than or equal to 1 for any network ., The software to implement the artificial chemistry and genetics , as well as the evolution experiments described in this manuscript , is available at http://public . kgi . edu/~ahintze .

Introduction, Results, Discussion, Methods

Biological networks have evolved to be highly functional within uncertain environments while remaining extremely adaptable ., One of the main contributors to the robustness and evolvability of biological networks is believed to be their modularity of function , with modules defined as sets of genes that are strongly interconnected but whose function is separable from those of other modules ., Here , we investigate the in silico evolution of modularity and robustness in complex artificial metabolic networks that encode an increasing amount of information about their environment while acquiring ubiquitous features of biological , social , and engineering networks , such as scale-free edge distribution , small-world property , and fault-tolerance ., These networks evolve in environments that differ in their predictability , and allow us to study modularity from topological , information-theoretic , and gene-epistatic points of view using new tools that do not depend on any preconceived notion of modularity ., We find that for our evolved complex networks as well as for the yeast protein–protein interaction network , synthetic lethal gene pairs consist mostly of redundant genes that lie close to each other and therefore within modules , while knockdown suppressor gene pairs are farther apart and often straddle modules , suggesting that knockdown rescue is mediated by alternative pathways or modules ., The combination of network modularity tools together with genetic interaction data constitutes a powerful approach to study and dissect the role of modularity in the evolution and function of biological networks .

The modular organization of cells is not immediately obvious from the network of interacting genes , proteins , and molecules ., A new window into cellular modularity is opened up by genetic data that identifies pairs of genes that interact either directly or indirectly to provide robustness to cellular function ., Such pairs can map out the modular nature of a network if we understand how they relate to established mathematical clustering methods applied to networks to identify putative modules ., We can test the relationship between genetically interacting pairs and modules on artificial data: large networks of interacting proteins and molecules that were evolved within an artificial chemistry and genetics , and that pass the standard tests for biological networks ., Modularity evolves in these networks in order to deal with a multitude of functional goals , with a degree depending on environmental variability ., Relationships between genetically interacting pairs and modules similar to those displayed by the artificial gene networks are found in the protein–protein interaction network of bakers yeast ., The evolution of complex functional biological networks in silico provides an opportunity to develop and test new methods and tools to understand the complexity of biological systems at the network level .

none, computational biology

journal.pcbi.1004908

Computational Identification of Genomic Features That Influence 3D Chromatin Domain Formation

High-throughput chromatin conformation capture ( Hi-C ) has emerged over the past years as an efficient approach to map long-range chromatin contacts 1–3 ., This technique has allowed the study of the 3D architecture of chromosomes at an unprecedented resolution for many genomes and cell types 4–7 ., Multiple hierarchical levels of genome organization have been revealed: compartments A/B 1 , sub-compartments 8 , topologically associating domains ( TADs ) 4 , 5 and sub-TADs 7 ., Among those domains , TADs represent a pervasive structural feature of the genome organization ., TADs are stable across different cell types and highly conserved across species ., A current challenge is to identify the molecular drivers of topological arrangements of higher-order chromatin organization ., There is a growing body of evidence that insulator binding proteins ( IBPs ) such as CTCF , and cofactors such as cohesin , act as mediators of long-range chromatin contacts 5 , 6 , 9–11 ., In human , depletion of cohesin predominantly reduces interactions within TADs , whereas depletion of CTCF not only decreases intradomain contacts but also increases interdomain contacts 12 ., The densest Hi-C mapping in human has recently revealed that loops that demarcate domains are often marked by asymmetric CTCF motifs where cohesin is recruited 8 ., In Drosophila , silencing of cohesin and condensin II have recently demonstrated their roles on long-range contacts 13 ., In addition , numerous IBPs , cofactors and functional elements colocalize at TAD borders 11 ., However it is unclear if all these proteins and functional elements , or specific combinations of them , play a role in TAD border establishment or maintenance ., Computational approaches that integrate protein binding ( chromatin immunoprecipitation followed by high-throughput DNA sequencing , ChIP-seq ) with Hi-C data may be well-suited to identify the key drivers of chromatin architecture ., Most computational approaches dedicated to chromosome conformation analysis have focused on correcting contact matrices for experimental biases 6 , 14–16 in order to assess more precisely the significance of contact counts 17 , 18 , to identify chromatin compartments 1 , 15 , 19 , or to 3D model chromosome folding 1 , 5 , 20–22 ., However few computational methods have been proposed to study the roles of DNA-binding proteins and functional elements in chromosome folding ., A simple yet widely used statistical method consists in assessing enrichment of a genomic feature around 3D domain borders by Fisher’s exact or Pearson’s chi-squared tests 4 , 5 , 7 ., An important caveat of enrichment test is that it only identifies those genomic features that colocalize at domain borders , but it cannot determine which genomic features influence the domain border establishment or maintenance ., For instance , two genomic features might be both found significantly enriched at domain boundaries , but only one of them might truly influence the domain border establishment or maintenance ., This is due to the colocalization ( correlation ) between the two genomic features ., Statistically speaking , correlation does not imply causation ., Other works focused on the prediction of 3D domain borders using ( semi ) non-parametric models and identified a subset of genomic features that are the most predictive of TADs 23 , 24 ., However a genomic feature can efficiently predict 3D domain borders without being influential 25 ., In this paper , we propose a multiple logistic regression to assess the influence of genomic features such as DNA-binding proteins and functional elements on topological chromatin domain borders ., Compared to enrichment test and non-parametric models , multiple logistic regression assesses conditional independence and thus can identify most influential proteins with respect to domain borders ., Moreover the multiple logistic regression model can easily accommodate interactions between genomic features to assess the impact of co-occurences on domain borders ., We illustrate our model using recent Drosophila and human Hi-C data allowing to probe TAD borders depending on multiple proteins and functional elements ., Using both simulated and real data , we show that our model outperforms enrichment test and non-parametric models such as random forests for the identification of known and suspected architectural proteins ., In addition , the proposed method identifies genomic features that positively or negatively impact TAD borders with a very high resolution of 1 kb ., The proposed multiple logistic regression models the influences of p genomic features on 3D domain borders:, ln P r o b ( Y = 1 | X ) 1 - P r o b ( Y = 1 | X ) = β 0 + β X ( 1 ), Where X = {X1 , … , Xp} is the set of p genomic features such as DNA-binding proteins and Y is a variable that indicates if the genomic bin belongs to a border ( Y = 1 ) or not ( Y = 0 ) ., The set β = {β1 , … , βp} denotes slope parameters , one parameter for each genomic feature ., The model can easily accommodate interaction terms between genomic features ( see Subsection Materials and Methods , Analysis of interactions ) ., By default , model likelihood is maximized by iteratively reweighted least squares to estimate unbiaised parameters ., However , when there are a large number of correlated genomic features in the model , L1-regularization is used instead to reduce instability in parameter estimation 26 ., We illustrate the proposed model using two scenarios and compare it with enrichment test ( Fig 1 ) ., In the first scenario , protein A positively influences 3D domain borders , while protein B colocalizes to protein A . In this scenario , enrichment test will estimate that the parameter associated with protein A βA > 0 and the parameter associated with protein B βB > 0 ., In other words , both proteins A and B are enriched at 3D domain borders ., Multiple logistic regression will instead estimate that parameters βA > 0 and βB = 0 ., This means that protein A positively influences 3D domain borders , while protein B does not ., This is because multiple logistic regression can discard spurious associations ( here between protein B and 3D domain borders ) ., One would argue that enrichment test can also be used to discard the spurious association if the enrichment of protein B when protein A is absent is tested instead ., However such conditional enrichment test becomes intractable when more than 3 proteins colocalize to domain borders , whereas multiple logistic regression is not limited by the numbers of proteins to analyze within the same model ., In the second scenario , the co-occurrence of proteins A and B influences 3D domain borders , but not the proteins alone ., Enrichment test will find that each protein alone is enriched at 3D domain borders ( βA > 0 and βB > 0 ) as well as their interaction ( βAB > 0 ) ., The proposed model will instead find that only the interaction between proteins A and B influences 3D domain borders ( βA = 0 , βB = 0 and βAB > 0 ) ., In addition to these two previous scenarios , another interest of the model is the possibility to study the negative influence of a protein ( or of a co-occurence of proteins ) on TAD border establishment of maintenance ., In other words , its presence counteracts the establishment or maintenance of 3D domain borders ., In such scenario , multiple logistic regression will estimate a parameter β < 0 ( see below ) ., Depending on the parameter estimation algorithm used ( likelihood maximization or L1-regularization ) , results are interpreted differently ., If likelihood maximization is used , then a protein beta parameter can be considered as significantly different from zero if the corresponding p-value is lower than the significance level computed by Bonferroni procedure ., If L1-regularization is used instead , then p-values are not computed ., A protein is considered as influential if its beta parameter is different from zero ., Using both algorithms , the beta parameter is the only measure used to quantify how strong is the influence of a protein on the 3D domain borders , and the p-value should not be used instead because it depends on the amount of data available ., Both algorithms are useful in practice ., Likelihood maximization allows to estimate beta parameters without any bias but influential proteins should be known in advance ., L1-regularization can be useful to select the influential proteins among a large set of correlated candidates , but estimates will be biased ., Several characteristics of the analyzed ChIP-seq and functional element data might prevent the accurate estimation of multiple logistic regression parameters β ., The matrix X of genomic features is sparse ( numerous values equal zero ) because genomic features are often absent from a particular genomic bin ., Sparsity of matrix X is known to prevent convergence of likelihood maximization for parameter estimation 27 ., Moreover some genomic features can be correlated ., For instance , different insulator binding proteins might bind to the same genomic regions ., For all these reasons , accurate estimation of parameters could fail in theory ., Hence we evaluated the accuracy of parameter estimation using simulations ., We simulated data that were similar to real ChIP-seq data ( see Subsection Materials and Methods , Data simulation , first paragraph ) ., Both genomic coordinate data ( e . g . , ChIP-seq peak coordinates ) and quantitative data ( e . g . , ChIP-seq signal intensity log C h I P I n p u t ) were generated ., From the simulated data , multiple logistic regression model parameters were then estimated by maximum likelihood ., We first simulated 100 genomic coordinate and 100 quantitative datasets that comprised 6 proteins and learned models without considering any interaction terms ., In Fig 2a , we plotted true against estimated parameter values ., We reported a very good accuracy for parameter estimation for both genomic coordinate and quantitative data with R2 = 99 . 5% ( p < 1 × 10−20 ) and R2 > 99 . 9% ( p < 1 × 10−20 ) between true and estimated parameter values , respectively ., Because some proteins might be rare over the genome and only involved in some 3D domain borders , we studied parameter accuracy for simulated proteins with varied ChIP-seq peak numbers ., Parameter estimation was highly accurate even for proteins with a low number of peaks over the genome ( R2 = 97 . 4% for 50 peaks; S1 Fig ) ., In addition , we sought to assess how parameter estimation is affected by 3D domain border inaccuracy of few kilobases ., We observed that with a border inaccuracy equal or lower than 2 kb , parameter estimation was still accurate ( R2 > 70 . 9% , S2 Fig ) ., We then simulated 100 genomic coordinate and 100 quantitative datasets that comprised the same 6 proteins and learned models with all two-way ( e . g . X1 X2 ) interaction terms ., In Fig 2b , we plotted true against estimated parameter values corresponding to interaction terms only ., Parameter estimation accuracy was still high for both genomic coordinate data ( R2 = 94 . 6% , p < 1 × 10−20 ) and quantitative data ( R2 = 99 . 9% , p < 1 × 10−20 ) ., We concluded that model parameter estimation was accurate for both marginal and two-way interaction of genomic features ., We then sought to assess how multiple logistic regression ( MLR ) efficiently identifies genomic features that influence TAD borders , comparing with other approaches commonly used to assess the link between TAD borders and genomic features ., We compared our model with enrichment test ( ET ) 4 and non-parametric model 23 ., For the non-parametric model , we used random forests ( RF ) which are very similar to the model used in 23 , but for which a scalable implementation allowed high resolution analysis ( https://github . com/aloysius-lim/bigrf ) ., For this purpose , we first simulated 100 datasets comprising 11 genomic features {X1 , X2 , … , X11} that were similar to real ChIP-seq data ( see Subsection Materials and Methods , Data simulation , second paragraph ) ., Among the genomic features , variables X1 and X10 were chosen to be causal with an odds ratio of 4 , which was comparable to odds ratios estimated from real data ( see below ) ., We compared beta parameters from multiple logistic regression with beta parameters from enrichment test and variable importances from random forests ( Fig 3a ) ., Enrichment test correctly identified causal variables X1 and X10 as the most enriched ( beta median = 1 . 3 ) , but also found highly enriched non-causal variables ( beta median = 1 ) ., Random forests detected X3 and X8 as the most influential variables for prediction ( variable importance median >2 . 75 ) , although they were not causal genomic features ., In contrast , multiple logistic regression correctly identified X1 and X10 as influential variables ( beta median = 0 . 93 ) and discarded non-causal variables ( beta median = −0 . 03 ) ., We next simulated more complex scenarios for which the causal variables and their number were randomly chosen for each simulation ., In addition , simulations were carried out for different odds ratios to study the influence of effect size ., As previously , we compared multiple logistic regression with enrichment test and random forests ., For each method , we computed the percentage of models that correctly ranked first the causal variables in terms of beta parameter or variable importance ( Fig 3b ) ., We observed that both enrichment test and multiple logistic regression successfully ranked first the causal variables even for a low odds ratio of 2 ( 93% of models ) , whereas random forests mostly failed even for the easiest scenario ( 44% of models for an odds ratio of 8; in the next paragraph , we will see that random forests poorly performed here partly due to high data sparsity ) ., We then compared empirical type I error rate for a significance threshold α = 10−5 between enrichment test and multiple logistic regression for which p-values on beta coefficients were available ( Fig 3c ) ., Even for a high odds ratio of 8 , MLR had a low error rate of 16% ., Conversely enrichment test showed a high error rate of 75% even for an odds ratio of 2 ., We also compared MLR with ET and RF using real data in human ., For this purpose , we analyzed new 3D domains detected from recent high resolution Hi-C data at 1 kb for GM12878 cells for which 69 ChIP-seq data were available 8 ., Multiple lines of evidence indicate that CTCF and cohesin serve as mediators of long-range contacts 5 , 6 , 9–11 , 28 ., However several proteins also colocalize or interact with CTCF , including Yin Yang 1 ( YY1 ) , Kaiso , MYC-associated zing-finger protein ( MAZ ) , jun-D proto-oncogene ( JUND ) and ZNF143 29 ., In addition , recent work has demonstrated the spatial clustering of Polycomb repressive complex proteins 30 ., Using the large number of available proteins in GM12878 cells , we could compare MLR with ET and RF to identify known or suspected architectural proteins CTCF , cohesin , YY1 , Kaiso , MAZ , JUND , ZNF143 and EZH2 ., For this purpose , we computed receiver operating characteristic ( ROC ) curves using Wald’s statistics for ET , beta parameters for MLR , and variable importances for RF ., We carried out computations at the very high resolution of 1 kb ( see Subsection Materials and Methods , Binned data matrix ) ., ROC curves revealed that MLR clearly outperformed ET and RF to identify architectural proteins ( AUCMLR = 0 . 827; Fig 3d ) ., Lower performance of ET ( AUCET = 0 . 613 ) was likely due to its inability to account for correlations among the proteins ( average correlation = 0 . 19 ) ., Regarding RF , its low performance ( AUCRF = 0 . 558 ) could be explained by its well-known inefficiency with sparse data ( at 1kb , there were 99 . 4% of zeros in the data matrix X ) ., At a lower resolution of 40 kb ( 88 . 5% of zeros ) , RF performed much better ( AUCRF = 0 . 746 ) but still lower than MLR ( AUCMLR = 0 . 815; S3 Fig ) ., To further validate MLR results with real data , we analyzed the impacts of single nucleotide polymorphisms ( SNPs ) in the consensus CTCF motif in human ., SNPs play an important role in common genetic diseases and recent works have uncovered differential long-range contacts due to variations in the CTCF motif 31–33 ., SNPs in the consensus CTCF motif are thus expected to affect , and most likely to decrease , the influence of CTCF motif on 3D domain border establishment or maintenance ., We then tested if MLR was able to detect the impacts of SNPs on CTCF motif ., For this purpose , we included within the same MLR model the wild-type ( WT ) motif and the three alternative alleles for a given position in the motif ., For instance , for the first position , the MLR comprised genomic coordinates of the WT motif CCANNAGNNGGCA and the genomic coordinates of the mutated motifs ACANNAGNNGGCA , GCANNAGNNGGCA and TCANNAGNNGGCA ., Over 27 mutated CTCF motifs , 25 showed beta coefficients that were lower than the one of WT CTCF motif , indicating that the corresponding SNPs diminished the influence of CTCF motif on TAD borders as expected ( Fig 4 ) ., Because correlations among the motif variables were very low ( average correlation <0 . 01 ) , ET performed as efficiently as MLR to detect the influences of SNPs ( AUCET = 0 . 926 and AUCMLR = 0 . 926 ) , but RF was inaccurate ( AUCRF = 0 . 638; S4 Fig ) ., For instance , for the first position , we observed that all three alternative alleles ( A , G and T ) diminished the influence of the motif with respect to 3D domain borders ., Some mutations even canceled the influence of CTCF motif ( for instance , alleles A and T on position 2 ) ., On the last position , allele G had a higher influence than the WT motif ., This result was actually consistent with the ambiguity between allele A and G in the motif ., Similar results were obtained for consensus BEAF-32 motif CGATA in Drosophila ( S5 Fig ) ., Using both simulated and real data , we concluded that multiple logistic regression correctly identified causal variables and discarded spurious associations of non-causal variables with TAD borders while both enrichment test and random forests failed ., In addition , multiple logistic regression successfully predicted expected effects of SNPs on CTCF and BEAF-32 motifs known to influence long-range contacts in human and Drosophila , respectively ., These predicted effects of SNPs could further serve to identify new regulatory variants in the context of genome-wide association studies ., We implemented the proposed model such that it can deal with either genomic coordinate data or quantitative data ., However , in the present study , we chose to focus on genomic coordinate data as in 11 , 34 ., An advantage of this approach was that both DNA-binding proteins and functional elements could be included within the same model ., In addition , we observed that logistic regression models built from genomic coordinate data usually outperformed those obtained with quantitative data in terms of deviance ratio and AIC ( model deviance ratios and AICs are given in S1 Table ) ., The influences of genomic features such as DNA-binding proteins or gene transcription on TAD border establishment or maintenance can be estimated by the proposed multiple logistic regression ., Using Drosophila Kc167 cell Hi-C data at 1 kb resolution , we assessed the effects of insulator binding proteins , cofactors , gene transcription and functional elements on TAD borders ., Although TADs were computed from 1 kb resolution Hi-C data , genomic features were binned at an even higher resolution of 50 bp in order to better discriminate between genomic features that influence TAD borders and those that do not , and to reduce standard errors of model parameters ( see Subsection Materials and Methods , Binned data matrix ) ., In this subsection , we first focused on the effects of insulator binding proteins in driving TAD borders 35 ., In Drosophila , there are five subclasses of insulator sequences 36 ., Each subclass is bound by a particular type of insulator binding protein ( IBP ) : suppressor of hairy wing ( Su ( Hw ) ) , Drosophila CTCF ( dCTCF ) , boundary-element-associated factor of 32 kDa ( BEAF-32 ) , GAGA binding factor ( GAF ) , and Zeste-White 5 ( ZW5 ) 10 ., In addition , the general transcription factor dTFIIIC was recently identified as a new IBP 11 ., We assessed enrichments of these IBPs within TAD borders ( Fig 5 ) ., We observed enrichments for all these IBPs ( all coefficients β ^ > 1 . 34 and all p-values p < 1 × 10−20 ) ., BEAF-32 was the most enriched IBP with a coefficient β ^ = 2 ., 71 , corresponding to an odds ratio O R ^ = 15 ., 03 , whereas GAF was the least enriched IBP with a coefficient β ^ = 1 ., 34 , corresponding to an odds ratio O R ^ = 3 ., 82 ., Multiple logistic regression yielded different results ( Fig 5 ) ., All beta coefficients decreased reflecting colocalization among the proteins ( average correlation of 0 . 28 ) ., Despite these correlations , the tight 95% confidence intervals reflect that betas were estimated with low standard errors ., This is due to the very large number of observations ( >1 million ) compared to the low number of variables ( 6 variables ) obtained for a binning at 50 bp ., There were clear differences of betas among the IBPs compared with enrichment analysis 5 , 6 ., Only BEAF-32 showed high and significant beta ( BEAF-32: β ^ = 1 . 92 , p < 1 × 10−20 ) ., For other IBPs , betas were significant but much lower ( β ^ < 0 . 95 , p < 1 × 10−20 ) ., Thus although dCTCF , dTFIIIC , GAF and Su ( Hw ) were enriched at TAD borders , multiple logistic regression revealed that they weakly influence TAD borders ., High enrichments of these proteins are due to their correlations with BEAF-32 ., For instance , previous work showed that numerous dCTCF sites align tightly with BEAF-32 37 ., These results supported the role of BEAF-32 as most influential IBP of TAD borders ., There has been an ongoing debate to know whether transcription or architectural proteins are the main cause of TAD border demarcation 6 ., Using enrichment test , we observed that active transcription start sites ( TSSs ) were enriched at TAD borders ( β ^ = 1 . 82 , p < 1 × 10−20 ) , as well as architectural proteins such as BEAF-32 ( β ^ = 2 . 72 , p < 1 × 10−20 ) ., Using multiple logistic regression , we then estimated the effects of transcription and of architectural proteins on TAD borders within the same model ( S6 Fig ) ., We observed that active TSSs had a significant positive effect in TAD border establishment/maintenance ( β ^ = 0 . 42 , p < 1 × 10−20 ) ., This effect was much lower than the one of architectural protein BEAF-32 ( β ^ = 2 . 59 , p < 1 × 10−20 ) ., Our model thus reveals that architectural protein BEAF-32 contributes much more to TAD-based organization than transcription ., However one might argue that the comparison between active TSSs and BEAF-32 was not straightforward because the latter represented two distinct genomic features , a functional element and a protein , respectively ., Hence for a proper comparison between transcription and architectural proteins , we compared within the same multiple logistic regression the effects of the short isoform of Drosophila Brd4 homologue ( Fs ( 1 ) h-S ) , a major transcriptional factor involved in transcriptional activation , with the long isoform ( Fs ( 1 ) h-L ) , a recently identified architectural protein 38 ., We observed that Fs ( 1 ) h-S had a significant positive effect on TAD borders ( β ^ = 1 . 87 , p < 1 × 10−20 ) , but which was lower than the one of Fs ( 1 ) h-L ( β ^ = 2 . 60 , p < 1 × 10−20 ) ., Our results thus highlighted the prevalent roles of architectural proteins compared to transcription , which was highly consistent with recent results suggesting a lower impact of transcription 13 ., Recent work supported the idea that IBPs may favor long-range contacts by recruiting cofactors directly involved in stabilizing long-range contacts 8–10 ., In Drosophila , several cofactors were identified: condensin I , condensin II , Chromator , centrosomal protein of 190 kDa ( CP190 ) , cohesin 10 , 13 , 39 , 40 and Fs ( 1 ) h-L 38 ., We first analyzed by multiple logistic regression all abovementioned cofactors in their own to understand their relative contribution to TAD borders ( S7 Fig ) ., Among the cofactors , CP190 had the highest influence on TAD borders in agreement with previous findings 5 ( β ^ = 1 . 12 , p < 1 × 10−20 ) ., Because cofactors were expected to be recruited by IBPs to the chromatin 8 , 9 , 39 , 40 , we then regressed cofactors with all IBPs and all IBP-cofactor interactions ( see S2 Table ) ., We observed that CP190 still presented a high beta ( β ^ = 1 . 13 , p < 1 × 10−20 ) , which reflect that additional IBPs are able to recruit these cofactors in concordance with recent results 41 ., An important question is to know if IBPs demarcate TAD borders depending on the presence of specific cofactors 10 ., To answer this question , we assessed if the co-occurence of an IBP with a cofactor could affect TAD borders by estimating the corresponding statistical interaction IBP-cofactor ( Fig 6 ) ., Among the significant positive interactions , we reported effects for Su ( Hw ) with Rad21 ( β ^ = 0 . 44 , p = 3 × 10−7 ) , and lower effects of Su ( Hw ) with Chromator ( β ^ = 0 . 29 , p = 2 × 10−4 ) , BEAF-32 with condensin I ( Barren ) ( β ^ = 0 . 27 , p = 2 × 10−5 ) , dTFIIIC with Fs ( 1 ) h-L ( β ^ = 0 . 21 , p = 0 . 001 ) , dCTCF with condensin I ( Barren ) ( β ^ = 0 . 23 , p = 2 × 10−3 ) ., These positive interactions reflected synergistic effects of IBPs with cofactors ., We did not report any significant positive statistical interaction between dCTCF and cohesin as observed in human 8 ., In contrast to vertebrates , Drosophila CTCF does not appear to rely on cohesin to establish or maintain interactions 42 ., Of interest , our method further highlighted strong and significant negative interactions that revealed antagonistic effects at domain borders , in particular for BEAF-32 with cofactor CP190 ( β ^ = - 0 . 80 , p < 1 × 10−20 ) ., As such , our model may allow to retrieve both synergistic and antagonistic influences of co-factors , which may better reflect the complexity behind the establishment or maintenance of TAD borders ., We sought to further investigate a wide variety of functional elements such as insulators and regulatory sequences ., Results are reported in Fig 7 ., Insulators were by far the most influential functional elements with respect to domain borders ( β ^ = 5 . 07 , p < 1 × 10−20 ) , as established in human 8 , 31 ., Regarding other functional elements , we found positive effects for repeat regions ( β ^ = 0 . 71 , p < 1 × 10−20 ) , and especially for tandem repeats on TAD borders ( β ^ = 1 . 10 , p = 5 × 10−9 ) ., Repeat regions were previously reported to spatially cluster together 43 ., In addition , snoRNA genes had a positive influence on domain borders ( β ^ = 1 . 37 , p = 1 × 10−7 ) , which may reflect their role in higher-order chromatin structure 44 ., Furthermore , a negative impact on TAD border was detected for regulatory sequences ( β ^ = 1 . 87 , p = 6 × 10−10 ) , strengthening the hypothesis that functional long-range contacts involving regulatory elements could compete with structural contacts 45 ( see Discussion ) ., We next analyzed the effects of DNA-binding proteins on 3D domains of human genome where fewer architectural proteins have been uncovered 29 ., To investigate the possible contributions of these proteins , we analyzed new 3D domains detected from recent high resolution Hi-C data at 1 kb for GM12878 cells for which a large number of ChIP-seq data were available 8 ., Over the 69 proteins analyzed , 51 proteins presented very high and significant enrichments ( all coefficients β ^ > 3 and all p-values p < 1 × 10−20 ) ., Multiple logistic regression instead detected 15 proteins with significant positive effects on domain borders ( all coefficients β ^ > 0 . 5 and all p-values p < 5 × 10−4; S3 Table ) ., Our analyses confirmed that , in contrast to Drosophila , CTCF and cohesin ( subunit Rad21 ) presented the highest effects among all factors ( CTCF: β ^ = 1 . 90 , p < 1 × 10−20; cohesin: β ^ = 1 . 91 , p < 1 × 10−20 ) , in complete agreement with numerous studies showing their important roles in shaping chromosome 3D structure in mammals 8 , 9 , 12 ., ZNF143 had the third highest effect ( β ^ = 1 . 85 , p < 1 × 10−20 ) , in total agreement with a very recent study demonstrating its role in long-range contacts 46 ., In addition , multiple logistic regression identified EZH2 , the catalytic subunit of the Polycomb repressive complex 2 ( PRC2 ) , as a protein that significantly impacted TAD borders ( 4th highest effect: β ^ = 1 . 32 , p < 5 × 10−11 ) ., In contrast , multiple logistic regression estimated a null beta for candidate architectural proteins JUND ( β ^ = 0 . 04 , p = 0 . 85 ) , Kaiso ( β ^ = 0 . 43 , p = 0 . 10 ) and a very low beta for MAZ ( β ^ = 0 . 23 , p = 3 × 10−4 ) ., Although these three proteins colocalize or interact with CTCF , our model suggests that they might not impact TAD borders ., We also notably identified several factors associated with transcriptional activation that had significant negative influences on TAD borders ., These proteins included RXRA ( β ^ = - 1 . 37 , p = 3 × 10−4 ) , P300 ( β ^ = - 1 . 22 , p = 1 × 10−10 ) , BCL11A ( β ^ = - 0 . 82 , p = 1 × 10−9 ) and ELK1 ( β ^ = - 0 . 74 , p = 4 × 10−9 ) , reinforcing the view that transcription could also interfere with TAD borders depending on context ., In the previous subsection , analyses of DNA-binding proteins were limited by available ChIP-seq data ., Here we alleviated this limitation by analyzing transcription factor binding site ( TFBS ) motifs available from the large MotifMap database 47 ., Given the large number of TFBS motifs ( 544 motifs ) , we used L1-regularization for parameter estimation ., We identified 213 positive drivers ( all coefficients β ^ > 1 ) and 75 negative drivers ( all coefficients β ^ < 1 ) , meaning that a large number of TFBSs actually play a role in TAD border establishment or maintenance ., CTCF motifs ranked first ( β ^ = 45 . 34 ) in complete agreement with recent studies 8 , 31 ., But our model also uncovered other TFBSs whose roles in TAD borders are less well known such as EGR-1 ( β ^ = 34 . 04 ) , p53 ( β ^ = 25 . 55 ) , MIZF ( β ^ = 22 . 46 ) , GABP ( β ^ = 21 . 94 ) and many others ( for a complete list , see S4 Table ) ., For instance , p53 is a major tumor suppressor gene and the most frequently mutated gene ( >50% ) in human cancer 48 ., Regarding negative drivers , we identified ALX4 ( β ^ = - 35 . 82 ) , EGR4 ( β ^ = - 26 . 72 ) , ZNF423 ( β ^ = - 23 . 97 ) ., All these results highlighted the great potential of TFBS motif analysis allowing the study of a very large number of DNA-binding proteins ., Here , we describe a multiple logistic regression ( MLR ) to assess the roles of genomic features such as DNA-binding proteins and functional elements on TAD border establishment/maintenance ., Based on conditional independence , such regression model can identify genomic features that impact TAD borders , unlike enrichment test ( ET ) and non-parametric models ., Using simulations , we demonstrate that model parameters can be accurately estimated for both marginal genomic features ( no interaction ) and two-way interactions ., In addition , we show that our model outperforms enrichment test and random forests for the identification of genomic features that influence domain borders ., Using recent experimental Hi-C and ChIP-seq data , the proposed model can identify genomic features that are most influential with respect to TAD borders at a very high resolution of 1 kb in both Drosophila and human ., The proposed model could thus guide the biologists for the design of most critical Hi-C experiments aiming at unravelin

Introduction, Results, Discussion, Materials and Methods

Recent advances in long-range Hi-C contact mapping have revealed the importance of the 3D structure of chromosomes in gene expression ., A current challenge is to identify the key molecular drivers of this 3D structure ., Several genomic features , such as architectural proteins and functional elements , were shown to be enriched at topological domain borders using classical enrichment tests ., Here we propose multiple logistic regression to identify those genomic features that positively or negatively influence domain border establishment or maintenance ., The model is flexible , and can account for statistical interactions among multiple genomic features ., Using both simulated and real data , we show that our model outperforms enrichment test and non-parametric models , such as random forests , for the identification of genomic features that influence domain borders ., Using Drosophila Hi-C data at a very high resolution of 1 kb , our model suggests that , among architectural proteins , BEAF-32 and CP190 are the main positive drivers of 3D domain borders ., In humans , our model identifies well-known architectural proteins CTCF and cohesin , as well as ZNF143 and Polycomb group proteins as positive drivers of domain borders ., The model also reveals the existence of several negative drivers that counteract the presence of domain borders including P300 , RXRA , BCL11A and ELK1 .

Chromosomal DNA is tightly packed up in 3D such that around 2 meters of this long molecule fits into the microscopic nucleus of every cell ., The genome packing is not random , but instead structured in 3D domains that are essential to numerous key processes in the cell , such as for the regulation of gene expression or for the replication of DNA ., A current challenge is to identify the key molecular drivers of this higher-order chromosome organization ., Here we propose a novel computational integrative approach to identify proteins and DNA elements that positively or negatively influence the establishment or maintenance of 3D domains ., Analysis of Drosophila data at very high resolution suggests that among architectural proteins , BEAF-32 and CP190 are the main positive drivers of 3D domains ., In humans , our results highlight the roles of CTCF , cohesin , ZNF143 and Polycomb group proteins as positive drivers of 3D domains , in contrast to P300 , RXRA , BCL11A and ELK1 that act as negative drivers .

invertebrates, functional genomics, computational biology, dna-binding proteins, enzymology, animals, invertebrate genomics, animal models, insulators, drosophila melanogaster, model organisms, materials science, genome analysis, epigenetics, drosophila, enzyme chemistry, chromatin, research and analysis methods, chromosome biology, proteins, gene expression, biological databases, materials by attribute, insects, animal genomics, arthropoda, biochemistry, cell biology, database and informatics methods, genetics, biology and life sciences, cofactors (biochemistry), physical sciences, genomics, genomic databases, organisms

journal.pcbi.1000806

Functional Rotation of the Transporter AcrB: Insights into Drug Extrusion from Simulations

The acquisition of multidrug resistance ( MDR ) by bacteria , both in hospitals and in the community , has become one of the most serious impediments to improved healthcare 1–6 ., Unfortunately , MDR is not restricted to antimicrobials , being common to antimalarials , herbicides , and anticancer agents as well 4 ., A key role in MDR is played by efflux pumps , which feature some characteristics with respect to common membrane transport systems 7 , 8 ., Indeed , while the latter typically are highly specific for their substrates , efflux pumps possess a broad specificity for a wide range of chemically unrelated molecules and drugs 3–5 , 9 , 10 ., MDR is of particular concern in Gram-negative bacteria , since this class includes several human pathogens , e . g . , Pseudomonas aeruginosa and Enterobacter aerogenes 4 , 5 , 11 , 12 ., Genetic and biochemical data 12–17 have shown that the major efflux systems in these bacteria constitute a tripartite complex spanning the periplasmic space across both the inner and the outer membrane 18 , 19 ., These efflux systems contain an inner-membrane transporter of the Resistance-Nodulation-Division ( RND ) superfamily 6 , 20 , 21 and extrude a large variety of toxic compounds , including novel experimental antimicrobials 22 ., In E . coli , the system is composed of the outer membrane protein TolC 23 , the periplasmic membrane fusion protein AcrA 24 , 25 , and the inner-membrane cation-drug antiporter AcrB 26 , 27 ., The active part of the efflux pump - AcrB - ( see Fig . 1A ) is primarily responsible for the uptake and selectivity of the substrate as well as for the energy transduction ., Its structure has first been solved as a symmetric homotrimer 27 ., Three main domains have been identified in each monomer: the transmembrane domain embedded in the inner membrane , which provides the energy using the transmembrane proton gradient; the pore/pumping domain in the periplasm , that is supposed to contain the gates from which substrate uptake and extrusion toward TolC occur 28 , 29; and the TolC docking domain , containing a central funnel and presumably being in contact with TolC ., More recently , AcrB has been crystallized as an asymmetric trimer with 30 and without 31 , 32 a substrate bound in the interior of the protein ., Each monomer was found in a different conformation ( hereafter Loose , Tight , and Open , or L , T , and O , respectively , following Ref . 31 ) ., In the structure of Murakami et al . 30 , the drugs doxorubicin and minocycline were found inside a binding pocket in the T monomer ., The three conformations of AcrB have been interpreted as states of a transport cycle , schematically represented in Fig . 1B , which occurs via a three-step functional ( not physical ) rotation 30 , 31 ., Following the hypotheses formulated in Refs ., 11 , 28 , 29 , the substrate should enter the pore domain of the transporter via the L and/or T monomer , either from open clefts in the periplasm or through grooves between helices at the interface between pore and transmembrane domain ., Then , the substrate should accomodate into a binding pocket when the monomer assumes the T conformation and move out toward the TolC docking domain upon a subsequent change to the conformation O . The proposed mechanism is primarily based on the available crystal structures , and has been confirmed only indirectly 33 , 34 ., Recently , it has been subject of a critical review 28 ., In particular , it is not known how conformational changes of the protein cause the extrusion of the drug , and to what extent diffusion is important in the process ., A direct and atomistic-level description of the interplay between structure and dynamics of the conformational changes might render the proposed pictures of the function less speculative and allow to acquire knowledge on the structure-dynamics-function relationship ., Such insights will be of support for the analysis of the huge amount of genetic , mutagenetic , and other biochemical data on RND transporters 3–6 , 9 , 11 , 35 ., Additionally , they will constitute valuable information for a structure-based design of efficient antibiotics and inhibitors , by identifying possible target and key domains in the different steps of the extrusion process ., In this respect , molecular dynamics ( MD ) simulations already pinpointed important atomic-level details of the functioning of TolC 36 , 37 and MexA 38 , a homologue of AcrA ., Despite the importance of AcrB , no computational studies of this transporter have been reported so far ., Here , we present a first attempt to address the relationship between functional rotation and extrusion of substrates: starting from the structural information available on the complex of AcrB with doxorubicin 30 , we simulated the proposed final extrusion step of the functional rotation ., This was done via targeted molecular dynamics ( TMD ) simulations 39 , which enables to mimic the conformational changes of the protein without explicitly considering the proton transfer and the related energy transduction , which would require the introduction of quantum-mechanical calculations ., TMD has been successfully applied to study conformational changes in large systems as 40 and MurD 41 , and it has recently been shown to provide reliable transition paths as compared to other methods used to sample conformations of proteins 42 ., Note that in this work we are not investigating the issue of substrate specificity of AcrB , which would require additional compounds to be considered ., In the following we show that doxorubicin leaves the binding pocket upon induction of functional rotation , although its total extrusion into the TolC docking domain is not observed ., The main aspects as well as the possible limiting factors of the process are discussed ., Furthermore , we investigate the presence of a peristaltic-like mechanism and characterize its underlying atomic rearrangements ., Initially , doxorubicin is found within a binding pocket which is located between of the subdomains PC1 and PN2 30 and formed by the residues F136 , Q176 , F610 , F615 , F617 , and F628 35 , as shown in the inset of Fig . 1A ., During the transition , these subdomains undergo conformational changes , thereby displacing the drug ., In general , the whole binding region , which contains the described binding pocket has a quite large internal volume , probably with more than one binding site 4 ., Fig . 2 displays the calculated distance between the centers of mass ( CoMs ) of the binding pocket and doxorubicin , , as a function of the simulation time , along with the values of the interaction energy ., At the end of the TMD simulations , the RMSD of the protein with respect to the target ( ) is Å ( Fig . S1 ) , indicating that the transition has been accomplished ., Furthermore , the substrate has moved away from the binding pocket by a total distance of Å toward the gate to the central funnel , formed by the residues Q124 , Q125 , and Y758 32 ., As shown in the inset of Fig . 2 , the interaction energy increases significantly as the transition proceeds , thereby denoting an unbinding event ., The initial and final positions of the drug of one of the TMD simulations are shown in Fig . 3 , as well as the structural changes of the binding pocket and of the gate to the central funnel ., The displacement of doxorubicin toward the gate is confirmed by the profile of the distance between the CoMs of the three residues forming the gate and that of doxorubicin ( data not shown ) ., During the transition , this distance decreases by Å , which is a clear indication of the movement of the drug along the path that was identified by Sennhauser et al . 32 ., Note that the magnitude of displacement is essentially independent of the initial orientation of doxorubicin within the binding pocket ( see Fig . S2 ) ., Additionally , the obtained displacements are almost insensitive to randomly reinitializing the initial velocities of all atoms or to extending the simulation time to 5 and 10 ns ( Fig . S3 ) ., In these longer simulations , the major movement of doxorubicin occurs at a different relative time , with respect to the total TMD simulation time , although the final displacements are very similar to those seen in the shorter runs ., This indicates a minor dependence of our general results on the simulation time and fortifies the reliability of our calculations ., To assess the stability of doxorubicin in the final position at the end of the TMD runs , we performed two sets of four standard MD simulations starting from the final TMD configurations ( see Figs . 2 and S4 ) ., In the first case , we restrained the atoms of the protein , in order to keep the backbone in the final conformation ., In the second one , we left the system completely unrestrained and free to relax ., In half of the unrestrained simulations , the drug moves further away from the binding pocket by 2 Å in the direction of the gate; in all the remaining runs ( 2 unrestrained and 4 restrained ) , it oscillates around its final TMD position ., Nevertheless , doxorubicin does not move back toward the binding pocket in any of these simulations ., From the visual inspection of the final position of doxorubicin as shown in Figs ., 3A and B , it is clear that the drug did not enter the central funnel of the TolC docking domain during the transition ., Certainly , the real time scale of the process is out of reach by the computational tools used in the present work , and such a limitation might be a reason for the absence of the complete extrusion in our simulations ., Indeed , diffusion could play a relevant role in driving out the drug from AcrB , but this process would occur on a time scale hardly approachable by our protocol ., Apart from methodological issues , additional factors have been suggested to be necessary for the full extrusion of the substrate ., For example , the necessity of cooperativity effects associated with the binding of a second substrate ( absent in our simulations ) to a neighboring monomer has been invoked to interpret kinetic data 43 ., Furthermore , a more involved pattern of configurations assumed by the monomers and connected to the extrusion process has also been inferred from the analysis of crystallographic structures 28 , 44 ., A further possible reason might be the absence of the membrane fusion protein AcrA ., Its contribution to the functionality of the efflux system seems to go beyond a simple structural linker between TolC and AcrB ., Surely , a deeper understanding of this interplay will benefit from the simulations of the entire system exploiting the model recently proposed by Symmons et al . 25 ., Note that upon induction of the conformational transition , the subdomain PC2 moves inward to close the entrance and is followed by PN1 which opens the exit 31 ( Fig . 3B ) ., The distance between the CoMs of PN2 and PC1 declines ( see Fig . S5 ) accompanied by a rotation of the two subdomains , thereby resulting in a shrinkage of the binding pocket ., Thus , the motions of the subdomains appear to be the first requirement for the squeezing of the drug out of the binding pocket ., However , the largest displacements between the CoMs of the subdomains do occur in the first half of the TMD simulation , while most of the drug displacement is seen in the second half ( see Figs . 2 , S5 , and Video S1 ) ., Interestingly , the RMSD of the residues of the binding pocket from the target does not drop much until almost half of the TMD simulation is over ., Then , it starts to decrease in correlation with the movement of the drug ( Fig . S1 ) , indicating that more specific and local conformational changes are involved in the unbinding of doxorubicin ., Thus , our attention focused on the action of specific groups of residues ., A peristaltic pumping was proposed as the extrusion mechanism by Pos and coworkers in 2006 31 ., To identify possible fingerprints of the peristaltic action and correlations between motions of residues and drug displacement , we compared the latter with the evolution of the minimum distances , , between selected couples of residues in the binding pocket ., In particular , we selected those pairs of residues whose distances decline predominantly during the transition , namely F136–F615 , F136–F617 , F136–F628 , and Q176–F615 ., In Fig . 4 , the evolution of their average minimum distances over 5 TMD simulations ( lower panel ) is shown together with three representative configurations associated therewith ( upper panel ) ., Interestingly , the changes in the distances among the selected residues occur in a step-wise fashion , with residue pairs at the “bottom” of the binding pocket closing first , and those at the “top” last , producing a zipper-like motion ., The first reduction of affects the pair F136–F628 , but the substrate essentially keeps its position in the binding pocket ., Successively , the residues F136 and F617 approach each other , and starts to increase ( I in lower panel of Fig . 4 ) ., The configurations assumed by the three residues are displayed in snapshot I of the upper panel in the same figure ., At about one third of the TMD simulation time , the distance of F136–F615 starts to decrease , and this reduction correlates with a large movement of the drug ( II in Fig . 4 ) ., Note that this is the largest reduction ( Å ) in the distance among the observed pairs of residues ., At approximately the same time , the squeezing motion between Q176 and F615 takes place , which is also related to a substantial displacement of the drug ( III in Fig . 4 ) ., These two amino acids happen to act as a clamp for the planar rings of doxorubicin ., The phenyl ring of F615 is atop of one of the rings belonging to the drug and the Q176 amide is on the other side ., While the drug is moving toward the gate , the connection between the ring of F615 and the drug is changing from one planar ring of the drug to the next one in a stepwise fashion ., Once the residues F615 and Q176 squeeze the substrate out of the binding pocket and thereby close the return path of doxorubicin ( see snapshot III in the upper panel of Fig . 4 ) , the drug is able to move further away from the binding pocket ., Concerning each individual TMD simulation , the connection between the zipper-like closure of the binding pocket and the drug displacement can be seen in 4 out of 5 different 1-ns-long simulations ( Figs . S6A , C , D , and E ) ., A three-step mechanism can be roughly recognized in the graphs , with a sequential closure of the pairs from the innermost ( F136–F628 ) to the outermost one ( Q176–F615 ) ., The remaining run of this set ( Fig . S6B ) could be viewed as a borderline case in which the last step is very short ., Despite this , the closure of the binding pocket maintains a basically sequential character , where the outermost pairs ( Q176–F615 and F136–F615 ) close after the innermost ones ( F136–F617 and F136–F628 ) ., Additionally , three longer TMD simulations ( two of 5 ns and one of 10 ns ) were performed to assess the dependence of our results from the simulation time ( Figs . S6F , G , and H , respectively ) ., As expected , the molecular details of the process ( final displacement , side chain conformation and dynamics ) are slightly sensitive to the simulation protocol ( see also Fig . S3 ) ., Nevertheless , a sequential closure of the binding pocket is still detectable in all panels of Fig S6 ., In addition to the four out of the five 1-ns-long simulations mentioned above , in one out of the two 5-ns-long ones evidences of a three-step mechanism are recognizable ( see caption of Fig . S6 for an extended discussion ) ., Unfortunately , a meaningful statistics , needed for a thorough discussion of the possible limits inherently present in the TMD protocol , is out of range for these longer trajectories ., It is worthwhile to point out that the results of recent mutagenesis experiments 35 have evidenced a significant impact of the mutation F610A on the minimum inhibitory concentration of doxorubicin , while other mutations , including those of the phenylalanines 136 , 178 , 615 , 617 , and 628 to alanine , showed smaller effects ., According to our simulations , F610 is not prominently involved in the zipper-like action , but might act as binding partner when doxorubicin enters the pore domain , and/or might close the escape from the binding pocket toward the periplasm ., Upon mutation to alanine , these actions might not be efficient anymore ., Additional studies are required to gain insight into the effect of the F610A mutation ., To enhance the understanding of the results presented above , we investigated the dynamical coupling between squeezing motions of the binding pocket and other specific residues located beyond it ., In particular , we chose those residues lining the path from the binding pocket toward the exit gate ., This path ( hereafter called BP-Gate path and sketched in Fig . 3C ) is formed , with reference to the initial conformation , by the residues 48–50 , 85–89 , 126 , 163 , 177–181 , 273–276 , and 767–772 of the occupied monomer T as well as residues 67–70 and 113–117 of the neighboring monomer O . A series of TMD simulations have been performed in which we kept the BP-Gate path of the T monomer unsteered and forced only the rest of the protein , thereby applying the same bias as in the previous TMD simulations ., According to our results , doxorubicin leaves the binding pocket also in these simulations , but the overall displacement is smaller by Å with respect to the one shown in Fig . 2 ., Indeed , the BP-Gate path remains too narrow for doxorubicin to leave the binding region and to move toward the exit gate ., Furthermore , the drug is tilted by with respect to the final position in Fig . 3A ( see Fig . S7 ) , which also hinders further motion toward the gate ., This result emphasizes the importance of a concerted opening of the BP-Gate path in addition to the zipper-like closure of the binding pocket ., Since the position and the orientation of amino acids seem to be important for the displacement of the drug from the binding pocket , we further extended our set of simulations to shed more light on this aspect ., In the dynamics described so far , all non-hydrogen atoms have been targeted , which corresponds to a forced movement of the side chains during the TMD simulation ., To analyze the importance of these movements for the drug displacement in comparison to the influence of the backbone/subdomain , we performed a series of TMD simulations where only the atoms were targeted ., This also allowed to test the influence of the biasing force on our results , as a large fraction of the protein is now free to move ., We observed a significant displacement of the drug during this set of TMD simulations ( see Fig . S8 ) , in qualitative agreement with those obtained by targeting all heavy atoms , hinting at the importance of subdomain motions for the displacement of the substrate ( see Fig . 5 ) ., On average , the displacement is reduced by Å with respect to the one reported in Fig . 2 ., Clearly , the number of possible paths explored by the drug is expected to increase when targeting only the , due to the larger flexibility of the protein ., Consistently , a displacement comparable to that shown in Fig . 2 is observed only in 3 out of 10 TMD simulations ( data not shown ) ., In addition , in some of the 7 remaining runs , doxorubicin does not move straight toward the exit gate , but also turns slightly aside where the interior along the BP-Gate path leaves space to roam ., These results indicate that the arrangement of the side chains is able to significantly influence the maximal displacement of the drug ., Additionally , we performed a simulation with a lower force constant ( see Tab . S1 ) ., The aim was to obtain an indication of the minimal force required to accomplish the conformational changes in the protein , especially along the BP-Gate path ., It turns out that , during the entire TMD simulation , the distance of doxorubicin from the binding pocket is lower by a couple of Å with respect to that in previous simulations with larger force constants ( Fig . S9 ) ., This is related to a larger RMSD of the binding pocket from the target along the simulation ( inset in Fig . S9 ) , which , although very small , has an important effect on the displacement of the drug ., In combination with the results of the TMD simulations where only atoms have been targeted , these findings highlight the importance of individual residues including their side-chain conformations for the displacement and subsequent extrusion of doxorubicin ., Analyzing the asymmetric crystal structures of AcrB 30–32 , it is reasonable to suppose that drugs exit the transporter from the monomer in the O conformation ., Therefore , we have considered the direct transition at first ., However , the possibility of a functional transition from T to O via the L conformation cannot be ruled out a priori ., Thus , we carried out simulations for the two steps of the reverse cycle direction , i . e . , and ( Fig . 6 ) ., The investigation is important for two reasons ., Firstly , the direction has been suggested to be the functional one from analyses of structural data 30–32 , but it lacks a direct proof ., Secondly , the comparison between the two directions should give a better picture of the conformational changes and drug-amino-acid interactions which are mainly involved in the displacement of the drug ., Interestingly , the specific movements of PN2 and PC1 , which have been described above as responsible for the shrinkage of the binding pocket during , can also be observed during ( data not shown ) ., Indeed , the substrate tends to leave the binding pocket in both cycle directions ., However , in contrast to , the drug displacement never exceeds Å for , hence doxorubicin does not approach the exit gate ., This can be attributed to the quite large internal volume of the binding region 4 ., Therefore , substrates may exploit their flexibility and change their orientation ., Importantly , the drug does not move remarkably during the second step of the reverse cycle as well ., Again , this points to the need of a concerted closure of the binding pocket and widening of the channel toward the exit gate ., The molecular dynamics underlying the functioning of many active transporters , which include efflux transporters of the RND family , are not fully understood yet ., Although the increasing number of crystal structures permits us to have a closer look at the atomic details of the structure , the dynamical aspects are not caught , and only hypotheses can be advanced concerning the functional mechanisms ., MD simulations with atomistic detail are an appropriate tool to investigate structure-function-dynamics relationship in these systems ., In this work , we performed TMD simulations to investigate the relations between supposed functional motions in AcrB 30–32 and the extrusion of the antibiotic doxorubicin without explicitly considering the energy supply associated with the proton gradient across the inner membrane ., Our results show a detachment of the drug from its initial binding pocket within the T monomer ., Moreover , during the step of the functional rotation , doxorubicin travels by Å and approaches the gate to the central funnel ., Importantly , this movement is believed to be part of the suggested extrusion process in AcrB ., Our data also support the proposed peristaltic pumping mechanism , and highlight the atomistic dynamics at its basis ., In particular , there is evidence to suggest a zipper-like squeezing of the binding pocket , which leads to an unbinding of the substrate along the direction of the cycle ., The closing of the binding pocket is initially caused by the movements of adjacent subdomains , whereas the rearrangements of individual residues lining the binding pocket strongly influence the detachment of doxorubicin in the end ., The molecular details of the extrusion process depend slightly on the TMD simulation protocol ( simulation length , targeted atoms ) , but the main features are robust against these changes ., While investigating the feasibility of the cycle in the reverse direction , additional simulations have shown a similar squeezing of the binding pocket during the transition ., However , no substantial movement of the drug toward the gate has been seen ., This could mainly be due to the lack of concerted widening of the BP-Gate path and the exit gate ., Moreover , even if such movements do occur during the subsequent step , they are not coupled to squeezing of the binding pocket , and do not cause any significant movement of the substrate ., Altogether , these results strongly points at as the legit direction of the functional rotation ., Although a substantial movement of the substrate was seen in our TMD simulations of the transition , the drug never reached the central funnel of the TolC docking domain , which is a necessary step to achieve the full extrusion of the drug out of AcrB ., One possibility to explain this is that further movement of the drug might just be directed diffusion within a confined geometry occurring on a time scale much larger than that captured in the simulations ., In addition , the motion of the drug might further be enhanced by attractive interactions between the substrate and residues around the gate or even TolC , or by the presence of other substrates ., Finally , the influence of the neighboring monomers as well as the other proteins constituting the efflux pump have to be understood ., In the long run , it would be very important to model the whole tripartite efflux pump , i . e . , AcrB together with TolC and AcrA ., This could complete the picture of the protein-protein interactions involved and their cooperative effects on the drug extrusion ., Nonetheless , using the present results it should be possible to obtain a better understanding of the structure-function relationship in RND transporters and its connection to dynamical aspects ., Finally , molecular insights on the efflux mechanism in AcrB might be of help for the research on human RND transporter , e . g . , the Niemann-Pick C1 disease protein and the hedgehog receptor Patched 28 ., For our simulation setup , the crystal structure from Ref . 32 was chosen ., After addition of hydrogen atoms , a restrained structural optimization was performed ., The structure of doxorubicin was taken from Ref . 30 and placed into the system in the same relative position within the binding pocket as original ., The latter structure was not used since several loop residues ( 499 to 512 ) of the pore domain were not resolved , and the resolution was lower with respect to that in Ref . 32 ., The combination of a crystal structure with a substrate from another structure was possible since the binding pocket of the protein accommodates the drug very well; indeed , doxorubicin keeps its position during the equilibration ., Moreover , the RMSD between the of the structures from Refs ., 30 , 32 is less than 1 Å ., After the placement of the drug , a second relaxation was performed ., The protein-substrate complex was inserted into a pre-equilibrated POPE lipid bilayer , which is parallel to the x-y plane , and solvated in TIP3P water with a physiological KCl concentration of 0 . 1 M . At the end of the buildup phase , all lipid and water atoms which overlapped with the protein were artificially removed; the total number of atoms of the system is 451 , 962 ., This setup leads to a periodic box size of ., The AMBER force field parm99 45 was used for the protein , the TIP3P parameters for water 46 , and Aaqvists parameters for the ions 47 ., For doxorubicin several parameters were taken from the GAFF force field 48 while the missing ones were generated using modules of the AMBER package 49 ., In particular , atomic restrained electrostatic potential ( RESP ) charges were derived using antechamber , after a structural optimization performed with Gaussian03 50 ., The GAFF parameters for the POPE lipids were generated following the protocol in Ref ., 51 ., The unbiased and the targeted MD simulations were both performed with the program NAMD 2 . 7b1 52 ., After an initial energy minimization , the system was gradually heated up to 600 K and finally quenched to 310 K . All these simulations were performed in the presence of restraints on the phospholipids and the heavy atoms of the the protein ., A time step of 1 fs was used for the integration of equations of motion ., Furthermore , periodic boundary conditions were employed , and electrostatic interactions were treated using the particle-mesh Ewald ( PME ) method , with a real space cutoff of 12 Å and a grid spacing of 1 Å per grid point in each dimension ., The van der Waals energies were calculated using a smooth cutoff ( switching radius 10 Å , cutoff radius 12 Å ) ., Furthermore , the simulations were performed in the NpT ensemble and the temperature was kept at 310 K by applying Langevin forces to all heavy atoms with the Langevin damping constant set to ., The pressure was kept at 1 . 013 bar using the Nosé-Hoover Langevin piston pressure control ., The functional rotation was simulated by means of TMD 39 ( built-in module of NAMD ) which allows to induce conformation changes between two known states ., To prevent any hindrance on the T monomer by the neighboring ones , we also steered those toward their next state ., Note that the TMD algorithm has recently been demonstrated to produce reliable transition paths as compared to other methods 42 ., In this respect , to assess the influence of the biasing force on the dynamics of the system , we performed a series of TMD simulations using different values for the force constant per atom ( ) and the simulation time ( 1 , 5 , and 10 ns ) ., The results discussed in the main paper refer to simulations of 1 ns with , which are consistent with the literature 40 , 41 , 53 ., All TMD simulations performed are detailed in Tab ., S1 together with the comparison among distances between CoMs of doxorubicin and the binding pocket and the final positions of the drug ( Fig . S2 ) ., The setup , the analyses as well as the atomic-level figures , were performed using VMD 54 .

Introduction, Results, Discussion, Methods

The tripartite complex AcrAB-TolC is the major efflux system in Escherichia coli ., It extrudes a wide spectrum of noxious compounds out of the bacterium , including many antibiotics ., Its active part , the homotrimeric transporter AcrB , is responsible for the selective binding of substrates and energy transduction ., Based on available crystal structures and biochemical data , the transport of substrates by AcrB has been proposed to take place via a functional rotation , in which each monomer assumes a particular conformation ., However , there is no molecular-level description of the conformational changes associated with the rotation and their connection to drug extrusion ., To obtain insights thereon , we have performed extensive targeted molecular dynamics simulations mimicking the functional rotation of AcrB containing doxorubicin , one of the two substrates that were co-crystallized so far ., The simulations , including almost half a million atoms , have been used to test several hypotheses concerning the structure-dynamics-function relationship of this transporter ., Our results indicate that , upon induction of conformational changes , the substrate detaches from the binding pocket and approaches the gate to the central funnel ., Furthermore , we provide strong evidence for the proposed peristaltic transport involving a zipper-like closure of the binding pocket , responsible for the displacement of the drug ., A concerted opening of the channel between the binding pocket and the gate further favors the displacement of the drug ., This microscopically well-funded information allows one to identify the role of specific amino acids during the transitions and to shed light on the functioning of AcrB .

In nature , bacteria have to resist several toxic threats to be able to survive , from bile acids in intestines up to antibiotics ., The Escherichia coli bacterium , which usually is a commensal inhabitant of human intestines , can also acquire pathogenic properties which would harm the human body ., To dispose of toxic compounds , E . coli has developed a protein machinery which is called “efflux pump” ., Here , we studied the dynamics of the transporter protein AcrB , a component of the E . coli major efflux system , in complex with an antibiotic ( doxorubicin ) ., We used computer simulations to complement the existing experimental data ., Our purpose was to gain more detailed insights into the pumping mechanism at the molecular level ., In our simulations the drug leaves the binding pocket upon induction of functional rotation in the protein , although a complete extrusion was never observed ., A peristaltic motion , which starts with a zipper-like closure of the interior of the protein , is an important step for the extrusion of the drug ., Interestingly , such a peristaltic mechanism of pumping has been suggested before on the basis of structural data ., The molecular details obtained in this study shall deepen the understanding of the functioning of the efflux pump .

computational biology/molecular dynamics, computer science/applications, biochemistry/membrane proteins and energy transduction

journal.pcbi.1005418

Sequential inference as a mode of cognition and its correlates in fronto-parietal and hippocampal brain regions

Model-based approaches to cognition posit that agents continually perform online inference about the current state of the world , based on incoming sensory information ., Typically , these approaches assume that the agent optimises beliefs about the current state of the world , referred to as Bayesian filtering ., However , in many situations ( for example , when parsing language ) it is more appropriate to optimise beliefs about a sequence of states ., Since the joint probability of a sequence of states is not , in general , the same as the ( product of marginal ) probabilities of the individual states considered individually , this leads to two alternative definitions of optimality: optimality of inference about individual states , and optimality of inference about sequences of states ., These diverging goals are captured in the sum-product and max-sum algorithms for exact inference 1 ) ., In the context of offline data analysis , it is common to calculate the most likely sequence of states across an entire data set using the Viterbi algorithm , an application of the max-sum approach 2 ., However , for embodied agents performing online inference , these schemes are not an option because future outcomes are unobservable , while inference over long sequences becomes computationally intractable ., One possibility is that agents instead perform windowed sequential inference , in other words they perform inference about short sequences of states stretching back into the past ( Fig 1b ) ., Compared with Bayesian filtering this entails relatively minor increases in computational cost and represents a plausible hypothesis regarding implementation of actual cognitive processes in human subjects ., The advantage of representing ‘a short history’ of states is that the most likely ( posterior ) distribution over states becomes a more accurate approximation to the true posterior , where the true posterior entails conditional dependencies between states and different times ., For example , if I am hungry at 11 AM I am more likely to be taking lunch at 1 PM ., One cannot represent this belief in terms of the statements “I was hungry at 11 AM and I was lunching at 1 PM” ., Although a Bayesian filter would correctly infer a higher probability of having lunch at 1 PM , given I was hungry earlier , its posterior belief about the current state having lunch has nothing to say about preceding hunger ., Sequential inference becomes even more prescient when we consider Non-Markovian processes ., Bayesian filters assume that states of the world evolve in a Markovian fashion , such that the preceding state completely specifies the next state in a probabilistic sense ., However , in real-world situations ( e . g . language ) , this Markovian assumption is often violated ., For example , the semantic violation at the end of a sentence depends on the sequence of preceding words , not just the penultimate word ., In this sense , inferring sequences with deep temporal structure necessarily requires the brain to perform some form of sequential inference ., The importance of considering information contained at the level of entire sequences has been elegantly demonstrated in work on speech recognition using machine learning , where models that learn at the level of entire sequences consistently outperform those that learn at the level of individual frames ( short epochs ) 3–6 ., Establishing whether human subjects perform sequential inference has important implications both for models of cognition and their neurobiological underpinnings ., In particular , sequential inference requires that agents explicitly represent and update beliefs about states in the past , as well as the present ., This mandates that neuronal circuits should also represent past states , and suggests that brain areas involved in processes such as maintaining and manipulating information about the past , for example prefrontal cortex and hippocampus ( i . e . , as organs of succession ) , might play a crucial role ., Importantly , filtering and sequential inference strategies make quantitatively distinguishable predictions about behaviour on appropriate tasks ( Fig 2 ) , permitting us to compare the evidence for these strategies through behavioural modelling Thus , to test our hypothesis that human subjects perform sequential inference , we developed a simple computational scheme to implement sequential inference within the context of a probabilistic reversal task ( Fig 1a ) ., On each trial of this task , subjects were presented with superimposed images of either a young face and an old house , or an old face and a modern house ., Subjects were asked to respond ( either ‘old’ or ‘young’ ) and were given probabilistic feedback based on whether the response was correct ., The correct response was determined by the ( unknown ) task relevant category ( either ‘face’ or ‘house’ ) , which the subjects had to infer , based on feedback ., Crucially , the task relevant category or condition reversed periodically requiring subjects to track switches in the category condition; in order to maintain performance ( see Methods for more details ) ., We applied sequential inference models to behavioural data from 43 younger and 36 older adults ( Fig 1 ) ., We hypothesised that behaviour would show evidence of sequential inference , and as this depends on working memory type resources it might also show an age-related decline ., To identify potential neuronal substrates of sequential inference , we analysed structural magnetic resonance imaging ( MRI ) scans to test for the correlates of sequential inference in terms of grey matter density using voxel-based morphometry ( VBM ) 7 ., We hypothesised that an ability to exploit sequential inference would be positively correlated with grey matter density in brain regions involved in constructing models of the environment and maintaining on-line information , specifically the anterior or dorsolateral prefrontal cortex and hippocampus ., Overall , subjects made correct choices ( defined in terms of the actual task contingencies ) on 74 . 5% of trials ( SEM: 0 . 795 ) ., As expected , younger adults made more correct choices ( mean: 77 . 9% SEM: 0 . 765 ) than older subjects ( mean: 70 . 4% SEM: 1 . 172 ) ., This difference was statistically significant ( p < 0 . 0001 , Wilcoxon rank sum test ) , suggesting , in line with a previous literature ( see 8 for review ) , younger adults were considerably better at decision making on this probabilistic reversal learning task ., To confirm the appropriateness of the Bayesian modelling approach ( see Methods ) , we performed a preliminary model comparison where we compared the performance of simple Bayesian filtering ( S1 ) with three models based on Q-learning ., These consisted of a simple model in which each action value was updated independently ( Q1 ) , a model in which all values were simultaneously updated ( Q2 ) , and a model in which all values were simultaneously updated but with separate learning rates for positive and negative feedback ( Q3 ) ., In keeping with previous findings 9–11 , random effects model comparison favoured S1 in both younger ( exceedance probability > 0 . 99 ) and older ( exceedance probability = 0 . 94 ) groups ( Table A in S1 Text ) ., This suggests that the behaviour of the subjects do indeed takes into account the structure of the task , in particular the fact that it involves abrupt shifts ( reversals ) , rather than gradual but continuous changes ., To test whether subjects performed sequential inference , we compared the behavioural fits of pure filtering model ( S1 ) with those in which subjects performed inference over sequences of lengths two to five ( S2-5 ) ., Random effects model selection provided evidence that both younger and older adults performed inference over sequences of length two or more ( Table 1 , Fig 3 ) ., The favoured model in both groups ( with an exceedance probability of 0 . 99 or greater ) was S2 , in which agents perform joint inference over the current state and the preceding state , but with evidence of significant variability in sequence length between subjects ., Bayesian parameter averaging over the model space showed that younger adults had significantly higher values for both r and v than older adults ( Younger: r = 0 . 95 , v = 0 . 86 . Older: r = 0 . 89 , v = 0 . 72 . Both p < 0 . 001 , Wilcoxon rank sum test ) ., This corresponds to beliefs that the environment is more stable , meaning a reversal is less likely to occur , and that feedback was more informative ., In younger adults , these beliefs also reflect the true contingencies in the task ., It thus appears that both age groups perform inference over a similar sequence length , while the age group effect in choice accuracy might be better captured by the certainty of the prediction at each given state , which we investigate in more depth elsewhere 12 ., Note that since we fixed the precision parameter in our model fitting , due to the redundancy between model parameters 9 , 13 , some of these group differences may in fact reflect the influence of changes in precision γ , which governs the stochasticity of action selection ( Eq 4 ) ., Given that our main model comparison suggested similar distributions of sequential inference strategies across age groups , we pooled across subjects , including age group and model fit quality as indexed by the mean BIC score over the model space for each subject as confounding regressors in subsequent VBM analyses ., Measures of model fit such as the BIC and pseudo-R2 indicated that all models explained less data variance in older adults ( Table 1 ) ., This is unsurprising given the differences in parameter estimates described above , which indicate that older subjects’ behaviour is more stochastic and harder to predict ., We next performed voxel-based morphometry 7 to explore how regional variations in grey matter density are related to interindividual differences in sequential inference ., Our motivation here rested on well described relationships between regional grey matter density and cognitive function across a variety of domains 14 ., This led us to hypothesise that grey matter density would provide a marker that would allow us to identify regions that play a key role in implementing sequential inference ., To characterise between-subject variability in cognitive strategy , we used two key , and complementary measures ., First , as a measure of how strongly sequential inference ( as opposed to filtering ) influenced subjects’ behaviour , we defined a measure ΔLL as the difference in the accuracy with which the best sequential inference model and the filtering model predicted behaviour as quantified by the difference in log likelihood ., Second , we defined a measure L which indexes the length of sequence considered by each subject based on the winning model for that subject ., These were entered into a multiple regression model , along with control regressors encoding age group , gender , total intracranial volume , individual subject parameter estimates ( r and v ) , and the mean BIC score across the model space for each subject ., Note there was no correlation between estimates of L and ΔLL themselves ( R = 0 . 020 , p = 0 . 861 ) ) ., Across subjects , ΔLL showed a positive association with grey matter density in the left anterior prefrontal cortex ( putatively Brodmann area 10 ) , a relationship significant at the whole brain level ( peak voxel coordinates –20 53 20 , t70 = 5 . 08 , p = 0 . 017 FWE-corrected for whole brain ) ( Fig 4 , Table B in S1 Text ) ., This suggests that this region may play a key role in determining a propensity to sequential inference , consistent with a dependence of sequential inference on higher-level cognitive functions , such as working memory maintenance and metacognitive functions , widely believed to be subserved by the anterior prefrontal cortex 15–18 ., Between-subject differences in the sequential length regressor L were correlated with grey matter density in the left posterior parietal cortex ( -29–80 39 , t70 = 4 . 84 , p = 0 . 039 FWE-corrected for whole brain ) ( Fig 5 , Table C in S1 Text ) ., Regions of the posterior parietal cortex are widely implicated in memory recollection 19–22 , and maintenance in working memory 23 , 24 ., In addition , based on its key role in encoding sequences 25–28 , we performed a region of interest analysis in the hippocampus , using coordinates derived from previous work on recollecting temporal sequences 27 ., Grey matter density in bilateral hippocampus positively correlated with L ( Fig 5 , Table C in S1 Text ) ( Left: –23–30–17 , t70 = 3 . 75 , p < 0 . 007 FWE-corrected for hippocampus ROI volume . Right: 21–30–12 , t70 = 3 . 68 , p < 0 . 008 FWE-corrected for hippocampus ROI ) ., This provides clear evidence that the hippocampus plays a role in supporting sequential inference strategies ., Finally , to assess whether grey matter density in the anterior prefrontal cortex also showed a relationship with sequence length , we performed an additional region of interest analysis using the prefrontal cluster identified in our ΔLL analysis ( thresholded at p < 0 . 001 uncorrected ) ., Importantly , because these regressors are entered into a single multiple regression analysis , we control for the effect of the log likelihood regressor ., This analysis showed a clear positive relationship between grey matter density in anterior prefrontal cortex and sequence length ( –21 56 21 , t70 = 2 . 76 , p = 0 . 031 FWE-corrected for ROI ) , further implicating this region in implementing sequential inference ., There was no relationship with ΔLL in the angular gyrus cluster or hippocampus ROIs ., Post hoc analyses suggested that the relationship between our measures of interest and grey matter density in these regions were similar across age groups , suggesting that the neuronal substrates of sequential inference do not alter over the course of healthy ageing ( see S1 Text ) ., No areas showed a significant negative relationship with ΔLL or L , and no significant effects obtained for any of the other regressors ( including the parameter estimates r and v ) , other than age and total intracranial volume , which correlated with widespread changes ( decreases for age , increases for total intracranial volume ) in grey matter density ., To explore possible relationships among our measures of sequential inference and performance on other cognitive tasks , we used a letter n-back task to measure working memory and Raven’s matrices to test non-verbal reasoning 29 ., The n-back task is described in more detail in 30 ., Briefly , subjects were presented with a series of letters and asked to indicate with a button press for each letter whether it is the same as the letter 1 , 2 or 3 letters back ., To test for inter-individual differences in fluid intelligence , an abbreviated version of Ravens matrices was used ( odd matrices from levels C , D and E , resulting in 18 matrices ) ., Subjects were given 20 minutes to complete the matrices ., We calculated partial correlations , controlling for the effects of age group and gender ., Working memory performance , as assessed by ( Hits—False alarms ) , averaged across one-back , two-back and three-back conditions , showed a positive correlation with L that showed trend significance ( R = 0 . 218 , p = 0 . 057 ) ., No clear relationship was found with ΔLL ( R = -0 . 041 , p = 0 . 724 ) ., No clear relationships were found between Raven’s matrices scores and either L ( R = 0 . 101 , p = 0 . 408 ) or ΔLL ( R = 0 . 043 , p = 0 . 727 ) ., Note that four younger and three older subjects were excluded from the Raven’s matrices analyses , and five younger and five older subjects from the working memory analyses , because they did not complete the relevant tasks ., No structural correlates of working memory performance or Raven’s matrices scores were found in additional VBM analyses ., Sequential inference represents a significantly different approach to solving inference problems from Bayesian filtering ., In this framework agents are required to infer multiple states , and their dependencies , simultaneously rather than just infer the current state ., This requires a capability to store information about previous observations , as well as maintain and update information about joint distributions ., Here , we provide evidence that both younger and older adults make use of such strategies , inferring the joint probability of sequences of states rather than inferring the current state in isolation and use this inference to inform their choice behaviour ., Our findings represent an important departure from existing models of human performance on inference tasks , and provides new insights into the cognitive and computational strategies used by human subjects for coping with a changing and uncertain environment ., Performing inference over sequences of states allows an agent to consider processes that violate the Markov assumption , under which the current state is determined only by its immediate precursor ., This allows agents to deal with problems that have deep temporal structure , as is common in many real-world situations such as language processing ., Indeed in models used for speech recognition , training at the level of sequences has been shown to produce considerable benefits over training at the level of individual frames 3–6 , highlighting the benefits of considering deeper structure ., To see the limitations imposed by the Markov assumption , consider the task of predicting whether it will rain today or not ., The weather yesterday undoubtedly provides some information about this , but there is also extra information contained in observations taken on previous days , since the weather is determined by processes at timescales from the relatively rapid ( individual rain clouds ) to the very slow ( seasonal fluctuations , El Niño ) ., Sequential inference thus equips agents with greater flexibility , which is likely to be important for understanding both normative strategies and human behaviour ., Moving beyond Bayesian filtering also has major implications for learning ( the so-called ‘dual estimation problem’ 31 ) , which we will consider in future work ., In addition , using a model-based analysis we were able to relate between-subject differences in sequential inference performance to grey matter density in left anterior prefrontal and left posterior parietal cortex , as well as bilateral hippocampus ., This is consistent with the established role of these regions in supporting the computations necessary for performing inference over extended sequences of states , given an established relationship between morphometric features of a region and its level of functional engagement 14 ., A striking ( very significant ) finding in our voxel-based morphometry was the link between left anterior prefrontal cortex , encompassing Brodmann area 10 , and the strength of evidence for sequential inference ., This region is believed to play a role in higher order executive cognitive processes 16 , 32 , 33 , memory retrieval 34 and the manipulation of internally generated information 35 ., It is also implicated in metacognition 18 , 36 ., In particular , it has been proposed that the anterior prefrontal cortex plays a key role in ‘cognitive branching’ , where multiple cognitive processes or options for action need to be maintained simultaneously and integrated together 15 , processes with similarities to the type of updating subjects perform based on our model ., Additionally , a similar region of prefrontal cortex is implicated in retrospective ( ‘metacognitive’ ) judgements about decision confidence , albeit with evidence to suggest this is lateralised to the right hemisphere 18 , 36 ., Unfortunately , it is not possible—based on our data—to draw fine-grained inferences about the precise cognitive processes that this region supports during sequential inference , something we will address in future computational and experimental work ., We observed that interindividual differences in sequence length , likely to be associated with increasing mnemonic demands , positively correlated with grey matter density in a region of the left posterior parietal cortex and bilateral hippocampus ., Areas of the posterior parietal cortex are activated in neuroimaging studies of memory recall , but the precise functional role of this activity remains the subject of much on-going debate 19–22 ., Additionally , the parietal cortex has been implicated in maintaining information in working memory 23 , 24 ., Either or both processes could be involved here and drive the observed relationship ., Alternatively , the relationship between sequential inference and grey matter density in this area might reflect a role in some other cognitive operation that underpins sequential inference , perhaps in concert with the hippocampus ., The association between the length of sequence considered by a subject and grey matter density in the hippocampus is consistent with its known role in encoding , maintaining and recalling sequences 25–28 ., Our data suggest a new role for such retrospective representations , namely in supporting sequential inference ., More speculatively , it is tempting to link the hippocampus’ putative role to the internally generated forward and backward sweeps through state space it is believed to support 37–39 ., Such sweeps are typically considered in the context of planning and navigation , but they are also ideally suited for use in inference and learning more generally 37 , 40 ., In this study , we considered behaviour and brain data collected from both younger and older adults ., Although there were clear differences in task performance ( and , correspondingly , in model parameter estimates ) between groups , similar distributions of sequential inference strategies were observed in both groups , suggesting that these differences were unlikely to be explicable in terms of the deployment of different cognitive models ., Closely related to this , a post hoc analysis of our structural data suggested similar relationships between our measures of sequential inference and regional grey matter density in both groups ., Taken together , these results suggest that ( at least within the restrictions of our data ) , the deployment of sequential inference is largely conserved over the course of healthy ageing ., The results have implications for future studies using reversal paradigms with probabilistic outcomes , particularly in humans 11 , 41–44 ., Our results suggest that most subjects perform sequential inference ., For many purposes ( for example when deriving regressors for functional neuroimaging ) , the differences between filtering and sequential inference model predictions are likely to be unimportant ., However , they may be relevant for understanding between-subject variability , for example in patient groups who show reversal learning impairments 9 , 45–49 ., A number of studies have suggested that medial prefrontal and orbitofrontal regions are important in reversal learning , linking stimuli and outcomes 42 , 50–52 ., This does not conflict with our findings , which instead predict that lesions of the anterior prefrontal cortex , posterior parietal cortex or hippocampus might result in subtler behavioural abnormalities , reflecting a decrease in sequential inference performance relying on such links between stimuli and outcomes ., More generally , we propose that sequential inference modelling is a plausible approach for explaining behaviour across a wide variety of paradigms that engender decision uncertainty , for example in reward learning tasks with slowly drifting outcome probabilities 53 , 54 ., We anticipate this will be a productive area for future research ., Sequential inference is a potential explanation for postdictive phenomena in perception 55–57 ., In such phenomena , for example the flash lag illusion 56 , the perception of a stimulus is influenced by stimuli presented after it ., This retrospective influence could be explained if perception depends upon inference about the joint probability of events within some finite temporal window , since this will lead to subjects perceiving the most likely sequence of events , and hence allow later observations to inform percepts about earlier time points ( for a related suggestion see 58 ) ., Our study provides the first behavioural evidence that human subjects perform simultaneous inference over both current and past states , a process likely to be important for adaptive behaviour ., The link to regional cortical morphometry is of particular interest as the processes that support sequential inference include many that are supported by these regions ., The findings also naturally suggest future lines of enquiry , including exploring the capacity for sequential inference in supporting adaptive behaviour in other cognitive contexts engendering uncertainty as well as in patient groups who manifest abnormal performance on inference tasks ., Our sample comprised 79 participants encompassing two age groups: 43 younger adults ( 18 male , mean age = 26 . 4 years , range = 20–33 years ) and 36 older adults ( 20 male , mean age = 66 . 4 years , range = 60–73 years ) ., Data from five younger adults and 2 older adults were excluded due to technical problems during data acquisition ., The educational level of the participants was comparatively high: 43% of the younger adults had attended the Gymnasium , a University high school preparatory track and 51% were currently enrolled at University ., Most of the older adults held academic high school diploma ( 54% ) or vocational school diploma ( 39% ) ., All subjects were right-handed ( Oldfield Questionnaire: LQ > 80; 59 ) ., None of the participants reported cardiovascular pathology , psychotropic medication usage , history of neurological or psychiatric episodes or substance abuse ., The study was approved by the local ethics committee of the Charité , University Medicine Berlin , and written consent was obtained from each participant prior to participation ., Participants were paid 10 Euros per hour of the experiment ., In a separate test session , the Digit Symbol Substitution test ( DSS ) 60 and a modified version of the Spot-a-Word test 61 were assessed as markers of fluid and crystallized intelligence , respectively ., As expected , DSS performance decreased from early to late adulthood ( t = -3 . 99 , p < . 01 ) , whereas performance on the Spot-a-Word showed a trend for an increase with age ( Z = 1 . 72 , p = . 08 ) ., The observed dissociation between lifespan age gradients of these two tests in our sample is consistent with established empirical evidence on the development of these two facets of intelligence ( cf . 62 ) and indicates that our sample falls within a representative range of age-comparative cognitive testing ., Subjects performed a probabilistic reversal task across four sessions in the scanner during fMRI , giving a total of 128 trials ., We will report the fMRI data in future papers ., Here , our focus is on intersubject behavioural variability in the depth or prevalence of sequential inference and its anatomical correlates as measured with structural MRI ., The probabilistic reversal task involved focusing on either a face or house attribute of translucent greyscale images comprising faces and houses ( Fig 1 ) ., Face and house stimuli were divided into either young and old faces or houses of modern and old styles respectively ., Subjects had to learn whether to focus on the face or house dimension of the superimposed stimuli ., On each trial , subjects were tasked categorise ( the face or house ) as young ( modern ) or old , with a left and right button press ., A young face was always paired with an old house and vice versa ., In this manner , feedback as to a correct young/old categorization was informative with respect to task set ( with respect to faces or houses ) ., Feedback was probabilistic , with 85% reliability ., If the proportion of gains exceeded 80% on the most recent 10 trials , a reversal of the task relevant category ( face or house ) was implemented within the next 1–3 trials ., Subjects were informed about the possibility of this change but not its actual timing or performance dependency ., Subjects were instructed to try to obtain as many rewards as possible ., This was achieved by inferring the task relevant ( i . e . more frequently rewarded ) category as well as switches therein ., Subjects were familiarised with the task in a practice sessions that included at least one reversal ., There were 4 runs in total , amounting to a total of 128 trials for the probabilistic reversal task ., Within a trial , each stimulus was presented for 2 seconds , followed by a variable interval of 1–7 seconds ( mean 1 . 25 seconds ) , during which a fixation cross was shown ., This was followed by a feedback stimulus presented for 1 second and a variable interval of 2–8 seconds ( mean 3 . 25 seconds ) with a fixation cross ( Fig 1 ) ., Face stimuli were taken from the FACES database 63 , house stimuli were selected from an internet search ., Individual face and house stimuli were adjusted in brightness , overlapping face and house stimuli were adjusted separately for each specific stimulus pairing to ensure equal subjective saliency of face and house stimuli 64 ., The gender of the face stimuli was balanced within tasks , as was the number of young ( modern ) and old stimuli ., Before the task , subjects were familiarized on a different stimulus subset with the type of face and house stimuli that were used during the experiment , as well as with the categorization of individual or overlapping stimuli into young ( modern ) or old face and houses ., Each stimulus was presented for 2 seconds , followed by a variable interval of 1–7 seconds ( mean 1 . 25 seconds ) , during which a fixation cross was shown ., This was followed by a feedback stimulus presented for 1 second and a variable interval of 2–8 seconds ( mean 3 . 25 seconds ) with a fixation cross ., To assess the contribution of cognitive function to differences in sequential inference , subjects performed a working memory ( n-back ) task , as well as the Raven’s matrices test of nonverbal reasoning ., Performance measures on these tasks were used as additional covariates to analyse behaviour and brain structure ., To model subjective inference , we used a simple Hidden Markov Model ( HMM ) in line with the previous literature 9–11 ., Here , agents infer the hidden state xt corresponding to the current task condition or context; in other words , the task relevant category , face or house ., ( We arbitrarily define xt = 1 as the ‘face’ condition and xt = 2 as the ‘house’ condition ) ., The outcome of each trial is indicated by ot ( ot = 1 represents positive feedback , ot = 2 represents negative feedback ) ., The visual cue presented on each trial is represented by yt , where yt = 1 corresponds to the old face / modern house pair , and yt = 2 corresponds to the young face / old house pair ., Actions selected by the subject are encoded as at = 1 for an ‘old’ response and at = 2 for the ‘young’ / ‘modern’ response ., The parameter r encodes the probability of a reversal between trials , and v encodes the cue validity ( the probability of receiving positive feedback given that the agent has made the correct decision , and negative feedback if not ) ., Thus , the generative model considered by the agent takes the form:, P ( xt\u2009|\u2009ot , at , yt , xt−1 , r , v ) =P ( ot\u2009|xt , at , yt , v ) P ( xt|xt−1 , r ) P ( ot ), ( 1 ), P ( xt=i|xt−1=j , r ) =EijE=1−rrr1−r, ( 2 ), P ( ot=1|xt=i , at=j , yt=k , v ) =AijkA1**=v1−v1−vvA2**=1−vvv1−v, ( 3 ), Action selection followed a softmax decision rule 54 based on the agent’s current beliefs such that:, P ( at=i|yt=1 , γ ) =eγP ( xt=i|o1:t−1 ) ∑j=1 , 2eγP ( xt=j|o1:t−1 ) P ( at=i|yt=2 , γ ) =1−P ( at=i|yt=1 , γ ), ( 4 ), Where γ is the precision parameter that governs the stochasticity of ch

Introduction, Results, Discussion, Methods

Normative models of human cognition often appeal to Bayesian filtering , which provides optimal online estimates of unknown or hidden states of the world , based on previous observations ., However , in many cases it is necessary to optimise beliefs about sequences of states rather than just the current state ., Importantly , Bayesian filtering and sequential inference strategies make different predictions about beliefs and subsequent choices , rendering them behaviourally dissociable ., Taking data from a probabilistic reversal task we show that subjects’ choices provide strong evidence that they are representing short sequences of states ., Between-subject measures of this implicit sequential inference strategy had a neurobiological underpinning and correlated with grey matter density in prefrontal and parietal cortex , as well as the hippocampus ., Our findings provide , to our knowledge , the first evidence for sequential inference in human cognition , and by exploiting between-subject variation in this measure we provide pointers to its neuronal substrates .

When studying human cognition , it is often assumed that agents form and update beliefs only about the current state of the world , an approach known as Bayesian filtering ., However , in many situations there are advantages to making inferences about the most likely sequence of states that have occurred , which involves simultaneously updating beliefs about the present and the past , based on incoming information ., Currently , very little is known about whether humans adopt such sequential inference strategies , and if they do , about the neuronal mechanisms involved ., We addressed this by applying computational modelling to data collected during a probabilistic reversal task ., At a group level , subjects’ behaviour showed clear evidence of sequential inference , and between-subject differences in the strategies adopted were reflected in variations in brain structure in the prefrontal and parietal cortices , as well as the hippocampus ., Our results provide new insight into the strategies employed in human cognition , as well as the neuronal substrates of sequential inference .

medicine and health sciences, diagnostic radiology, elderly, decision making, nervous system, prefrontal cortex, brain, social sciences, neuroscience, magnetic resonance imaging, age groups, brain morphometry, cognitive psychology, morphometry, cognition, brain mapping, neuroimaging, research and analysis methods, imaging techniques, behavior, people and places, voxel-based morphometry, psychology, hippocampus, parietal lobe, diagnostic medicine, radiology and imaging, anatomy, central nervous system, biology and life sciences, population groupings, cognitive science, cerebral cortex

journal.pgen.1007240

Methods for fine-mapping with chromatin and expression data

Discerning the genetic and molecular basis of complex traits is a fundamental problem in biology ., Genome-wide association studies have revealed that the majority of variants associated with disease lie in noncoding regulatory sequences 1 , 2 ., Identifying the target genes of these variants and the mechanisms through which they act remains an open problem 3 ., Recent efforts to systematically characterize how genetic variation impacts more granular molecular phenotypes have yielded thousands of single nucleotide polymorphisms ( SNPs ) that associate with local and distal histone modifications—termed histone quantitative trait loci ( hQTLs ) 4–7 ., Furthermore , recent studies have identified many expression quantitative trait loci ( eQTLs ) that co-localize with hQTLs , implying there may exist a shared genetic influence on epigenetic traits and gene expression 8–11 ., Therefore , one proposed mechanism by which regulatory variants may affect gene expression and thereby impact traits is through changes in chromatin state 10 ., However , this putative chain of causality whereby the effects of SNPs on expression are mediated by chromatin modifications has yet to be established ., This is further compounded by the complex space of plausible causal directions connecting transcription factor binding , DNA methylation , chromatin variation , and gene expression ., Since laboratory experiments are very costly , there is a need for statistical methods that can accurately prioritize the causal SNP and chromatin mark within an implicated region under a plausible causal model ., However , even if the causal direction is given , pinpointing the exact SNP and mark within a genomic region is very challenging due to the confounding effects of linkage disequilibrium ( LD ) among SNPs and correlations among marks 5 , 6 , 10 , 12–14 ., Methods to investigate the relationships between the genome , the epigenome , and expression have largely focused on quantifying the overlap between hQTLs and eQTLs 10 , 14 , 15 ., Previous studies have sought to identify hQTLs by selecting the SNP with the strongest p-value for association to a local chromatin mark and to local gene expression 10 , 14 , 15 ., Moreover , various methods exist for the fine-mapping of SNPs that may be concurrently affecting two traits , including eCAVIAR 16 and Coloc 17 ., Although these methods can be applied to jointly analyze SNP , chromatin , and expression data , they do not model the causal path whereby SNPs impact expression through chromatin alteration ., Here we propose a fine-mapping framework , pathfinder , that explicitly models the hierarchical relationships between genome , chromatin , and gene expression to predict both the causal SNP and the causal mark within a gene region that are influencing expression of a given gene ., Our framework assumes a causal model where a SNP impacts a chromatin which in turn alters gene expression ., In our framework we refer to a “causal” SNP as any SNP that disrupts inter-individual variation of chromatin state either through a direct biological mechanism ( e . g . , chromatin accessibility ) or indirectly through an unobserved biological mechanism ., Similarly , we refer to a “causal” chromatin mark as either a mark that biologically alters expression or that tags an underlying epigenetic regulatory mechanism of expression ., Our framework takes as input the strength of association ( as quantified through the standard Z-scores ) between all SNP/mark pairs and all marks to expression as measured in a given set of individuals ., To explicitly account for the correlation structure among SNPs and marks , we use a Matrix-variate Normal distribution to model all Z-scores jointly ., By construction , this allows our probabilistic model to assign posterior probabilities for each SNP , mark , and path ( where paths include all possible SNP-mark combinations ) to be causal in the region ., A key advantage of our approach is that it produces well-calibrated posterior probabilities for causality ., Thus , pathfinder can be used to prioritize variants and marks for validation experiments ., In simulations we compare against several existing methods , demonstrating that pathfinder outperforms alternative approaches with respect to both accuracy and calibration ., This is largely because our comparators do not take into account mark-expression associations ., In some cases , these additional associations may help distinguish between two potentially causal paths that have comparable evidence for causality ., For example , in cases where a SNP is associated with expression of a local gene and is also associated with two local chromatin marks , knowledge of the impact of each mark on gene expression may help distinguish between two possible paths for causality ., Finally , we analyze genotype , chromatin and expression data from 65 African-ancestry and 47 European-ancestry individuals ., We show that the top causal SNPs proposed by pathfinder tend to lie in more functional regions and disturb more regulatory motifs than expected by chance ., We also present evidence that most of the top paths reported by pathfinder demonstrate consistency with our proposed sequential model , thus strengthening the case for our method’s applicability to empirical biological data ., Here we introduce a hierarchical statistical method for fine-mapping of causal SNPs and chromatin marks ( e . g . , histone modifications ) that may be concordantly influencing gene expression within a genomic region ., We build upon previous insights that a vector of Z-scores is well-described by a Multivariate Normal ( MVN ) distribution parameterized by LD 13 , 18 , 19 to model association statistics between chromatin marks and gene expression ., We analyze all chromatin peaks across four mark types ( DHS , H3K4me1 , H3K4me3 , and H3K27ac ) jointly in the same framework; we refer to a “mark” as a chromatin peak at a particular location , and “mark types” as DHS , H3K4me1 , H3K4me3 , and H3K27ac ., To simultaneously take into account both SNP LD and the correlations between chromatin marks , we use the Matrix-variate Normal distribution to jointly model association statistics between all SNPs and marks within a region ., Our method takes as input SNP-mark and mark-expression associations within a region centered around a particular gene , as well as correlations among all SNPs ( LD ) and correlations among all considered marks ., Pathfinder enumerates over all possible causal paths , considering one causal SNP and one causal mark for each path , and outputs a posterior probability for each path to be causal , which can subsequently be used to prioritize SNPs and marks for validation ., We compute marginal probabilities for individual SNPs ( or marks ) to be causal by summing the posterior probabilities over all paths that contain the SNP ( or mark ) ., For simplicity , in this work we refer to a “causal” mark as a mark that either causally drives inter-individual variation of gene expression or is correlated to an underlying causal mechanism ( e . g . transcription factor binding ) , though it may not be biologically causal for expression ., The advantage of our method over existing approaches is that it integrates mark-expression associations which may help distinguish between two paths with otherwise comparable evidence for causality ., We illustrate a scenario in Fig 1 ., Consider a genetic region where SNP g1 has a strong association with two local marks h1 and h2 , as well as a significant association with gene expression ., Using only SNP-mark and SNP-expression effects , we are unable to discern whether SNP g1 influences expression through mark h1 or h2 ., However , if we consider mark-expression effects , we see that mark h1 has a strong association with gene expression where mark h2 does not ., This additional information helps support the hypothesis that there is a causal path from SNP g1 to mark h1 to gene expression ., We used simulations to compare pathfinder’s performance against alternative methods with respect to SNP- , mark- , and path-finding efficiency as well as the calibration of its posterior probabilities ., We generated genetic , chromatin , and gene expression data for 10 , 000 50kb regions , each centered around a single gene , over 100 individuals , using SNP LD and mark correlations derived from 65 Yoruban ( YRI ) individuals ( see Methods ) ., We define a “mark” as an individual peak location for any mark type in the dataset ( DHS , H3M4me1 , H3K4me3 , or H3K27ac ) ., For each gene , we randomly assigned a single causal pathway from one SNP to one mark to gene expression ., We then ran our methods on all regions individually and assessed their ability to correctly prioritize the true causal path in each region ( Methods ) ., We compare against an independent fine-mapping approach ( whereby we fine-map SNP-mark associations and mark-expression associations independently and take the product of the resulting probabilities to produce posterior probabilities for paths ) , a Bayesian network analysis 20 , a naive ranking ( where we rank SNP-expression and mark-expression associations to prioritize SNPs and marks within a region; for path-finding , we rank the product of these two ) , a formal colocalization method 17 , and finally , against overlaps between eQTLs and hQTLs within a region centered around a gene of interest ( see Methods ) ., Unlike the first four approaches , the overlap methods do not produce rankings , but yield candidate sets of causal SNPs , marks , and paths ., For this reason , we present these results in a separate analysis using an alternative metric for comparison ., We find that pathfinder has consistently better performance than the other ranking approaches with respect to all three features—SNP- , mark- , and path-mapping within a region ( Fig 2 ) ., For example , association ranking , Coloc , Bayesian network analysis , and independent fine-mapping accumulate 55% , 62% , 47% , and 13% of the top paths on average in order to recapture 90% of the causal paths , whereas our method only requires 8% of the top paths ., Note that SNP-expression association ranking is equivalent to running a basic eQTL analysis , which does not take into account chromatin data , in order to identify causal SNPs ., A similar improvement in accuracy was observed for the size of the credible sets , defined as the number of SNPs required to capture a given percentage of the causal variants ( S1 Table ) ., Next , we evaluated pathfinder’s performance compared against standard analyses that investigate overlaps between hQTLs and eQTLs within a genomic region ., In such experiments , the variant with the strongest association to each local chromatin mark is selected , as well as the variant with the strongest association to local gene expression ., In addition , marks are filtered to ensure a 10% FDR ( see Methods ) ., This produces a set of candidate marks , as well as one candidate SNP per mark , and one SNP deemed causal for gene expression in the region ., Implicitly , the overlap of these variants suggests a set of candidate SNPs , marks , and paths for the region ., For the same set sizes , pathfinder identifies 96% of the causal marks versus 74% in the standard overlap approach ( Fig 3 ) ., SNP-finding accuracy is comparable between the two methods ., We next assessed the calibration of the posterior probabilities for causality output by pathfinder ., Our method has slightly deflated credible sets for SNP- and path-finding , but well-calibrated credible sets for mark-finding ( Fig 4 ) ., In contrast , the independent fine-mapping approach has consistently inflated credible sets—that is , it captures more causal paths than expected , but also has drastically larger credible set sizes ., For example , when accumulating 90% of the posterior probabilities over all regions , pathfinder captures 88% of the true causal paths within the top 380 candidate paths , whereas independent fine-mapping captures 94% of the causal paths within the top 1026 candidate paths ., Similar outcomes were attained for the 50% and 99% credible sets ( S1 Fig ) ., Overall , pathfinder’s credible sets are less biased and narrower than those obtained through the independent fine-mapping approach ., Finally , we investigated the effects of simulation and method parameters on pathfinder’s accuracy ., Firstly , we varied the causal SNP and mark effect sizes such that the variance explained of mark and gene expression ranged from 0 . 1 to 0 . 5 ., As anticipated , increased heritability leads to better performance ( See Fig 5A–5C ) ., Secondly , in order to assess the impact of SNP LD and mark correlations on SNP- and mark-finding performance , we stratified our existing simulations based on the mean correlation of the causal SNP or mark to all other SNPs or marks ( See Fig 5D–5I ) ., We grouped our simulations into three categories: low , medium , and high correlations ., As anticipated , SNP-finding performance decreases slightly as SNP LD increases ., Notably , mark-finding performance is actually improved at higher SNP LD ., This is due to the redundancy in information about SNP-mark associations at the causal mark when these effects are exhibited across multiple correlated SNPs ., SNP- and mark-finding performance , however , do not seem to be significantly affected by mark correlations in our simulations—at least not at the level of variation exhibited in our data ., In addition to stratifying our existing simulations by LD , we also assessed the impact of using European rather than African LD in the same regions , as European LD is known to be more extensive ., Here we retained the YRI mark and expression data in order to isolate the effect of SNP correlations ., The credible set sizes computed from the CEU dataset do not substantially differ from those obtained in YRI ( S2 Table ) ., This result demonstrates that the more extensive LD observed in European individuals will not significantly affect pathfinder’s performance ., Thirdly , we evaluated the effect of the prior variance tuning parameter on fine-mapping performance ( See Fig 5J–5L ) ., The prior variance is an estimate of the variance explained by the causal SNP and mark in the region , as we do not know a priori what the causal effect sizes are ., We show that the optimal range for the prior variance parameters is between 5 and 10 , in simulations with a variance explained of 0 . 25 on both levels ., Overall , performance does not seem to change drastically in response to variations in the prior variance , even significantly outside of this optimal range ., Our hierarchical model makes several key assumptions that may sometimes be violated in empirical data ., Firstly , pathfinder assumes that a single causal SNP and a single causal mark are driving the associations within a region , where in reality there may exist multiple true causal SNPs or marks 13 , 19 ., Secondly , pathfinder assumes that SNP effects on gene expression are mediated by a chromatin mark , which may not be the case in real data ., We therefore assessed the performance of our method when these two assumptions are violated in various ways , diagrammed in Fig 6 ., First , we investigate violations 1–3 , which include multiple causal pathways throughout the region ., Path-mapping accuracy , measured by the proportion of causal paths identified , is reduced in all three scenarios ( Fig 6 ) ., Note that the number of causals identified does not necessarily decrease , but rather the proportion , as there are more causal paths in each region ., SNP- and mark-finding accuracy under these violations are also compromised , but with two notable exceptions ., In the multi-causal-SNP scenario , mark-finding accuracy increased in comparison with the single-SNP simulations; for example , only 8% of marks were selected ( versus 18% in the single causal simulations ) to capture 90% of the causal marks ., In the multi-causal-mark scenario , SNP-finding accuracy increased ., Intuitively , this is due to the redundancy in the signal that is captured by the Matrix-variate Normal distribution ., We next investigate violations 4–5 , in which an additional SNP or mark influences gene expression directly ., We observe in these two scenarios that performance is reduced for SNP- , mark- , and path-finding , but not drastically ., For example , in order to capture 90% of the causal paths , pathfinder must select on average 25% and 28% of paths under violations 4 and 5 , respectively ( compared with 15% under standard simulations ) ., Because anti-correlated marks ( e . g . activating and repressing marks ) often tend to act in the same region , we also assess pathfinder’s behavior specifically when two marks have opposite effects on expression ., As expected , pathfinder’s performance does not decline in the presence of anti-correlated peaks ( S2 Fig ) ., Finally , we discuss pathfinder’s performance under violations where the causal order is modified ( violations 6–7 ) ., Under violation 6 , where a single causal SNP affects gene expression directly , which in turn affects a single mark , pathfinder actually captures a higher proportion of the affected marks and overall paths ., For example , in order to capture 90% of the causal paths , pathfinder must select on average only 3% of the top-ranked paths ( compared with 15% under standard simulations ) ., In violation 7 , where the SNP has independent effects on the mark and the gene expression , we show that pathfinder’s accuracy in finding the causal mark and path is significantly reduced ., Note that in this case , the “path” is not truly a path but a SNP/mark pair , as effects of the SNP on mark and gene expression are independent ., Our power in distinguishing between these two models depends on the prior variance explained parameter ., Under violation 7 , the variance explained in gene expression by the causal mark is much smaller than expected , thus reducing our confidence in the true causal configuration ., We conclude that under the SNP→expression→mark violation , pathfinder will identify causal paths very confidently even if they do not follow the assumed SNP→mark→expression model ., Therefore a high posterior probability for a path may not be sufficient evidence for causality ., On the other hand , when SNP effects on mark and expression are independent , pathfinder is less likely to produce false positives ., For these reasons , we recommend a pre- or post-filtering step to retain only those regions that show some prior evidence for the SNP→mark→expression model using a conditional analysis or partial correlation approach ( Methods ) ., For completeness , we also assess existing methods under these simulations ( S3 Fig ) ., Most notably , the simple association-ranking approach shows a distinct improvement under violations 6 and 7 , in which SNPs have a direct effect on gene expression ., This is expected as pathfinder assumes the causal effect to be mediated by chromatin ., A similar improvement can be observed for Coloc under violation 7 , in which the SNP affects both chromatin and gene expression directly ., We evaluated the behavior of our hierarchical fine-mapping method when applied to empirical data ., We performed these analyses on data from 65 YRI individuals whose genotypes were obtained through 1000 Genomes , and whose PEER-corrected H3K4me1 , H3K4me3 , H3K27ac , DHS , and RNA expression levels in lymphoblastoid cell lines ( LCLs ) were obtained from 10 ., In each region , we analyzed all four mark types jointly ( H3K4me1 , H3K4me3 , H3K27ac , and DHS ) by including all peaks spanning the region for each mark type ., Each peak of each mark type was therefore treated as a single chromatin mark ., We filtered the 14 , 669 regions using a two-step regression analysis to yield 1 , 317 regions that showed evidence for the sequential model of SNPs affecting histone marks which in turn affect gene expression ( see Methods ) ., pathfinder’s runtime scales approximately as s3t3 , where s and t are the number of SNPs and marks within a region , respectively ., On average , each 50kb region contained 160 SNPs and 25 marks ., Most runs were completed in under a few minutes ., The most dense region contained 331 SNPs and 66 marks and took approximately 21 minutes ( S4 Fig ) ., In Table 1 , we report the average 50% , 90% , and 99% credible set sizes produced when running pathfinder on real data ., We compare against basic eQTL mapping , where we fine-map SNPs to gene expression ignoring chromatin data ., We show that the credible set sizes are significantly narrower when running pathfinder with all three levels of data , consistent with our findings in simulations ., For example , eQTL mapping requires an average of 45 . 3 SNPs in order to capture 90% of the posterior probability for SNP causality , whereas pathfinder only requires 28 . 4 SNPs ., If we define a gene to be fine-mapped if 99% of the posterior probability mass for SNP causality is contained within the top 10 SNPs or fewer , then standard eQTL mapping fine-maps 46 of the genes in our data , whereas pathfinder fine-maps 73 of the genes ., Notably , pathfinder also requires only 1 . 8 marks on average in order to capture 90% of the posterior probability for causal marks ., In 82% of the regions where the top two marks capture more than 90% of the posterior probability , these two marks are two distinct peaks of the same mark type ., The mean variance explained observed in the top path chosen by pathfinder , across all conforming regions , were 0 . 38 ( s . e . 0 . 01 ) for the SNP-mark effect and 0 . 20 ( s . e . 0 . 01 ) for the mark-expression effect ( S5 Fig ) ., These effects are reasonably consistent with the 25% variance explained we used in simulations at each level ( see Simulations ) ., The correlation between the SNP-mark and mark-expression effect size magnitudes in the top selected paths across all regions was 0 . 03 ( p = 0 . 400 ) ., That is , the strength of the SNP-mark effect did not seem to correlate with the strength of the mark-expression effect ., We assessed the relative impacts of each type of histone mark by computing the proportion of probability mass assigned to each mark type in aggregate over all regions ( S3 Table ) ., H3K4me3 is the most informative mark type in this data , capturing 31% of the total probability mass despite being the least prevalent of all four mark types , constituting only 13% of all marks ., We also report the size of pathfinder’s credible sets when applied to empirical CEU data rather than YRI in Table 2 ., These two datasets are not directly comparable , as the types of epigenetic marks and their quantities differ substantially ., Nonetheless , we demonstrate that pathfinder’s performance on the CEU dataset does not drastically diverge from its behavior in YRI ., Data pre-processing strategies such as PCA and PEER correction may substantially impact the number of mark-expression correlations that are retained 21 ., We find that credible set sizes for PEER-corrected data are narrower , giving a slight but significant improvement in performance ( S4 Table ) ., As our pre-filtering step was designed to preserve only regions in which SNP effects on gene expression are mediated by chromatin , we expected a large majority of the analyzed regions to show evidence for this mechanism ., To confirm this , we investigated whether the top paths prioritized by our method demonstrate consistency with this causal model ., We defined a set of top paths as those which were ranked first in a region and whose posterior probabilities for causality were assigned by pathfinder to be greater than 0 . 1 ., This resulted in 480 total top paths ., Out of 480 top paths , only 12 had a significant ( p < 0 . 05/480 ) partial correlation between SNP and gene expression after controlling for chromatin ., However , 193 paths had a significant partial correlation between SNP and chromatin after controlling for gene expression ., This finding suggests that the top paths are more consistent with the SNP→mark→expression model than with a SNP→expression→mark model ., Next we examined the relationship between the product of the effect sizes between SNP-mark and mark-expression against the overall SNP-expression association ( Fig 7 ) ., We expect this relationship to be correlative; if truly mediated by the mark in question , the overall SNP-expression effect size should be proportional to the product of the two contributing effect sizes ., Note that we weight our correlation by the reported posterior probability for each path , such that the paths we have more confidence in will contribute more to this metric ., We find a high correlation ( r = 0 . 91 ) between these effect size vectors for our top paths , as compared with a correlation of r = 0 . 36 when running the same analysis on random paths within each region ., This result indicates that pathfinder is identifying many pathways that are likely to be following its causal model ., In Table 3 , we list the top ten paths prioritized by pathfinder across all real data regions ., Most SNPs implicated in these paths are known to alter several regulatory motifs and often lie in an enhancer region or a promoter region of the genes whose expression they affect ., 59% ( s . e . 2% ) of the SNPs implicated in the top paths fall into active ChromHMM states ( 1–7 ) in LCLs , including active TSS , flanking active TSS , transcription at gene 5’ and 3’ , strong transcription , weak transcription , genic enhancers , and enhancers ., Only 47% ( s . e . 2% ) of random paths fall into these active states ( p = 0 . 001834 ) ., Moreover , on average , SNPs in the top paths disturbed 5 . 35 ( s . e . 0 . 26 ) regulatory motifs , whereas random SNPs chosen at the same regions only disturbed 4 . 40 ( s . e . 0 . 20 ) motifs on average ( p < 0 . 001 ) ., We did not , however , observe a similar change in transcription factor binding affinity at these motifs ( δ = 5 . 26 vs δ = 5 . 27 , ( p = 0 . 511 ) ) ., As an example , in Fig 8A–8D , we display the genomic context for the top region reported by pathfinder , including average mark signals for DHS , H3K4me1 , H3K4me3 , and H3K27ac , stratified by genotype , in a 4kb region centered around the TSS of the NDUFA12 gene ., The implicated SNP lies within the NDUFA12 TSS ., Fig 8E plots the gene expression signal against that of the top mark , stratified by genotype ., In S6 Fig , we show associations for the top region reported by pathfinder , spanning a 50kb region centered around the NDUFA12 TSS ., Next we examined the spatial relationships between the SNP , mark , and TSS implicated in the top paths reported by pathfinder ( Fig 9 ) ., SNP to mark and mark to TSS distances were significantly lower in our selected paths compared with randomly chosen paths at the same regions ., The average distance from SNP to mark in pathfinder’s top paths was approximately 11 . 7kb , compared to 15 . 3kb in randomly chosen paths ( p < 0 . 001 ) ., The average distance from mark to TSS in selected paths was approximately 8 . 6kb , compared to 9 . 7kb in randomly chosen paths ( p = 0 . 026 ) ., SNP to TSS distances were not significantly different in top versus random paths ( p = 0 . 108 ) , with top SNPs lying on average 11 . 7kb away from the TSS and random SNPs lying 12 . 4kb away ., 5% of top SNPs lied within 2kb of the TSS while 15% lied within 2kb of the corresponding peak ., 23% of peaks in the top paths lied within 2kb of the gene TSS ., S7 Fig displays all three distances where top paths are stratified by mark type ., To further validate the top paths chosen by pathfinder , we determined the extent to which SNPs in these paths overlap with eQTLs that have been identified in LCLs using the larger scale Geuvadis data set 22 ., 21% of the top paths contained SNPs that were also identified as eQTLs from the Geuvadis data set ., In comparison , when randomly choosing paths at the same regions , only 11% overlapped with eQTLs ( p < 0 . 001 ) ., Simply choosing the SNP with the highest association with gene expression in each region ( equivalent to standard eQTL-mapping ) resulted in an overlap of 24% with existing eQTLs ., These results contradict the improvement in accuracy demonstrated in simulations when using pathfinder ., We suspect this discrepancy is due either to imperfect locus ascertainment ( i . e . , a number of loci may include SNPs that directly affect gene expression rather than indirectly through chromatin ) or the fact that the Geuvadis eQTLs were also selected using standard fine-mapping approaches and we may thus expect a stronger agreement between the two resulting eQTL sets ., We also investigated the extent to which pathfinder’s top SNPs overlap with eQTLs that have been experimentally validated through differential expression in an LCL dataset 23 ., Here , we define the set of validated eQTLs to be those whose p-values for differential expression passed a threshold of 0 . 01 ., We find that 2 . 2% ( or 13 ) of pathfinder’s top SNPs overlap with this validated set , where choosing the SNP with the highest association with gene expression in each region resulted in an overlap of 2 . 3% ( also 13 SNPs ) ., Finally , we investigated whether any of the top paths reported by pathfinder could be found within GWAS hit regions for various autoimmune diseases , as our data were collected from LCLs ., These autoimmune diseases included Celiac disease , Crohn’s disease , PBC ( Primary Biliary Cirrhosis ) , SLE ( Systemic Lupus Erythematosus ) , MS ( Multiple Sclerosis ) , RA ( Rheumatoid Arthritis ) , IBD ( Irritable Bowel Disease ) , and UC ( Ulcerative Colitis ) ., We restricted to GWAS hits with variants associated to the trait with p < 5 × 10−8 ., We found that 19 of our 480 top paths were contained in a GWAS-implicated region ., In Table 4 , we report the paths that localized within autoimmune GWAS regions ., In order to determine whether our top paths are truly enriched in GWAS regions , we established how many of these paths appear in an equivalent number of random regions that have not been implicated by an autoimmune GWAS ., We centered each random region around a SNP that was matched for a similar MAF and LD score as the GWAS tag SNP ., We ran this analysis 100 times to define a null distribution for the number of top paths found in a background region ., We found that 19 out of 480 top paths was not a significant enrichment ( p = 0 . 44 ) ., In this work we proposed a hierarchical fine-mapping framework that integrates three levels of data—genetic , chromatin , and gene expression—to pinpoint SNPs and chromatin marks that may be concordantly influencing gene expression ., A key contribution of our approach is the ability to model the correlation structure in the association statistics using a Matrix-variate Normal distribution ., Our approach is superior to existing methods , demonstrating the advantage of using a probabilistic approach that takes into account the full sequential model ., Moreover , pathfinder produces well-calibrated posterior probabilities , and is thus a reliable method for the prioritization of SNPs and marks for functional validation ., We conclude by addressing some of the limitations of our method ., Most notably , our method is based upon the SNP→mark→expression assumption ., In many genomic regions that show simultaneous evidence for SNP to mark and SNP to gene expression effects , this model will not necessary hold true ., In simulations , we show that under the SNP→expression→mark violation , pathfinder may identify causal paths very confidently , leading to false positives under the proposed model ., When a SNP is in fact independently influencing a mark and gene expression , pathfinder is less likely to produce false positives ., However , the risk of mis-appropriating our method in this way can be reduced by requiring genomic regions to show evidence for our causal model ., We recommend a pre-filtering step before running pathfinder on real data that we outline in Methods ., In our empirical da

Introduction, Results, Discussion, Materials and methods

Recent studies have identified thousands of regions in the genome associated with chromatin modifications , which may in turn be affecting gene expression ., Existing works have used heuristic methods to investigate the relationships between genome , epigenome , and gene expression , but , to our knowledge , none have explicitly modeled the chain of causality whereby genetic variants impact chromatin , which impacts gene expression ., In this work we introduce a new hierarchical fine-mapping framework that integrates information across all three levels of data to better identify the causal variant and chromatin mark that are concordantly influencing gene expression ., In simulations we show that our method is more accurate than existing approaches at identifying the causal mark influencing expression ., We analyze empirical genetic , chromatin , and gene expression data from 65 African-ancestry and 47 European-ancestry individuals and show that many of the paths prioritized by our method are consistent with the proposed causal model and often lie in likely functional regions .

Genome-wide association studies ( GWAS ) have revealed that the majority of variants associated with complex disease lie in noncoding regulatory sequences ., More recent studies have identified thousands of quantitative trait loci ( QTLs ) associated with chromatin modifications , which in turn are associated with changes in gene regulation ., Thus , one proposed mechanism by which genetic variants act on trait is through chromatin , which may in turn have downstream effects on transcription ., In this work , we propose a method that assumes a causal path from genetic variation to chromatin to expression and integrates information across all three levels of data in order to identify the causal variant and chromatin mark that are likely influencing gene expression ., We demonstrate in simulations that our probabilistic approach produces well-calibrated posterior probabilities and outperforms existing methods with respect to SNP- , mark- , and overall path-mapping .

genome-wide association studies, dna-binding proteins, social sciences, simulation and modeling, probability distribution, mathematics, genome analysis, epigenetics, molecular genetics, chromatin, research and analysis methods, economic models, chromosome biology, proteins, gene expression, histones, molecular biology, economics, probability theory, biochemistry, cell biology, normal distribution, genetics, biology and life sciences, physical sciences, genomics, computational biology, human genetics

journal.pcbi.1002038

Neurobiologically Realistic Determinants of Self-Organized Criticality in Networks of Spiking Neurons

Self-organized criticality is increasingly postulated to underlie the organization of brain activity 1–2 ., The notion of self-organized criticality describes an unsupervised emergence of critical dynamics in complex systems dominated by internal interactions 3–4 ., Critical dynamics emerge at the transition between randomness ( subcritical dynamics ) and order ( supercritical dynamics ) , and are characterized by self-similar ( power-law-distributed ) spatial and temporal properties of system events ( e . g . neural activations ) ., The occurrence of these dynamics in the brain is theoretically appealing and is increasingly empirically supported ., Theoretically , and increasingly empirically , critical dynamics are associated with optimized information transmission and storage 5–8 , maximized dynamic range 9–10 and successful learning 11 ., Empirically , multielectrode array recordings of spontaneous activity from organotypic cortical slice cultures 5–6 and dissociated cortical neuron cultures 12–13 show power-law scaling of distributed “avalanche” activity of neuronal ensembles ., Multielectrode array recordings of spontaneous cortical activity in the awake rhesus monkey also show power-law scaling of avalanches 14 , suggesting that these dynamics are not confined to in vitro preparations ., The temporal and spatial statistics of EEG , ECoG , MEG and fMRI signals likewise show power-law scaling 15–18 , although the relationship of these large-scale brain signals to avalanches of neuronal ensembles may not be straightforward ., Brain dynamics are thought to be strongly influenced by neuroanatomical connectivity 19–22 ., Consequently , self-organized critical brain dynamics may be influenced by properties of neuroanatomical organization , such as hierarchical modularity , small-worldness and economical wiring 23–26 ., Hierarchical modularity is a self-similar organization in which functionally specialized neural clusters ( e . g . cortical lobes ) contain smaller and more specialized neural clusters ( e . g . cortical nuclei , cortical columns ) at multiple spatial scales ., Small-worldness is an organization which combines modularity and robust between-module connectivity ., Economical wiring is an organization which contains predominantly short connections ., The presence of an intuitive association between self-similar brain structure ( i . e . hierarchical modularity ) and self-similar brain dynamics ( i . e . self-organized criticality ) , has not been previously examined ., The relationship between brain structure and dynamics is reciprocal: while the structure strongly constrains the dynamics , the dynamics continuously modify the structure through mechanisms such as activity-dependent synaptic depression 27 and spike-timing-dependent plasticity 28–29 ., We previously showed that this reciprocal relationship is associated with an unsupervised emergence of modular small-world structural connectivity , in a large-scale model of spontaneous brain activity 30 ., We now ask whether realistic structural organization is associated with the emergence of self-organized critical dynamics ., A number of modeling studies recently reported self-organized critical avalanche dynamics in neuronal networks with nontrivial topology and activity-dependent plasticity 31–33 ., These studies focused on conceptual features of network organization and plasticity , and hence omitted neurobiologically realistic features such as membrane leakage , axonal delays and spike-timing-dependent plasticity ., Other studies are increasingly beginning to examine these relationships in more realistic networks 34–35 ., Most studies however , remain constrained by assessment of power-law distributions with unreliable linear least-squares-based methods 36 ., In contrast , we aim to systematically and rigorously examine the relationship between anatomical connectivity , synaptic plasticity and self-organized criticality , in a realistic network model of neuronal activity ., To this end , we extend a recent model of nonperiodic synchronization in networks of leaky integrate-and-fire neurons 37 to incorporate large , sparse , hierarchical modular connectivity , spike-timing-dependent plasticity and other neurobiologically realistic features such as axonal conduction delays and neuronal inhibition ., We hypothesize that the neurobiologically realistic features of our model will facilitate the emergence of self-organized critical dynamics ., The studied leaky integrate-and-fire neuron evolves according towhere is the membrane potential , is the membrane capacitance , is the leakage conductance , is the resting potential and and are the external current and synaptic current , respectively ., When exceeds a constant threshold , the neuron is said to spike and is reset to the value for an absolute refractory period ., The external current maintains a constant low level of background neuronal activity , while synaptic currents couple anatomically connected neurons ., In the model , we set and ., We set for clarity , but any other value ( e . g . ) results in equivalent dynamics , as long as the above relationship between , and holds ., We discuss these and other aspects of the integration scheme in the Supplementary Information ( Text S1 ) ., For a postsynaptic neuron , we modeled synaptic currents with decaying exponentials , where the outer sum is over all presynaptic neighbors of , the inner sum is over all previous spike times of each presynaptic neighbor , is the synaptic weight from to , and are the slow and fast decay constants , and is a magnitude parameter ., Synaptic coupling incorporated axonal delays , set to uniformly distributed random integers between and ., These values are in the range of empirically estimated axonal delays 38 ., For computational simplicity we used the same distribution of axonal delays for all hierarchical levels ., We note that long-range cortical connections are often more thickly myelinated than short-range connections so there is no simple relationship between inter-level distance and axonal delay ., Synaptic weights changed at every spike of a neuron incident to the synapse , according to a spike-timing-dependent plasticity ( STDP ) rule ( Figure 1b ) ., The STDP rule potentiates when the postsynaptic neuron spikes shortly after the presynaptic neuron , and depresses when neuron spikes shortly before neuron ., More specifically , when or spike , changes as , withwhere and are the latest spike times of and , and are time constants and and are weight dependence functions , The weight dependence functions keep all weights between and , and rescale weight changes by the weight constants and , and by the rate constant ., The above functions enable soft weight bounds , or multiplicative weight dependence ., Alternative functions , where is the Heaveside step function , enable hard weight bounds , or additive weight dependence ., The choice between soft and hard weight bounds has important implications for synaptic weight distributions ( Figure 1c–e ) ., The unimodal distribution associated with soft weight bounds has more experimental support 39 , although both hard and soft weight bounds are extensively used in computational studies ., We used soft bounds in most simulations , but also explored the robustness of our results to the presence of hard bounds ., Parameter values of the model were adapted from the Thivierge and Cisek 37 study and are shown in Table 1 ., In the present study , we find that the postsynaptic-response magnitude and STDP learning rate parameters facilitate important internal interactions in the network ., We show that high values of these parameters are required to compensate for the relatively small number of neuronal synapses in our networks ., We also show that these values may be substantially reduced in larger networks with greater numbers of synapses ., Each network comprised neurons , subdivided into modules ., Each module comprised neurons , of which neurons were inhibitory and excitatory ., Inhibitory neurons only formed synaptic connections with all excitatory within-module neurons ., On the other hand , excitatory neurons could potentially form synaptic connections with excitatory or inhibitory neurons in all modules ., Initially , excitatory neurons only formed synaptic connections with all other within-module neurons ., Subsequently , excitatory synapses were probabilistically rewired within seven hierarchical levels ( Figure 1a ) ., The density of intermodular connections , , within each level , was set using power-law ( ) , exponential ( ) or linear ( ) scaling functions , with , and determining density drop-off rates ( Figure 2a ) ., Synapses were rewired in a way that preserved the total number of synapses per neuron 40 but not connection reciprocity ., For each network , rewiring occurred progressively from the outermost to the innermost hierarchical level ., The location of synapses in each network was kept fixed during simulations ., The wiring cost associated with each scaling function was computed by estimating the number of synapses in each hierarchical level for that function , equating the cost of each synapse with the number of its hierarchical level ( e . g . synapses in level were assigned a cost of ) , and averaging the cost over all synapses ., Higher density drop-off rates were associated with lower wiring cost ( Figure 2b ) ., The low wiring cost was in turn associated with higher clustering coefficients and higher characteristic path lengths in the network ( Figure 2b–d ) ., Clustering coefficients and characteristic path lengths are simple measures of modular organization and between-module connectivity , respectively 41 ., We integrated subthreshold neuronal dynamics exactly , interpolated neuronal spike times between intervals and recorded neuronal activity at bins 42 ., We began all simulations by setting all synaptic weights to and setting all membrane potentials to uniformly distributed random values from to ., We discarded five minutes of initial activity , ensuring in each case that synaptic weights converged to a stable distribution ., We recorded five minutes of subsequent activity and described this activity in terms of module spikes ., Module spikes represent simultaneous activations of large numbers of within-module neurons , and hence correspond to network spikes described in empirical data 43–44; we used the term module spike , rather than network spike , to avoid potential confusion with global network synchrony ., We explicitly note that module spikes are conceptually distinct from individual neuron spikes ., We determined the occurrence of module spikes with a shuffling algorithm that preserved individual spike frequency but destroyed global patterns of network activity ., In this algorithm , spike times of all excitatory within-module neurons are randomly shuffled between active time bins ., Module spikes are then said to occur when the number of simultaneously active neurons in the original data exceeds a threshold corresponding to the number of simultaneously active neurons in of the shuffled data ., For each module , we averaged the spike threshold from shuffled matrices ., It is also possible to describe network activity in terms of individual neuron spikes , rather than in terms of module spikes ., In our simulations , neurons were likely to spike in module-specific groups , and neuronal spikes were hence strongly correlated with module spikes ( Figure 3 ) ., We concentrated on module spike patterns because these describe activations of neuronal ensembles and have clear parallels with population spikes observed through changes of local field potentials in empirical studies of self-organized criticality 5 , 12 ., Neuronal spike patterns are studied in more detail elsewhere , e . g . in memory consolidation 45 ., We also note that neuronal activity is likely to occur at every time point in large networks; consequently descriptions of avalanches of individual neuron spikes require a global network threshold to remove background activity ., In our simulations , this threshold resulted in minimal event sizes of neurons , which , together with maximal event sizes of neurons , made rigorous detection of power-law scaling computationally prohibitive ., We defined an avalanche as a sequence of temporally continuous ( in bins ) module spikes , preceded and followed by a period of inactivity 5 ., Correspondingly , we defined the avalanche size as the number of module spikes in the avalanche , and the avalanche duration as the total time between onset and conclusion of the avalanche ., The minimal avalanche has size module and duration ., The maximal avalanche may be arbitrarily large because modules can be potentially active multiple times in the same avalanche ., More realistically , the overwhelming majority of avalanches in our simulations , especially in simulations with neurobiologically realistic connectivities ( Figure 7a ) , did not exceed the system size of modules ., Probability distributions of avalanche sizes and durations allow a concise quantification of network dynamics ., For instance , subcritical dynamics are characterized by small avalanche sizes and rapidly decaying avalanche size distributions , while supercritical dynamics are characterized by large avalanche sizes and slowly decaying avalanche size distributions ., Critical dynamics are characterized by avalanche sizes and durations that follow power-law distributions , with a cumulative distribution function , where is avalanche size or duration , is the scaling exponent , and are upper and lower cut-offs and is the generalized Hurwitz zeta function ., The functions explicitly incorporate an upper cut-off , as distributions are necessarily bounded by system size 46 ., In the following , we set to the maximal event size in each distribution ., We rigorously assessed the presence of power-law scaling in avalanche distributions , by adapting the methods described in Clauset et al . 36 ., We hence estimated using the method of maximum likelihood ., This method is mathematically robust and accurate for large number of samples ( in our simulations ) , unlike linear least-squares-based methods commonly used in previous studies ., For a given , we estimated by numerically maximizing the log-likelihood function , where , are the observed values of , such that for all ., We imposed the condition and this conservative condition ensured that we considered a wide range of events ., We then chose the , pair that minimized the Kolmogorov-Smirnov statistic , where is the cumulative distribution function of the data and is the cumulative distribution function of the fitted model ., We formally assessed the power-law goodness-of-fit , by generating synthetic power-law distributions with equivalent , , and ., For each generated dataset we individually estimated and , and computed the statistic as above ., This procedure gives a -value as the fraction of instances in which the statistic of the generated data exceeds the statistic of the original data ., We deemed that 47 did not allow to reject the power-law hypothesis , and hence suggested power-law scaling ., Smaller or larger -values ( ) did not qualitatively change our results ., We imposed three additional conditions to ensure meaningful power-law scaling ., Firstly , we required that maximal avalanche sizes approach system limits ( modules ) , to ensure that power laws did not reflect rapidly decaying subcritical dynamics ., Secondly , we required that avalanche distributions extracted from corresponding shuffled module spike matrices had goodness-of-fit ., Thirdly , we directly compared power-law and exponential distribution fits , by computing the log-likelihood ratio for the best-fitting power-law and exponential distributions ., The corresponding probability distribution , cumulative distribution and log-likelihood functions for the exponential distribution are , respectively , where is the exponential parameter ., The log-likelihood ratio compares two distributions and identifies a distribution which fits the data better ., A significance test on the log-likelihood ratio gives a -value on the statistical significance of this comparison 48 , 36 ., We deemed that indicated a statistically significant difference in fit between distributions ., We did not attempt to compare power-law and log-normal distribution fits because it is very difficult to differentiate these two distributions and hence such comparisons are typically inconclusive 36 ., We summarized the presence of power-law scaling in each distribution with a single statistic ., For each distribution , equaled the goodness-of-fit -value for the power-law model if the distribution additionally fulfilled the above three conditions; alternatively was set to ., We averaged over independent simulations for each type of connectivity , and considered to indicate power-law scaling ., We initially examined dynamics emergent on nonhierarchical modular networks ( Figure 4a ) ., We gradually randomized these networks by rewiring excitatory connections in a way that increased the number of connections between modules ., At one extreme , ordered nonhierarchical networks had no intermodule synapses ., At the other extreme , random nonhierarchical networks had homogeneously distributed intra- and intermodule excitatory synapses ., Between these two extremes , nonhierarchical networks had a varying number of homogeneously distributed intermodular excitatory synapses ., The location of synapses in each network was fixed during simulations , but synaptic weights continuously fluctuated according to the STDP rule ., All nonhierarchical networks had a connectivity-independent neuron spike rate of , and a stable weight distribution ( Figure 1b ) ., In addition , these networks had module spike rates of ., Ordered networks had no intermodular connections , and correspondingly showed subcritical uncoordinated dynamics ., Random networks had large numbers of intermodular connections and correspondingly showed supercritical globally synchronous dynamics ., A narrow range of network topologies between these two extremes was associated with critical dynamics , characterized by power-law distributions of avalanche sizes and durations ( Figure 4b , c ) ., Distributions of inter-avalanche intervals likewise changed from subcritical to supercritical , but did not follow consistent power laws at this transition ( Figure 4b ) ., Despite the stable weight distributions , activity-dependent fluctuations in synaptic weights continuously occurred ( Figure 5a , b ) ., In order to investigate the impact of these fluctuations on global network dynamics , we examined the effect of freezing plasticity after five minutes of initial transient simulation ., This procedure fixed the values of individual weights , and hence preserved the same neuronal spike rate of ., However , this procedure dramatically disrupted within-module neuronal synchrony: module spike rate dropped to less than and dynamics on all networks became highly subcritical ( Figure 5c , d ) ., Module spike rate remained negligible despite increases in external current , and consequent increases in neuronal spike rate ., Furthermore , module spike rate remained negligible with an even more stringent control condition , which allowed synaptic weight changes at spike times , but made these changes by randomly drawing weights from the distribution in Figure 1c , rather than according to the STDP rule ( results not shown ) ., On the other hand , as we show below , a change from soft to hard bounds in the STDP rule preserved equivalent dynamics , despite changing the weight distribution ( Figure 1c–e ) ., In addition , halving the STDP learning rate preserved equivalent dynamics when network size was doubled ., Together , these findings indicate that the precise patterns of STDP-driven fluctuations enabled the formation of coherent within-module dynamics in our model ., Nonhierarchical connectivity is neurobiologically implausible , because of the high wiring cost associated with a large number of long-range connections , and because hierarchical modularity is evident in multiscale neuroanatomical organization 25 ., We hence examined a more plausible connectivity by defining a framework in which connections were probabilistically placed within explicit spatial hierarchical levels , according to predefined power-law , exponential and linear scaling functions ( see Methods and Figure 2 ) ., Figure 6 compares the critical regimes associated with nonhierarchical connectivity ( Figure 6a ) , and with hierarchical power-law , exponential and linear ( Figure 6b–d ) connectivities ., The rows in Figure 6b–d represent different wiring costs for each hierarchical organization ., Most strikingly , low-cost power-law and exponential connectivities were associated with a broad critical regime ., This regime was especially evident for the power-law connectivity with ( fourth row in Figure 6b ) , as this was the only studied connectivity simultaneously associated with a broad regime of power-law distributed avalanche sizes and power-law distributed avalanche durations ., Connectivities with higher wiring cost , such as all linear connectivities , showed narrow critical regimes ., Connectivities with very low wiring cost did not show broad critical regimes , presumably because the numbers of long range connections in these connectivities were insufficient to enable the emergence of large events ., Figure 7a , b shows statistically significant power-law distributions of avalanche sizes and durations for the optimal power-law , exponential and linear connectivities ., The greater number of power-law distributions for the power-law and exponential connectivities , compared with linear connectivity , is clearly visible ., Figure 7c illustrates the values of power-law exponents for connectivities in which avalanche sizes and durations simultaneously followed statistically significant power laws ., Exponents of avalanche size distributions associated with power-law connectivities were close to and hence accurately resembled empirically estimated exponents of neuronal avalanche size distributions at the same bin size 5 , 14 ., Exponents decreased with increasing network randomization ., We sought to disambiguate the association between modularity and the broad critical regime by examining dynamics emergent on lattices with optimal power-law connectivity , but no explicit modular structure ( Figure 8a ) ., For this purpose , we constructed lattices of the same size and degree as the hierarchical connectivity networks , and we randomized these lattices by distributing off-diagonal connections according to the power-law density scaling function with ., In this way , we could focus on the effect of hierarchical modularity by retaining most other features of original network organization , including wiring cost ., Dynamics on these lattice networks had substantially reduced module spike rates ( ) and were associated with a rapid phase transition and a loss of the broad critical regime ( Figure 8c , top ) ., An increase in external current restored the original module spike rate of and consequently broadened the critical regime , although not to the original level ( Figure 8c , middle ) ., On the other hand , when modularity was implicitly reintroduced by rearranging inhibitory synapses into modules ( Figure 8b ) , a broad critical regime reappeared without changes in external current ( Figure 8c , bottom ) ., These findings suggest that modularity of inhibitory connections facilitated coherent within-module dynamics ., We explored robustness of the broad critical regime ( for the optimal power-law density scaling function ) to other meaningful changes in neurobiologically relevant parameters , such as changes in external current , changes in conduction delays , changes in the postsynaptic response , presence of neuronal inhibition , changes in the STDP rule and changes in network size ( Figure 9 ) ., Theoretically , self-organized criticality emerges in systems with low external drive and strong internal interactions , and the responses of our model to variation of parameters were meaningful in this context ., It is worth noting that we assessed the strength of external drive by the associated neuronal spike rate ., Specifically , we considered the external current of to represent a low external drive even though this value substantially exceeds the minimal value of required to sustain neuronal activity ( see Text S1 for details ) ., In our simulations the broad critical regime was robust to moderate variations of external current and delays ( Figure 9a , b ) , but began to disappear when external current exceeded ( as external drive became too strong ) , or when delay lengths were quadrupled to the range of ( as internal interactions lost spike precision ) ., The regime was narrowed when the postsynaptic response weakened ( Figure 9c , top ) , but was preserved when the STDP learning rate was reduced ( Figure 9c , bottom ) ., In both cases , we controlled for changes in neuronal spike rate by increasing external current ., The regime was broadened by a stronger postsynaptic response and by a higher STDP learning rate ( results not shown , as the associated parameter values are unrealistically high ) ., We hypothesized that our network models required strong postsynaptic responses and fast STDP learning rates to compensate for the small number of synaptic connections of each neuron ., Excitatory neurons in our model connected with only other neurons , while in vivo each neuron is thought to have thousands of synapses ., We compensated for the small number of connections in our model by setting the postsynaptic-response magnitude of each neuron to a value which could theoretically exceed the neuron spike threshold and by using an instantaneous STDP learning rate that substantially exceeds empirically observed values ( Table 1 ) ., When we doubled our module size to neurons , and consequently doubled our network size to neurons , we were able to simultaneously halve the values of postsynaptic-response magnitudes and STDP learning rates and hence bring these values much closer to empirically observed values 49 ., Specifically , the broad critical regime in these larger networks was preserved when the postsynaptic-response magnitude was halved , the STDP learning rate was halved , and the external current was reduced from to ( Figure 9d , top ) ., Alternatively , the regime was preserved when the postsynaptic-response magnitude was halved , the STDP learning rate remained unchanged , and the external current was halved ( Figure 9d , bottom ) ., These findings show that realistically large numbers of synaptic connections are likely to facilitate strong internal interactions in the presence of biologically realistic parameter values ., In addition to these variations , the broad critical regime did not qualitatively change when inhibitory synapses were removed , provided the loss of inhibition was controlled by reductions in external current ( Figure 9e ) ., The broad critical regime was likewise preserved when soft weight bounds were changed to hard weight bounds in the STDP rule ( Figure 9f ) ., We found that despite seemingly stable neuronal activity , spike-timing-dependent plasticity enabled coherent within- and between-module neuronal activity ., Furthermore , we showed that two variations of the STDP rule produced distinct weight distributions , but enabled a broad critical regime on conducive network topologies ., In contrast , fixed or randomly altered synaptic weights were associated with subcritical dynamics and negligible module spike rates ., STDP may facilitate coherent within-module activity by intermittently potentiating and depressing synapses between reciprocally connected neurons ., In small networks , simulations showed that intermittent synaptic potentiation and depression was associated with pairwise neuronal synchrony , fluctuations of synaptic weights and continuous reversal of phase differences between reciprocally connected pairs of neurons ( results not shown ) ., In our networks , within-module weights were potentiated during module spikes , and depressed between module spikes ( Figure 5b ) ., These activity-dependent fluctuations hence clearly played an important role in facilitating neuronal ensemble synchronization ., Recent studies have shown the importance of short-term synaptic depression in self-organized critical dynamics in networks of spiking neurons , but have not concurrently considered the effects of STDP 33 , 35 ., Our study illustrates the importance of STDP in self-organization and hence provides a alternative generative model of critical dynamics in networks of spiking neurons ., A principled comparison of the role of these two forms of plasticity in self-organized criticality is hence an important subject of future research ., The distinct mechanism of these forms of plasticity may also allow to disambiguate their role empirically with pharmacological manipulations in real neuronal systems ., Modular networks with low wiring cost showed a broad critical regime ., Modular networks with high wiring cost showed a narrow critical regime , possibly due to high numbers of costly long-range connections , which enabled a rapid onset of globally synchronous , supercritical dynamics ., Lattice networks with low wiring cost showed a narrowed critical regime due to uncoordinated inhibition and a consequent loss of coherent ensemble dynamics ., Modularity and low wiring cost were hence simultaneously required for self-organized criticality to emerge ., This simultaneous requirement is notable , as both properties are thought to be ubiquitously present in neuroanatomical organization ., In an early comprehensive exposition , Jensen 4 addressed the potentially confusing meaning of self-organization to criticality: “self-organization to criticality will definitely occur only under certain conditions; one will always be able to generalize a model sufficiently to lose the critical behavior . Hence the question becomes just what is relevant in a given context . This is where a super-general approach must be supplemented by insight from the specific science to which a given system belongs . ”, In this spirit , we examined neurobiologically meaningful variations in parameters such as external current and conduction delays ., We found that the broad critical regime was generally preserved despite variations of these parameters and , consequently , finetuning was not required for self-organized critical dynamics to emerge ., More specifically , strong synaptic interactions with low external current ( i . e . short delays , strong postsynaptic responses , high STDP learning rate ) favored a broad critical regime , while weak synaptic interactions with high external current ( i . e . long delays , weak postsynaptic response , low STDP learning rate ) favored a narrow critical regime ., These findings indicate that critical dynamics primarily emerged through internal interactions , rather than external drive ., The findings hence provide further evidence for the self-organizing nature of the observed dynamics ., The strong postsynaptic response and STDP learning rate in our model compensated for the relatively low synaptic connectivity , and could be markedly lowered in larger networks without detriment to the broad critical regime ., We found that inhibitory neurons in our model did not explicitly enable a broad critical regime ., In contrast , recent network simulations of simple stochastic neurons by Benayoun et al . 50 show that inhibitory neurons enable self-organized criticality by balancing the network ., However , the differences in neuronal dynamics , and the absence of statistically significant power laws in the Benayoun et al . study , make it difficult to directly compare our findings ., We do show however , that the presence of inhibitory neurons in our networks was compatible with

Introduction, Methods, Results, Discussion

Self-organized criticality refers to the spontaneous emergence of self-similar dynamics in complex systems poised between order and randomness ., The presence of self-organized critical dynamics in the brain is theoretically appealing and is supported by recent neurophysiological studies ., Despite this , the neurobiological determinants of these dynamics have not been previously sought ., Here , we systematically examined the influence of such determinants in hierarchically modular networks of leaky integrate-and-fire neurons with spike-timing-dependent synaptic plasticity and axonal conduction delays ., We characterized emergent dynamics in our networks by distributions of active neuronal ensemble modules ( neuronal avalanches ) and rigorously assessed these distributions for power-law scaling ., We found that spike-timing-dependent synaptic plasticity enabled a rapid phase transition from random subcritical dynamics to ordered supercritical dynamics ., Importantly , modular connectivity and low wiring cost broadened this transition , and enabled a regime indicative of self-organized criticality ., The regime only occurred when modular connectivity , low wiring cost and synaptic plasticity were simultaneously present , and the regime was most evident when between-module connection density scaled as a power-law ., The regime was robust to variations in other neurobiologically relevant parameters and favored systems with low external drive and strong internal interactions ., Increases in system size and connectivity facilitated internal interactions , permitting reductions in external drive and facilitating convergence of postsynaptic-response magnitude and synaptic-plasticity learning rate parameter values towards neurobiologically realistic levels ., We hence infer a novel association between self-organized critical neuronal dynamics and several neurobiologically realistic features of structural connectivity ., The central role of these features in our model may reflect their importance for neuronal information processing .

The intricate relationship between structural brain connectivity and functional brain activity is an important and intriguing research area ., Brain structure ( the pattern of neuroanatomical connections ) is thought to strongly influence and constrain brain function ( the pattern of neuronal activations ) ., Concurrently , brain function is thought to gradually reshape brain structure , through processes such as activity-dependent plasticity ( the “what fires together , wires together” principle ) ., In this study , we examined the relationship between brain structure and function in a biologically realistic mathematical model ., More specifically , we considered the relationship between realistic features of brain structure , such as self-similar organization of specialized brain regions at multiple spatial scales ( hierarchical modularity ) and realistic features of brain activity , such as self-similar complex dynamics poised between order and randomness ( self-organized criticality ) ., We found a direct association between these structural and functional features in our model ., This association only occurred in the presence of activity-dependent plasticity , and may reflect the importance of the corresponding structural and functional features in neuronal information processing .

neuroanatomy, neural networks, computational neuroscience, biology, neuroscience

journal.pntd.0007231

A computational method for the identification of Dengue, Zika and Chikungunya virus species and genotypes

In the recent years , an increasing number of outbreaks of Dengue ( DENV ) , Chikungunya ( CHIKV ) and Zika ( ZIKV ) viruses have been reported in Asia and the Americas 1–3 ., The predominant mosquito species transmitting DENV , CHIKV and ZIKV , are Aedes aegypti and Aedes Albopictus , which are widely distributed in tropical and sub-tropical regions 4 ., In the past few years , several studies have reported concurrent outbreaks of DENV , CHIKV and ZIKV in the same geographical area 5 , 6 ., Currently , unprecedented outbreaks of DENV , CHIKV and ZIKV are co-occurring in Brazil ., In 2017 , the Brazilian Ministry of Health estimated that approximately 251 , 000 suspected cases of DENV , 185 , 000 suspected cases of CHIKV and close to 18 , 000 suspected ZIKV cases had occurred in Brazil 7 ., Monitoring virus genotype diversity is crucial to understand the emergence and spread of outbreaks , both aspects that are vital to develop effective prevention and treatment strategies ., Both DENV and CHIKV epidemics are associated with a mortality and morbidity that puts a significant economic burden on the affected regions 8 , 9 ., While infections with ZIKV are rarely fatal , as stated before , ZIKV infections may result in Guillain-Barré syndrome and congenital malformations 10 , 11 ., Genomic surveillance of epidemics at the appropriate resolution and consistently classifying the reported genetic sequences , also enables the identification of strains associated with greater epidemic potential 12 or disease severity 13 ., However , methods that consistently classify arbovirus sequences at the level of species and sub-species ( i . e . serotype and/or genotype ) are currently lacking ., Additionally , whole genome sequences are often not available in routine clinical settings , forcing the use of shorter gene sequences to classify at viral species or sub-species level ., It has however insufficiently been explored which genomic regions are most suitable for accurate classification ., A new computational method for the identification of DENV/CHIKV/ZIKV sequences , with respect to species and sub-species ( i . e . serotype and/or genotype ) , is presented ., The classification method is implemented in the Genome Detective software tool , which was validated on a large dataset by assessing the classification performance of whole-genome sequences , partial-genome sequences and products from next-generation sequencing methods ., Furthermore , the suitability of different genomic regions for virus classification was evaluated ., An efficient method to classify virus sequences with respect to their species and sub-species ( i . e . serotype and/or genotype ) was developed ., This method was implemented in Java and this implementation was integrated in an easy-to-use web interface ., A detailed description of the method and its implementation can be found in the ‘Classification method and implementation’ Methods subsection ., Two different methods were used to verify the suitability of sub-genomic regions for genotyping purposes: a boot-scanning method and a likelihood-mapping method ( see Methods ) ., For DENV , the only sub genomic region that supports confident genotype assignment across the four different serotypes was the envelope gene ., For CHIKV , the envelope region E1 was the only region that allowed consistent assignment ., The boot-scanning analysis showed that for ZIKV , segments of around 1 , 200–1 , 500 base pairs support the genotype assignment with bootstrap > 70% ( Fig 3 ) ., This was the case over the entire genome , with the exception of the end of the genome ( i . e . the non-coding region ) and near the NS3 region , where bootstraps fell below 60% ., Our likelihood-mapping analyses show that for DENV , the envelope , NS1 , NS3 and NS5 had good phylogenetic signal across all four serotypes ., For CHIKV , the envelope E2 gene had the best signal but this region did not provide good boot-scanning support for the classification of the ECSA genotype ( Fig 3 ) ., For ZIKV , the envelope , NS1 , NS2A , NS3 , NS4A , NS4B and NS5 regions had good phylogenetic signal ., A detailed overview of the results of the likelihood-mapping analysis can be found in the S2 Table of the Supporting Information ., In summary , these analyses show that the envelope genes of the reference datasets of the three pathogens ( DENV , 1 , 485 nucleotides; CHIKV , 1 , 317 nucleotides; ZIKV , 1 , 525 nucleotides ) are the most suitable targets for reliable genotype classification ., Our automated method provided specificity , sensitivity and accuracy of 100% for the identification of complete genomes for all viral species and genotypes compared to the gold standard , a manual classification ., For a detailed overview of the DENV , CHIKV and ZIKV assignment performance , we refer to the Supporting Information S3 Table ., Only ten of 4118 DENV whole-genomes could not be classified at the genotype level , either by manual phylogenetic analysis or by our automated method ., Notably , the seven sequences ( AF298807 , KF864667 , EU179860 , JQ922546 , KF184975 , KF289073 , EF457905 of DENV-Sero1 were outliers in the phylogenetic tree ( see Supporting Information , S1 Fig ) ., We tested all ten sequences for recombination using boot-scanning ( see Supporting Information , S2 Fig ) and the recombination detection program RDP4 36 ., We only found sequence AY496879 to be a clear recombinant of DENV genotype 3I and 3II ., The two other sequences ( DENV-Sero2 KF744408 and DENV-Sero3 JF262783 ) were also identified as a divergent outlier ., Our analysis shows that the classification results for the envelope sub-genomic region at the species and genotype level were similar to that obtained using whole-genome sequences and largely in agreement with the gold standard , a manual classification ., For DENV , most of the genotypes were classified with great accuracy ( i . e . specificity and sensitivity > 99% ) using the envelope gene ., The exception was DENV-sero2 genotype IV , of which 41 envelope sequences were available and for which 33 were correctly identified ( i . e . sensitivity 80 . 49% , specificity 100% ) ., The CHIKV sequences covering the E1 region were accurately classified for all three genotypes ( i . e . 100% sensitivity and specificity ) ., All the ZIKV envelope sequences were classified with 100% sensitivity and specificity ., For a detailed overview of the DENV , CHIKV and ZIKV assignment performance refer to Supporting Information S4 Table ., Since a good phylogenetic signal was reported for the DENV and ZIKV NS5 region and the CHIKV E2 region , a classification analysis was performed for these regions as well ., For the DENV NS5 region a sensitivity of 57 , 48% and specificity of 31 , 35% ) was observed ., Nearly all ZIKV NS5 sequences were correctly assigned to the African genotype ( i . e . sensitivity of 97 . 72% and specificity of 100% ) ., This indicates that the ZIKV NS5 region might also be used for genotype classification ., For CHIKV , the E2 region showed perfect accuracy , similar to the E1 region ( i . e . specificity and sensitivity of 100% ) ., However , our previous boot-scanning support showed that the genetically variable E2 region may cause problems for some strains to be correctly identified as ECSA genotype ., In summary , our results suggest that the envelope region of DENV and ZIKV and the E1 envelope region of CHIKV are suitable for genotyping purposes ., In addition , these regions contain the largest number of sequences in public databases , which easily allows for a wide range of comparative analyses and validation experiments ., Emerging infectious diseases caused by viral pathogens still represent a major threat to public health worldwide , as recently demonstrated by outbreaks of Ebola , Zika , Middle East Respiratory Syndrome ( MERS ) and Yellow Fever virus ., Fast and accurate real-time monitoring of outbreaks and surveillance of on-going epidemics is crucial to anticipate viral spread and to design effective prevention or treatment strategies ., To this end , an accurate and reliable method for the classification of ZIKV/DENV/CHIKV arboviruses was developed: The ArboTyping tool ., The ArboTyping tool implements a classification pipeline that consists of a BLAST-based species assignment and phylogenetic assessment to identify subspecies ( i . e . genotypes ) with respect to a set of reference strains , as exemplified for other virus species by previous work 29–31 ., To enable accurate classification , a set of reference sequences that cover the extent of diversity within species and subspecies , was carefully selected ., The classification performance of the ArboTyping tool was assessed on a dataset of whole-genome sequences ., All whole-genome sequences in this dataset that could be confidently assigned a species and genotype with the gold standard , a manual classification procedure , were concordant with the typing tool ., There were , however , 10 sequences that could not be classified using the manual classification procedure: further analyses show that these 10 sequences consist out of 3 outlier sequences , 2 clades of outlier sequences ( 3 sequences in each outlier clade ) and 1 recombinant sequence ., As these outliers have been previously identified 43 , these results need to be further investigated to assess whether these outliers form new genotypes 44 ., However , whole-genome sequences are currently not routinely available and the suitability of the different genomic regions was evaluated with respect to their use for classification ., Since the envelope gene is a popular target for phylogenetic classification , there is a large availability of envelope sequences in public databases ., Therefore , the performance of the ArboTyping tool was evaluated on a large dataset of envelope sequences ( i . e . Global-ENV dataset ) ., For these envelope sequences , a classification performance close to the tool’s performance on whole-genome sequences was reported ., While the availability of sequence products originating from other genomic regions is currently low , it can be expected that these regions will increase in relevance given the interest in developing antiviral agents that target non-structural proteins ., Therefore , more detailed studies to assess the classification performance of other genomic regions are warranted 44 ., In this manuscript , we focus on the classification of consensus sequences on the species and sub-species level ., However , Genome Detective , the framework in which our tools are integrated , is also a virus discovery toolchain 41 ., Genome Detective’s user interface allows users to supply raw next-generation sequence reads that can be automatically assembled into a consensus and passed to the ArboTyping tool ., Details on the methods used to assemble reads in Genome Detective and an extensive validation using raw NGS reads can be found in 41 ., In conclusion , the new method presented here allows the fast , accurate and high-throughput classification of DENV , CHIKV and ZIKV species and genotypes ., Species can be classified using different sequencing products ( i . e . whole-genome sequences , envelope sequences and individual next-generation sequencing reads ) and genotypes can be classified most confidently when using envelope sequences or whole-genome sequences ., This method accommodates the need to consistently and accurately classify DENV/CHIKV/ZIKV sequences , which is essential to implement epidemic tracing and to support outbreak surveillance efforts ., Additionally , we present a solid framework that has the potential to serve as the foundation for many other arbovirus classification tools ., These tools are also useful to be integrated in data management environments 45 ., Our method is implemented in the Genome Detective software framework , suitable for many virus typing tools ., The web application that makes our tool available through an easy-to-use web interface is available online via a dedicated server that is hosted at http://www . krisp . org . za/tools . php .

Introduction, Results, Discussion

In recent years , an increasing number of outbreaks of Dengue , Chikungunya and Zika viruses have been reported in Asia and the Americas ., Monitoring virus genotype diversity is crucial to understand the emergence and spread of outbreaks , both aspects that are vital to develop effective prevention and treatment strategies ., Hence , we developed an efficient method to classify virus sequences with respect to their species and sub-species ( i . e . serotype and/or genotype ) ., This tool provides an easy-to-use software implementation of this new method and was validated on a large dataset assessing the classification performance with respect to whole-genome sequences and partial-genome sequences ., Available online: http://krisp . org . za/tools . php .

Dengue ( DENV ) , Chikungunya ( CHIKV ) and Zika ( ZIKV ) are considered major public health challenges ., In addition to the epidemic caused by DENV , which has been described in many tropical countries , the introduction of CHIKV and ZIKV in these countries is a major public health concern ., These arboviruses are primarily transmitted by mosquitoes of the species Ae ., Aegypti and its related diseases result in increased financial costs associated with diagnosis and treatment ., To support the design of efficient diagnosis , prevention and treatment strategies , a bioinformatics tool has been developed for the genotyping of these viruses based on appropriate evolutionary models in an automatic , accurate and rapid manner ., A set of virus reference sequences was obtained from GenBank and used for the development of the tools ., This process involved the alignment of the reference sequences followed by phylogenetic tree reconstructions ., To assign the genotypes uploaded by the user , the tool analyses the sequences one by one , genotypes through identification , alignment and phylogenetic reconstruction ., This computational method allows the high-throughput classification of these virus species and genotypes in seconds ., As shown experimentally , genotypes are classified most confidently using the envelope gene or complete genome sequences .

taxonomy, medicine and health sciences, pathology and laboratory medicine, togaviruses, pathogens, microbiology, alphaviruses, viruses, phylogenetics, data management, chikungunya virus, rna viruses, phylogenetic analysis, genome analysis, research and analysis methods, sequence analysis, computer and information sciences, genomics, sequence alignment, bioinformatics, blast algorithm, medical microbiology, microbial pathogens, evolutionary systematics, flaviviruses, viral pathogens, database and informatics methods, genetics, biology and life sciences, computational biology, evolutionary biology, organisms, zika virus

journal.pcbi.1005698

A systems approach reveals distinct metabolic strategies among the NCI-60 cancer cell lines

Aerobic glycolysis indicates the incomplete oxidation of glucose to lactate under normoxic conditions 1 and has been a focus of cancer research in recent decades 2 ., However , cancer cells are increasingly thought to employ heterogeneous metabolic strategies beyond aerobic glycolysis 3–6 ., Many cancer cells generate substantial amounts of energy through mitochondrial oxidative phosphorylation 2 , 7 , 8 ., Moreover , cancer cells use additional fuels , such as glutamine and fatty acids , to support proliferation 3 , 9 ., These carbon sources can be used in different ways , e . g . , different parts of the tricarboxylic acid ( TCA ) cycle can be employed for glutaminolysis 5 , 8 , 10 , 11 ., Reductive carboxylation involves only two TCA cycle reactions that run in reverse direction without producing energy , whereas glutaminolysis in the forward direction does yield energy 5 , 8 , 11 ., In addition to various metabolic strategies , cancer cells display robustness towards environmental changes , such as , nutrient supply or oxygenation 12–14 ., Even though these differences in metabolic phenotypes are known to exist , the variance in the metabolism of cancer cell lines has not been exhaustively analyzed using extracellular metabolomic data ., Liquid chromatography-tandem mass spectrometry ( LC-MS ) was used to determine the metabolites that were consumed and released by the cancer cell lines included in the NCI-60 panel of the National Cancer Institute’s ( NCI’s ) Developmental Therapeutics Program ( DTP; http://dtp . nci . nih . gov ) 15 ., By combining the obtained metabolomic profiles with doubling times and transcriptomic data , rapid proliferation was associated with cellular glycine requirements 15 ., However , most of the intracellular pathways that gave rise to distinct metabolomic profiles remained undetermined ., Metabolism can be investigated using constraint-based modeling 16 , 17 , which involves the application of physico-chemical principles and often assumes the system to be in a steady-state 16 ., Limitations on metabolite uptake and secretion rates can be added to the model to increase the precision of the predictions by eliminating network states that exceed these constraints 18 ., A reconstruction of the human metabolism is readily available 19 , 20 , and numerous analytical methods are used to investigate the metabolic differences that arise due to the imposed constraints 21 , 22 ., Metabolomic data derived from body fluids and cell culture supernatant have previously been integrated with metabolic reconstructions 7 , 23 , 24 ., One existing challenge in the integration of extracellular metabolomic data is incomplete data ., Analytical techniques identify only a subset of the metabolome due to the chemical diversity among small molecules and because the analysis is often a priori limited to a defined set of targeted metabolites 25 ., Hence , the information on which substrates are taken up by the cells is incomplete ., Similarly , the management of data derived from cells grown in serum is difficult because the quantitative and qualitative composition of the serum is unknown ., However , the quality of computational predictions depends on the extent to which a model’s solution space can be reduced by integrating available data ., Ideally , only biologically relevant network states would remain to be investigated 18 ., Novel approaches are necessary to overcome these difficulties and enable the rapid classification of metabolic phenotypes based on metabolomic profiles ., Such approaches could have a broad impact on many biological fields including biomedicine ., We developed a novel method termed minExCard to complete the uptake and secretion profile , by predicting a minimal set of metabolite exchanges in addition to the ones measured , to complete the metabolome ., We applied the method to the comprehensive targeted extracellular metabolomic data set from Jain et al . , which was generated from the NCI-60 cell lines grown in medium enriched with serum 15 ., Using minExCard we generated 120 condition-specific models from extracellular metabolomic data ., Our models utilized different biochemical routes to supply the cells with energy and were distinctively affected by network perturbations ., We distinguished different oxotypes based on the range of allowable oxygen uptake rates ., We identified a distinctive tissue pattern for melanoma cell lines that was supported by protein and RNA expression levels from melanoma cell lines and primary melanoma ., This work demonstrates how analysis of extracellular metabolomic data in the metabolic model context , and the combination of multiple analysis strategies , can lead to unprecedented insight into cell metabolism ., Published metabolomic profiles comprising the uptake and secretion of metabolites from and into the culture medium were integrated with the metabolic model ( Fig 1A ) 15 ., The metabolomic data consisted of two samples per cell line ., Because there was considerable variation between samples ( Fig A in S1 Text ) , we generated one condition-specific cell line model for each sample rather than averaging the data for each cell line ., The metabolome is dynamic and constitutes a snap-shot of the phenotype elicited by the cultivated cells over the duration of the experiment and under a specific set of environmental conditions ., We refer to the models as condition-specific since they are tailored only according to the metabolomic profiles ., Generic cell-line specific models would need to be generated from data sets of different experimental conditions and the existing literature for the same cell line , to ensure that it can carry out all the functions observed for these cells under any set of environmental conditions ., To generate a condition-specific model , the global model was constrained using the metabolite uptake and secretion rates measured for the respective samples ., Next , a minimal set of , on average 17 ± 3 , exchange reactions needed to sustain a minimal growth phenotype ( Vbiomass , min = 0 , 008 U ) together with the imposed uptake and secretion rates were identified based on the model structure by minimizing the number of exchange reactions ( using minExCard ) ., An analysis of the expression of genes associated with the metabolites additionally required in the MCF-7 models ( which required the highest number of added exchange reactions ) , revealed that extracellular transport and metabolism of these added metabolites could indeed appear in MCF-7 cells ( see S1 Text , S1A Table , 26 ) ., However , the gene expression data was only used to validate the added exchanges , but not for the generation of the condition-specific models since the transcriptomic data originated from a different experiment than the metabolomic data ., All other metabolite exchanges and internal reactions that were no longer used by the model were removed to produce an individual condition-specific cell line model for each sample ( Fig 1A ) ., The 120 models differed with respect to the numbers of reactions , metabolites , and genes ( Fig 1B and 1C , Fig B in S1 Text ) ., Many of the models could substantially exceed the maximally possible growth rates expected for any human cell ( S1B Table ) ., The capability of the models to grow at realistic rates was analyzed by applying constraints on the biomass objective function based on reported growth rates ( +/-20% ) for the individual cell lines , and flux balance analysis revealed whether the model remained feasible with these constraint ., Only 14 models were infeasible when constrained using the experimental growth rates ( see S1 Text , S1C Table ) because the feasible range of flux rates through the biomass reactions exceeded or did not reach up to the experimental growth rates , even when assuming a 20% error range ., ACHN-2 and UACC-257 were limited to experimental growth rates just by the metabolite uptake and secretion profile and the minimal growth constraint ( S1 Text , S1B and S1D Table ) ., Considering a lower error of 5% or constraining both upper and lower bound to the growth rate , the ACHN-2 model became infeasible ( S1D Table ) ., The predicted growth rates for the HCT-116 models using sampling Vmedian , biomass = 0 . 038 U , corresponding to a doubling time of 18 . 2 hrs , deviated at most 7% from the growth rate reported by Jain et al . and others ( S1D Table , 15 , 27 ) ., Taken together , the diversity of the models and their ability to predict realistic growth rates suggested that they were a good starting point to investigate metabolic heterogeneity between the cell lines ., Metabolic strategies yield different amounts of ATP , e . g . , full oxidation of glucose to CO2 can yield 32 ATP and aerobic glycolysis can yield two ATP 28 , 29 ., Herein , we used the ATP yield as an estimator for distinct pathway utilization ., For this analysis , we divided the sum of flux through all reactions in the model that produced ATP by the individual glucose uptake ., There was a large range of ATP yields across the models ( Fig 2A , ATP yield: min = 2 . 92 , max = 55 . 27 , S1B Table ) that exceeded the theoretical measure for aerobic glycolysis ., An exact fit with the theoretical ATP yields was not expected because the models could use all substrates as defined by the uptake profile and ATP-producing reactions present in the condition-specific model and not only glucose ( Fig A in S1 Text ) ., As a sanity check , we tested for maximum ATP hydrolysis flux from only O2 and glucose as carbon source ., ATP hydrolysis flux from glucose did not exceed the theoretical measures 28 , 29 in any of the 120 cancer models ( S1E Table ) ., Upper bounds on exchange reactions were opened for the sanity check ., Rank-ordered ATP yields nearly continuously increased and were occasionally interrupted between groups of models ( Fig 2A , Fig C in S1 Text ) ., One interruption was associated with the switch of the major ATP-producing reaction ., Models with an ATP yield < 4 . 21 ( ’glycolytic’ models , n = 38 , Fig 2A ) produced the highest fraction of ATP through phosphoglycerate kinase ( PGK ) ., In contrast , models with an ATP yield > 7 . 26 produced ATP primarily via ATP synthase ( ’OxPhos’ models , n = 82 , Fig 2A ) ., Thus , the ATP yield and ATP production strategy divided the models into glycolytic and OxPhos phenotypes ., The distinction of the models was significantly associated with the ratios of glucose uptake to lactate secretion ( ttest , p< 0 . 01 ) , and glucose uptake to glutamine uptake ( ttest , p< 0 . 0002 ) ., Taken together , the distinction of the glycolytic and the OxPhos models emerged from the ratios of fluxes of metabolites , which are associated with the observed Warburg phenotype and , which were imposed on the models as individual flux constraints ., Consideration of differences in the utilization of the TCA cycle , i . e . , ATP production of succinate-CoA ligase , enabled the further identification of two OxPhos subtypes ( Fig D in S1 Text ) ., This division was not obvious according to ATP yield ( Fig E in S1 Text ) ., In addition to ATP , cells need cofactors to support proliferation ., Distinct strategies used in the models produced different cofactors and enabled the division of glycolytic models into two subtypes ( Fig 2B and 2C , S1F–S1J Table ) ., The two OxPhos subtypes were further subdivided into a total of six subtypes ( Fig 2B and 2C , S1J Table ) ., The glycolytic subtypes differed only in the major FADH2-producing reaction ( Fig 2B , I and II ) ., Two OxPhos subtypes were associated with high TCA cycle contribution to ATP production , which was associated with a high utilization of cytosolic malic enzyme as a leading NADPH source ( Fig 2B , IV and VII ) ., The four remaining OxPhos subtypes predominantly used either isocitrate dehydrogenase ( IDH , Fig 2B , V and VIII ) or dihydroceramide desaturase ( Fig 2B , III and VI ) for NADPH production ., Glyceraldehyde-3-phosphate dehydrogenase was the primary NADH producer in OxPhos models with relatively more glycolysis-based ATP production , whereas 2-oxoglutarate dehydrogenase was favored in models with a higher contribution of ATP synthase ( Fig 2C ) ., Thus , the predicted strategies for cofactor production enabled further refinement for the classification of glycolytic and OxPhos models ., Thus far , we stratified the models based on the imposed constraints and the distinct use of central metabolic pathways ., In the following , we predict the behavior of each model towards environmental and genetic perturbations ., Fluctuations of nutrients and oxygen supply during transformation shape the individual metabolic network and may influence the robustness of cancer cells towards environmental changes 30 ., Variations of glucose uptake , glutamine uptake , oxygen uptake , and lactate secretion ( phenotypic phase plane analysis ( PhPP ) ) led to two major observations 31 ., First , the size and form of the solution spaces varied across models ( Fig 3 ) ., Using the form and size of the solution spaces as visual clues ( Fig G in S1 Text ) , we divided the models into six distinct clusters ( Figs 2E and 3 , S1K Table , S1 Text ) ., Second , the solution space , which contains all possible network states and which was defined by variations in oxygen uptake , divided the models into three groups ( Fig 2D ), ( i ) Glycolytic models could only grow at low oxygen uptake rates ( Figs 2D and 3 cluster 4 ) ., The group of OxPhos models comprised, ( ii ) models growing only at high oxygen uptake rates ( Figs 2D and 3 cluster 1–3 ) and, ( iii ) models that were indifferent with respect to oxygen uptake rates ( Figs 2D and 3 cluster 5–6 ) ., The latter two groups provided a separation of the OxPhos models that was distinct from the previous analysis ., Thus , the models could be further divided according to their robustness towards oxygen uptake ., In silico gene knock-outs can predict novel drug targets 32 ., Single gene deletion of 1215 unique human genes ( all isozymes of one gene were constrained to zero at once ) was performed for each of the 120 models ., The number of essential genes varied across models ( min = 132 , max = 272 , S1B Table ) and was not associated with any phenotype ., A total of 55 genes were essential to all models and could constitute metabolic targets for all previously defined phenotypes ( S1L Table ) ., These numbers of essential genes predicted by our models were higher compared to those predicted for generic cell-or tissue specific models ., This was caused by the vast reduction of exchange reactions and fixed uptake and secretion fluxes , which prevented that upon a gene knock-out , the models could switch to using different metabolic fuels or pathways connected to changes in gene expression ., The flux ranges and the direction of flux of the exchange reactions were fixed , causing any reaction that was linked to the exchanges to become essential for the model ., Whether a gene was essential under changing environmental conditions and whether cells in vivo could evade the effect by changing the metabolic pathways used to generate energy , cannot be answered by our models ., However , models build from transcriptomic data could be used instead ., Such models have previously revealed the switch to pathways requiring higher oxygen uptake when glycolytic enzymes were inhibited 33 ., However , the condition-specific models , which are ‘frozen’ to the metabolic properties elicited at the time , highlight inhibition of which genes necessitate changes in metabolic flux and changes in gene expression ., Cancer cells use the TCA cycle in different ways 5 , 8 ., Reductive carboxylation involves the TCA cycle reactions isocitrate dehydrogenase and aconitase , and occurs in the mitochondria or the cytosol ., The gene IDH1 encodes the cytosolic isocitrate dehydrogenase and the gene IDH2 encodes the mitochondrial isocitrate dehydrogenase ., Interestingly , in silico IDH2 knock-out terminated growth in four models ( SK-MEL-28 , SK-MEL-28-2 , MALME-3-2 , and BT-549 ) and reduced growth in 12 additional models ., A flux variability analysis ( FVA ) revealed that the four models had to employ reductive carboxylation ( S1M Table 34 , whereas this pathway remained optional for the other models even when constrained to experimental growth rates ( S1D Table ) ., In agreement with an observed increase in reductive carboxylation under hypoxic conditions 5 , a reduction of the oxygen uptake rate ( lb = ub = −100 fmol/cell/hr ) rendered 14 additional models dependent on reductive carboxylation ( S1M Table ) ., Fifteen models , including the four reductive carboxylation models , belonged to PhPP cluster 4 , which was characterized by a heavily constricted solution space at low oxygen uptake rates compared with , e . g . , the cluster 4C models ( Fig 3 ) ., The remainder belonged to cluster 1B ., Our models were therefore not only able to predict reductive carboxylation but also able to further reproduce the co-occurrence of low oxygenation and reductive carboxylation in cancer cell lines ., Phosphoglycerate dehydrogenase ( PHGDH ) was another essential gene shared among the four models with obligate reductive carboxylation ., Interestingly , SK-MEL-28 and MALME-3M had previously been associated with amplifications of PGDH due to 1p12 gain 4 , 35 ., The correct prediction of the dependency of SK-MEL-28 and MALME-3M on PHGDH provides additional support for the presented approach and for the predicted dependency of SK-MEL-28 on reductive carboxylation ., Because the oxotype played an essential role in determining the phenotype and because tissues are known to be differentially oxygenated 36 , we questioned whether tissue origin impacted the oxotype of the cancer ., In total , 49 cell line model pairs had the same oxotype ( Fig 4 ) ., Breast , colon , and non-small cell lung cancer models were spread across oxotypes ., Leukemia , prostate , renal , and CNS cell line models predominantly depended on high oxygen uptake rates ., In contrast , melanoma cell lines were clearly separated from the other cell lines by predominantly relying on low oxygen uptake rates ( Fig 4 ) ., Thus , the oxotypes enabled us to distinguish melanoma cell lines from other cancer cell lines ., Most melanoma models were predicted to be glycolytic and having a low oxotype ( Fig 4 , S1J and S1K Table ) ., A reverse flux through the TCA cycle was essential for a small subset of melanoma models without additional constraints limiting the oxygen uptake ., To validate that melanomas indeed use the mitochondrial isocitrate dehydrogenase , we analyzed protein abundance and RNA expression data from the Human Protein Atlas 37 ., IDH1 protein abundance was low or not detectable in normal skin cell types ( hypergeometric p ( x = 5 ) = 0 . 047 , Table 1 , 1 ) , skin cancer , and melanoma ., In comparison , IDH2 protein levels were medium in normal skin cell types ( hypergeometric p ( x = 5 ) = 0 . 006 , Table 1 , 2 ) and detected in more than 50% of the skin cancers and melanomas ( Table 1 ) ., Thus , the data supported a prevalence of IDH2 for normal skin cell types , skin cancers , and melanoma at the protein level ., Reductive carboxylation has been associated with the loss of the von Hippel-Lindau tumor suppressor ( VHL ) in renal cancer cell lines 38 ., HIF1α protein is no longer degraded , which is associated with the expression of glucose transporters and glycolytic enzymes 39 , 40 ., Since the process of HIF stabilization is connected to hypoxia , this process has also been referred to as pseudo-hypoxia 5 ., To validate the predicted glycolytic phenotype and the low ‘oxotype’ , we analyzed HIF1α and VHL protein , and RNA levels ., HIF1α protein abundance was low in normal skin tissue ( hypergeometric p ( x = 5 ) = 0 . 019 , Table 1 , 3 ) and low or medium in the majority of skin cancers and melanomas ( Table 1 ) ., HIF1α RNA expression was overall high in human melanoma and epidermoid carcinoma cell lines ( Table 2 ) ., The VHL protein detection was unreliable in all normal skin cell types ( Table 1 ) ., Interestingly , VHL protein was not detected in skin cancers or in melanomas ( Table 1 ) ., The absence of VHL was even more distinctive in skin cancers as compared to renal cancers where VHL levels were medium or high in 7 out of 12 patient samples ( Table 1 ) ., Moreover , RNA expression was low in two melanoma cell lines , an epidermoid carcinoma , an immortalized normal keratinocyte cell lines ( hypergeometric p ( x = 4 ) = 0 . 044 , Table 2 , 1 ) , and A549 cells , which were predicted to be low oxotype ( Table 2 ) ., Hence , the lack of VHL emerged as a prominent feature of normal skin and melanoma ., We opted to build our models solely from metabolomic data , without consideration of the genotypic and other omics data , to evaluate whether such models could provide novel biological insights ., This approach was particularly interesting for connecting the melanoma cell lines with the reverse flux through the IDH2 and psydohypoxia 5 ., However , when only constrained based on the metabolomic data , our 786-O models predicted net reductive carboxylation was optional , which stood in contrast to net reverse flux observed for these cells ., Limiting the oxygen uptake in the models to the minimum emphasized the reverse flux in the TCA cycle ., Overall , this highlights that oxygen consumption in an experiment determines the observable metabolic phenotype , in addition to the growth medium composition ( or environmental condition ) ., Addition of , e . g . , transcriptomic data could further define the phenotype 62 ., There is no shortage in transcriptomic data for the NCI-60 cell lines ., However , since the models build herein are condition-specific , the data used should originate from the same experiment to resemble the metabolic phenotype displayed in the experiment ., The presented computational modeling approach is applicable to many cellular systems and represents a valuable starting point to investigate metabolic strategies of individual cell lines as well as to envision clinical applications ., Further development of this approach could help realize personalized clinical applications utilizing metabolomic data ., Immortalized cell lines , such as the NCI-60 cell lines , have a limited clinical relevance since they are monoclonal and accumulate mutations due to the high passage numbers 63 ., One way to increase the clinical relevance of our work would be to extend the presented work to omics data generated from patient-derived primary tumor cells ., Methods exist to cultivate primary tumor cells , or selected sub-populations of the same , e . g . , tumor-initiating stem cells; and to retain phenotype and genotypes using tissue-specific supplements and environmental conditions 63 ., Extracellular metabolomic data or multiple omics data derived from such personalized cell cultures could then be used in conjunction with the presented approach to gain a better understanding of an individual’s cancer , and to predict appropriate treatment strategies ., The global model constitutes a subset of Recon 2 20 ., This subset is the same as that used in a previous study 62 ., Units ( U ) are given in fmol/cell/hr ., The MetaboTools function setMediumConstraints was used to apply the following constraints to the global model 61 ., Essentially , infinite constraints were set to lb = −2 , 000 U and ub = 2 , 000 U . All exchange reactions in the model were initially set to lb = −2 , 000 U and ub = 2 , 000 U . Subsequently , constraints were set for exchange reactions of ions ( lb = −100 U ) , vitamins ( lb = −1 U ) , essential amino acids ( lb = −10 U ) and compounds such as water or protons ( lb = −100 U ) ., Oxygen uptake was constrained to lb = −1 , 000 U and ub = 0 U . This range was defined based on reported oxygen uptake rates of a cancer cell line ( 2 . 85 ⋅ 10-6ml O2/105cells/min = 646 . 013 U 64 ) ., Additionally , the lower bounds of the superoxide anion and hydrogen peroxide exchanges ( i . e . , uptake flux ) were set to zero to prevent the generation of models that did not require oxygen uptake ., Reaction fluxes are usually in units of mmol/gDW/hr ., Here , however , the metabolite uptake and secretion profiles were mapped in the unit fmol/cell/hr 15 ., We assumed a unitary cell weight of 10-12 g , which was in the range of the dry weight ( 3 . 645 ⋅ 10-12 g ) that we calculated for lymphocytes in an earlier study 62 ., In that study , the dry weight was inferred from the dry mass ( range 35–60 ng 65 ) and cellular volume ( 4000 μm3 66 ) of the human osteosarcoma cell line U2OS , which we related to the cell volume of lymphocytes ( 243 μm3 ) 67 ., By calculating 4000/243 = 16 . 46 , 60 pg/16 . 46 = 3 . 645 pg ( 3 . 645 ⋅ 10-12 g ) 62 ., According to 1mmol/gdw = 1012fmol/1012 cells , no biomass scaling was necessary ., The lower bound ( lb ) of the biomass objective function was fixed to a minimal value of 0 . 008 U to match the lb defined for the slowest growing cell line in the data set ( HOP-92 , 88 hrs ) 27 , ensuring that the model building resulted in functional models with non-zero growth ., S1Q Table lists the reactions and constraints of the global model ., We used published metabolomic data 15 ., There were two quantitative extracellular metabolomic profiles for each of the NCI-60 cell lines ., These profiles defined the uptake and secretion rates of 115 metabolites 15 ., From the entire set of detected metabolites , we used only the calibrated ( quantitative ) uptake and secretion fluxes ., Fluxes were provided in the unit fmol/cell/hr ( U ) and were incorporated as such into the model ., Throughout the manuscript , fluxes are reported in the unit fmol/cell/hr ( U ) ., Metabolite identifiers in the data were mapped to the metabolite abbreviations in the global model ., The metabolite aminoisobutyrate was not part of the global model and was excluded ., We identified the existing metabolite exchange reactions based on the metabolite abbreviations ., If there was no exchange reaction in the model but if the metabolite itself was part of the model , a new exchange reaction was added to the model ., In addition to the exchange reactions , transport reactions need to be present in the model to account for transport of metabolites between the extracellular space and the cytosol of the model ., Transport reactions need to be added for all metabolites for which we added exchange reactions ., These transport reactions were identified from the literature ., If no transporter for the metabolite could be identified , we added a diffusion reaction ., The additions that we made to the model based on the metabolomic data comprised 44 transport and 37 exchange reactions ( S1R Table ) ., The global model used to generate the cancer models comprised 3 , 935 reactions and 2 , 833 metabolites ., The Integration of the metabolomic data was performed as detailed in a protocol that provides extensive support ( including workflows , code , and tutorials ) for the data integration , model generation , and model analysis , carried out in this study 61 ., Consider the optimization problem, min θ ( v ) s . t . S · v = 0 , l b ≤ v ≤ u b , ( 1 ), where v ∈ R n is a vector of reaction rates , θ ( v ) is a scalar valued objective function and S ∈ R m × n is the stoichiometric matrix consisting of m metabolites and n reaction rates as defined by the metabolic reconstruction ., The lower and upper bounds , lb and u b ∈ R n respectively , constrain the sign and magnitude of the reaction rate , with the convention that a net forward rate is positive ., In flux balance analysis ( FBA 68 ) , the objective is to minimize θ ( v ) : = cT ⋅ v , a linear sum of reaction rates , where c ∈ R is a parameter vector that specifies the linear contribution of each reaction rate to the objective function ., When minimizing a single reaction rate , every entry of c is zero , except one ., Typically , there is an infinite number of optimal reaction rate vectors that produce an optimal value of the objective function ., To obtain a unique flux vector , we first solve Problem ( 1 ) with θ ( v ) : = cT ⋅ v , then fix the rate of the previously optimized reaction and again solve Problem ( 1 ) except with θ ( v ) : = 1 2 v T · v . This procedure returns a unique reaction rate vector that minimizes the square of the Euclidean norm of the reaction rates , subject to optimality with respect to the original objective function 21 ., In flux variability analysis ( FVA ) , one uses linear optimization to compute the minimal and maximal rate of each reaction , subject to θ ( v ) : = cT ⋅ v being minimal as computed in Problem ( 1 ) 34 ., The presence of an exchange and transport reactions does not ensure that a metabolite can be consumed or secreted by the model because anabolic and/or catabolic pathways may not be present or unknown 20 ., We used the MetaboTools function prepIntegrationQuant to generate individual uptake and secretion profiles for each sample in the data set: To identify the subset of metabolites that the model could consume and secrete , we performed FBA while enforcing small uptake ( ub = −0 . 0001 U ) or secretion ( lb = 0 . 0001 U ) for all mapped metabolite exchanges ., All metabolites that could not be consumed ( 14 ) or secreted ( 14 ) by the model were discarded ( S1S Table ) ., Among them was homoserine 4-hydroxybenzoate , which could be neither consumed nor secreted by the model ., Therefore , data for 112 metabolites could be mapped ., Note that these 112 metabolites included those that could only be consumed , only be secreted , or by both consumed and secreted ( S1S Table ) ., The identification of metabolites that are not part of a metabolic reconstruction is common , and pathways for these metabolites need to be added in future releases of the human metabolic model 20 , which served as a starting point ( see also above ) ., If the uptake of a metabolite was possible in the global model but secretion was not , only metabolite secretion was discarded from the metabolic profiles , while uptake remained present , and vice versa ., After the sets of ‘qualitatively’ feasible metabolite exchanges were identified , we mapped the sets of metabolite uptake and secretions of a sample to the global model using the MetaboTools function setQuantConstraints 61: We mapped a minimum of 95 and a maximum of 105 exchanges to the models ( S1T Table ) ., These exchanges were split into uptake and secretion ., The number of metabolite uptakes mapped to the model ranged between 34 and 58 , and the number of secretions enforced in the model varied between 42 and 67 ., We imposed each detected , quantitative flux x as a constraint to the bounds of the respective metabolite exchange reaction while considering a 20% allowance around x ( lb = 0 . 8x U and ub = 1 . 2x U ) ., The constraint pairs for one sample were mapped to the global model one by one ., After constraints were placed on one exchange reaction , FBA was performed to check if the model was still able to grow ., Although the global model was able to perform all qualitative metabolite exchanges that were mapped , certain quantities or combinations of constraints could still render the model infeasible ., In case of infeasibility , the original bounds of the model were restored , and we proceeded to the next set of constraints ., Quantitative constraints rendered 27 preliminary cell line models infeasible ( Fig 1A ) ., Of these 27 models 25x2 , 1x1 , and 1x4 exchange constraints were restored during the data integration ( S1B Table ) ., Although 464 of the reactions in the global model can exchange metabolites across the boundary of a cell , the exchange of only 115 metabolites was actually quantified in the metabolomic profiles that we employed ., The incompleteness of the metabolic profiles results from limits to the scope of individual metabolomic platforms , e . g . , oxygen uptake rates that were not reported ., This issue was co

Introduction, Results, Discussion, Materials and methods

The metabolic phenotype of cancer cells is reflected by the metabolites they consume and by the byproducts they release ., Here , we use quantitative , extracellular metabolomic data of the NCI-60 panel and a novel computational method to generate 120 condition-specific cancer cell line metabolic models ., These condition-specific cancer models used distinct metabolic strategies to generate energy and cofactors ., The analysis of the models’ capability to deal with environmental perturbations revealed three oxotypes , differing in the range of allowable oxygen uptake rates ., Interestingly , models based on metabolomic profiles of melanoma cells were distinguished from other models through their low oxygen uptake rates , which were associated with a glycolytic phenotype ., A subset of the melanoma cell models required reductive carboxylation ., The analysis of protein and RNA expression levels from the Human Protein Atlas showed that IDH2 , which was an essential gene in the melanoma models , but not IDH1 protein , was detected in normal skin cell types and melanoma ., Moreover , the von Hippel-Lindau tumor suppressor ( VHL ) protein , whose loss is associated with non-hypoxic HIF-stabilization , reductive carboxylation , and promotion of glycolysis , was uniformly absent in melanoma ., Thus , the experimental data supported the predicted role of IDH2 and the absence of VHL protein supported the glycolytic and low oxygen phenotype predicted for melanoma ., Taken together , our approach of integrating extracellular metabolomic data with metabolic modeling and the combination of different network interrogation methods allowed insights into the metabolism of cells .

Altered metabolism is characteristic for many human diseases including cancer ., Disease progression and treatment efficacy vary between patients ., Hence , we need personalized approaches to define metabolic disease phenotypes ., This definition will enable us to unravel the underlying disease mechanisms and to treat individuals more efficiently ., Computational modeling increasingly supports the analysis of disease mechanisms and complex data sets ., The interpretation of extracellular metabolomic data sets is particularly promising since this data type is proximal to the actual metabolic phenotype altered in human diseases ., Moreover , it might enable us to directly interpret disease states from serum samples in the future ., Herein , we took a first step towards this ambitious goal ., We generated a large set of cancer metabolic models from extracellular metabolomic data and computationally stratified the models based on their metabolic characteristics into different phenotype groups ., Melanoma emerged as an interesting example of how our approach can provide insights into the intracellular metabolism from extracellular measurements ., Taken together , this work paves the way to generate condition-specific models from extracellular metabolomic data and demonstrates the many ways by which distinct phenotypes can be stratified and phenotype-specific intervention targets can be predicted .

cell physiology, medicine and health sciences, oxygen, cancers and neoplasms, cell metabolism, oncology, physiological processes, oxygen metabolism, metabolomics, metabolites, pharmacology, drug metabolism, melanomas, chemistry, pharmacokinetics, biochemistry, cell biology, physiology, secretion, biology and life sciences, physical sciences, metabolism, chemical elements

journal.pcbi.1004050

Angiogenic Activity of Breast Cancer Patients’ Monocytes Reverted by Combined Use of Systems Modeling and Experimental Approaches

Elucidating the various cell signaling cascades , pathway crosstalk , and how they influence final cell fate and behavior is crucial for defining therapeutic intervention points aimed at driving a cell towards a desired state ., To this end , modeling approaches can be used to perturb a biological system in silico to test hypotheses on a scale that would be unfeasible to test experimentally ., Boolean models have been extensively used in the past to simulate the behavior of cells based on their network activity 1 ., In a Boolean modeling approach , the nodes in a regulatory network represent the state of activation of a gene ( protein , receptor or ligand ) using discrete variables ( On or Off ) ., The state of the network at a given instant can change depending on the state of the other nodes and can ultimately stabilize into attractors of either a single state ( steady state ) or an oscillating set of states ( cycling attractors ) 2 ., Introducing perturbations in a biological regulatory network can change the attractors and even transition the system from one attractor to another one ., The Boolean steady state of the network has been shown to correspond to the cellular states for various regulatory networks in the past 3 ., Boolean modeling of steady state transitions helps in understanding the influence of perturbations on system wide behavior and has been used to identify the key molecular mechanisms controlling gene expression 4 , 5 , 6 and regulation 7 , 8 , cell differentiation 9 and signal transduction 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 ., Most of these models were developed in synergy by wet and dry laboratories ., However , to date , only few of them have reported experimental validations ( in primary cells ) of the proposed in silico predictions 12 , 15 , 16 ., In the present study we describe the application of a Boolean modeling based approach to investigate the molecular mechanisms underlying the angiogenic function of tumor monocytes from breast cancer patients and the experimental validation of in silico predictions derived from this modeling ., The formation of tumor-associated vasculature , a process also referred to as tumor angiogenesis , is essential for tumor progression ., Tumor vessels can form from local pre-existing capillaries ., This process is promoted by the recruitment of bone marrow-derived angiogenic cells ( i . e . mainly monocytes , dendritic cells and neutrophils ) at tumor sites 21 , 22 , 23 ., Clinical studies have demonstrated in a variety of human solid tumors a positive correlation between increased micro-vessel density , infiltration of tumor-associated macrophages ( TAM ) 24 and unfavorable prognosis in cancer patients 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 ., Recently , monocytes expressing the TIE-2/tek receptor tyrosine kinase ( TEM: TIE-2 expressing monocytes ) have been identified in peripheral blood and tumors of humans and mouse 34 , 35 ., In experimental mouse models , TEM recruited to tumors accounted for apparently all angiogenic activity of bone marrow-derived cells since their selective ablation fully suppressed angiogenesis and induced tumor regression 34 ., Hence , TEM appear to be key players in tumor angiogenesis but the tumor micro-environmental signals and the related signaling pathways governing their functions remain to be elucidated ., Of particular interest from a disease standpoint is how TEM can be directed away from potentiating tumor angiogenesis and progression to monocytes being immunologically potent cells ., The VEGFR-1 ( Vascular Endothelial Growth Factor Receptor-1 ) , TGFBR-1 ( Tumor Growth Factor β Receptor-1 ) , TNF-R1 ( Tumor Necrosis Factor Receptor-1 ) pathways have been reported to regulate tumor angiogenesis 36 , 37 , but their activities have not been examined in human TEM ., While we have previously reported that TIE-2 and VEGFR kinase activities drive immunosuppressive function of TEM in human breast Cancer 38 , in this study , we investigated the contribution of these pathways along with TGFBR-1 and TNF-R1 pathways to TEM pro-angiogenic activity ., We observed that the pro-angiogenic activity of TEM increased drastically from blood to tumor in breast cancer patients ., We constructed an integrative and predictive model of TEM behavior to predict in silico all minimal perturbations that can transition the highly pro-angiogenic phenotype of breast tumor TEM into a weak pro-angiogenic phenotype and vice versa ., By experimentally validating our computational predictions , we demonstrate here that the inferred regulatory network captured accurately patient TEM behavior ., Thus , the contribution of the computational approach was not only essential to predict and tune TEM pro-angiogenic activity but also to identify the key underlying components and pathways of their pro-angiogenic activity ., Finally , gene expression profiling of TEM transitioned to a weak pro-angiogenic phenotype confirmed that TEM infiltrating carcinoma of the breast remain plastic cells that can be reverted from pro-angiogenic and protumoral cells to immunological potent monocytes ., The angigoenic profile of TEM was investigated in a group of 40 newly diagnosed breast cancer patients ( Table 1 ) ., We characterized by flow cytometry the phenotype of TEM from patient peripheral blood and freshly dissociated tumor specimens obtained at time of surgery ( see Material and Methods ) ., Based on our immunostaining and flow cytometry protocol we observed that TEM did not constitute a distinct subset of monocytes ., In contrast , all monocytes showed expression of TIE-2 , which was particularly low in patient blood and substantially higher on monocytes isolated from tumor tissue ( S1 Fig . and Table 2 ) ., Thus , CD11b+ , CD14+ monocytes from patient blood and tumor tissue were referred to as “TEM” and compared with respect to receptor and cytokine expression ., However , tumor TEM co-expressed VEGFR-1 and TGFR-1 at significantly higher levels compared to peripheral blood TEM ( Table 2 ) ., We next assess the pro-angiogenic activity of TEM using the in vivo corneal vascularization assay 39 ., The cornea itself is avascular and was injected with TEM isolated from patient peripheral blood and tumor tissue ., Thus , any growth of new vessels from the peripheral limbal vasculature must be due to injected TEM and reflect their pro-angiogenic activity ., Tumor TEM showed a heterogeneous and consistently high pro-angiogenic activity inducing cornea and iris vascularization ., By contrast , blood TEM were unable to induce de novo vascularization of the cornea but did increase the pre-existing vascular network of the iris ( Fig . 1A ) ., Thus , tumor and blood TEM show distinct pro-angiogenic phenotypes with the expression levels of TIE-2 , VEGFR-1 and TGFR-1 mirroring their pro-angiogenic activity ( Fig . 1A and Table 2 ) ., Finally , secretions were profiled in the conditioned medium of patient-isolated TEM and revealed that tumor TEM are paracrine inducers of tumor angiogenesis by releasing high levels of angiogenic factors ( i . e . VEGF , bFGF , and ANG-1 ) and MMP9 ( matrix metalloproteinase 9 ) ( Fig . 1B ) ., Blood and tumor TEM display a mixed M1-like ( tumor-associated macrophages releasing inflammatory molecules ) and M2-like ( immunosuppressive macrophages polarized by anti-inflammatory molecules ) phenotype , with secretion of both the pro- and anti-inflammatory cytokines IL-12 and IL-10 , respectively ( Fig . 1B ) ., Given that TEM circulating in the blood infiltrate tumor tissue where they further differentiate 34 , our data suggest that the tumor microenvironment shapes their highly pro-angiogenic phenotype ., The identification of the ligands and the pathways controlling the highly pro-angiogenic activity of tumor TEM is of paramount significance because it represents the rationale for a treatment directing TEM away from being cells supporting tumor growth ., The strategy we selected to reach this goal combined computational and experimental approaches to simulate and predict the behavior of patient TEM subjected to various ligand combinations ., Given the limited amounts of patient specimens and the low frequency of TEM ( TEM represented 6 . 7% ±2 . 5% of peripheral blood mononuclear cells and 22% ±2 . 7% of the tumor hematopoietic infiltrate ) , only a limited number of ligand combinations could be investigated experimentally ., The availability of limited amounts of patient TEM was partially overcome by taking advantage of our recently developed model system of TEM differentiated in vitro by exposing CD34+ cord blood hematopoietic progenitors to breast cancer cell conditioned culture medium 38 , 40 ., In vitro differentiated TEM ( thereafter named ivdTEM ) are angiogenic 38 , 40 and display an intermediate phenotype relative to blood and tumor TEM ( Table 2 ) ., Consistent with their phenotype ( Table 2 ) , ivdTEM released intermediate amounts of angiogenic factors relative to blood and tumor TEM ( Table 3 ) ., Moreover , the in silico modeling and predictions helped us to focus on the most clinically relevant monocytic ligands and to spare precious patient specimen ., The workflow of our approach consists of five steps ( Fig . 1C ) : 1 ) experimental measurement of the responses of TEM differentiated in vitro to a set of ligands , 2 ) construction of a dynamic regulatory network based on these experimental data , 3 ) in silico prediction of the treatments altering TEM behavior , 4 ) experimental validation of computationally predicted treatments using ivdTEM and 5 ) validation the best predicted treatments in patient TEM ( Fig . 1C ) ., Finally , to help shed light on possible molecular mechanisms underlying TEM pro-angiogenic transformation , we selected several treatment combinations and measured genome wide expression profiles for the TEM differentiated in vitro , comparing the state of the cells before and after treatment ., Our strategy was to expose TEM to several treatments to identify the ligands and pathways critically controlling their pro-angiogenic activity ., TEM differentiated in vitro were exposed to angiogenic factors ( VEGF , PlGF and ANG-1 , ANG-2 which are the ligands of VEGFR-1 and TIE-2 respectively ) in combination with either TGF-β or TNF-α and the changes in their phenotype , angiogenic activity and paracrine secretion profile were examined ., These experimental results were used as the foundations for a computational model that would allow predicting treatments increasing or dampening TEM proangiogenic activity ., First , changes in TEM phenotype were evaluated by flow cytometry 36h post treatment ., Globally , treatments combined with TGF-β or TNF-α displayed a stronger impact on TEM phenotype than single treatments with however , the exception of TGF-β ., Overall , CD11b , CD14 , VEGFR-1 and TIE-2 expression displayed larger changes in response to treatment than CCR5 , TNF-R1 and TGFBR-1 ( Fig . 2A ) ., A hallmark of TGF-β treatments was a strong decrease in VEGFR-1 and CD11b expression and an increase in TIE-2 expression ( Fig . 2A ) ., By contrast , TNF-α treatments had no impact on VEGFR-1 expression and TNF-α increased TIE-2 expression when combined with PlGF or ANG-2 ( Fig . 2A ) ., We assessed the impact of the treatments on the pro-angiogenic activity of TEM using in vitro HUVEC ( Human Umbilical Vascular Endothelial Cells ) sprouting assay ( see Methods ) ., Treated TEM were applied to HUVEC grown on microcarrier beads and embedded in a fibrin gel to measure their aptitude to induce HUVEC sprouting i . e . the initial step of blood vessel formation ., Single treatments show no significant impact on TEM proangiogenic activity relative to untreated cells with the exception of TGF-β which significantly reduced TEM pro-angiogenic activity ( Fig . 2B ) ., Interestingly , combining TGF-β with PlGF further decreased VEGFR-1 expression ( Fig . 2A ) and TEM proangiogenic activity ( Fig . 2B ) suggesting that TGF-β synergized with PlGF to reduce TEM proangiogenic activity ., We examined the impact of combined treatments on TEM using in vivo corneal vascularization assay ., Indeed , in vitro sprouting assay was preferred for quantification but is however less reliable because it does not recapitulate the intricate balance of signals from growth factors , mural cells and extracellular matrix of in vivo angiogenesis ., TNF-α in combination with ANG-2 ( or PlGF ) significantly increased TIE-2 expression whilst leaving VEGFR-1 expression unchanged ( Fig . 2A ) , and raised TEM pro-angiogenic activity ( Fig . 2C . Cornea and iris vascularization in AU: control: 1; untreated: 1 . 81; TNF-α+Ang-2: 4 . 58 ) ., Conversely , TGF-β in combination with VEGF resulted in a comparable induction of TIE-2 but decreased VEGFR-1 expression ( Fig . 2A ) , and reduced TEM pro-angiogenic activity ( Fig . 2C . Cornea and iris vascularization in AU: TGF-β+VEGF: 1 . 36 ) ., Taken together these results show , for the first time , that both Tie2 and VEGFR1 pathways control TEM pro-angiogenic activity ., Furthermore , TIE-2 and VEGFR1 pathways synergized with the TNF and TGF pathway to induce and reduce TEM pro-angiogenic activity respectively ., Finally , we examined the impact of the different ligand treatments on TEM secretions ., Thus , cumulated TEM secretions from ivdTEM were measured experimentally and the secretions for TEM were mathematically inferred ( ivdTEM correspond to double positive DP cell population , see Materials and Methods and S2 Fig . ) and display in Fig . 2D ., Of note , none of the single or double treatments we have examined experimentally ( Fig . 2D ) shifted completely the paracrine secretion profile of TEM differentiated in vitro toward that of blood or tumor TEM ( compare Fig . 2D and 1B ) ., These results suggest that transitioning ivdTEM into blood or tumor TEM requires a model to simulate computationally the impact of a larger number of ligand combinations on TEM behavior ., The limited amounts of patient TEM and the combinatorial nature of the ligands precluded experimental testing of all the ligand combinations and was the rationale for building an integrative and predictive model of TEM behavior ., We used TEM differentiated in vitro to derive a dynamical regulatory network from experimental data obtained with a selected number of ligands ( Fig . 2 ) and used then as a proxy to assess the clinically most relevant ligand combinations ., To create the models , data sets of receptor expression ( Fig . 2 and S2 Table ) and paracrine secretion profiles ( Fig . 2 and S3 Table ) were combined to infer relevant relationships ( or links ) between ligands and receptors ., Briefly , relevant links were identified based on the amplitude of their expression or secretion changes , their reproducibility , and their coherent variations across the treatments ( see Methods ) ., Based on these criteria , amongst 924 possible links ( 7 receptors × 11 secreted factors × 12 treatments ) we retained 74 relevant links ( S4 Table ) ., Globally , TNF-α , TGF-β and PlGF appeared as key regulators of TEM network ., However , TNF-α in contrast to TGF-β , was strongly regulated by other factors ( Fig . 3 ) ., Dynamical Boolean modeling was then performed by integrating the retained links into an algorithm for computing Minimal Intervention Set ( MIS ) of TEM regulatory network ., Given a regulatory network , MIS patterns represent a set of simultaneous perturbations ( or treatments ) to force the network into a desired steady state , where a subset of nodes remain at a fixed expression level of either low or high 41 , 42 ., The term minimal implies that no other sub-set of an MIS pattern can lead to the desired steady state behavior ., However , for a given network , there can be more than one MIS patterns to generate the same steady state ., The MIS algorithm proposed by Garg et al 43 , 44 was used for assessing TEM regulatory network by computationally predicting all possible set of up to three simultaneous treatments that can force the TEM network into a weakly ( i . e . blood TEM ) or highly ( i . e . tumor TEM ) pro-angiogenic phenotype ., Relative to their blood counterparts , tumor TEM display a higher pro-angiogenic activity ( Fig . 1A ) , a paracrine profile shifted toward angiogenesis ( Fig . 1B ) and higher levels of, TIE-2 and VEGFR-1 ( Table 2 ) ., Therefore blood and tumor TEM can be viewed as two distinct cell steady state behaviors and ivdTEM as an intermediate state ( Tables 2 and 3 ) ., Using the regulatory network model of TEM differentiated in vitro we predicted the minimal treatments required for transitioning tumor TEM to blood TEM and vice versa ., Because the expression levels of TIE-2 and VEGFR-1 controlled ( Fig . 2 ) and mirrored ( Fig . 1B ) TEM pro-angiogenic activity , we assigned to TIE-2 and VEGFR-1 nodes a fixed polarity of either both over-expressed or down-modulated for highly pro-angiogenic ( i . e . tumor TEM ) or weakly pro-angiogenic ( i . e . blood TEM ) steady states respectively ., Computationally predicted minimal perturbations sets ( MIS ) are reported in Table 4 ., It is interesting to note that all the predicted treatments were composed of at least two , and mostly three simultaneous perturbations ., Only one treatment , combining three perturbations , was predicted by the model to promote TEM pro-angiogenic activity ( TNF-α , ANG-2 and PlGF; Table 4 ) ., Conversely , eleven distinct treatments were predicted to dampen TEM proangiogenic activity and resulted in three main groups ( Table 4 ) ., The first group of treatments combined TIE-2 tyrosine kinase inhibitor with TGF-β and a ligand of VEGFR-1 or TIE-2 ., Treatments from the second group involved VEGFR-1 kinase inhibitor , and the third group of treatments associated TGF-β with TNF-α and a ligand of TIE-2 or VEGFR-1 ( Table 4 ) ., It is worth noting here that we assumed that possible compensatory mechanisms resulting from the blocking of the receptor signaling ( rather than knocking down the receptor ) do not significantly affect the angiogenic activity ., Results showed that this assumption was valid for the particular case of the receptors under study ., With currently available tools , VEGFR-1 kinase activity is almost impossible to manipulate ., Indeed , to date , all available VEGFR-1 kinase inhibitors also inhibit VEGFR2 and VEGFR3 to a lesser extent , thus preventing experimental validation of any treatment of the second group ., For experimental validations , we therefore selected the combined TNF-α/ANG-2/PlGF treatment , predicted to promote angiogenesis , two treatments of the first group ( TIE-2inhibitor/TGF-β/PlGF , and TIE-2 inhibitor/TGF-β/ANG-2 ) , one treatment of the third group ( PlGF/TGF-β/TNF-α ) and a TIE-2 kinase inhibitor alone ., These experimental validations were first conducted in TEM differentiated in vitro ., As predicted , the TNF-α/ANG-2/PlGF combined treatment induced TIE-2 and VEGFR-1 expression ( Fig . 4A ) and increased their proangiogenic activity ( Fig . 4B ) ., Importantly , this combined treatment induced TIE-2 and VEGFR-1 expression and TEM pro-angiogenic activity more efficiently than PlGF/TNF-α ( Fig . 4A ) and PlGF or ANG-2 single treatments ( Fig . 2A and B ) ., These results validate our in silico prediction and reveal the synergistic effect of TIE-2 , VEGFR-1 and TNF-α pathways in controlling TEM pro-angiogenic activity ., The predicted inhibitory effect of the other treatments was assessed on TEM pre-treated with, TNF-α/ANG-2/PlGF , which display an increased pro-angiogenic phenotype compared to untreated cells ( Fig . 4A and B ) ., This pre-treatment increased the dynamic range and therefore the sensitivity of detecting inhibitory effects ., TIE-2 kinase inhibitor/TGF-β combined with ANG-2 or PlGF significantly decreased TIE-2 and VEGFR-1 receptor expression ( Fig . 4A ) consistently reduced their pro-angiogenic activity ( Fig . 4B ) ., The PlGF/TGF-β/TNF-α treatment was not as effective , but still reduced their pro-angiogenic activity ., These combined treatments were synergistic and minimal since TIE-2 kinase inhibitor ( Fig . 4A and B ) or single treatments alone ( Fig . 2A and 2B ) or double treatments ( PLGF/TGF-β or TIE-2inhibitor/TGF-β display on Fig . 2B and Fig . 4B , respectively ) were not sufficient to decrease VEGFR-1 expression and TEM pro-angiogenic activity ., By contrast , PlGF/TGF-β/TNF-α heterogeneously decreased TIE-2 and VEGFR-1 expression ( Fig . 4A ) and TEM pro-angiogenic activity ( Fig . 4B ) ., In summary , from these validation experiments we found that the best computationally predicted treatment promoting TEM pro-angiogenic activity was TNF-α/ANG-2/PlGF and the best dampening activity was found using TIE-2 kinase inhibitor/TGF-β associated with a ligand of TIE-2 or VEGFR-1 ., Having identified the critical ligands and pathways controlling TEM plasticity , we next examined in TEM differentiated in vitro whether differential gene expression might also contribute to the molecular basis of TEM plastic behavior ., This analysis may shed light on the molecular mechanisms underlying the observed TEM responses ., To this end , we selected VEGF/TNF-α , ANG-2/TGF-β and PlGF/TGF-β treatments for gene expression profiling using Affimetrix whole genome microarrays , because these treatments were present in 17 , 16 and 14 , respectively of the 74 links ( treatment/receptor/cytokine ) retained in TEM regulatory network ( S4 Table and Fig . 3 ) ., All the other treatments occurred less frequently ., Hierarchical clustering demonstrated that TGF-β-based treatments ( ANG-2/TGF-β and PlGF/TGF-β ) clustered separately from VEGF/TNF-α and control treatments ., A total of 398 genes were significantly ( p<0 . 05 ) and differentially expressed between the two clusters among which 369 and 72 genes were altered by TGF-β/ANG-2 and TGF-β/PlGF treatments respectively ( S5 Table , NT unique lists ) while 43 were regulated in common ( S5 Table , NT intersect list ) ., Enrichment analyses of the gene expression data against known pathways and functional gene categories were conducted as described in Materials and Methods ., No enrichment of specific pathways of interest was observed due to the fact that the gene annotations were too general and did not correspond to specific functions of monocytes ., Therefore , the 398 differentially expressed genes were annotated and classified in categories manually ( S5 Table ) ., Similar expression profiles were obtained for untreated and TNF-α/VEGF treated cells consistent with their weak impact on TEM functional angiogenic phenotype ( Fig . 2 ) ., By contrast , ANG-2/TGF-β and PlGF/TGF-β treatments inhibited TEM pro-angiogenic activity ( Fig . 2 ) and down-modulated the expression of pro-angiogenic genes ( Fig . 5 and S5 Table ) ., Furthermore and interestingly , the expression of VASH1 ( vasohibin 1 ) and UCN ( urocortin ) genes coding for anti-angiogenic proteins was simultaneously up-regulated ( Fig . 5 and S5 Table ) ., In response to both ANG-2/TGF-β and PlGF/TGF-β treatments , 95% of the genes functionally related to the cell cycle displayed a down-modulated expression indicating that TEM stopped proliferating with profound changes in their metabolism but without , however undergoing apoptosis ( the expression of metabolism and apoptosis related genes was down-modulated for 76% and 88% of them respectively ) ., TEM treated with TGF-β/ANG-2 or TGF-β/PlGF show the expression of some genes ( P2RY12 , TMCC3 , NPDC1 , IFFO1 , UBASH3B , C11orf52 , SLC4A7 , TMEM87A , NPL , EMB , PCNA , DNA2 , TMEM86A , MMP12 , CTSD , AXL , RASGRP3 , TUBB , FCGR1A , CR1 , MX2 ) previously ascribed to mouse TAM 45 , 46 , 47 ., However , in response to ANG-2/TGF-β and PlGF/TGF-β treatments , TEM down-modulated the expression of genes involved in macrophage differentiation ( Fig . 5 ) and started to acquire the profile ( RGS1 , CXCL11 , CXCL9 , STAT1 , IFIH1 , ISG20 , NT5C3 , ADC , PDGFRL , TNF-ASF12 , IFIT5 , RGS10 , TRAF3IP3 , CIDEB , APOBEC3A , PYGL , RRM1 , MAF , NLRC4 , IL10 , MYC , DUT , POLE4 , CXCL17 ) of dendritic cells matured in vitro by exposure to lipopolysaccharide and interferon-gamma 48 , 49 ., Along these lines , genes encoding for dendritic cell markers , antigen processing and adaptive immune response were upregulated while genes involved in immune suppression show markedly decreased expression ( Fig . 5 and 38 ) ., Finally the expression of genes related to adhesion and migration were up- and down-regulated respectively indicating that TEM mobility was strongly reduced; an observation consistent with the arrest of their cell cycle and the alteration of their differentiation program ( Fig . 5 and S5 Table ) ., Along these lines , we observed experimentally that ivdTEM treated with PlGF/TGF-β/TIE-2i display reduced mobility towards the human epithelial tumor cell line MDA-231 ( S3A Fig . ) and slowed down the growth of MDA-231 cells ( S3B Fig . ) Taken together , our results suggest that ANG-2/TGF-β and PlGF/TGF-β treatments are not only anti-angiogenic but also shift the gene expression profile of monocytes toward the one of cells promoting immune surveillance , thereby limiting tumor growth ., We next sought to validate the computationally predicted treatments in TEM isolated from patient breast carcinoma ., Tumor TEM were exposed to TIE-2 kinase inhibitor combined with TGF-β and simultaneously engaged their VEGFR-1 using VEGF ( alternatively PlGF , Table 4 and Fig . 4 ) ., This combined treatment strongly reduced the pro-angiogenic activity of tumor TEM in the mouse cornea vascularization assay ( Fig . 6A and B ) and decreased the expression of TIE-2 and VEGFR-1 ( Fig . 6D ) ., Furthermore , this treatment reduced the secretion of IL-6 , IL-8 , MMP9 , bFGF and VEGF , consistent with a paracrine profile shifted toward a M1-like phenotype and closer to the one of blood TEM ( Fig . 6C and 1B ) ., Conversely , TEM from patient blood exposed to the combined treatment of TNF-α/PlGF/ANG-2 increased their pro-angiogenic activity in the mouse cornea vascularization assay ( Fig . 6B ) and was associated with significantly higher secretion of IL-1β , IL-6 , IL-10 , MMP9 and VEGF ( Fig . 6C ) and increased expression of TIE-2 and VEGFR1 ( Fig . 6D ) ., These results highlighted the validity of our combined experimental and computational approach to revert the pro-angiogenic phenotype of TEM and revealed , for the first time , that tumor TEM remain plastic cells representing attractive targets for anti-angiogenic therapies ., We addressed the question whether or not the expression levels of ANG-2 and PIGF when considering the overall breast tumor have impact on the survival ( considered here as relapse free survival ) ., To this end we analyzed a dataset including tumor expression profiles and clinical data of 1809 breast cancer patients 50 and compared two subsets of patients: those with lowest and highest expression values for ANG-2 , PIGF and CD14 ( as TEM marker ) , using as threshold the first and fourth quartile respectively ., These quartiles were computed independently for each gene , and the two groups of selected patients resulted from the intersection of them all ( Fig . 7B-F ) ., The Kaplan-Meier plot showed a clear separation between patients with low ( n = 40 ) and high ( n = 62 ) expression for these three genes , with a p-value of 0 . 0257 derived from log-rank analysis ( Fig . 7B and F ) ., Interestingly , we observed that the same analysis repeated for patients with high and low levels of ANG-2 and CD14 or PIGF and CD14 ( and not for the remaining gene ) resulted on p-values not statistically significant ( 0 . 0587 and 0 . 521 respectively , Fig . 7C-E ) , suggesting that the synergistic effect of the corresponding pathways is required to have a significant impact on the survival ., These results suggest that TEM infiltrating a tumor microenvironment enriched in Ang-2 and PlGF , which synergistically trigger TEM angiogenic activity through Tie-2 and VEGFR-1 ( Fig . 4 and 5 ) , may contribute to a worse patient survival ., Further , tumor size correlated positively with the amounts of PlGF and Ang-2 content in the tumor microenvironment ( Fig . 7A , P<0 . 01 ) while no significant correlation was observed with VEGF , Ang-1 , MCP-1 , SDF-1 , TGF-α and TNF-β ., Moreover , we measured by reverse phase protein arrays that in tumors the extent of TEM infiltration was significantly and linearly correlated with PLGF content ( Fig . 7A ) thus highlighting that Tie-2 and VEGFR-1 axes , as well as their cognate angiogenic TEM ligands Ang-2 and PlGF represent attractive therapeutic targets in breast cancer ., The key relevance of this study is a comprehensive understanding of the behavior of TEM in breast tumor vascularization ., This goal was achieved by constructing an integrative and predictive model of TEM behavior based on experimental data ., This model was interrogated to identify combined treatments that would alter TEM pro-angiogenic activity ., Quite remarkably , four of the five predicted combined treatments that we validated experimentally proved to be extremely efficient at inhibiting or promoting tumor TEM proangiogenic activity , demonstrating the robustness of our model ., Furthermore , this study demonstrates that the synergistic effect of these treatments relies on crosstalk between TNF-R1 , VEGFR-1 , TGF-β and TIE-2 pathways resulting in altered angiogenic activity ( Figs . 2 , 4 and 5 ) , modulated expression of angiogenic receptors ( Fig . 4A ) and shifted paracrine profile ( Fig . 6C ) ., Taken together , our results highlight crosstalks between TIE-2 , VEGFR-1 , TGF-β and TNF-α pathways of outstanding importance to promote ( TNF-α/ANG-2/PlGF ) or abrogate ( TGF-β/TIE-2 inhibitor/VGFR1 or TIE-2 ligand ) patient TEM pro-angiogenic activity ., Another contribution of this study is an effective approach to model relatively sparse data from distinct individuals ( newborns and patients ) , who are inherently heterogeneous in nature ., This challenge was overcome by a sustained and tight collaboration between the experts in the fields of computational and experimental sciences throughout all steps of the workflow ( Fig . 1C ) ., By setting up a rigorous experimental design we identified coherent variations and links across biological replicates and data sets , which provided a robust basis to reconstruct the TEM signaling network ( Fig . 3 ) ., Furthermore , the modeling framework was an integral part of our experimental strategy , enabling the model predictions to address the biological questions , an issue that is of particular importance in systems biology 1 , 51 , 52 ., In a traditional approach , it would have been unfeasible to experimentally test the complete set of up to three simultaneous perturbations using 12 distinct ligands , which would have led to 596 ligand combinations ., The physiologically relevant combinations of ligands were discovered by applying the recently proposed MIS algorithm 43 , 44 to predict all minimal perturbations in the inferred regulatory network that can transition TEM into desired steady states ( Table 4 ) ., The in silico minimal perturbations predicted by applying the MIS algorithm on the inferred ivdTEM regulatory network comprised not only a handful of the set of perturbations ( or ligand combinations ) and they were all shown to be experimentally valid when tested on ivdTEM and patient TEM ( Figs . 4 and 5 ) ., The in silico prediction algorithm helped us to focus on the most clinically relevant monocytic ligands and to unravel treatments abrogating TEM pro-angiogenic activity at breast tumor sites ., These results highlight the importance of mutual relationship between experimental and computational sciences ., Furthermore , the combined computational and experimental approach followed in this study may provide a general strategy to study the behavior of limited cell subsets from patients in cancer and other diseases ., The main outcome of this modeling strategy for experimental and clinical oncology is the validation of treatments abrogating tumor TEM pro-angiogenic activity and thus simultaneously revealing their functional plasticity

Introduction, Results, Discussion, Materials and Methods

Angiogenesis plays a key role in tumor growth and cancer progression ., TIE-2-expressing monocytes ( TEM ) have been reported to critically account for tumor vascularization and growth in mouse tumor experimental models , but the molecular basis of their pro-angiogenic activity are largely unknown ., Moreover , differences in the pro-angiogenic activity between blood circulating and tumor infiltrated TEM in human patients has not been established to date , hindering the identification of specific targets for therapeutic intervention ., In this work , we investigated these differences and the phenotypic reversal of breast tumor pro-angiogenic TEM to a weak pro-angiogenic phenotype by combining Boolean modelling and experimental approaches ., Firstly , we show that in breast cancer patients the pro-angiogenic activity of TEM increased drastically from blood to tumor , suggesting that the tumor microenvironment shapes the highly pro-angiogenic phenotype of TEM ., Secondly , we predicted in silico all minimal perturbations transitioning the highly pro-angiogenic phenotype of tumor TEM to the weak pro-angiogenic phenotype of blood TEM and vice versa ., In silico predicted perturbations were validated experimentally using patient TEM ., In addition , gene expression profiling of TEM transitioned to a weak pro-angiogenic phenotype confirmed that TEM are plastic cells and can be reverted to immunological potent monocytes ., Finally , the relapse-free survival analysis showed a statistically significant difference between patients with tumors with high and low expression values for genes encoding transitioning proteins detected in silico and validated on patient TEM ., In conclusion , the inferred TEM regulatory network accurately captured experimental TEM behavior and highlighted crosstalk between specific angiogenic and inflammatory signaling pathways of outstanding importance to control their pro-angiogenic activity ., Results showed the successful in vitro reversion of such an activity by perturbation of in silico predicted target genes in tumor derived TEM , and indicated that targeting tumor TEM plasticity may constitute a novel valid therapeutic strategy in breast cancer .

Tumor vascularization is essential for tumor growth and cancer progression ., In breast cancer , monocytes are angiogenic , i . e . able to induce tumor vascularization ., In patients , blood circulating monocytes drastically increase their angiogenic activity when reaching the tumor , suggesting that the tumor microenvironment shapes their angiogenic activity ., The identification of the tumor signals inducing the angiogenic activity of monocyte is of paramount significance because it represents the rationale for anti-angiogenic therapies in breast cancer ., This goal was achieved by constructing an integrative model of monocyte behavior based on experimental data ., The model predicted treatments abrogating the angiogenic activity of monocytes , which were experimentally validated in monocytes isolated from patient breast carcinoma ., Importantly , these treatments reverted angiogenic monocytes into immunological potent cells ., The main outcome of this modeling strategy for experimental and clinical oncology is the identification of effective treatments abrogating the angiogenic activity of monocytes and thus simultaneously revealing their functional plasticity .

journal.pbio.1002097

Spontaneous Cdc42 Polarization Independent of GDI-Mediated Extraction and Actin-Based Trafficking

Cell polarization is an evolutionary , ancient cellular property that , in eukaryotes , centers around the Rho-family GTPase Cdc42 ., Cdc42 , which cycles between active GTP-bound and inactive GDP-bound forms , is locally activated by Guanine nucleotide Exchange Factors ( GEFs ) and accumulates at presumptive sites of polarity ., Active Cdc42 then promotes the activation of numerous effectors , including p21-activated kinases ( PAK ) , nucleators of actin cytoskeleton assembly , and the exocyst complex for polarized exocytosis 1–3 ., Collectively , these pathways transduce the location of Cdc42 activity into effective cell polarization , which underlies essential processes such as proliferation , migration , and signal transduction ., Consistent with its central role , the misregulation of Cdc42 and other Rho-GTPases has been implicated in multiple human conditions , such as congenital diseases or infection 4 ., Thus , one critical question is , “What are the mechanisms that promote the local activation and accumulation of Cdc42 ? ”, Rho-family GTPases are associated with cellular membranes ., The vast majority , including Cdc42 , carries a C-terminal CAAX box , which serves as signal for prenylation on the cysteine residue and insertion in the endoplasmic reticulum membrane 5 ., From there , the Rho proteins can be distributed through the trafficking system up to the plasma membrane , where Cdc42 localizes ., Rho-family GTPases , including Cdc42 , can also be extracted from membranes by so-called Guanine nucleotide Dissociation Inhibitors ( GDIs ) , which shield the prenyl group and keep the Rho protein in a soluble cytosolic form 6 ., While local activation of Cdc42 may depend on the trivial presence of a pre-localized activator , groundbreaking work in the budding yeast has shown that Cdc42 displays the ability to polarize spontaneously , whereby both its active form and the total protein pool become dynamically polarized , even in absence of pre-established landmarks ., Spontaneous polarization—also known as symmetry breaking—is observed in many cell types when the spatial cues normally directing cell polarization , such as external chemo-attractants or internal landmarks , are absent 7 , 8 ., It also occurs naturally , for instance , in germinating yeast spores , which establish polarization in absence of any known pre-localized landmarks 9 ., Spontaneous polarization relies primarily on positive feedback mechanisms that amplify initial stochastic noise into robust polarization 7 , 8 ., One such feedback mechanism involves the formation of a protein complex between a Cdc42-GTP-binding effector—a PAK—and a Cdc42 GEF , which propagates Cdc42 activation around clusters of Cdc42 activity 10–13 ., However , this autocatalytic self-amplifying system does not by itself explain accumulation of Cdc42 at the site of activity , which is thought to arise from coupling to dynamic recycling of Cdc42 ., Two modes of Cdc42 recycling from and to the plasma membrane , with distinct dynamic properties , have been proposed ., The first slow mode relies on Cdc42 trafficking on vesicles through endo- and exocytosis 14–18 ., As Cdc42 promotes the assembly of actin cables , which serve as tracks for the delivery of secretory vesicles , this , in principle , constitutes a mechanism by which to enrich Cdc42 at sites of Cdc42 activity 19 , 20 ., However , the low concentration of Cdc42 on secretory vesicles has raised debate about whether this feedback would indeed reinforce Cdc42 polarization 18 , 21 , 22 ., The second fast recycling mode depends on GDI-mediated extraction of Cdc42-GDP 23–25 ., As the GDI interaction can be competed out by the GEF , this may also enhance Cdc42 delivery and membrane re-insertion at sites of GEF localization 23 , 24 ., Because simultaneous block of vesicle secretion and GDI deletion leads to loss of GFP-Cdc42 polarization 23 , 25 , the current view is that spontaneous polarization of Cdc42 requires these two recycling routes ., However , it is important to note that the N-terminally tagged GFP-Cdc42 fusion used in all dynamic studies to date is not fully functional ( see below; 18 , 23 , 26 , 27 ., In addition to positive feedbacks , the existence of negative feedback and competition mechanisms has been revealed by the oscillatory behavior of polarity patch components 26 , 28 ., These oscillations were observed in both budding and fission yeasts , with a distinct outcome: whereas these oscillations resolve into a single patch at the prospective bud site in the former , they persist throughout bipolar extension at cell poles in the latter 29 ., In fission yeast , bipolar growth occurs after passage through S-phase and requires the Tea1-Tea4 landmark complex , which is deposited at cell poles by microtubules and feeds into the Cdc42 activation cycle 30 , 31 ., The presence of oscillations in both organisms , as well as the existence of mutant conditions allowing the rare formation of two simultaneous buds in Saccharomyces cerevisiae , suggests that the transition from one to several polarization sites may be an intrinsic , modular property of the Cdc42 spontaneous polarization system 24 , 26 , 32–35 , though the specific mechanisms remain unclear ., Here we describe a functional , fluorescently tagged Cdc42 allele in fission yeast , which reveals that Cdc42 is mobile at the plasma membrane independently of GDI and vesicle trafficking ., Engineered Cdc42 alleles targeted to the plasma membrane in a prenylation-independent manner demonstrate that Cdc42 spontaneously polarizes , often to multiple sites , independently of these recycling pathways in both fission and budding yeast cells ., Our work further reveals that inactive Cdc42 displays fast lateral diffusion and slows down and accumulates as a consequence of its local activation ., To better understand the mechanisms underlying Cdc42 dynamics in live cells , we set out to construct a functional fluorescent fusion protein in fission yeast ., Existing fusions in various organisms fuse fluorescent proteins to Cdc42 N-terminus , as the C-terminus is subject to post-translational modification by prenylation ( Fig . 1A ) ., However , when this has been tested by gene replacement , these N-terminal fusions compromise Cdc42’s functions , yielding temperature-sensitivity and failure to associate with post-Golgi vesicles in S . cerevisiae 18 , 23 , 26 or altered cell morphology in Schizosaccharomyces pombe 27 ., Indeed , the GFP-cdc42 strain displayed slow growth and aberrant morphology at all tested temperatures ( see Fig . 1D–F ) ., We chose the alternate approach of placing a fluorescent protein gene within the cdc42 reading frame , a strategy previously tried with success on other proteins 36 , 37 ., Potentially permissive sites for fluorophore insertion were determined by examining Cdc42 crystal structure and looking for solvent-exposed poorly-conserved external loops distant from the switch regions and α2 helix that mediate the interface of most known interactors ( Fig . 1A ) 38 ., The α3′ helix fit these criteria well and the linker-SGGSACSGPPG- was inserted following amino acid Q134 ., Expression of Cdc42-Q134-linker from a plasmid complemented the cdc42-1625 temperature sensitive mutant at restrictive temperature ( Fig . 1B ) ., Insertion of either GFP or mCherry at this site , generating sandwich fusions Cdc42-GFPSW or Cdc42-mCherrySW , likewise complemented the cdc42-1625 mutant ( Fig . 1B ) ., We next engineered strains expressing the sandwich fusions as the sole source of Cdc42 from its native genomic promoter ., Replacement of cdc42 in diploid cells followed by germination of haploid spores yielded colonies of equal size ( Fig . 1C ) ., Proper integration in the genome was confirmed by diagnostic PCR and Southern blotting ( S1A–C Fig ) ., Remarkably , cells expressing Cdc42-mCherrySW or Cdc42-sfGFPSW ( superfolder-GFP ) showed growth rate , cell width and length at division , and division plane positioning indistinguishable from wild type , even at temperatures up to 36°C ( Fig . 1D–F , S1D Fig ) ., We note that Cdc42-GFPSW was deficient at elevated temperatures ( Fig . 1F ) , suggesting that the slow rate of GFP folding slightly impairs the functionality of the fusion protein 39 , 40 ., We thus used Cdc42-mCherrySW or Cdc42-sfGFPSW in all subsequent experiments ., Cdc42-mCherrySW was enriched at the cell tips and division sites ( Fig . 1G ) ., Significant levels of Cdc42-mCherrySW were also found along the cell sides and on internal membranes including the nuclear and presumably vacuolar membranes ., Cdc42-sfGFPSW showed similar localization ( S1E Fig ) ., In cells depleted of the exocyst member Sec8 , in which exocytic vesicles accumulate but fail to fuse at the cell tip 41 , 42 , Cdc42-mCherrySW accumulated sub-apically , confirming that Cdc42 is trafficked on exocytic vesicles ( Fig . 1G ) ., Finally , we found no synthetic interactions with any of the mutants used below ., Thus , at least during mitotic growth , the mCherry and sfGFP sandwich constructs appear to be functional fusions to Cdc42 ., To quantitatively define the distribution and activity of Cdc42 at the plasma membrane , we co-imaged Cdc42-mCherrySW with CRIB-3GFP , a probe that selectively binds active Cdc42-GTP ( CRIB stands for Cdc42- and Rac-interactive binding domain ) 43 ., The distribution of CRIB closely mirrored that of Cdc42 enrichment at cell poles ( Fig . 2A ) ., At cell poles with strong CRIB localization , Cdc42 was enriched 3-fold ( ± 0 . 7 , n = 40 ) over its levels at cell side ( Fig . 2B ) ., At cell poles with low CRIB levels , these correlated with low Cdc42 enrichment ( S2 Fig ) ., Normalization of the Cdc42 and CRIB distribution profiles to their maximum and minimum yielded overlapping curves with identical decay rates , suggesting a very tight correlation between enrichment of Cdc42 and the active form ( Fig . 2B right ) ., Examination of Cdc42-mCherrySW and CRIB-3GFP in a panel of mutants shown or predicted to regulate Cdc42 activity further strengthened this correlation ( Fig . 2C ) ., Indeed , Cdc42 tip enrichment strongly correlated with CRIB tip enrichment across all mutants examined ( linear regression r2 = 0 . 96; Fig . 2D–E ) ., Two mutants—orb2-34 , a largely inactive allele of the PAK kinase Shk1/Pak1 proposed to act in a negative feedback to inhibit Cdc42 activity 28 , and deletion of the Tea4 landmark 44 , 45—showed higher average Cdc42 activity and enrichment at their single growing cell tip ., Deletion of the Cdc42 GEF Scd1 46 showed dramatic loss of Cdc42 local activity and enrichment ., By contrast , Cdc42 activity and enrichment were not as severely affected by deletion of the putative scaffold Scd2 , which forms a complex with Scd1 and Cdc42 46 ., Finally , deletion of the second Cdc42 GEF Gef1 47 , 48 , or of the only predicted GDI Rdi1 49 , had no or minor effect on Cdc42 local activity and enrichment ., In summary , these data indicate that the accumulation of Cdc42 at growing cell poles represents the active form ., We used fluorescence recovery after photobleaching ( FRAP ) experiments to measure the mobility of Cdc42 at the plasma membrane ., Using an identical 0 . 9 μm bleach spot in all experiments , we found that Cdc42-mCherrySW fluorescence recovers significantly faster at cell sides than cell poles , with recovery halftimes of 1 . 0 ± 0 . 3 versus 4 . 6 ± 2 . 0 s−1 , respectively ( Fig . 3A , B ) ., Thus , Cdc42 is highly mobile at the plasma membrane , but significantly slower at cell tips ( Student’s t test , p = 3 . 5 x 10−5 ) ., We considered whether cell geometry may cause this difference by examining Cdc42 mobility at cell tips lacking Cdc42 activity or at sites of activity on cell sides ., Cdc42 mobility at the non-growing cell tip of tea4Δ cells , which has vastly reduced Cdc42 activity and enrichment ( Fig . 3C , D ) , was significantly higher than at the other cell tip and more similar to the cell sides ., Conversely , we generated zones of Cdc42 activity and enrichment at cell sides by treating cells with the actin depolymerizing drug Latrunculin A ( LatA ) for 30–40 min ( Fig . 3C , D , see below ) ., This leads to progressive loss of CRIB from cell poles and formation of dynamic zones of CRIB on cell sides 42 ., Cdc42 was enriched in these zones and displayed significantly slower mobility , whereas Cdc42 from depleted cell poles showed fast mobility ., Thus , the geometry of the cell tip does not constrain Cdc42 mobility , and Cdc42 mobility can be slowed down also at cell sides ., Measurement of Cdc42 FRAP halftimes in the panel of regulator mutants described above further established a correlation between the levels of active Cdc42 , as detected by CRIB , and Cdc42 slow mobility ( high FRAP halftimes ) at cell tips ( Fig . 3E , F; linear regression r2 = 0 . 82 ) ., Cdc42 mobility was high ( low FRAP halftimes ) at the sides of all mutants , though some differences were noticed in comparison to wild type ., We also tested the mobility of the Cdc42 GTP-locked allele , Cdc42Q61L-mCherrySW expressed from plasmids under control of an inducible promoter in a cdc42-sfGFPSW strain ., Long-term induction led to cell rounding , as previously reported for untagged Cdc42Q61L ( S3A Fig ) 50 ., FRAP experiments performed after short-term induction before cell shape change showed slow mobility of Cdc42Q61L-mCherrySW at both cell tips and cell sides , with halftimes similar or higher than those of wild-type Cdc42 at cell tips ( S3B , C Fig ) ., We note that Cdc42Q61L-mCherrySW expression had no effect on the dynamics of Cdc42-sfGFPSW on cell sides , but led to reduction in its halftime at cell tips , suggesting titration of some factor for Cdc42 activation or stabilization ., Cdc42 dynamics remained slow at cell tips and fast at cell sides in both channels when wild-type Cdc42-mCherrySW was co-expressed from plasmids in a cdc42-sfGFPSW strain ., We conclude that Cdc42-GTP exhibits slower mobility than Cdc42-GDP at the plasma membrane ., We were surprised to discover that deletion of rdi1 did not affect Cdc42 mobility at cell tips in the experiment above ( Fig . 3F , Fig . 4A ) ., Though Rdi1 is the sole predicted GDI in S . pombe , its deletion yields only a minor morphological phenotype , with cells slightly shorter and wider than wild-type cells at division ( S4A–D Fig ) ., Disruption of actin cables in formin for3Δ mutant 51 , interference with endocytosis in end4Δ mutant 52 , or disruption of all actin structures by treatment with 200 μM LatA for a short time ( 5–10 min ) , also had no or minor effect on Cdc42 mobility ( Fig . 4A ) ., Remarkably , Cdc42 mobility at cell tips was even maintained in rdi1Δ mutant cells treated with LatA ., Further collapse of the membrane trafficking system by treatment with Brefeldin A ( BFA ) also failed to slow down Cdc42 dynamics at cell tips ( Fig . 4A ) ., We note , however , that rdi1 deletion slightly slowed down Cdc42 mobility at cell sides especially in combination with actin cytoskeleton disruption , though the absence of known actin structures or trafficking pathways at cell sides suggests the effect of actin disruption may be indirect ., These data indicate that Cdc42 dynamics at the plasma membrane occurs largely independently of GDI-mediated membrane extraction and vesicle trafficking ., Furthermore , long-term ( 30–40 min ) LatA treatment led to the formation of new zones of active Cdc42 polarization at cell sides , which were dynamic , forming and disappearing over time , even in rdi1Δ cells ( S4E Fig ) ., Finally , zones of active Cdc42 formed spontaneously in spores 9 , even upon removal of both GDI and actin structures ( Fig . 4B ) ., Thus , Cdc42 mobility and its ability to locally accumulate require neither GDI nor actin-dependent vesicle trafficking , though the actin cytoskeleton is required for maintenance of an active Cdc42 zone at a stable location ., The FRAP measurements described above may reflect exchange of Cdc42 between the membrane and the cytosol or lateral diffusion along the membrane ., In case of lateral diffusion , the rate of fluorescence recovery decreases with increasing size of the bleach zone ., Photobleaching of Cdc42 over wider ( 4–6 μm ) zones at the sides of wild-type , rdi1Δ , and rdi1Δ cells treated with LatA yielded significantly slower recovery , suggesting significant contribution of lateral diffusion to Cdc42 dynamics ( Fig . 4C–E ) ., The recovery on the cell sides was fitted with a model that accounts for 2-D membrane diffusion and uniform exchange with a fast-diffusing cytoplasmic pool ( Fig . 4D , E ) ., Cytoplasmic exchange results in exponential recovery over time while membrane diffusion results in algebraic recovery of the intensity at the center of the bleached and marginally detectable broadening of the bleached region over time ., The model predicts the evolution of the initial Gaussian-shaped bleach profile as a function of distance along the cell contour and time , with the diffusion coefficient and exchange time as fitting parameters ., Fits to the diffusion-dominated recovery of narrow bleached regions give a diffusion coefficient ranging between 0 . 15 and 0 . 35 μm2/s in wild-type and rdi1Δ cells ( Fig . 4E ) ., Use of this range of diffusion coefficient values provides good fits to the recovery of large bleached zones , with a cytoplasmic exchange time longer than 20 s in wild-type and rdi1Δ cells ( Fig . 4D and S5F Fig ) ., These values for the exchange time suggest that membrane removal , with or without GDI , is a small contribution to kinetics over the diffusion time across the cell side ., In rdi1Δ cells treated with LatA , diffusion coefficients of 0 . 1–0 . 2 μm2/s and no exchange component provided good fits ., We conclude that lateral diffusion of inactive Cdc42 at cell sides is a major component of Cdc42 mobility ., Photobleaching of half-cell tips also showed recovery from the sides of the bleached zone , with concomitant fluorescence loss in the adjacent tip region , indicating lateral movement along the tip membrane at a slower rate compared to cell sides ( S5A–E Fig ) ., Diffusion at cell tips may reflect spatially dependent inter-conversion between fast diffusing Cdc42-GDP and less mobile Cdc42-GTP , which complicates a precise calculation of the Cdc42-GTP diffusion coefficient ., However , measurements of the rate of bleached region broadening , as well as fits to a model of diffusion-dominated recovery over a small bleached tip area , suggest at least a 10-fold smaller lateral diffusion coefficient for Cdc42-GTP ., We used fluorescence correlation spectroscopy ( FCS ) to investigate the mobility properties of Cdc42 further ., At the cell sides , the FCS autocorrelation function showed two components with distinct diffusion regimes ( Fig . 4F , S5G Fig ) ., The sub-ms component is attributed to cytosolic diffusion , while the slower component depends on the presence of prenylation and hence is associated to a membrane-bound species ., A fit with a two-component model revealed a diffusion rate of 0 . 18 μm2/s for the membrane-associated species , in agreement with the FRAP fit above ., At the cell tips , FCS did not detect a slower diffusing species , instead only revealing diffusion similar to that observed on cell sides ( Fig . 4F and S5G–I Fig ) ., Our inability to detect a slower diffusing components stems from the fact that in standard FCS , to obtain statistical relevance the measuring time has to be 103–104-fold longer than the diffusion time to be determined 53 ., This would require a recording time of 15–150 min with an accuracy of 100 nm , incompatible with cell and optical focus stability ., As FRAP measures a large ensemble of fluorophores over a much larger area , it can detect much slower diffusing components , but without resolving multiple diffusing components , especially if one ( e . g . , the slow diffusing component ) is dominating ., Therefore , by resolving the faster component , our FCS measurements are complementary to the FRAP experiments and suggest that Cdc42-GDP is able to diffuse into the cell tip region ., In summary , Cdc42-GDP diffuses at rates of about 0 . 2 μm2/s , whereas Cdc42-GTP diffuses at least 10-fold slower ., To strengthen our findings that GDI and vesicle trafficking only play a minor role on Cdc42 dynamics and further test the role of Cdc42 membrane attachment in cell polarization , we engineered Cdc42 alleles with alternative plasma membrane targeting mechanisms ( Fig . 5A ) ., Membrane targeting is essential to Cdc42 function as shown by the fact that removal of the CAAX sequence yielded a diffuse , non-functional Cdc42 allele unable to complement the cdc42-1625 temperature-sensitive mutant ( S6A , B Fig ) ., Remarkably , two distinct Cdc42 alleles in which the CAAX sequence is replaced by a trans-membrane sequence ( cdc42-psy1TM ) , or an amphipathic helix ( cdc42-ritC ) , were able to complement the cdc42-1625 mutant when expressed from plasmids ( Fig . 5B ) and were viable when integrated as single cdc42 copy at the native cdc42 genomic locus ., All experiments presented below use strains with these alleles as single cdc42 copy ., ( H ) Average profiles of fluorescence intensity along cortical traces with standard deviation ., n = 40 ., ( I ) Halftimes of Cdc42-mCherrySW-ritC FRAP recovery at cell tips and sides ., n ≥ 18 ., The two values are statistically significantly different ( Student’s t test p-value = 0 . 0056 ) ., ( J ) Cdc42-mCherrySW-ritC localization following depletion of exocyst component Sec8 ., Note that Cdc42-mCherrySW-ritC fusion does not accumulate sub-apically as wild type does ( see Fig . 1G , right ) ., Bars = 5 μm ., Cdc42-psy1TM , containing the trans-membrane domain of the t-SNARE syntaxin-like protein Psy1 54 , localized to the plasma membrane , where it was almost immobile at cell sides and displayed slow turnover at cell tips ( FRAP halftime > 1 . 5 min ) , similar to endogenous Psy1 ( Fig . 5D , E ) ., This allele did not accumulate at cell poles , where it was less abundant than along cell sides ( Fig . 5F ) ., Remarkably , however , these cells localized CRIB-3GFP and polarized growth to both cell poles ( Fig . 5C ) , indicating Cdc42-psy1TM is active at cell poles ., We hypothesize that the membrane insertion of Cdc42-psy1TM prevents its rapid recycling and accumulation at sites of activity ., We conclude that accumulation of Cdc42 is not absolutely necessary for , and occurs as a consequence of , its local activation ., Though viable , Cdc42-psy1TM cells displayed irregular shapes with variable cell width , with low amounts of CRIB-3GFP also detected on cell sides ( Fig . 5C inset; S6C Fig ) ., We tested whether this allele supports the formation of new sites of polarization upon long-term LatA treatment ., This led to increased occurrence of CRIB zones on cell sides , similar to our observations in wild-type cells , though the zones were often less well defined , and Cdc42-psy1TM was not enriched in these zones ( S6D Fig ) ., While this result shows that Cdc42-psy1TM can support spontaneous polarization to some extent , it also suggests that this almost immobile Cdc42 allele is compromised in its ability to form a focal , well-defined growth zone ., We were unable to examine whether this allele can support spontaneous polarization in spores , because of difficulty in obtaining homozygous cdc42-psy1TM mutant zygotes ., The Cdc42-psy1TM allele also showed synthetic defects with deletion of the landmark Tea1 ( S6F Fig ) ., Tea1 , which marks cell poles for growth , is required for bipolarity as well as for the maintenance of the rod shape , as tea1 deletion produces curved , occasionally T-shaped , monopolar cells 55 ., Double cdc42-psy1TM tea1Δ mutants were slow-growing and displayed very aberrant shapes , suggesting that Cdc42-psy1TM activation at cell tips largely relies on upstream polarization cues ., The Cdc42-ritC fusion contains the C-terminal amphipathic helix of a heterologous mammalian protein Rit , previously shown to efficiently localize to the plasma membrane but not to endomembrane systems 56 , 57 ., Cdc42-ritC localized specifically to the plasma membrane and was not detected on endomembranes ( Fig . 5G ) ., It also did not accumulate sub-apically in cells depleted of the exocyst member Sec8 , indicating that this allele is not trafficked on exocytic vesicles ( Fig . 5J ) ., This allele is also predicted not to be a substrate for GDI , because the prenyl group is required to bind GDI 49 , 58 , 59 ., Remarkably , cdc42-ritC mutant cells grew at near wild-type rates at 25°C and 30°C , though it was compromised at high temperatures , and showed only minor morphological defects , with slightly wider and shorter cells than wild type ( S6C , G Fig ) ., Cdc42-ritC accumulated at cell poles , where it was active and enriched 3 . 1-fold ( ±1 . 0 , n = 40 ) over its levels at cell sides , similar to wild-type Cdc42 ( Fig . 5H ) ., It also showed slower FRAP recovery at cell poles compared to cell sides , though the FRAP halftime at cell sides was about 4-fold slower than wild type ( Fig . 5I ) ., The minor phenotype displayed by cdc42-ritC mutant cells is consistent with the minor role played by GDI and vesicle trafficking in Cdc42 dynamics ., We used four distinct assays to test whether Cdc42-ritC was able to break symmetry in absence of upstream polarity cues ., First , upon long-term LatA treatment , Cdc42-ritC , like wild-type Cdc42 , enriched to novel active zones on cell sides , which are likely devoid of landmarks ( S6E Fig ) ., Second , after cell wall digestion generating round protoplasts , both wild-type Cdc42 and Cdc42-ritC enriched in dynamic peripheral active zones during protoplast recovery ( Fig . 6A ) ., Third , cdc42-ritC mutant spores germinated and polarized growth as efficiently as wild-type spores ( Fig . 6B and S7A ) ., Finally , in absence of the landmark Tea1 , cdc42-ritC mutants formed T-shapes upon re-feeding , indicating polarization at cell sides in absence of the landmark ( Fig . 6C ) ., Unexpectedly , and in contrast to tea1Δ single mutants , cdc42-ritC tea1Δ double mutants grew in a bipolar manner in exponential phase ( Fig . 6D ) ., This finding may explain the reduced efficiency in the formation of T-shapes , because of competition with growing cell poles 60 ., We conclude that Cdc42-ritC is able to break symmetry in absence of upstream cues ., Together with our dissection of Cdc42 dynamics , these data show that spontaneous polarization of Cdc42 activity and localization does not require GDI-mediated extraction and actin-based vesicle trafficking ., We were intrigued by the observation that Cdc42-ritC confers bi-polarity in absence of Tea1 ., In addition to the role of the Tea1/Tea4 landmark , bipolar growth normally is controlled by the cell cycle and occurs only after passage through S-phase , such that cdc10-v50 mutant cells blocked in G1 phase remain monopolar 31 , 61 ., Remarkably , both Cdc42-ritC and Cdc42-psy1TM promoted bipolar growth in cdc10-v50 G1-arrested cells ( Fig . 6E ) ., Cdc42-ritC and Cdc42-psy1TM mutant cells also displayed clear bipolarity when examined in time-lapse imaging ( S7B Fig ) ., Thus , both Cdc42 alleles with altered plasma membrane targeting override the normal regulation to promote bipolar polarization and growth ., Our observations that Cdc42 polarizes independently of GDI and actin-based trafficking conflict with data in S . cerevisiae in which simultaneous disruption of GDI and actin blocks GFP-Cdc42 recycling and polarization 23 , 25 ., We tested whether targeting of Cdc42 to the plasma membrane by an amphipathic helix would permit cell polarization and viability also in S . cerevisiae ( Fig . 7A ) ., We replaced the endogenous cdc42 gene in a diploid strain with a cdc42-ritC or a cdc42-ritC-GFP allele ., Sporulation yielded four viable spores , of which two grew slowly and carried the mutant allele ( Fig . 7B ) ., Thus a Cdc42 allele directly targeted from the cytosol to the plasma membrane independently of GDI also confers viability in the budding yeast ., Cdc42-ritC-GFP efficiently polarized in haploid mutant cells , but often accumulated at two or more sites simultaneously ( Fig . 7C–G ) , even upon actin disruption ( Fig . 7F ) ., A large fraction of these cells formed multiple buds or aberrant growth projections , which could grow concurrently ( Fig . 7C–G ) ., Examination of simultaneous double bud formation on time-lapse movies showed 45 events in 250 cells ., Finally , these cells formed an abnormal budding pattern , budding at random locations , suggesting override of the normal landmarks at the previous bud scar ( Fig . 7G–I ) 62 ., These data are entirely consistent with our results in the fission yeast and suggest that Cdc42 can also polarize to naïve sites at the plasma membrane independently of GDI and vesicle-mediated transport in the budding yeast ., Living systems are able to spontaneously break symmetry and self-organize in ordered patterns ., These patterns generally reflect the steady state of a dynamic protein flux ., Thus , live fluorescently tagged alleles have become indispensable experimental tools ., However , for small , highly conserved proteins that have multiple binding partners , it can be challenging to preserve functionality of the tagged molecule ., The highly conserved polarity regulator Cdc42 GTPase is one such small protein ., N-terminal GFP fusions previously used to derive much of our knowledge on Cdc42 localization and dynamics compromise Cdc42 functionality and localization in yeasts 18 , 23 , 27 ., N-terminal GFP-Cdc42 fusions have also been abundantly used in more complex eukaryotic organisms to derive information about Cdc42 localization and dynamics 63–68 ., We note , however , that in these systems , functionality is more difficult to test ., We present here an improved internally tagged version of Cdc42 and its use in revealing new biology of this important polarity factor ., We note that , besides the position of the fluorescent marker , its folding properties need to be taken into consideration , as use of mCherry or sfGFP , but not GFP , which folds considerably slower 39 , 40 , resulted in functional fusions ., All functional tests conducted here indicate these sandwich fusions do not compromise Cdc42 function , though we cannot rule out that phenotypes may be revealed in other , more sensitive backgrounds ., Our approach is , in principle , generally applicable for Cdc42 , or indeed for any small GTPase , in all organisms , though the specific site for fluorescent protein insertion will need to be carefully selected and tested ., The ability of Cdc42 to spontaneously polarize—i . e . to display local zones of accumulation—relies on positive feedback mechanisms ., It has been proposed in the budding yeast to depend on two Cdc42 recycling routes from and to the plasma membrane: GDI-mediated extraction and trafficking on vesicles ., In fission yeast , these two recycling routes very likely exist: Cdc42 is a probable GDI substrate because GDI binds Cdc42 49 , and GDI deletion causes modest reduction in Cdc42 dynamic turnover at cell sides ., However , GDI deletion does not overtly affect the ability of Cdc42 to polarize in spores or vegetative cells ., Cdc42 may also traffic on vesicles since it is detected on secretory vesicles upon block in exocytosis ., Yet , short-term pharmacological treatment blocking vesicle trafficking causes no or very modest changes in Cdc42 dynamics ., We note , however , that the actin cytoskeleton plays an important role in the longer-term stability of the polarized zone , which progressively diminishes at cell poles and spontaneously re-appears on cell sides upon sustained LatA treatment 42 ., This may be similar to observations in the budding yeast , in which actin disruption causes flickering of the polarity patch and prolongs naturally observed oscillations 20 , 26 ., We conclude that the actin cytoskeleton ( and thus polarized vesicle transport and endocytosis ) does not by itself significantly modify

Introduction, Results, Discussion, Materials and Methods

The small Rho-family GTPase Cdc42 is critical for cell polarization and polarizes spontaneously in absence of upstream spatial cues ., Spontaneous polarization is thought to require dynamic Cdc42 recycling through Guanine nucleotide Dissociation Inhibitor ( GDI ) -mediated membrane extraction and vesicle trafficking ., Here , we describe a functional fluorescent Cdc42 allele in fission yeast , which demonstrates Cdc42 dynamics and polarization independent of these pathways ., Furthermore , an engineered Cdc42 allele targeted to the membrane independently of these recycling pathways by an amphipathic helix is viable and polarizes spontaneously to multiple sites in fission and budding yeasts ., We show that Cdc42 is highly mobile at the membrane and accumulates at sites of activity , where it displays slower mobility ., By contrast , a near-immobile transmembrane domain-containing Cdc42 allele supports viability and polarized activity , but does not accumulate at sites of activity ., We propose that Cdc42 activation , enhanced by positive feedback , leads to its local accumulation by capture of fast-diffusing inactive molecules .

Cell polarization is a critical feature of most cells that underlies their functional organization ., A central polarity factor called Cdc42 , a small GTPase targeted to the plasma membrane by prenylation , promotes cell polarization in its active GTP-bound form ., Cdc42 is a key polarity factor because it accumulates at presumptive sites of polarity , which previous work suggested involves Cdc42 recycling on and off the plasma membrane ., In addition , its activity can spontaneously polarize cells in a single location by self-enhancing positive feedback mechanisms , even in the absence of any pre-localized landmarks ., In this study , we constructed the first functional fluorescently tagged allele of Cdc42 that replaces the endogenous genomic copy in Schizosaccharomyces pombe ., This allowed measurements of Cdc42 dynamics at the plasma membrane by live microscopy ., Unexpectedly , this approach revealed that Cdc42 primarily moves through lateral diffusion , rather than on and off the plasma membrane ., Engineered Cdc42 alleles with alternative membrane-targeting mechanisms demonstrated that Cdc42 activity , indeed , polarizes in the absence of known pathways that recycle Cdc42 on and off the membrane ., We further show that the active form , Cdc42-GTP , is less mobile than Cdc42-GDP ., We thus propose that Cdc42 polarization occurs as a consequence of its local activation—either through self-enhanced feedback or in response to upstream cues—by a reduction in the active Cdc42 diffusion rate .

This study of fission yeast reveals that the active and inactive forms of the small GTPase Cdc42 have different rates of lateral diffusion in the membrane, providing insights into how it becomes spontaneously polarized, thereby determining the polarity of the cell.

journal.pntd.0003841

Comparison of the Mitochondrial Genomes and Steady State Transcriptomes of Two Strains of the Trypanosomatid Parasite, Leishmania tarentolae

Kinetoplastid protists have an unusual mitochondrial genetic architecture: the mitochondrial genes + cryptogenes encoded in the 20–40 catenated maxicircles , and multiple guide RNAs ( gRNAs ) encoded mainly in the ~10 , 000 catenated minicircles 1 ., The gRNAs contain information for correction of DNA-encoded mRNA frameshifts at the RNA level 2 ., This unusual genetic organization reflects the evolution in these early protists of a post-transcriptional RNA modification phenomenon known as U-insertion/deletion RNA editing ., The mechanism of this type of RNA editing involves hybridization of the 5’ end of the gRNA to the “pre-edited” mRNA sequence just downstream of the first editing site , endonuclease cleavage at the first upstream mismatch , followed either by the 3’ addition of U residues to the 5’ mRNA fragment which base pair with A or G “guiding nucleotides” in the gRNA and ligation of the two RNA fragments , or removal of unpaired 3’ U residues before RNA ligation 2 ., The proteins responsible for this process are components of a 1 MDa core complex ( RNA editing core complex or RECC ) 3 containing three endoribonucleases , two 3’-5’ U-specific exonucleases , a 3’ uridylyltransferase or TUTase and two RNA ligases , in addition to more than 10 proteins of uncertain function 4 ., In addition there are a number of accessory complexes which interact dynamically with the RECC forming a yet poorly defined “editosome” complex sedimenting around 40S 5 ., The accessory complexes exhibit gRNA binding 6 , 7 , RNA binding 8 , RNA helicase 9 , 10 , 3’ TUTase 11–14 and poly A polymerase activities 15 , 16 ., Medium resolution cryoEM structures of the RECC particles from Leishmania tarentolae and Trypanosoma brucei have been published 17 , 18 and several crystallographic structures of editing proteins are also known 19–26 ., In L . tarentolae , there are approximately 10 , 000–20 , 000 minicircles and 20–50 maxicircles all catenated into a single giant network of DNA ( kinetoplast or kDNA ) 27–29 that is situated in the mitochondrial matrix linked to the flagellum basal body by fibers extending from the kDNA region to the mitochondrial membrane and from the mitochondrial membrane to the organelle ( TAC complex ) 30 , 31 ., The extent of editing varies from a few uridines at a few sites to hundreds of uridines at hundreds of sites ( pan-editing ) ., Pan-editing proceeds 3’ to 5’ with the overall polarity being determined by multiple overlapping gRNAs , one of which initiates editing of a short “block” of nucleotides , creating a sequence to which the second overlapping gRNA hybridizes , and so on , forming an editing cascade 32 ., The mitochondrial genome of trypanosomatid protists consists of the maxicircle genome , which encodes 6 genes , transcripts of which are never edited , and 12 cryptogenes , transcripts of which are edited , and the minicircle genome , which encodes small guide RNAs ( gRNAs ) that contain the information for the precise insertion/deletion of uridine residues in the cryptogene transcripts to create translatable mRNAs 2 , 33 , 34 ., Seven of the gRNAs are encoded in the maxicircle genome and the remainder are encoded in the thousands of catenated minicircles ., In L . tarentolae , the minicircles are approximately 850 bp and there is a single gRNA gene per molecule ., A minicircle “sequence class” is defined as a group of minicircles with homologous sequences encoding a specific gRNA ., Guide RNAs which differ in sequence due to the existence of G/C and G/U base pairs but contain the same editing information are termed “redundant gRNAs” ., Replication of the kDNA and the nuclear DNA is fairly synchronous in the trypanosomatid cell cycle 35 , 36 , giving rise to G2 networks with double the number of minicircles ., Division of the kinetoplast network involves non-mitotic scission yielding two daughter networks ., The frequencies of different minicircle classes in the network of L . tarentolae were previously shown to change significantly during continuous culture from year to year 37 ., The complete loss of a single minicircle class encoding a non-redundant gRNA for an essential protein would be lethal ., Since division of the single kDNA network involves a non-mitotic scission of the catenated structure , it would be essential for the cell to have the minicircle classes distributed randomly throughout the network so that each daughter network contains a complete complement of gRNAs ., A novel mode of DNA replication which involves decatenation of closed minicircles from the network followed by replication and then recatenation at two peripheral antipodal nodes while the network is rotating or oscillating may have evolved to help accomplish this 27 ., A computer model of minicircle replication and network segregation assuming random distribution of minicircles to daughter networks and several other reasonable assumptions indicated that the frequencies of different minicircle classes spontaneously fluctuate over one thousand generations and that minicircles encoding gRNAs for non-functional editing cascades become the majority of the 10 , 000 molecules in the network 38 ., In fact , this appears to be the case in the UC strain , in which the kDNA network was found to contain several high copy number minicircle classes that encode such “orphan” gRNAs 37 ., We showed previously that the old laboratory UC strain ( Tar II ) of L . tarentolae isolated by Parrot in 1939 39 was defective in editing of several of the normally pan-edited genes and therefore in the protein products which were apparently not required in culture 40 ., The recently isolated LEM125 strain was found to contain minicircle-encoded gRNAs which would allow editing of a number of mRNAs not edited in the UC strain ., This model however was brought into question by the finding that a strain of L . mexicana isolated in 1972 and kept in culture contained an apparently complete set of pan-edited RNAs 41 , suggesting that some yet unknown property of Leishmania may play a role in maintaining the intactness of the editing system ., In order to understand the detailed role and plasticity of minicircle-encoded gRNAs and the details of the editing process , a complete inventory of the mitochondrial genome and the minicircle transcriptome is essential and that is presented in this paper ., There is one study in the literature on the gRNA transcriptome of insect phase T . brucei 42 but no mapping was done and the minicircle genome was not investigated ., In our study , minicircles from both L . tarentolae strains were sequenced and the number of classes determined ., In addition , mitochondrial RNA libraries were sequenced and the reads mapped to minicircles , encoded gRNAs and pre-edited and mature maxicircle edited sequences ., We confirmed and quantitated the differences between the defective kDNA genome of the UC cells and the more robust kDNA genome of the LEM125 cells ., The UC strain was originally obtained from Dr . W . Trager ., It has been maintained from 1968 to the present in the L . S . lab at UCLA in Brain Heart Infusion Medium with 10 μg/ml hemin at 27°C ., This is the original Parrot TarII strain ( ATCC 30267 , 30143 ) which was isolated from Tarentola mauritanica in Algeria in 1939 ., The LEM125 strain was obtained from Dr . J . A . Rioux who isolated it from a gecko in southern France in 1981 43 ., It was kept frozen in liquid nitrogen and samples were thawed to start cultures ., For culture of LEM125 , 10% inactivated fetal calf serum and 10 μg/ml hemin were added to BHI ., Purified kinetoplast-mitochondria 44 from the UC and LEM125 45 , 46 strains of L . tarentolae were frozen and kept in Trizole ., RNA was isolated using the Qiagen miRNeasy Micro Kit in which the sample homogenization in QiaZol was substituted by Trizole ., RNA was fractionated into large ( >200 nt ) and small ( <200 nt ) fractions using the Ilumina RNeasy MinElute Cleanup Kit ( cat . no . 74204 ) ., Small RNA libraries were generated using the SeqMatic TailorMiX miRNA sample prep kit with some modifications: The gRNA 5 triphosphate was removed by treating the RNA with Antarctic phosphatase from NEB ( using NEB protocols ) ., A 5 monophosphate was added back to the RNA for subsequent adapter ligation using T4 polynucleotide kinase from NEB ( again using NEB protocols ) ., Adapter ligation , reverse transcription , and PCR steps followed standard protocols for small RNA library generation ., Large RNA libraries were generated using the SeqMatic Directional RNA kit ., The RNAs were fragmented in MgCl2 and the fragment ends were treated with T4 PNK ., Then adapters were ligated followed by RT-PCR just as in the small RNA procedure ., For sequencing using the MiSeq v2 platform 47 , small RNA was run on a 150 bp single end read and large RNA on a 2x150 bp paired end read ., In one preparation , total cell RNA was used to generate libraries using SeqMatic’s TailorMix Directional RNA sample prep kit using the company’s protocols ., The total RNA was sheared to an average length of 200 bp ., The sheared fragments were then ligated with adapters and converted into cDNA ., PCR was then performed with primers containing barcoded Illumina adapter sequences ., The cDNA was then subjected to paired-end 2 X 150 bp sequencing ., All libraries were validated using the Agilent Bioanalzyer ., The percentage of minicircle specific reads in the libraries was low , with the majority of reads specific for genomic RNAs and maxicircle rRNAs , indicating that the isolated kinetoplast fractions were contaminated with genomic RNAs ., The mapping of reads to minicircles however was very specific and not affected by this contamination ., In fact we were able to use libraries of reads prepared from total cell RNA and obtain quite similar results ., In all mapping experiments using small RNA reads , the Bam files from reads from purified mitochondria were merged with the Bam files from reads from total cell RNA ., The unmapped values for LEM small and LEM large are quite high , perhaps for technical reasons , but this does not affect our results due to the high specificity of mapping we observe ., To determine contamination of libraries , RNA sequence reads were mapped to genomic , maxicircle , and all minicircle sequences in a single step ., Reads were characterized by which sequence they aligned to or if they failed to align to any of these sequences ., See Table 1 ., In order to obtain the complete complement of minicircle sequence classes , kinetoplast DNA was isolated by the CsCl sedimentation method 48 from stationary phase UC and LEM125 cells ., The kinetoplast DNA in a microtube was cleaved with the Covaris S2 Focused-Ultrasonicator using conditions ( Duty cycle 2% , Intensity 4 , 200 cycles per 20 sec burst ) that yielded approximately a single random cleavage per molecule ., DNA libraries were generated using the Illumina TruSeq DNA sample preparation kit ., All libraries were validated using the Agilent Bioanalyzer High Sensitivity DNA Assay ., The average UC library size was 871 bp and the LEM library size 963 bp ., The DNA libraries were subjected to PacBio sequencing 47 to obtain full length minicircle sequences 49 and avoid the problem of the presence of the conserved region affecting assembly of shorter sequences ., CCS reads , which are circular consensus sequences generated by multiple passes around each circular SMRTbell construct , were used for minicircle sequence assembly ., The CCS sequences ( Fig 1A ) were filtered for size ( 800–100 nt ) and for minicircle sequences using the highly conserved CSB3 motif and subsequently rearranged in the same polarity to terminate with the 5’ end of this motif so as to have the encoded gRNAs in a 5-3 orientation ., The rearranged reads were mapped to all known minicircle sequences and unmapped reads were separated as a subgroup ., A multiple alignment of the unmapped reads was performed with the MAFFT method 50 and a tree was constructed ., Consensus sequences were generated based on the alignments of reads in each branch segmented from the tree ., All consensus sequences were then mapped to all known minicircles and any unmapped sequences were selected as novel minicircle candidates ., The novel minicircles were verified by identification of novel gRNAs as described previously and added to the database ., Then the whole process was cycled with mapping all rearranged reads against this combined minicircle database until no consensus sequences could be generated ., The filtered CCS sequences of once cleaved minicircles for both strains were assembled against the 114 identified minicircles , using the Geneious program ., Table 2 shows the number of CCS sequences which assembled to each minicircle sequence ., These numbers were used to generate approximate minicircle copy numbers per network as described in the text ., Informatics analysis was mainly performed using the Broad Institute Galaxy system 51 installed on the Hoffman2 cluster at UCLA ., The short RNA FastQ files and the long RNA paired FastQ files were filtered to remove adapters and for quality ., In addition , posttranscriptionally added 3 oligoU sequences in both the short and long RNA FastQ files were removed to avoid mapping problems ., PolyA or Poly AU 3 sequences were not filtered since we are only interested in the mitochondrial RNA sequences ., The processed RNA reads were mapped to the 114 L . tarentolae minicircle reference sequences using Bowtie 49 ., Quantification and FPKM ( Fragments Per Kilobase of transcript per Million mapped reads ) analysis 52 were performed using in-house command line scripts created by S . D . and visualized using Excel ., The average depth of coverage for the mapped gRNA peaks is ~150 fold for UC and ~50 fold for LEM125 ., New minicircle sequences: BankIt1789720 mc3 KP456020 BankIt1789720 mc4 KP456021 BankIt1789720 mc7 KP456022 BankIt1789720 mc12 KP456023 BankIt1789720 mc13 KP456024 BankIt1789720 mc14 KP456025 BankIt1789720 mc19 KP456026 BankIt1789720 mc22 KP456027 BankIt1789720 mc24 KP456028 BankIt1789720 mc28 KP456029 BankIt1789720 mc32 KP456030 BankIt1789720 mc33 KP456031 BankIt1789720 mc38 KP456032 BankIt1789720 mc41 KP456033 BankIt1789720 mc48 KP456034 BankIt1789720 mc52 KP456035 BankIt1789720 mc55 KP456036 BankIt1789720 mc59 KP456037 BankIt1789720 mc60 KP456038 BankIt1789720 mc61 KP456039 BankIt1789720 mc62 KP456040 BankIt1789720 mc71 KP456041 BankIt1789720 mc76 KP456042 BankIt1789720 mc82 KP456043 BankIt1789720 mc83 KP456044 BankIt1789720 mc86 KP456045 BankIt1789720 mc89 KP456046 BankIt1789720 mc91 KP456047 BankIt1789720 mc92 KP456048 BankIt1789720 mc98 KP456049 BankIt1789720 mc101 KP456050 BankIt1789720 mc102 KP456051 BankIt1789720 mc103 KP456052 BankIt1789720 mc105 KP456053 BankIt1789720 mc108 KP456054 BankIt1789720 mc109 KP456055 BankIt1789720 mc110 KP456056 BankIt1789720 mc111 KP456057 BankIt1789720 mc112 KP456058 BankIt1789720 mc113 KP456059, Sequences of once cleaved minicircles from both the UC and LEM125 strains were obtained using the PacBio platform 53 and assembled into contigs against the known minicircle database using the Geneious software package 51 ., Minicircles were identified using several criteria as diagrammed in Fig, 2 . These criteria are size ( 800–1000 bp ) , the presence and relative location of the three short conserved sequences , CSB1 ( 5-GAACGCCCCT-3 ) , CSB2 ( 5-GAACGGGG-3 ) and CSB3 ( 5-ATGTGGTTGGGG-3 ) 54 which are involved in DNA replication , and also the presence and relative location of the “bend” region 55–58 ., The Variable or Divergent Region , which represents the minicircle sequence outside the conserved region , is indicated by brackets ., The intrinsically bent DNA region is located a characteristic distance from the gRNA gene ( see S1 Fig for length distribution ) and contains 4–5 T tracts together with variable length A and G tracts that together repeat each turn of the helix ( S2 Fig ) ., See Fig 1A for length distributions of 22 , 680 PacBio CCS sequences of once cleaved kDNA and Fig 1B for the final minicircle data set of 114 sequence classes from the LEM125 strain , of which 41 were novel , showing a peak of around 850–870 bp ., Putative gRNA genes were identified by computer analysis , as discussed in Materials and Methods ( see S3 Fig ) ., The conserved location of the known gRNA genes ( 240–340 nt from the 5’ ends of the CSB3 sequences ) assisted in this identification ( S1 Fig ) ., Table 2 has a list of the final set of 114 minicircle classes together with the”mc numbers” and the putative encoded gRNAs if known ., We define a minicircle sequence class by sequence homology and by the specific encoded gRNA ., Since the kDNA network contains approximately 10 , 000 catenated minicircles , there must be multiple copies of most if not all of the 114 sequence classes ., In order to determine the approximate frequency of the minicircle sequence classes , we assumed that the number of CCS sequences that assemble to specific minicircles is proportional to the relative frequency of these minicircles in the network ., Table 2 shows the number of CCS sequences which assembled to each minicircle and the calculated copy numbers of each minicircle class for both strains ., This data is shown graphically in Fig, 3 . The LEM125 strain has one class with ~2 , 000 mc’s per network of 10 , 000 mcs with the remainder of the 114 classes each containing less than 200 copies per network ., The UC strain , however , has only 20–24 total sequence classes with two of these containing around 3 , 000 and 5 , 000 copies per network and two containing around 600 copies each ., The multiply aligned CCS sequences for each sequence class showed a background of random changes , due to sequencing errors or mutations ., There were also however in 61% of the sequence classes conserved patterns of single nucleotide substitutions , as shown in Fig 4 for the mc54 minicircle by the vertical boxed columns ., Another example is in Fig 5 which shows 9 extracted regions from a multiple alignment of CCS sequences that form another specific minicircle sequence class ., These extracted regions contain 14 columns with similar patterns of nucleotide substitutions ( highlighted ) ., Most of the conserved single nucleotide changes occur within the minicircle variable region ( Fig 6 ) ., The mutations indicated in color in Figs 4 and 5 represent highly correlated patterns ., In the example in Fig 5 , 14 mutations are identical in 13 CCS sequences ., The probability of a nucleotide changing to another is 1/3 , and the probability of identical changes in the 13 CCS sequences is ( 1/3 ) 12 ., The probability of similar changes at all 14 different sites is: ( ( 1/3 ) 12 ) 13 which is less than 10−75 ., In light of the extremely small probability of this happening by chance , we propose a model in which a single minicircle in a sequence class encoding a specific gRNA had multiple single nucleotide changes which were selected for perhaps by affecting transcription or processing or through some indirect affect such as growth in culture ., Multiple rounds of replication of this minicircle would give rise to progeny minicircles that have the specific patterns we have observed ., Further research is required to fully explain this intriguing phenomenon ., Putative minicircle-encoded gRNAs are identified by computer analysis , which involves searching for regions of complementarity , allowing both G-C and G-U base pairs , between the 12 mature pan-edited sequences and the minicircle sequences reversed ., The duplex length criterion is >19 continuous base pairs with one or two allowed mismatches in longer duplexes due to sequence errors or to flexibility in the editing mechanism ., The entire set of edited RNA/gRNA duplexes can be seen in S3–S7 Figs and the identified gRNAs are listed in Table, 2 . Interestingly , there are 14 minicircles ( mc101-114 ) which do not encode identifiable gRNAs for the L . tarentolae edited sequences ., However , use of the related L . mexicana mature edited sequences led to the identification of 5 additional putative gRNAs for ND3 and G4 encoded by mc102 , 103 , 110 , 113 and 114 ., The remaining 10 minicircles do not contain identifiable gRNAs ., The absence of a single overlapping gRNA could break the 3-5 editing cascade and prevent the generation of a mature mRNA and thereby translation of that product ., The computer-identified gRNAs for both strains and also the maxicircle-encoded gRNAs were mapped onto the mature edited maxicircle transcripts ., The extracted annotated editing domains from both strains are compared side by side in Figs 7–9 ., The edited sequences are in green and the editing domains in orange ., The mapped minicircle-encoded gRNAs are indicated as red arrows ., The 7 maxicircle-encoded gRNAs which are identical in both strains and are shown as blue arrows mediate editing of the Cyb , Murf2 and ND7 transcripts ( Figs 7–9 ) ., The difference in the overall number of minicircle-encoded gRNAs between these strains is striking ., LEM125 has fairly complete sets of overlapping gRNAs with multiple “redundant” gRNAs for the A6 , G4 , ND3 and ND8 genes ( Figs 7–9 ) ., In the case of RPS12 , a maxicircle-encoded gRNA covers a single gap in the cascade ( Fig 8 ) ., LEM125 ND9 editing however apparently lacks at least two overlapping gRNAs ( Fig 9 ) , but we show below that the RNA library contains mature edited ND9 mRNA sequences , suggesting that our minicircle dataset lacks at least two sequence classes ., LEM125 G4 editing apparently lacks two overlapping gRNAs ( Fig 9 ) and LEM125 G3 editing lacks one gRNA required for complete editing , but we discuss below the possibility that the published 5 edited sequences for G3 may contain errors ., The UC strain on the other hand only has complete sets of overlapping minicircle-encoded gRNAs and therefore minicircle sequence classes for editing of the CO3 and A6 genes ( Fig 7 ) , and there is a striking paucity of gRNAs for editing of the ND3 , ND8 , ND9 , G3 and G4 genes ( Figs 8 and 9 ) ., UC RPS12 editing apparently lacks several overlapping gRNAs ( Fig 8 ) , but we show evidence below for the presence of mature edited UC RPS12 mRNAs , suggesting that the UC minicircle dataset lacks several minicircle RPS12 gRNA sequence classes ., In order to verify transcription of the putative gRNA genes , libraries for NGS sequencing were constructed for small and large mitochondrial RNA fractions ( <200 nt and >200 nt ) as described in Materials and Methods ., These reads were used to map onto all known minicircle sequences ., All minicircle sequences are in the same polarity with the gRNAs in the 5 to 3 orientation ., The encoded gRNAs are annotated below each respective minicircle as small boxes ., The linearized minicircles are concatenated in tandem in an order determined by the positions of the various gRNAs in the editing cascades starting with the 3 most gRNAs ., A 40 nt filler nucleotide sequence separates each minicircle ., Several representative minicircle mapping images are shown in Fig 10 and the entire dataset can be seen in S8–S11 Figs ., Also , see S17–S19 Figs for the complete dataset of mapping of short RNA reads from both strains to the 100 gRNA sequences directly ., In Fig 10 , minicircles mc1-mc4 encode gRNAs involved in the CO3 cascade of editing and minicircles mc5—mc7 encode gRNAs involved in the A6 cascade of editing ., The reads are mapped using Bowtie to the concatenated linearized minicircle sequences ., Lines are drawn from each encoded gRNA to indicate the putative mapped gRNA peak ., The sizes of the mapped peaks indicate the number of reads that map to each region ., It should be noted that in IGV the scales are automatically sized to fill the field ., The variable region of each minicircle which contains the gRNA gene is annotated ., Interestingly , there are additional peaks , both sense and antisense , not coincident with the gRNA genes and not conserved between minicircles ., There are no striking differences in abundance of the steady state transcripts from either strand in the minicircle variable ( VR ) or conserved regions ., The mc4 mapped gRNA peak is shown expanded in Fig 10 to illustrate the fairly homogeneous 5’ ends and the 3’ end heterogeneity ., The gND9X gRNA mapped peak is shown expanded to the nucleotide level in Fig 11 to illustrate that the mapped gRNA reads initiate 3–4 nucleotides upstream of the beginning of the gRNA/mRNA complementarity ., In some cases however the mapped gRNAs initiate a few nucleotides downstream of the gRNA/edited mRNA duplex start , but there is an overall preference for transcription initiation at the -3 and -4 positions , which are mainly A and C . In most cases due to the 3’ heterogeneity , the reads only map to a portion of the 3’ end of the gRNA gene ., See S12–S16 Figs for multiple examples ., The large RNA reads in many cases cover large portions if not the entire minicircles on both strands , as shown for three representative minicircles in Fig 12 ., The boxes in Fig 12 show the extensive expression of both strands covering almost the entire minicircle ., This data suggests that transcription is bidirectional and complete , and , since the 5 end of the mature gRNAs is an unprocessed triphosphate 2 , that 3 end processing is required for maturation of the gRNAs ., Since any 3 oligo U tails were filtered from the Fastq sequences prior to mapping , the percent of bona fide gRNAs mapping cannot be determined , and it is possible that some of the mapped reads are derived from small RNAs that lack the 3 oligo U tail ., This requires further investigation ., gRNA/mRNA duplex motifs for the L . tarentolae edited sequences could not be detected by computer analysis of mc101-mc114 ., However , putative gRNAs were identified in several of these minicircles for L . mexicana ND3 and G4 genes ( mc102 , 103 , 110 , 113 , 114 ) ., As shown in Fig 13 , in several minicircles without identifiable gRNAs ( mc101 , 104 , 106 , 107 ) , peaks of mapped reads showing characteristics of gRNA peaks are present in the 240–350 bp regions ( circled in Fig 13 for mc106 and mc107 ) , but analysis of the consensus read sequences of these peaks against mature edited mRNA sequences does not yield any gRNA/edited mRNA duplexes ., The function of these peaks of reads is unknown ., One possibility is that they encode gRNAs for misedited 59 , partially edited 59 or alternatively edited 60 RNAs , but this was not investigated ., The apparent absence of gRNA genes in some minicircles is a novel and puzzling finding which should be investigated further ., The FPKM method was used to quantitate the mapped steady state expression products ., Fig 14 shows the expression of specific total minicircle steady state transcripts as a percentage of total minicircle expression for both strains ., Fig 15 shows the expression of specific gRNA steady state transcripts as percentage of total gRNA transcripts ., There is as much as a 10 fold variation in the abundance of steady state transcripts between different classes in both strains , but there is no correlation between relative abundance of transcripts and minicircle function such as location of the gRNA in the editing cascades ., In general the UC strain shows a higher relative abundance of transcripts than the LEM125 ., The gRNA distribution in Fig 15 differs significantly from the minicircle transcript distribution in Fig 14 , which could be a function of rates of transcription and turnover ., One possibility for these observations might be the minicircle copy number differences ., The data in Figs 16 and 17 compare the percent expression of total minicircle and gRNA steady state transcripts to the percent of the different minicircle sequence classes in the network for both strains ., The most abundant LEM125 minicircle class ( mc67 ) shows the largest extent of expression , but the second most abundant minicircle class ( mc49 ) shows relatively little expression ., In general , LEM125 minicircle copy number and LEM125 ( Fig 16 ) minicircle expression of steady state transcripts show a moderate positive Pearson correlation 61 ( r = 0 . 6233 ) while the correlation in the case of the UC strain ( Fig 17 ) is somewhat weaker ( r = 0 . 4971 ) ., Scatter plots of this data are shown in Fig 18 ., Another possibility is that individual minicircles in different sequence classes may differ innately in expression ., Fig 19 shows the normalized total minicircle steady state transcript abundance divided by the LEM125 minicircle copy number and Fig 20 shows the same data for the UC minicircles ., In both there is a large variation in transcript abundance for different minicircles and there is no apparent gRNA functional correlation ., A similar analysis of expression of the normalized steady state minicircle gRNA is shown in Fig 21 for LEM125 and Fig 22 for the UC strain ., Maxicircle genes are encoded on both strands and some genes overlap ( Fig 23 ) ., Seven gRNAs are also encoded in the maxicircle ( blue arrows ) ( eight including the CO2 in cis gRNA sequence ) ., There is also a large variable or divergent region with AT-rich repetitive sequences that vary in size and sequence between trypanosomatid species and the function of which is unknown ., Twelve of the 18 pre-edited structural genes are edited post transcriptionally and termed “cryptogenes” , and the remainder are termed “never edited” ., Both are shown in green in Fig 23 and the never edited genes are in orange in the right panels in Figs 24 and 25 ., Little is known about transcription of maxicircle genes , other that there are some polycistronic transcripts which must be processed 62 , 63 ., Transcription of the gRNAs has the additional complication in that the gRNAs encoded in the maxicircle have 5’ triphosphates , as in the case of minicircle-encoded gRNAs 2 and may represent primary transcripts , but promoters have not been identified 5 of the gRNA genes ., Large RNA libraries from both strains were mapped to the maxicircle genomic sequence ., As shown in the example in Fig 24 and Fig 25 , there are no significant differences between the mapping of steady state RNAs between the strains ., The polarity and identity of each gene are shown on the right ., To study strain variations in the extent of editing of the maxicircle gene transcripts , we performed mapping of RNA reads from both strains onto the pre-edited ( i . e . , genomic ) and mature edited maxicircle gene sequences ., In most cases the large RNA reads were used , but the small RNA reads gave better visualization for the ND8 and G4 genes ., See S20 and S21 Figs for the complete dataset of IGV mapping images ., Fig 26 shows a representative mapping experiment using the ND9 pre-edited and edited sequences and large RNA reads from both strains ., The absence of ND9 editing in the lower right panel is clear ., An FPKM analysis of the mapping data of all pre-edited and mature edited maxicircle genes for both strains is shown in Fig 27 ., The LEM125 strain shows barely detectable levels of editing for A6 , G3 , G4 and ND8 , which is puzzling since there are complete cascades of minicircle-encoded overlapping gRNAs for A6 , and ND8 ( Figs 7 and 8 ) ., However , since edited sequences were previously obtained by RT PCR for the LEM125 A6 , ND8 and G4 genes in our 1994 project 40 , lack of detection in the current experiments may be due to a low level of expression and/or an insufficient number of reads in the database for detection ., The LEM125 G4 editing cascade , however , apparently lacks two overlapping gRNAs ( Fig 9 ) , the loss of which would break the editing cascade ., It is of course possible that the database of reads is also insufficient to detect G4 gRNAs , but it is also possible that the specific minicircle classes encoding these two G4 gRNAs were lost in the last 20 years of sporadic culture of LEM125 cells ., In the case of the UC strain , however , the absence of editing of ND3 , ND8 , ND9 , G3 and G4 is consistent with the lack of cascades of overlapping

Introduction, Materials and Methods, Results, Discussion

U-insertion/deletion RNA editing is a post-transcriptional mitochondrial RNA modification phenomenon required for viability of trypanosomatid parasites ., Small guide RNAs encoded mainly by the thousands of catenated minicircles contain the information for this editing ., We analyzed by NGS technology the mitochondrial genomes and transcriptomes of two strains , the old lab UC strain and the recently isolated LEM125 strain ., PacBio sequencing provided complete minicircle sequences which avoided the assembly problem of short reads caused by the conserved regions ., Minicircles were identified by a characteristic size , the presence of three short conserved sequences , a region of inherently bent DNA and the presence of single gRNA genes at a fairly defined location ., The LEM125 strain contained over 114 minicircles encoding different gRNAs and the UC strain only ~24 minicircles ., Some LEM125 minicircles contained no identifiable gRNAs ., Approximate copy numbers of the different minicircle classes in the network were determined by the number of PacBio CCS reads that assembled to each class ., Mitochondrial RNA libraries from both strains were mapped against the minicircle and maxicircle sequences ., Small RNA reads mapped to the putative gRNA genes but also to multiple regions outside the genes on both strands and large RNA reads mapped in many cases over almost the entire minicircle on both strands ., These data suggest that minicircle transcription is complete and bidirectional , with 3’ processing yielding the mature gRNAs ., Steady state RNAs in varying abundances are derived from all maxicircle genes , including portions of the repetitive divergent region ., The relative extents of editing in both strains correlated with the presence of a cascade of cognate gRNAs ., These data should provide the foundation for a deeper understanding of this dynamic genetic system as well as the evolutionary variation of editing in different strains .

U-insertion/deletion RNA editing is a unique post-transcriptional mRNA modification process that occurs in trypanosomatid parasites and is required for viability ., The participation of guide RNAs which are transcribed from the thousands of catenated minicircles in determining the precise sites and number of U’s inserted and deleted to create translatable mRNAs is novel and significant in terms of the recently realized importance of small RNAs in biology ., This study contributes the necessary bioinformatics foundation for a deeper understanding of this important genetic system in molecular detail using a model trypanosomatid , Leishmania tarentolae ., We used Next Generation Sequencing methods to determine the complete maxicircle and minicircle genomes and to map maxicircle pre-edited and edited transcripts and minicircle transcripts ., The transcription of minicircle-encoded guide RNAs was confirmed and novel information about minicircle gene expression was obtained ., The biological context involved a comparison of two strains of the parasites , one recently isolated and having an intact mitochondrial genetic system and the other an old lab strain that has developed a partially defective mitochondrial genome ., The data are important for an understanding of the mitochondrial genomic complexity and expression of this dynamic genetic system .

journal.pgen.1004458

Age-Associated Sperm DNA Methylation Alterations: Possible Implications in Offspring Disease Susceptibility

The effects of advanced paternal age have only recently become of interest to the scientific community as a whole ., This interest has likely arisen as a result of recent studies that suggest an association with increased incidence of diseases and abnormalities in the offspring of older fathers ., Specifically , offspring sired by older fathers have been shown to have increased incidence of neuropsychiatric disorders ( autism , bipolar disorder , schizophrenia , etc . ) 1–3 , trinucleotide repeat associated diseases ( myotonic dystrophy , spinocerebellar atixia , Huntingtons disease , etc . ) 4–7 , as well as some forms of cancer 8–11 ., Though these are intriguing data , we know very little about the etiology of the increased frequency of diseases in the offspring of older fathers ., Among the most likely contributing factors to this phenomenon are epigenetic alterations in the sperm that can be passed on to the offspring ., These studies are in striking contrast to the previously held dogma that the mature sperm are responsible only for the safe delivery of the paternal DNA ., Intriguingly , with increased investigation has come mounting evidence that the sperm epigenome is not only well suited to facilitate mature gamete function but is also competent to contribute to events in embryonic development ., It has been established that even through the dramatic nuclear protein remodeling that occurs in the developing sperm , involving the replacement of histone proteins with protamines , some nucleosomes are retained 12 ., Importantly , histones are retained at promoters of important genomic loci for development , suggesting that the sperm epigenome is poised to play a role in embryogenesis 12 ., In addition , recent reports suggest that hypomethylated regions with high CpG density also appear to drive nucleosome retention 13 ., Similarly , DNA methylation marks in the sperm have been identified that likely contribute to embryonic development as well 12 , 14 ., These data strongly support the hypothesis that the sperm epigenome is not only well suited to facilitate mature sperm function , but that it also contributes to events beyond fertilization ., Looking past fertilization and embryogenesis , sperm appear to contribute to events manifesting later in life ., The remarkable claim that sperm , independent of gene mutation , may be capable of affecting phenotype in the offspring was initially proposed as a result of large retrospective epidemiological studies observing changes in the frequency of diseases in the offspring of fathers who were exposed to famine conditions in the early 19th century 15 , 16 ., Recently , many studies utilizing animal models have discovered similar patterns that comport with the epidemiological data ., Specifically , in male animals fed a low protein diet , offspring display altered cholesterol metabolism in hepatic tissue 17 ., However , the etiology of this phenomenon is poorly understood ., Despite this , there are multiple likely candidates that may drive these effects , such as DNA methylation ., Methylation marks at cytosine residues , typically found at cytosine phosphate guanine dinucleotides ( CpGs ) , in the DNA are capable of regulatory control over gene activation or silencing ., These roles are dependent on location relative to gene architecture ( promoter , exon , intron , etc . ) ., Since these heritable marks are capable of driving changes that may affect phenotype , they represent a possible mechanism to explain the increased disease susceptibility in the offspring of older fathers ., Additionally , in both sexes , aging alters DNA methylation marks in most somatic tissues throughout the body ., In one of the first large studies to address the question of age-associated methylation alterations , Christensen et al . identified over 300 different CpG loci with age-associated methylation alterations in many tissues 18 ., One recent study compared age-associated DNA methylation alterations in blood , brain , kidney and muscle tissue and identified both common and unique methylation alterations between different tissues 19 ., Additionally , recent work suggests that DNA methylation can be used to predict the age of an organism based on tissue methylation profiles 20 ., This study also supports previous reports which identify global hypomethylation as a hallmark of aging in most somatic tissues 21 ., Because of its prevalence in other cell types , age-associated DNA methylation alteration is likely to occur in sperm as well ., In further support of this idea is work demonstrating that frequently dividing cells typically have more striking methylation changes associated with age than do cells which divide less often 22 ., In this study we have analyzed the age associated sperm DNA methylation alterations that are common among the individuals in our study population to determine the magnitude of sperm DNA methylation changes over time and whether specific regions are consistently altered with age ., To assess global methylation in the samples in question we performed pyrosequencing analysis of long interspersed elements ( LINE ) , a commonly used tool for the analysis of global methylation in many tissues 23 , 24 ., We identified significant global hypermethylation with age in sperm DNA as previous data from our lab suggests ( Figure 1 ) 25 ., Specifically , there was significant hypermethylation with age based on a paired analysis ( p\u200a=\u200a0 . 028 ) or by stratifying the samples by age alone and performing linear regression analysis ( p\u200a=\u200a0 . 0062 ) ., In addition to the global analysis , we performed a high resolution ( CpG level ) analysis of methylation alterations with age ., To perform this we utilized Illuminas Infinium HumanMethylation 450K array ., Each sample was hybridized and analyzed on an array and the results were compared to detect changes in methylation that are consistent with age ., We utilized a sliding window analysis , coupled with regression analysis ( average methylation at identified window versus the age at collection ) as an additional filter ( any window whose regression p-value was >0 . 05 was excluded from downstream analysis ) , to compare changes that are common between paired samples ., A Benjamini Hochberg corrected Wilcoxon Signed Rank Test FDR of <\u200a=\u200a0 . 0001 and an absolute log2 ratio >\u200a=\u200a0 . 2 ( effectively a change in methylation of approximately 10% or greater ) was used as our threshold of significance ., Raw FDR values have been transformed for visualization in figures and reference in this text ( ( −10 log10 ( q-value FDR ) ) , such that a transformed FDR value of 13\u200a=\u200a0 . 05 , 20\u200a=\u200a0 . 01 , 25\u200a=\u200a0 . 003 , 30\u200a=\u200a0 . 001 , and 40\u200a=\u200a0 . 0001 ., With this approach we have identified multiple age-associated intra-individual regional methylation alterations that consistently occur within the same genomic windows in most or all of the donors screened ., Specifically , we identified a total of 139 regions that are significantly hypomethylated with age ( Log2 ratio ≤−0 . 2 ) and 8 regions that are significantly hypermethylated with age ( Log2 ratio ≥0 . 2; Table S1 ) ., The average significant window is approximately 887 base pairs in length and contains an average of 5 CpGs with no fewer than 3 in any significant window ., Of the 139 hypomethylated regions , 112 are associated with a gene ( at either the promoter or the gene body ) , and of the 8 hypermethylated regions 7 are gene associated ., The 8 hypermethylated regions that were found did change in all donor samples , however they did not increase DNA methylation levels beyond 0 . 1 fraction methylation ., In one case we identified 3 significantly hypomethylated windows within a single gene ( PTPRN2 ) ., Thus there were a total of 110 genes with age-associated hypomethylation ., A previous report analyzing multiple somatic tissues suggests that the magnitude of DNA methylation alterations that occurs with age is fairly subtle with an average percent change per year ( measured as slope ) at a single CpG of approximately 0 . 05% to 0 . 15% 19 ., Our data , while still subtle , suggest that there may be a stronger effect of age on the methylation alterations in sperm compared with somatic cells ., Briefly , in the four tissues screened by Day et al . ( blood , brain , kidney and muscle ) they identified a total of 8 individual CpGs with a methylation change per year of >0 . 4% and a single CpG with a yearly change of >0 . 5% ., By comparison , our data have revealed a total of 26 genomic windows ( not just individual CpGs ) whose average fraction methylation change is >0 . 4% per year and 13 genomic windows with an average fraction methylation change per year of >0 . 5% ( Figure 2A–B ) ., Specifically in hypermethylated regions , the average fraction methylation change was 0 . 304% per year ( ranging from 0 . 08% to 0 . 95% per year ) ., In hypomethylated regions the average fraction methylation change was 0 . 279% per year ( ranging from 0 . 08% to 0 . 92% per year ) ., Considering the entire reproductive lifespan of a male , it is not unreasonable to anticipate an average change of 10–12% at these significantly altered regions ., Importantly , these alterations all occur in windows with an average initial fraction methylation of <0 . 6 at the first collection and the majority ( 67% of altered regions ) are also considered to have intermediate methylation based on conventional standards ( fraction DNA methylation levels between 0 . 2 and 0 . 8; Figure 2B ) ., Despite the increased magnitude of age-associated alterations in sperm when compared to somatic cells these changes are still quite subtle when considering the possible biological impacts at the 119 regions of age-associated alteration that are found at genes ( gene bodies , promoters ) ., Gene promoters were defined based on Illuminas array annotation , in general these fall within 1000 bps of the associated gene ., The significant loci identified in our analyses are located at various genomic features ., The majority of regions that undergo age-associated hypomethylation occurred at CpG shores , whereas hypermethylation events are more commonly associated with CpG islands , and these differences are significant in both cases ( p\u200a=\u200a0 . 0015 and p\u200a=\u200a0 . 0056 respectively; Figure 2C ) ., It should be noted that while we did observe these significant changes there are slight differences in the baseline fraction methylation at islands and shores between regions with hypomethylation events and those with hypermethylation events ( at the highest an absolute fraction methylation change of 0 . 16 ) ., We additionally analyzed the co-localization of windows of age associated methylation alterations with known regions of nucleosome retention in the mature sperm , as well as regions where specific histone modifications are found based on previous work from our laboratory 12 ., We found that approximately 88% of regions that are hypomethylated with age are found within 1 kb of known nucleosome retention sites in the mature sperm ( Figure 2D ) ., Interestingly , loci that are hypermethylated with age are far less frequently found in regions of histone retention , with only approximately 37 . 5% being associated with sites where nucleosomes are found , though there are only 8 regions of significance on which to base this analysis ., This difference was significant based on a fishers exact test ( p\u200a=\u200a0 . 002 ) ., Similarly , 23% of loci with age-associated hypomethylation are associated with H3K4 methylation and 45 . 3% are associated with H3K27 methylation ., The same co-localization is very rare with hypermethylation events ( p\u200a=\u200a0 . 0107 ) ., Additionally , we analyzed chromosomal enrichment of these marks to determine if there are specific chromosomal regions that are more susceptible to age-related methylation alterations ., We found a random distribution of significant age-associated methylation alterations throughout the entire genome with what appears to be enrichment at telomeric and sub-telomeric loci , however this apparent enrichment failed to reach significance ( Figure 3 ) ., To confirm our array data we selected 21 regions found to be significant by our array analysis and subjected them to targeted bisulfite sequencing ( on the MiSeq platform ) to confirm that the CpGs tiled on the array reflected the entire CpG content within the windows of interest ., Specifically , we amplified via PCR , bisulfite converted DNA from each donor ( young and aged collections ) ., The PCR was designed to produce amplicons of approximately 300–500 bp that were located within 21 of the regions of significant methylation alteration we identified by array ., Our depth of sequencing was quite robust with an average of 2 , 252 ( SE ±371 . 6 ) reads per amplicon in each sample ., The minimum number of average reads for any one amplicon was 313 ., In 20 of the 21 gene regions that were analyzed , the array and MiSeq data were similar in both direction and relative magnitude ( Figure 4A ) ., In the one case that did not show a similar trend ( hypomethylation with age by array and no change by MiSeq ) the amplicon was outside the region of the two CpGs that drove the significance of the window ., When comparing the methylation of the approximately 300 bp amplicon to the CpG tiled on the array in that same region only , and not the array CpGs over the entire 1000 bp window , the data are in agreement ., Taken together , the sequencing run confirmed that our array data is a good representation of the methylation status at all CpGs in our regions of interest ., To confirm that the sites identified on the array were not only altered in the samples we investigated , but that these loci are also altered with age in the sperm of non-selected individuals in the general population , we have performed an analysis on an independent cohort of individuals from two age groups: young , defined as <25 years of age ( n\u200a=\u200a47 ) , and aged , defined as ≥45 years of age ( n\u200a=\u200a19 ) ., Average age in the young cohort was 20 . 46 years of age ( SE ±0 . 18 ) , and in the aged cohort 47 . 71 years of age ( SE ±0 . 77 ) ., We performed a multiplex sequencing run on sperm DNA from these individuals to probe for 15 different regions of interest that were identified with the array data ., Briefly , we PCR amplified 15 regions ( using bisulfite converted DNA ) from each individual ( 47 young , and 19 aged ) ., The PCR was designed to produce amplicons of approximately 300–500 bp that were located within 15 regions of significant methylation alteration identified by array ., Our depth of sequencing was , again , quite robust with approximately 3 , 645 ( SE ±853 . 4 ) reads per amplicon in each sample with a minimum average number of reads for any one amplicon of 263 ., From these data we have confirmed that these genomic regions clearly undergo age-associated methylation alterations ( Figure 4B ) ., Interestingly , the average magnitude of alteration is also much higher in our independent cohort than in our initial paired donor sample study ( approximately 2 . 2 times greater on average ) ., This is particularly remarkable when considering that the average age difference in the independent cohort study was 27 . 2 years , effectively 2 . 3 times greater than the average age difference of 12 . 6 years seen in the paired donor analysis ., This further supports our regression data in the paired donor study , which generally suggest a linear relationship of methylation alterations with age at most of the identified genomic loci ., To address the question of the dynamics of sperm population changes associated with the approximately 0 . 281% change per year identified in this study we subjected our next generation sequencing data from the paired donor samples to a novel analysis where we compared the sperm population shifts between the young and aged samples ., Because the MiSeq platform produces data for each single nucleotide sequence ( each representing the methylation status in a single sperm ) we are able to determine average methylation at each region for all of the amplicons analyzed ., We identified 3 general patterns in methylation profile population shifts that resulted in the age–associated methylation alterations we identified ., First , we identified regions whose methylation at an age <45 was strongly hypomethylated , and the methylation profile in individuals >45 years of age is virtually the same , though it is more strongly hypomethylated ., In these cases the change is still strikingly significant , but the magnitude of fraction DNA methylation change is minimal ., Second , we see a single population in samples collected at <45 years of age that is shifted toward more hypomethylation in samples collected at >45 years of age ., Last , we identified a bimodal distribution in samples collected <45 years of age that , in samples >45 years of age , is stabilized into a single population ( Figure 5 ) ., This could be indicative of at least two sperm subpopulations , which are biased to a single , more hypomethylated sperm population with age ., In every case the results suggest that all of the alterations we detected with the array are the result of the entire sperm population being altered in similar subtle ways and not a result of a dramatic alteration in a small portion of the sperm population ., The genes affected by the age associated methylation alterations ( those that have alterations that occur at their promoter , or gene body ) were analyzed by Pathway , GO and disease association analysis ., The results indicate that no one GO term or Pathway is significantly altered in our gene group ., Similarly , there were no significant diseases or disease classes associated with the genes identified in this study based on results of the disease association tool on DAVID ., However the most significant disease hits ( those that were significant prior to multiple comparison correction ) have both been suggested to have increased incidence in the offspring of older fathers , namely myotonic dystrophy and schizophrenia 2 , 7 ., To directly investigate the disease association ( s ) in our set of genes we searched the National Institute of Healths ( NIH ) genetic association database ( GAD ) ., We investigated all 117 genes that were determined to have age associated methylation alterations ( 110 hypomethylated; 7 hypermethylated ) for their various disease associations ., From these a total of 46 genes have been confirmed to be associated with either a phenotypic alteration or a disease based on GAD annotation ., We identified 4 diseases that were most commonly associated with our set of genes ( those disease that are associated with at least 2 genes identified in our study; diabetes mellitus , hypertension , bipolar disorder and schizophrenia ) ., To further investigate these associations , we analyzed the frequency of genes associated with these 4 diseases in our gene set and compared it to their frequency in all 11 , 306 genes known to be associated with either a phenotypic alteration or a disease ., Only bipolar disorder appeared to be more frequently associated with our identified genes than the background set of genes , based on chi-squared analysis with multiple comparison correction ( Bonferonni ) of the 117 age associated genes identified in our analyses ( p\u200a=\u200a0 . 012 ) ., Interestingly , schizophrenia also appeared to trend toward increased frequency ( p\u200a=\u200a0 . 07; figure 6 ) ., However , it is important to note that these are not considered significant enrichments if considering correction for comparisons with all genes in the genome ( omitting the filter for a disease connection ) ., The frequency of genetic association between our gene set and the background gene set was statistically similar for both hypertension and diabetes mellitus ., To investigate the attributes of regions that we determined to be most susceptible to methylation alterations , we evaluated the co-localization of significantly altered loci in our study with regions of nucleosome retention in the mature sperm ., It appears that hypomethylation events are most commonly associated with sites of nucleosome retention ., It should be noted that our criteria for sites of nucleosome retention is simply that our sites of alteration occur within 1 kb of known retention sites and thus there may be a greater degree of complexity in the actual sites of methylation alteration than we have identified ., The actual nature of methylation patterns at a higher resolution in these regions ( whether the affected regions are flanking or directly associated with histones ) is difficult to elucidate due to the nature of array tiling in many of the loci we identified ., Interestingly , this same co-localization was not seen with hypermethylation events ., Though co-localization patterns are significantly different between the hypomethylation and hypermethylation events , it should be noted that the sample size is quite small in the hypermethylation group ( 8 significant windows ) ., It should also be noted that while the co-localization of histones and the hypomethylation events we observed in our study are significant , the methylation marks observed are likely established earlier in spermatogenesis and thus may not be affected by the nucleosome architecture in the fully matured sperm ., In addition , the alterations identified in this study are not localized everywhere that histones are retained , thus nucleosome retention alone cant be the independent driving force of regional susceptibility to methylation alterations ., It should be further noted that our approach was not targeted to observe changes in chromatin co-localization patterns and as such this represents a secondary analysis of these patterns with the use of a “promoter array . ”, As a result of observing only a selected portion of the genome , there are clear biases that are introduced that should be taken into account when considering these findings ., Recent literature suggests an interesting hypothesis of “selfish spermatogonial selection” that may have application in this study as well 29 ., Briefly , the hypothesis states that some gene mutations that are causative of abnormalities in the offspring are beneficial to spermatogenesis and become enriched throughout the aging process in spermatogonial stem cells ., Thus , sperm carrying these mutations become more frequent in the population to the detriment of the offspring ., Similarly , it is possible that the age-associated methylation alterations we have identified may be in regions that are important to spermatogenesis and thus would be selected for ., While the genes identified herein are not well known spermatogenesis hotspots , they may lie close to other genes that are important in development and thus may be subject to a looser chromatin state leaving these genes more susceptible to methylation perturbations ., The hypomethylation events we identified could occur as a result of either active or passive demethylation ., For example , regional transcription activity at loci important in spermatogenesis would likely be accompanied by a relaxed chromatin structure that could result in increased frequency of DNA damage over time ., Established methylation marks located within this region could then be passively removed through repair mechanisms in the developing sperm ., If the removal of this mark is either beneficial or has no effect on spermatogenesis it will persist , and over time similar marks could accumulate at nearby CpGs ultimately leading to the profile we identified in our study ., It should also be noted that the accumulation of de novo mutations could lead to a similar profile ., It is clear that the number of mutations in the sperm increase with age , and if these mutations involve deamination of cytosine residues the resulting sequence could appear as a loss of methylation with the technologies utilized herein ., However , the mutation load , and specifically these C to T transitions , in sperm are stochastic in nature and thus cannot be the primary driving factor for the genomic hotspots of age-associated hypomethylation seen in virtually all of the individuals screened 30 ., Alternatively , active enzymatic removal of methylation marks in the sperm might drive age-associated methylation changes ., For this to be mechanistically plausible we would have to assume that hypomethylation in the windows we identified is always beneficial to spermatogenesis ., While either of these mechanisms is plausible , it is likely that the effects we have identified involve some combination of both ., The mechanics of hypermethylation events with age are more difficult to elucidate , as this , by definition , has to be an active targeted process involving methyltransferase enzymes ., However , some evidence from this study indicates DNA sequence may be an important driver of age-related hypermethylation ., Of the 7 windows that we identified with gene-associated hypermethylation with age , 4 are associated with the FAM86 family of genes that are categorized not by protein function or genomic location but sequence similarity ., This strongly suggests that , at least in part , age associated hypermethylation events at specific loci are driven , either directly or indirectly , by DNA sequence ., Interestingly , this family of genes ( FAM86 ) with unknown function has recently been categorized with a larger family of methyltransferase genes , though it remains unclear what the FAM86 target ( s ) is/are ( DNA , Histone , other proteins , etc . ) ., It is important to note that in addition to these regional hypermethyaltion events , globally DNA methylation is markedly increased as well ., The possible role of chromatin modifications ( histone tail modifications , etc ) in this process is also important to note , as what we have identified may be linked to regional histone methylation , acetylation , etc ., Such histone modifications may reflect underlying transcriptional changes during spermatogenesis ., Taken together , the mechanisms that drive age-related methylation alterations in the sperm remain elusive , but likely involve both active and passive methylation modification ., It is important to consider two questions in determining the biological impacts of the identified methylation changes in this study ., First , are the methylation changes described herein capable of transcriptional alterations ?, Second , are these methylation changes capable of avoiding embryonic methylation reprogramming ?, Regardless of the mechanism by which these methylation marks are altered in the sperm over time , it is striking that these changes occur with such consistency between individuals and have such a tight association with age that was seen in both the paired donor analysis and the independent cohort analysis ., This is in stark contrast to the relative stability of the sperm methylome seen over time within each individual in the majority of the genome ., One limitation of these findings , however , is the magnitude of alterations we have discovered ., As described earlier the average fraction methylation alteration per year was approximately a change of 0 . 281% ., Though this seems relatively small , when expanded to include the possible reasonable reproductive years in a male the change would be 10–12% ., The increased magnitude of change with increasing age is strongly supported by our independent cohort study where an increase in the age difference between two groups was directly correlated with an increase in the magnitude of methylation alterations at virtually every locus screened in a relatively linear manner ., Importantly , based on our analysis of complete nucleotide sequences from our sequencing data it appears that this decrease of 10–12% reflects changes to random CpGs within windows of susceptibility in all sperm , which would manifest in an individual sperm as a mosaically methylated region ., The resultant 10–12% change in methylation within every individual sperm ( effectively 1 out of every 10 CpGs are demethylated ) suggests that every sperm carries similar , more subtle , alterations within these regions on average ., It is important to note that because we only investigated a portion of the regions of interest in our sequencing run ( used for confirmation of array results ) and the amplicons we probed made up only a portion of the regions of interest , we can not make a definitive overarching statement about the dynamics of methylation profile population shifts in sperm as a result of age ., Despite this , the consistency of population shifts in the regions we were able to observe suggests that other regions of interest would likely follow similar patterns ., Regardless , the resultant age-associated epigenetic landscape alterations may contribute to disease susceptibility in the offspring despite the small degree of change though the increased risk to the offspring may be relatively small ., Figure 7 illustrates the alterations seen at two representative loci from our analysis , Dopamine receptor D4 ( DRD4; ENSG00000170956 ) and tenascin XB ( TNXB; ENSG00000168477 ) ., The heritability of such marks is more difficult to elucidate mainly because the current study does not directly address this question ., However , this issue needs to be addressed as the identified age-associated methylation alterations in the mature sperm may be removed through the embryonic demethylation wave ., Despite the fact that there is no direct evidence of methylation alteration heritability at the specific loci presented in this work , the observed age-associated changes at regions known to be of significance in diseases with increased incidence in the offspring of aged males is striking and warrants further study ., The intriguing localization of these alterations suggests that the methylation profile in the mature sperm , at specific loci , either contributes to the increased incidence of associated abnormalities in the offspring or that they reflect ( are downstream of ) changes that are actually causative of the associated abnormalities in the offspring ., Moreover , it has been previously proposed that epigenetic alterations are among the most likely candidates to transmit such transgenerational effects , and we have identified methylation alterations that appear capable of contributing to the various pathologies associated with advanced paternal age ., Despite this , future work must still be performed to determine the real impact these marks have on transcription and thus phenotype and disease ., Taken together , these subtle yet highly significant , age-associated alterations to the sperm methylation profile are intriguing because of their location and consistency , but more work is required to elucidate the biological impact of these marks ., There are many genes identified in our study that , if biologically affected , may result in pathologies in the offspring ., DRD4 is one of the most widely implicated genes in the pathology of both schizophrenia and bipolar disorder as well as many other neuropsychiatric disorders 31 , 32 ., Interestingly , the entire DRD4 gene itself is hypomethylated with age ( Figure 7 ) ., TNXB has also been suggested to be associated with schizophrenia based on multiple studies , though the data are controversial 33 , 34 , and virtually the entire 1st exon of TNXB is hypomethylated with age ., Additionally , DMPK ( ENSG00000104936 ) , a gene identified in our study , is known to be associated with myotonic dystrophy , a disease for which advanced paternal age is a risk factor 7 ., In fact , increases in trinucleotide repeats in DMPK are believed to be the cause of myotonic dystrophy type 1 ., Importantly , previous data suggests that altered methylation marks may affect trinucleotide instability 35 ., These examples represent only a portion of the genes that were identified in our study and support the hypothesis that age-associate

Introduction, Results, Discussion, Methods

Recent evidence demonstrates a role for paternal aging on offspring disease susceptibility ., It is well established that various neuropsychiatric disorders ( schizophrenia , autism , etc . ) , trinucleotide expansion associated diseases ( myotonic dystrophy , Huntingtons , etc . ) and even some forms of cancer have increased incidence in the offspring of older fathers ., Despite strong epidemiological evidence that these alterations are more common in offspring sired by older fathers , in most cases the mechanisms that drive these processes are unclear ., However , it is commonly believed that epigenetics , and specifically DNA methylation alterations , likely play a role ., In this study we have investigated the impact of aging on DNA methylation in mature human sperm ., Using a methylation array approach we evaluated changes to sperm DNA methylation patterns in 17 fertile donors by comparing the sperm methylome of 2 samples collected from each individual 9–19 years apart ., With this design we have identified 139 regions that are significantly and consistently hypomethylated with age and 8 regions that are significantly hypermethylated with age ., A representative subset of these alterations have been confirmed in an independent cohort ., A total of 117 genes are associated with these regions of methylation alterations ( promoter or gene body ) ., Intriguingly , a portion of the age-related changes in sperm DNA methylation are located at genes previously associated with schizophrenia and bipolar disorder ., While our data does not establish a causative relationship , it does raise the possibility that the age-associated methylation of the candidate genes that we observe in sperm might contribute to the increased incidence of neuropsychiatric and other disorders in the offspring of older males ., However , further study is required to determine whether , and to what extent , a causative relationship exists .

There is a striking trend of delayed parenthood in developed countries due to secular and socioeconomic pressures ., As a result , physicians commonly consult with concerned patients inquiring about the impact of advanced age on their ability to conceive healthy offspring ., The concern has more frequently surrounded the effects of advanced maternal age , but recent evidence suggests negative effects of advanced paternal age as well ., Specifically , studies have demonstrated increased incidence of neuropsychiatric and other disorders in the offspring of older males ., In this study we have investigated a commonly hypothesized mechanism for this effect , namely sperm DNA methylation alteration ., Our data indicate that specific genomic regions of DNA methylation are commonly altered with age , suggesting that some regions of the sperm genome are more susceptible than others to age-related epigenetic changes ., Importantly , a significant portion of these alterations occur at genes known to be associated with schizophrenia and bipolar disorder , both of which display increased incidence in the offspring of older fathers ., These data will be important in driving future studies aimed at determining the impact that these methylation alterations may have on offspring health and will thus enable couples at advanced reproductive ages to be more informed of possible risks .

urology, medicine and health sciences, genetics, epigenetics, biology and life sciences, infertility

journal.pgen.1008116

Infection mechanisms and putative effector repertoire of the mosquito pathogenic oomycete Pythium guiyangense uncovered by genomic analysis

Mosquitoes are a major threat to global health since they are vectors of numerous devastating diseases , including malaria , dengue fever , Zika virus and other arboviruses , which together result in hundreds of millions of cases and several million deaths annually 1 ., Existing commonly used control methods for reducing disease rely on the application of residual synthetic pesticides ., However , intensive and repeated use of pesticides leads to ongoing development of resistance , environmental pollution and toxicity to human and non-target organisms 2 ., Control strategies utilizing naturally occurring microbial pathogens have therefore emerged as a promising alternative ., A particular focus has been on biological control agents 2 ., Among them , the oomycete Lagenidium giganteum and the fungal pathogens , Beauveria bassiana and Metarhizum anisopliae , are well characterized , promising agents for mosquito larvae control , and have been produced commercially for field tests 2–4 ., However , so far , available agents for mosquito control are rather limited ., Recently , a new mosquito-pathogenic oomycete , Pythium guiyangense X . Q . Su was isolated from infected larvae of Aedes albopictus from Guizhou , China 5 ., It is a virulent pathogen of a wide range of mosquito larvae and is safe to non-target organisms 6 , 7 ., The pathogen also shows robust adaptability to a variety of natural environments and can be easily mass-produced 7 ., All these properties of P . guiyangense make it of interest for practical applications as a potential mosquito control agent ., However , little is known about the molecular mechanisms underlying the pathological processes on its mosquito hosts ., It belongs to the genus Pythium ( kingdom Stramenopila; phylum Oomycota , class Peronosporomycetes ) 8 ., Within the oomycetes , the genus Pythium is a genetically diverse group with a broad host range ., For example , many Pythium species are important plant pathogens causing a variety of diseases 9 , 10 ., On the other hand , P . undulatum can infect fish and P . insidiosum is a well-known pathogen that is capable of infecting human and other mammals 11 ., To date , only P . guiyangense has been proposed for mosquito control 5 ., The availability of genome sequences of a variety of pathogenic oomycetes , including Pythium species , provides a unique opportunity for comparative analysis between P . guiyangense and other oomycetes with respect to the evolution of pathogenicity ., A number of genome sequences of plant pathogenic oomycetes are now available 12–14 ., In addition , the genomes of mycoparasitic Pythium species that infect fungi , the human pathogen P . insidiosum , and the fish pathogens S . parasitica and S . diclina have also been sequenced 15 , 16 ., Oomycetes secrete an arsenal of effectors into the host to manipulate the host immune system and enable parasitic infection 17 ., These effectors have been a central question in the study of plant-oomycete interactions , including extracellular proteins such as toxins and hydrolases , and cell-entering proteins such as the RxLR ( Arg-X-Leu-Arg ) and Crinkler ( CRN ) effectors 18 ., P . guiyangense may share conserved virulence effectors with other pathogenic oomycetes because of their close evolutionary relationships ., Here , we determined the infection cycle of P . guiyangense and demonstrated an unusual process , in which mycelia were devoured by the mosquito larvae and the mycelia inside digestive system could effectively initialize infection ., Then we produced a high-quality genome sequence assembly , which represents the first draft genome from an insect pathogenic oomycete ., Based on the transcriptome , effector prediction , and comparative genome analyses with other Pythium species , we investigated its insect-killing mechanisms and finally identified several cytoplasmic effectors having virulence functions in insect cells , thus expanding the roles of oomycete effectors ., The P . guiyangense isolate was reported to be highly virulent on mosquito larvae , but infection was not quantitated 5 , 19 ., We created a quantitative virulence assay by inoculating early second-instar larvae of Aedes albopictus or Culex pipiens pallens with P . guiyangense mycelia ., The cumulative survival curves revealed that daily survival of A . albopictus and Cx ., pipiens pallens larvae quickly declined from 3~4 days post-inoculation ( dpi ) onwards , reaching 76% mortality for A . albopictus and 69% for Cx ., pipiens pallens by 10 dpi ( S1A Fig ) ., A . albopictus larvae died faster than Cx ., pipiens pallens larvae , reaching 50% survival by day 6 compared to day 8 ( S1A Fig ) ., Furthermore , P . guiyangense could infect all the tested stages of Cx ., pipiens pallens , including eggs , larvae , pupae and adults , resulting a visible accumulation of mycelia by 3–4 dpi ( S1 Video ) and all tissues were fully covered by mycelia ( S1B Fig ) ., Together , the results confirmed that P . guiyangense is highly efficacious in killing all life stages of Aedes albopictus and Cx ., pipiens pallens ., To investigate the infection process of P . guiyangense , early second-instar larvae of Cx ., pipiens pallens were incubated with mycelia or swimming zoospores ., Our results showed that zoospores attached to almost any part of the mosquito larval cuticle ( Fig 1A showing zoospore attachments on the thorax and abdomen ) ., Then , germination of the cysts occurred and appressorium-like swellings appeared at the tip of the germ tubes as visualized using scanning electron microscopy ( SEM ) ( Fig 1B ) ., Penetration hyphae emerging from the appressorium traversed the insect integument ( Fig 1C ) and invaded the hemocoel of larvae ., Eventually the mycelia filled the whole body , then emerged through the inner cuticle and formed sporangia ( Fig 1D ) ., We also observed that Cx ., pipiens pallens larvae readily ingested P . guiyangense mycelia even in an adequate food environment ( Fig 1E , S1C Fig , S2 Video ) ., A thick section of a moribund larva visualized with SEM showed that , following feeding , the midgut was completely packed with mycelia that could initialize infection ( Fig 1F ) ., After fixation , embedding in paraffin , and sectioning , microscopic observations showed that the midgut epithelium , muscles , and connective tissues appeared disrupted in the P . guiyangense-infected larvae ( Fig 1G and 1H ) ., After 48 hpi , infected larvae appeared almost devoid of internal organs or tissues and the whole body was permeated with mycelia ( Fig 1I ) ., Thus invasion through the digestive tract is an effective route of infection by P . guiyangense ., Taken together , these observations define two routes of invasion , namely infection through the exterior cuticle and through the digestive tract ., A high quality genome sequence of P . guiyangense was generated using a hybrid strategy that combined sequences from Pacific Biosciences long reads and Illumina short reads ., The genome assembly indicated in an estimated P . guiyangense genome size of 110 Mb and annotation predicted 30 , 943 protein-coding genes ( Table 1 ) ., The assembled genome consisted of 239 contigs with an N50 contig length of 1 , 009 kb ., To assess the completeness of the genome assembly , CEGMA analysis , which identifies orthologs of 248 ultra-conserved core eukaryotic genes ( CEGs ) , was used to identify core genes in the P . guiyangense genome ., The results revealed complete matches to 97 . 6% of CEGs and at least partial matches to 98 . 4% of CEGs within the P . guiyangense assembly; these results compared to only 78 . 2–94 . 4% of complete CEGs and 91 . 5–95 . 6% of partial CEGs in other Pythium genomes ( S1 Table ) ., Taken together , the comparison with other assembled Pythium genomes including P . insidiosum , P . ultimum , P . aphanidermatum , P . arrhenomanes , P . irregulare and P . iwayamai , revealed that the P . guiyangense assembly represented the best quality among the sequenced Pythium genomes so far ., Whole transcriptome sequencing ( RNA-seq ) was performed using Illumina sequencing of RNAs from P . guiyangense mycelia and from early second-instar larvae 24 hr after inoculation with P . guiyangense mycelia ., Transcripts from each individual library matched approximately 69% of the genes , and together matched approximately 74% of the genes ( S2 Table ) ., A total of 3 , 354 genes ( 10 . 8% ) were differentially expressed ( >4-fold expression difference and statistical GFOLD value > 1 or < -1 ) between the two samples; 1 , 654 genes were up-regulated in the infection stage while 1 , 700 genes were down-regulated ., Functional enrichment analysis revealed that genes encoding tyrosine kinase-like ( TKL ) kinases , subtilisin proteases , kazal-type protease inhibitors , and elicitin proteins , were over-represented among the differentially expressed genes ( S3 Table ) ., To validate the differentially expressed genes , we selected 18 genes that belonged to the above mentioned over-represented gene families and that were up- or down-regulated based on RNA-Seq data , and then measured their transcript levels by the qRT-PCR assay ., The qRT-PCR results showed that the transcriptional patterns of 17 among the 18 genes were consistent with RNA-Seq results ( S2 Fig ) , which further supported the general reliability of the RNA-Seq data ., Our comparative genome analysis revealed that the genome size ( 110 Mb ) and predicted gene number of P . guiyangense ( 30 , 943 genes ) were approximately twice those of the other sequenced Pythium species ( Table 1 ) ., To explore the potential mechanisms underlying such a large genome size , we initially analyzed the repetitive DNA content within the P . guiyangense genome and found that the repeat sequence content was 6% , similar to that of P . ultimum ( 7% ) which has a genome size of only 43 Mb , thus excluding the possibility that high repeat content was responsible for the large genome size , which was observed in Ph . infestans 14 ., Previously , hybridization between two parental species has been reported in yeast and Phytophthora evolution 20 , 21 ., Most ( 84% ) of the Pythium core genes ( present in all the 7 Pythium genomes ) were present in two copies in the P . guiyangense genome , consistent with a hybrid origin , which is similar to the yeast 20 ., Typically , the copied core genes shared the highest sequence similarity with genes derived from the P . irregulare genome , however , the two copied genes were about 8% different in nucleotide sequence ., To further confirm the hypothesis of hybridization , internal synteny is selected as a useful indicator , which could be used to evaluate hybridization between two parental genomes 22 , 23 ., Therefore , we characterized internal synteny across the P . guiyangense genome using the MCScanX program 24 ., In the whole genome , 468 conserved synteny blocks with an average size of 192 kb were identified ( Fig 2A , S4 Table ) ., These synteny blocks covered 74% of all the contigs , and together spanned 84% of the genome , suggesting that the P . guiyangense genome could be classified into two subgenomes ., To further compare the genetic relatedness of the two parental subgenomes , the average nucleotide identity ( ANI ) was calculated , and the ANI between the two subgenomes revealed approximately 91% identity ., Notably , a total of 11 , 068 pairs of homologous genes were identified in these synteny blocks ( Fig 2B , S4 Table ) , consistent with a hybrid origin ., We estimated the rates of synonymous substitutions per synonymous site ( Ks ) of 11 , 068 pairs of homologous genes ., This analysis showed a synonymous site divergence peak of Ks = 0 . 35 ( Fig 2C ) , indicating that the two subgenomes were relatively diverse ., Taken together , we inferred that P . guiyangense was a hybrid genome derived from two distinct parental species ., To further investigate the parental species of P . guiyangense , we systematically analyzed CEGs in the 7 sequenced Pythium genomes ., The majority of CEGs were present as two copies in the P . guiyangense genome but only one copy in each of the other Pythium genomes ( Fig 2D and 2E ) ., A total of 167 CEGs that contained 2 copies in P . guiyangense and also had orthologs in other Pythium species were utilized for phylogenetic analysis ., For each phylogenetic tree , the two copies of the P . guiyangense CEG always clustered together most closely , and then clustered with the orthologs from the other Pythium species ( one tree based on the KOG1439 protein is shown in Fig 2F as an example ) , indicating that the parental species of P . guiyangense were not represented in the data set ., In addition , cytochrome oxidase II ( cox II ) and β-tubulin genes , which have been widely used as phylogenetic maker genes , were available in 35 Pythium species and contained 2 copies in P . guiyangense ., Phylogenetic analyses of the two genes showed that the two P . guiyangense orthologs were more similar to one another than the nearest known species ( P . orthopogon ) ( S3A and S3B Fig ) , indicating that the parental species of P . guiyangense were not represented based on current information ., We speculate that the parental species of P . guiyangense are more closely related to each other than to the known Pythium species ., In parallel with the genome analysis , we noticed that an unusual high percentage of P . guiyangense zoospores contained two nuclei rather than one ( S3C Fig ) ., Among 500 observed zoospores , nearly 22% of them contained two nuclei in P . guiyangense while in P . aphanidermatum and Ph . capsici , all the spores had only one nucleus ( S3C Fig ) ., We then found that the P . guiyangense zoospores containing only one nucleus could also breed similar percent of zoospores containing two nuclei , and PCR amplifications resulted in presence of both of the two copied genes ., This observation suggested that P . guiyangense might be a dikaryon , and its relationship with the complex genome is still under investigation ., To establish the phylogenetic relationship of P . guiyangense among oomycetes , a phylogenetic tree was constructed based on 248 CEGs from P . guiyangense and other 12 oomycetes , with the diatoms as outgroups ( Fig 3A ) ., The tree clearly showed that P . guiyangense was clustered within the clade formed by the plant pathogenic Pythium species , and was distantly related to other genera , including Hyaloperonospora and Phytophthora ., This phylogeny was consistent with that in previous publications 15 , 25 ., These results imply that P . guiyangense , along with the mammalian pathogen P . insidiosum share common ancestors with the plant pathogenic Pythium species ( Fig 3A ) ., To identify the genes responsible for host adaptation in P . guiyangense , the OrthoMCL tool was used to cluster the seven Pythium proteomes on the basis of protein sequence similarity ., A total of 25 , 602 ( 83% ) P . guiyangense genes had orthologs in other Pythium species ., Among these , P . guiyangense shared 13 , 000 core genes with the other Pythium species ., In addition , 5 , 341 genes were identified to be specific to P . guiyangense ( S4 Fig ) ., To gain insights into the features of species-specific genes in P . guiyangense , we compared the frequency of occurrence of protein family domains and identified highly over-represented domains included kinase ( PF00433 ) , kazal inhibitor ( PF00050 ) , elicitin ( PF00964 ) , and protease ( PF02902 ) ( Fig 3B , S5 Table ) ., These gene families were also enriched among genes differentially expressed during infection ., By searching with the HMM profiles of kinase domains derived from KinBase , 471 unique protein kinases ( 943 kinases in total ) were identified in the P . guiyangense genome , greatly surpassing the numbers in plant pathogenic Pythium genomes , which range from 152 to 192 ( Table 2 ) ., Intriguingly , other two animal pathogenic oomycetes , P . insidiosum and S . parasitica , also have expanded kinomes , coding for 286 and 538 kinases , respectively 15 , 16 ., We further classified the kinases into 9 families defined by Hanks and Hunter 26 ., Five families , including TKL ( tyrosine kinase-like ) , CAMK ( calcium/calmodulin-dependent kinase ) , CMGC including cyclin-dependent kinases ( CDKs ) , mitogen-activated protein kinases ( MAP kinases ) , glycogen synthase kinases ( GSK ) and CDK-like kinases , AGC ( cAMP-dependent , cGMP-dependent and protein kinase C ) and other were noticeably expanded in P . guiyangense ( Fig 4A ) ., Among the 5 expanded families , a total of 220 unique TKL genes were identified in P . guiyangense kinome ., A comparison of the locations of TKL genes in the P . guiyangense and P . ultimum genomes revealed extensive rearrangements , which resulted from species-specific expansions at the locations of these genes ( Fig 4B ) ., Forty-six unique kinases belonging to AGC family and 50 unique members of the CAMK family were identified from P . guiyangense ., Based on the RNA-Seq data , a total of 92 kinase genes were differentially expressed at the infection stage , including 52 TKL kinase genes ( S6 Table ) ., These results suggest that many of the protein kinases may be involved in regulation of infection processes and adaptation to the mosquito hosts ., Since major structural and physiological differences were observed between plant cell walls and insect cuticles , we compared the repertoire of plant cell wall and cuticle degrading enzymes encoded in the P . guiyangense genome to other oomycete genomes ., Several groups of plant cell wall degrading enzymes , such as GH53 , GH78 , CE5 , GH10 and GH11 , and GH12 were completely absent in P . guiyangense ( S7 Table ) ., Genes encoding 12 unique pectin/pectate lyases ( PL1 , PL3 and PL4 ) , two unique GH28 and 1 unique GH43 involved in pectin backbone degradation were identified in the P . guiyangense genome; however , RNA-Seq data showed that none of these genes exhibited up-regulation during mosquito infection processes ., P . guiyangense had more genes encoding proteases potentially involved in insect cuticle degradation than plant pathogenic Pythium species ( Fig 4C ) ., A total of 307 unique proteases ( 615 genes in total ) were encoded in the P . guiyangense genome , compared to an average of 260 proteases in the plant pathogenic Pythium species ( Table 2 ) ., The two animal pathogen genomes also had large numbers of proteases ( Table 2 , Fig 4C ) ., Among them , genes encoding cysteine- , metallo- and serine-proteases were particularly highly expanded in P . guiyangense ( Fig 4C ) ., The subtilisin serine-protease family had the highest relative expansion with 32 unique genes in P . guiyangense ( Table 2 , Fig 4C ) ., Phylogenetic analysis revealed that over half of the subtilisin proteases were recently expanded in P . guiyangense due to lineage-specific gene duplications ( S5 Fig ) ., Based on the RNA-Seq data , 31% of the total subtilisins were significantly up-regulated during mosquito infection ., The peptidase_C1 and carboxypeptidases also exhibited significant expansion in P . guiyangense ( Table 2 , Fig 4C ) ., Protease inhibitors regulate various biological and physiological processes in all living systems as modulators of protease activity 27 ., Among them , the kazal-type protease inhibitor ( KPI ) family is one of the best characterized 27 ., A total of 19 unique kazal inhibitors were identified in the P . guiyangense genome , which exceeded those in plant pathogenic Pythium species ( Table 2 ) ., The animal pathogen , P . insidiosum also encoded larger numbers of kazal inhibitors ., A phylogenetic tree was constructed using the Pythium kazal inhibitors , and the majority of genes derived from P . guiyangense and P . insidiosum formed clusters that were species-specific ( S6A Fig ) , implying that these genes were retained and diversified independently in these two animal pathogens ., During mosquito infection , 25% of the P . guiyangense kazal inhibitors were up-regulated , and four of these exhibited transcript levels over 40 times those in the mycelia sample ( S6B Fig ) ., The transcriptional patterns of the 4 kazal inhibitors at three infection time points were analyzed by qRT-PCR , and results revealed that all the 4 genes were up-regulated during the infection process ( S6C Fig ) ., Further analysis demonstrated that all of the up-regulated kazal inhibitors contained signal peptides , indicating that they could play important roles in the pathogenesis ., A common feature of many plant pathogenic oomycetes is the secretion of a variety of apoplastic ( extracellular ) proteins to promote infection , some of which can be detected by the host immune system ., These include elicitins ( ELIs; lipid-binding proteins ) , elicitin-like ( ELL ) proteins and Nep1-like proteins ( NLP ) 28 ., Ten unique ELI genes were identified in the P . guiyangense genome ., In contrast , only 2 ELI genes were found in the P . irregulare genome , and none were identified in the other Pythium and S . parasitica genomes ( Fig 5A , Table 2 ) ., Based on phylogenetic analysis of the elicitin domains , P . guiyangense ELIs were distributed into two clades , and one clade included genes from diverse species while the second clade only contained P . guiyangense ELIs ( Fig 5B ) ., Moreover , 19 of the 20 ELI genes were physically clustered in the P . guiyangense genome , suggesting that ELIs were expanded in a species-specific manner ., In contrast to the ELI genes , ELL genes were widely distributed in all the detected Pythium genomes ., Both P . guiyangense and P . insidiosum had more ELL genes ( 45 and 50 unique genes ) than the plant pathogenic Pythium species ( 23–40 genes ) ( Fig 5A , Table 2 ) ., Further phylogenetic analysis showed that over half of the ELL genes were distributed in nine clades which were specific to P . guiyangense and contained at least four members; thus many ELL genes were specifically expanded in P . guiyangense ., Based on the P . guiyangense transcriptome analysis , 45% of the ELI genes were differentially expressed , and all were down-regulated during infection ( Fig 5C ) ., In contrast , 31% of the ELL genes were up-regulated while 15% were down-regulated during infection ( Fig 5C ) ., This observation suggested that the diverse ELIs and ELLs had a variety of different functions relative to growth and infection ., Another common apoplastic effector family is the necrosis and ethylene-inducing-like proteins ( NLP ) genes ., Many NLPs , but not all , can trigger cell death and defense responses in plants 29 ., Only 1 unique NLP gene was found in P . guiyangense and none were found in P . insidiosum ( Table 2 ) ., This NLP protein belonged to type 1 NLP subfamily with two conserved cysteine residues ., Transcriptional analysis revealed no significant change during infection ., These observations suggest that NLP proteins may not participate in oomycete-animal interactions ., Crinkler ( CRN ) , a large class of cytoplasmic effectors , was first identified in Ph . infestans as a family of proteins that could cause plant cell death and defense responses 30 ., A total of 38 CRN candidates were predicted in P . guiyangense ( S8 Table ) , compared to 10–46 predicted CRN proteins in the other Pythium species using the same method ( Table 2 ) ., Examination of protein alignments of P . guiyangense CRN effectors revealed considerable conservation of the characteristic LxLFLAR/K and HVLVxxP motifs , which were similar to those observed in plant pathogenic Pythium species 13 , 25 ., Based on five secretion signal predictors , 74% of CRN candidates in P . guiyangense contained a potential signal peptide or non-classical secretion signal ( S8 Table ) , suggesting that the majority of P . guiyangense CRN proteins might be secreted into mosquito hosts ., A homology network of the oomycete CRN proteins was generated to investigate the evolutionary relationships between P . guiyangense and other oomycetes ., The network is composed of 633 nodes in which each node represents an individual CRN protein ., The network contains 34 , 149 edges that link nodes if the node proteins are homologous based on an all-versus-all BlastP search with an E-value cutoff of 10−10 ., As shown in Fig 6A , the network was comprised of a crowd of disconnected clusters and a small number of singletons ., P . guiyangense CRN proteins were mainly distributed in 3 large and 3 small clusters ( cluster I-VI represented by red dotted circles ) ., Cluster I and III were composed primarily of P . guiyangense CRN proteins , with some of these proteins having homology to Pythium proteins ., Notably , cluster II , IV and V only contained CRN proteins derived from P . guiyangense , revealing that these CRN proteins did not share significant sequence similarity with other oomycete CRN proteins ., Moreover , all of the P . guiyangense CRN proteins showed sequence divergence of at least 50% with the most similar CRN protein in any plant pathogenic Pythium species , indicating that the CRN proteins are highly divergent between insect pathogenic Pythium species and plant pathogenic Pythium species ., To explore the possible functions of P . guiyangense CRN proteins in insect cells , twenty-six CRN genes were expressed in Spodoptera frugiperda cell ( Sf9 ) lines; successful expression of the proteins was confirmed with western blots or by detecting fluorescence signals under the fluorescence microscope ( S7A and S7B Fig ) ., The cell counting Kit-8 assay was used to determine protein toxicity to cells , with the Bacillus thuringiensis Delta-Endotoxin Cry1C as a positive control 31 ., The results showed that 7 CRN proteins ( CRN31 , 33 , 34 , 36 , 37 , 38 , and 28 ) significantly decreased the viability of Sf9 cells while the remaining CRN proteins produced responses similar to the negative control ( Fig 6B ) ., Notably , CRN31 appeared to be the most toxic to Sf9 cells ., To further validate the toxicity of these 7 CRN proteins , a prokaryotic expression system was used to obtain recombinant CRN proteins ( S7C Fig ) ., E . coli crude extracts containing the expressed proteins were then incubated with Sf9 cells and with mosquito Aedes albopictus C6/36 cells , respectively , to determine the toxicity using the cell counting Kit-8 assay ., The results showed that CRN31 and CRN28 significantly reduced the viability of Sf9 cells and C6/36 cells ( Fig 6C and 6D ) ., Since the transcript levels of the CRN31 and CRN28 genes were not elevated during infection at 24 hpi as measured by RNA-seq ( S8 Table ) , we used qRT-PCR to test whether the two CRN genes were significantly up-regulated during earlier infection stages ( 1–4 hpi ) ( S7D and S7E Fig ) ., CRN31 exhibited the highest transcript level change with a 65 fold change at 2 hpi while CRN28 showed 4–5 fold changes at 1–3 hpi ( S7D and S7E Fig ) ., Together these results suggested that CRN31 and possibly CRN28 might act as cell-killing effectors during insect infection ., In this study , we have determined the mode of infection of P . guiyangense on mosquito larvae ., In our experiments , P . guiyangense caused up to 76% mortality for A . albopictus and 69% for Cx ., pipiens pallens larvae ., Analogous to most of the entomopathogenic fungi , P . guiyangense hyphae emerging from germinating zoospore cysts entered their host directly through the exterior cuticle , propagated inside hosts , and produced sporangia to start a new cycle of infection ., Another infection route of P . guiyangense was through the ingestion of mycelia by larvae ., Mycelia in the digestive tract progressively destroyed internal tissues of the larval midgut , leading to host death ., The most common invasion route for aquatic insect pathogens , including Metarhizium anisopliae , Aspergillus clavatus and Beauveria Bassiana , was through ingestion of spores to infect their host 4 , 32 , 33 ., P . guiyangense has evolved a similar strategy to initiate infection in the digestive system ., Overall , P . guiyangense utilizes cuticle penetration and ingestion of mycelia into the digestive system to infect mosquito larvae ., We speculate that firm adhesion of zoospores to the mosquito larvae epicuticle is critical for the success of the P . guiyangense pathogen which involves a combination of passive hydrophobic and electrostatic forces as well as protein interaction ., Hydrophobins found in the outer layer of the spore cell wall of Beauveria Bassiana , mediate adhesion to the arthropod cuticle 34 , 35 ., Hydrolytic enzymes , Mad1 and Mad2 , also assisted in attachment of the fungi to insects 36 ., To identify the factors that promote attachment and ingestion of P . guiyangense by the mosquitoes would be interesting to further explore in the future ., To probe the molecular basis underlying the interactions of P . guiyangense with insects , a high-quality genome assembly and transcriptome sequences were generated for P . guiyangense ., Our results reveal that P . guiyangense is probably a hybrid genome derived from two parental species ., Natural interspecies hybridization events have been described in the genus Phytophthora such as Ph . andina , Ph . nicotianae and Ph . cactorum 37 , 38 ., It is believed that interspecies hybridization has the potential to create new strains that have a new or expanded host range 21 ., Considering the distinct hosts , we speculate that the hybrid feature of P . guiyangense contributes to its adaptation of the mosquito host ., The two parental subgenomes of P . guiyangense are approximately 9% different in nucleotide sequence , suggesting that the two parents are relatively diverse , however , the potential parents are still mysterious based on limited Pythium data ., We will pay close attention to the new information of Pythium and update the concerns in future study ., The phylogenetic analysis of the currently sequenced oomycete pathogens together with two diatoms demonstrated P . guiyangense is closely related to three plant pathogenic Pythium species ( P . irregulare , P . iwayamai and P . ultimum ) but has a slightly more distant relationship with the mammalian pathogen , P . insidiosum ., This finding suggested that as a facultative mosquito pathogen , P . guiyangense , may have evolved from a common ancestor with the plant pathogens ., This result is highly concordant with recent analysis indicating the mosquito oomycete pathogen , L . giganteum has also evolved from a plant pathogen 3 ., In conjunction with the transcriptome analysis , oomycete genome comparisons identified several gene families that might contribute to P . guiyangense virulence ., In this study , 471 putative unique kinases ( 943 kinases in total ) were identified in the P . guiyangense genome ., Comparison with other sequenced oomycete genomes revealed that the genomes of the animal pathogens , P . guiyangense , P . insidiosum and S . parasitica , also encoded significantly more kinases than the plant pathogenic Pythium genomes 15 , 16 ., Transcriptome analysis revealed that a total of 92 kinases were differentially expressed during infection of P . guiyangense against mosquito larvae , implying that protein kinases may be involved in regulating virulence ., We also found that genes involved in insect cuticle degradation were expanded in P . guiyangense while proteins for plant cell wall penetration were absent or lost functions ., The P . guiyangense genome encoded a significantly larger number of proteases than plant pathogenic Pythium species , including cysteine- , metallo- , and serine-proteases ., Transcript levels of 31% of the total subtilisin-like serine proteases were significantly elevated when P . guiyangense invaded mosquito larvae ., Some of these proteases were reported as key virulence determinants in entomopathogenic fungi 39 , supporting a potential role of these proteases in P . guiyangense infection ., A large number of Kazal proteinase inhibitors ( KPIs ) were characterized from P . guiyangense and 25% of these genes were up-regulated during infection of mosquito larvae , suggesting KPIs may be involv

Introduction, Results, Discussion, Materials and methods

Pythium guiyangense , an oomycete from a genus of mostly plant pathogens , is an effective biological control agent that has wide potential to manage diverse mosquitoes ., However , its mosquito-killing mechanisms are almost unknown ., In this study , we observed that P . guiyangense could utilize cuticle penetration and ingestion of mycelia into the digestive system to infect mosquito larvae ., To explore pathogenic mechanisms , a high-quality genome sequence with 239 contigs and an N50 contig length of 1 , 009 kb was generated ., The genome assembly is approximately 110 Mb , which is almost twice the size of other sequenced Pythium genomes ., Further genome analysis suggests that P . guiyangense may arise from a hybridization of two related but distinct parental species ., Phylogenetic analysis demonstrated that P . guiyangense likely evolved from common ancestors shared with plant pathogens ., Comparative genome analysis coupled with transcriptome sequencing data suggested that P . guiyangense may employ multiple virulence mechanisms to infect mosquitoes , including secreted proteases and kazal-type protease inhibitors ., It also shares intracellular Crinkler ( CRN ) effectors used by plant pathogenic oomycetes to facilitate the colonization of plant hosts ., Our experimental evidence demonstrates that CRN effectors of P . guiyangense can be toxic to insect cells ., The infection mechanisms and putative virulence effectors of P . guiyangense uncovered by this study provide the basis to develop improved mosquito control strategies ., These data also provide useful knowledge on host adaptation and evolution of the entomopathogenic lifestyle within the oomycete lineage ., A deeper understanding of the biology of P . guiyangense effectors might also be useful for management of other important agricultural pests .

Utilization of biocontrol agents has emerged as a promising mosquito control strategy , and Pythium guiyangense has wide potential to manage diverse mosquitoes with high efficiency ., However , the molecular mechanisms underlying pathological processes remain almost unknown ., We observed that P . guiyangense invades mosquito larvae through cuticle penetration and through ingestion of mycelia via the digestive system , jointly accelerating mosquito larvae mortality ., We also present a high-quality genome assembly of P . guiyangense that contains two distinct genome complements , which likely resulted from a hybridization of two parental species ., Our analyses revealed expansions of kinases , proteases , kazal-type protease inhibitors , and elicitins that may be important for adaptation of P . guiyangense to a mosquito-pathogenic lifestyle ., Moreover , our experimental evidence demonstrated that some Crinkler effectors of P . guiyangense can be toxic to insect cells ., Our findings suggest new insights into oomycete evolution and host adaptation by animal pathogenic oomycetes ., Our new genome resource will enable better understanding of infection mechanisms , with the potential to improve the biological control of mosquitoes and other agriculturally important pests .

taxonomy, oomycetes, enzymes, computational biology, microbiology, enzymology, invertebrate genomics, fungal structure, developmental biology, fungi, plant science, mycelium, phylogenetics, data management, phylogenetic analysis, genome analysis, plant pathology, mycology, computer and information sciences, proteins, life cycles, evolutionary systematics, animal genomics, biochemistry, eukaryota, plant pathogens, genetics, biology and life sciences, proteases, genomics, evolutionary biology, larvae, organisms

journal.pgen.1000582

Statistical Power of Model Selection Strategies for Genome-Wide Association Studies

In genome-wide association studies ( GWAS ) , hundreds of thousands of markers are genotyped to identify genetic variations associated with complex phenotypes of interest ., The detection of truly associated markers can be framed as a model selection problem: a group of statistical models are considered to assess how well each model predicts the phenotype , and the selected models are expected to include all or some of the truly associated genetic markers and few , if any , markers not associated with the phenotype ., In the literature , three model-selecting procedures have been advocated: marginal search , exhaustive search , and forward search ., Marginal search analyzes markers individually and is the simplest and computationally least expensive among these three search methods ., Under certain assumptions , such as no interactions among covariates ( or markers in the GWAS context ) , Fan and Lv 1 proved that the truly associated covariates will be among those having the highest marginal correlations ., However , Fan and Lv acknowledged that marginal search may suffer when an important covariate is jointly associated as a group but marginally unassociated as individuals with the response ( phenotype ) ., In GWAS , the phenotypes are likely associated with multiple genes , their gene-gene interactions ( i . e . epistases ) , and gene-environment interactions ., Therefore , marginal search may not be optimal for the analysis of GWAS data ., In contrast to marginal search , exhaustive search and forward search simultaneously consider multiple markers in the model ., Exhaustive search examines all possible models within a given model dimension , and forward search identifies markers in a stepwise fashion ., As they consider interactions , they may gain statistical power compared to marginal search 2–5 ., In practice , exhaustive search bears a much larger computational burden because the number of models that need to be explored is an exponential function of the number of markers jointly considered ., For example , if 500 , 000 markers are genotyped , an exhaustive search of all marker pairs would study around 1011 candidate models ., This requires significant computational resources , especially when permutations are needed to establish overall significance levels , e . g . for the purpose of appropriately accounting for dependencies among markers ., Because of this computational burden , it is difficult or even impossible to assess the power of exhaustive search through simulation studies ., Based on limited simulations and real data analysis , conflicting results exist in the literature on the relative merit of exhaustive search and forward search ., Because exhaustive search considers many more models , it may increase the probability that the truly associated markers do not rise to the top as more models involving unrelated markers may outperform the true models simply due to chance ., Forward search explores a smaller model space , allowing a less stringent threshold for significance ., However , forward search may miss the markers that have a strong interaction effect but weak marginal effect ., Through limited simulation studies , Marchini and colleagues 4 , 5 concluded that exhaustive search is more powerful in finding truly associated markers in the presence of epistasis ., On the contrary , based on the analysis of a real data set for yeast , Storey and colleagues 2 , 3 recommended sequential forward search ., They reported that exhaustive search suffers from lower power because a substantial increase in the number of models ., By analytically demonstrating the conditions under which exhaustive search is better than forward search , and the reverse , our research systematically explains these contradictory results ., It is clear that the optimal model selection strategy depends on the underlying genetic model , which is unknown to researchers ., In the most extreme case , if the underlying genetic model has no marginal association , an exhaustive search is the only way to find influential genes ., On the other hand , for a model with purely additive genetic effects , marginal or forward search will be the most effective ., For the cases between these two extremes , the optimal model selection strategy should achieve a delicate balance between computational efficiency , statistical power , and a low false positive rate ., Without the knowledge of underlying models , it is necessary to evaluate the different methods by thoroughly comparing them across a large genetic model space , in which both computationally intensive simulations and limited real data analysis are difficult to fully explore ., In this article , we derive the analytical results for statistical power of marginal search , exhaustive search , and forward search ., These formulas can significantly reduce the computational burden in power estimation ., To implement the formulas , we developed an R package markerSearchPower ., We demonstrate through simulations that our results are accurate ., Through our results , we can systematically assess different SNP search methods across a large model space and efficiently identify the optimal one ., Our derivation approaches are general and can be applied to the model selection procedures in other random predictor settings ., The rest of this article is organized as follows: in the Results section , we present the model set-up , the validation of our analytical results through simulations , and the comparisons among three model selection strategies; in the Discussion section , we summarize the power comparison results and discuss our methodological contributions; and in the Methods section , we outline the derivations of asymptotic distributions and power calculations ., The Text S1 available online gives statistical details of proofs and derivations , extended power comparisons , and relevant formulas for the estimates of distribution parameters ., A genetic model relates phenotype to genotypes , and this relationship can be rather complex ., In general , statistical power depends on the effects of risk alleles , allele frequencies in the population , epistasis , as well as environmental risk factors and their interactions with genetic factors ., We focus on a model commonly used in the literature , which offers valuable insights into the relative performance of model selection methods ., Assume that genotype data are available from p independent single nucleotide polymorphisms ( SNPs ) ., Our results can be generalized to other types of markers ., We use Xi1 , … , Xip , i\u200a=\u200a1 , … , n , to denote the genotypes for the ith sampled individual , for SNPs 1 , … , p , respectively ., Let the alleles at the jth SNP be Mj and mj with frequencies pj and qj\u200a=\u200a1−pj , respectively ., Under the assumption of Hardy-Weinberg equilibrium and additive allelic effects , we use the following coding for this SNP: ( 1 ) We focus on the scenario that two of these SNPs , indexed by 1 and 2 , are truly associated with a quantitative outcome Y through the following genetic model ( 2 ) where εi∼N ( 0 , σ2 ) is independent of the genotypes ., The interaction term represents the epistatic effect , and its coefficient b3 measures the direction and magnitude of this effect ., Based on the observed data , we fit the following models using Ordinary Least Squares ( OLS ) involving one or two SNPs: ( 3 ) ( 4 ) The subscripts in the above models index the SNP ( s ) included in these models ., Based on models ( 3 ) and ( 4 ) , three model selection methods seek candidate markers according to the corresponding test statistics ., In marginal search , we fit simple linear model ( 3 ) and compare the T-statistics 6 Tj for j\u200a=\u200a1 , … , p ., A model , and thus its involved SNP , is selected if the corresponding T-statistic is among the largest from all tests ., In two-dimensional exhaustive search , we fit regression model ( 4 ) for all SNP pairs and compare the F-statistics 6 Fjk for all j<k where j , k∈{1 , … , p} ., The models with the highest values of the F-statistics are selected ., In forward search , we first conduct a marginal model selection through model ( 3 ) and select the jth SNP if |Tj| is the largest ., With Xj , we then add another SNP Xk ( k≠j ) for different SNPs , and choose models in format ( 4 ) which generate the highest F-statistics ., Two criteria are adopted to decide if the chosen models are correct ., On one hand , we could be rather stringent and call a model correct only if it matches the true underlying genetic model ., This is consistent with the concept of “joint significance” in Storey et al . 2 ., On the other hand , we could be more generous and call a model correct if it contains at least one of the truly associated markers ., This is consistent with the null hypothesis used in some published simulation studies 4 , 5 ., Accordingly , we consider two definitions of power for a model selection procedure: Under power definition ( A ) , the null model is any model other than the true genetic model; under power definition ( B ) , the null model is any model containing neither true SNP ., We evaluated the accuracy of the asymptotic results derived in the Methods section by comparing the analytical results with those from simulations ., To estimate power through simulation studies , we generated 1 , 000 data sets with n subjects and p candidate SNPs assuming Hardy-Weinberg equilibrium , as indicated in ( 1 ) ., The quantitative trait values were generated through true model ( 2 ) involving two true SNPs ., We then used marginal search , exhaustive search , and forward search to identify SNPs associated with the trait ., Under power definition ( A ) , the target model ( s ) were the true model ( or models with one true SNP in marginal search ) , and the other models were considered null models ., Under power definition ( B ) , the target models were those containing at least one true SNP , and the rest were considered null models ., The empirical power estimated from these simulations was the proportion of that datasets that we were able to successfully find the target model ( s ) through model selection procedures , under the control of a pre-specified number ( R ) of falsely discovered null models ., Such control offers a fair comparison of power among the three model selection methods and is numerically equal to the detection probability ( DP ) control 7 , which is the probability of including a “correct model” when selecting R ( or R+1 in marginal search under power definition ( A ) ) of the most significant models ., In the first set-up for model ( 2 ) , we considered n\u200a=\u200a100 subjects , p\u200a=\u200a300 SNPs , genetic effects b1\u200a=\u200ab2\u200a=\u200a0 . 1 , b3\u200a=\u200a2 . 4 , allele frequency of each SNP qj\u200a=\u200a0 . 3 , j\u200a=\u200a1 , … , p , and variance σ2, =\u200a3 . Table 1 summarizes the calculated power and the simulated power under definitions ( A ) and ( B ) ., The second set-up is the same as the first except b3\u200a=\u200a1 . 4 ., For this set-up , Table 2 shows the results under definitions ( A ) and ( B ) ., The two values of b3 represent large and small interaction terms with which the simulation generated a broad spectrum of power values ., In both set-ups , the analytical power is very close to the empirical power based on simulations ., We chose these two set-ups in which the power was reasonably large to approximate most practical settings ., The chosen value of p is much smaller than that in GWAS ( in the 100 , 000s ) for the feasibility of simulation ., As discussed in the Methods section , the asymptotic results are derived by assuming a large p ., Therefore , we expect better approximations if p has a value similar to those in a real GWAS ., The simulation results shown in Table 1 and Table 2 demonstrate that our analytical results provide good approximations to the true power , which is the basis for comparing the performance of these model search methods in a practical GWAS ., We now consider a more realistic setting with a sample size of 1000 individuals ( n ) and a total of 300 , 000 SNPs ( p ) ., We assumed a genetic model of form ( 2 ) with σ2\u200a=\u200a3 and varied the values of b1\u200a=\u200ab2 as well as that of b3 from −1 to 1 by a step size of 0 . 1 ., To simplify the discussion , we assumed all SNPs had the same allele frequency of qj\u200a=\u200a0 . 3 , j\u200a=\u200a1 , … , p ., Note that this setting can be changed without affecting the qualitative nature of the comparison results ., Figure 1 gives the 3D plots of statistical power over the genetic model space for different model selection methods ( in columns ) under two power definitions ( A ) and ( B ) ( in rows ) , when controlling the number of false discoveries to be R\u200a=\u200a10 ., These figures illustrate that marginal search and forward search cannot detect the marginal association of the influential SNP 1 or 2 in a certain region of the model space , while exhaustive search can ., This portion of the model space is represented by the region where the power of marginal search and that of forward search are very close to 0 , no matter how large the genetic effect is ., According to formulas ( 8 ) and ( 16 ) in the Methods section , the marginally non-detectable region for SNP 1 , where b1+b3 ( p2−q2 ) =\u200a0 , depends on the additive genetic effect b1 , epistatic effect b3 , and the allele frequency p2 of SNP, 2 . The non-detectable region for SNP 2 is analogous by symmetry ., In exhaustive search , such region does not exist , as indicated by formula ( 12 ) ., So , exhaustive search can better identify the signals when they are counterbalanced ., In order to better visualize the difference of model selection methods , we show the power differences between different methods ., The left , middle , and right columns of Figure 2 and Figure 3 present the power difference between marginal search and exhaustive search , between marginal search and forward search , and between forward search and exhaustive search , respectively ., For a specific comparison , the red areas represent negative values , indicating the former method has lower power , and the green areas represent positive values , indicating the former method has higher power ., The dashed contours in these plots represent the heritability of the genetic model , i . e . , the proportion of the total variation due to genetic effects , which is defined asUnder our model set-up , In each plot , there are two areas in which the difference of power is close to 0 ., First , in the central area where the signal is weak ( small H2 ) , all model selection procedures have low power and tend to fail to pick up the true SNPs ., Second , in the edge areas where the signals are strong , all model selection procedures have similarly good power ., The light colored areas represent these two special situations in which there is little difference in power among model selection methods ., To compare marginal search and exhaustive search , the left columns of Figure 2 and Figure 3 exhibit the power difference under power definitions ( A ) and ( B ) , respectively ., Exhaustive search has significant advantage in the red areas where the interaction effect b3 is large or b1+b3 ( p2−q2 ) is small ., Such advantage is more pronounced under power definition ( A ) than under power definition ( B ) ., Marginal search performs better in the green areas where b3 is small and b1 and b2 are both moderate ., There are two reasons for the better performance of marginal search ., First , with a small interaction term b3 in these green areas , marginal search well detects the signals when the two-marker genetic effects are projected onto a marginal space through the simple regression of form ( 3 ) ., At the same time , with moderate b1 and b2 , the power for these two methods is not close to 0 or 1 , so that they are distinguishable ., Second , marginal search considers fewer models so that the desired models are more likely to be found from the models with the best fit ., Under different power definitions , the performance of forward search relative to that of marginal search can change ., Capable of including interaction terms , forward search has an advantage over marginal search in finding the full correct model under power definition ( A ) , as shown by the red areas in the middle column of Figure, 2 . Based on the analytical formulas in the Methods section , there is a positive correlation between the test statistics in the first and second steps of forward search ., Therefore , if one of the associated SNPs can be picked up in the first step , the contribution of the epistatic term makes forward search more powerful to identify the second correct SNP ., Under power definition ( B ) , the middle column of Figure 3 shows that marginal search always has similar or slightly better power than forward search , because forward search is less likely than marginal search to pick up a true SNP if an incorrect SNP is chosen first ., The power of forward search will not improve greatly even if the number of false discoveries R increases ., As shown in the right column of Figure 2 , exhaustive search under power definition ( A ) always has a similar or higher power to detect the true model when compared to forward search ., Although forward search can also detect the interaction terms through joint analysis , its ability to capture the interaction terms is restricted , especially when marginal effect is small in the deep red areas of b1+b3 ( p2−q2 ) ≈0 ., Under power definition ( B ) , forward search is more powerful than exhaustive search when R , the number of controlled false discoveries , is small , but is less powerful when R is large ., With small R ( e . g . R\u200a=\u200a1 ) , forward search benefits from considering fewer models and is better than exhaustive search in the green areas of Figure, 3 . This benefit is reduced for larger R and will eventually be dominated by the advantage of exhaustive search ., Since the first step of forward search is essentially a marginal search , the advantage of exhaustive search over marginal search also applies to forward search ., This is reflected in the right columns of Figure 2 and Figure 3 , where the red areas are similar to those in the left columns ., As reflected by the change of red/green areas between the first and the second rows in both Figure 2 and Figure 3 , if we raise the number of allowed false discoveries R , the power of marginal search will increase the most , followed by the power of exhaustive search , and then the power of forward search ., With the same increase in R , marginal search includes a much higher proportion of the models with true SNPs than exhaustive search ., For forward search , the increase of power is smaller because it is more difficult to identify a correct SNP in the second step when an incorrect SNP is more likely to be selected in the first step ., We also explored additional model set-ups in Text S1 Section 3 with n\u200a=\u200a100 , p\u200a=\u200a1000 , R\u200a=\u200a1 , 5 , and 10 , qj\u200a=\u200a0 . 3 and 0 . 5 , j\u200a=\u200a1 , … , p , and σ2, =\u200a3 . The values of the genetic effects b1\u200a=\u200ab2 and b3 varied from −2 to 2 by a step size of 0 . 2 ., When qj\u200a=\u200a0 . 5 , the graphs are symmetric about b1\u200a=\u200ab2\u200a=\u200a0 and b3\u200a=\u200a0 ., In general , the patterns are similar to those shown in Figure 2 and Figure, 3 . In the following we provide an example to show how to apply our approach to calculating and comparing the power of model selection methods in empirical analysis ., Because there are no consistently replicated interaction effects from real studies , we constructed hypothetical interaction models based on real data so that the marginal associations between traits and markers were matched , while allowing the interaction term to vary ., Specifically , we calculated power based on a set of genetic models derived from a genome-wide association study of adult height by Weedon et al . 8 ., Based on the reported 20 loci that putatively influence adult height , we set up a two-marker genetic model composed of SNPs rs11107116 and rs10906982 , each of which showed moderate marginal effect ., According to the Supplementary Table 4 in the original publication , the estimated marginal effects of rs11107116 and rs10906982 are respectively 0 . 045s . d ., and 0 . 046s . d ., with a sample standard deviation ( s . d . ) of height of 6 . 82 cm ., Assuming different levels of interaction between the two SNPs ( quantified by b3 ) , we estimated the parameters b1 , b2 , and σ2 using model ( 2 ) so that the marginal effects matched the observed values ., The Methods section gives the details of how these parameters were estimated ., We used the set-up of Weedons study: sample size n\u200a=\u200a16 , 482 , number of candidate SNPs p\u200a=\u200a402 , 951 , and the frequencies of the height-increasing allele for rs11107116 and rs10906982 p1\u200a=\u200a0 . 77 and p2\u200a=\u200a0 . 48 , respectively ., Figure 4 shows the comparisons among the power of the three model selection methods over different values of b3 ., For the detection of both SNPs , graphs A ( R\u200a=\u200a1 ) and C ( R\u200a=\u200a20 ) indicate that if the magnitude of epistasis b3 is large , exhaustive search ( red dashed curve ) has significant advantage over forward search ( green dotted curve ) , which is better than marginal search ( black solid curve ) ., If b3 is small , marginal search has higher power than the other two ., For the detection of at least one of the two SNPs , graphs B ( R\u200a=\u200a1 ) and D ( R\u200a=\u200a20 ) indicate that marginal search is similar or better than forward search; both methods are not affected by the variation of b3 ., The relative performance of exhaustive search strongly depends on the magnitude of epistasis ., Comparing graphs B ( R\u200a=\u200a1 ) and D ( R\u200a=\u200a20 ) , it is clear that marginal search is superior over a larger region when a larger false discovery number R is tolerated ., With R\u200a=\u200a20 , graphs C and D indicate that exhaustive search is better than marginal search to find both or at least one of the SNPs when the magnitude of b3>0 . 3 or 0 . 6 , respectively ., We studied the statistical significance of the interaction terms with the simulated data ( 1 , 000 runs ) when b3 equals these two cutoffs ., When b3\u200a=\u200a0 . 3 , 11 . 4% of the simulations had the Bonferroni p-values ( adjusted by the number of all possible pairs of the 20 found loci ) that exceeded the significant threshold at 0 . 05 ., Therefore , a small epistatic effect , rarely showing significance from the observed data , can still make an exhaustive search more powerful than a marginal search under power definition ( A ) ., Under power definition ( B ) , when b3\u200a=\u200a0 . 6 , 87 . 3% of the Bonferroni adjusted p-values were significant ., That is , to make exhaustive search more powerful than marginal search for finding either SNP , a true epistatic effect needs to be large enough to often identify a statistically significant interaction ., This example demonstrates that the value of the interaction term and the number of false discoveries affect the relative performance of model selection methods , which can be one of the reasons for the conflicting results about the power of model selection methods in the existing literature 2 , 4 ., Therefore , the suspected values of parameters such as epistatic effects can affect the researchers choice of model selection methods ., Our power calculation for model selection strategies is different from a traditional power calculation for multiple regression models 9 ., The traditional approach is to calculate the probability of accepting a specific multiple-regression model and rejecting the null hypothesis that the response and the covariates have no association , when controlling the type I error rate ., This power calculation focuses on models instead of model selection methods , as it does not address any procedure of model selection ., In contrast , our analytical approach is to calculate the probability that a model selection method can pick up the models that contain the true covariates ( true SNPs in GWAS ) ., Our analytical approach leads to new insights into model selection methods than simulations and limited real data analysis ., Furthermore , our approach addresses a critical limitation of prior studies 4 , 5 that do not distinguish the models with all correct predictors from those with only a subset of the correct predictors ., In those studies , the null distribution assumes the test statistic is from a model without any of the true predictors , and the alternative distribution assumes the statistic is from any model containing at least one true predictor ( or , when considering the power for finding both true loci , the models with either true locus are ignored from the null distribution ) ., This is a common problem of traditional multiple testing for model selection method , as pointed out by Storey et al . 2 , who stated that “there is no statistically rigorous method to test for joint linkage , which exists only if both loci have nonzero terms in the full model . ”, To address this issue , all involved models ( including true , partially true , and wrong models ) are considered and ranked by how well they fit the observed data ., Our power calculation distinguishes the case that model selection procedures find the true model based on power definition ( A ) from the case that the procedures find a partially true model based on definition ( B ) ., We have derived the null and alternative distributions for each case , and thus provide the basis for model performance comparisons ., To compare the power of model selection methods , our approach explicitly considers the correlation structures among the test statistics for the null and alternative hypotheses , which achieves more accurate assessment of model selection methods than Bonferroni-corrected type I error control that is commonly used in the literature 4 , 5 ., Bonferroni-based control is usually a conservative control when the test statistics are dependent on each other ., As illustrated by both simulations ( results not shown ) and the theoretical derivations in the Methods section , the considered models and their test statistics usually exhibit complex correlation structures ., Therefore Bonferroni-based control is not optimal as it only considers the number of models evaluated ( that is , the number of hypothesis tests ) and ignores correlation structures generated by different search strategies ., The adequacy of our approach has been demonstrated through a good agreement between the analytical and the simulation results shown in Table 1 and Table 2 ., Furthermore , our study of correlation structures improves the understanding of the mechanism of different search strategies discovering genetic signals ., For example , in forward search , the failure of the first stage is likely to cause the failure of the second stage even if there is a large epistatic effect , because the test statistics for the true predictors are positively correlated between the two stages ., To obtain the significance threshold , we control the number of false discoveries at R depending on how the power is defined ., This control is practically meaningful and equals to the detection probability ( DP ) control 7 as discussed in the Results section ., Furthermore , controlling the number of false discoveries is related to controlling the type I error rate ., Since the type I error rate is defined as the probability of rejecting a hypothesis given it is a true null , with the definition of null models corresponding to the power definition ( A ) or ( B ) , the estimation of component-wise type I error rate could be considered as The model selection problem is also a large-scale simultaneous hypothesis testing problem ., A widely applied significance control criterion in this scenario is the false discovery rate ( FDR ) 10 ., The false discovery number control in our study is also related to the control of the false discovery proportion ( FDP ) , which is an estimate of FDR ., Under power definition ( A ) where power ( R ) denotes the power calculated based on the number of selected null models R , and i indicates the number of correct models: i\u200a=\u200a2 for marginal search , and i\u200a=\u200a1 for exhaustive search and forward search ., Through the simulations in the Results section , our derivation of asymptotic distributions is shown to be accurate for moderately small genetic effects when the sample size n\u200a=\u200a100 ., Since the asymptotic derivation assumes large sample size , the power calculation results should provide accurate approximations for reasonably smaller genetic effects in GWAS which have a much larger number of observations in general ., The asymptotic derivation has several benefits ., First , we can derive the results for the models with random predictors ., Because genotypes are randomly observed in genetic studies , it is necessary to consider such models ., Traditional methods for deriving the non-central F distributions for the test statistics are based on fixed predictors 7 , 11 , 12 ., As functions of predictor variables , these non-central parameters are not statistically consistent when genotypes are random ., Although one may integrate the power over all possible configurations of markers 13 , it is very cumbersome unless n is small ., Our method , based on asymptotic theorems , provides a satisfactory solution for models with random predictors ., Our novel approach presented here can be applied to derive the distributions of such models test statistics ., Second , the derived asymptotic multivariate normal distributions for theoretical null and alternative hypotheses allow us to incorporate complex correlations among the test statistics into power calculation based on population parameters ., For a given GWAS data set , the correlations presented in the data may also be addressed by empirical estimation of the null hypothesis 14 , 15 ., Third , the ideas behind the asymptotic derivation can be applied to study the distributions for hypothesis testing and power calculation in general as long as the statistics have certain functions of random variables ., We have assumed that the markers are independent in this paper ., There may be linkage disequilibrium ( LD ) among SNPs ., However , LD in general is weak among tagging SNPs 16–18 ., Furthermore , simulations based on real GWAS data ( results not shown ) indicate that even in the presence of LD , our analytical results are quite accurate when more false positives are acceptable , i . e . a large R value ., In addition , the analytical power approximations are more accurate for power definition ( B ) than for definition ( A ) ., In general , when the dependency among true SNPs and the ensemble of unrelated SNPs is weak or moderate , our power calculation provides acceptable approximations ., In reality , the underlying true model could be more complicated than model ( 2 ) with more related SNPs and interactions ., Our analytical results of power calculation can be extended through the approaches similar to the one we developed here ., Although the genetic models studied are simple , our results provide insights into the relative performance of different model selection procedures ., To calculate the power of model selection procedures shown in the Results section , we first derive general results on the asymptotic distributions ., Let Zi\u200a= ( Zi1 , … , Zis ) , i\u200a=\u200a1 , … , n , be n independent and identically distributed ( iid ) random vectors of dimension s ., Assume the mean vector is θ\u200a=\u200aE ( Zi ) = ( θ1 , … , θs ) with θj\u200a=\u200aE ( Zij ) and the variance-covariance matrix is Σ\u200a=\u200aCov ( Zi ) with ( Σ ) jk\u200a=\u200aCov ( Zij , Zik ) , j , k\u200a=\u200a1 , … , s ., Let , where ., Considering a real valued function of , if ,

Introduction, Results, Discussion, Methods

Genome-wide association studies ( GWAS ) aim to identify genetic variants related to diseases by examining the associations between phenotypes and hundreds of thousands of genotyped markers ., Because many genes are potentially involved in common diseases and a large number of markers are analyzed , it is crucial to devise an effective strategy to identify truly associated variants that have individual and/or interactive effects , while controlling false positives at the desired level ., Although a number of model selection methods have been proposed in the literature , including marginal search , exhaustive search , and forward search , their relative performance has only been evaluated through limited simulations due to the lack of an analytical approach to calculating the power of these methods ., This article develops a novel statistical approach for power calculation , derives accurate formulas for the power of different model selection strategies , and then uses the formulas to evaluate and compare these strategies in genetic model spaces ., In contrast to previous studies , our theoretical framework allows for random genotypes , correlations among test statistics , and a false-positive control based on GWAS practice ., After the accuracy of our analytical results is validated through simulations , they are utilized to systematically evaluate and compare the performance of these strategies in a wide class of genetic models ., For a specific genetic model , our results clearly reveal how different factors , such as effect size , allele frequency , and interaction , jointly affect the statistical power of each strategy ., An example is provided for the application of our approach to empirical research ., The statistical approach used in our derivations is general and can be employed to address the model selection problems in other random predictor settings ., We have developed an R package markerSearchPower to implement our formulas , which can be downloaded from the Comprehensive R Archive Network ( CRAN ) or http://bioinformatics . med . yale . edu/group/ .

Almost all published genome-wide association studies are based on single-marker analysis ., Intuitively , joint consideration of multiple markers should be more informative when multiple genes and their interactions are involved in disease etiology ., For example , an exhaustive search among models involving multiple markers and their interactions can identify certain gene–gene interactions that will be missed by single-marker analysis ., However , an exhaustive search is difficult , or even impossible , to perform because of the computational requirements ., Moreover , searching more models does not necessarily increase statistical power , because there may be an increased chance of finding false positive results when more models are explored ., For power comparisons of different model selection methods , the published studies have relied on limited simulations due to the highly computationally intensive nature of such simulation studies ., To enable researchers to compare different model search strategies without resorting to extensive simulations , we develop a novel analytical approach to evaluating the statistical power of these methods ., Our results offer insights into how different parameters in a genetic model affect the statistical power of a given model selection strategy ., We developed an R package to implement our results ., This package can be used by researchers to compare and select an effective approach to detecting SNPs .

mathematics/statistics, genetics and genomics

journal.pgen.1000705

Acquisition of Aneuploidy Provides Increased Fitness during the Evolution of Antifungal Drug Resistance

Candida albicans is the most prevalent fungal pathogen of humans and is commonly treated with fluconazole because of its low toxicity , low cost and oral availability ., The acquisition of drug resistance is an evolutionary process that occurs because antimicrobials rarely kill an entire population 1 ., The survivors are subject to strong natural selection for resistant phenotypes in the presence of a drug ., The increasing use of prolonged courses of fungistatic antifungal therapies increases the incidence of acquired antifungal drug resistance ( reviewed in 2–4 ) ., Fluconazole is especially prone to result in resistance as it is fungistatic , not fungicidal and the effective size of surviving populations is large ., Furthermore , resistant subpopulations appear to be maintained in the host , since previously treated patients have a higher incidence of resistance to subsequent fluconazole treatments 5 ., The evolution of drug resistance depends on phenotypic variability , and the ultimate source of that variability has been considered to be mutations that alter gene expression or protein activities ( e . g . , 6 ) ., Recent studies indicate that copy number variation ( CNV ) , including short segmental CNV and whole chromosome aneuploidy , are important contributors to genetic variability in human diseases , as well as to the acquisition of resistance to chemotherapeutic agents by tumor cells 7 , 8 ., Indeed , in S . cerevisiae , the increased frequency of CNV and aneuploidy over point mutations during experimental evolution is an indication of the fitness benefit these mutations convey 9 ., Fungi exposed to antifungal agents also exhibit high levels of aneuploidy 10–13 ., In addition to genetic changes in DNA sequence and/or copy number , reproductive output , used to estimate fitness , is an important component of a resistance phenotype 14 ., The ability of a pathogen with an altered genotype to survive in the presence and in the absence of drug greatly influences the degree to which that genotype will proliferate in the population and the degree to which it can be targeted if drug regimens are changed ., However , the fitness effect of CNVs in the presence and absence of drugs has not been studied systematically ., In particular , the dynamics of aneuploidy acquisition , fixation and change in a population over the course of physiologically relevant drug treatment have not been examined ., Molecular mechanisms of resistance to fluconazole are well documented and include alterations of two general processes ., First , lanosterol 14-alpha-demethylase , which catalyzes a critical step in the ergosterol biosynthesis pathway ( encoded by ERG11 ) is the target of the azole drugs and alterations in this enzyme structure or increases in the level of the protein confer resistance ( reviewed in 4 ) ., Second , drug efflux via ABC transporters ( encoded by CDR1 and CDR2 ) or via the major facilitator superfamily efflux pump ( encoded by MDR1 ) decreases effective intracellular drug levels , allowing cells to survive in the presence of higher extracellular drug concentrations ., Increased activity of these processes can be achieved through point mutations in genes encoding the proteins 15 or in transcription factors that regulate them 16–18 ., Expression levels 19 or physical copy numbers of these genes can also be increased via genome rearrangements such as whole chromosome and segmental aneuploidies 11 , 20 ., Alteration in gene copy number is a major mechanism for environmental adaptation of asexual yeast populations 21 , 22 ., In C . albicans , karyotype variability appears in many clinical isolates 23 as well as in some laboratory strains 24 , 25 ., Aneuploidy is especially common in fluconazole resistant ( FluR ) strains: approximately 50% of FluR strains carried a whole chromosome or segmental aneuploidy , while only 10% of fluconazole sensitive ( FluS ) strains exhibit any type of aneuploidy 11 ., One specific segmental aneuploidy , i ( 5L ) ( an isochromosome composed of two identical chromosome arms ( Chr5L ) flanking a centromere ) , increases gene copy numbers of Chr5L at least 2 fold relative to Chr5R , and appears in >20% of drug resistant strains ( 12/57 strains analyzed ) 11 , and A . S . and J . B . , unpublished data ) ., The appearance of i ( 5L ) is highly correlated with the appearance of increased FluR , and this FluR is primarily due to the presence of two genes on Chr5L that contribute additively and independently to FluR 20: ERG11 is located ∼150 kb from the left telomere and TAC1 , a transcription factor that activates CDR1 and CDR2 expression 17 , 26 , is located ∼48 kb from the centromere ., Despite the high prevalence of CNVs in FluR isolates , no systematic characterization of CNV dynamics has been performed on isogenic C . albicans strains throughout the course of fluconazole treatment ., The evolution of drug resistance has been studied by following patient isolates over time ., These studies are useful because they provide information about the evolutionary pressures occurring in the patient ., For example , data from isolates taken from a patient that acquired FluR have revealed the homozygosis of point mutations in ERG11 27 and in TAC1 26 ., We recently found that i ( 5L ) was acquired twice , in two genetically distinct bloodstream isolates from an individual patient , and that its appearance correlated with increases in the fluconazole minimal inhibitory concentration ( MIC ) 20 , indicating that active changes in chromosome copy number are an important mechanism for the evolution of antifungal resistance in the clinical setting ., However , studies of chronological patient samples are limited because the consecutive events that led to establishment of a specific strain are only inferred 28 ., Experimental evolution permits more direct observation of the dynamics of genetic and genomic changes in identical starting populations exposed to a known selective pressure ., Multiple , parallel experiments can be performed with controlled conditions of population size and selection pressure in an environment which facilitates sampling of the population throughout the evolutionary process ., In addition , they permit analysis of the fitness consequences of those genetic changes and of the frequency of a given genomic change in the population ., Parallel evolution of similar genetic changes under identical selection conditions provides strong evidence that these mutations provide an adaptive advantage 29 ., For example , evolution of E . coli for 20 , 000 generations resulted in strains with similar expression profiles 30 ., Experimental evolution of haploid and/or diploid S . cerevisiae in limiting nutrient conditions led to amplification of chromosome regions between highly similar genetic loci 21 , 31 , 32 , as well as reproducible segmental aneuploidies that were specific to the selection environment 29 ., In addition , one diploid strain gave rise to trisomy of three whole chromosomes 29 ., In the presence of drug , cells that acquire drug resistance are generally more fit than the ancestral strain ., In the absence of drug , the relative fitness of these two strains varies 6 , 33 ., In a previous study , Cowen et al ( 2000 ) performed experimental evolution of a single drug sensitive C . albicans strain , T118 , a strain isolated from an oral swab from a patient that was HIV positive ., They used a single colony to seed a liquid culture for one round of overnight growth and then divided this culture into 12 independent T118-derived populations: 6 in the absence of drug ( N1–N6 ) and 6 in the presence of drug ( fluconazole , D7–D12 ) ., Serial cultures were propagated for ∼330 generations of growth ., Each time a culture grew , the amount of fluconazole added to each population was double the MIC ., Three independent drug-treated populations acquired very high levels of FluR and we previously found that i ( 5L ) was present in these three populations ( D9-330 , D11-330 , and D12-165 ) 11 ., Here we studied the evolutionary dynamics of aneuploidy in these twelve batch culture lineages by performing genetic sampling throughout the entire evolution experiment as well as of individuals within mixed subpopulations at times when gross chromosomal rearrangements arose ., We performed contour-clamped homogeneous electric field ( CHEF ) karyotype analysis followed by Southern hybridization to detect gross chromosomal rearrangements , along with comparative genome hybridization ( CGH ) analysis to detect alterations in chromosome copy number ., In addition , we determined the frequency of genome changes by analyzing multiple clones from many of the populations ., Following the evolution of initially isogenic populations enabled us to address several questions about the evolution of aneuploidy in C . albicans: how early did aneuploidy arise in these populations ?, What were the dynamics of aneuploid chromosome acquisition and loss in the presence and absence of drug selection ?, Did specific aneuploidies arise with similar dynamics in different populations ?, Furthermore , we performed experiments to ask about the fitness effects of the aneuploidies in the presence and absence of fluconazole ., We found no evidence for aneuploidy or karyotype changes in the untreated populations or parental strain at any time point ., Nonetheless , we found that two specific aneuploid chromosomes , i ( 5L ) and trisomy of Chr7 , were detectable within the first drug-exposed passage of all three populations that became highly FluR ., Furthermore , additional aneuploidies appeared and disappeared over the course of the experimental evolution ., Interestingly , in most cases these aneuploid cells , which carried almost 20% more total DNA content than the progenitor cells , had a clear growth advantage in the presence of fluconazole ., Thus , exposure to an antifungal drug led to positive selection of specific aneuploidies and gross chromosomal rearrangements ., To follow genome dynamics during the evolution of FluR under controlled experimental conditions , we analyzed three independent fluconazole treated populations ( D9 , D11 and D12 ) that developed very high levels of FluR ( MIC peaks of ∼96 µg/ml ) ( 6 and Figure 1A ) ., We also analyzed six non-drug treated populations derived from the same progenitor strain ., In a previous study , we detected i ( 5L ) and other aneuploidies in samples with the highest MIC values from populations that had undergone an estimated 330 or 165 doublings ( D9-330 , D11-330 and D12-165 ) 11 ., The i ( 5L ) in the tested D9 and D11 samples co-migrated with Chr7 ( ∼950 kb ) , the size expected ( ∼945 kb ) for independent i ( 5L ) composed of two copies of Chr5L plus CEN5 ., In the D12-165 sample , the extra Chr5L sequences were found in a much larger band that co-migrated with Chr2 ( ∼2 . 2 Mb ) ., Southern analysis demonstrated that this large band was composed of an intact i ( 5L ) attached through a telomere-telomere fusion to the left telomere of an intact copy of Chr5 to form an attached isochromosome 5L ( att-i ( 5L ) ) 11 ., While i ( 5L ) was detected in all three populations , the dynamics of the appearance of i ( 5L ) and other aneuploidies was not known ., We did not know when it had arisen and whether or not it had become fixed in the population ., Furthermore , while strains containing i ( 5L ) often carried other aneuploid chromosomes as well , it was not clear if those other aneuploidies were more or less stable than the i ( 5L ) in the populations ., To address questions concerning the dynamics of genome change in these populations , we analyzed the untreated populations ( N1 through N6 ) and all of the time points from the three drug treated populations ( D9 , D11 , and D12 ) on CHEF karyotype gels stained with EtBr ( Figure 1B and Figure 2A–2C ) , and then analyzed them by Southern hybridization with a CEN5 probe ( Figure 1C and Figure 2D–2F ) , which detects the i ( 5L ) that co-migrates with Chr7 11 ., In D9 , the EtBr-stained karyotypes of early cultures appeared generally unchanged , other than a minor change in Chr5 Major Repeat Sequence ( MRS ) length ( A . S . and J . B . , unpublished data ) and the appearance of a new band below Chr4 in three later populations ( Figure 2D , arrow , discussed below ) ., Surprisingly , Southern analysis revealed a band corresponding to the size of i ( 5L ) , in all the evolved cultures , including the ∼3 . 3 generation isolate ., However , the intensity of hybridization in D9-3 . 3 was lower than in the other generations ( Figure 2D ) ., Similar results were seen in population D11 , except that hybridization of the CEN5 probe to i ( 5L ) was stronger than in D9-3 . 3 ( Figure 2E ) ., In the D12 CHEF karyotypes , the acquisition of the att-i ( 5L ) also was evident within the earliest ∼3 . 3 generation isolate ( Figure 2E and 2F ) and the shorter Chr5 homolog ( MTLα , data not shown ) ‘disappeared’ at the same time ., Importantly , this att-i ( 5L ) was composed of 3 copies of the MTLα copy of Chr5L 11 ., In contrast , the i ( 5L ) in D9-3 . 3 and D11-3 . 3 were composed of 2 copies of the MTLa copy of Chr5L , indicating that the i ( 5L ) in D12 arose independently from the i ( 5L ) found in D9 and D11 populations ., At later generations 260 , 300 and 330 , the att-i5L ‘disappeared’ and the MTLα homolog of Chr5 ‘reappeared’ ., This is consistent with the idea that two events occurred: an i ( 5L ) formed from the MTLα homolog of Chr5L and it also became attached to the shorter homolog of Chr5 to form the att-i ( 5L ) ., Later in the evolution of the culture , the i ( 5L ) portion of the att-i ( 5L ) was lost ., Thus , in all three cultures that evolved high levels of FluR , either i ( 5L ) or att-i ( 5L ) was detectable at the earliest sampling of the cultures ., Importantly , neither i ( 5L ) nor any other gross chromosomal rearrangement was detected in the progenitor T118 or in populations that were not treated with drug ( Figure 1B and 1C and data not shown ) ., Furthermore , i ( 5L ) was not detected in early time points from drug-treated populations that did not develop high levels of FluR ( D7 , D8 and D10 , data not shown ) ., In addition , populations of T118 progenitor cells plated on fluconazole concentrations do not contain drug resistant colonies ( data not shown ) ., Taken together with the appearance of two different forms of i ( 5L ) generated from different MTL alleles , this suggests that the different forms of i ( 5L ) either arose late during growth of the original progenitor colony used to seed all 12 cultures or that the two different isochromosomes arose within the first 24 hours of drug exposure ., In either case , growth in fluconazole was selective for the i ( 5L ) ., While only semi-quantitative , Southern analysis of CHEF gels suggested that i ( 5L ) may not be present in all cells in some of the populations such as D9-3 . 3 ., To address this issue more directly , we analyzed individual colonies from a number of the time points , to ask if the i ( 5L ) was present in each of the progenitor cells that gave rise to the colonies ., Again , CHEF karyotype gels were first analyzed with EtBr and were then subjected to Southern analysis using the CEN5 probe ., An example of the results for colonies derived from the D9-3 . 3 population is shown ( Figure 3 ) ., Similar analyses were performed for clones from other populations ( Figures S1 , S2 , S3 ) and the results are summarized in Table 1 ., As expected for a mixed population of adaptive mutants , 3 of 16 ( 19% ) of the D9-3 . 3 clones ( individual colonies ) contained the i ( 5L ) ., By the next time point tested ( D9-140 ) all clones carried the i ( 5L ) , indicating that it had become fixed in the population ., Of note , there was heterogeneity in the genome structures of different clones ., For example , the MTLα homolog of Chr5 was lost in clone D9-3 . 3-E ( Figure 3A and 3B and data not shown ) ., Despite the higher proportion of i5L at the D11-3 . 3 time point , i ( 5L ) did not become fixed as rapidly in this population ., After 3 . 3 doublings , 94% ( 15 out of 16 clones ) contained i ( 5L ) ; after 50 doublings i ( 5L ) was detected in 73% of the population and by 140 doublings the number decreased to 50% of the clones tested ., From 200 doublings on , all clones included an i ( 5L ) , suggesting that it had swept the population after 200 doublings ., In D12-3 . 3 and D12-50 , the att-i ( 5L ) was present in all clones analyzed , suggesting that it appeared early after drug exposure ., Although CHEF analysis detected a small amount of independent i ( 5L ) ( not attached ) in population D12-50 ( Figure 2F ) , it was not detectable in any of the 10 clones analyzed ., Interestingly , the loss of att-i ( 5L ) appears to have been abrupt as well: it was present in all of the D12-230 clones ( 15/15 colonies ) , but only in 20% of the D12-260 clones ( 3/15 colonies ) ., The early appearance of i ( 5L ) in all three populations was surprising and could be due to several possible mechanisms ., First , it is possible that a subpopulation of the progenitor cells contained i ( 5L ) and that their increased fitness in the presence of drug caused them to be rapidly selected 34 ., We consider this unlikely because no i ( 5L ) was detected in early cultures from strains D7 , D8 and D10 under drug selection ., In the D8 population , i ( 5L ) never appeared and in D7 and D10 , which acquired transient peaks of FluR ( MIC ∼4 to 8 ) , i ( 5L ) appeared only in the peak populations ( A . M . S . , data not shown ) ., Furthermore , since two types of i ( 5L ) ( attached MTLα/α/α and independent MTLa/a ) appeared in independent populations , both types would have had to be present in a few cells in the progenitor population and thus each population would have been heterogeneous for different types of i ( 5L ) ., Second , it is possible that each i ( 5L ) formed during the 24-hour period of fluconazole stress ., Once formed , i ( 5L ) could sweep through the populations if it provided very strong selective advantage in the presence of drug , such that clones that had acquired it out-competed clones within the population that had not acquired it ., To ask if this was the case , we compared the fitness of clones with or without i ( 5L ) by directly competing each clone with the drug sensitive ancestor ( T118 ) , in the presence and absence of fluconazole , by measuring the reproductive output of each competitor in the final mixture ( Figure 3C ) ., In the presence of fluconazole , all clones at generation ∼3 . 3 that contained an i ( 5L ) exhibited significantly increased fitness relative to the clones that had not acquired i ( 5L ) : D9-3 . 3-D , D11-3 . 3-A and D12-3 . 3-A , were 24% , 46% and 54% more fit than the progenitor , respectively ., In contrast , the non-i ( 5L ) clones ( D9-3 . 3-A and D11-3 . 3-D ) , and the non-drug treated strain ( N1-3 . 3 ) exhibited fitness levels that were not significantly different from the progenitor in the presence of drug ., This indicates that i ( 5L ) confers a strong selective advantage in the presence of the drug ., Furthermore , the growth rate and maximum cell density achieved by strains containing i ( 5L ) was much higher than that of sibling strains lacking i ( 5L ) ( data not shown ) ., Modeling of cell numbers indicates that if i ( 5L ) arose within the first 2 hours of fluconazole exposure , it could have reached >0 . 2% of the population within the first growth cycle ., Thus , selective advantage alone during 24 hours in fluconazole cannot explain how i ( 5L ) reached levels of 15–100% of the population of ∼1×10∧6 cells ., Importantly , cells were stored in the selective growth medium ( plus glycerol ) immediately following the experiment ., We cannot rule out the possibility that resuscitation of those strains may have involved some additional selection that allowed cells containing i ( 5L ) to reach higher proportions of the population ., A third mechanism by which i ( 5L ) may have become a larger proportion of the population is during subsequent non-selective propagation of the strains ., Such propagation was necessary for transfer of the strains between labs and for growth and analysis of single colonies for CHEF analysis ., We estimate that cells underwent ∼50–70 divisions in the absence of drug prior to CHEF analysis ., In fitness assays conducted under the conditions used for the evolution experiment ( RPMI medium with or without drug ) , the D9-3 . 3 , D11-3 . 3 , and D12-3 . 3 clones that contained the i ( 5L ) had no significant reduction in fitness relative to the progenitor in the absence of drug ( Figure 3C ) ; furthermore , D12-3 . 3 had a significant increase in fitness relative to the progenitor in the absence of fluconazole and D11 consistently exhibited a slight increase in fitness under these conditions ., Thus the D12-3 . 3 population could have easily accumulated more cells carrying i ( 5L ) during the nonselective growth necessary for CHEF gel analysis ., This may be the case for D11-3 . 3 as well ., Interestingly , the proportion of cells containing i ( 5L ) in the ‘3 . 3’ populations reflects the relative fitness advantage of those strains under non-selective conditions ., Thus , we conclude that i ( 5L ) arose early in all three populations and reached appreciable proportions of the population during growth in the first cycle of exposure to fluconazole , but that the very high levels of i ( 5L ) found in the populations analyzed is due to that selection plus some additional selective advantage under no drug conditions ., Comparative Genome Hybridization ( CGH ) performed on a microarray platform that includes probes covering most ORFs in the genome provides a comprehensive view of copy number changes ., We used CGH to analyze D9 , D11 and D12 populations as well as a number of single colony clones that previously had been analyzed by CHEF karyotype gels and Southern hybridization ( e . g . , Figure 3 , Table 1 ) ., In general , strains carrying the ∼945 kb i ( 5L ) on CHEF gels also carried two extra copies of Chr5L , but there were exceptions ., In D11-200 , -260 and -330 , there were >5 copies of Chr5L , suggesting that they contained two copies of i ( 5L ) ( 4 copies of Chr5L ) in addition to the normal two copies on intact Chr5 homologs ( Figure S7 ) ., CGH of D9 strains was consistent with the CHEF karytoype gel analysis: no i ( 5L ) or other aneuploidies were detected in clone D9-3 . 3-A , while i ( 5L ) was detected in clone D9-3 . 3-D ( Figure 4A and 4B ) ., In addition , CGH revealed that Chr3 , Chr4 and Chr7 were trisomic in clone D9-3 . 3-D ., Thus , clone D9-3 . 3-D included multiple aneuploidies that were not evident on the CHEF karyotype gels and its MIC ( ∼3 . 0 µg/ml ) , was higher than that of clone D9-3 . 3-A ( ∼1 . 5 µg/ml ) , which had no obvious aneuploidies ., This is consistent with the idea that aneuploidies in D9-3 . 3-D ( including , but not limited to i ( 5L ) ) confer increased drug resistance ., Importantly , the additional whole chromosome trisomies did not impair fitness under either the selective or non-selective growth conditions ( Figure 3C ) and were observed at generation 330 ( discussed below ) ., Clones from population D11-3 . 3 ( Figure 4C and 4D ) were generally similar to the clones from population D9-3 . 3 ., D11-3 . 3-A was trisomic for Chr3 and Chr7 ( but not for Chr4 ) and carried i ( 5L ) ., Furthermore , as in D9-3 . 3 clones , the MIC of strains with multiple aneuploidies was slightly higher ( ∼2 . 0 µg/ml ) than the MIC of strains lacking them ( ∼0 . 5 µg/ml ) ., For D12-3 . 3 , all single colony clones contained i ( 5L ) , and thus we could not compare their growth to sister clones lacking i ( 5L ) ., Importantly , the degree to which D9-3 . 3 , D11-3 . 3 and D12-3 . 3 had increased fitness in the presence and absence of drug correlated positively with the proportion of the population that contained aneuploidies ( Figure 3C and Table 1 ) ., Aneuploidies detected by CGH in evolved populations and their respective individual clones ( Figures S6 , S7 ) , are summarized in Table 2 and revealed several interesting findings: First , the accumulation of multiple aneuploidies correlated with the highest MIC ., Both D9 and D11 accumulated more aneuploidies than D12 over the evolution experiment and also reached a higher final MIC ( 96 µg/ml ) than D12 ( 8 µg/ml ) ., This is consistent with the idea that aneuploidy can promote evolvability under strong selection 22 , 29 , 35 ., Second , whenever i ( 5L ) was present , Chr7 trisomy was also evident , suggesting that extra copies of Chr7 may enhance the fitness of strains carrying i ( 5L ) ., However , loss of i ( 5L ) , detected only in the D12 series , was not accompanied by coincident loss of Chr7 ( described below ) ., Thus , i ( 5L ) and Chr7 do not co-segregate during all changes in chromosome content ., Third , we observed dynamic appearance and subsequent disappearance of different clones/aneuploidies: in D9-3 . 3 the aneuploid genotype ( i ( 5L ) +\u2009trisomy of Chrs 3 , 4 & 7 ) was present in ∼15% of the population; in D9-165 trisomy of Chr3 & Chr4 was not evident; in D9-230 and D9-300 , a new chromosome band ( Figure 2D arrow , discussed below ) appeared and then disappeared; and , in D9-330 , in addition to i ( 5L ) , Chr5R was monosomic , Chrs4 , 6 and 7 were trisomic and Chr3 had a segmental trisomy ., Nonetheless , whole chromosome and segmental aneuploidies detected in single clones at generation 3 . 3 were highly predictive of the aneuploidies detected in the D9 and D11 populations at generation 330 ., For example , the aneuploid chromosomes of clone D9-3 . 3-D ( i ( 5L ) , Chr3 , Chr4 , & Chr7 ) were present in population D9-330 , except that the Chr3 trisomy became a segmental trisomy ., This suggests that , once it becomes aneuploid , the C . albicans genome is dynamic and continues to change ., Yet , under fluconazole there was strong selective pressure for a similar repertoire of aneuploidies ., Finally , no aneuploidy was detected in the progenitor strain ( T118 ) , in the endpoints of each N1 population ( N1-330 to N6-330 ) , nor in single colonies from within each endpoint population ( N1-330-A to N6-330-A ) ( Figures S4 , S5 ) ., Thus , during extended growth in vitro , in the absence of selective pressure , the karyotype did not change ., A new band with apparent size of ∼1 . 5 Mb appeared in the later D9 populations ( D9-230 , D9-260 and D9-300 , Figure 2D , upper arrow ) while i ( 5L ) remained detectable in these populations ., Clones from D9-260 grew with two distinct colony size phenotypes ( Figure 5A ) : small colonies that grew slowly ( clones D9-260-a to -e ) and colonies with a wild-type size and growth rate ( clones D9-260-F to -I ) ., CHEF gel analysis indicated that the small colonies contained the ∼1 . 5 Mb SNC as well as i ( 5L ) while the larger colonies retained i ( 5L ) only ( Figure 5B ) ., CGH analysis of small and large colonies indicated that both types of colonies contained 2–4 additional copies of Chr5L and were trisomic for Chr7 ( Figure 6A and 6B ) ., In addition , small colony D9-260-a was trisomic for Chr6 ., To ask if the ∼1 . 5 Mb SNC included either Chr5R , Ch6 and/or Ch7 DNA fused to Chr5L DNA , we probed Southern blots of the CHEF with probes from Chr6 ( two from 6L and one from 6R ) , CEN7 , the right arm of Chr5 , and MTLα on Chr5L ( probes are listed in Table S1 ) ., None of these probes hybridized to the ∼1 . 5 Mb SNC ( Figure 5B and data not shown ) ., This suggests that neither Chr5R , nor Chr6 , nor Chr7 are fused to Chr5L in the novel SNC ., We then isolated the ∼1 . 5 Mb SNC from the CHEF gel , labeled it with Cy3 and analyzed the contents of this single chromosome by CGH 21 ., As expected , the band included DNA from all of Chr5L including CEN5 ., Strikingly , it also included a segment of Chr3R beginning just to the left of DYN1 ( orf19 . 5999 ) ( Figure 6C ) ., While this type of analysis cannot directly determine copy number ( because one chromosome is hybridized relative to a whole genome ) , the signal from Chr5L was approximately twice as high as the signal from the Chr3R segment ., This is consistent with the size of the fragment and implies that the ∼1 . 5 Mb SNC is composed of two copies of Chr5L ( most likely organized as an isochromosome , ∼945 kb ) attached to a ( ∼600 kb ) segment from Chr3R ( 5L-CEN5-5L::3 ( orf19 . 5999→tel ) ) ., We refer to the ∼1 . 5 Mb SNC as i ( 5L ) -3R ., The detection of a Chr3R segment in the isolated ∼1 . 5 Mb SNC was surprising since no obvious increase in the number of Chr3R copies was evident in the whole genome CGH of small colony D9-260 clones ( Figure 6A and Figure S6B ) ., Southern analysis of whole chromosome CHEF gels confirmed that CDR1 , a gene just to the right of DYN1 and within the Chr3R segment , is present on the ∼1 . 5 Mb SNC as well as on intact Chr3 ., and CDR2 , a gene just to the left of DYN1 , is not ( Figure 5C ) ., A CEN3 probe , located farther to the left of the Chr3R segment breakpoint , is also absent from the ∼1 . 5Mb SNC ( Figure 5C ) ., Consistent with the fusion of Chr3R to an i ( 5L ) , the ∼1 . 5 Mb SNC did not include an Sfi1 restriction site ( Figure S8A ) ., Also , since both the independent i ( 5L ) and the i ( 5L ) -3R SNC in these clones carry only the MTLa homolog ( Figure S8B ) , the event that gave rise to the ∼1 . 5 Mb SNC likely involved a non-reciprocal recombination event in which one Chr3R segment was copied onto the end of the i ( 5L ) that was already present in the population ., The stability of the i ( 5L ) -3R SNC was highly variable , with some small colony clones giving rise to all small colonies and some giving rise to small and large colonies ., CHEF gel analysis indicates that all of these large colony derivatives had lost the ∼1 . 5 Mb SNC ( Figure S8C ) ., In fact , loss of the SNC occurred in isolate D9-260-a during subsequent propagation for CHEF analysis ( ∼20 generations in YPD ) ( Figure S8A , single colony lane “a” ) ., These observations prompted analysis of fitness and chromosome stability in clones with and without the SNC ., The slow growth of clones containing the ∼1 . 5 Mb SNC is consistent with the idea that this SNC caused a burden on cell growth ., Indeed , it caused a growth disadvantage in the absence and in the presence of fluconazole relative to the progenitor strain ( Figure 6D ) ., The MIC of this subpopulation ( 48–64 µg/ml ) accounts for the MIC of the original D9-260 population ( 64 µg/ml ) ., Despite the very slow growth of the small D9-260 clones in the presence of drug , the large D9-260 clones grew even less well in fluconazole and had a much lower MIC ( 8 µg/ml ) ., This implies that the SNC is a beneficial yet costly mutation ( Figure 6D ) ., Consistent with the observation that the i ( 5L ) -3R SNC in clone D9-260-a was unstable during culture , a double ellipse of growth formed in the e-strip assay ( Figure 6A , insert ) one at 64 µg/ml and the other at 8 µg/ml ., We propose this is due to loss of the SNC during growth of the colonies on the plate ., In D12 , the att-i ( 5L ) was maintained through 230 doublings and then was quickly lost ., CGH analysis of these strains detected the i ( 5L ) DNA as well as trisomy of Chr7 ( Figure 7A ) ., No other aneuploidies were evident in the strain ., Thus , an extra copy of Chr7 accompanied the acquisition of i ( 5L ) in all three of the cultures derived from T118 that reached higher MICs ( D9 , D11 and D12 ) ., A drop in MIC accompanied the loss of the att-i ( 5L ) , and the intact MTLα Chr5 homolog , whose mobility was altered upon att-i ( 5L ) formation , was restored ( Figure 8A ) ., A similar loss of att-i ( 5L ) and restoration of the original Chr5 homolog was seen in transformants derived from D12-165 ( Figure 8B ) ., Importantly , the drop in MIC upon loss of att-i ( 5L ) and retention of Chr7 trisomy ( Figure 7B ) suggests that the major contribution to fluconazole resistance came from genes on i ( 5L ) ,

Introduction, Results, Discussion, Materials and Methods

The evolution of drug resistance is an important process that affects clinical outcomes ., Resistance to fluconazole , the most widely used antifungal , is often associated with acquired aneuploidy ., Here we provide a longitudinal study of the prevalence and dynamics of gross chromosomal rearrangements , including aneuploidy , in the presence and absence of fluconazole during a well-controlled in vitro evolution experiment using Candida albicans , the most prevalent human fungal pathogen ., While no aneuploidy was detected in any of the no-drug control populations , in all fluconazole-treated populations analyzed an isochromosome 5L i ( 5L ) appeared soon after drug exposure ., This isochromosome was associated with increased fitness in the presence of drug and , over time , became fixed in independent populations ., In two separate cases , larger supernumerary chromosomes composed of i ( 5L ) attached to an intact chromosome or chromosome fragment formed during exposure to the drug ., Other aneuploidies , particularly trisomies of the smaller chromosomes ( Chr3–7 ) , appeared throughout the evolution experiment , and the accumulation of multiple aneuploid chromosomes per cell coincided with the highest resistance to fluconazole ., Unlike the case in many other organisms , some isolates carrying i ( 5L ) exhibited improved fitness in the presence , as well as in the absence , of fluconazole ., The early appearance of aneuploidy is consistent with a model in which C . albicans becomes more permissive of chromosome rearrangements and segregation defects in the presence of fluconazole .

C . albicans , the most prevalent human fungal pathogen , acquires resistance to fluconazole by genetic alterations that often include changes in the number of chromosomes or chromosome arms ( aneuploidy ) ., Here we demonstrate that chromosomal rearrangements resulting in increased gene dosage are the predominant means of acquired resistance to the antifungal drug fluconazole in replicated experimental populations of C . albicans ., A specific aneuploidy , isochromosome 5L , which is composed of two copies of the left arm of Chr5 , occurs with high frequency and is detectable soon after fluconazole exposure ., The early appearance of aneuploidy in some populations is consistent with a model in which C . albicans becomes more permissive of chromosome rearrangements and segregation defects in the presence of fluconazole ., The results presented here indicate that the C . albicans genome is highly plastic and imply that exposure to an antifungal drug induces genome reorganization events , some of which provide a fitness advantage in the presence of drug .

evolutionary biology/microbial evolution and genomics, genetics and genomics/comparative genomics, evolutionary biology/evolutionary and comparative genetics, microbiology/microbial evolution and genomics, genetics and genomics/chromosome biology

journal.pgen.1005615

Association of the Long Non-coding RNA Steroid Receptor RNA Activator (SRA) with TrxG and PRC2 Complexes

Histone H3 modifications involving lysine 4 trimethylation ( H3K4me3 ) and lysine 27 trimethylation ( H3K27me3 ) represent activating and repressive histone marks , respectively ., However , when present together , as they are in bivalent sites , they mark genes that are poised for induction ., Genes carrying the bivalent modification include those involved in differentiation of pluripotent stem cells ., Two distinct histone modification machineries , associated with the trithorax group ( TrxG ) complex and with polycomb repressive complex 2 ( PRC2 ) , are responsible for methylating H3K4 and H3K27 , respectively ., TrxG complexes comprise at least four protein components , WDR5 , RBBP5 , ASH2L and an H3K4 methyltransferase such as MLL ( MLL1-4 ) , whereas EZH2 , EED and SUZ12 are core components of PRC2 ., Establishment of bivalent domains involves delivery of these two complexes to their target regions ., Both MLL1 and MLL2 containing complexes deliver trimethyl marks to H3K4 , and MLL2 is required for this modification at bivalent sites in mouse embryonic stem cells 1 , 2 ., CpG islands ( CGIs ) have been reported to play an important role in recruitment of TrxG and PRC2 complexes via several CGI-binding proteins 3 ., In addition , TrxG complex has been shown to be recruited directly by DNA sequence-specific transcription factors Oct4 4 and estrogen receptor α ( ERα ) 5 ., Similarly , at least one component of the PRC2 complex , SUZ12 , can be targeted directly by the transcription factor CTCF 6 ., Moreover , PRC2 target genes can recruit the complex through interaction with short RNAs transcribed from the 5’ ends of those genes 7–9 ., We note that although under some solvent conditions PRC2 may exhibit non-specific interaction with RNA 9 , 10 , the experiments reported here , carried out in nuclear extracts or in PBS buffer , clearly show specificity for SRA ., A growing number of long non-coding RNAs ( lncRNAs ) have been implicated in recruitment of TrxG or PRC2 complexes to their target genes 11 ., Two groups of lncRNAs may be categorized according to whether TrxG or PRC2 complexes bind to them , defining activating and repressive lncRNAs respectively ., The first category of activating lncRNAs , which recruit TrxG complexes to their target genes via WDR5 , includes Hottip 12 , NeST 13 and NANCI 14 ., In contrast , examples of lncRNAs belonging to the second category of repressive lncRNAs , which recruit PRC2 complex to its binding sites , are Xist 15 , Hotair 16 and Braveheart 17 ., The complete PRC2 complex has been shown to bind highly selectively to Hotair and RepA/Xist , as compared with control RNA 18 ., Of the three core components of PRC2 comprising EZH2 , SUZ12 and EED , it has been shown recently that EZH2 and SUZ12 possess a high affinity for RNA binding , whereas EED helps to increase RNA binding specificity to the complex 18 ., Recently , a novel technique , Chromatin Isolation by RNA Purification ( ChIRP ) , has provided a powerful method to map the location of lncRNAs genome-wide 19 ., Using this technique , the lncRNA HOTAIR was shown to co-localize with the PRC2 complex and H3K27me3 genome-wide , supporting its functional role in tethering PRC2 to target genes ., Similar techniques have been utilized to map the distribution of the lncRNA Xist , which also has a domain that recruits the PRC2 complex , along the X chromosome 20 , 21 ., Although these and other lncRNA species have been shown to deliver either “activating” or “silencing” histone modifications , it is not clear whether they can function coordinately to create bivalent domains ., The lncRNA steroid receptor RNA activator ( SRA ) can be recruited to DNA through interactions with proteins that bind either directly or indirectly to DNA 22 ., For example , SRA has been shown to interact directly with ERα 23 , which binds to specific DNA sequences , and to co-activate ERα target genes 24 ., It also forms a complex with the DEAD box RNA helicase p68 , which in turn interacts with the DNA binding protein MyoD 25 ., We have reported previously that SRA and p68 form a complex with CTCF and are crucial for insulator function of CTCF at the IGF2-H19 locus 26 ., Furthermore , it has been shown that SRA can interact with EZH2 27 , suggesting that it might be involved in silencing functions associated with the PRC2 complex ., In addition , SRA also interacts with HP1 gamma and LSD1 to repress progesterone receptor target genes 28 ., In possible contradiction of that repressive function is the observation that knockdown of SRA in HeLa cells results in decreased expression of the majority of significantly changed genes 29 ., In this study , we show that the lncRNA SRA is capable of binding TrxG and PRC2 ., Direct interaction with the complexes is specific for sense SRA as compared with the control , its anti-sense counterpart ., SRA-p68 interaction strengthens recruitment of a TrxG complex but does not affect PRC2 ., We find that CTCF binding sites that are also occupied by SRA , are more likely to have bivalent marks ., We also find that SRA/p68 associates with NANOG , a master transcription factor in pluripotent stem cells ., These results show that SRA can associate with TrxG and PRC2 complexes to deliver either activating or repressive histone modifications , and that the choice can be modulated by proteins with which it associates ., They also suggest a mechanism in which the bivalent state may be controlled at certain sites , including those occupied by NANOG , through recruitment of SRA and its associated histone modifying enzymes in pluripotent stem cells ., To confirm that SRA interacts with the RNA helicase p68 and CTCF 26 , an RNA pull down assay was performed using nuclear extract from human pluripotent stem cells NTERA2 and in vitro transcribed biotinylated antisense SRA and sense SRA ., Western blot analysis showed that sense SRA specifically recruits p68 and CTCF ( Fig 1A and S1 Fig ) supporting our previous report 26 ., p72 , another RNA helicase known to interact with SRA , was also pulled down by SRA ., Next , to detect a possible association between SRA and TrxG and/or PRC2 in nuclear extract , the RNA pull down assay was employed to probe for WDR5 and EZH2 proteins , respectively ., Both WDR5 and EZH2 were pulled down selectively by sense SRA suggesting that SRA interacts with TrxG and PRC2 complexes ( Fig 1A ) ., WDR5 is shared by several TrxG complexes: interaction with both MLL1 and MLL2 was detected in these pull down experiments ( Fig 1A ) , as were related complexes containing histone methyltransferases SETD1A and SETD1B ( S2 Fig ) ., RNA immunoprecipitation experiment showed that SRA was retrieved by anti-WDR5 and anti-SUZ12 , indicating an association between SRA and TrxG/PRC2 complexes in vivo ( S3 Fig ) ., An in vitro RNA pull down assay similarly revealed an interaction between SRA and either recombinant TrxG or PRC2 complexes indicating that the binding between SRA and the two epigenetic machineries is direct ( Fig 1B ) ., The selective properties of the SRA sense strand , in contrast to the antisense strand , are consistent with a specific interaction between the RNA and the two histone modifying complexes ., To determine which components of TrxG and PRC2 mediate the interaction with SRA , individual recombinant proteins were used in the RNA pull down ., Among major TrxG components , sense SRA specifically retrieved WDR5 , whereas it pulled down both the EED and SUZ12 components of the PRC2 complex ( Fig 1C ) ., This result indicates that SRA interacts with TrxG through WDR5 and with PRC2 via EED and SUZ12 ., Purified EZH2 , when not part of the PRC2 complex , shows no selective affinity for sense as compared to anti-sense SRA ( Fig 1C ) ., To a lesser extent this is true for RBBP5 , which as an isolated component shows some binding to anti-sense SRA , unlike other members of the TrxG complex ( Fig 1B ) ., It is clear however that the full complexes , and most of their components , exhibit selective binding to sense SRA ., Domain mapping analysis , in which the 5’ or 3’ halves of the SRA molecule are separately tested for their ability to interact with TrxG and PRC2 complexes , suggests that the TrxG and PRC2 complexes preferentially bind to the 5’ and 3’ regions of SRA , respectively ( Fig 1D and S4 Fig ) ., We note that the secondary structure of SRA 30 harbors distinct domains that might be specialized to interact with the TrxG and PRC2 complexes ., These observations raise the question whether SRA might simultaneously bind to both TrxG and PRC2 , thereby in principle allowing for delivery of both activating and silencing marks ., Co-immunoprecipitation experiments were performed using recombinant TrxG and PRC2 complexes in the presence of either antisense or sense SRA ., Immunoprecipitation of RBBP5 resulted in an enrichment of EED when sense SRA was present in the reaction ( Fig 1E ) ., Similarly , immunoprecipitation of EZH2 led to an enrichment of WDR5 in the presence of sense , but not antisense SRA ., These results indicate that TrxG , PRC2 and SRA are present in the same complex ., However they do not distinguish between a complex in which a single SRA molecule binds both TrxG and PRC2 , and , for example , a complex containing two or more SRA molecules , each separately carrying either TrxG or PRC2 ., Nonetheless , the experiment in Fig 1D suggests that the binding domains on SRA for each complex are largely independent of each other and should be capable of binding both complexes at once To determine whether SRA displays the same bi-faceted binding properties in vivo , shRNA silencing of SRA was employed to deplete SRA expression in NTERA2 ( S5 Fig ) ., Immunoprecipitation of RBBP5 co-precipitated EZH2 in control knockdown cells ( Fig 1F ) ., However , this interaction of EZH2 and RBBP5 was reduced in SRA knockdown cells ., This result is consistent with the in vitro interaction assay and suggests that SRA may be capable of delivering both activating and silencing histone modifications to sites where it is bound ., The lncRNA SRA and RNA DEAD box helicase p68 have been implicated as acting together in transcriptional regulation , yet their mechanism of action remains elusive ., If SRA in the absence of other components can recruit both the TrxG and PRC2 complexes , what role does p68 play ?, We therefore sought to establish whether p68 might modulate SRA/TrxG/PRC2 interactions , altering the affinity of SRA for these complexes ., An SRA pull down assay shows that the amount of interacting TrxG complexes is increased when p68 is present in the reaction ( Fig 2A ) ., This property of p68 to promote TrxG recruitment by SRA is not due to an interaction between p68 and TrxG , since p68 does not directly associate with TrxG complexes ( S6 Fig ) ., In contrast , the ability of SRA to pull down PRC2 complex is not altered by p68 ( Fig 2A ) ., We obtained similar results using the p68 homolog p72 ., To confirm in vivo the function of p68 in promoting SRA and TrxG interaction , RNA immunoprecipitation was carried out with an antibody recognizing RBBP5 after using shRNA to knock down p68 ( S7 Fig ) ., The result shows that enrichment of SRA bound to TrxG complex , but not to PRC2 , was reduced in p68 knockdown cells ( Figs 2B and S8 ) ., These results thus reveal a role of p68 in facilitating interaction between the lncRNA SRA and the activating epigenetic machinery of the TrxG complex ., SRA interacts directly with TrxG and PRC2 complexes ., The function of PRC2 involves methylation of histone H3 lysine 27 ., The TrxG complex carrying MLL2 is responsible for trimethylation of histone H3 lysine 4 in mouse embryonic stem cells 1 , 2 , particularly at bivalent sites ., We therefore asked whether SRA might be present at bivalent domains ., To this end , we utilized the ChIRP technique 19 to pull down the lncRNA SRA from chromatin of the human pluripotent stem cells NTERA2 ., Using next generation sequencing , we identified 7 , 899 SRA-binding sites genome-wide ( see Methods ) ., Comparing SRA with profiles of H3K4me3 and H3K27me3 in NTERA2 generated by the ENCODE project , we find that 1 , 570 and 735 sites representing 20% and 9 . 3% of total SRA binding sites possess respectively either the H3K4me3 or H3K27me3 modification exclusively ( Fig 3A ) ., Among SRA binding sites , 894 regions representing 11% have the bivalent domain signature ( Fig 3A , 3D and S9 Fig ) ., Taken together , about 40% of SRA sites carry at least one of these modifications ., Of all bivalent domains we mapped , 8% are associated with SRA binding ., Gene classification analysis reveals that SRA-bound regions are associated with differentiation and embryonic development genes ( Fig 3B ) ., This result is consistent with the observed interaction in vitro and in vivo between SRA and TrxG/PRC2 complexes , and with a role for SRA in targeting histone modifications , including bivalent modifications , in pluripotent stem cells ., Because p68 facilitates interaction between SRA and WDR5 containing complexes , we asked whether sites of H3K4me3 modification might be enriched at genomic regions occupied by both SRA and p68 relative to those occupied by SRA alone ., Chromatin immunoprecipitation ( ChIP ) sequencing of p68 in NTERA2 identified 14 , 131 binding sites genome wide; functions of many associated genes are involved in embryonic development ( Fig 3C ) ., It is obvious from our data that many sites of H3K4 or H3K27 methylation are associated neither with p68 nor SRA , consistent with the existence of multiple mechanisms for delivering those modifications ., However if we focus on the role of SRA and its interaction with p68 , we find that 16% of SRA binding sites are also occupied by p68 ( Fig 3D–3F and S10 Fig ) ., Furthermore 21% of SRA/p68 binding sites are located at bivalent sites that harbor both H3K4me3 and H3K27me3 marks ( S11 Fig ) ., Interestingly , we observe a significant 19% ( 47% versus 28% ) increase ( p-value < 10−4 , Fisher’s exact test ) in sites carrying the H3K4me3 modification at genomic regions occupied by both SRA and p68 compared with those occupied by SRA but lacking p68 ( Fig 3E ) ., To investigate whether p68 facilitates modification of H3K4me3 , we performed ChIP-PCR of the histone mark at selected p68-bound genes upon silencing of p68 ., Depletion of p68 led to a decrease in H3K4me3 occupancy at half of the p68-bound genes we examined ( S12 Fig ) ., On the other hand , the presence of p68 at SRA binding sites has an insignificant effect on the extent of H3K27me3 modification ( 23% versus 20% ) ( Fig 3F ) ., The genome-wide accumulation of H3K4me3 at p68-associated SRA binding sites thus suggests a role in vivo for p68 in facilitating SRA mediated H3K4 methylation , consistent with our observations in vitro that p68 stabilizes SRA-TrxG interaction ., We have previously shown that CTCF , a DNA binding protein , interacts with p68/SRA in nuclear extracts , and that p68 binding is essential to CTCF dependent insulator function at the human IGF2/H19 imprinted locus 26 ., However unlike the interactions of SRA with TrxG or PRC2 , the interaction between CTCF and p68/SRA is indirect ( S13 Fig ) ., Analysis of SRA ChIRP data from the pluripotent stem cells NTERA2 cells shows that not all CTCF sites are associated with SRA ., Nonetheless , recruitment of SRA by CTCF increases the probability that the site will also be bivalent: 14 . 3% of sites occupied by both CTCF and SRA also carry bivalent marks , whereas only 7 . 3% of CTCF sites not associated with SRA are bivalent ( S14 Fig , p-value < 10−4 ) ., The presence of SRA at CTCF binding sites thus correlates with the presence of bivalent domains ., We next asked whether the core transcription factors NANOG , OCT4 and SOX2 , which have been shown to occupy sites at bivalent genes in human pluripotent stem cells 31 , 32 , might interact with SRA as a means to recruit the lncRNA to their target genes ., RNA pull down experiments using either nuclear extract or recombinant proteins reveal a direct association between SRA and NANOG , but our data do provide evidence for such association of OCT4 or SOX2 ( Fig 4A and 4B ) ., Further , co-immunoprecipitation of p68 and NANOG in the presence of sense or antisense SRA shows that SRA facilitates specific complex formation between p68 and NANOG ( Fig 4C ) ., Using a publicly available ENCODE database of NANOG ChIP-seq in human embryonic stem cells , we find that 16% of SRA binding sites detected in NTERA2 cells overlap with NANOG ( S15 Fig ) ., Unlike for CTCF , we do not find a correlation between SRA co-localization with NANOG and the abundance of bivalent domains ., However , 16 . 5% of NANOG-SRA binding sites also show bivalent association ( S16 Fig ) ., At NANOG-SRA binding sites , the H3K4me3 mark associates with 75% of regions when p68 is present ( NANOG/SRA/p68/K4Me3 vs all NANOG/SRA/p68 ) compared with 51% of this modification at these regions without p68 ( NANOG/SRA/K4Me3 no p68 vs all NANOG/SRA no p68 ) ( S16B Fig , p-value < 10−4 ) ., In contrast , a 5% reduction of H3K27me3 co-occupancy is observed for NANOG-SRA binding sites when p68 is present ( S16C Fig ) ., Thus , similar to the above observation for all SRA associated sites , the presence of p68 at NANOG-SRA binding sites appears to facilitate the establishment of H3K4 methylation ., As the TrxG and PRC2 complexes are important for reprogramming of somatic cells toward induced pluripotent stem cells 4 , 33 , 34 , we tested whether SRA is also important for this process ., Human fibroblasts were transfected with a plasmid encoding OCT4 , SOX2 , c-MYC and KLF4 , and were grown under feeder-free human pluripotent stem cell conditions for 30 days ., We find that , when SRA expression is depleted , the numbers of alkaline phosphatase and SSEA3 positive colonies are reduced ( Fig 4D and 4E ) ., This observation suggests that , similar to TrxG and PRC2 complexes , SRA is a crucial factor for the reprogramming of fibroblasts toward induced pluripotent stem cells ., Additionally , we find that silencing of SRA leads to a decrease in number of cells expressing the pluripotent stem cell marker SSEA3 , while the number of cells expressing the differentiation marker A2B5 is increased ( S17 Fig ) ., This result indicates that SRA is important for maintaining the stem cell state ., However , because silencing of SRA results in a decrease in self-renewal , we are unable to carry out experiments to study the effects of SRA depletion on histone modifications while maintaining the stem cell identity of NTERA2 cells ., The enzymatic mechanisms and cofactors underlying H3K4 and K27 trimethylation have been well characterized ., However , little is known about mechanisms that could selectively generate a bivalent domain , which carries both kinds of methylation marks ., In the present study , we have identified SRA as a lncRNA interacting with both the TrxG and PRC2 complexes ., As discussed in the Introduction , several lncRNAs have been shown to bind either to TrxG or PRC2 12 , 13 , 15–17 ., To date , the only lncRNA known to interact with both TrxG and PRC2 is Fendrr 35 ., However , it is not known whether the interaction between Fendrr and the two histone modifying complexes is direct , or whether Fendrr can deliver those complexes simultaneously ., In NTERA2 cells , 11% of SRA-binding sites genome-wide overlap with bivalent domains , and another 29% are associated with sites carrying either H3K4me3 or H3K27me3 ., This suggests that , depending upon the site , SRA can deliver either or both of these modifications , in the latter case consistent with the presence of a bivalent mark ., Although SRA possesses a potential to interact with both TrxG and PRC2 , 20% of SRA-binding sites are occupied by H3K4me3 but not H3K27me3 , whereas only 9% of SRA-binding sites are marked by H3K27me3 but not H3K4me3 ., Our finding therefore supports a preferred role of SRA as a transcriptional co-activator 29 ., SRA frequently functions with p68 as a complex that can in turn interact with a variety of DNA-binding transcription factors such as MyoD ., But as shown here for SRA-NANOG , SRA in some cases does not require the assistance of p68 ., Our data nonetheless show that the presence of p68 enhances interaction between SRA and the TrxG complex in experiments carried out either with purified components or with nuclear extracts ( Fig 2 ) ., The role of p68 in increasing SRA-TrxG interaction is analagous to that of ATRX , which increases interaction between Xist and PRC2 36 ., Consistent with these observations , the presence of p68 at SRA sites in NTERA2 cells in vivo increases the co-occupancy between SRA and H3K4me3 from 29% to 52% ( Fig 3E ) ., These findings reveal the mutual relationship between p68 and SRA in transcriptional activation ., Many DNA-binding transcription factors have been reported to interact with SRA , either directly or indirectly 22 ., Our study shows that SRA directly interacts with the homeodomain transcription factor NANOG , which occupies regulatory elements of many genes associated with bivalent domains in human pluripotent stem cells 31 , 32 ., We find that SRA and NANOG share binding sites genome-wide ., NANOG is a key transcription factor required for self-renewal of human and mouse embryonic stem cells 37–39 and for establishment of pluripotency 40 ., Similar to the latter function of NANOG , TrxG and PRC2 complexes are also important for reprogramming of the pluripotent state 4 , 33 , 34 ., Our results suggest that NANOG recruits SRA and its associated TrxG and PRC2 complexes as part of the mechanism for establishing the pluripotency of induced pluripotent stem cells , and at least in some cases plays a role in establishing and/or maintaining bivalent domains ( see model in S18 Fig ) ., Our results also show that SRA localization sites are widespread in the genome , and that they are likely to be involved at those sites in delivery of both activating and silencing histone modifications ., The SRA/TrxG/PRC2 complexes can be recruited directly or indirectly to binding sites on DNA through interaction with a variety of transcription factors , only some of which have so far been identified ., CTCF is a ubiquitous factor that appears to contribute to establishment of bivalent states at sites where SRA is also present ., In addition to recruiting both MLL1 and MLL2 , which trimethylate H3K4 , SRA recruits both SETD1A and SETD1B , raising the possibility that it may mediate histone H3 monomethylation as well as trimethylation ., Many other factors ( such as MyoD and NANOG ) are lineage specific; it will be important to investigate in other cell types the interaction of the SRA/TrxG/PRC2 complexes with lineage specific transcription factors , and their role in establishing patterns of histone modification important for regulation of gene expression ., A plasmid containing SRA sequence ( BC067895 . 1 ) was purchased from Open Biosystems ., The SRA coding sequence was subcloned into pLITMUS28i ( New England Biolabs ) for in vitro transcription ( see below ) ., The following plasmids were used for in vitro transcription/translation: pSG5-MYC encoding p68 and p72 ( gift from Prof . Frances V . Fuller-Pace , University of Dundee , UK ) ; pcDNA3 . 1-NANOG ( Addgene ) ., See S1 Table for the list of antibodies used in this study ., Human pluripotent stem cell line NTERA2 was grown in DMEM supplemented with 10% FBS ( Gibco ) at 37°C under a humidified atmosphere of 5% CO2 in air ., At confluent , cells were passaged every three days using 0 . 25% trypsin ( Gibco ) ., For establishment of NTERA2 stable knockdown cell lines , the plasmids pMLP-shRNA targeting SRA , p68 or scramble control ( transOMIC ) were linearized by NdeI and transfected into 1x106 cells using nucleofector ( Amaxa ) according to manufacturer’s protocol ., Cells were immediately grown in DMEM-F12 plus 10% FBS ., On day 3 , stable cell lines were selected using puromycin at 3 μg/ml final concentration ., RNA pull down experiments were performed as previously described 41 ., First , DNA fragments encoding full length , 5’ and 3’ domains of lncRNA SRA were cloned into pLITMUS28i ( New England Biolabs ) , and the DNA sequence was confirmed by sequencing ., To generate antisense or sense SRA transcripts , the plasmid containing full length SRA was linearized by StuI or BglI , respectively ., Biotinylated SRA was in vitro transcribed using HiScribe T7 In Vitro transcription kit ( New England Biolabs ) in the presence of biotin-14-CTP ( Invitrogen ) according to the instruction manuals ., Transcribed RNA products were DNase-treated ( Ambion ) , purified by ethanol precipitation and verified by northern blotting ., For RNA pull downs using nuclear extract , 3 μg of in vitro transcribed RNA was prepared in RNA structure buffer ( Tris-Cl pH 7 . 5 , 0 . 1 M KCl , 10 mM MgCl2 ) and incubated at 78°C for 3 min ., The RNA was then gradually cooled down to 37°C ., Five hundred micrograms of NTERA2 nuclear extract , prepared using NE-PER Nuclear Protein Extraction Kit ( Pierce ) , was mixed with the RNA in immunoprecipitation buffer ( PBS plus 0 . 1% Triton X-100 , 1 mM DTT , protease inhibitor cocktail , PMSF , 80 U RNase inhibitor ) in a total volume of 500 μL ., The reaction was incubated for 4 hr at 4°C with rotation ., MyOne Streptavidin C1 beads were prepared according to manufacturer’s recommendation , and used at 50 μL per sample ., The RNA-beads complex was further incubated overnight ., Beads were washed five times with immunoprecipitation buffer and boiled with 50 μL of SDS loading buffer ., Twenty microliters was loaded onto Novex precast gel ( Invitrogen ) ., For RNA pull down using recombinant proteins , 0 . 3 μg of RNA was used per pull down reaction with 3 μg of protein complex or 1 μg of individual protein ., TrxG and PRC2 complexes were purchased from BPS Bioscience and Cayman Chemical ., NANOG , OCT4 and SOX2 were purchased from Fitzgerald Industries International ., The RNA helicases p68 and p72 were in vitro translated using the TNT Coupled Reticulocyte Lysate System ( Promega ) ., A plasmid encoding luminescence protein was used as negative control ( Promega ) ., Recombinant NANOG was also produced by in vitro translation using a Wheat Germ System ( Promega ) ., All in vitro translated proteins were verified by western blotting ., Ten microliters of translated protein product was used per RNA pull down reaction ., For in vitro co-immunoprecipitation in the presence of antisense or sense SRA , the RNAs were transcribed without Biotin-14-CTP ., Three micrograms of TrxG and PRC2 complexes or 10 μL of in vitro translated p68 and NANOG were used for co-immunoprecipitation in 200 μL of immunoprecipitation buffer ., Antibodies for immunoprecipitation were used at 3 μg including mouse anti-RBBP5 ( MABE220 , Upstate ) , mouse anti-EZH2 ( MA5-15101 , Thermo Scientific ) and rabbit anti-DDX5 ( A300-523A , Bethyl Laboratories ) ., For co-immunoprecipitation using nuclear extract , 500 μg of NTERA2 nuclear extract was mixed with 3 μg of relevant antibodies in a total of 500 μL of immunoprecipitation buffer ., The reaction was incubated for 4 hr at 4°C with rotation ., Protein A and protein G conjugated magnetic beads were prepared according to manufacturer’s recommendation ( Invitrogen ) , and used at 50 μL per sample ., The complex was then further incubated overnight ., Beads were washed five times with immunoprecipitation buffer and boiled with 50 μL of SDS loading buffer ., Twenty microliters was loaded onto Novex precast gel ( Invitrogen ) ., RNA was extracted using TRIzol reagent ( Invitrogen ) and DNase-treated ( DNA-free kit , Ambion ) ., Complementary DNA synthesis was performed with 1 μg RNA using a Maxima First Strand cDNA Synthesis Kit ( Thermo Scientific ) ., qPCR was carried on by using Power SYBR Green PCR Master Mix ( Applied Biosystems ) in a total volume of 20 μl each well with 7900HT real-time PCR system ( Applied Biosystems ) ., Gene expression was normalized by expression level of ACTB ., Primer sequences are available upon request ., Twenty million cells were fixed with 1% formaldehyde in PBS for 10 min at room temperature ., The fixation was quenched by adding glycine at 125 mM final concentration and incubated further for 5 min ., Cells were washed and collected by centrifugation at 1500 rpm for 5 min ., Nuclear extract was prepared by using NE-PER Nuclear Protein Extraction Kit ( Pierce ) ., Three micrograms of antibody was added to 500 μg of the nuclear extract in immunoprecipitation buffer ( PBS , 1 mM DTT , protease inhibitor cocktail , PMSF , 80 U RNase inhibitor ) in a total volume of 500 μL ., The complex was incubated at 4°C for 4 hr ., Protein A and protein G conjugated magnetic beads were used at 50 μL per sample ., The complex was then further incubated overnight ., Beads were washed five times and resuspended in 100 μL proteinase K buffer ( 10 mM Tris-Cl pH 7 . 5 , 100 mM NaCl , 1 mM EDTA , 0 . 5% SDS ) with 5 μL proteinase K ( New England Biolabs ) ., Samples were incubated at 50°C for 45 min with shaking , and boiled at 95°C for 10 min ., Samples were mixed with 500 μL Qiazol by vigorous vortexing , and were incubated at room temperature for 10 min ., RNA extraction was then performed using miRNeasy mini kit ( Qiagen ) ., qPCR was employed to detect RNA binding ., ChIP was performed according to the manufacturer’s instruction ( Active Motif ) ., Briefly , 2 x 107 cells were fixed with 1% formaldehyde in PBS for 10 min at room temperature ., The fixation was then quenched by adding glycine ., Cells were washed and collected by centrifugation at 1500 rpm for 5 min ., Nuclei were sonicated twice using Bioruptor ( Diagenode ) at maximum power , 30 sec ON and 30 sec OFF for 7 . 5 min to obtain chromatin fragments ranging from 200–1000 bp ., Fifty micrograms of sheared chromatin was used per IP with 3 μg antibody ., Retrieved DNA fragments were purified by QIAquick PCR Purification Kit ( Qiagen ) or ethanol precipitation ., Primer sequences for ChIP are listed in S2 Table ., ChIRP analysis was performed according to published protocols with minor modifications based on ChIRP and Capture Hybridization Analysis of RNA Targets ( CHART ) techniques 19 , 42 , 43 ., Briefly , 3x107 cells were fixed with 1% glutaraldehyde for 10 min at room temperature with shaking ., The fixation was stopped by adding glycine ., Crosslinked cells were washed with PBS , and resuspended in 1 ml swelling buffer ( 25 mM HEPES pH 7 . 3 , 10 mM KCl , 0 . 1% NP-40 , 1 mM DTT , PMSF ) ., Samples were incubated at 4°C for 30 min with shaking , and were collected by centrifugation ., The pellet was resuspended with 350 μL of ChIRP lysis buffer , and was sonicated using Bioruptor ( Diagenode ) at maximum power , 30 sec ON and 30 sec OFF for 7 . 5 min of 6 cycles to obtain chromatin fragments ranging from 100–1000 bp ., Sheared chromatin was then collected by centrifugation ., Two hundred micrograms of sheared chromatin sample was pre-cleared for 1 hour using 100 μL of Ultralink-streptavidin beads ( Pierce ) at room temperature with shaking ., The sample was then centrifuged , and supernatant was collected ., The pre-cleared chromatin was used per hybridization reaction with 10 μL of 100 μM pooled 3’ Biotin TEG oligonucleotide probes ( Integrated DNA Technologies ) ., SRA probes were designed to cover SRA transcript at nucleotide position 124 to 1473 ( accession number NR_045587 . 1 ) ( See S3 Table for the probe sequences ) ., LacZ probes were employed as negative control 19 ., The sample and the probes were hybridized at 37°C for 4 hours with shaking ., Once the hybridization was completed , 100 μL of C-1 magnetic beads ( Invitrogen ) was mixed with the sample to pull down the biotinylated probes ., DNA was eluted in the presence of 12 . 5 mM D-Biotin ( Invitrogen ) ., DNA was ethanol precipitated and subjected to library preparation ., Library preparation was performed using TruSeq ChIP Sample Preparati

Introduction, Results, Discussion, Materials and Methods

Long non-coding RNAs ( lncRNAs ) have been recognized as key players in transcriptional regulation ., We show that the lncRNA steroid receptor RNA activator ( SRA ) participates in regulation through complex formation with trithorax group ( TrxG ) and polycomb repressive complex 2 ( PRC2 ) complexes ., Binding of the SRA-associated RNA helicase p68 preferentially stabilizes complex formation between SRA and a TrxG complex but not PRC2 ., In human pluripotent stem cells NTERA2 , SRA binding sites that are also occupied by p68 are significantly enriched for H3K4 trimethylation ., Consistent with its ability to interact with TrxG and PRC2 complexes , some SRA binding sites in human pluripotent stem cells overlap with bivalent domains ., CTCF sites associated with SRA appear also to be enriched for bivalent modifications ., We identify NANOG as a transcription factor directly interacting with SRA and co-localizing with it genome-wide in NTERA2 ., Further , we show that SRA is important for maintaining the stem cell state and for reprogramming of human fibroblasts to achieve the pluripotent state ., Our results suggest a mechanism whereby the lncRNA SRA interacts with either TrxG or PRC2 ., These complexes may then be recruited by various DNA binding factors to deliver either activating or silencing signals , or both , to establish bivalent domains .

Long non-coding RNAs ( lncRNAs ) can play an important role in regulation of gene expression ., In a number of cases , individual lncRNAs have been shown to interact with either the trithorax group ( TrxG ) or polycomb repressive complex 2 ( PRC2 ) protein complexes , which deliver histone modifications associated respectively with transcriptionally active or inactive chromatin ., Here we show that the lncRNA , SRA , unusually forms complexes with both TrxG and PRC2 complexes ., Consistent with this property , some SRA binding sites in human pluripotent stem cells overlap with bivalent domains , which carry both kinds of histone modifications ., We find that SRA complexed with the helicase protein , p68 , shows enhanced binding of TrxG complex , but not of PRC2 ., This is reflected in genome wide enriched ‘activating’ histone modifications at SRA sites also occupied by p68 ., We show that in human pluripotent stem cells SRA also interacts with NANOG , a principal determinant of pluripotency , and is important for maintenance of the pluripotent state ., SRA may be involved in the delivery of histone modifications associated with either activation or silencing of gene expression , and in some cases could deliver both .

journal.pcbi.0040009

A Quantitative Study of the Division Cycle of Caulobacter crescentus Stalked Cells

C . crescentus is a dimorphic bacterium that inhabits freshwater , seawater , and soils , where it plays an important role in global carbon cycling by mineralizing dissolved organic materials 26 ., C . crescentus normally undergoes an asymmetric cell division resulting in two different progeny cells ( Figure 1 ) : a motile , flagellated swarmer cell and a sessile stalked cell 22 , 23 , 27 ., The nascent stalked cell then enters immediately into a new round of cell division and produces , about 90–120 min later , a new swarmer cell ., The nascent swarmer cell swims around for 30–45 min before it differentiates into a stalked cell and initiates the DNA replication–division cycle ., In this paper , we restrict our attention to the division cycle of stalked cells ., Figure 2 depicts central elements of the cell division regulatory network in C . crescentus ., Caulobacter crescentus has 3 , 767 protein-encoding genes 28 , of which 553 are cell cycle regulated 29 ., Two master-regulator proteins control more than 25% of cell cycle–regulated genes: the transcription factor CtrA 30 directly regulates 95 genes ( including divK , ccrM , podJ , pleC , ftsZ , and ftsQ ) 31 , 32 , whereas GcrA controls 49 genes 15 , 29 , 32 ., There is also weak evidence from microarray data 32 that CtrA can up-regulate dnaA ., In addition , DNA synthesis in C . crescentus is under direct control by CtrA 33–35 , which binds to the origin of DNA replication and inhibits initiation of DNA synthesis 30 ., CtrA is present at a high level in swarmer cells , whereas in stalked cells , it changes from low to high level during the cell cycle 15 , 36 , 37 ., The abundance and activity of the CtrA protein is regulated through gene transcription , protein degradation , and phosphorylation ., Expression of ctrA is under control of two promoters , ctrA P1 and ctrA P2 31 , 36 , 38 ., The weaker ctrA P1 promoter is activated in the early stalked cell ( ∼35 min after the initiation of DNA replication 39 , 40 ) by GcrA protein 15 and inhibited by high levels of CtrA itself 36 ., The stronger ctrA P2 promoter is activated later , in predivisional cells , by the CtrA protein itself 36 ., In addition , the ctrA P1 promoter is only activated from a new strand of hemimethylated DNA 31 , 40 ., The ctrA P2 promoter is not active in swarmer cells , even though these cells have high levels of CtrA 36 ., Furthermore , expression from ctrA P2 is inhibited in predivisional cells by conditions that inhibit DNA replication 41 ., These facts indicate that ctrA P2 has regulators other than CtrA itself 36 ., Proteolysis of CtrA ( and CtrA∼P ) is significantly accelerated by the phosphorylated form of DivK protein , DivK∼P , via the ClpXP protease pathway 42 , or with the help of some other ( as yet unknown ) histidine phosphotransferases 43 ., Recently , RcdA and CpdR proteins have been reported to be involved in CtrA degradation in combination with ClpXP 44 , 45 ., When this proteolysis pathway is activated , the half-life of CtrA in vivo is 5 min or less 38 ., CtrA is active when phosphorylated 46 , 47 , a reaction carried out by a histidine kinase , CckA 46 , 48 , and a histidine phosphotransferase , ChpT 49 ., In addition , CtrA is also phosphorylated by a tyrosine kinase , DivL 50 ., CtrA is rapidly dephosphorylated in vivo ., The activity of CckA was shown recently to be down-regulated by a DivK∼P 44 , 45 , 49 , thereby linking the phosphorylation and proteolysis pathways of CtrA ., But otherwise , how the kinase and phosphatase reactions are regulated to control the fraction of active CtrA is poorly understood ., GcrA is an activator of components of the replisome and of the segregation machinery 15 , and also regulates genes such as ctrA , pleC , and podJ 15 , 19 ., GcrA protein concentration varies through the cell division cycle , peaking early in the cycle in stalked cells and reaching its minimum in a swarmer cell , after cell division ., The DNA replication-initiating protein , DnaA , is required for gcrA expression 18 ., In addition , transcription of gcrA is repressed by the CtrA protein 15 ., DNA replication proceeds in three phases: initiation , elongation , and termination ., The origin of DNA replication ( Cori ) in C . crescentus has one potential binding site for DnaA , a protein involved in initiating DNA synthesis 51 ., The DnaA binding site partially overlaps with five CtrA binding sites in Cori 33–35 ., CtrA represses initiation of DNA replication 30 ., Thus , DNA replication is only initiated when DnaA level is high and CtrA level is low ., In addition , DNA replication cannot be re-initiated until the origin stie has been fully methylated 52 , 53 ., These conditions prevail during the swarmer-to-stalked cell transition , and just after division in the stalked cell compartment 34 ., Once initiated , DNA synthesis continues bidirectionally along the circular chromosome , with an average speed of ∼20 . 5 kb/min in minimal broth , finishing in the late predivisional cell 54 ., Elongation of newly replicating DNA strands requires a complex machinery , many components of which are under GcrA control 15 ., Several cell cycle–related genes ( ctrA , gcrA , dnaA , ftsZ , and ccrM ) have GANTC methylation sites in their promoters 19 , 31 , 40 , 52 , 53 , 55 , 56 ., Hence , the expression of these genes may be sensitive to the methylation state of the promoter ., DNA replication transforms a fully methylated gene ( both strands methylated ) into a pair of hemimethylated genes ( only one strand methylated ) ., At some later time , the unmethylated strands become methylated by the action of CcrM to return the genes to the fully methylated state 53 ., These methylation transitions may affect the expression of cell cycle–related genes 53 ., Methylation of Cori is also necessary for initiating a new round of DNA synthesis 34 ., These methylation effects provide feedback from the progression of DNA replication to the cell cycle control system ., In C . crescentus and other α-proteobacteria , CcrM is the methyltransferase that accounts for methylation of newly synthesized DNA strands ., ccrM transcription is activated by CtrA only from a hemimethylated chromosome for about 20 min , in a late predivisional cell ( its expression peaks at ∼105 min in the 150-min swarmer cell cycle ) 57 ., Lon protease is required for CcrM degradation 58 ., The half-life of CcrM is less than 10 min in vivo 39 ., Thus , CcrM activity is limited to a short portion of the predivisional cell phase , just before cell division ., The multicomponent Z-ring organelle , which forms and constricts at the mid-cell plane , plays an important role in compartmentation of the predivisional cell and its subsequent division 27 ., Compartmentation lasts about 20 min 59 ., After the late predivisional cell is divided into two progeny cells , the Z-ring is disassembled and degraded ., The Fts proteins ( FtsZ , FtsQ , FtsA , and FtsW ) have been identified as crucial elements of the Z-ring ., ftsZ expression is positively and negatively regulated by CtrA 29 , 60 , and it may also by regulated by DNA methylation since the ftsZ promoter has a methylation site 40 , 53 ., The ftsQ gene is expressed only after CtrA-mediated activation in the late predivisional cell 41 ., The FtsQ protein localizes predominantly to the mid-cell plane of the predivisional cell , consistently with the appearance of the Z-ring 61 , 62 ., The FtsA protein exhibits the time course similar to FtsQ 61 ., divK transcription is activated by CtrA in late predivisional cells , which results in a slight elevation of DivK protein , otherwise present throughout the cell cycle at a nearly constant level 42 , 63 ., The total amount of DivK∼P , the form that promotes CtrA degradation , does not appear to undergo dramatic changes during the cell cycle ., It is 50% ± 20% lower in swarmer cells than in predivisional cells 63 ., However , DivK and DivK∼P are dynamically localized during the cell division cycle 63–68 ., Membrane-bound proteins DivJ and PleC , which localize at stalked and flagellated cell poles , respectively , regulate this process 64 , 65 by having opposite effects on DivK phosphorylation ., DivJ is a kinase that continuously phosphorylates DivK at the stalked cell pole , and PleC promotes the continuous dephosphorylation of DivK∼P at the flagellated cell pole 64 , 67 ., Hence , opposing gradients of DivK and DivK∼P are established between the two cell poles ., Full constriction of the Z-ring disrupts the diffusion of DivK between the two poles 59 , 64 ., As a result , DivK∼P accumulates in the nascent stalked cell compartment and unphosphorylated DivK accumulates in the nascent swarmer cell compartment ., High DivK∼P promotes CtrA degradation in the stalked cell compartment 42 , 43 , whereas high CtrA is maintained in the swarmer cell compartment 16 ., The nonuniform distribution of DivK and DivK∼P , and their corresponding effects on CtrA degradation , contribute largely to the different developmental programs of swarmer and stalked cells in C . crescentus ., In addition , recent investigations indicate that CtrA phosphorylation is also at least partially under the control of DivK∼P ( as mentioned above ) , which shows that DivK∼P not only controls the stability of CtrA , but also its activity 44 , 45 ., To simulate the molecular regulation of a wild-type stalked-cell division cycle , we solve the equations in Table 1 subject to the parameter values and initial conditions in Tables 2 and 3 ., Figure 4 illustrates how scaled protein concentrations and other variables of the model change during repetitive cycling of a stalked cell ., The duration of a wild-type stalked-cell division cycle in our simulations is 120 min ( ∼90 min for S phase and ∼30 min for G2/M phase ) , as typically observed in experiments 22 , 23 , 59 ., The main physiological events of the division cycle can be traced back to characteristic signatures of protein expression , as described in the Introduction ., The division cycle starts with initiation of DNA replication ( Figure 4A ) from a fully methylated origin site by elevated DnaA , when CtrA is low and GcrA is sufficiently high ( to induce production of required components of the replication machinery ) ( Figure 4C and 4D ) ., Immediately after DNA replication starts , Cori is hemimethylated ., As DNA synthesis progresses , certain genetic loci become hemimethylated in order along the chromosome ( Figure 4B ) ., Consequently , the regulatory proteins are produced and reach their peak concentrations sequentially ., By contrast , dnaA expression seems to be activated by full methylation 55 , so its expression rate declines immediately after DNA replication starts ., The effect of methylation on dnaA expression is minor compared to the regulatory signals coming from GcrA and CtrA ., When the replication fork passes the ccrM locus , the gene becomes available for transcription , but is not immediately expressed , because CtrA level is low ., In a predivisional cell , at approximately 35 min after start of DNA replication , the replication fork passes the ctrA gene ( Figure 4B ) , and its expression is immediately activated by GcrA ( Figure 4C ) and then further up-regulated by CtrA itself ., Later on , when CtrA level becomes high , expression of the ccrM gene and , later , hemimethylated fts genes ( at ∼65 min ) , are expressed by the activation from high-level CtrA ( Figure 4D ) ., High CtrA down-regulates gcrA expression ., When DNA replication is finished , the new DNA strands are methylated by elevated CcrM in about 20 min ., DNA methylation shuts down production of CtrA , CcrM , and Fts proteins ., Meanwhile , elevated Fts proteins promote Z-ring formation and constriction ( Figure 4D ) , which separates the predivisional cell into two compartments , thereby restricting access of DivK and DivK∼P to only one of the old poles of the cell ., As a result , in the stalked cell compartment , most DivK is converted into DivK∼P , accelerating CtrA proteolysis there ( Figure 4C ) ., In a nascent stalked cell , low CtrA concentration releases gcrA expression , and GcrA protein level rises ., Then , low CtrA , high GcrA , and high DnaA drive the nascent stalked cell into a new round of DNA synthesis from the fully methylated chromosome ., These computed properties of the model agree reasonably well with what is known ( or expected ) about cell cycle progression in C . crescentus ., In Figure 5 , we compare our simulation with experimental data ., The data , collected from literature , were obtained by different research group with various experimental techniques ., In most cases , experimental uncertainties of the data were not reported , but it is reasonable to assume that the error bounds are quite generous ., Therefore , based on a visual comparison , we conclude that our model is in reasonable agreement with experimental observations ., The only serious objection that might be raised is to our simulation of DivK∼P ( Figure 5C , green curve ) , which increases rapidly in the stalked-cell compartment after the Z-ring closes and DivK∼P is cut off from its phosphatase at the swarmer cell pole ., Jacobs et al . 62 reported roughly constant levels of DivK∼P in predivisional stalked cells , i . e . , until just before Z-ring constriction , and significant differences of DivK∼P levels between stalked cells and swarmer cells ., Our waveform for DivK∼P is consistent with this report and predicts that there should be a distinct peak of DivK phosphorylation in the stalked cell compartment at the end of the division cycle ., This peak seems to be an inevitable consequence of the current belief that , upon Z-ring constriction , DivK becomes dephosphorylated in the swarmer cell compartment and remains heavily phosphorylated in the stalked cell compartment ., The phenotypes of mutant cells provide crucial hints for deciphering the biochemical circuitry underlying any aspect of cell physiology ., A mathematical model must be consistent with known phenotypes of relevant mutants ., To make this test , we simulate cell cycle mutants of C . crescentus using exactly the same differential equations , parameter values , and initial conditions as for wild-type cells ( Tables 1 , 2 , and 3 ) , except for those modifications to parameters dictated by the nature of the mutation ( Table 4 ) ., Our simulations of 16 classes of mutants are in agreement with experimentally observed phenotypes , as described here ., We propose ( Figure 3 ) a realistic mechanism for regulating the cell division cycle of stalked cells of C . crescentus ., The mechanism includes three master-regulatory proteins ( GcrA , DnaA , and CtrA ) , a DNA methylase ( CcrM ) , Z-ring components ( Fts proteins ) , and an end-of-cycle protein ( DivK ) in its inactive and active ( phosphorylated ) forms ., Cytokinesis is represented by a phenomenological variable that describes the extent of constriction of the Z-ring ., DNA synthesis is described in terms of initiation , elongation , and termination ., We assume that initiation of DNA replication requires high DnaA and GcrA , low CtrA , and full methylation of the origin site , and that the rate of DNA elongation is independent of DnaA , GcrA , and CtrA , and is almost linear ., Transcription of some genes occurs only from an unmethylated DNA sequence; hence , the expression of such genes depends on their location on the newly synthesized DNA strand ., Compartmentation in the predivisional cell is assumed to result in localization of phosphorylated DivK to the stalked compartment of the dividing cell , promoting CtrA degradation there ., These assumptions are formulated as a mathematical model ( Table, 1 ) consisting of 16 nonlinear , ordinary differential equations for seven proteins , the state of the Z-ring , the progression of DNA synthesis , and the methylation state of five gene sites on the DNA ., The rate equations entail 44 parameters ( rate constants , binding constants , and thresholds; Table, 2 ) that need to be determined by fitting the model to specific experimental observations ., For the present , parameter estimation is done by trial and error , so we can only claim that our model equations and parameter set are sufficient to account for many properties of cell cycle control in C . crescentus ., Because we fit the model to many different mutant phenotypes , we have a wealth of data to fix the parameters and to provide meaningful confirmation of the mechanism ., Table 2 is in no sense an optimal parameter set , nor can we quantify how robust the system is , although our experience suggests that the model is quite hardy ., Our present model is based heavily on an earlier conjecture 17 that the C . crescentus cell cycle is controlled by a bistable switch , created by positive feedback in the molecular circuitry of the ctrA gene ., In that conjecture , the switch is flipped from the off-state ( CtrA low ) to the on-state ( CtrA high ) by GcrA accumulation as cells enter S phase , and then switched back to the off-state by DivK activation ( phosphorylation ) as cells divide ( the CtrA–DivK negative feedback loop ) ., The original model did not account for the ways in which gene expression is linked to DNA methylation , thereby anchoring the protein interaction network to the progression of DNA replication forks ., By incorporating DNA synthesis and methylation into the Brazhnik–Tyson model , the present model provides a more satisfactory account of cell cycle regulation in C . crescentus , and it can be tested by comparison to a broad spectrum of mutant phenotypes ., Because the new model successfully reproduces the behavior of wild-type and mutant cells in many quantitative details , we conclude that our present understanding of the control system ( Figure 3 and Table 1 ) , properly interpreted , is accurate and adequate ., On the other hand , the proposed mechanism must be considered as an evolving hypothesis that will be continually examined , revised , and improved as new observations tell us more about the control system ., Some obvious improvements to the model include refined criteria for DNA initiation , regulated phosphorylation of CtrA , spatial localization of proteins , inclusion of a swarmer cell compartment , and an account of the swarmer-to-stalk cell transition ., Finally , most of division-control proteins ( such as CtrA , DivK , CcrM , FtsZ , and FtsQ ) are conserved among α-proteobacteria 72 , suggesting that the computational model proposed here for C . crescentus may prove applicable to other types of α-proteobacteria , including symbiotic nitrogen-fixing genera ( Rhizobia ) and pathogenic genera ( Brucella spp . , Coxiella spp , etc . ), To understand the molecular logic of cell cycle regulation in C . crescentus , we constructed a mathematical model of the temporal dynamics of the regulatory genes and proteins ., Following standard rules of chemical kinetics , we converted the wiring diagram in Figure 3 into a set of rate equations describing the temporal dynamics of the model ., Justification of our approach is described in detail in 17 ., Our model includes: Seven proteins: DnaA , GcrA , CtrA , CcrM , DivK ( inactive ) , and DivK∼P ( phosphorylated , active form ) , and a “representative” Fts protein ., Two phenomenological variables , Z ( the state of closure of the septal Z-ring ) and I ( introducing a delay between activation of ccrM transcription and later activation of CcrM protein production ) ., The progression of DNA replication ( including initiation , elongation , and termination ) and its methylation ( including probabilities of hemimethylation of ccrM , ctrA , dnaA , and fts genes , and of the replication origin site , Cori ) ., Accordingly , our mathematical model consists of 16 nonlinear differential equations presented in Table 1 , including 28 kinetic constants ( ks ) , 11 binding constants ( Js ) , and five thresholds ( θs ) ., Our choice of parameter values is given in Table 2 ., A common trend in developing complex models in molecular cell biology is to start from a simple coarse-grained ( “phenomenological” ) model and then refine and expand it step by step ( as data become available ) into an increasingly more comprehensive model ., ( A good example is the progression of models of the budding yeast cell cycle 2 , 4 , 73 . ), We have taken this approach in our study of the C . crescentus cell cycle ., We have limited the scope of our model so that it can be based largely on experimental observations , is not overwhelmed with assumptions , and is able to make predictions ., Obviously , at any stage of modeling there will be facts that have not yet been incorporated and thus are out of the scope of the model ., Our modeling assumptions are described here ., First , we propose to model , at this stage , only the average behavior of cells and do not address naturally occurring fluctuations in cell cycle progression ., Second , the rise of DivK∼P in stalked compartments after constriction of the Z-ring is a necessary , but not sufficient , condition for CtrA degradation ., In our coarse-grained model of CtrA proteolysis , we use DivK∼P as a signal for starting rapid degradation of CtrA ., In other words , DivK∼P determines when the degradation of CtrA is turned on , but the how ( the machinery that degrades CtrA , involving RcdA , CpdR , and ClpXP ) is assumed to be there when needed and is not modeled at present ., Third , CtrA is activated by phosphorylation ( by kinases CckA and DivL ) , and a complete model of the Caulobacter cell cycle should take this into account ., Unfortunately , little is known about the phosphorylation and dephosphorylation of CtrA and how these processes are temporally regulated ., During the division cycle of wild-type cells , the levels of CtrA and CtrA∼P rise and fall together 22 , 46 , so we need not distinguish between the two forms ., Therefore , in the current model , we keep track of CtrA synthesis and degradation only , assuming that CtrA∼P is a fixed fraction of total CtrA ., This assumption , though a great oversimplification , is harmless enough for most of the mutants we consider in this paper ., But it seems to cause serious problems for exactly those mutants ( ctrAop , ctrAΔ3 , ctrAD51E , and ctrAD51EΔ3 in wild-type background ) that interfere with normal synthesis , degradation , or activation of CtrA 34 ., Later versions of the model will have to include CtrA∼P as a variable , when we have a better of idea of the mechanisms controlling CtrA phosphorylation ., It is known that DivK∼P promotes the proteolysis of CtrA∼P 42 and negatively regulates CckA activity , thereby reducing phosphorylation of CtrA 49 , 74 ., Hence , DivK∼P works to eliminate CtrA∼P activity by two independent pathways ., We lump these two effects together as a single DivK∼P promoted reaction for removing active CtrA ., Fourth , the dnaA locus is very close to the origin site ( Cori ) 28 ., Within its promoter , potential CtrA and DnaA boxes and methylation sites exist for regulating its expression 20 , 34 , 52 , 55 ., GcrA is a repressor for dnaA expression 15 , and CtrA seems to be an activator 32 ., However , DnaA protein concentration varies very little during the Caulobacter cell cycle 55 ., Although we include the regulatory signals in the model , they do not much affect the dynamics of a stalked cell because DnaA level is nearly constant throughout the cell cycle due to DnaAs long half-life ., Fifth , initiation of DNA replication is triggered by the combined conditions of low CtrA , high DnaA , and fully methylated DNA origin site ., In addition , initiation requires sufficient replication machinery , which is correlated to a high level of GcrA ., We combine these regulatory effects into a single term ., We assume that once initiation of DNA replication is successful , DNA elongation starts immediately ., Elongation of new DNA strands is linear in time until it finishes , based on experimental data indicating that the speed of DNA replication in C . crescentus is almost constant 54 ., Sixth , full constriction of the Z-ring requires accumulation and activation of a number of proteins , including FtsZ , FtsQ , FtsA , and FtsW , some of which are stimulated by CtrA ., To simplify the model , we use Fts as a combined component to relay the signal from CtrA to Z-ring constriction ., The transition from Z-ring open ( =, 1 ) to fully constricted ( = 0 ) is modeled as a Goldbeter-Koshland ultrasensitive switch 75 ., Seventh , we include the effects of DNA methylation on gene expression in our model because these effects mediate important feedback loops between DNA synthesis and the master regulatory proteins , and because DNA methylation can be a useful target for new drug development ., In our model , the genes ccrM , dnaA , ctrA , and fts as well as the origin of DNA replication are regulated by methylation ., Methylation plays a minor role in the regulation of GcrA production 19 , so we disregard it in our model ., We allow a modest contribution of DNA methylation to regulating the production of DnaA ., ccrM gene expression is significantly affected by its methylation state 40 , 57 ., The activity of ctrA-P1 is known to depend on hemimethylation 36 , and the activity of ctrA-P2 seems to depend in some other way on DNA replication 37 ., For simplicity , we assume that both ctrA promoters are turned on by hemimethylation of the gene ., Among fts genes , the ftsZ promoter has a methylation site 40 , 53 , but the ftsQ promoter does not 41 ., Scanning the ftsQ gene for the consensus sequence GANTC using the Regulatory Sequence Analysis Tools ( http://rsat . ulb . ac . be/rsat/ ) , we found a GAGTC segment in the coding sequence , suggesting that the ftsQ gene might also be affected by methylation ., Since our “Fts” variable is a combination of Fts proteins , we conclude that our fts gene should be regulated by methylation ., The effects of methylation on gene promoters and Cori are described by probabilities to be methylated or hemimethylated during the cell cycle ., The probabilities ( h . . variables ) are in turn controlled by the progression of DNA replication and by the activity of CcrM 52 , 53 ., Eighth , ccrM transcription is tightly regulated by CtrA protein , but accumulation of CcrM protein shows a noticeable delay from the transcriptional activation of its gene 37 , resulting in delayed activation of DNA methylation 57 ., This delay is mimicked in our model by an intermediate variable I in the CtrA-to-CcrM pathway ., Ninth , we recognize the importance of spatial controls in the Caulobacter cell cycle ., However , at this stage , we are trying to model the stalked cell cycle as far as possible without explicitly tracking the spatial localization of regulatory proteins ., That would require a more sophisticated mathematical framework and is planned for the next stage of the model ., As the result of this simplification , our model makes no distinction between the stalked and swarmer parts of the predivisional cell ., Right after compartmentation and before cytokinesis , we keep track of proteins in the stalked cell compartment only ., At this stage , the distinction between swarmer and stalked cells is made by the phosphorylation state of DivK ( being completely phosphorylated in the stalked compartment ) ., Tenth , we assume cells grow steadily in time , with a mass-doubling time of about 120 min and with the accumulated material shed at each division in the swarmer cell ., In the present model , there is no coupling between cell growth and division , as in our models of eukaryotic cell proliferation 10 ., Hence , there is no need for us to keep track of cell size , except to notice that if cell division is delayed or blocked , then the stalked cell will grow longer than normal and eventually be described as having a filamentous morphology ., Parameter values for our model ( Table, 2 ) were determined from available experimental data , wherever possible ., Rate constants of degradation were estimated from experimentally observed half-lives of proteins ., Rate constants of protein synthesis were adjusted to fit variations of protein concentration observed in experiments ., Parameter values of Z-ring dynamics were set to be consistent with observed durations of the open ( ∼100 min ) and constricted ( ∼20 min ) states of the Z-ring 59 ., Rate constants of DivK phosphorylation and dephosphorylation were estimated from the difference of DivK∼P concentration before and after Z-ring closing in predivisional cells 63 ., Successful initiation of DNA replication depends on satisfying four requirements: low CtrA ( CtrA < θCtrA ) , high DnaA ( DnaA > θDnaA ) , high GcrA ( GcrA > θGcrA ) , and a fully methylated origin site ( hcori < θCori ) ., The thresholds were adjusted to position the onset of the S phase correctly in wild-type cells ., Replication-fork progression ( elongation ) begins at each successful initiation ( Ini = 0 . 05 ) and stops when DNA replication is complete ( Elong = 1 ) ., The constant rate of elongation is consistent with an 80-min delay for copying the chromosome ., Due to the constant rate of DNA replication , those genes that must be hemimethylated in order to be transcribed will be expressed in a temporal sequence determined by their positions on the chromosome from the origin of replication 39 , 76 ., To model this effect , the variable hgene is set to 1 ( hemimethylated ) when Elong = distance of gene from Cori ., Some time thereafter , when CcrM activity is high , the hgene decays exponentially back to 0 ( fully methylated ) ., Most Hill function exponents are assumed to be 2 , with a higher value ( nH = 4 ) where sharper switching was required ., Initial conditions ( Table 3 ) were taken to represent the beginning of a stalked cell cycle in a wild-type cell ., The phenotypes of relevant mutants were collected from the literature ., To simulate each mutant , we use exactly the same equations ( Table, 1 ) and parameter values ( Table, 2 ) except for values of those parameters directly affected by the mutation ( Table 4 ) ., Mutations are introduced in our model after 120 min of simulation of the wild-type cell ., For gene deletion , the rate of synthesis of the corresponding protein is set to zero ., For gene overexpression , an additional constant rate of synthesis of the corresponding protein is introduced into the equations , because proteins are typically overexpressed from an extra copy of the gene under control of an inducible promoter ., For heat- or cold-sensitive mutants , the relevant rate constant ( s ) retains its wild-type value at the permissive temperature and is set to zero at the restrictive temperature ., For partial deletions , the relevant parameter value is assumed to lie between 0% and 100% of the wild-type value , according to the experimental characterization of the mutation ., Equations of the model were solved numerically with Matlab 2006a ( The MathWorks ) ., Machine-readable files for reproducing our simulations are made available in Text S1 and on our Web site ( http://mpf . biol . vt . edu/research/caulobacter/pp/ ) .

Introduction, Results, Discussion, Materials and Methods

Progression of a cell through the division cycle is tightly controlled at different steps to ensure the integrity of genome replication and partitioning to daughter cells ., From published experimental evidence , we propose a molecular mechanism for control of the cell division cycle in Caulobacter crescentus ., The mechanism , which is based on the synthesis and degradation of three “master regulator” proteins ( CtrA , GcrA , and DnaA ) , is converted into a quantitative model , in order to study the temporal dynamics of these and other cell cycle proteins ., The model accounts for important details of the physiology , biochemistry , and genetics of cell cycle control in stalked C . crescentus cell ., It reproduces protein time courses in wild-type cells , mimics correctly the phenotypes of many mutant strains , and predicts the phenotypes of currently uncharacterized mutants ., Since many of the proteins involved in regulating the cell cycle of C . crescentus are conserved among many genera of α-proteobacteria , the proposed mechanism may be applicable to other species of importance in agriculture and medicine .

The cell cycle is the sequence of events by which a growing cell replicates all its components and divides them more or less evenly between two daughter cells ., The timing and spatial organization of these events are controlled by gene–protein interaction networks of great complexity ., A challenge for computational biology is to build realistic , accurate , predictive mathematical models of these control systems in a variety of organisms , both eukaryotes and prokaryotes ., To this end , we present a model of a portion of the molecular network controlling DNA synthesis , cell cycle–related gene expression , DNA methylation , and cell division in stalked cells of the α-proteobacterium Caulobacter crescentus ., The model is formulated in terms of nonlinear ordinary differential equations for the major cell cycle regulatory proteins in Caulobacter: CtrA , GcrA , DnaA , CcrM , and DivK ., Kinetic rate constants are estimated , and the model is tested against available experimental observations on wild-type and mutant cells ., The model is viewed as a starting point for more comprehensive models of the future that will account , in addition , for the spatial asymmetry of Caulobacter reproduction ( swarmer cells as well as stalked cells ) , the correlation of cell growth and division , and cell cycle checkpoints .

cell biology, in vitro, microbiology, computational biology, eubacteria

journal.pcbi.1007173

Large vessels as a tree of transmission lines incorporated in the CircAdapt whole-heart model: A computational tool to examine heart-vessel interaction

The CircAdapt platform , a zero-dimensional whole-heart model developed in our lab , historically focussed on cardiac mechanics ., It has been successfully used for simulating haemodynamics during cardiac conductance disorders , valve pathologies , and changes in afterload 1 , 2 , 3 , 4 ., Lacking a distributed model of the vascular system , the current CircAdapt model is yet unable to simulate heart-vessel interaction at the level of arterial wave dynamics ., Arterial pulse waves , constituting a component of ventricular afterload , appear to have implications in age-related changes in left ventricular mass , and left ventricular hypertrophy 5 , 6 ., So-called wave intensity analysis ( WIA ) allows characterisation of both pulse wave magnitude and propagation direction , thereby requiring synchronous and co-localised measurements of blood pressure and blood flow velocity signals 7 ., WIA applied to patient measurement data is sensitive to synchronisation errors and the signal processing characteristics of the measurement devices 8 , which hampers or limits detailed studies on heart-vessel interaction , especially concerning causal relationships ., Computational models of whole-circulation mechanics , such as CircAdapt , allow for well-controlled simulations , facilitating comprehensive study of single- and multi-factorial relationships between arterial system properties and cardiac structure and function ., In the present study we introduce and demonstrate the CircAdapt-TL model ( Fig 1 ) : a whole-circulation model with an integrated segmental transmission line ( TL ) module , describing vascular wave propagation , reflection and transmission ., We verify the numerical implementation of the TL model by a benchmark comparison of the model to the established pulse wave propagation ( PWP ) model of Kroon et al . 11 ., Additionally , we demonstrate operation and output of the integrated CircAdapt-TL model , by simulating systemic normotensive- and hypertensive conditions ., We evaluate the implications of modelling vascular wave transmission on aortic haemodynamics by comparing simulated left ventricular- and aortic pressure tracings of the integrated CircAdapt-TL model with the tracings obtained with the systemic circulation lumped into the existing CircAdapt non-linear three-element windkessel ( 3WK ) model ( i . e . , neglecting wave transmission effects ) ., Further evaluation includes WIA applied to simulated carotid arterial pressure and flow waveforms in semi-quantitative comparison with WIA applied to patient measurements ., Our vascular module describing wave transmission in vascular networks will be integrated into the existing CircAdapt platform ( www . circadapt . org ) ., This model platform has a modular setup , currently consisting of a 0D whole-heart mechanics model , valve haemodynamics model , and non-linear three-element windkessel models of the pulmonary and peripheral circulations ( Fig 1 ) ., In the next section , we introduce the governing equations , modelling assumptions and implementation of our new vascular module in detail ., To model pressure-flow waves within segments of blood vessels , we assume 1 ) blood vessels to be thick-walled , longitudinally constrained non-linear elastic tubes , 2 ) blood to be incompressible and Newtonian and 3 ) that gravity forces can be neglected ., Furthermore , we assume 4 ) no leakage of blood to small side-branches that are not explicitly modelled ., Application of the laws of balance of mass and momentum , and subsequent integration over the tube’s cross-sectional area yield the governing equations 12:, C∂ p ∂ t + ∂ q ∂ z = 0 , ( 1 ) L ( ∂ q ∂ t + ∂ ∂ z ∫ A v z 2 d A ) + ∂ p ∂ z = f , ( 2 ), where p = p ( z , t ) is the pressure at the axial vessel coordinate z , and q = q ( z , t ) the flow rate at that coordinate ., Furthermore , A denotes cross-sectional lumen area , and L and C are the tube inertance and compliance per unit length , respectively ., Term L ∂ ∂ z ∫ A v z 2 d A represents the convective acceleration term , with vz the axial blood velocity ., Term f represents friction force per unit volume caused by viscous properties of the blood , defined f = 2πr0τw/A0 13 ., Here , symbol τw denotes wall shear stress , r0 reference radius , and A0 reference cross-sectional lumen area , respectively ., After neglecting the convective acceleration term and assuming an approximate velocity profile to estimate τw 13 , the governing equations can be rewritten to the telegrapher’s equations:, - ∂ q ∂ z = C ∂ p ∂ t , ( 3 ) - ∂ p ∂ z = L ( α 0 ) ∂ q ∂ t + R ( α 0 ) q , ( 4 ), with L ( α0 ) and R ( α0 ) a characteristic Womersley number-dependent inertance and resistance term , defined by, L ( α 0 ) = g ( α 0 ) ρ A 0 , ( 5 ) R ( α 0 ) = h ( α 0 ) 8 π η A 0 2 , with ( 6 ) α 0 = r 0 ρ ω 0 / η ., ( 7 ) The functions g ( α0 ) and h ( α0 ) were derived by Bessems et al . 13 and are detailed in S1 Text , Section ‘Derivation of the attenuation constant , wave speed and wave impedance’ ., The characteristic Womersley number ( α0 ) describes the ratio of instationary inertia forces and viscous forces , governed by vessel radius ( r0 ) , characteristic angular frequency ( ω0 = 2π/T , with T the cardiac cycle duration ) , blood dynamic viscosity ( η ) and blood density ( ρ ) , respectively ( Table 1 ) ., To solve the governing equations , we also need a constitutive law to relate ( changes in ) transmural pressure ( ptrans ) to ( changes in ) current cross-sectional area ( A ) ., The rationale of this method is to calculate R and L based on an approximated velocity profile for which the viscous boundary layer thickness is approximated for the characteristic frequency 13 ., We formulated a non-linear power-law to phenomenologically capture the experimentally observed non-linear pressure-area relation of arteries and veins 16 , 20:, p trans ( A ) = - p ext + p 0 ( ( 1 + b ) ( A A 0 ) 1 + k / 3 - 2 1 + b - b A 0 A ) , and C = d A d p trans , ( 8 ), with p0 a reference pressure , A0 a reference cross-sectional area , and k the vessel stiffness coefficient ., Furthermore , b is a small fraction to simulate collapse of the tube with negative transmural pressure ( Table 1 ) and pext represents a prescribed external pressure ( if present ) ., Now we can solve the resulting governing equations in either the time domain or frequency domain 21 ., We explicitly chose a time-domain approach , since this permits using non-linear boundary conditions as already present in the CircAdapt platform 2 ., Our solving method uses a TL model ., A detailed overview of our solving method is provided in S1 Text , Section ‘Solving strategy’ ., The terminal end of a tube was coupled to a non-linear three-element windkessel ( 3WK ) element 10 ., We assumed the windkessel compliance to be pressure-dependent , and scaled by an estimate of the tissues’ vessel bed length 22 ., As a consequence , wave impedance also becomes pressure-dependent ( Fig 1 ) :, C AV= l AV d A d p AV and Z wave , AV = ρ A d p AV d A , ( 9 ), with AV , the subscript for the arterial and venous contributions , i . e . AV = art , ven ., Such approach enables simulating large changes in haemodynamic load ( e . g . exercise or hypertension ) without requiring to manually adjust the 3WK parameter values ., The derivatives in the aforementioned equations were calculated at the connection point ( i . e . a node ) of a tube with a 3WK , using the constitutive relation as given in Eq 8 ., The parameter lAV represents the characteristic length of a peripheral bed ., We estimated the vessel bed length using the relation given by l AV = 6 q AV 1 / 3 , with qAV the mean peripheral flow through any terminal tube ., To obtain first-order approximations of lAV among all peripheral beds , we utilised this relation in conjunction with flow distribution estimates as reported in Table B in S1 Text ., Furthermore , using a physiology textbook 18 , we estimated that in rest 21% of the cardiac output is directed to the head , 47% to the abdomen , 18% to the pelvis and lower extremities , and 14% to the upper extremities , respectively ., The peripheral resistance ( Rp ) was defined via a flow source , controlled by the instantaneous arterio-venous pressure difference ( Fig 1 ) 10:, R p = p art - p ven q AV ., ( 10 ), The atria and ventricles of the heart were modelled as contractile chambers ., The ventricles are surrounded by three cardiac walls: the left ventricle free wall , interventricular septum and right ventricle free wall ( Fig 1 ) ., Ventricles are mechanically coupled , based on force equilibrium in the junction of the ventricular walls 9 ., The atria are surrounded by the left atrial wall and right atrial wall ( Fig 1 ) ., The cardiac chambers are considered as contractile cavities formed by the one-fibre model , relating myofibre stress to cavity pressure using the assumption that myofibre stress is homogeneously distributed within the myocardial wall 23 ., The phenomenological model of myofibre mechanics was previously described 2 ., The one-fibre model is used to calculate myofibre stress from myofibre strain ., Total Cauchy myofibre stress experienced by cardiac tissue comprises of a summation of active stress , present in the actin filaments and separate microstructural contributions ( i . e . titin and the extracellular matrix , assumed to act in parallel ) ., Transmural pressure is calculated from wall tension , derived from total Cauchy stress and wall curvature using Laplace’s law 2 ., Cavity pressures are calculated by adding the transmural pressures to the pericardial pressure surrounding the myocardial walls ., As commonly used in other cardiac models , the pericardium was assumed a compliant bag , modelled using a non-linear relation relating pericardial pressure and volume 24 ., The pulmonary circulation was modelled as 3WK ( see Section ‘Arterio-venous impedance module’ ) , connecting the pulmonary artery with the pulmonary veins 25 ., Full details of the cardiac model can be retrieved from Walmsley et al . 2 and Lumens et al . 9 ., Valve flow ( qvalve ) was generated using the unsteady Bernoulli equation , assuming incompressibility and inviscid , irrotational flow:, ρ l valve A valve ∂ q valve ∂ t = Δ p - 1 2 ρ q valve | q valve | ( 1 A valve 2 - 1 A p 2 ) , ( 11 ), with the term on the left hand side the unsteady inertia , governed by blood density , effective valve length ( lvalve ) and valve cross-sectional area ( Avalve ) 1 ., The first term on the right hand side denotes the pressure difference ( Δp ) and the second term is the change in kinetic energy , with Avalve and Ap cross-sectional areas of the valve and proximal to the valve , respectively ., For Avalve , a phenomenological valve opening and closing function depending on the pressure gradient was used 1 ., In case Δp > 0 , Avalve instantaneously increases towards an effective valve area representing a completely opened valve ., In case Δp < 0 , on the other hand , flow gradually decreases due to inertia ., Furthermore , Avalve gradually decreases towards a quasi-closed state , with small leakage to avoid division by zero 1 ., Agreement between pressure and flow waves of the TL model and PWP model for five tubes in the model domain is graphically depicted in Fig 4 ., Root mean square errors ( Eq 13 ) for pressures and flows for all tubes are given in Table 2 ., Between models , we found good agreement in terms of pressure and flow waveforms for proximal arteries ( e . g . aorta , carotid , subclavian and vertebral arteries ) , expressed by relative errors δp ≤ 1 . 5% and δq ≤ 5 . 6% At the distally located interosseous artery , the difference between both models slightly increased , expressed by δp , 25 equal to 2 . 9% and δq , 25 equal to 5 . 3% ., Nevertheless , the shape of the pressure and flow waveforms as well as absolute systolic and diastolic pressure and flow values were highly similar ( Fig 4 ) ., In Figs 2 and 3 , pressure and flow waveforms in normotension are displayed for arteries and veins at three regions ( i . e . the central- , arm- and leg region ) ., Arterial pressure waveforms at distal locations are characterised by an increase in pressure amplitude , as well as a reduction in peak width ., The arterial pressure waveforms at distal locations show a more prominent dicrotic notch compared to the pressure waves at proximal locations ., For veins , a biphasic pressure waveform can be distinguished , with venous flow and pressure in anti-phase ( Fig 3 ) ., In the REF–TL simulation , pulse wave velocity ( PWV ) was 5 . 5 m s–1 , representing a PWV value commonly found in subjects aged < 30 years 29 ., For the HYP simulation , pulse wave velocity ( PWV ) was 8 . 0 m s–1 , representing a PWV value clinically associated with early aortic stiffening , and commonly found in subjects aged > 50 years 29 ., The blood pressure values in the REF–TL simulation were within the normal range ( Table 3 ) ., As shown in Fig 5 , simulating systemic hypertension ( HYP ) caused arterial pressure to increase ., This resulted in an increase in left ventricular pressure and left atrial pressure , whereas pulmonary artery pressure and pulmonary venous pressure slightly increased ( Fig 5 ) ., The HYP–TL simulation showed an increase in systolic blood pressure ( psys ) from 128 to 193 mmHg and an increased diastolic blood pressure ( pdia ) from 75 to 92 mmHg ( Table 3 ) ., Fig 6 shows LV and ascending aortic pressure tracings obtained using the CircAdapt–3WK model and the CircAdapt–TL model , respectively ., The aortic pressure tracings of the REF–TL and HYP–TL simulation showed pressure augmentation ( i . e . a systolic pressure boost ) as well as an dicrotic notch , whereas for CircAdapt–3WK model simulations , these waveform characteristics were absent ., In the REF–TL simulation , systolic pressure augmentation occurred after time of peak systolic pressure , whereas in the HYP–TL simulation this occurred prior to the time of peak systolic pressure ., Wave intensity tracings ( dI+ , dI− , and dI , respectively ) of the REF–TL and HYP–TL simulation were calculated for the left common carotid artery ( Fig 7A ) ., The carotid arterial wave intensity tracings of the REF–TL simulation indicate a forward compression wave ( FCW ) followed by a backward compression wave ( BCW ) ., At end-systole , there is a forward expansion wave ( FEW ) associated with the deceleration of the rate of myocardial contraction 30 ., In the REF–TL simulation , the onset of the BCW occurred 38 ms after onset of left ventricular ejection , whereas for the HYP–TL simulation the delay was 28 ms ( Fig 7A ) ., Peak wave intensity of the BCW was approximately equal for the HYP–TL simulation ( 4 . 19 ⋅ 105 W m–2 s–2 ) as compared to the REF–TL simulation ( 4 . 23 ⋅ 105 W m–2 s–2 ) ( Fig 7A ) ., Overall , the pattern of the simulated wave intensity tracings was similar to measured carotid arterial wave intensity tracings as reported by Hughes et al . 31 ( Fig 7B ) ., A simplification in the TL model is that convective acceleration is neglected ., However , the influence of convective acceleration on arterial pressures and flows is believed to be small 41 ., Moreover , it was found that inclusion of convective acceleration in an arterial model domain , similar to the one used in the present study , changed pressure and flow waveforms in the various arteries only slightly ( i . e . a root mean square error of 1 . 3 mmHg for thoracic aortic pressure waveform and 11 . 3 ml s–1 for thoracic aortic flow waveform , respectively 15 ) ., We expect , however , that the effect of convective acceleration will be more important when simulating exercise conditions ., Therefore , in such studies the modelling error related to convective acceleration needs to be properly considered ., By excluding cerebral and coronary vessels from our model domain , we neglect the presumed influence that wave reflections and re-reflections from head and neck vessels or vessels in the myocardium may have on observed ascending aortic and carotid waveforms 15 , 42 ., Our model neither contains a skeletal-muscle pump model nor does it incorporate venous valves ., Hence , the present vascular model will not account for these functional aspects with postural changes ., For studies with emphasis on venous haemodynamics , the CircAdapt platform allows for a straightforward implementation of venous valves , using for instance the existing valve module as a starting point ., Like all distributed models of 1D wave transmission , our model cannot capture the complex pressure losses or local wall shear stresses when applied to disease conditions ( e . g . stenosis or aneurysm ) ., This requires either use of calibrated loss models or coupling of detailed 3D models of stenoses and aneurysms to 1D models , respectively 43 , 44 ., Reymond et al . 45 reported for the case of an apparently healthy aorta , that pressure and flow waveforms from a 3D CFD model and from a 1D PWP model are highly similar ., The latter finding supports our notion that , in general , distributed models of wave transmission are well suited to examine and quantify heart-vessel interaction at the level of pressure and flow waveform characteristics ., The utility of the CircAdapt–TL model should be further tested by direct comparisons against detailed haemodynamic data from humans ., We consider the concurrent use of in vivo as well as simulated data as most valuable , because both arms bring complementary assets ., The model allows error free assessment of phase relationships between signals and in vivo data enables characterisation of biological and pathological variability ., In the future , we aim to further extend the CircAdapt–TL model with the cardiac adaptation module of Arts et al . 46 ., This module contains a homeostatic control loop that senses offsets in mechanical load ( e . g . as present in chronic hypertension ) , and in response , imposes geometrical ( i . e . cavity volume and wall volume ) adaptation of the heart ., We believe that modelling cardiac adaptation is vital in assessing candidate haemodynamic indices ., Key clinical studies in this field include that of Hashimoto et al . 6 ., They found a positive association between left ventricular mass , and wave reflection magnitude derived from pressure and flow velocity measurements , following antihypertensive treatment in left ventricular hypertrophy patients ., However , a limitation of such clinical studies is that for non-invasive acquisition , pressure signals are obtained at distal measurement sites ( e . g . at the radial artery ) and therefore require a transfer function to obtain an estimate of the aortic pressure signal ., Moreover , for clinically-gathered data , correct synchronisation of pressure and flow velocity signals is crucial , because only a small ( e . g . 5 ms ) misalignment can cause substantial changes in derived wave ( intensity ) quantities 7 ., We validated and incorporated a one-dimensional vascular module into the CircAdapt platform ., The resulting CircAdapt–TL model enables fast simulation of whole-heart mechanics , pressure and flow waveforms at various locations along the arterial and venous systems , and allows detailed haemodynamics studies ., The CircAdapt–TL model provides a valuable tool for testing hypotheses concerning heart-vessel interaction and evaluating existing haemodynamics indices .

Introduction, Models, Results, Discussion

We developed a whole-circulation computational model by integrating a transmission line ( TL ) model describing vascular wave transmission into the established CircAdapt platform of whole-heart mechanics ., In the present paper , we verify the numerical framework of our TL model by benchmark comparison to a previously validated pulse wave propagation ( PWP ) model ., Additionally , we showcase the integrated CircAdapt–TL model , which now includes the heart as well as extensive arterial and venous trees with terminal impedances ., We present CircAdapt–TL haemodynamics simulations of:, 1 ) a systemic normotensive situation and, 2 ) a systemic hypertensive situation ., In the TL–PWP benchmark comparison we found good agreement regarding pressure and flow waveforms ( relative errors ≤ 2 . 9% for pressure , and ≤ 5 . 6% for flow ) ., CircAdapt–TL simulations reproduced the typically observed haemodynamic changes with hypertension , expressed by increases in mean and pulsatile blood pressures , and increased arterial pulse wave velocity ., We observed a change in the timing of pressure augmentation ( defined as a late-systolic boost in aortic pressure ) from occurring after time of peak systolic pressure in the normotensive situation , to occurring prior to time of peak pressure in the hypertensive situation ., The pressure augmentation could not be observed when the systemic circulation was lumped into a ( non-linear ) three-element windkessel model , instead of using our TL model ., Wave intensity analysis at the carotid artery indicated earlier arrival of reflected waves with hypertension as compared to normotension , in good qualitative agreement with findings in patients ., In conclusion , we successfully embedded a TL model as a vascular module into the CircAdapt platform ., The integrated CircAdapt–TL model allows detailed studies on mechanistic studies on heart-vessel interaction .

Arterial pulse wave characteristics show associations with left ventricular hypertrophy in clinical studies ., However , in such studies , measurement and signal processing errors limit assessment of causality between wave characteristics and left ventricular hypertrophy ., When validated , a computational model would allow comprehensive causality studies on heart-vessel interaction without such errors ., In the present study , we integrated a novel vascular module describing wave transmission in vascular networks into the CircAdapt model of whole-heart mechanics ., A benchmark comparison between our vascular module and a previously validated but more complex method , showed good agreement in terms of pressure and flow waveforms ., The extended CircAdapt model is now also able to quantitatively describe vascular haemodynamics , including wave dynamics .

medicine and health sciences, body fluids, blood, anatomy, blood vessels, arteries, simulation and modeling, cardiovascular anatomy, physiology, aorta, biology and life sciences, hypertension, blood pressure, vascular medicine, veins, research and analysis methods, systolic pressure

journal.pgen.1006900

Replication stress affects the fidelity of nucleosome-mediated epigenetic inheritance

In eukaryotic cells , genomic DNA are packaged into arrays of nucleosomes 1 , each comprised of a 147bp DNA fragment wrapped around a histone octamer core ., The combination of histone variants and the large repertoire of covalent modifications on histones result in a highly complex biochemical signature of the nucleosome , which encodes important epigenetic information 2 , 3 ., Overall , the nucleosomal organization of chromatin–including the positions of nucleosomes relative to the underlying DNA sequence and the biochemical signatures that they carry–has a profound impact on the functional state of the genome ., In order to maintain the identity of the cell , nucleosomal organization must be preserved through cell divisions ., On the other hand , it is conceivable that controlled alteration in cell type , such as cell differentiation during development , would require nucleosomal organization amendable for reprogramming ., Despite its profound biological significance , the mechanisms on regulating or influencing the precision of chromosomal epigenetic inheritance are not well understood ., In this study , we examine the effect of replication perturbation on the fidelity of chromatin duplication and epigenetic inheritance and explore the underlying mechanisms ., During cell division , chromatin is duplicated in conjunction with DNA synthesis at the replication fork , through a process called replication coupled ( RC ) nucleosome assembly 4 , 5 ., The process can be divided into three major steps ., First , pre-existing nucleosomes ( also known as parental nucleosomes ) immediately in front of the replication fork are disrupted so that the template DNA is accessible to the replication machinery ., Second , shortly after replication fork passage , the core components of the parental nucleosomes–the ( H3-H4 ) 2 tetramers in specific–are recycled to assemble nucleosomes on one of the daughter strands behind the replication fork ., Finally , newly synthesized histones are incorporated on the other daughter strand to form nucleosomes de novo ., Although the two daughter strands can be distinguished based on whether they originate from the leading or lagging strand in replication , in general , there appears to be no strand-specificity for histone recycling and de novo nucleosome assembly ., To achieve precision in duplication of epigenetic markers on histones , the recycled parental H3-H4 molecules need to be incorporated at their original loci on one of the daughter strands ., Furthermore , the nucleosomes assembled de novo on the other strand need to be positioned at the corresponding sites ., The newly incorporated histone molecules should also be of the proper variant type and obtain the biochemical modifications matching that of the parental histones ., Currently , little is known on how the precision is achieved for any of the above steps ., An important aspect of chromatin duplication is that the parental H3-H4 histones are transferred from the template chromatin to the replicated strands as intact ( H3-H4 ) 2 tetramers 6–8 ., An alternative mode of inheritance–splitting the ( H3-H4 ) 2 tetramer into two dimers and passing them equally to the two replicated strands–may occur in the minor sub-population of nucleosomes that contain the H3 . 3 variant but not in the majority that contain the canonical H3 . 1 variant 8 ., The DNA replication machinery is directly implicated in RC-nucleosome assembly by interacting with , and potentially coordinating the actions of , histones , histone chaperones , histone modifying enzymes , and chromatin remodeling factors9 , 10 ., Among the replication proteins , the helicase MCM2-7 is thought to play critical roles in evicting the nucleosomes ahead of the replication fork as well as assembling nucleosomes behind the fork ., MCM2-7 forms a stable complex with the histone chaperone Asf1 that is bridged by a histone H3-H4 dimer 11 , 12 ., The Asf1/H3-H4/MCM complex may represent an intermediate for parental histone recycling or new nucleosome assembly ., However , it remains unclear how each ( H3-H4 ) 2 tetramer is integrated into the proper site on one of the daughter strands ., In the budding yeast S . cerevisiae , genome-wide tracking of the parental histone H3 molecules through several generations and quantitative modeling of experimental data lead to a model that the parental ( H3-H4 ) 2 tetramers are re-incorporated within the distance of one to two nucleosomes ( ~400bp ) of the original site ., Thus , nucleosomal inheritance may be somewhat “sloppy” 13 ., Such sloppiness , if confirmed , would preclude single or small number of nucleosomes as efficient carrier of epigenetic information ., Further experimental evidence is needed to directly test this model ., We used Position Effect Variegation ( PEV ) as an indicator of chromatin epigenetic stability to quantify the precision of nucleosomal inheritance ., PEV , referring to variable expression patterns in a gene due to its translocation to a specific position in the genome , was broadly observed 14 , 15 ., PEV phenomenon was originally discovered in specific fruit fly strains , associated with the white gene translocation adjacent to centromeric heterochromatin ., There , the variegated expression states of the translocated white gene propagate clonally in adult eyes , causing mosaic eye coloration patterns 14 ., In fission yeast , heterochromatin spreading is also responsible for PEV associated with the mating type locus ., Within the centromeric core region , PEV is also observed but due to a different mechanism 16 , 17 ., Here , the positioning of nucleosomes containing the specific histone H3 variant CENP-A/Cnp1 is variable within the centromere , and Cnp1 occupancy inversely correlates with the expression levels of the underlying reporter genes ., Regardless of the biochemical characteristics of local chromatin region responsible for underlying gene silencing , one common property of PEV is that the variegated gene expression states are inherited in a clonal fashion ., In both budding yeast and fission yeast , inheritance of variegated gene expression is vividly demonstrated by sectoring in yeast colony coloration 16 , 18 , 19 and by tracking the gene expression status through the cell generations directly at single cell level 16 ., Changes in the coloration patterns of the colonies serves as a convenient indicator of changes in the epigenetic marker underlying gene silencing ., In addition to using PEV as readout for epigenetic stability at specific loci , we wish to assess the impact of replication stresses at the whole epi-genome level by determining the genome-wide heterochromatin distribution in cells under stresses ., Finally , we sought to explore the possible evolutionary conservation and the physiological significance of reduced epigenetic fidelity due to replication stress by testing the effects of perturbing replication on the development process in fly and worm ., In fission yeast , variegated expression of ade6+ inserted in the centromere ( cnt2::ade6+ ) is readily visualized: the ON or OFF states of ade6+ correspond to white or red color , respectively , of the colonies grown with low supply of adenine ., We previously have shown that cnt2::ade6+ expression inversely correlates with Cnp1 occupancy on ade6+ ., Furthermore , by tracking the colony coloration through cell lineages , we have demonstrated that the state of ade6+expression , and thereby , Cnp1 occupancy on ade6+ , is inherited through cell generations , but can change abruptly within one generation at low rates 16 ., We explored the possible association between the fidelity of centromeric Cnp1 nucleosome position inheritance and the progression of DNA replication ., Hydroxyurea ( HU ) is broadly used to study DNA damage-independent replication fork arrest 20–22 ., HU is an inhibitor of ribonucleotide reductase ( RNR ) , the enzyme responsible for the synthesis of dNTPs ., Depletion of dNTP pools through HU treatment leads to replication fork arrest and subsequent genomic instability23 , 24 ., We first tested whether HU treatment enhances the rate of switching in the expression state of cnt2::ade6+ ., Yeast cells originated from a red colony ( i . e . , cnt2::ade6+ silenced , with minor white sectors ) were treated transiently ( for four to six generation times ) with low concentrations of HU , prior to plating on the media for characterization of the progeny colonies coloration ., Consistent with the epigenetic inheritance of the cnt2::ade6+ silent status , in the absence of HU , most of the progeny colonies were red ( with minor white sectors ) ., However , with increasing concentrations of HU , more white ( with or without minor red sectors ) colonies were formed ( Fig 1A , upper ) ., Similarly , HU treatment of cells that originated from a white colony also gave rise to increased switching in colony coloration , albeit from white to red ( Fig 1A , lower ) ., We previously have established a pedigree analysis assay to quantify the rate of switching in cnt2::ade6+ expression states per cell division 16 ., Using this assay , we measured the switching rate of cnt2::ade6+ ( OFF to ON ) in wild type cells is 6 . 5% , and is increased to 8 . 5% in wild type cells treated with 1mM HU ( Fig 1B lower panel ) ., These results show that HU treatment enhances switching in cnt2::ade6+ expression status , suggesting that replication perturbation due to HU treatment ( which inhibits ribonucleotide reductase and causes depletion of dNTP pools ) could reduce the fidelity of centromeric epigenetic inheritance ., To test whether depletion of dNTP pools by genetic perturbation would have the same effect , we also measured the CEN-PEV switching rate in cdc22-3 , a mutation in the large subunit of ribonucleotide reductase25 ., The result shows that the switching rate of cnt2::ade6+ ( OFF to ON ) in cdc22-3 at 25°C is increased to 9 . 7% comparing with 6 . 5% in wild type cells ( Fig 1B lower panel ) , suggesting that replication perturbation caused by suboptimal dNTP levels reduces the fidelity of centromeric epigenetic inheritance ., Likewise , colonies formed at the constant presence of HU exhibit higher degrees of sectoring than those formed in the absence of HU ., This reflects enhanced , continually ongoing switching in cnt2::ade6+ expression state throughout the time course of colony formation ( Fig 1B ) ., Noticeably , colonies that exhibited high degree sectoring when grown in the presence of HU , once re-plated on media without HU , reverted to wild type degree sectoring ( Fig 1C ) ., This suggests that change in the degree of sectoring directly correlates with HU treatment and that such changes are not genetic ., To further confirm this notion , we examined the ade6 gene in four of these red colonies by PCR amplification and DNA sequencing and found no mutation ., Together , these results indicate that HU-induced perturbation of DNA replication promotes switching in cnt2::ade6+ expression status , and once switched , the expression states are inherited ., HU treatment disturbs replication progression as well as RC nucleosome assembly ., Specifically , continuing unwinding template DNA combined with pausing in DNA synthesis leads to excessive formation of ssDNA on template and concurrent accumulation of parental H3-H4 histones evicted from template chromatin 26 ., ssDNA activates the S phase checkpoint , which in turn , halts the MCM helicase , preventing further ssDNA formation and histone eviction 27 , 28 ., When replication resumes , incorporation of the accumulated histones onto the daughter strands would be disordered due to the loss of their original positioning on the template chromatin 26 , thus contributing to the enhanced variegation in the centromere ., According to this model , there would be a correlation between excessive ssDNA formation and the reduction in the fidelity of nucleosome inheritance ., In particular , mutations that cause prolonged unwinding of the template DNA and excessive formation of ssDNA should also reduce the fidelity of nucleosome inheritance ., We chose two mutants to test this prediction: deletion of the S phase checkpoint gene cds1 ( rad53/chk2 ) —cds1-D and a c-terminal truncation of the Mcm4 subunit of the MCM helicase -mcm4-84c ., cds1-D inactivates the replication checkpoint 29 , 30; whereas mcm4-84c renders the MCM helicase unresponsive to inhibition by an activated checkpoint without apparent compromising of its helicase activity 28 ., Both mutants are hypersensitive to HU ( S1 Fig ) , and are shown to form ssDNA excessively upon replication pausing as evidenced by enhanced chromatin association of single strand binding protein , Ssb2/Rfa2 28 , 31 ., We confirmed this by quantifying the Ssb2-GFP foci signal in S phase cells ( recognized by the presence of a medial septum–a morphological signature of S phase cells ) ., The Ssb2-GFP foci signal is significantly increased in both cds1-D and mcm4-84c mutants compared with wild type at the same HU doses ( Fig 2A ) ., In order to test whether the increased Ssb2-GFP signal results from the change in the Ssb2-GFP protein level , we have performed the western blotting assay , and found the similar protein level between wild type and mutant cells with or without HU treatment ( S2 Fig ) ., We then compared the rate of switching in cnt2::ade6+ expression states upon HU treatment in wild type and mutant cells ., In wild type cells , HU treatment enhances switching rate in cnt2::ade6+ expression status using the pedigree analysis assay 16 ., Meanwhile , we measured significant increases in the rate of switching in cds1-D cells ( 9 . 1% without HU treatment to 10 . 1% treated with 0 . 5mM HU ) and mcm4-84c cells ( 13 . 5% without HU treatment to 14 . 9% treated with 1mM HU ) respectively ( Fig 2B ) ., Both mutants also exhibited higher rates of switching than wild type cells under all tested conditions ., Consistently , we also found that mutant colonies exhibited more complex sectoring patterns with HU treatment compared to no HU treatment , and much more complex sectoring patterns in comparison to wild type at all conditions ( S1 Fig ) ., Together , these results suggest that excessive ssDNA formation caused by HU treatment is correlated with the reduction in the fidelity of epigenetic inheritance ., Conversely , if a mutation causes DNA replication stalling without excessive formation of ssDNA , it should not affect the fidelity of nucleosome inheritance ., MCM helicase unwinds the template DNA and is postulated to drive the eviction of the parental histones ., We thus reasoned that perturbation of the MCM helicase function might cause replication perturbation without causing excessive unwinding of template DNA or accumulation of parental histones ., To test this , we examined the effect of a temperature sensitive mutation in the MCM helicase ( mcm4-M68ts ) that conditionally disrupts replication initiation 32 ., At a semi-permissive temperature ( 29°C ) , the biological activity of MCM is compromised so that the survival of mcm4-M68 cells is strictly dependent on the Chk1-dependent DNA damage checkpoint that is non-essential in wild type cells ., Interestingly , the Cds1p-dependent intra-S phase checkpoint is not activated or required for cell survival under this stress condition 33 ., Indeed , we found only insignificant level of Ssb2-GFP foci signal in S phase mcm4-M68 cells at 29°C comparable to wild type ( Fig 3A ) , but observed a significant cell cycle delay ( elongated cell morphology ) and reduction in cell viability ( S3 Fig ) , confirming that MCM activity was compromised ., Consistent with minor increase in ssDNA formation , the rate of switching in cnt2::ade6+ expression state increased insignificant in mcm4-M68 cells compared to wild type at 29°C ( Fig 3B ) ., Mutant colony morphology also exhibited wild type level complexity in sectoring ( S1 Fig ) ., Together , these results suggest a correlation between excessive ssDNA formation and the enhanced switching of centromeric PEV , indicating a reduction of fidelity in Cnp1 nucleosome position inheritance ., Given that HU treatment enhancing centromeric PEV correlates with excessive ssDNA formation , we sought to test whether other replication stresses that cause increased ssDNA formation should also cause enhanced centromeric PEV ., We tested genetic perturbations in three distinct complexes of the replication machinery: deletion of ctf8 , a non-essential subunit of the Ctf18-RFC clamp-loader complex 34; and deletion of ssb3 , a non-essential subunit of the ssDNA binding protein complex RPA 31 , partial inactivation of Psf1 , a subunit of the GINS complex essential for replication initiation and elongation 35 ., Conditional inactivation of Psf1 , an essential protein , is achieved by fusing Psf1 to a steroid hormone-binding domain ( HBD ) tag that is tightly associated with the protein chaperone Hsp90 , rendering the fusion protein inactive by steric hindrance ., The HBD fusion protein can be kept active by the addition of β-estradiol , which binds to HBD and displaces Hsp90 ., psf1-HBD cells depend on the presence of β-estradiol for viability 36 ., Microscopic examination of S phase cells reveal increased Ssb2-GFP foci signal in all three mutants compared to wild type ( Fig 3A ) ., Noticeably , psf1-HBD cells with a high level of β-estradiol , which fully supported cell viability ( “proficient” condition ) , still exhibit an elevated switching rate ( 10 . 4% in comparison to 6 . 5% in wild type ) and enhanced ssDNA levels , indicating that the HBD tag alone may quantitatively disturb the GINS complex function ., Reducing the level of β-estradiol ( “deficient” condition ) further exacerbate the defects ( the switching rate increases to 13 . 9% ) ., Consistently , quantification of the switching rate ( cnt2::ade6+ OFF to ON ) by pedigree analysis show higher switching rate in all mutants compared to wild type cells ( Fig 3B ) , mutant colony morphology also exhibits more complex sectoring patterns ( S1 Fig ) ., We further wish to test whether reduced epigenetic inheritance stability caused by replication stress is not only reflected by the enhanced switching rate of cnt2::ade6+ OFF to ON , but also the changed switching rate of cnt2::ade6+ ON to OFF ., Consistently , the switching rate of cnt2::ade6+ in wild type cells is 6 . 5% ( OFF to ON ) and 3 . 4% ( ON to OFF ) , and increased to 8 . 5% ( OFF to ON ) and 5 . 6% ( ON to OFF ) with 1mM HU treatment ., And we also quantified the two switching rates of cnt2::ade6+ simultaneously in two replication mutants , ssb3D and ctf8D ., While significant increases in switching rate of cnt2::ade6+OFF to ON were detected in both mutants ( 9 . 8% in ssb3D and 10 . 6% in ctf8D ) , no increase or slight reduction in ON to OFF rate was found ( 2 . 55% in ssb3D and 3% in ctf8D , respectively ) ., We are unclear about the discrepancy between the measurements of the two switching rates ., One possible reason may be that , the pedigree analysis is less suited to capture rare switching events ( ON to OFF ) quantitatively ., All together , in all three mutants tested above in which various parts of the replication machinery is perturbed genetically and with excessive accumulation of ssDNA , centromeric PEV is enhanced ., We further reasoned that replication stresses might affect the inheritance of other chromatin features in addition to Cnp1/CENP-A nucleosome occupancy within the centromeres ., To test this idea , we examined two PEV systems associated with the mating type region caused by stochastic heterochromatin ( histone H3K9me2/3 modifications ) spreading ., The mating type region of fission yeast contains three gene loci , among which only mat1 is actively transcribed and determines the cell mating type ., mat2 and mat3 act as genetic information donors for a gene conversion process at mat1 that causes mating type switching 37 ., mat2-mat3 region , in wild type cells , is silenced tightly by heterochromatin formation via histone H3 lysine 9 methylation 38 , 39 ., Furthermore , histone hypoacetylation also contributes to its silencing 40 ., ade6+ inserted in the silencing domain between the boundary and mat2 ( L ( BglII ) ::ade6+ ) exhibits the typical variegated expression pattern 17 ., Alternatively , a cis-DNA element–cenH–within the mating type region is sufficient to initiate heterochromatin formation at an ectopic site in the genome ., ade6+ juxtaposed to cenH at an ectopic site ( ura4::cenH-ade6+ ) also exhibits variegated expression 39 ., We tested replication stress on PEV of L ( BglII ) ::ade6+and ura4::cenH-ade6+ , and found that HU increased ade6+ silencing in a dosage—dependent manner for both reporter constructs ( Fig 4 ) ., Regardless cells originated from red or white colonies , when treated with HU , ade6+ silencing state was promptly established and persisted , resulting in red colonies with little or no white sectors ., Between these two reporter constructs , L ( BglII ) ::ade6+ exhibited a dramatic change , resulting in predominant or nearly all red colonies with HU treatment ( Fig 4A–4C ) ., In comparison , ura4::cenH-ade6+ exhibited a moderate but clear , unilateral increase in ade6+ silencing ( Fig 4D–4F , the number of red colony is increased upon HU pulse treatment from 85% to 93% ( start from a red colony ) , and the number of white colony is decreased from 97% to 90% ( start from a white colony ) ) ., Such unilateral switching to silencing state is in sharp contrast to the observation in centromeric PEV using the same test , which resulted in colonies with enhanced bi-lateral switching ., Upon re-plating to HU-absence media , white colonies re-emerged from L ( BglII ) ::ade6+and ura4::cenH-ade6+ red colonies , suggesting that ade6+ silencing is due to epigenetic instead of genetic changes ., To further confirm this notion , we examined the ade6 gene in four of these red colonies by PCR amplification and DNA sequencing and found no mutation ( S4 Fig ) ., Studies in the budding yeast have raised the concern that in certain experimental settings , reporter genes ( URA3 and ADE2 ) may exhibit gene-specific transcription responses , rendering them unsuitable for characterizing the heterochromatin-induced silencing effects 41 , 42 ( also see comments in 43 ) ., To assess whether or not enhanced switching in ade6+ expression status in our experiments is reporter gene-specific , we tested the effect of replication stress on a native gene , mat2-P , at its endogenous locus ., Mutations in specific genes ( e . g . , clr1 , a zinc finger protein gene ) partially compromise transcriptional silencing at the mating type region 37 ., The leaky expression of mat2-P in stable M cells ( Mat1-Mmst0—a genetic modification at mat1 that locks the cell in the h- mating type ) leads to haploid meiosis and sporulation , producing aneuploid , non-viable spores ., Spore formation is detected by iodine vapor staining of the colonies or by microscopic examination of the cells ., Importantly , iodine vapor staining reveals sectoring patterns , indicating that the silencing/leaky expression states of mat2-P are clonally inherited 44 , 45 ., We examined mat2-P leaky expression in clr1-deletion ( clr1-D ) 46 , 47 colonies under replication stress conditions ., The number of iodine staining patches and the intensity of staining diminished in an HU dose-dependent manner ., Quantification of haploid meiosis ( H . M . phenotype ) within the colonies confirms a reduction in meiosis upon HU treatment ( S5 Fig ) ., h-/h+ diploid colonies under the same conditions are stained strongly by iodine vapor , suggesting that the low level of HU treatment used in this study does not inhibit meiosis or sporulation per se ., These results suggest that low level HU treatment suppresses the leaky expression of mat2-P in clr1-D cells ., To verify that this silencing effect is caused by heterochromatin on mat2-P , we further tested this notion by anti-H3K9me2 ChIP , and found heterochromatin is compromised in clr1-D strain ( S5 Fig ) ., HU treatment enhances H3K9me2 enrichment at mat2-P , demonstrating that heterochromatin underlies the gene silencing here ., Thus , replication stress perturbs the inheritance of the expression states of a native gene similar to that of ade6+ reporter at the mating type region , arguing against the possibility of a gene-specific response to HU treatment ., In all , these results show that , unlike centromeric PEV in which the variation is enhanced , replication stress stimulates enhanced silencing unilaterally on two independent PEV systems mediated by H3K9me2/3 , and the phenomenon is not reporter gene-specific ., Net enhanced silencing of heterochromatin-associated PEV by replication stress at multiple loci in the genome may be explained by a possible mechanism that the heterochromatin domains are expanded ., Consistent with this hypothesis , Singh and Klar previously have shown that cdc22-3 causes heterochromatin silencing and H3K9 methylation spreading at the silent mat locus25 ., To test whether such effect is broadly seen throughout the genome , we compared heterochromatin distribution on whole genome-wide in wild type cells with or without HU treatment and cdc22-3 mutant cells by ChIP-Seq ., In specific , chromatin immunoprecipitation was performed in these cells using an antibody against histone H3 dimethylated at lysine 9 ( H3K9me2 ) ., And the immunoprecipitated DNA was then subjected to high throughput sequencing to determine the specific location and relative abundance of H3K9me2 throughout the genome ., The result showed there is no appreciable difference in wild type cells with or without HU treatment ., However , comparing between wild type and cdc22-3 mutant cells , significant difference can be seen at mating type locus and sub-telomeric regions ( Fig 5A and 5B . Please see below for more in-detailed analysis of the ChIP-seq results ) ., We are unclear why short-term HU treatment in wild type cells didn’t cause significant changes in heterochromatin distribution whereas genetic perturbation of cdc22-3 mutation did ., We speculate that this may be because cells within a culture treated with HU temporarily are highly heterogeneous in terms of epigenome perturbation ., Any changes in epigenome at the specific locus in a small percentage of cells may not be detected readily by ChIP-seq ., Throughout the fission yeast genome , two types of heterochromatin have been found–constitutive heterochromatin domains and facultative heterochromatin islands 48 , 49 ., The former are strong , persistent heterochromatin domains , including peri-centromeric , sub-telomeric regions and the mating-type locus ., The latter encompasses ~30 loci , the majority of which are meiosis genes 49 ., In wild type cells , we have detected H3K9me2 mainly at the constitutive heterochromatin domains , identical to previous reports 49 ( S6 Fig ) ., We have also detected a number of heterochromatic islands with relatively low levels of H3K9me2 , most in agreement with previous reports ., A few heterochromatic islands are different from those reported in recent studies 49 , 50 ., The discrepancies might be caused by the difference in sensitivity of the experimental tools ( ChIP-chip vs ChIP-Seq ) , or different data processing methods ., In cdc22-3 mutant , we detected alterations in some of the heterochromatin domains in comparison to wild type ( Fig 5A and 5B ) ., Specifically , H3K9me2 enrichment at the sub-telomeric regions is expanded by 5-20kb ., And the heterochromatin at the mating type locus spreads beyond the normal boundaries to the neighboring genes , just as described in Singh and Klar’s work25 ., To confirm the heterochromatin expansion , we tested the effect of HU on mating type locus using reporter genes inserted outside the wild type mating type boundaries 25 ., Consistently , we have observed enhanced reporter gene silencing under HU treatment ( S7 Fig , ura4 gene silencing cells exhibit resistance at 5-FOA plate ) , similar to what was previously reported 25 ., Expansion of the heterochromatin domains is specific to the regions described above ., No change was detected in the peri-centromeric regions of any chromosomes ., This suggests that alteration in heterochromatin expansion induced by replication stress is locus-specific ., Noticeably , we observed that the detected alterations of heterochromatin in mutant cells were variable among biological repeat samples ( Fig 5C ) , whereas the positions of heterochromatin domains are highly consistent among wild type biological repeats ( S6 Fig ) ., In mutant Sample 1 ( cdc22-3_1 ) , the heterochromatin of Tel1 L and Tel2 L was shorter than the parallel biological repeats ( cdc22-3_2 and cdc22-3_3 ) , whereas the heterochromatin of Tel2 R , Tel3 L , and Tel3 R was longer than the parallel biological repeats ( Fig 5C ) ., The variation among the samples is unlikely due to technical reasons , as such variation is seen only in the sub-telomeric regions , while other regions of the genome are highly consistent ., Such sample-specific chromatin structure changes may indicate they are sporadic events among genetically identical cells/cultures and are epigenetically inheritable ., To validate this notion , we examined the variation of heterochromatin spreading using ChIP-PCR among seven independent yeast colonies derived from the same ancestor cells ( Fig 5D ) ., Using primers to amplify a fragment nearby tel2R ( SPBPB2B2 . 08 , with low level of H3K9me2 enrichment in wild type cells ) along with primers set that amplified a fragment of tel2R ( with high level of H3K9me2 enrichment in wild type cells ) as a control , we found that three to ten folds enrichment of SPBPB2B2 . 08 gene fragment among different samples , whereas constant levels of mei4 and dg are detected throughout all the colonies ( Fig 5D ) ., Thus , varied expansion of heterochromatin at sub-telomeric regions from genetically identical cells ( cdc22-3 mutation in this case ) occurs presumably randomly and once established , is relatively stable ., Chromatin organization and its inheritance through the cell linage are crucial for cell fate specification and cell identity maintenance during development in metazoans ., We postulated that replication stress might induce chromatin change and alter gene expression and therefore , perturb the development process ., To test this notion , we first ask whether HU treatment may affect the inheritance pattern of epigenetic marks in Drosophila , using PEV of the white gene expression as the readout ., The X chromosome inversion In ( 1 ) wm4 brings the white locus near to the heterochromatin ., Spreading of the silent chromatin marks causes a highly variable mosaic pattern in eye coloration ., Fly larvae are treated with HU and the eye coloration patterns are quantified in adult flies ., Flies are sorted into five bins based on the degree of pigmentation ( Fig 6A ) ., Bin 1 contains flies with nearly pure white eyes ( silenced w locus ) with only a few pigmented omatidia ., The eyes of flies in bin 3 contain roughly equal sectors of red and white tissue ., Bin 5 flies have eyes that were solid red ( fully active w locus ) ., When parallel cultures of flies are fed with 6 mM HU throughout larval development , roughly half the animals that form pupae die at that stage , but those that reach adulthood are placed in the same five bins based on eye pigmentation ., We have found a strong shift in the distribution towards whiter eyes ( Kolmogorov-Smirnov comparison p< 0 . 001 ) , indicating greater probability that the w locus is silenced ( Fig 6A ) ., The same result is obtained for females carrying two copies of the wm4 locus and males with only one ., This suggests that replication fork pausing in Drosophila reduces the fidelity in chromatin organization duplication ., We also test whether HU treatment affects cell fate specification during vulva development in the nematode C . elegans ., A number of genes implicated in vulva development encode chromatin factors , including heterochromatin bi

Introduction, Results, Discussion, Materials and methods

The fidelity of epigenetic inheritance or , the precision by which epigenetic information is passed along , is an essential parameter for measuring the effectiveness of the process ., How the precision of the process is achieved or modulated , however , remains largely elusive ., We have performed quantitative measurement of epigenetic fidelity , using position effect variegation ( PEV ) in Schizosaccharomyces pombe as readout , to explore whether replication perturbation affects nucleosome-mediated epigenetic inheritance ., We show that replication stresses , due to either hydroxyurea treatment or various forms of genetic lesions of the replication machinery , reduce the inheritance accuracy of CENP-A/Cnp1 nucleosome positioning within centromere ., Mechanistically , we demonstrate that excessive formation of single-stranded DNA , a common molecular abnormality under these conditions , might have correlation with the reduction in fidelity of centromeric chromatin duplication ., Furthermore , we show that replication stress broadly changes chromatin structure at various loci in the genome , such as telomere heterochromatin expanding and mating type locus heterochromatin spreading out of the boundaries ., Interestingly , the levels of inheritable expanding at sub-telomeric heterochromatin regions are highly variable among independent cell populations ., Finally , we show that HU treatment of the multi-cellular organisms C . elegans and D . melanogaster affects epigenetically programmed development and PEV , illustrating the evolutionary conservation of the phenomenon ., Replication stress , in addition to its demonstrated role in genetic instability , promotes variable epigenetic instability throughout the epigenome .

In this study , we found replication stresses reduce the fidelity of nucleosome-mediated epigenetic inheritance ., Using Position Effect Variegation ( PEV ) in centromere as an indicator of chromatin epigenetic stability , we quantified the precision of nucleosomal inheritance and found replication stresses reduce the fidelity of nucleosome-mediated epigenetic inheritance ., Further analysis of genome-wide heterochromatin distribution showed that replication stresses affect chromatin structure by expanding of heterochromatin with locus specificity ., Mechanistically , we provide evidence suggesting that excessive formation of single-stranded DNA might have correlation with the reduction in fidelity of centromeric chromatin duplication ., Finally , we demonstrated replication stress perturb the development process by reducing the fidelity of chromatin organization duplication in fruit fly and worm , illustrating the broadness and the evolutionary conservation of the phenomenon ., Together , our results shed light on the importance of replication stresses cause epigenetic instability in addition to genetic stability .

cell cycle and cell division, cell processes, dna-binding proteins, dna replication, epigenetics, dna, synthesis phase, chromatin, heterochromatin, chromosome biology, proteins, gene expression, histones, genetic loci, nucleosomes, biochemistry, cell biology, nucleic acids, genetics, biology and life sciences

journal.pgen.1008335

Telomere-binding proteins Taz1 and Rap1 regulate DSB repair and suppress gross chromosomal rearrangements in fission yeast

The integrity of chromosomal DNA can be compromised by mutations that vary in size , ranging from small perturbations , such as point mutations and short insertions/deletions , to large changes , such as deletions , duplications , inversions , and translocations of long chromosome segments ., The latter are collectively called genomic rearrangements or gross chromosomal rearrangements ( GCRs ) , which have profound implications in cancers as well as genetic diseases ., Recent advances in DNA sequencing technology have enabled us to trace the history of GCRs in cancer cells , and it is now well-known that cancer development is accompanied by the frequent occurrence of GCRs 1 ., Thus , elucidation of the molecular mechanism underlying GCR control is of critical importance in understanding the progression of cancer malignancy ., Previous studies have pointed to the requirement of chromosome maintenance mechanisms for suppression of GCRs , including DNA repair and telomere protection pathways 2 , 3 ., The telomere is a huge DNA-protein complex that is located at the termini of linear chromosomes ., In humans , telomeric DNA comprises hexanucleotide TTAGGG repeats and consists of a double-stranded ( ds ) region and a single-stranded ( ss ) overhang ., The telomeric dsDNA recruits TRF1-TRF2-Rap1 , whereas the ss telomeric DNA recruits POT1-TPP1 , and these two subcomplexes are bridged by TIN2 to form a complex known as shelterin ( reviewed in 4 ) ., This shelterin complex helps cells distinguish telomeres from DNA double-strand breaks ( DSBs ) that must be repaired ., For instance , TRF2 depletion brings about the frequent occurrence of chromosome end-to-end fusions , which is due to deregulation of the non-homologous end joining ( NHEJ ) repair pathway at telomeres ., Resultant dicentric chromosomes are unstable , leading to another round of chromosomal rearrangements ( reviewed in 5 ) ., It is thus evident that telomere protection by the shelterin complex is vital for repressing GCRs ., While the shelterin complex primarily serves to protect telomeric DNA , the telomere-associated DNA polymerase named telomerase is implicated in GCRs 6 , 7 ., On the one hand , telomerase is able to elongate the telomere repeat sequence using its RNA subunit as a template , thereby counteracting gradual telomere shortening at each round of DNA replication ., At the same time , however , telomerase poses a potential threat to genome stability ., In budding yeast , telomerase promotes GCRs through de novo addition of telomere repeats to DSB sites , resulting in terminal deletion of chromosomal DNA 7 ., It has been reported that de novo telomere addition is suppressed through two mechanisms: activation of Pif1 helicase , which was proposed to remove telomerase from DSBs; and inhibition of Cdc13 accumulation by DNA damage signaling 8–10 ., However , a previous study showed that fission yeast Pif1 is not a negative regulator of telomerase 11 ., In human cells , recruitment of telomerase to telomeres and the activity of telomerase are regulated by the shelterin complex ( reviewed in 12 ) ., However , it is still unclear whether shelterin is also involved in the regulation of de novo telomere addition at non-telomeric sites ., Fission yeast , Schizosaccharomyces pombe , serves as a useful model to dissect the functions of shelterin , because this unicellular organism shares most of the shelterin components with humans ., Fission yeast shelterin is composed of six proteins: Taz1 , Rap1 , Poz1 , Tpz1 , Pot1 , and Ccq1 ., Among these , Taz1 , Tpz1 and Pot1 are orthologs of human TRF1/2 , TPP1 , and POT1 , respectively ., Fission yeast Rap1 and human Rap1 are also homologous to each other , sharing several domains including a single BRCT domain at their N termini ., Taz1 and Rap1 form a subcomplex that binds to telomeric ds DNA , while Poz1 , Tpz1 , Ccq1 , and Pot1 form another subcomplex at the telomeric ss DNA ., Similar to human shelterin , these two subcomplexes at telomeric ds and ss DNA are bridged by the physical interaction between Rap1 and Poz1 13 , 14 ., To date , the shelterin components in fission yeast have been extensively investigated ., Taz1 , Rap1 , and Poz1 negatively regulate telomerase activity and promote telomere heterochromatin formation 13 , 15–17 ., On the other hand , Tpz1 and Pot1 are essential for telomere protection , and thus telomere DNA is aggressively degraded after deletion of the tpz1+ or pot1+ gene 13 , 18 ., Ccq1 recruits telomerase to telomeres through direct binding to telomerase 19 , 20 ., Taz1 and Rap1 prevent telomere end fusions that would otherwise be caused by aberrant activation of the NHEJ repair pathway at telomeres 21 , 22 ., Taz1 and Rap1 also tether telomeres to the nuclear periphery via inner nuclear membrane ( INM ) protein Bqt4 in vegetative cell growth 23 ., As such , the shelterin components perform distinct functions , even though they form a complex ., It is known that disruption of shelterin can trigger frequent GCRs through breakage of dicentric chromosomes formed by chromosome end-to-end fusion 5 ., However , it is unclear whether the shelterin complex has an additional GCR-suppressive function apart from preventing such chromosome end-to-end fusions; this uncertainty can be ascribed to technical limitations in precisely measuring the occurrence rate of GCRs in mammalian cells ., In budding yeast , an assay has been developed to measure the GCR rates , aptly termed the “GCR assay” 24 , 25 ., GCR rates are deduced from loss of two tandem counter-selective markers inserted in a non-essential chromosomal region ., In this study , we adopted the GCR assay to fission yeast and examined whether the individual shelterin components as well as other telomere-binding proteins suppress GCRs in non-telomeric regions ., We found that a fraction of the shelterin components , including Taz1 and Rap1 , are required for GCR suppression ., Deletion of DNA ligase IV , which is essential for NHEJ , did not rescue the increased GCR rates in taz1Δ and rap1Δ mutant cells , suggesting that Taz1 and Rap1 do not prevent GCR via suppressing NHEJ , unlike the Taz1- and Rap1-dependent protection of telomeres from fusion ., Instead , derepression of telomerase is responsible for the increased GCR rates in taz1Δ and rap1Δ strains ., Dissection of the Rap1 protein identified the N-terminal BRCT domain as an important domain for the GCR suppression ., Moreover , when DSBs are site-specifically induced at a non-telomeric locus by I-SceI endonuclease , Taz1 and Rap1 are required for cellular survival and for inhibiting erroneous repair ., We propose that Taz1 and Rap1 prevent GCRs by regulating telomerase activity and DSB repair , even in non-telomeric regions ., To measure GCR rates in fission yeast , we applied the assay system that was previously developed for budding yeast ( Fig 1A ) 24 ., We constructed a DNA cassette containing two neighboring marker genes , ura4+ and TK ( the latter encodes herpes virus thymidine kinase ) in tandem ., Cells expressing ura4+ and TK are sensitive to 5-fluoroorotic acid ( 5-FOA ) and 5-fluoro-2’-deoxyuridine ( FUdR ) , respectively ., As expected , fission yeast cells with this marker cassette integrated at approximately 150 kb from the right telomere of chromosome I ( the precise location is described in Materials and Methods ) showed sensitivity to both of the drugs ( 5-FOA/FUdR ) ( S1A Fig ) ., This strain is expected to become resistant to both drugs when the ura4+ and TK genes undergo simultaneous deletions and/or loss-of-function point mutations ., However , such simultaneous point mutations seem highly unlikely because the probability of simultaneous point mutations occurring in two specific genes is thought to be quite low ( ~10−14/cell division , given that the spontaneous incidence of loss-of-function mutations for each gene , independently , is ~10−7 , see Methods ) 26 ., Thus , as in the budding yeast GCR assay system , the vast majority of drug-resistant survivors in our assay should be derived from GCRs that result in simultaneous loss of the two marker genes ., Because an essential gene closest to the marker cassette is sec16+ , which is located about 16 . 8 kb centromeric from the cassette , and there is no essential gene telomeric to the cassette , our system can detect GCRs that take place within this ~16 . 8-kb region ( Fig 1A ) ., Hereafter , we will refer to this GCR target region as the “breakpoint region” ., Because it lacks any sequence that shares apparent homology with other chromosome regions , our GCR assay is expected to detect GCRs that are mediated by no or little homology ., In the GCR assay , we counted the number of colonies on a plate with or without 5-FOA/FUdR and estimated GCR rates per cell division using fluctuation analysis 24 ., In the case of wild-type cells , a GCR rate determined in our system was 2 . 6 × 10−9 per cell division ( Fig 1B ) ., This rate is actually far greater than the expected probability of dual independent point mutant survivors ( 10−14 per cell division ) , confirming that our system primarily detects GCRs ., We then isolated 5-FOA/FUdR-resistant clones and performed DNA sequencing at the breakpoint region ( See Materials and Methods ) ., Based on the sequencing data , GCRs were classified into deletion and translocation types ( Fig 1C , wild-type ) ., In the deletion type , DSBs led to deletion of the chromosomal terminus containing the drug selection cassette ., The sequencing analysis detected ectopic telomeric DNA repeats at the breakpoints , suggesting that de novo telomere addition healed the DSB ( Fig 1D ) ., Twelve out of fifteen wild-type-derived GCR clones examined here belonged to this type ., In two other clones , breakpoints were fused with unique sequences from the left arm of chromosome I ( opposite the right arm where the original marker cassette had been located prior to the rearrangement ) in a head-to-tail orientation ( same direction towards telomeres ) ., Such fusions could be derived from either break-induced DNA replication or DNA recombination ( Fig 1D , Translocation as diagramed in S1C Fig ) ., Indeed , the breakpoint junctions consisted of 7 or 8 bp of microhomology in these survivors ( Fig 1D ) ., In both types of GCRs , the locations of the breakpoints seemed to be uniformly distributed in the breakpoint region , rather than clustering at a particular hotspot ( Fig 1E ) ., In the last of the 15 survivors , we established the loss of the marker cassette but could not determine the precise change in the breakpoint region sequence ., As we expected , we did not obtain any clones with simultaneous point mutations in both ura4+ and TK , validating the usefulness of our assay system to specifically evaluate GCR rates ., Of note , the GCR rate measured in our fission yeast system is comparable to that in the original budding yeast system: 2 . 6 × 10−9 /cell division in a 16 . 8 kb-long breakpoint region in wild-type fission yeast ( this study ) vs . 2 . 27 × 10−9 in a 19 . 2 kb breakpoint region in wild-type budding yeast 27 ., If we assume that GCRs occur randomly throughout the genome , then GCR rates would be expected to be proportional to the DNA length of the breakpoint region for the GCR assay ., The GCR rates normalized to unit length are 1 . 5 × 10−10/cell division/kb in fission yeast and 1 . 2 × 10−10/cell division/kb in budding yeast ., Thus , wild-type cells of the two yeast species showed comparable normalized GCR rates ., In addition , GCRs in wild-type fission yeast cells are mostly associated with terminal deletions , whereas translocations are relatively rare , just as in budding yeast 24 ., We thus asked whether the GCR suppression mechanism identified by the budding yeast GCR assay system also functions in fission yeast ., Budding yeast strains lacking nuclease FEN-1 or Mre11 show a 914- and 628-fold increases in GCR rates , respectively 20 ., We therefore tested the effects of loss of FEN-1 and Mre11 in rad2Δ and mre11Δ S . pombe cells and observed a ~100-fold increase in GCR rates ( Fig 1B ) ., In budding yeast , Pif1 helicase suppresses telomerase-mediated telomere elongation at native telomeres and DSBs through destabilizing annealing of telomerase RNA template and single-stranded telomere DNA substrates 28 ., We examined the impact of inactivating pfh1 , the fission yeast homologue of PIF1 on GCRs ., pfh1-mt* is a mutant that lacks nuclear functions but retains the essential mitochondrial functions 29 ., pfh1-mt* showed 32-fold higher GCR rates than wild type , similar to the results reported for the budding yeast corresponding mutant , pif1-m2 ( S1D Fig ) 7 ., From these similarities observed in the two distinct yeast species , we surmise that the regulatory mechanism suppressing GCRs is evolutionarily conserved , underscoring the significance of studying the GCR mechanism in fission yeast ., Since most of the GCR survivors that we isolated were derived from de novo telomere addition , we investigated a possible involvement of the telomere proteins in GCR regulation ., In fission yeast , Taz1 directly binds to both Rap1 and ds telomeric DNA , thereby recruiting Rap1 to telomeres ( Fig 2A ) ., We found that taz1Δ and rap1Δ cells showed greatly increased GCR rates , 1 . 1 × 10−7 and 0 . 85 × 10−7 /cell division ( 42-fold and 33-fold higher than wild-type cells ) , respectively ( Fig 2B ) ., A Rap1-I655R mutant , in which the recruitment of Rap1 to telomeres is diminished due to a compromised Taz1-Rap1 interaction 30 , showed an increased GCR rate that was comparable to taz1Δ or rap1Δ ( Fig 2B , 2 . 6 × 10−7 /cell division , a 97-fold increase over wild-type ) , suggesting that Taz1 represses GCRs primarily through the physical interaction with Rap1 ., Consistent with this notion , taz1+ and rap1+ were found to be epistatic: a taz1Δ rap1Δ double mutant ( 1 . 2 × 10−7 /cell division ) showed similarly increased GCR rates compared to each single mutant taz1Δ or rap1Δ ( Fig 2B ) ., We determined the sequences of GCR breakpoints in taz1Δ and rap1Δ survivors in GCR assay ., We found only deletion type GCRs in taz1Δ and rap1Δ survivors , although the fractions of deletion in these mutants are not significantly higher than in wild type ( Fig 2C & 2D ) ., These results imply that the two shelterin components Taz1 and Rap1 function in the same pathway to prevent GCRs ., We also measured GCR rates of wild-type , taz1Δ , and rap1Δ strains at 20°C ., It is known that taz1Δ , but not wild-type or rap1Δ delay the cell cycle progression and lose viability due to chromosomal entanglement at this temperature 31 ., We found that taz1Δ , but not wild-type and rap1Δ , showed more than one order of magnitude higher GCR rates at 20°C than at 32°C ( S2 Fig ) ., Given the correlation between the increased GCR rate and cold-sensitivity among the three strains , it is possible that the chromosome entanglement contributes to the high GCR rate with taz1Δ at 20°C ., Future study is necessary for concluding the molecular link between these phenotypes ., In sharp contrast to taz1+ and rap1+ deletion , deletion of the poz1+ gene , which encodes another Rap1-interacting shelterin component , did not affect the GCR rate ( 3 . 9 × 10−9 /cell division , Fig 2B ) ., Interestingly , taz1Δ poz1Δ and rap1Δ poz1Δ double mutants showed lower GCR rates than the taz1Δ and rap1Δ single mutants , demonstrating that Poz1 is required for the derepression of GCRs in taz1Δ and rap1Δ cells ( Fig 2E ) ., Consistently , the abrogation of Poz1-Tpz1 binding by a I501A/R505E mutation in Tpz1 32 , another shelterin component that directly binds to Poz1 , similarly suppressed the increased GCR rates in taz1Δ and rap1Δ mutant backgrounds ( Fig 2E ) ., This result suggests that Poz1 recruitment promotes GCRs in taz1Δ and rap1Δ , given that Poz1-Tpz1 binding is essential for telomere localization and function of Poz1 14 , 33 ., We noticed that poz1+ deletion and Tpz1-I501A/R505E mutation individually caused strong reduction in GCR rates in rap1Δ but not much in taz1Δ cells ., These results suggest that the increased GCR rates in taz1Δ and rap1Δ are caused by different mechanisms ., It was reported that the formation of Rap1-Poz1-Tpz1 trimer is a hierarchical process in vitro 34 ., First , Poz1 and Tpz1 form a dimer ., The Rap1-binding domain of Poz1 undergoes allosteric changes upon the Poz1-Tpz1 dimer formation , which greatly increases the affinity with Rap1 , and induces the Rap1-Poz1-Tpz1 trimer formation ., Therefore , it is possible that the Tpz-Poz1 dimer stably exists in rap1Δ but not in taz1Δ ., Such unusual shelterin subcomplexes may contribute to the differential effects of poz1+ deletion in taz1Δ and rap1Δ , as revealed in Fig 2E ., These results suggest that genetic interaction of Taz1 , Rap1 , and Poz1 regarding GCR suppression is complex , similarly to that as for telomere length regulation and cold sensitivity 20 , 22 ., We also examined Stn1 , a non-shelterin protein that binds to telomeric ssDNA ., Because Stn1 is essential for telomere protection , we investigated a temperature sensitive mutant stn1-1 , which has slightly elongated telomeres at semi-permissive temperature 25°C 35 ., We found a moderately increased GCR rate at that temperature ( Fig 2B ) , 1 . 6 × 10−8 /cell division , a 6-fold increase over wild-type ., Because both Taz1 and Rap1 are multi-functional ( see below ) , we set out to dissect which specific function ( s ) is related to the GCR inhibition ., It is known that , in taz1Δ and rap1Δ cells , but not in poz1Δ cells , telomeres are prone to fuse to each other by NHEJ when cells are arrested at G1 phase 21 , 36 ., It is thus possible that a failure to suppress NHEJ in taz1Δ and rap1Δ could lead to formation of dicentric chromosomes , which would trigger DSBs which could result in the observed chromosome terminal deletions ., In order to examine whether NHEJ is responsible for frequent GCRs in taz1Δ and rap1Δ mutant cells , we deleted the DNA ligase IV-encoding lig4+gene , which is essential for NHEJ in fission yeast ., It was reported that a lack of lig4+ suppresses the frequent telomere fusions in taz1Δ and rap1Δ 21 , 22 ., We found that disruption of lig4+ in taz1Δ and rap1Δ did not significantly suppress the increased GCR rates observed with taz1Δ and rap1Δ ( Fig 3A ) , suggesting that NHEJ is dispensable for the high incidence of GCRs in taz1Δ and rap1Δ cells ., Because fission yeast in exponentially growing phase shows very short G1 phase , and NHEJ is active only in G1 but not in S and G2 phase , it was possible that NHEJ was dispensable for GCRs due to a small fraction of cells staying in G1 phase ., We therefore arrested cells in G1 phase through nitrogen starvation , and measured the GCR frequency ., Briefly , cells exponentially growing in YES media were divided into two groups , which were incubated in EMM media with ( N+ ) or without ( N- ) ammonium sulfate for 24 hr , respectively , and then transferred to YES media for growth overnight ., Then equal numbers of N+ and N- cells were subjected to the GCR assay ., We found that taz1Δ ( N- ) cells showed an approximately two-fold increase in GCR frequencies compared to taz1Δ ( N+ ) cells ( S3A Fig ) ., Because taz1Δ ( N- ) cells were G1-arrested and/or lost viability 21 while taz1Δ ( N+ ) cells actively proliferated in EMM media ( with or without supplementing nitrogen ) , the total number of cell divisions was greater in taz1Δ ( N+ ) cells than in taz1Δ ( N- ) cells ., Therefore , the two-fold increase of GCR frequencies is most likely an underestimate of a larger GCR rate ( which is normalized per cell division ) in taz1Δ ( N- ) compared to that in taz1Δ ( N+ ) ., Interestingly , GCR frequencies in taz1Δ ( N- ) were partially suppressed by ligase IV deletion , while those in taz1Δ ( N+ ) were not ., Taken together , NHEJ also contributes to the increase of GCR frequencies of taz1Δ cells in G1 phase ., Because G1 cells are rare in cells with unperturbed cell cycles , this effect is negligible in exponentially growing cell populations ., These results suggest that the break-fusion-bridge cycle via formation of telomere-fusion-mediated dicentric chromosomes plays a minor role , if any , in the increased GCR rate in cycling taz1Δ cells ., Both Taz1 and Rap1 are essential for heterochromatin formation at telomeres and their adjacent regions , subtelomeres 37 ., To examine whether telomere heterochromatin structure is important for suppression of GCRs , we deleted the clr4+ and swi6+ genes , both of which encode essential factors for heterochromatin formation ( S3B Fig ) ., Deletion of swi6+ in the wild-type background led to a small increase in the GCR rate , suggesting a potential contribution of heterochromatin to the suppression of GCRs ., We examined poz1-W209A mutation ., The shelterin component Poz1 is required for telomere silencing , and the poz1-W209A mutation is known to specifically disrupt the telomere heterochromatin regulatory function , among others 38 ., No increase in the GCR rate was observed in poz1-W209A strain ( S3C Fig ) ., In contrast , deletion of clr4+ in rap1Δ background showed small but significant decrease GCR rates , although the underlying mechanism is unclear ., From these results , we conclude that heterochromatin does not play a significant role in suppressing GCR except in rap1Δ ., In fission yeast , Taz1 and Rap1 , but not Poz1 , tether telomeres to the INM via binding of Rap1 to INM protein Bqt4 in vegetative cell growth 23 , 39 ., It is thus possible that the telomere tethering to the INM contributes to GCR suppression through regulation of chromosome positioning within the nucleus ., We found that bqt4Δ cells showed moderately increased GCR rates ( 4 . 3 × 10−8 /cell division , Fig 3B ) ., The GCR rate was also significantly increased by deletion of bqt3+ ( 3 . 0 × 10−8 /cell division ) , whose protein product Bqt3 stabilizes Bqt4 23 ., It was reported that the Ku70/80 complex and two INM proteins Lem2 and Man1 also promote tethering of telomeres to the nuclear envelope , although Man1 plays a minor role 40 , 41 ., Deletion of pku70+ or lem2+ , but not man1+ , led to moderately higher GCR rates ( 2 . 9 × 10−8 and 4 . 2 × 10−8 /cell division , respectively ) than the wild-type strain ( Fig 3B ) ., These results imply that tethering of telomeres to the nuclear envelope facilitates GCR suppression ., Bqt4 localizes to the INM through its C-terminus transmembrane domain , and its N-terminal half is necessary and sufficient for binding Rap1 23 ., While telomeres are dissociated from the nuclear envelope in bqt4Δ , expression of an artificial fusion protein between Rap1 and an N terminus-truncated Bqt4 ( Rap1-GFP-Bqt4ΔN ) in bqt4Δ resumed telomere clustering at the nuclear envelope 23 ., With our GCR assay , we found that bqt4Δ cells expressing the Rap1-GFP-Bqt4ΔN fusion protein from bqt4 promoter showed only a slightly lower GCR rate than bqt4Δ cells expressing GFP-Bqt4ΔN , in which telomeres are not tethered to the INM ( Fig 3B , 2-fold difference ) ., Moreover , the rap1-5E mutant ( consisting of S213E , T378E , S422E , S456E , S513E mutations ) , in which the interaction between Rap1 and Bqt4 is impaired 39 , displayed a comparable GCR rate to wild-type cells ( Fig 3B ) ., We also found that simultaneous deletion of bqt4+ significantly increased GCR rates in taz1Δ and rap1Δ cells ( Fig 3C ) ., These results suggest that Rap1-Bqt4 binding plays a minor role in suppressing GCRs , and that Bqt4 regulates GCRs at least in part by a Taz1- and Rap1-independent mechanism ., By the same token , this result suggests that Rap1 utilizes Bqt4-independent mechanisms for suppressing GCRs ., Taz1 and Rap1 suppress telomerase-mediated telomere DNA elongation 42 , 43 ., Given that all GCRs examined in taz1Δ and rap1Δ were terminal deletions involving de novo telomere additions at breakpoints , it was likely that deregulated telomerase reactions facilitated GCRs through enhanced de novo telomere addition in taz1Δ and rap1Δ cells ., Inactivation of trt1+ , the gene encoding the catalytic subunit of telomerase , leads to chromosome self-circularization 42 , making the GCR assay results difficult to compare with other cases ., We therefore explored the effect of a Pof8 disruption on GCR rates ., Pof8 is involved in maturation of telomerase RNA , and deletion of pof8+ leads to telomere shortening without extensive chromosome circularization , in contrast to a trt1+ deletion 44–47 ., We found that taz1Δ pof8Δ and rap1Δ pof8Δ cells , which also do not show chromosome circularization , showed GCR rates which were lower than taz1Δ and rap1Δ cells , and similar to wild type cells ( Fig 4A ) ., These results suggest that telomerase activity is essential for the high GCR rates in taz1Δ and rap1Δ ., In contrast , the GCR rate of a rad2Δ pof8Δ strain was in between that of rad2Δ alone and wild type cells , suggesting that Taz1 and Rap1 specifically suppress telomerase-dependent GCRs ., We considered two possibilities for how telomerase activity affects GCRs in taz1Δ and rap1Δ: ( 1 ) increased telomerase accessibility directly facilitates de novo telomere addition at breakpoints , or ( 2 ) abnormally elongated native telomeres indirectly affect non-telomeric GCRs ., To determine which is the case , we examined GCR rates using cells with circular chromosomes in the presence or absence of Trt1 4 ., It is known that circular chromosomes in trt1Δ do not contain telomere DNA sequences 48 ., For this purpose , we introduced a Trt1-expressing plasmid into trt1Δ cells with circularized chromosomes ., In this setting , the majority of the trt1Δ cells harboring the Trt1 plasmid maintained circular chromosomes I and II ( S4 Fig ) ., As for trt1Δ taz1Δ , chromosomal configuration depends on the order of gene deletions during the strain preparation ., When trt1+ is deleted first , followed by taz1+ deletion , the strain contains circular chromosomes ., In contrast , linear chromosomes are maintained when taz1+ is deleted first , followed by trt1+ deletion 42 ., Below , we will describe experiments using trt1Δ taz1Δ maintaining circular chromosomes , except otherwise noted ., We also confirmed that trt1Δ taz1Δ expressing ectopic Trt1 retains circular chromosomes ( S4 Fig ) ., When we subjected circular chromosome-containing cells to the GCR assay , it was expected that circular chromosomes needed to undergo complicated changes , such as two independent DSBs at the both sides of the selection cassette , and healing of the two DSBs by telomere addition to produce linear chromosomes ., Consistently , all of the various strains maintaining circular chromosomes ( except Trt1-overproducing trt1Δ taz1Δ ) showed GCR rates below the detection sensitivity of the assay ( Fig 4B ) ., When trt1Δ , trt1Δ taz1Δ and trt1Δ rap1Δ ( all containing circular chromosomes ) were transformed with Trt1-expressing plasmids , trt1Δ taz1Δ showed a significant increase in GCR frequency , while trt1Δ and trt1Δ rap1Δ did not ( Fig 4B ) ., These results suggest two points: first , Taz1 prevents GCR formation independent of its specific DNA binding to telomere DNAs , since circular chromosomes lack all telomere DNAs 48; second , Taz1 has additional roles , which are not shared by Rap1 , in preventing GCR formation from circular chromosomes ., When we deleted poz1+ in trt1Δ taz1Δ cells , followed by over-expression of Trt1 , GCR rates were decreased , suggesting that Poz1 promotes GCRs in taz1Δ cells in the absence of telomere DNA ( Fig 4B , compare lanes 4 and 8 ) ., rap1Δ trt1Δ cells showed similar GCR rates to wild type even after Trt1 re-expression ., We confirmed that both trt1Δ taz1Δ poz1Δ cells and rap1Δ trt1Δ cells maintained circularization of chromosomes I and II before and after Trt1 re-expression ( S4 Fig ) ., In contrast to circular chromosomes-containing trt1Δ taz1Δ , linear chromosome-maintaining trt1Δ taz1Δ ( see above ) , showed significantly increased GCR rates compared to linear-chromosome-containing wild-type cells ( Fig 4B ) ., Ectopic Trt1-over-expression further increased the GCR rates to the level of taz1Δ cells ., To further dissect the precise mechanism of GCR repression by Rap1 , we exploited previously reported sequential N-terminal Rap1 truncations , Rap1-A to G 31 ( Fig 5A ) ., Among these , we found that only the Rap1-G mutant showed an increased GCR rate ., Because the Rap1-A to F mutant strains all retain the Poz1-binding domain ( Rap1 457–512 amino acids ) but Rap1-G does not , the results raised the possibility that Rap1-Poz1 binding is required for the GCR suppression ., However , deletion of the Poz1-binding domain alone did not increase GCR rates ( Rap1ΔP , Fig 5A and 5B ) ., Further dissection of Rap1 revealed that simultaneous deletion of the BRCT domain at the N terminus as well as the Poz1-binding domain led to an increase in GCR rates that was comparable to that in rap1Δ cells ( Rap1-AΔP , Fig 5A and 5B , and S4 Fig ) ., The phenotype of the rap1-AΔP strain was similar to rap1Δ regarding GCR suppression; GCRs from the rap1-AΔP strain primarily showed terminal deletion ( Fig 5C ) , and deletion of pof8+ canceled the increased GCR rates ., These results indicate that the BRCT domain and the Poz1-binding domain redundantly suppress GCRs ., Since the Rap1-A mutant , which lacks the BRCT domain , maintains normal telomere length , we suggest that the BRCT domain does not regulate telomerase action at native telomeres , while the Poz1-binding domain suppresses GCRs through inhibition of telomerase at both telomeres and non-telomeric DSBs ., The Rap1 BRCT domain may be involved in a general DNA repair pathway , failure of which causes various consequences including erroneous telomere addition by telomerase at non-telomeric regions ., Given that GCRs are thought to arise from aberrant DSB repair , the telomerase-independent GCR repression mechanism could potentially include DSB processing ., In order to examine this possibility , we constructed a conditional , site-specific DSB induction system ( Fig 6A ) ., The DNA sequence-specific endonuclease I-SceI was expressed under the control of a tetracycline-inducible promoter , and a single I-SceI cut site ( I-SceIcs ) was integrated at approximately 150 kb centromeric from the right telomere of chromosome I , exactly at the same locus as the marker cassette that was inserted in our GCR assay strains ., Addition of anhydrotetracycline ( ahTET ) to the culture media leads to a DSB at the I-SceIcs ., Indeed , two hours after ahTET addition , quantitative PCR amplification of genomic DNA using primers flanking the I-SceIcs decreased to 40–50% of control levels in wild-type , taz1Δ , rap1Δ , and poz1Δ backgrounds , demonstrating that DSBs were induced in these strains with similar efficiencies ( Fig 6B ) ., With this system , we examined how efficiently the wild-type and mutant cells could repair the DSB ., We transiently induced DSB formation at I-SceIcs by culturing cells in liquid media containing ahTET for two hours ., After that , ahTET was washed out and the cells were spread onto ahTET-free plate media ., Cells that were unsuccessful in repairing the I-SceI DSB ( or healing it , e . g . by de novo telomere addition ) would not form colonies ., We examined genomic DNA extracted from 10 colonies each from wild type , taz1Δ , rap1Δ strains and confirmed that none of them contained mutation in I-SceIcs , indicating that GCRs were not involved in generating survivors ., Subsequent to the transient DSB induction in wild-type cells , the frequency of colony formation decreased to 62% of control ( uncut ) levels ( Fig 6C ) ., Strikingly , taz1Δ and rap1Δ cells showed further lower viabilities ( 37% and 33% , respectively ) ., This result suggests that Taz1 and Rap1 promote DSB repair ., taz1Δ pof8Δ cells showed similar survival with taz1Δ , consistent with the idea that the survivors occur not through telomerase-mediated GCRs , but through DSB repair , and suggesting that Taz1 promotes DSB repair independently of telomerase regulation .

Introduction, Results, Discussion, Materials and methods

Genomic rearrangements ( gross chromosomal rearrangements , GCRs ) threatens genome integrity and cause cell death or tumor formation ., At the terminus of linear chromosomes , a telomere-binding protein complex , called shelterin , ensures chromosome stability by preventing chromosome end-to-end fusions and regulating telomere length homeostasis ., As such , shelterin-mediated telomere functions play a pivotal role in suppressing GCR formation ., However , it remains unclear whether the shelterin proteins play any direct role in inhibiting GCR at non-telomeric regions ., Here , we have established a GCR assay for the first time in fission yeast and measured GCR rates in various mutants ., We found that fission yeast cells lacking shelterin components Taz1 or Rap1 ( mammalian TRF1/2 or RAP1 homologues , respectively ) showed higher GCR rates compared to wild-type , accumulating large chromosome deletions ., Genetic dissection of Rap1 revealed that Rap1 contributes to inhibiting GCRs via two independent pathways ., The N-terminal BRCT-domain promotes faithful DSB repair , as determined by I-SceI-mediated DSB-induction experiments; moreover , association with Poz1 mediated by the central Poz1-binding domain regulates telomerase accessibility to DSBs , leading to suppression of de novo telomere additions ., Our data highlight unappreciated functions of the shelterin components Taz1 and Rap1 in maintaining genome stability , specifically by preventing non-telomeric GCRs .

Tips of chromosomes , telomeres , are bound and protected by a telomere-binding protein complex called shelterin ., Most previous studies focused on shelterin’s telomere-specific role , and its general role in genome maintenance has not been explored extensively ., In this study , we first set up an assay measuring the spontaneous formation rate per cell division of gross chromosomal rearrangements ( GCRs ) in fission yeast ., We found that the rate of GCRs is elevated in mutants defective for shelterin components Taz1 or Rap1 ., Detailed genetic experiments revealed unexpectedly that Taz1 and Rap1 have a novel role in repairing DNA double-strand breaks ( DSBs ) and suppressing GCRs at non-telomeric regions ., Given that shelterin components are conserved between fission yeast and humans , future studies are warranted to test whether shelterin dysfunction leads to genome-wide GCRs , which are frequently observed in cancers .

chromosome structure and function, cell cycle and cell division, cell processes, cloning, telomeres, mutation, fungi, model organisms, non-homologous end joining, experimental organism systems, dna, molecular biology techniques, schizosaccharomyces, research and analysis methods, saccharomyces, chromosome biology, animal studies, chromosomal aberrations, schizosaccharomyces pombe, molecular biology, yeast, biochemistry, chromosomal deletions, point mutation, eukaryota, cell biology, nucleic acids, genetics, biology and life sciences, yeast and fungal models, saccharomyces cerevisiae, dna repair, organisms, chromosomes

journal.pcbi.1003531

Waste Not, Want Not: Why Rarefying Microbiome Data Is Inadmissible

Despite its current popularity in microbiome analyses rarefying biological count data is statistically inadmissible because it requires the omission of available valid data ., This holds even if repeated rarefying trials are compared for stability as previously suggested 17 ., In this article we demonstrate the applicability of a variance stabilization technique based on a mixture model of microbiome count data ., This approach simultaneously addresses both problems of ( 1 ) DNA sequencing libraries of widely different sizes , and ( 2 ) OTU ( feature ) count proportions that vary more than expected under a Poisson model ., We utilize the most popular implementations of this approach currently used in RNA-Seq analysis , namely edgeR 41 and DESeq 13 , adapted here for microbiome data ., This approach allows valid comparison across OTUs while substantially improving both power and accuracy in the detection of differential abundance ., We also compare the performance of the Gamma-Poisson mixture model against a method that models OTU proportions using a zero-inflated Gaussian distribution , implemented in a recently-released package called metagenomeSeq 40 ., A mathematical proof of the sub-optimality of the rarefying approach is presented in the supplementary material ( Text S1 ) ., To help explain why rarefying is statistically inadmissible , especially with regards to variance stabilization , we start with the following minimal example ., Suppose we want to compare two different samples , called A and B , comprised of 100 and 1000 DNA sequencing reads , respectively ., In statistical terms , these library sizes are also equivalent to the number of trials in a sampling experiment ., In practice , the library size associated with each biological sample is a random number generated by the technology , often varying from hundreds to millions ., For our example , we imagine the simplest possible case where the samples can only contain two types of microbes , called OTU1 and OTU2 ., The results of this hypothetical experiment are represented in the Original Abundance section of Figure 1 ., Formally comparing the two proportions according to a standard test could technically be done either using a χ2 test ( equivalent to a two sample proportion test here ) or a Fisher exact test ., By first rarefying ( Figure 1 , Rarefied Abundance section ) so that both samples have the same library size before doing the tests , we are no longer able to differentiate the samples ( Figure 1 , tests ) ., This loss of power is completely attributable to reducing the size of B by a factor of 10 , which also increases the width of the confidence intervals corresponding to each proportion such that they are no longer distinguishable from those in A even though they are distinguishable in the original data ., The variance of the proportions estimate is multiplied by 10 when the total count is divided by 10 ., In this binomial example the variance of the proportion estimate is , a function of the mean ., This is a common occurrence and one that is traditionally dealt with in statistics by applying variance-stabilizing transformations ., We show in Text S1 that the relation between the variance and the mean for microbiome count data can be estimated and the model used to find the optimal variance-stabilizing transformation ., As illustrated by this simple example , it is inappropriate to compare the proportions of OTU i , , without accounting for differences in the denominator value ( the library size , sj ) because they have unequal variances ., This problem of unequal variances is called heteroscedasticity ., In other words , the uncertainty associated with each value in the table is fundamentally linked to the total number of observations ( or reads ) , which can vary even more widely than a 10-fold difference ., In practice we will be observing hundreds of different OTUs instead of two , often with dependendency between the counts ., Nevertheless , the difficulty caused by unequal library sizes still pertains ., The uncertainty with which each proportion is estimated must be considered when testing for a difference between proportions ( one OTU ) , or sets of proportions ( a microbial community ) ., Although rarefying does equalize variances , it does so only by inflating the variances in all samples to the largest ( worst ) value among them at the cost of discriminating power ( increased uncertainty ) ., Rarefying also adds artificial uncertainty through the random subsampling step , such that Figure 1 shows the best-case , achieved only with a sufficient number of repeated rarefying trials ( See Protocol S1 , minimal example ) ., In this sense alone , the random step in rarefying is unnecessary ., Each count value could be transformed to a common-scale by rounding ., Although this common-scale approach is an improvement over the rarefying method here defined , both methods suffer from the same problems related to lost data ., Simulation A is a simple example of a descriptive experiment in which the main goal is to distinguish patterns of relationships between whole microbiome samples through normalization followed by the calculation of sample-wise distances ., Many early microbiome investigations are variants of Simulation A , and also used rarefying prior to calculating UniFrac distances 27 ., Microbiome studies often graphically represent the results of their pairwise sample distances using multidimensional scaling 42 ( also called Principal Coordinate Analysis , PCoA ) , which is useful if the desired effects are clearly evident among the first two or three ordination axes ., In some cases , formal testing of sample covariates is also done using a permutation MANOVA ( e . g . vegan::adonis in R 43 ) with the ( squared ) distances and covariates as response and linear predictors , respectively 44 ., However , in this case we are not interested in creating summary graphics or testing the explanatory power of sample covariates , but rather we are interested in precisely evaluating the relative discriminating capability of each combination of normalization method and distance measure ., We will use clustering results as a quantitative proxy for the broad spectrum of approaches taken to interpret microbiome sample distances ., Simulation B is a simple example of microbiome experiments in which the goal is to detect microbes that are differentially abundant between two pre-determined classes of samples ., This experimental design appears in many clinical settings ( health/disease , target/control , etc . ) , and other settings for which there is sufficient a priori knowledge about the microbiological conditions , and we want to enumerate the OTUs that are different between these microbiomes , along with a measure of confidence that the proportions differ ., For this form of analysis , the microbiome counts for each simulated experiment are generated by sampling from a single multinomial derived from the OTU proportions observed in one environment of the Global Patterns dataset ., To create an effect , the simulated samples of an experiment were divided into two equally-sized classes , test and null , and a perturbation was applied ( multiplication by a defined value ) to the count values of a random subset of OTUs in the test class only ., See part B of Figure 2 for a simple example ., Each of the randomly perturbed OTUs is differentially abundant between the classes , and the performance of downstream tests can be evaluated on how well these OTUs are detected without falsely selecting OTUs for which no perturbation occurred ( false positives ) ., False negatives are perturbed OTUs that went undetected ., This approach for generating simulated experiments with a defined effect size ( in the form of multiplicative factor ) was repeated for each combination of median library size , number of samples per class , and the nine microbial environments included in the Global Patterns dataset ., Each simulated experiment was subjected to various approaches for normalization/noise-modeling and differential abundance testing ., We surveyed various publicly available microbiome count data to evaluate the variance-mean relationship for OTUs among sets of biological replicates , a few examples of which are shown here ( Figure 3 ) ., In every instance the variances were larger than could be expected under a Poisson model ( overdispersed , φ>0 ) , especially at larger values of the common-scale mean ., By definition , these OTUs are the most abundant , and receive the greatest interest in many studies ., For rarefied counts the absolute scales are decreased and there are many fewer OTUs that pass filtering , but overdispersion is present in both cases and follows a clear sample-wide trend ., See the dispersion-survey section of Protocol S1 for additional examples of overdispersed microbiome counts ., The consistent ( though non-linear ) relationship between variance and mean indicates that parameters of a NB model , especially φi , can be adequately estimated among biological replicates of microbiome data , despite a previous weak assertion to the contrary 39 ., In simulations evaluating clustering accuracy , we found that rarefying undermined the performance of downstream clustering methods ., This was the result of omitted read counts , added noise from the random sampling step in rarefying , as well as omitted microbiome samples with small library sizes that were accurately clustered by alternative procedures on the same simulated data ( Figure 4 ) ., The extent to which the rarefying procedure performed worse depended on the effect-size ( ease of clustering ) , as well as the typical library size of the samples in the simulation and the choice of threshold for the minimum library size ( Figure 5 ) ., We also evaluated the performance of alternative clustering methods , k-means and hierarchical clustering , on the same tasks and found similar overall results ( Protocol S1 ) ., In additional rarefying simulations we investigated the dependency of clustering performance on the choice of minimum library threshold , ., We found that samples were trivial to cluster for the largest library sizes using most distance methods , even with the threshold set to the smallest library in the simulation ( no samples discarded , all correctly clustered ) ., However , at more modest library sizes typical of highly-parallel experimental designs the optimum choice of size threshold is less clear ., A small threshold implies retaining more samples but with a smaller number of reads ( less information ) per sample; whereas a larger threshold implies more discarded samples , but with larger libraries for the samples that remain ., In our simulations the optimum choice of threshold hovered around the 15th-percentile of library sizes for most simulations and normalization/distance procedures ( Figure 5 ) , but this value is not generalizable to other data ., Regions within Figure 5 in which all distances have converged to the same line ( ) are regions for which the minimum library threshold completely controls clustering accuracy ( all samples not discarded are accurately clustered ) ., Regions to the left of this convergence indicate a compromise between discarding fewer samples and retaining enough counts per sample for accurate clustering ., In simulations evaluating performance in the detection of differential abundance , we found an improvement in sensitivity and specificity when normalization and subsequent tests are based upon a relevant mixture model ( Figure 6 ) ., Multiple t-tests with correction for multiple inference did not perform well on this data , whether on rarefied counts or on proportions ., A direct comparison of the performance of more sophisticated parametric methods applied to both original and rarefied counts demonstrates the strong potential of these methods and large improvements in sensitivity and specificity if rarefying is not used at all ., In general , the rate of false positives from tests based on proportions or rarefied counts was unacceptably high , and increased with the effect size ., This is an undesirable phenomenon in which the increased relative abundance of the true-positive OTUs ( the effect ) is large enough that the null ( unmodified ) OTUs appear significantly more abundant in the null samples than in the test samples ., This explanation is easily verified by the sign of the test statistics of the false positive OTU abundances , which was uniformly positive ( Protocol S1 ) ., Importantly , this side-effect of a strong differential abundance was observed rarely in edgeR performance results under TMM normalization ( not shown ) but not with RLE normalization ( shown ) , and was similarly absent in DESeq ( 2 ) results ., The false positive rate for edgeR and DESeq ( 2 ) was near zero under most conditions , with no obvious correlation between false positive rate and effect size ., Although rarefied counts and proportions both performed relatively poorly , count proportions outperformed rarefied counts in most simulations due to better sensitivity , but also suffered from a higher rate of false positives at larger values of effect size ( Figure 6 , Protocol S1 ) ., The rarefying normalization procedure was associated with performance costs in both sample-clustering and differential abundance statistical evaluations , enumerated in the following ., Due to these demonstrated limitations and proven sub-optimality , we advocate that rarefying should not be used ., In special cases the costs listed above may be acceptable for sample-comparison experiments in which the effect-size ( s ) and the original library sizes are large enough to withstand the loss of data ., Many early descriptive studies fall into this category – for example comparing functionally distinct human body sites or environments 48 – and the ability to accurately distinguish those vastly-different microbiome samples is not in question , even with rarefied counts ., However , for new empirical data the effect size ( s ) are unknown and may be subtle; and consequently , rarefying may undermine downstream analyses ., In the case of differential abundance detection , it seems unlikely that the cost of rarefying is ever acceptable ., In our simulations , both rarefied counts and sample proportions resulted in an unacceptably high rate of false positive OTUs ., As we described theoretically in the introduction , this is explained by differences among biological replicates that manifest as overdispersion , leading to a subsequent underestimate of the true variance if a relevant mixture model is not used ., We detected overdispersion among biological replicates in all publicly available microbiome count datasets that we surveyed ( Figure 3 , Protocol S1 ) ., Failure to account for this overdispersion – by using proportions or rarefied counts – results in a systematic bias that increases the Type-I error rate even after correcting for multiple-hypotheses ( e . g . Benjamini-Hochberg 52 ) ., In other words , if overdispersion has not been addressed , we predict many of the reported differentially abundant OTUs are false positives attributable to an underestimate of uncertainty ., In our simulations this propensity for Type-I error increased with the effect size , e . g . the fold-change in OTU abundance among the true-positive OTUs ., For rarefied counts , we also detected a simultaneous increase in Type-II error attributable to the forfeited data ., It may be tempting to imagine that the increased variance estimate due to rarefying could be counterbalanced by the variance underestimate that results from omitting a relevant mixture model ., However , such a scenario constitutes an unlikely special case , and false positives will not compensate for the false negatives in general ., In our simulations both Type-I and Type-II error increased for rarefied counts ( Figure 6 , Protocol S1 ) ., Fortunately , we have demonstrated that strongly-performing alternative methods for normalization and inference are already available ., In particular , an analysis that models counts with the Negative Binomial – as implemented in DESeq2 13 or in edgeR 41 with RLE normalization – was able to accurately and specifically detect differential abundance over the full range of effect sizes , replicate numbers , and library sizes that we simulated ( Figure 6 ) ., DESeq-based analyses are routinely applied to more complex tests and experimental designs using the generalized linear model interface in R 61 , and so are not limited to a simple two-class design ., We also verified an improvement in differential abundance performance over rarefied counts or proportions by using an alternative mixture model based on the zero-inflated Gaussian , as implemented in the metagenomeSeq package 40 ., However , we did not find that metagenomeSeqs AUC values were uniformly highest , as Negative Binomial methods had higher AUC values when biological replicate samples were low ., Furthermore , while metagenomeSeqs AUC values were marginally higher than Negative Binomial methods at larger numbers of biological replicates , this was generally accompanied with a much higher rate of false positives ( Figure 6 , Protocol S1 ) ., Based on our simulation results and the widely enjoyed success for highly similar RNA-Seq data , we recommend using DESeq2 or edgeR to perform analysis of differential abundance in microbiome experiments ., It should be noted that we did not comprehensively explore all available RNA-Seq analysis methods , which is an active area of research ., Comparisons of many of these methods on empirical 62 , 63 and simulated 14 , 64 , 65 data find consistently effective performance for detection of differential expression ., One minor exception is an increased Type-I error for edgeR compared to later methods 62 , which was also detected in our results relative to DESeq and DESeq2 when TMM normalization was used ( not shown ) – but not after switching to RLE normalization ( Figure 6 , Protocol S1 ) ., Generally speaking , the reported performance improvements between these methods are incremental relative to the large gains attributable to applying a relevant mixture model of the noise with shared-strength across OTUs ., However , some of these alternatives from the RNA-Seq community may outperform DESeq2 on microbiome data meeting special conditions , for example a large proportion of true positives and sufficient replicates 66 , small sample sizes 14 , or extreme values 67 ., Although we did not explore the topic in the simulations here described , a procedure for further improving differential expression detection performance , called Independent Filtering 68 , also applies to microbial differential abundance ., Some heuristics for filtering low-abundance OTUs are already described in the documentation of various microbiome analysis workflows 29 , 30 , and in many cases these can be classified as forms of Independent Filtering ., More effort is needed to optimize Independent Filtering for differential abundance detection , and rigorously define the theoretical basis and heuristics applicable to microbiome data ., Ideally a formal application of Independent Filtering of OTUs would replace many of the current ad hoc approaches that often include poor reproducibility , poor justification , and the opportunity to introduce bias ., Some of the justification for the rarefying procedure has originated from exploratory sample-wise comparisons of microbiomes for which it was observed that a larger library size also results in additional observations of rare species , leading to a library size dependent increase in estimates of both alpha- and beta-diversity 24 , 69 , especially UniFrac 70 ., It should be emphasized that this represents a failure of the implementation of these methods to properly account for rare species and not evidence that diversity depends on library size ., Rarefying is far from the optimal method for addressing rare species , even when analysis is restricted solely to sample-wise comparisons ., As we demonstrate here , it is more data-efficient to model the noise and address extra species using statistical normalization methods based on variance stabilization and robustification/filtering ., Though beyond the scope of this work , a Bayesian approach to species abundance estimation would allow the inclusion of pseudo-counts from a Dirichlet prior that should also substantially increase robustness to rare species ., Our results have substantial implications for past and future microbiome analyses , particularly regarding the interpretation of differential abundance ., Most microbiome studies utilizing high-throughput DNA sequencing to acquire culture-independent counts of species/OTUs have used either proportions or rarefied counts to address widely varying library sizes ., Left alone , both of these approaches suffer from a failure to address overdispersion among biological replicates , with rarefied counts also suffering from a loss of power , and proportions failing to account for heteroscedasticity ., Previous reports of differential abundance based on rarefied counts or proportions bear a strong risk of bias toward false positives , and may warrant re-evaluation ., Current and future investigations into microbial differential abundance should instead model uncertainty using a hierarchical mixture , such as the Poisson-Gamma or Binomial-Beta models , and normalization should be done using the relevant variance-stabilizing transformations ., This can easily be put into practice using powerful implementations in R , like DESeq2 and edgeR , that performed well on our simulated microbiome data ., We have provided wrappers for edgeR , DESeq , DESeq2 , and metagenomeSeq that are tailored for microbiome count data and can take common microbiome file formats through the relevant interfaces in the phyloseq package 32 ., These wrappers are included with the complete code and documentation necessary to exactly reproduce the simulations , analyses , surveys , and examples shown here , including all figures ( Protocol S1 ) ., This example of fully reproducible research can and should be applied to future publication of microbiome analyses 71–73 .

Introduction, Methods, Results/Discussion

Current practice in the normalization of microbiome count data is inefficient in the statistical sense ., For apparently historical reasons , the common approach is either to use simple proportions ( which does not address heteroscedasticity ) or to use rarefying of counts , even though both of these approaches are inappropriate for detection of differentially abundant species ., Well-established statistical theory is available that simultaneously accounts for library size differences and biological variability using an appropriate mixture model ., Moreover , specific implementations for DNA sequencing read count data ( based on a Negative Binomial model for instance ) are already available in RNA-Seq focused R packages such as edgeR and DESeq ., Here we summarize the supporting statistical theory and use simulations and empirical data to demonstrate substantial improvements provided by a relevant mixture model framework over simple proportions or rarefying ., We show how both proportions and rarefied counts result in a high rate of false positives in tests for species that are differentially abundant across sample classes ., Regarding microbiome sample-wise clustering , we also show that the rarefying procedure often discards samples that can be accurately clustered by alternative methods ., We further compare different Negative Binomial methods with a recently-described zero-inflated Gaussian mixture , implemented in a package called metagenomeSeq ., We find that metagenomeSeq performs well when there is an adequate number of biological replicates , but it nevertheless tends toward a higher false positive rate ., Based on these results and well-established statistical theory , we advocate that investigators avoid rarefying altogether ., We have provided microbiome-specific extensions to these tools in the R package , phyloseq .

The term microbiome refers to the ecosystem of microbes that live in a defined environment ., The decreasing cost and increasing speed of DNA sequencing technology has recently provided scientists with affordable and timely access to the genes and genomes of microbiomes that inhabit our planet and even our own bodies ., In these investigations many microbiome samples are sequenced at the same time on the same DNA sequencing machine , but often result in total numbers of sequences per sample that are vastly different ., The common procedure for addressing this difference in sequencing effort across samples – different library sizes – is to either ( 1 ) base analyses on the proportional abundance of each species in a library , or ( 2 ) rarefy , throw away sequences from the larger libraries so that all have the same , smallest size ., We show that both of these normalization methods can work when comparing obviously-different whole microbiomes , but that neither method works well when comparing the relative proportions of each bacterial species across microbiome samples ., We show that alternative methods based on a statistical mixture model perform much better and can be easily adapted from a separate biological sub-discipline , called RNA-Seq analysis .

mathematics, statistics (mathematics), ecology, medical microbiology, biology and life sciences, microbiology, physical sciences, biostatistics, contingency tables, statistical methods, microbial ecology

journal.ppat.1007368

An unusually high substitution rate in transplant-associated BK polyomavirus in vivo is further concentrated in HLA-C-bound viral peptides

Viral evolutionary rates can vary strongly depending on the method used to estimate them 1 , 2 ., Among Baltimore groups , the fastest evolving entities are single-stranded ( ss ) RNA and reverse-transcribing ( RT ) viruses , with rates ranging between 10−2 and 10−5 substitutions per site per year ( s/s/y ) ., The rates of double-stranded ( ds ) RNA and ssDNA viruses range between 10−3 and 10−6 s/s/y , whereas dsDNA viruses evolve more slowly ( 10−3 and 10−8 s/s/y ) 3 , 4 ., It is important to note that only few estimates on dsDNA viruses are published ., In fact , higher estimates are based on specific genes , as estimated for human papillomavirus 16 ( E6 and E7 ) , human adenovirus ( hexon ) , or JC virus ( VP1 ) , which are in the order of 10−3 s/s/y 4 , 5 ., Regarding estimates based on dsDNA complete genomes , all of them range between 10−5 and 10−7 s/s/y 3 , 5 ., This finding confirms that viruses are fast evolving entities whereas humans have much lower evolutionary rates ( 10−8–10−9 s/s/y ) ., However , the well-established co-divergence of viral populations with their hosts suggests the possibility of low evolutionary rates in viruses as well ., For example , polyomaviruses were historically considered to be examples of human-virus co-divergence , and have been used as markers for human migration patterns , with proposed estimates ranging from 1 . 41 × 10−7 to 4 × 10−8 s/s/y 6 , 7 ., Detailed studies are needed to better understand dsDNA virus evolution in vivo , especially in viruses that can be considered as potential pathogens ., In vertebrates , the major driving force in anti-viral immunity is the high level of polymorphism in human leukocyte antigen ( HLA ) genes ., Despite a few recent reports 8 , 9 , limited information is presently available on the extent of viral variability in vivo , especially at the whole viral genome level , and only a few studies have tackled this variability in conjunction with the HLA genotype of infected individuals ., Consequently , viral escape mutants—i . e . , viruses that produce mutated peptides that are no longer able to bind to cognate HLA molecules—have been mainly studied for limited model epitopes in in vitro systems and in highly relevant RNA viruses such as HIV , HCV , influenza or dengue ( see the following historical references 10 , 11; for a recent review and full bibliography on the subject see 12 ) ., It is not surprising that RNA viruses can adapt to circumvent the immune responses 4 , but little is known about viral escape in DNA viruses ., A better understanding of the epitopes involved in viral escape from the immune system could be useful for the development of vaccines and specific treatments ., Here , we initiate a dual approach using the BK virus ( BKV ) as a model ., BKV , which was detected for the first time in 1971 , is a 5 . 1 kb dsDNA virus of the Polyomaviridae family that harbors six genes ( Agnogene , VP1 to VP3 , large T antigen “LTA” and small t antigen “stA” ) 13 ., The primary infection occurs essentially in childhood and the virus infects up to 90% of the human population ., The virus remains persistent throughout life , primarily in the urinary tract 14 ., High-level replication mainly occurs in immunocompromised hosts and , more specifically in those receiving modern immunosuppressive regimens , notably post-kidney transplantation ., BKV-associated diseases , especially BKV-associated nephropathy , affect 1–10% of transplant recipients 15 , 16 and may lead to loss of the allograft and even death 17 ., There are no specific prophylactic or curative treatments , and early diagnosis , as well as quick restoration of immunity ( through dampening of immunosuppression ) , remain the most effective strategies to control the disease ., Access to the virus in the bloodstream and/or urine within a transplant setting , where HLA alleles of both donors and recipients are known , provides a unique opportunity to study viral evolution in vivo in the context of the individual’s ( both recipient and donor ) HLA class I genotype ., A retrospective cohort of 96 patients—225 samples—that underwent solid organ ( N = 83 ) or hematopoietic cell transplantations ( N = 13 ) , harboring a minimum of 104 viral copies/mL in whole blood or urine , was selected ., Quantitative real-time PCR showed that the viral titers in blood ( 8 . 98 × 104 ± 2 . 47 × 104 copies/mL ) were significantly lower than those in urine ( 2 . 16 ×109 ± 3 . 94 × 108 copies/mL ) ( Mann-Whitney U = 315 . 0 , two-tailed , P < 0 . 0001 ) ., After complete deep genome sequencing of all 225 samples and alignment to the BKV Dunlop reference strain ( GenBank accession number NC001538 ) , an average of 110 ± 3 polymorphisms per sample was observed with an average median coverage of 3043 ± 78 reads/position ( S1 Table , GenBank accession numbers KT896230-KT896454; see Methods ) ., In total , 37 . 88% of all amino acid positions in the Agnoprotein , 12 . 43% in VP1 , 9 . 97% in VP2 , 11 . 21% in VP3 , 8 . 20% in LTA and 8 . 72% in stA , were found to be polymorphic ( S2 Table ) ., Agnogene is the only gene that is not under apparent selective constraints ( Nei-Gojobori test , P = 0 . 8663 ) , while the others are under purifying selection ( Nei-Gojobori test , P < 0 . 0001 , Fig 1 , see Methods ) ., Only a few single nucleotide insertions or deletions were detected in the viral genes ( S3 Table ) ., Due to methodological limitations ( short reads ) the non-coding control region was not included in the analyses ., The occurrence of mutations is the main process generating genetic variability , but other processes , such as genetic drift , gene flow , selection and recombination , are responsible for shaping the genetic structure and variation of viral populations ., Here , we present evidence that BKV is under strong purifying selection even in the immunocompromised host ., Several specific features of the Polyomaviridae ( e . g . , limited size of the genome , small number of genes and overlapping transcription units ) likely account for this outcome ., In addition , the prevalence of purifying selection in essential genes is anticipated in all viruses as there is a requirement to complete the viral cycle , even in immunocompromised hosts ., Most mutations in coding regions must be deleterious , and a high substitution rate implies the accumulation of mutations with deleterious effects 18 ., This phenomenon is well known in RNA viruses , which have high mutation rates and short replication times ., Similar results have been shown comparing mutational fitness effects and evolution in ssRNA and ssDNA viruses 19 , 20 ., Our study supports the hypothesis , in concordance with other recent findings 21 , that the evolutionary rate gap between small dsDNA and RNA viruses might not be as wide as previously thought ., A recent study in lentiviruses has revealed that the combined effects of sequence saturation and purifying selection can explain the time-dependent pattern of rate variation ., Purifying selection acts on the genetic diversity over long timeframes by removing a large number of transient deleterious mutations that are still present within short timeframes 4 ., Phylogenetic analysis with all BKV complete genomes available from GenBank ( Fig 2A ) suggested the existence of three large groups or genotypes represented by serotypes I , II/III , and IV , with subtypes within genotypes ., Limited differences ( short branch lengths ) between the previously designated genotypes II and III suggested the existence of only one genotype II/III with two subtypes ( in contrast to more pronounced differences between serotypes II and III ) ., A similar phylogenetic classification was observed by analyzing only the VP1 gene ( Fig 2B ) ., Incidentally , this finding indicated that the current BKV classification should be revised due to inconsistencies between serotyping and genotyping ., Next , to establish the genotype of our samples , one reference strain of each genotype and subtype was used for the phylogenetic analysis ( Fig 2C ) ., Most of our samples ( 80 . 88% ) belonged to genotype I , whereas genotypes IV and II/III were less represented ( 13 . 78% and 5 . 3% respectively ) ., The clustering was patient-dependent but independent of the sample origin ( urine or blood ) and suggested that some samples likely contained a mixture of genotypes ., This mixture might be due to multiple lifelong infections or the replication of viruses from the recipient and/or the donor ., Intra- and inter-patient evolutionary rates were estimated ., BKV sequences from samples with possible recombination or a mixture of genotypes according to the RDP output 22 were removed from the analysis ( see Methods ) ., We estimated an intra-patient substitution rate for BKV in transplanted patients in the range of 4 . 90 × 10−4–1 . 22 × 10−3 substitutions per nucleotide site per year ( s/s/y ) ., No differences between substitution rates in solid organ and hematopoietic cell transplant recipients were found ( t-test , P = 0 . 2581 ) ., To estimate the inter-patient evolutionary rate , the best substitution ( molecular clock ) and demographic model according to marginal likelihood analyses was the relaxed log-normal uncorrelated clock with Bayesian skyline demographic prior ., The estimated inter-patient evolutionary rate ranged from 1 . 00 × 10−5–2 . 15 × 10−4 ( 95% HDI ) for a maximum sampling interval of 568 days ., The estimate was quite robust to different demographic and molecular clock models ( S4 Table ) ., The evolutionary rates based on the maximum likelihood and least-squares methods implemented in treedater were similar when applied to the whole data set ( 4 . 30 × 10−3 s/s/y ) but with large parametric bootstrap confidence intervals ( in the 10−20 to 1014 range ) , thus preventing their consideration as reasonable estimates ., However , when the dataset was reduced to the sequences of genotype I ( n = 56 ) the average evolutionary rate was estimated at 1 . 33 × 10−4 ( 95% CI = 3 . 13 ×10−6–5 . 59 × 10−3 ) ., These values were close to those obtained with the Bayesian approach described previously ., It is usually assumed that RNA viruses evolve at a rate of 10−4 s/s/y , while dsDNA can be close to 10−8 s/s/y 23 ., ssDNA viruses with small genomes evolving at rates similar to those of RNA viruses have been reported previously 24 , 25 , as illustrated by the canine parvovirus , with a substitution rate of 1 . 7 × 10−4 s/s/y 26 ., In the case of dsDNA , many evolutionary rates have been calculated under the assumption of co-divergence between viral and human populations , as observed for polyomaviruses ., Recently , the substitution rate for JC polyomavirus was evaluated at 1 . 7 × 10−5 s/s/y 27 ., Based on this result , Bayesian analyses suggested the substitution rate of BKV to be on the order of 10−5 s/s/y 5 , 28 , while another study found only minor nucleotide substitutions in the genes encoding late proteins 29 ., Here we estimated a substitution rate for BKV on the order of 10−3–10−5 s/s/y ( Fig 3 ) ., Our experimental results show , for the first time using whole-genome sequencing of in vivo viral populations ( in a large monocentric cohort ) , that the genomic evolutionary rate of a dsDNA virus can be as high as that of RNA viruses ., It is important to note that the sampling window of sequences may affect the estimates of evolutionary rates , because very short timescales can inflate them ., A recent study has shown that estimates of evolutionary rates were lower for broader sampling levels and longer timeframes for both , DNA and RNA viruses , suggesting that the time dependence of substitution rates is ubiquitous among all viruses 4 ., For example , lentivirus evolutionary rates from serial samples over a few years within a single patient or host are in the order of 10−3 s/s/y 30 , reflecting those observed in this study in a small dsDNA virus ., In addition , a previous study comparing the evolution of ssRNA and ssDNA viruses has shown that small genomes ( < 5 kb ) can evolve rapidly 24 regardless of their encoding material , and that the well-known correlation between genome size and mutation rate 70 can also hold for evolutionary rates ., Here , we show that small dsDNA genomes can also evolve as fast as single-stranded ones ., Although BKV uses the host DNA polymerase for its replication , the virally-encoded Agnoprotein inhibits dsDNA break repair activity , thereby potentially increasing the error rate during BKV DNA replication 71 ., Interestingly , cell tropism of RNA viruses was recently suggested as a key factor in their capacity to evolve , since viruses replicating in epithelial cells ( as BKV ) are characterized by rapid replication and higher substitution rates 72 ., To investigate the relationship between the evolutionary rate of the virus and the immunosuppressive drug regimen—hence the strength of the immune system—we analyzed such information in our kidney transplant recipient cohort ( the largest subgroup in our cohort ) ., Kidney transplant patients were given either anti-thymocyte globulin ( ATG ) ( immunological high-risk patients ) or anti-Interleukin-2 receptor ( anti-IL-2R ) ( immunological low-risk patients ) as induction treatments , and tacrolimus ( immunological high-risk patients ) or cyclosporine ( immunological low-risk patients ) as maintenance therapy ., Mycophenolate mofetil and steroids were also part of both drug regimens ( for high- and low-risk patients ) ., Evolutionary analysis of the different subgroups showed no significant differences in the mutational load ( full negative binomial mixed model regression with random effect intercept to account for repeated measures ) nor in inter-patient substitution rates where ranges overlapped between treatments ( ATG 6 . 12 × 10−4–1 . 03 × 10−5 s/s/y , Anti-IL-2R 8 . 60 × 10−4–1 . 36 × 10−5 s/s/y , tacrolimus 4 . 64 × 10−4–9 . 31 × 10−6 s/s/y , and cyclosporine 1 . 72 × 10−3–1 . 11 × 10−5 s/s/y ) ., To investigate the genetic immune escape mechanism of BKV , potential T-cell epitopes presented by HLA class I were predicted using both donor and recipient HLA alleles , combined with the viral substitutions found herein ( S1 Fig , S2 , S5 and S6 Tables , see Methods ) ., In this way , we determined the putative HLA ligandome of the virus as linked to the individual’s cognate HLA genotype ., Interestingly , the two codons in VP2 that appeared to be under positive selection corresponded to codons within predicted epitopes ., The VP2 103 codon , the one with the highest level of significant difference , was found in three predicted HLA-C epitopes ( KFFDDWDHKVSTV , FFDDWDHKV and FFDDWDHKVSTV ) , and codon 340 was located within two HLA-A predicted epitopes ( TTNKRRSR and TTNKRRSRSSR ) ., We also found a higher fraction of observed amino acid substitutions within HLA-C epitopes compared with the fraction of amino acid substitutions outside of HLA-C epitopes ( one-sided Wilcoxon signed test , P = 3 . 71 × 10−10 ) ., The opposite behavior was observed for HLA-A and -B presented epitopes ( one-sided Wilcoxon signed , HLA-A: P = 4 . 17 × 10−29; HLA-B: P = 1 . 35 × 10−26 ) ( Fig 4 ) ., This difference in contribution of HLA loci was independent of the transplantation type ( solid organ or hematopoietic ) or the origin of the HLA loci ( whether from the donor or the recipient ) as assessed by a three-way ANOVA ( P = 0 . 7947 ) ., Therefore , our results suggest that HLA-C might be specifically involved in the immune response against BKV through its peptide selection capacity for viral peptides ., A possible mechanistic explanation for this finding stems from the amply documented interaction of HLA-C with natural killer ( NK ) and T cells expressing the killer cell immunoglobulin-like receptors ( KIR ) ., Notably , the relevance of KIR and HLA-C interactions has been described for viral infections 73 , 74 , and the involvement of NK cells in the immune response against BKV has also been reported 75 , 76 , although further investigations should be done to confirm this hypothesis ., High evolutionary rates in RNA viruses allow them to escape immune pressures ., Interactions between HLA epitopes and viruses have been described for a variety of RNA viruses , such as HIV , HCV , influenza or dengue , while little is known about immune escape in DNA viruses ., A few studies in HPV-16 or herpes simplex virus have been done to improve vaccine design and drug development , but those studies have only examined a fraction of the proteins and not at whole-genome sequencing data 77–79 ., This work , to our knowledge , is the first in which predicted epitopes from whole genome sequencing have been studied in an in vivo cohort , in conjunction with cognate HLA alleles , to understand the mechanism involved in immune escape in a DNA virus ., Our results of viral escape combined with the high evolutionary rate described herein suggest that a combination of drugs should be used as potential treatment against BKV , as commonly used in highly variable viruses such as HIV and HCV , due to the variable viral populations present in a single patient as observed in our study ., The present work describes an unusually fast evolutionary rate for BKV in vivo and charts its interaction with the immune system—through the analysis of cognate HLA alleles—whilst considering the whole viral genome and not only candidate epitopes ., It further offers a blueprint for similar analyses in other viruses and helps to better rationalize anti-viral therapy and candidate vaccine development ., Our results suggest that small dsDNA viruses should be treated as RNA viruses due to their similarities in evolution and immune escape ., Thus , a combination of drugs might be necessary for the treatment of BKV , as used for fast evolving RNA viruses ., It is important to note that new analytic methods for the study of the evolutionary rates are needed to better understand the effect of time spans and improve the comparison between estimates ., Ninety-six transplanted patients between 2012 and 2013 from the Strasbourg University Hospitals ( France ) with high levels of post-transplant BKV viruria—as detected by routine BKV testing at the hospital’s clinical virology laboratory—were enrolled in this study ., Sixty-eight patients underwent kidney transplantation , 12 were lung recipients , 3 received double ( kidney-heart; heart-lung or kidney-pancreas ) transplants and 13 hematopoietic stem cell transplantation ., A total of 225 samples , including 197 urine ( from 94 patients ) and 28 whole blood ( from 13 patients ) were included ., Urine samples were collected longitudinally for 36 patients ., All patients were enrolled in the study following the Helsinki guidelines ., Written informed consent for genetic testing was obtained from all patients and the study was approved by the Strasbourg University Hospitals institutional review board ( RNI DC-2013-1990 ) ., Urine and whole blood samples were collected , and DNA was purified using the QIAxtractor instrument ( Qiagen , Hilden , Germany ) , following the DX protocol ., Extracted DNA was stored at -80°C until analysis ., Blood and urine specimens were assessed using the BK virus R-gene quantification kit ( Biomérieux , Lyon , France ) following the manufacturer’s recommendations ., DNA was amplified by Phusion Polymerase ( New England Biolabs , MA , USA ) using specific overlapping primers ., Nested PCR was performed for samples with a low BKV DNA load ( usually blood samples ) ., PCR products were purified using the GeneJET DNA purification Kit ( ThermoFisher Scientific , Waltham , MA , USA ) and quantified with Qubit ( ThermoFisher Scientific , Waltham , MA , USA ) ., Twenty-one urine-blood paired samples were used for sequencing by the Sanger method using an ABI Prism 3130 Genetic Analyzer ( ThermoFisher Scientific , Waltham , MA , USA ) ., Bi-directional sequencing was performed with the Big Dye Terminator v3 . 1 kit ( ThermoFisher Scientific , Waltham , MA , USA ) following the manufacturer’s recommendations ., Chromatograms were analyzed with the Staden package ( 24 ) to obtain the consensus sequence for each sample ., These consensuses were obtained to compare with the results after the next-generation sequencing assembly to validate our pipeline ., All 225 urine and blood samples were sequenced by NGS ., PCR products from the same samples were pooled in equimolar amounts and library construction with barcodes was performed according to the Fragment Library Preparation protocol using the AB Library Builder System ( ThermoFisher Scientific , Waltham , MA , USA ) ., Libraries were quantified by Qubit ( ThermoFisher Scientific , Waltham , MA , USA ) and then pooled in equimolar amounts for Template beads preparation using the SOLiD EZ beads System ( ThermoFisher Scientific , Waltham , MA , USA ) ., Template beads were subjected to sequencing using SOLiD 5500 ( ThermoFisher Scientific , Waltham , MA , USA ) with the paired-end 75 bp / 35 bp workflow ., Sequences were assembled against the Dunlop reference strain ( GenBank accession number NC001538 ) using LifeScope software ( ThermoFisher Scientific , Waltham , MA , USA ) ., Comparison with Sanger sequencing was performed to ascertain the correct assemblies ., To quantify the variability per sample , mutations were analyzed with SeqMan software ( DNASTAR , Madison , Wisconsin , USA ) ., For each sample , we obtained a list of variants with their genomic location , coverage , and quality metrics , among others ., To establish a cutoff for variant calling , we introduced internal controls including, ( a ) a clone from the Dunlop reference strain , pBK ( BKV34-2 ) plasmid ( ATCC 45025 ) prepared by minipreparation ( ThermoFisher Scientific , Waltham , MA , USA ) ;, ( b ) PCR amplicons from the same clone; and, ( c ) PCR amplicons in duplicate from three of the samples ., These controls were processed using the same sequencing methodology to establish the rate of sequencing and PCR errors ., The final list of variants was selected by means of a Fishers exact one-sided test comparing evidence obtained from the data for every potential polymorphism to the estimated error rate using our internal controls ., Based on this analysis , BKV sequence variants found in less than 0 . 5% of reads were removed from the analysis ., Sequences were aligned and assembled against the Dunlop strain by Muscle implemented in MEGA version 6 80 with default parameters in order to compare and determine point mutations , insertions , deletions , and other sequence variations ., For better analysis of coding regions , individual datasets per gene were obtained ., Further analysis of synonymous and non-synonymous substitutions and the Nei-Gojobori test of neutrality were performed with MEGA version 6 80 ., Phylogenetic analyses of the whole genome consensus sequences obtained from all samples , and for each gene separately , were performed using MEGA version 6 80 ., Maximum likelihood phylogenetic trees were constructed with the general time reversible model ( GTR ) of nucleotide substitution with gamma distribution to account for rate heterogeneity among sites , as this model achieved the lowest AIC score ., Similar analyses were performed for 309 BKV complete genome sequences collected from GenBank ( all items found by searching the NCBI nucleotide database for “BK polyomavirus complete genome” ) ., To genotype the populations in the different samples , two approaches were performed ., First , phylogenetic trees with all our samples and one of the reference strains for each genotype and subtype were obtained following the methodology explained previously ., We determined the genotype as the shortest branch distance to one reference ., The second approach was based on the methodology proposed by Luo and colleagues , in which point mutations specifically reported in particular genotypes are described 81 ., To estimate the evolutionary rates of BKV , intra- and inter-patient analyses were performed ., Upon multiple alignment , consensus sequences were tested using RDP software 22 for potential recombination , and those with positive results using at least two different methods implemented in the RDP package were removed from the ensuing analyses ., Samples showing mixtures of genotypes were also excluded since they could interfere with the calculation of the substitution rate ., To estimate the intra-patient substitution rate , we used urine samples from twenty-five patients collected at different times ( the first positive samples and after 6 months ) ., To calculate substitutions per site per year , we considered all the different genomic positions between two different times that were fixed in the populations ., All the substitutions that reverted to the reference base were not included since the possibility of them already being present in the ancestral population at a low frequency could not be ruled out ., Thereby only substitutions appearing de novo and exhibiting a high proportion in the population ( fixed substitutions , more than 80% of the reads ) were included in this approach ., With this methodology , we obtained conservative estimates ., To estimate the inter-patient substitution rate , the consensus sequence for the first available urine sample of each patient with a known date of sampling was selected ., After being tested by RDP , a dataset of 79 BKV sequences was used to estimate the inter-patient evolutionary rate ( sequences from 15 patients were potential recombinants ) ., A maximum likelihood phylogenetic tree was obtained using Phyml 82 with the GTR model with gamma distribution and invariant sites to account for heterogeneity among sites ., This model was determined to be the most appropriate for this dataset with jModeltest 83 ., TempEst analysis was conducted to detect a correlation between genetic divergence and sampling time , and it assured a temporal signal in our inter-patient dataset ( S2 Fig ) 84 ., We used Bayesian estimates of the evolutionary rate with dated tips as implemented in BEAST 85 ., Based on previous results by Firth et al . 5 , we considered three molecular clock models ( strict , relaxed log-normal uncorrelated , and relaxed exponential uncorrelated ) and two demographic models ( constant population size and Bayesian skyline ) ., The GTR model with a gamma distribution and invariant sites was used as the nucleotide substitution model in all combinations ., Model selection was performed through computation of the marginal likelihood using path sampling and stepping stone sampling analyses 86 ., A lognormalPrior with a mean of 1 × 10−6 and a standard deviation of 1 . 0 was used for the substitution rate ., Two independent runs of 30 million steps with 10% burn-in were used to obtain the median and 95% high probability density intervals for the relevant parameters in each model ., In all cases , the effective sample size was > 200 , as checked with Tracer v . 1 . 5 ( available from http://beast . bio . ed . ac . uk ) ., In addition , we used the recently developed method of Volz and Frost which uses maximum likelihood and least squares to estimate evolutionary rates and dates based on relaxed molecular clocks ., The method is implemented in the R package treedater 87 ., To predict BKV-encoded T-cell epitopes that can be presented by HLA alleles , HLA high-resolution typing ( 2 fields ) was done at the Etablissement Français du Sang Grand Est ( Strasbourg ) using a sequence-specific oligonucleotide technology ., High-resolution typing data of HLA-A , -B and -C of 75 available donor / recipient pairs were used in each analysis , using the recipient’s viral populations in each case ( S5 Table ) ., NetMHC 3 . 4 88 was used to predict the peptide binding affinities of potential HLA class I epitopes occurring in BKV Dunlop reference proteins to HLA class I alleles of the patients and donors ., Peptides eliciting a predicted IC50 of less than 50 nM were considered epitopes ., IC50 values represent the concentration of the peptide that will displace 50% of a standard peptide from the HLA molecule ., The lower the IC50 value , the stronger is the affinity of the peptide for the tested HLA molecule ., According to the NetMHC parameters , peptides with IC50 < 50 nM were considered high-affinity binders ., IC50 values of 5 nM and 500 nM were also tested , but a cutoff of 50 nM was chosen as the best indicator ( at a 5 nM threshold not enough peptides were predicted to bind; at 500 nM all possible peptides within a given proteins were predicted to bind ) ., Furthermore , all predicted epitopes were tested with NetChop 3 . 1 89 to predict whether the epitopes could have been produced by the human proteasome using default parameters ., All strong binding peptides with a high likelihood of being correctly cleaved ( score prediction higher than the default threshold of 0 . 5 ) were included in further analyses ., To calculate the fraction of substituted amino acids within and outside of HLA epitopes , the substitutions detected in the specific viral populations of each patient were mapped onto viral reference proteins , and the number of substitutions that occurred within and outside of the predicted epitopes were calculated for each protein and HLA allele of each patient and donor respectively ., The counts were normalized to the number of potentially mutable amino acids per category ( i . e . , within or outside of epitopes ) , to make them comparable across proteins of varying length ., Statistical comparison of the internal and external fractions was performed with a one-sided Wilcoxon signed test for each HLA allele to identify the direction of the difference ., The P-values were Bonferroni corrected to account for multiple testing .

Introduction, Results and discussion, Materials and methods

Infection with human BK polyomavirus , a small double-stranded DNA virus , potentially results in severe complications in immunocompromised patients ., Here , we describe the in vivo variability and evolution of the BK polyomavirus by deep sequencing ., Our data reveal the highest genomic evolutionary rate described in double-stranded DNA viruses , i . e . , 10−3–10−5 substitutions per nucleotide site per year ., High mutation rates in viruses allow their escape from immune surveillance and adaptation to new hosts ., By combining mutational landscapes across viral genomes with in silico prediction of viral peptides , we demonstrate the presence of significantly more coding substitutions within predicted cognate HLA-C-bound viral peptides than outside ., This finding suggests a role for HLA-C in antiviral immunity , perhaps through the action of killer cell immunoglobulin-like receptors ., The present study provides a comprehensive view of viral evolution and immune escape in a DNA virus .

Little is known about the mechanisms of evolution and viral immune escape in double-stranded DNA ( dsDNA ) viruses ., Here , we study the evolution of BK polyomavirus and observe the highest genomic evolutionary rate described so far for a dsDNA virus , in the range of RNA viruses , which usually evolve rapidly ., Furthermore , the prediction of viral peptides to determine immune escape suggests a specific role of HLA-C in antiviral immunity ., These findings are helpful for future advances in antiviral therapies and provide a step forward in our understanding of in vivo viral evolution in humans .

taxonomy, organismal evolution, medicine and health sciences, body fluids, genome evolution, pathology and laboratory medicine, chemical compounds, pathogens, microbiology, organic compounds, viruses, urine, phylogenetics, data management, rna viruses, amino acid substitution, phylogenetic analysis, dna viruses, microbial evolution, amino acids, computer and information sciences, evolutionary rate, proteins, medical microbiology, microbial pathogens, molecular evolution, chemistry, evolutionary systematics, polyomaviruses, viral evolution, biochemistry, anatomy, organic chemistry, evolutionary processes, virology, viral pathogens, genetics, physiology, biology and life sciences, physical sciences, genomics, evolutionary biology, computational biology, organisms

journal.pntd.0000672

A Non Mouse-Adapted Dengue Virus Strain as a New Model of Severe Dengue Infection in AG129 Mice

Dengue ( DEN ) virus belongs to the Flaviviridae family , Flavivirus genus , and is the causative agent of DEN disease , a mosquito-borne illness that is endemic in subtropical and tropical countries 1 ., With approximately half of the worlds population residing in DEN endemic regions 2 and more than 50 million new infections projected to occur annually 3 , DEN certainly poses as a global economic and health threat ., Infection with one of the four DEN serotypes can be asymptomatic or trigger a wide spectrum of clinical manifestations , ranging from mild acute febrile illness to classical dengue fever ( DF ) , and to severe dengue hemorrhagic fever/dengue shock syndrome ( DHF/DSS ) , characterized by fever , hemorrhagic tendency , thrombocytopenia , and capillary leakage according to the WHO guidelines 4 ., Despite the increasing attention and research efforts devoted to DEN in recent years , the cellular and molecular mechanisms responsible for DEN pathogenesis remain largely unknown ., Current hypotheses for the development of severe DEN that involve dysfunction of the host immune system include enhancing mechanisms induced by sub-neutralizing cross-reactive antibodies and memory T cells 3 , 5 ., Other non-enhancing mechanisms implicating the immune system include auto-immune responses against cross-reactive viral components , such as DEN non-structural 1 ( NS1 ) protein 6 , 7 ., Platelet lysis , nitric oxide-mediated apoptosis of endothelial cells and complement activation have also been proposed to mediate thrombocytopenia and vascular leakage 8 ., In addition , host genetic predisposition 9–11 and virus virulence 12 , 13 were reported as risk factors for the development of severe DEN ., No effective drugs or vaccines against DEN are currently available on the market 14 ., Undeniably , progress in deciphering the mechanisms responsible for DEN pathogenesis and in developing effective prophylactic and/or therapeutic treatments has been impeded by the lack of suitable animal models 15 ., Humans and mosquitoes represent so far the only natural hosts for DEN virus ., Non-human primates have been reported to be permissive to DEN infection but no apparent clinical symptoms of the disease were observed 16 , 17 , although a recent study reported signs of hemorrhage in rhesus macaques intravenously infected with a high dose of a DEN2 virus strain 18 ., In addition , since the infected animals develop transient viremia and antibody responses , they have been useful for evaluating the efficacy of vaccine and antiviral candidates prior to clinical trials in humans 19 , 20 ., However , for ethical and economical reasons , non-human primates do not represent a sustainable option for DEN research ., Alternatively , the mouse model has been explored 15 ., However , most of the DEN virus laboratory strains and clinical isolates do not replicate efficiently in mice ., Mouse-adapted DEN virus strains displayed a higher infectivity but led to irrelevant clinical manifestations such as paralysis 21 , 22 ., Alternatively , a variety of mouse genetic backgrounds have been explored that displayed greater susceptibility to DEN infection 23–30 ., Among them , AG129 mice , deficient in interferon ( IFN ) - α/β and -γ receptors , were shown to allow effective replication of DEN virus 30–33 ., However , great heterogeneity in the susceptibility of these mice to DEN virus strains , even within the same serotype , was reported 32 with none or few of DEN disease manifestations 30 ., Moreover , administration of high viral doses was necessary to trigger a virulent phenotype which resulted in the animals death within few days at the peak of viremia 30 ., This is in contrast to humans for whom signs of severe DEN generally occur during or after defervescence when DEN virus is no longer detectable in the patients blood 3 , 34 , 35 ., Here we describe a unique non mouse-adapted strain of DEN virus serotype 2 ( D2Y98P ) which is highly infectious in AG129 mice upon intraperitoneal administration ., Infection with a high viral dose of D2Y98P resulted in an acute model of infection with mice dying at the peak of viremia , whereas infection with a low viral dose led to asymptomatic dissemination and replication of the virus followed by death of the animals after the virus has been cleared from its host ., All the animal experiments were carried out under the guidelines of the National University of Singapore animal study board ., The virus strain used in this study ( D2Y98P ) derives from a 1998 DEN2 Singapore human isolate that has been exclusively passaged for about 20 rounds in Aedes albopictus C6/36 cells ., C6/36 cells ( ATCC# CRL-1660 ) were maintained in Leibovitzs L-15 medium ( GIBCO ) supplemented with 5% fetal calf serum ( FCS ) , and virus propagation was carried out as described previously 32 ., Virus stocks were stored −80°C ., When necessary , heat-inactivation of the virus was performed at 55°C for 15 min ., Plaque assay was carried out to quantify the number of infectious viral particles using BHK-21 ( Baby Hamster Kidney , ATCC# CCL-10 ) cells as described previously 36 with slight modifications ., Briefly , BHK cells were cultured to approx ., 80% confluency in 24-well plates ( NUNC , NY , USA ) ., The virus stock was 10-fold serially diluted from 10−1 to 10−8 in RPMI 1640 ( GIBCO ) ., BHK-21 monolayers were infected with 100 ul of each virus dilution ., After incubation at 37°C and 5% C02 atmosphere for 1 hr with rocking at 15 min intervals , the medium was decanted and 1 ml of 1% ( w/v ) carboxymethyl cellulose in RPMI supplemented with 2% FCS was added to each well ., After 4 days incubation at 37°C in 5% CO2 , the cells were fixed with 4% paraformaldehyde and stained for 30 min with 200 µl of 1% crystal violet dissolved in 37% formaldehyde ., After thorough rinsing with water , the plates were dried and the plaques were scored visually ., AG129 129/Sv mice deficient in both alpha/beta ( IFN-α/β ) and gamma ( IFN-γ ) interferon receptors were obtained from B&K Universal ( UK ) ., They were housed under specific pathogen-free conditions in individual ventilated cages ., Eight to 9 week-old mice were administered with 107 to 102 plaque forming units ( PFU ) of D2Y98P via the intraperitoneal ( ip . ) route ( 0 . 4 ml in sterile PBS ) ., Where indicated , mice were inoculated with the same dose and volume of heat-inactivated D2Y98P ., Systemic antibody titres against D2Y98P were determined by enzyme-linked immunoadsorbent assay ( ELISA ) as described previously 32 ., Briefly , 96-well plates ( Corning costar , NY , USA ) were coated overnight at 4°C with 105 PFU of heat-inactivated D2Y98P virus in 0 . 1M NaHCO3 buffer at pH 9 . 6 ., Two-fold serially diluted serum samples ( 1∶25 to 1∶25 , 600 ) were added to the wells and incubated for 1 hr at 37°C ., HRP-conjugated anti-mouse IgM ( Chemicon ) or IgG ( H+L ) ( Bio-rad ) secondary antibody were used at a 1∶3 , 000 dilution ., Detection was performed using SigmaFast™ O-phenylenediamine dihydrochloride substrate ( Sigma Aldrich ) according to the manufacturers instructions ., The reaction was stopped with 75 µl of 1M H2SO4 and absorbance was read at 490 nm using an ELISA plate reader ( Bio-rad model 680 ) ., ELISA titres were defined as the reciprocal of the highest serum dilution that equals to 3 times the absorbance reading from uninfected mouse serum sample ., PRNT was carried out as described previously 36 with modifications ., Briefly , mouse serum samples were heated at 56°C for 30 min to inactivate complement ., Two-fold serial dilutions of the sera ( 1∶10 to 1∶10 , 240 in RPMI 1640 ) were mixed in 96-well plates with an equal volume containing 30 PFU of D2Y98P , and incubated at 37°C for 1 hr with rocking every 15 min ., Each mix ( 100 µl ) was transferred onto BHK monolayers grown in 24-well plates , and incubated at 37°C for 1 hr ., The mix was decanted , and plaque assay was carried out as described above ., The percentage of plaque reduction was derived relative to the control consisting of virus mixed with uninfected serum: 1- ( number of plaques in test wells/number of plaques in control wells ) *100 ., Fifty percent neutralization titres ( PRNT50 ) were determined for each sample by fitting a variable sigmoidal curve in GraphPad Prism 5 . 00 ( GraphPad Software ) ., Data are expressed as the reciprocal of the highest serum dilution for which PRNT50 is obtained ., Blood samples were collected in 0 . 4% sodium citrate and centrifuged for 5 min at 6 , 000 g to obtain plasma ., The presence of infectious viral particles was determined by plaque assay as described above ., To assess the levels of infectious virus in the tissues from infected mice , the animals were euthanized and perfused systemically with 50 ml sterile PBS ., Whole tissue from the brain , intestines , liver and spleen were harvested from individual mice , kept on ice and their wet weights were recorded prior to any further processing ., Samples were then trimmed and homogenized using a mechanical homogenizer ( Omni ) for 5 minutes in 1 ml RPMI 1640 at medium speed on ice ., Thoroughly homogenized tissues were clarified by centrifugation at 14 , 000 rpm for 10 min at 4°C to pellet debris ., The supernatant was filter-sterilized using a 0 . 22 µm diameter pore size filter and the volume was recorded ., The level of infectious virus within the filtrate is thus considered representative of the total level of infectious virus present in the harvested organ ., Ten-fold serial dilutions of each filtrate ( from neat to 1∶105 ) were assayed in a standard virus plaque assay on BHK-21 cells as described above ., Triplicate wells were run for each dilution of each sample ., Data are finally expressed as log10 mean ± SD in PFU per gram of wet tissue with a limit of sensitivity set at 1 . 0 log10 PFU/g of tissue ., Five mice per time point per group were assessed ., Results are representative of two experiments ., Mice were euthanized , and tissues were harvested and immediately fixed in 10% formalin in PBS ., Fixed tissues were paraffin embedded , sectioned and stained with Hematoxylin and Eosin ( H&E ) ., Vascular leakage was assessed using Evans Blue dye as a marker for albumin extravasation as described previously 30 , 37 with modifications ., Briefly , 0 . 2 ml of Evans blue dye ( 0 . 5% w/v in PBS ) ( Sigma Aldrich ) were injected intravenously into the mice ., After 2 hrs , the animals were euthanized and extensively perfused with sterile PBS ., Vascular permeability in the tissues was determined visually and quantitatively; the tissues were harvested and weighed prior to dye extraction using N , N-dimethylformamide ( Sigma; 4 ml/g of tissue wet weight ) at 37°C for 24 hrs after which absorbance was read at 620 nm ., Data are expressed as fold increase in OD620nm per g of tissue wet weight compared to the uninfected control ., Cytokine ( IFN-γ , TNF-α and IL-6 ) expression levels were measured in individual serum samples using individual detection kits ( R&D ) , according to the manufacturer instructions ., After incubation with detection antibodies and streptavidin-PE complexes , absorbance was read at 450 nm ., Five mice per group and per time point were used ., Mouse blood samples were collected in K2EDTA and serum tubes ( Biomed Diagnostics ) ., Whole blood was immediately analysed for cell counts using automated hematology analyzer Cell Dyn – 3700 ( Abbott ) ., Serum alanine ( ALT ) and aspartate ( AST ) aminotransferases , and albumin levels were quantified using chemistry analyzer COBAS C111 ( ROCHE ) ., The results were analyzed using the unpaired Student t test ., Differences were considered significant ( * ) at p value <0 . 05 ., To test the infectious potential of the D2Y98P strain , AG129 mice were intraperitoneally ( ip . ) infected with 10-fold serially diluted viral doses ranging from 107 to 102 PFU ., Survival rates indicated that infection with 104 PFU and above induced 100% mortality whereas 20% and 90% survival rates were observed in animals infected with 103 and 102 PFU , respectively ( Fig . 1 ) ., Moreover , in mice infected with lethal doses , a clear correlation between viral dose and time-of-death was observed , with increased heterogeneity as the infectious dose is lower ., Upon infection with 107 and 106 PFU , initial clinical signs included ruffled fur and hunched posture , which further progressed to bloatedness , lethargy , diarrhoea-like symptoms , moribund state and finally death of the animals ., None of the mice exhibited paralysis or significant body weight loss during the course of infection ( Fig . 2A ) ., In contrast , upon infection with 105 PFU and below , no signs of diarrhoea were observed and near moribund state , rapid body weight loss was measured ( Fig . 2B ) ., Mice ip ., inoculated with heat-inactivated D2Y98P ( 107 PFU equivalent ) displayed none of the disease manifestations or death ., In addition , neither disease manifestation nor transient viremia was observed in immunocompetent Balb/c and C57Bl/6 mice ip ., infected with 107 PFU of D2Y98P ( data not shown ) ., Although both viral doses eventually induced 100% mortality in AG129 mice , ip ., infection with 107 and 104 PFU of D2Y98P gave very different disease kinetics , suggesting that different mechanisms and players are involved in the disease progression ., We thus decided to further characterize both the “acute” and “delayed” models of DEN infection ., Systemic virus titres were monitored over the course of infection for both viral doses ., In mice infected with 107 PFU , the peak of viremia ( 105 PFU/ml ) coincided with the animals death at 5 days p . i . ( Fig . 3A ) ., In contrast , in mice infected with 104 PFU , viremia peaked at around 104 PFU/ml at 6 days p . i . , followed by viral clearance from the blood circulation prior to animal death ( Fig . 3B ) , similar to the disease kinetic described in severe DEN patients 3 , 34 , 35 , 38 ., Furthermore , specific IgM and IgG antibody titres were monitored over the course of infection ., Significant IgM but weak IgG responses were measured in mice infected with 107 PFU which both peaked at the time of death , 5 days p . i . ( Fig . 3C ) ., Instead , in mice infected with 104 PFU , significant IgG antibody titers were detected which progressively increased over time , while the IgM antibody response peaked at day 10 p . i . and waned by day 18 p . i . ( Fig . 3D ) ., Neutralizing antibody titres correlated with the IgG antibody responses ( Fig . 3E&F ) ., Gross pathological examination of the organs within the intraperitoneal cavity from moribund animals infected with 107 PFU of D2Y98P revealed overt abnormalities that included a severely distended stomach , a significantly enlarged spleen and focal areas of haemorrhage in the liver , observable after systemic perfusion of the mice with saline ( Fig . 4A ) ., These features were not observed in moribund animals infected with 104 PFU ( data not shown ) ., Tissue tropism and kinetic of viral replication were determined in the intestines , liver , spleen , and brain from animals infected with either 107 or 104 PFU of D2Y98P ., No infectious viral particles were detected in the intestines ., In the spleen , liver and brain , the kinetic of the virus titers corresponded to the viremia profile; in animals infected with 107 PFU , virus titres in the infected organs increased logarithmically in conjunction with disease advancement , reaching their highest at the time of death ( Fig . 4B-D ) ., Instead , in animals infected with 104 PFU , the virus titres peaked at 5 or 6 days p . i . in the liver , spleen and brain , and progressively dropped until complete clearance by day 8 p . i . ( Fig . 4B-D ) ., Interestingly , the peak of virus titres achieved in the liver and spleen was comparable in both animal groups whereas peak titres in the brain ( Fig . 4D ) and plasma ( Fig . 3A&B ) were about 1 log higher in mice infected with 107 PFU ., Brain , spleen , liver and intestines were harvested from mice infected with 107 or 104 PFU of D2Y98P over the course of infection ., Histological examination of H&E stained-sections from animals infected with 107 PFU revealed progressive damage at both tissue and cellular levels which culminated at the time of death ( Fig . 5A ) ., The well defined limits of the splenic red and white pulp began to blur by day 3 p . i . ( data not shown ) and the spleen architecture was completely lost by day 5 p . i . ( Fig . 5A ) ., A larger magnification revealed the presence of apoptotic debris ., The liver displayed focal areas of haemorrhage and edema of cell masses ., Lymphoid aggregates and inflammatory infiltrates were also detected at the portal tract and within the sinusoidal spaces of the liver ( data not shown ) ., At the cellular level , extensive cytopathic effects that included hepatocyte swelling , cytoplasmic vacuolation and degeneration were observed ., Liver damage was reflected by the significantly increased levels of aspartate ( ALT ) and alanine ( AST ) transaminases measured in the serum of the infected animals ( Fig . 5B ) ., Interestingly , despite the absence of detectable virus particles in the intestines , these tissues displayed marked infiltration of inflammatory cells and extensive architectural distortion at moribund state ( Fig . 5A ) ., Severe detachment and disintegration of the intestinal villi resulting in a debris-filled intestinal lumen was noted ., In animals infected with 104 PFU of D2Y98P , no visible organ damage was noticeable at the peak of viremia , 6 days p . i . ( Fig . 5A ) ., However , at moribund state , the splenic architecture was severely impaired to an extent comparable to that observed in animals infected with 107 PFU ., In contrast , the liver and intestines were moderately affected with only localized areas of visible damage ., Moderate but significant increase in the systemic levels of ALT and AST was measured at moribund state ( Fig . 5B ) , indicative of some liver dysfunction ., Apart from slight vascular congestion , brain sections from both animal groups did not display any significant pathological changes at any time post-infection ( Fig . 5A ) ., Vascular leakage , a hallmark of severe DEN infection in humans , was investigated in D2Y98P-infected AG129 mice using Evans blue dye extrusion assay 30 , 37 ., At moribund state , severe vascular leakage was observed ( Fig . 6A ) and measured ( Fig . 6B ) in the spleen , liver and intestines from animals infected with 107 PFU compared to uninfected controls ., Consistently , significant decreased levels in serum albumin were measured in these infected animals , indicative of plasmatic proteins leakage ( Fig . 6C ) ., In animals infected with 104 PFU , marginal dye extrusion was observed in the liver , intestines and spleen at the peak of viremia ( 6 days p . i . ) whereas at moribund state , dye extrusion was markedly increased in all the organs examined ( Fig . 6A&B ) ., The extent of leakage in the liver and intestines was lesser than that observed in mice infected with 107 PFU , whereas dye extrusion in the spleen was as high as in the animals infected with 107 PFU ( Fig . 6B ) ., Interestingly , and in contrast to animals infected with 107 PFU , serum albumin concentration measured in animals infected with 104 PFU was significantly higher than that measured in uninfected control animals ( Fig . 6C ) , suggestive of hemoconcentration ., Enhanced cytokine production may lead to increased vascular permeability and has been proposed to contribute to DHF/DSS pathogenesis 39 , 40 ., The expression profile of three key pro-inflammatory cytokines , namely IFN-γ , IL-6 and TNF-α , was monitored over the course of infection in the serum of animals infected with 107 or 104 PFU of D2Y98P ., In animals infected with 107 PFU , the cytokine expression levels increased consistently over time and peaked at the time of death of the animals ( Fig . 7 ) ., In contrast , in animals infected with 104 PFU , the production of these pro-inflammatory cytokines corresponded to the viremia profile , peaking at day 6 p . i . , followed by a progressive decline to reach basal production levels at moribund stage ( Fig . 7 ) ., Of note , peak values of the systemic levels of these three cytokines were significantly higher in animals infected with 107 PFU compared to animals infected with 104 PFU ., Hematological disorders have been associated with DEN disease and tentatively used as diagnostic and prognostic markers 41 , 42 ., Total counts of red blood cells ( RBC ) , white blood cells ( WBC ) , lymphocytes , platelets and neutrophils were monitored in D2Y98P-infected mice over the course of infection ( Table 1 ) ., In animals infected with 107 PFU , significant increase in RBC concentration and hematocrit was measured at day 3 p . i . compared to uninfected controls , indicative of hemoconcentration ., At moribund state however ( day 5 p . i . ) , the levels of RBC and hematocrit dropped , suggestive of hemorrhage ., However , the levels of WBC , neutrophils and platelets increased substantially over time ., Transient depletion in lymphocyte counts was observed at day 3 p . i . followed by significant increase at day 5 p . i . In animals infected with 104 PFU , progressive increase in RBC counts and hematocrit was observed over the course of infection , indicative of hemoconcentration ., WBC , neutrophils , and platelets levels similarly increased progressively and reached peak values at 10 days p . i . At moribund state however , the levels measured were comparable to those measured in uninfected controls ., Transient lymphopenia was observed at the peak of viremia ( day 6 p . i . ) followed by a very significant increase at day 10 p . i . Basal lymphocytes level was measured at moribund state ., Altogether , the hematological parameters indicate that infection with 107 PFU of D2Y98P led to haemorrhage tendency , whereas infection with 104 PFU resulted in hemoconcentration ., Remarkably , no evidence of thrombocytopenia was observed in the infected animals as reflected by the platelets counts which were not found statistically different from the uninfected controls ., A growing number of immunocompetent , immunosuppressed and humanized mouse models of DEN infection have been explored , using an increasing number of mouse-adapted or cell-culture passaged DEN virus strains ., However , none of these have so far managed to recapitulate all the clinical symptoms and manifestations of DEN disease as observed in humans ., As humans and mosquitoes represent the only two natural hosts for DEN virus , it is unrealistic to hope address all the features of DEN pathogenesis in a single mouse model ., However , previous studies have shown that it is possible to reproduce , and thus study , one or few aspects of DEN pathogenesis in a specific mouse model of DEN infection defined by a particular mouse background infected with a specific DEN virus strain through a particular route of administration and at a particular infectious dose ., For example , a mouse model of DEN hemorrhage has recently been reported through intradermal infection of immunocompetent mice with a high dose of the non-mouse adapted DEN2 virus strain 16681 originally isolated from a DHF patient 43 , 44 ., Likewise , a humanized mouse strain infected subcutaneously with various DEN virus strains reportedly displayed clinical signs of DEN fever , including fever , viremia , erythema , and thrombocytopenia 45 ., Similarly , the AG129 mouse model has allowed the investigation of some aspects of DEN pathogenesis including virus tropism , vascular leakage , and pathogenesis in context of a functional adaptive immune system 33 ., Furthermore , the AG129 mouse background has proven useful for vaccine and drug testing 31 , 32 ., However , the lack of IFN α/β− and γ−signalling draws some limitations and calls for cautious interpretation of the findings and observations made in this mouse model ., Furthermore , the susceptibility of AG129 mice to DEN infection appears to greatly depend on the DEN virus strain 32 and a limited number only have so far been reported to result in a productive infection with no , few or irrelevant clinical manifestations 30 , 32 ., Moreover , administration of high viral doses was necessary to trigger a virulent phenotype which resulted in animal death within few days at the peak of viremia 30 ., Here we describe a non mouse-adapted DEN virus strain , D2Y98P , which is highly infectious in AG129 mice ., D2Y98P is a serotype 2 DEN virus strain originally isolated in 1998 from a Singapore DEN-infected patient whose disease status at the time of sample collection , and disease outcome are unfortunately not known ., The virus has been exclusively amplified in mosquito cells for less than 20 rounds ., Interestingly , an earlier passage ( P13 ) displayed a more attenuated virulent phenotype upon infection of AG129 mice ( G . Tan , personal communication ) ., This observation therefore suggests that mutation ( s ) have occurred in the viral genome upon amplification in mosquito cells that rendered the virus more virulent ., Identification of the nucleotide changes between the two virus passages is currently in progress in our laboratory ., Infection of AG129 mice with a high dose ( 107 PFU ) of D2Y98P induced an acute lethal DEN infection where the peak of viremia and virus titres in the infected organs coincided with death of the animals , accompanied by cytokine storm , massive organ damage , and severe vascular damage leading to haemorrhage ., It is thus likely that in this acute model of DEN infection , the pathological events are a consequence of both virus-induced cell death and massive inflammation reaction 39 , 40 ., Such virulent phenotype is similar to that described previously by Shresta and colleagues using the D2S10 DEN virus strain 30 ., In contrast , infection of AG129 mice with a lower dose ( 104 PFU ) of D2Y98P led to a transient asymptomatic systemic viral infection followed by death of the animals few days after viral clearance , similar to the disease kinetic described in humans 3 , 38 ., A strong neutralizing IgG antibody response was measured in the infected animals and is likely to be involved in the viral clearance ., Although increased vascular permeability ( as indicated by increased serum albumin concentration and Evans blue dye extrusion ) was observed in the moribund animals , the actual cause of the animals death remains elusive ., Apparent destruction of the splenic architecture and liver dysfunction at moribund stage are likely to contribute to the sickness ., Furthermore , as the disease progressed , infected animals appeared lethargic and displayed reduced motility ., This may result in reduced water intake and dehydration of the animal , hence contributing to the sharp body weight loss observed towards moribund stage and consequently leading to animal death ., Widespread immune activation in response to acute DEN infection has been well documented in DEN patients , and circulating levels of various pro-inflammatory cytokines were found to be elevated in patients with severe DEN 40 ., Likewise , the levels of three key pro-inflammatory cytokines implicated in DF/DHF , namely IL-6 , TNF-α and IFN-γ , were significantly elevated in the D2Y98P-infected AG129 mice and were directly dependent on the initial infectious dose ., Consistently , extensive damage of various organs including the spleen , liver and intestines was observed in animals infected with a high viral dose ( 107 PFU ) ., In contrast , lower levels of cytokine production in animals infected with a low viral dose ( 104 PFU ) correlated with milder organ damage except for the spleen that appeared at moribund stage , to be as extensively damaged as in animals infected with a high viral dose; the absence of infectious viral particles in the moribund animals excludes a direct virus cytopathic effect but rather suggests some immunological disorder that may arise from the overstimulation of immune cells possibly by persistent viral antigens ., In contrast to the liver and spleen , no histological damage or abnormalities were detected in the brain of animals infected with 107 PFU or 104 PFU , although infectious viral particles were readily detected in this tissue after systemic perfusion ., This observation suggests that the virus is capable of extravasating from the systemic circulation and cross the blood-brain barrier but may not effectively replicate in the brain ., Therefore , in this mouse model , and as reported in dengue patients 46 , 47 , meningitis and/or encephalitis may not contribute significantly to disease severity ., The action of a variety of cytokines , chemokines , and other soluble mediators on endothelial cells has been proposed to affect vascular permeability during DEN infection 39 ., Vascular leakage is a hallmark of DHF/DSS leading to hemoconcentration and hemorrhagic manifestations 41 , 48 , as observed in mice infected with 107 PFU of D2Y98P for whom focal areas of haemorrhage were observed in the liver , and low hematocrit and serum albumin levels were measured ., In this animal group , high levels of pro-inflammatory cytokines are likely responsible for the observed severe vascular leakage , particularly in the intestines where no infectious viral particles were detected ., However , in mice infected with 104 PFU , neither significant vascular leakage nor hemorrhage was detected at the peak of viremia despite elevated levels of IFN-γ , IL-6 and TNF-α ., Instead , increased vascular permeability was clearly observed at moribund stage where the production of these three cytokines has returned to basal level ., This observation suggests that other pro-inflammatory cytokines may be involved in the increased vascular permeability observed in this low viral dose infection model ., Indeed , in addition to IFN-γ , IL-6 and TNF-α , a number of cytokines , chemokines and other soluble mediators have been demonstrated or proposed to play a role in vascular leakage in DEN disease 39 ., Alternatively or additionally , other mediators previously proposed to increase vascular permeability such as immune complexes 49 , nitrite oxide production 39 , or cross-reactive anti-NS1 antibodies 6 , 7 , may be at play ., Furthermore , hemoconcentration and increased serum albumin level suggests that fluid only but not proteins or cells , leaks from the blood vessels ., Increased vascular permeability without morphological damage of the capillary endothelium is believed to be the cardinal feature of DSS 39 , 49 and thus appears to be reproduced in this mouse model of DEN infection ., Further investigation is however needed to decipher the actual mechanisms underlying this phenomenon ., Remarkably , thrombocytopenia , a hallmark of severe disease in DEN patients , was not detected in the animals infected with D2Y98P virus , regardless of the initial infectious dose ., Transient drop in platelet counts has been previously observed in a number of mouse models of DEN infection 15 including AG129 33 , ruling out the possibility that the lack of IFNγ signalling in these mice would impair the mechanism ( s ) involved in thrombocytopenia ., The absence of thrombocytopenia in our model may thus be inherent to the D2Y98P virus strain ., A number of immunological mechanisms and effectors have been proposed to play a role in thrombocytopenia during DEN infection 50–53 , but the differential ability of DEN virus strains to induce thrombocytopenia in a single model of DEN infection has never been investigated ., In conclusion , the attractiveness of the D2Y98P strain lies in its ability to induce , without the need for mouse-adaptation and upon peripheral administration of a low viral dose , a virulent phenotype in AG129 mice with a productive viral replication and dissemination accompanied by some relevant clinical manifestations , including disease kinetic , organ damage/dysfunction and increased vascular permeability ., T

Introduction, Materials and Methods, Results, Discussion

The spread of dengue ( DEN ) worldwide combined with an increased severity of the DEN-associated clinical outcomes have made this mosquito-borne virus of great global public health importance ., Progress in understanding DEN pathogenesis and in developing effective treatments has been hampered by the lack of a suitable small animal model ., Most of the DEN clinical isolates and cell culture-passaged DEN virus strains reported so far require either host adaptation , inoculation with a high dose and/or intravenous administration to elicit a virulent phenotype in mice which results , at best , in a productive infection with no , few , or irrelevant disease manifestations , and with mice dying within few days at the peak of viremia ., Here we describe a non-mouse-adapted DEN2 virus strain ( D2Y98P ) that is highly infectious in AG129 mice ( lacking interferon-α/β and -γ receptors ) upon intraperitoneal administration ., Infection with a high dose of D2Y98P induced cytokine storm , massive organ damage , and severe vascular leakage , leading to haemorrhage and rapid death of the animals at the peak of viremia ., In contrast , very interestingly and uniquely , infection with a low dose of D2Y98P led to asymptomatic viral dissemination and replication in relevant organs , followed by non-paralytic death of the animals few days after virus clearance , similar to the disease kinetic in humans ., Spleen damage , liver dysfunction and increased vascular permeability , but no haemorrhage , were observed in moribund animals , suggesting intact vascular integrity , a cardinal feature in DEN shock syndrome ., Infection with D2Y98P thus offers the opportunity to further decipher some of the aspects of dengue pathogenesis and provides a new platform for drug and vaccine testing .

The spread of dengue ( DEN ) worldwide combined with an increased severity of the DEN-associated clinical outcomes have made this mosquito-borne virus of great global public health importance ., Infection with DEN virus can be asymptomatic or trigger a wide spectrum of clinical manifestations , ranging from mild acute febrile illness to classical dengue fever and to severe DEN hemorrhagic fever/DEN shock syndrome ( DHF/DSS ) ., Progress in understanding DEN disease and in developing effective treatments has been hampered by the lack of a suitable animal model that can reproduce all or part of the diseases clinical manifestations and outcome ., Only a few of the DEN virus strains reported so far elicit a virulent phenotype in mice , which results at best in an acute infection where mice die within few days with no , few or irrelevant disease manifestations ., Here we describe a DEN virus strain which is highly virulent in mice and reproduces some of the aspects of severe DEN in humans , including the disease kinetics , organ damage/dysfunction and increased vascular permeability ., This DEN virus strain thus offers the opportunity to further decipher some of the mechanisms involved in DEN pathogenesis , and provides a new platform for drug and vaccine testing in the mouse model .

infectious diseases/neglected tropical diseases, infectious diseases/viral infections, virology/animal models of infection

journal.pntd.0002854

High Prevalence and Spatial Distribution of Strongyloides stercoralis in Rural Cambodia

Strongyloides stercoralis , a soil-transmitted nematode , is a neglected tropical helminthiasis 1 , 2 and endemic in tropical , subtropical and temperate settings where sanitary and hygiene conditions are poor 3 , 4 ., However , the worldwide prevalence of S . stercoralis is heterogeneously distributed 2 and the current estimation of infection remains underestimated due to the use of inadequate diagnostic method 5 ., The available information about S . stercoralis infection in developing countries mostly comes from studies in Brazil and Thailand 2 ., The gastrointestinal symptoms of the disease include diarrhea and abdominal pain , while dermatological symptoms include itching , rash ( urticaria ) and migrating larvae in the skin ( larva currens ) 6–9 ., However , more than 50% of all infections remain asymptomatic 4 ., Due to its particular ability for autoinfection , S . stercoralis is the only soil-transmitted helminth ( STH ) that can lead to systemic infection with high parasite densities and severe to potentially fatal complications , especially in immunosuppressed hosts 3 , 7 , 10 ., Ivermectin is recommended as the most effective treatment 11 ., The presence of S . stercoralis larvae in stool specimens is proof of infection 12 ., Koga-agar plate ( KAP ) culture 13 and the Baermann method 14 are specific diagnostic methods for strongyloidiasis ., However , their sensitivity is not satisfactory when testing a single stool sample in cases of chronic , uncomplicated strongyloidiasis 15–18 ., In Cambodia , data from several cross-sectional studies in community and hospital settings revealed S . stercoralis prevalences between 2 . 6% and 31 . 5% ., However , in all but three studies , a diagnostic approach with low sensitivity was used on a single stool sample 19–22 ., Three recent studies used a combined diagnostic approach ( KAP culture and Baermann technique ) on two 9 , 23 and three stool samples 16 ., We aimed to determine the prevalence , risk factors and spatial distribution of S . stercoralis infection in Preah Vihear province ., We conducted a cross-sectional study of S . stercoralis infection , using KAP culture and the Baermann method on two stool samples from each participant in 60 villages of Preah Vihear province , northern Cambodia ., The research was approved by the Ethics Committee of the Cantons of Basel-Stadt and Baselland ( EKBB , #16/10 , dated 1 February 2010 ) , Switzerland , and by the National Ethics Committee for Health Research , Ministry of Health , Cambodia ( NECHR , #004 , dated 5 February 2010 ) ., Written informed consent was obtained from each participant prior to the start of the study ., For participants between the ages of 1 and 18 years , written informed consent was obtained from the parents , legal guardian or appropriate literate substitute ., All participants were informed of the studys purpose and procedures prior to enrolment ., All participants infected with S . stercoralis were treated with a single oral dose of ivermectin ( 200 µg/kg BW ) 24 ., All other parasitic infections were treated according to the guidelines of the National Helminth Control Program of Cambodia 25 ., The study was conducted in 60 rural villages of Preah Vihear province , Northern Cambodia ( Figure 1 ) ., The villages were randomly selected from a list of all villages in six of the seven districts in Preah Vihear province ( total number of villages: 184 ) ., The district of Chhaeb was not included as most villages in this district are difficult to access by car , which was necessary to ensure the rapid transfer ( three hours by car ) of stool samples to one of the two temporary laboratories established in the health centers of Kulen and Rovieng districts ., A cross-sectional study was carried out from February to June 2010 among all the population living in 60 villages ., Fifteen households were randomly selected from the list of all households in the selected villages ., All household members one year of age and older were eligible for inclusion in the study and all household members present on the day of the survey were enrolled ., After obtaining written informed consent from participants , an individual questionnaire was administered to obtain demographic information ( age , gender , educational level and profession ) , personal risk-perception ( knowledge about worm infections ) , and behavioral data ( personal hygiene practices , wearing shoes , and latrine use ) ., The head of household was interviewed , based on a household questionnaire , about socioeconomic indicators such as house type , household assets , latrine and livestock ., All questionnaires were pre-tested ., After the interview , each participant was given a pre-labeled plastic container ( ID code , name , sex , age and date ) for stool sample collection ., The next morning , the filled stool container was collected and a second empty , pre-labeled one was handed out for a second stool sample of the following day ., Stool samples were transported at ambient temperature and arrived at the laboratory within three hours of collection ., Laboratory technicians from the National Center for Parasitology , Entomology and Malaria Control ( CNM ) , Phnom Penh , processed the stool specimens in one of two laboratories established in Kulen and Rovieng health centers , respectively ., First , a single Kato-Katz thick smear 26 was prepared using the WHO standard template and examined under a light microscope to detect helminth eggs ., Eggs were counted and recorded for each helminth species separately ., Second , KAP culture 13 was used to detect S . stercoralis larvae ., A hazelnut-sized stool sample was placed in the middle of the agar plate and the closed Petri dish was incubated in a humid chamber for 48 hours at 28°C ., Afterwards , the plates were visually examined for the presence of larval tracks ., The plates were then rinsed with sodium acetate-acetic acid-formalin ( SAF ) solution ., The eluent was centrifuged and the sediment was examined under a microscope for the presence of larvae ., Based on morphology , larvae were identified ( i . e . , size of buccal cavity , presence of genital primordium ( L1 ) , presence of forked tail-end ( L3 ) ) as either S . stercoralis or hookworm larvae ., Finally , the Baermann technique 14 was performed to detect S . stercoralis larvae ., A walnut-sized stool sample was placed on gauze inserted into a glass funnel and covered with water ., The apparatus was exposed for two hours to artificial light directed from below ., After centrifuging the collected liquid , the sediment was examined under a microscope for the presence of S . stercoralis larvae ., For quality control , the technicians were specifically trained on the morphological criteria for distinguishing hookworm and S . stercoralis larvae ., Throughout the study period , technicians were rigorously supervised by a qualified microscopist from the Swiss Tropical and Public Health Institute ( Swiss TPH ) , Basel , Switzerland ., Any unclear diagnosis was immediately discussed with both the qualified microscopist and the study supervisor ., Day and night land surface temperature ( LST ) , enhanced vegetation index ( EVI ) and land use/land cover ( LULC ) were extracted at 1×1 km resolution from Moderate Resolution Imaging Spectroradiometer ( MODIS ) Land Processes Distributed Active Archive Center ( LP DAAC ) , U . S . Geological Survey ( USGS ) Earth Resources Observation and Science ( EROS ) Center ( http://lpdaac . usgs . gov ) ., Rainfall estimates ( RFE ) at 0 . 1 degree ( about 10×11 km ) resolution were obtained from the National Oceanic and Atmospheric Administrations ( NOAA ) Climate Prediction Center ( CPC ) Famine Early Warning System ( FEWS ) Rainfall Estimates South Asia , version 2 . 0 ( http://www . cpc . ncep . noaa . gov/products/fews/SASIA/rfe . shtml ) ., Digital elevation data at a resolution of 90×90 m were retrieved from the NASA Shuttle Radar Topographic Missions ( SRTM ) Consortium for Spatial Information of the Consultative Group for International Agricultural Research ( CGIAR-CSI ) database ., Soil type data at a spatial resolution of 9×9 km , including bulk density , soil organic carbon content and pH , was extracted from the International Soil Reference and Information Centers ( ISRIC ) World Inventory Soil Emission Potentials ( WISE ) , version 1 . 0 ( http://www . isric . org ) ., The 18 land cover type 1 classes ( IGBP ) were merged into five categories according to similarity and respective frequencies ., Yearly means , as well as minima and maxima of EVI , monthly LST and RFE were calculated for May 2009 to April 2010 ., Overall , 3 , 560 individuals from 616 households ( average household size: 5; range: 1–12 ) were enrolled , of which 2 , 748 ( 77 . 2% ) participants submitted two stool samples ., The final analysis included 2 , 396 ( 67 . 3% ) participants with complete data records , i . e . , two stool specimens examined with all diagnostic tests and all questionnaires completed ., The median age of the participants was 20 years , with a range from 1 to 85 years ., One thousand three hundred and fifty-five ( 56 . 5% ) participants were females ., Half of the participants ( 48 . 5% ) were farmers and 33 . 0% were pupils ., The majority of participants ( 58 . 3% ) had attended primary school; one third ( 32 . 2% ) had not received primary education ., Seven intestinal parasite species were found in the stool samples ., Hookworm and S . stercoralis were most common , with a prevalence of 46 . 7% and 44 . 7% , respectively ., Taenia sp ., was found in 0 . 4% of participants , while Hymenolepis nana and Enterobius vermicularis were observed in 0 . 2% and 0 . 1% of participants , respectively ., Both Ascaris lumbricoides and Trichuris trichiura were observed in 0 . 3% of participants ., Of the 1 , 071 S . stercoralis cases , 642 ( 59 . 9% ) were co-infected with hookworm ., Table 1 summarizes the results of KAP culture and Baermann tests for the 1 , 071 S . stercoralis cases ( 44 . 7% ) detected ., KAP culture and the Baermann technique detected 877 and 823 cases , respectively ., The total of all positive cases diagnosed by any of the two methods was considered the “diagnostic gold standard” ., The sensitivity of the KAP culture was 81 . 9% , and that of the Baermann technique , 76 . 8% ., The negative predictive values were 87 . 2% and 84 . 2% , while the positive predictive values were 81 . 8% and 76 . 8% for KAP culture and Baermann technique , respectively ., Of 1 , 071 S . stercoralis cases , half were females ( 50 . 1% ) , half were farmers ( 51 . 1% ) , and 425 ( 39 . 7% ) cases were diagnosed in individuals under 16 years ., The majority ( 57 . 0% ) attended primary school , while one third ( 33 . 6% ) reported no schooling ., Figure 2 shows the smoothed age prevalence stratified by gender ., The prevalence of S . stercoralis increased rapidly with age , particularly in the first eight years of life , where after it leveled off in females but continued to rise slowly in males ., Prevalence rose from 31 . 4% in children , aged five , to 51 . 2% in participants older than 50 ., In all age groups , prevalence was higher in males than in females ., The multivariate GEE found that gender was significantly associated with S . stercoralis infection ( mOR: 1 . 7; 95% CI: 1 . 4–2 . 0; P<0 . 001 ) ., Compared to children under six years old , all age groups had a higher risk for infection ., Participants who reported having been treated for worms were less frequently infected with S . stercoralis than those who did not report taking anthelminthic drugs ( mOR: 0 . 7; 95% CI: 0 . 6–0 . 8; P<0 . 001 ) ., In addition , participants who usually defecated in latrines were significantly less infected with S . stercoralis than those who did not use latrines ( mOR: 0 . 6; 95% CI: 0 . 4–0 . 8; P\u200a=\u200a0 . 001 ) ., No additional predictor of S . stercoralis infection relating to personal disease perception and hygiene was found in the multiple regression analysis ., Looking at environmental factors , risk significantly decreased with increasing rainfall ( mOR: 0 . 8; 95% CI: 0 . 7–0 . 9; P\u200a=\u200a0 . 004 ) and soil organic carbon content ( mOR: 0 . 6; 95% CI: 0 . 5–0 . 9; P\u200a=\u200a0 . 003 ) ., The land cover class corresponding to croplands was associated with an increased risk for infection ( mOR: 1 . 7 , 95%CI: 1 . 2–2 . 4; P\u200a=\u200a0 . 004 ) ( Table 2 ) ., During the two weeks preceding examinations for S . stercoralis , 50 . 5% of participants reported an episode of diarrhea , 12 . 7% had experienced nausea and 59 . 1% complained about abdominal pain ., However , none of these clinical symptoms was significantly associated with S . stercoralis infection ., Population attributable risk analysis found that the number of strongyloidiasis cases would be reduced by 39% if all participants used a latrine for defecation ., The spatial model run without covariates indicated very little spatial correlation of infection risk , as indicated by the 1 km range ., The small residual ( unexplained ) within village variance ( σ ) also indicated a weak clustering tendency of S . stercoralis infection risk ., Parameters of these models are presented in Table 3 ., After introducing LST night , rainfall , soil carbon content and land cover , the model with an exchangeable random effect fitted the data slightly better , as indicated by the lower DIC ., Environmental covariates explained 45% of the village-level variability and the range dropped under a kilometer after covariates were introduced in the model ., Mixed bivariate logistic regressions revealed no association at 15% significance level between S . stercoralis infection risk and any yearly summary measure of altitude , LST day , EVI , soil pH or bulk density ., LST night ( P\u200a=\u200a0 . 072 ) , yearly means of rainfall estimates ( P<0 . 0001 ) , soil organic carbon content ( P\u200a=\u200a0 . 002 ) and land cover ( P\u200a=\u200a0 . 107 ) were associated with infection risk and were used to predict S . stercoralis infection risk throughout Preah Vihear province ., Apart from LST night , all covariates remained significant in the multivariate model and ORs were similar to those obtained in the multivariate GEE for the risk factor analysis ( data not shown ) ., Maps of the covariates used predict infection in Preah Vihear province are presented in Figure S1 ., Model validation revealed that both models were able to correctly predict prevalence for 100% of the test locations , within a 95% credible interval ., However the non-spatial model , i . e . with an exchangeable random effect , had slightly better predictive ability ( MSE: 0 . 0226 and 0 . 0229 , χ2: 13 . 22 and 13 . 59 for the non-spatial and spatial models , respectively ) ., Therefore , the non-spatial model was used to predict S . stercoralis infection risk in Preah Vihear province , Cambodia ., Figure 3 displays the S . stercoralis predicted median prevalence in Preah Vihear province ( Figure 3A ) , together with the uncertainty of the estimates ( Figure 3B ) as expressed by the error coefficient ( the ratio between the predicted median and its standard deviation ) ., The lower ( 2 . 5% ) and upper ( 97 . 5% ) credible intervals of the predicted S . stercoralis prevalence are presented in Figure ( 3C ) and ( 3D ) , respectively ., Results were consistent with observed prevalence at surveyed locations ., Many epidemiological aspects of S . stercoralis infection are poorly understood 34 ., The available information on the prevalence of S . stercoralis comes from studies on other STHs , where diagnostic methods with low-sensitivity for S . stercoralis and only a single stool sample were mostly used 1 , 2 , 16 ., To reach an acceptable estimate of the “true” prevalence of S . stercoralis , Siddiqui and Beck 12 , and Khieu et al . 16 proposed analyzing multiple stool samples with multiple diagnostic techniques simultaneously ., In our study of S . stercoralis among a rural population living in 60 villages in northern Cambodia , we examined two stool samples using two diagnostic techniques ( KAP culture and Baermann method ) specifically targeting S . stercoralis and found that 44 . 7% of the participants were infected ., Children under the age of six accounted for 5 . 5% of the infections , while prevalence increased with age ., Almost every second individual in our study population was infected with S . stercoralis ., To our knowledge , this is one of the highest prevalence ever reported , compared to other studies in highly endemic areas like Cambodia 2 , 16 , 35 , Laos 36 , Thailand 37 , Brazil 34 and China 38 , or in other countries ., The main reason for such high prevalence is likely to be due to the more rigorous diagnostic approach employed in our study ( number of stool specimen , multiple diagnostic methods ) , compared to the other studies , where a single method to examine a single fecal sample was used ., Yet , the prevalence we observed is also substantially higher than that of other studies using the similar diagnostic approaches ., Two recent studies in Kandal and Takeo provinces in Cambodia reported that about a quarter ( 24 . 4% ) of schoolchildren and 21 . 0% of the general population were infected , respectively 16 , 35; while Steinmann et al . , and Knopp et al . found a prevalence rate of 11 . 7% in a village in Yunnan , China and of 10 . 8% among schoolchildren in Zanzibar , respectively 39 , 40 ., Hence , other factors such socioeconomic and sanitary conditions are likely to contribute to the differences observed ., In the absence of a gold standard for diagnosing S . stercoralis , KAP culture 13 and the Baermann method 14 are widely used for detecting the parasite microscopically ., Our study found that KAP culture was more sensitive than the Baermann method , which is consistent with reports from Cambodia 16 , 35 , rural Côte d′Ivoire 41 , Brazil 42 and Honduras 43 ., However , the opposite was observed in studies in south-central Côte d′Ivoire 44 , Zanzibar 40 , China 39 and Uganda 45 ., This seems to indicate that neither method is superior ., As either technique will fail to identify a certain number of infections , the combined use of both methods is recommended for optimal sensitivity ., We found that about one third of children under six ( 59 of 188 children ) were already infected with S . stercoralis ., This hints at a high contamination of the environment , such that children easily become infected when playing on the ground around the house or barefoot in the village ., The fact that prevalence steadily increases with age can be explained by the fact that once infected at a young age , an infection can persist in an untreated individual for their entire life 46 , 47 ., Personal hygiene ( not using a toilet for defecating ) as a significant predictor of S . stercoralis infection was also observed in a study in south-central Cambodia 16 ., This connection is obvious: with proper disposal of the feces , contamination of the surrounding area with infective larvae decreases ., We calculated that 39 . 0% of S . stercoralis cases in the study area could be prevented if everyone were to defecate in a toilet ., The cycle of S . stercoralis transmission could thus be interrupted by improving personal hygiene and sanitation ., Strongyloidiasis is almost non-existent in countries where sanitation and human waste disposal have improved 48 ., S . stercoralis infections were ubiquitous the study setting and exhibited a weak tendency to spatial clustering in the Preah Vihear province , as indicated by the low location-specific variance parameter ., A low clustering tendency was also observed for hookworm , in the Region of Man , Côte d′Ivoire and Ghana 49 , 50 ., However , the lack of spatial correlation in this analysis is likely due to the studys small scale ., This does not preclude S . stercoralis infection risk from spatially clustering at country or regional level , since environmental factors delimit suitable ecological zones for parasites at larger scales ., 51 ., Still , even at this provincial scale , we found significant associations with rainfall , soil organic carbon content and croplands both in the predictive model and after adjusting for demographic and behavioral factors ., Our risk predictions yielded two broad risk zones: a lower risk zone in the East of the province and a higher risk zone in the West , characterized by lower rainfall and soil organic carbon content and a higher proportion of zones occupied by cropland ., Since there was no indication of spatial correlation , risk prediction was carried out using an exchangeable random effect and relied on the predictors only ., While a negative association between rainfall and infection risk was also identified in Thailand , a laboratory study found that S . stercoralis development was impaired by submersion of stools in water 52 ., Hypothetically , the decreased risk of S . stercoralis infection in the East of the Province where rainfall was higher , might relate to more extensive or long lasting flooding that could negatively affect S . stercoralis transmission ., Another possibility might be that higher rainfall in the East reduces parasite survival rates , as parasites are washed away by run-off water down steeper slopes ., We found that lower soil carbon content was associated with increased risk of infection ( in the West ) ., A full profile of soil type information was unavailable for this setting and soil organic carbon content depends on a complex interplay of environmental and soil features , so interpretation is limited ., But , in general , soil organic content tends to decrease with increased forest destruction , burning of savannas and land use for agriculture 53 ., Hence , the association of increased risk of infection with lower soil carbon contents in our setting might relate to human activities such as slash-and-burn practices that destroy forests to create agricultural lands ., Moreover , risk of infection was found to increase in croplands , a MODIS land cover category that specifically corresponds to soils that are alternately bare and cultivated ., In our setting , these are rice fields 54 ., Half ( 51 . 7% ) of the study villages are surrounded by rice fields and 54 . 9% of participants infected with S . stercoralis live in such environments ., Risk might be increased further by regular soil contamination by defecation around the fields and exposure during agricultural activities ., Indeed , open defecation was the usual habit for 88 . 5% of participants ., Finally , the small cluster size ( 1 km ) of infection risk suggests that S . stercoralis transmission occurs within villages rather than between them and may relate to the location of defecation sites within and close to the villages ., We conclude that S . stercoralis infection is highly prevalent in rural communities of Cambodia ., School-aged children and adults over 45 years were the most at risk for infection ., Almost 40% of infections could be avoided by proper personal hygiene ., Access to adequate treatment for chronic uncomplicated strongyloidiasis is low ., Given its potential to produce potentially fatal disseminated infections , further epidemiological data on this parasite in other endemic areas are urgently needed

Introduction, Materials and Methods, Results, Discussion

The threadworm , Strongyloides stercoralis , endemic in tropical and temperate climates , is a neglected tropical disease ., Its diagnosis requires specific methods , and accurate information on its geographic distribution and global burden are lacking ., We predicted prevalence , using Bayesian geostatistical modeling , and determined risk factors in northern Cambodia ., From February to June 2010 , we performed a cross-sectional study among 2 , 396 participants from 60 villages in Preah Vihear Province , northern Cambodia ., Two stool specimens per participant were examined using Koga agar plate culture and the Baermann method for detecting S . stercoralis infection ., Environmental data was linked to parasitological and questionnaire data by location ., Bayesian mixed logistic models were used to explore the spatial correlation of S . stercoralis infection risk ., Bayesian Kriging was employed to predict risk at non-surveyed locations ., Of the 2 , 396 participants , 44 . 7% were infected with S . stercoralis ., Of 1 , 071 strongyloidiasis cases , 339 ( 31 . 6% ) were among schoolchildren and 425 ( 39 . 7% ) were found in individuals under 16 years ., The incidence of S . stercoralis infection statistically increased with age ., Infection among male participants was significantly higher than among females ( OR: 1 . 7; 95% CI: 1 . 4–2 . 0; P<0 . 001 ) ., Participants who defecated in latrines were infected significantly less than those who did not ( OR: 0 . 6; 95% CI: 0 . 4–0 . 8; P\u200a=\u200a0 . 001 ) ., Strongyloidiasis cases would be reduced by 39% if all participants defecated in latrines ., Incidence of S . stercoralis infections did not show a strong tendency toward spatial clustering in this province ., The risk of infection significantly decreased with increasing rainfall and soil organic carbon content , and increased in areas with rice fields ., Prevalence of S . stercoralis in rural Cambodia is very high and school-aged children and adults over 45 years were the most at risk for infection ., Lack of access to adequate treatment for chronic uncomplicated strongyloidiasis is an urgent issue in Cambodia ., We would expect to see similar prevalence rates elsewhere in Southeast Asia and other tropical resource poor countries .

Data on the prevalence and distribution of Strongyloides stercoralis ( threadworm ) is scarce in many resource-poor countries ., We carried out a cross-sectional study during the dry season among 2 , 396 rural Cambodians of all ages ., We used a rigorous diagnostic approach , involving two stool samples per person and two examination techniques , namely , Koga agar plate culture and the Baermann method ., We predicted the spatial distribution of S . stercoralis using Bayesian Kriging analysis ., Almost half of the participants ( 44 . 7% ) were infected with S . stercoralis ., Of the S . stercoralis cases , 39 . 7% involved participants under 16 years old ., S . stercoralis infection prevalence was significantly higher in males than in females ., Participants younger than 10 years old had a lower risk of infection than did older participants ., Furthermore , our study showed that toilet use could prevent threadworm infections by 39% ., Infection prevalence in the province was negatively associated with rainfall and soil organic content and positively associated with land covered by rice fields ., We conclude that access to adequate treatment for S . stercoralis must be addressed in Cambodia ., Infection prevalence is likely to be similar in other countries of the region and the developing world .

public and occupational health, infectious diseases, helminth infections, medicine and health sciences, primary care, environmental epidemiology, epidemiology, global health, strongyloidiasis, neglected tropical diseases, spatial epidemiology, infectious disease control, tropical diseases, soil-transmitted helminthiases, parasitic diseases, health care

journal.ppat.1006269

Neisseria gonorrhoeae infects the human endocervix by activating non-muscle myosin II-mediated epithelial exfoliation

Microbial pathogens establish infection at the mucosal surface by colonization , disruption , and penetration of the epithelium 1 ., The epithelium is the first line of the host defense against microbial pathogens , providing a physical barrier and a sensor of invading pathogens 2 , 3 ., In the female reproductive tract ( FRT ) , this mucosal surface is composed of multilayered non-polarized squamous epithelial cells at the ectocervix and vagina , or monolayered polarized columnar epithelial cells at the endocervix and uterus ., Different from multilayered squamous epithelial cells that are held together by adherent junctions , the monolayer epithelium is sealed by the apical junction , which prevents the entry of pathogens through the paracellular space ( gate function ) and maintains the polarity of the apical and basolateral surfaces ( fence function ) 4–7 ., The apical junction is formed by the integral proteins , claudin , occludin , junctional adhesion molecules , E-cadherin , and the associated proteins zonula occludens-1 ( ZO1 ) and β-catenin 7 ., ZO1 and β-catenin link the apical junction to the actin cytoskeleton and signaling networks 7–11 ., The actin cytoskeleton and non-muscle myosin II ( NMII ) form a supporting ring at the apical junction 12–14 ., The contraction of the actomyosin ring can transiently open the “gate” of the apical junction , regulating the permeability of the epithelium 15–17 ., Over activation of the actomyosin ring can lead to the disassembly of the apical junction by inducing the endocytosis of junctional proteins 18 , 19 ., As a strategy of protecting the epithelium from pathogens , infected cells with associated microbes are shed 20 ., While the exfoliation of multilayered squamous epithelium is mediated by weakening cell-cell adhesion , exfoliation of polarized epithelial monolayers requires collaboration between NMII and apical junctional complexes ., Actomyosin and apical junctional proteins are recruited to the plasma membrane of epithelial cells in contact with an exfoliating cell ., NMII-generated forces in neighboring cells “squeeze” the exfoliating cell out while apical junctional complexes ensure that the epithelial barrier remains uncompromised 21–25 ., How bacteria break the epithelial barrier and escape from epithelial shedding to achieve infection remains elusive ., Neisseria gonorrhoeae ( GC ) , a Gram-negative bacterium , infects the mucosal surface of human genital tissues in men and women and causes one of the most common sexually transmitted infections , gonorrhea 26 ., In the FRT , the endocervix has been suggested as a primary site for GC to initiate infection that may lead to pelvic inflammatory disease 27 , 28 ., Previous studies , using epithelial cells , fallopian tube organ culture , and mouse vaginal infection models , have shown that GC can adhere to , invade into , and transmigrate across epithelial cells 29–31 ., However , how GC infect the polarized human columnar endocervical epithelial cells has not been well studied ., GC major surface molecules , including pili , lipooligosaccharide ( LOS ) , porin , and opacity-associate protein ( Opa ) , function concertedly for infection ., Opa has been suggested to be involved in GC adherence to , invasion into , and transmigration across polarized epithelial cells 32–36 , as well as GC-GC interaction by binding to LOS 37–39 ., Opa , pili and LOS undergo phase variation ., This phase variation has been implicated for the capability of GC to infect various locations of the FRT and generate different pathological conditions and complications 32 , 40 , 41 ., Most GC isolated from patients 42 and infected mice are Opa positive 43 , underscoring the importance of Opa in infections ., Opa has been shown to inhibit GC-induced exfoliation of squamous epithelial cells from the lower genital tract of mice by engaging carcinoembryonic antigen-related cell adhesion molecules ( CEACAMs ) and activating integrin , which enhances GC colonization 44 , 45 ., These data indicate that Opa phase variation is a major way for GC to modify their pathogenicity ., GC establishes infection by interacting with various receptors on epithelial cells , such as the binding of Opa to CEACAMs or heparin sulfate proteoglycans ( HSPG ) 34 , 46–48 ., These interactions alter signaling cascades in epithelial cells , such as phosphatidylinositol 3-kinase , phospholipase C , and Ca2+ flux ., The signaling leads to actin reorganization , which can drive microvillus elongation and the subsequent engulfment of GC 49 , 50 ., We have shown that GC-induced transactivation of epidermal growth factor receptor ( EGFR ) is critical for the optimal level of GC invasion into non-polarized epithelial cells and transmigration across polarized epithelial cells 51 , 52 ., GC interaction with polarized epithelial cells weakens the apical junction by inducing the disassociation of ZO1 and β-catenin from the junctional complex , consequently facilitating GC transmigration 51 ., Our recently published studies found a surprising role for Opa in inhibiting GC transmigration across polarized epithelial cells 38 ., How GC manipulate columnar endocervical epithelial cells through Opa for infection is unknown ., A major obstacle against addressing this question has been a lack of infection models that mimic all aspects of human infection ., In this study , we established a new ex vivo infection model , human endocervical tissue explants ., Using this model and polarized epithelial cells , as well as isogenic strains of GC expressing invariable Opa , we revealed the mechanistic links between GC infectivity , GC-induced exfoliation , apical junction disassembly , and signaling in polarized columnar endocervical epithelial cells , and novel roles of Opa phase variation in these events ., GC induce the exfoliation of polarized endocervical epithelial cells by disrupting the apical junction ., Opposite to GC-induced shedding of squamous epithelial cells , the exfoliation of columnar epithelial cells does not reduce GC adherence and invasion; instead , it increases GC penetration into the subepithelium ., Both GC-induced epithelial exfoliation and apical junction weakening require Ca2+-dependent redistribution of active NMII ., The expression of CEACAM-binding OpaH but not HSPG-binding OpaC inhibits GC-induced exfoliation and junctional disruption by interfering with NMII activation and reorganization as well as Ca2+ flux , while GC piliation promotes these events ., Our results suggest that GC modify the exfoliation process for infection by activating Ca+ flux and NMII redistribution in endocervical epithelial cells and change the magnitude of this process through regulating the levels of NMII activation and redistribution by Opa and pili phase variation ., We utilized human endocervical tissue explants and the polarized human colonic epithelial cell line T84 to determine whether GC-infected polarized epithelial cells undergo exfoliation ., Tissue explants that were cultured with the mucosal side up and T84 cells that were polarized on transwells were inoculated apically with a GC strain , MS11 that express phase variable Opa and pili ( MS11Pil+Opa+ ) at a MOI of ~10 for 6 or 24 h ., Thin sections of cryo-preserved endocervical tissues and T84 cells were stained with a DNA dye and GC-specific polyclonal antibodies and analyzed using and three-dimensional confocal fluorescence microscopy ( 3D-CFM ) ., Images showing both the mucosal and subepithelial sides of the endocervix and T84 monolayers were analyzed ., Epithelial cells at the top of the endocervical epithelium of tissue explants or T84 monolayers , indicated by white lines , were counted as exfoliating cells ( Fig 1A ) and quantified as the percentage of total epithelial cells ., After 24 h incubation , the exfoliation of GC-inoculated epithelial cells was significantly increased in both the endocervical epithelium ( Fig 1B and 1C ) and the T84 monolayer ( Fig 1D and 1E ) , compared to uninfected controls ., This indicates that polarized T84 monolayers behave similarly to the endocervical epithelium upon GC infection ., There was no significant increase in the percentage of GC-inoculated epithelial cells exfoliated from T84 monolayer after 6-h inoculation , compared to uninfected cells ( Fig 1E , left panel ) ., To determine if Opa has a role in the exfoliation of columnar epithelial cells , we inoculated endocervical tissue explants and polarized epithelial cells with MS11Pil+ΔOpa , a GC strain where all 11 opa genes were deleted 39 ., MS11Pil+ΔOpa increased the percentage of epithelial exfoliation from 32 . 3% to 66 . 3% in tissue explants ( Fig 1B and 1C ) and from 31 . 2% to 55 . 8% in T84 monolayers ( Fig 1D and 1E ) ., Even at 6 h , MS11Pil+ΔOpa-infected T84 cells exfoliated significantly more than the uninfected control ( Fig 1D and 1E ) ., To determine whether different Opa variants have similar effects on the exfoliation of endocervical epithelial cells , we utilized MS11Pil+ΔOpa strains that express invariant OpaH ( binding to CEACAMs ) or OpaC ( binding to HSPG ) ., We found that the exfoliation level of MS11Pil+OpaH-inoculated endocervical tissue explants was as low as that of MS11Pil+Opa+ infected explants , while the exfoliation level of MS11Pil+OpaC-inoculated explants was as high as that of MS11Pil+ΔOpa-infected explants ( Fig 1C ) ., These results indicate that GC induces the epithelial exfoliation from the endocervix and cell line-formed polarized monolayers , and the expression of CEACAM-binding OpaH but not HSPG-binding OpaC inhibits the exfoliation ., The similar inhibitory effect of MS11Opa+ and MS11OpaH on epithelial exfoliation suggests that MS11Opa+ expresses primarily CEACAM-binding Opa proteins ., To determine if GC-induced exfoliation of endocervical epithelial cells depends on NMII , we inhibited the activation of NMII using inhibitors specific for Rho-associated kinase ( ROCK ) , Y27632 , and myosin light chain kinase ( MLCK ) , ML-7 and PIK ., Polarized T84 cells and human endocervical tissue explants were treated with individual inhibitors for 1 h before and during incubation with GC ., We found that both the small chemical inhibitor ( ML-7 ) 53 and the catalytic site-targeted peptide inhibitor ( PIK ) 54 of MLCK reduced the exfoliation of MS11Pil+ΔOpa-infected ( Fig 2A and 2B ) but not MS11Pil+Opa+-infected epithelial cells from T84 monolayers ( S1A and S1B Fig ) ., In contrast , treatment with the ROCK inhibitor did not significantly change the percentage of epithelial exfoliation , no matter if epithelial cells were infected with MS11Pil+Opa+ ( S1A and S1B Fig ) or MS11Pil+ΔOpa ( Fig 2A and 2B ) ., Importantly , the treatment of MLCK inhibitor , ML-7 or PIK , also decreased the epithelial exfoliation of human endocervical tissue explants to the basal level no matter if it was based on the total number of epithelial cells ( Fig 2C and 2D ) or GC-associated epithelial cells ( S1C Fig ) ., As MLCK activation requires Ca2+-bound calmodulin 55 , 56 and the MLCK inhibitor PIK blocks the calmodulin-binding site in MLCK 54 , we investigated if GC-induced exfoliation of polarized epithelial cells depends on Ca2+ flux ., We utilized 2APB , an inhibitor that blocks Ca2+ release from intracellular stores 57 , 58 ., Treatment with 2APB also reduced the exfoliation of polarized T84 cells to the level similar to ML-7 and PIK ( Fig 2A and 2B ) ., As controls , we treated polarized T84 cells with the inhibitors alone , and found that ML-7 , PIK , and 2APB did not affect epithelial exfoliation , but the NMII motor inhibitor blebbistatin increased epithelial exfoliation without GC inoculation ( S2 Fig ) ., These results suggest that GC induce exfoliation of polarized epithelial cells via Ca2+- and MLCK- but not ROCK-dependent activation of NMII ., We have previously shown that GC can transmigrate across polarized epithelial cells , and Opa expression inhibits the transmigration 38 ., To determine whether such transmigration occurs in the endocervical epithelium and which Opa variant inhibits GC transmigration , we utilized the tissue explants and GC strains expressing single invariable Opa ., After incubating with piliated GC for 24 h , we examined GC transmigration across the endocervical epithelium by quantifying the percentage of GC-associated endocervical epithelial cells with GC staining in the basal side ( Fig 3A ) ., In infected endocervical tissue explants , the percentages of epithelial cells associated with penetrated MS11ΔOpa and MS11OpaC were significantly higher than those with penetrated MS11Opa+ and MS11OpaH ( Fig 3B ) ., However , there was no significant difference between the percentages of epithelial cells with penetrated MS11ΔOpa and MS11OpaC and between those with penetrated MS11Opa+ and MS11OpaH ( Fig 3B ) ., These results indicate that GC can penetrate into the subepithelium of the human endocervix in the tissue explant model , and the expression of CEACAM-binding OpaH , which reduces GC-induced epithelial exfoliation , but not HSPG-binding OpaC , which does not affect the exfoliation , inhibits GC penetration ., We next asked whether Opa-mediated inhibition of penetration is related to the efficiency of GC adherence using polarized human endometrial epithelial cells , HEC-1-B ( Fig 3C and 3D ) , and T84 monolayers ( Fig 3E and 3F ) ., Similar to what we observed in the endocervical tissue , the numbers of MS11Pil+ΔOpa transmigrating across polarized HEC-1-B ( Fig 3C ) and T84 monolayers ( Fig 3E ) were significantly higher than those of MS11Pil+Opa+ ., However , the expression of Opa had no significant effect on GC adherence to the apical surface of HEC-1-B ( Fig 3D ) and T84 cells ( Fig 3F ) ., Pili have been shown to be involved in GC transmigration across polarized epithelial cells 59 , 60 ., To examine the relationship between pili and Opa , we compare the transmigration and adherence efficiencies of piliated and non-piliated MS11Opa+ and MS11ΔOpa in polarized T84 cells ( Fig 3E and 3F ) ., We found that the numbers of non-piliated MS11Opa+ and MS11ΔOpa that adhered to and transmigrated across epithelial monolayers were significantly lower than their piliated strains ., However , Opa expression only reduced the transmigration of piliated but not non-piliated GC ( Fig 3E ) ., These results suggest that pili and Opa play opposing roles in GC transmigration , with pili promote GC transmigration , probably by enhancing adherence , and Opa inhibiting GC transmigration without affecting GC adherence ., The inhibitory effects of CEACAM-binding Opa on both GC-induced epithelial exfoliation and GC penetration in the endocervical tissue explants implicate a relationship between these two events ., To investigate this relationship , we determined whether inhibiting GC-induced exfoliation would affect the ability of GC to adhere to , invade into , and transmigrate across polarized epithelial cells ., Inhibition of GC-induced exfoliation by the Ca2+ ( 2APB ) and MLCK inhibitors ( ML-7 and PIK ) significantly reduced the transmigration of MS11Pil+ΔOpa across the polarized T84 monolayer ( Fig 4A ) ., However , none of these inhibitors had any significant effect on the adherence and invasion of MS11Pil+ΔOpa ( Fig 4B and 4C ) ., The ROCK inhibitor that did not affect GC-induced exfoliation also had no impact on GC adherence , invasion and transmigration ( Fig 4A–4C ) ., Treatment with the inhibitors alone did not significantly affect the barrier function of the epithelium and GC growth except that treatment of PIK longer than 6 h reduced the overall yield of gonococci to one-half ( S3 Fig ) ., Similar to the results obtained from polarized T84 cells , treatment with either ML-7 or PIK decreased the percentage of epithelial cells with basally associated GC among the total GC-associated epithelial cells from 27% to 7 . 1% ( Fig 4D ) , significantly inhibiting GC penetration into the endocervical epithelium ., Our results suggest that Ca2+ flux and the activation of NMII by MLCK in polarized epithelial cells , which are required for GC-induced epithelial exfoliation , also are critical for GC transmigration across and penetration into the human endocervical epithelium , but not for GC adherence and invasion ., The linkage between the efficiency of GC penetration into the epithelium with GC-induced epithelial exfoliation and apical junction disruption shown here and previously 51 implicate GC-induced junction disruption as an underlying cause of epithelial exfoliation ., To test this hypothesis , we determined whether Opa expression and the MLCK and Ca2+ inhibitors , which all inhibited GC-induced epithelial exfoliation , also prevent GC from disrupting the apical junction ., The structural integrity of the apical junction was evaluated by analyzing the distribution of E-cadherin using immunofluorescence ( IFM ) and 3D-CFM and quantifying the fluorescence intensity ratio ( FIR ) of E-cadherin at the cytoplasm to that at the apical junction ., In polarized T84 cells that were not inoculated with GC , E-cadherin staining was primarily localized at the apical junction ( Fig 5A , top panels ) ., Incubation with GC changed the continuous E-cadherin staining at the apical junction into puncta in the cytoplasm , indicating endocytosis of E-cadherin ( Fig 5A ) ., This led to a significant increase in the cytoplasm: junction FIR of E-cadherin in both MS11Pil+Opa+ and MS11Pil+ΔOpa-inoculated epithelial cells , compared to non-inoculated controls ( Fig 5B ) ., In particular , the magnitude of the increase in the FIR was significantly greater in MS11Pil+ΔOpa-infected than MS11Pil+Opa+-infected epithelial cells ( Fig 5B ) ., Our Western blot analysis did not find any significant changes in the protein level of the apical junctional protein ZO1 between epithelial cells inoculated with piliated MS11Opa+ , MS11ΔOpa , and no GC ( Fig 5C ) ., These results suggest that Opa expression suppresses GC-induced apical junction disassembly by inhibiting E-cadherin endocytosis ., We used inhibitors to determine the role of NMII and Ca2+ flux in GC-induced junction disassembly ., Treatment with the MLCK inhibitor ML-7 and the Ca2+ inhibitor 2APB , but not the ROCK inhibitor Y27632 , decreased the punctate staining of E-cadherin in the cytoplasm and the cytoplasm: junction FIR of E-cadherin to or below the control level in epithelial cells without GC inoculation ( Fig 5A and 5B ) ., Thus , Ca2+/MLCK inhibitors suppress GC-induced junction disassembly ., Our previous studies show that GC-induced junctional disassembly leads to a significant increase in the lateral diffusion between the apical and basolateral membrane but not in the permeability of epithelial monolayers 51 ., To determine whether Opa , MLCK and Ca2+ flux are involved in this functional alteration of the apical junction , we stained the basolateral surface of polarized T84 epithelial cells exclusively with CellMask dye for 15 min , after apical incubation with fluorescently labeled piliated GC for 6 h ., The appearance of basolaterally stained CellMask dye in the apical membrane indicates a decrease in the fence function of the apical junction ( Fig 5D ) ., In control cells where no GC were added , <10% of the cells showed the CellMask staining at the apical surface ., The percentage of cells with basolaterally labeled CellMask reaching the apical surface increased to 19 . 4% when MS11Pil+Opa+ was inoculated and to 62 . 2% when MS11Pil+ΔOpa was inoculated ( Fig 5E and 5F ) ., These results indicate that while both Opa+ and ΔOpa GC decrease the fence function of the apical junction , MS11Pil+ΔOpa caused a greater reduction than MS11Pil+Opa+ ., Moreover , the treatment with the MLCK inhibitor ML-7 or the Ca2+ inhibitor 2APB significantly lowered the percentage of epithelial cells with the CellMark staining leaked to the apical surface ( Fig 5E and 5F ) , thereby inhibiting the GC-induced fence function reduction ., We determined if MS11Pil+ΔOpa can induce the disruption of the apical junction in human endocervical tissue ., Sections of uninfected and infected tissue explants were stained for the junctional protein ZO1 , GC , and DNA and analyzed by 3D-CFM ( Fig 5G ) ., We quantified junction disruption by determining the percentage of GC-associated epithelial cells that lost continuous apical staining of ZO1 , using 3D reconstituted confocal images ( Fig 5G , right panels ) ., After a 24-h incubation with MS11Pil+ΔOpa , ZO1 staining at the apical junction of GC-associated epithelial cells appeared to be reduced ( Fig 5G , left panels ) , and 93 . 2% of GC-associated epithelial cells showed defective ZO1 staining ( Fig 5G , right panels , arrows ) , compared to 16 . 7% of uninfected cells ( Fig 5H ) ., In contrast to the recruitment of ZO1 to epithelial cells neighboring exfoliating cells in uninfected monolayers , no accumulation of ZO1 staining was observed around GC-infected exfoliating cells ( Fig 5I , arrows ) ., Furthermore , GC inoculation significantly changed the morphology of endocervical epithelial cells , with the cells losing their tall and columnar shape ( Fig 5G , left panels ) ., Treatment with the MLCK inhibitor ML-7 restored both the morphology ( Fig 5G left panels ) and apical distribution of ZO1 ( Fig 5H ) ., These data confirm the ability of GC to compromise the apical junction of the endocervical epithelial cells in a NMII-dependent manner in the human tissue explants ., These results together show that both Opa expression and Ca2+/MLCK inhibitors suppress GC-induced disruption of the apical junction , indicating that similar to GC-induced epithelial exfoliation , Ca2+ signal and MLCK-mediated NMII activation are required for GC-induced apical junction disruption while Opa expression inhibits the junction disruption ., Our finding of that GC induce both epithelial exfoliation and apical junction disassembly in a NMII-dependent manner suggests that GC regulate the activity of NMII in polarized epithelial cells ., We examined the cellular distribution of active NMII after 6-h incubation with GC , using antibody specific for phosphorylated myosin light chain ( pMLC ) and 3D-CFM ., In uninfected polarized T84 ( Fig 6A ) and HEC-1-B cells ( S4A Fig ) , pMLC was primarily localized at the apical junction ., The polarized distribution of pMLC at the apical surface was quantified by the FIR of pMLC at the apical to the lateral ( Apical: Lateral ) membrane areas in individual cells using CFM images scanning across the apical and basolateral surfaces ( Fig 6A–6C ) ., The polarized distribution pMLC at the apical junction was quantified by the FIR of pMLC at the junction to non-junction ( Junction: Non-junction ) areas of the apical region using CFM images scanning through the apical junction ( Fig 6D and 6E ) ., The apical inoculation of piliated MS11Opa+ and MS11ΔOpa caused significant increases in apical: lateral FIR in both polarized T84 ( Fig 6A–6C ) and HEC-1-B cells ( S4 Fig ) , compared to the no GC control ., There were also significant increases in the junction: non-junction FIR in infected polarized T84 cells , compared to non-infected cells ( Fig 6D and 6E ) ., Moreover , both the apical: lateral and junction: non-junction FIRs were significantly higher in MS11Pil+ΔOpa-infected than those in MS11Pil+Opa+-infected T84 cells ( Fig 6C and 6E ) , but this difference was not detected in HEC-1-B cells that do not express CEACAMs 61 ( S4 Fig ) ., In contrast , the apical: lateral FIR in epithelial cells infected by non-piliated MS11 , no matter if GC expressed Opa or no , were all significantly reduced to a similar level , compared to those infected by piliated MS11 ( Fig 6B ) ., We further noticed that NMII at the apical surface appeared to accumulate at GC adherent sites ( Fig 6A , middle panels , arrows ) ., To determine if GC inoculation changes the activation level of NMII , we quantified the amount of pMLC and MLC by Western blot ., Polarized T84 cells were incubated with or without piliated MS11Opa+ or MS11ΔOpa apically for 6 h before lysis and Western blot analysis ., The antibody staining density ratios of pMLC to MLC in MS11Pil+ΔOpa- but not MS11Pil+Opa+-inoculated cells were significantly higher than that in uninoculated epithelial cells ( Fig 6F , top panels , and Fig 6G ) ., However , GC inoculation did not significantly change the staining density ratio of MLC to tubulin ( Fig 6H ) ., Thus , MS11Pil+ΔOpa , but not MS11Pil+Opa+ , increases the activation level of NMII ., To explore the possibility of that GC-induced NMII redistribution occurs in vivo and the role of Opa phase variation , we incubated human endocervical tissue explants with piliated MS11Opa+ , ΔOpa , OpaH , or OpaC for 24 h ., Cryo-sections of the endocervical tissue were stained for pMLC , GC and nuclei ., In addition to its apical junction localization , pMLC was concentrated at the basal surface of the endocervical epithelial cells contacting with the basal membrane ( Fig 7A , upper panels ) ., When incubated with MS11Pil+Opa+ , there was a redistribution of pMLC from the basal to apical surface , resulting in a significant higher apical: lateral FIR in GC-inoculated tissue explants than that in no GC control ( Fig 7 ) ., Inoculation of MS11Pil+ΔOpa further increased the apical: later FIR of pMLC , similar to what we observed in polarized T84 ( Figs 7B and 6A–6C ) ., Expression of OpaH , but not OpaC , in MS11Pil+ΔOpa reduced the apical: lateral FIR back to the level in MS11Pil+Opa+-infected cells ( Fig 7B ) ., Furthermore , pMLC at the apical surface of the endocervical epithelial cells also concentrated at GC adherent sites ( Fig 7A , second row , white arrows ) , but not at the membrane of cells neighboring exfoliating cells ( Fig 7A , second row , orange arrows ) ., These observations confirm that GC increase the relative amount of activated NMII at the apical surface of the endocervical epithelial cells in human tissue explants ., Our results from both human endocervical tissue explants and polarized epithelial cell lines suggest that GC interactions via pili cause an accumulation of activated NMII at GC adherent sites and the apical membrane of columnar epithelial cells , and the expression of CEACAM-binding Opa suppresses the activation and redistribution of NMII ., The activation of NMII is mediated by the phosphorylation of MLC by MLCK downstream of Ca2+-activated calmodulin 62–65 and/or by ROCK downstream of Rho GTPases 62 , 66 ., Our findings that GC-induced epithelial exfoliation and apical junctional disruption , as well as GC transmigration , are inhibited by the MLCK and Ca2+ but not ROCK inhibitors suggest that MLCK mediates the activation and redistribution of NMII triggered by GC ., We determined the effects of the MLCK and ROCK inhibitors on GC-induced MLC redistribution and phosphorylation using 3D-CFM and Western blot ., Our 3D-CFM analysis found that treatment with the MLCK inhibitor ML-7 or PIK significantly reduced both the apical: lateral and junction: non-junction FIRs of pMLC in GC-infected epithelial cells ( Fig 6A and 6C–6E ) , as well as the accumulation of pMLC at GC adherent sites ( Fig 6A , bottom panels , arrows ) ., However , treatment with the ROCK inhibitor Y27632 further increased the junction: non-junction FIR of pMLC in MS11Pil+ΔOpa-inoculated epithelial cells , while having similar inhibitory effects as the MLCK inhibitors on the apical: lateral FIR of pMLC ( Fig 6A and 6C–6E ) ., Our Western blot analysis showed that treatment with either the MLCK or the ROCK inhibitor reduced the pMLC: MLC but not the MLC: tubulin density ration in MS11Pil+ΔOpa-inoculated epithelial cells to basal levels ( Fig 6F–6G ) ., Moreover , the MLCK inhibitors ML-7 and PIK significantly reduced the apical: lateral FIR of pMLC ( Fig 7 ) and pMLC accumulation at GC adherent sites ( Fig 7A , white arrows ) in MS11Pil+ΔOpa-inoculated endocervical tissue explants ., These results suggest that both MLCK and ROCK are involved in the activation of MLC phosphorylation induced by MS11Pil+ΔOpa , but MLCK and ROCK distinctly regulate the subcellular location of active NMII with MLCK promoting and ROCK inhibiting the accumulation of active NMII to the apical junction ., A major upstream signaling molecule of MLCK is calmodulin that is activated by Ca2+ 62–65 ., To investigate if Ca2+ is involved in GC-induced redistribution of active NMII , we determined if GC inoculation would induce Ca2+ flux in polarized epithelial cells ., We used two Ca2+ indicators , FluoForte ( Fig 8A–8C ) and Fluo-4 ( S5 Fig ) to determine the cytoplasmic Ca2+ level ., Polarized T84 ( Fig 8A–8C , S5A and S5B Fig ) and HEC-1-B ( S5C and S5D Fig ) were incubated apically with piliated or non-piliated MS11Opa+ or MS11ΔOpa for 4 h ., The cells were then loaded with the fluorescent Ca2+ indicator , and the cell membrane marked by the membrane dye CellMask ., Cells were imaged using 3D-CFM ( Fig 8A , S5A and S5C Fig ) ., The mean fluorescence intensity ( MFI ) of the Ca2+ dyes in individual cells was measured to estimate the cytoplasmic level of Ca2+ ( Fig 8B and 8C , S5B and S5D Fig ) ., Compared to uninoculated cells , polarized T84 cells and HEC-1-B inoculated with either MS11Pil+Opa+ or MS11Pil+ΔOpa exhibited significant increases in the MFI of both FluoForte ( Fig 8B ) and Fluo-4 ( S5B and S5D Fig ) ., The MFIs of both the Ca2+ indicators in MS11Pil+ΔOpa-inoculated epithelial cells were significantly higher than those in MS11Pil+Opa+-inoculated cells ( Fig 8B , S5B and S5D Fig ) ., However , the MFI of the Ca2+ indicator in epithelial cells inoculated with MS11Pil-ΔOpa , was significantly reduced compared to those infected by MS11Pil+ΔOpa , but similar to those infected by MS11Pil+Opa+ ( Fig 8B ) ., Treatment with the inhibitor specific for Ca2+ release from intracellular storages 2APB or the intracellular Ca2+ chelator BAPTA brought the MFI of the Ca2+ indicators in both MS11Pil+Opa+ and MS11Pil+ΔOpa-inoculated polarized epithelial cells back to the basal level as seen in uninoculated cells ( Fig 8C , S5B and S5D Fig ) ., These results suggest that GC interacting with the apical surface of polarized epithelial cells increases the cytoplasmic level of Ca2+ , by opening the intracellular Ca2+ storages ., Opa inhibits and pili may facilitate GC-induced Ca2+ flux ., To determine whether GC-induced redistribution of active NMII depends on Ca2+ , we treated polarized T84 cells with the Ca2+ inhibitor 2APB or BAPTA before and during incubation with piliated GC ., Both inhibitors decreased the apical: lateral ( Fig 8E ) and the junction: non-junction FIRs ( Fig 8F ) of pMLC in both MS11Pil+Opa+- and MS11Pil+ΔOpa-inoculated polarized epithelial cells , as well as the accumulation of pMLC at GC adherent sites ( Fig 8D , right panels ) ., The results in this and previous sections together suggest that Ca2+-dependent activation of MLCK is responsible for GC-induced accumulation of active NMII at the apical junction and GC adherent sites ., A primary challenge in our understanding of GC pathogenesis in the FRT is to mechanistically explain why a small percentage of GC infections lead to invasive diseases while the rest of the infections remain localized ., A major research obstacle is a lack of infection models that mimic the anatomic environment and process of GC infection in vivo ., This study utilized our newly established human endocervical tissue model with the support of the traditional polarized epithelial cells ., Our results demonstrate that GC can penetrate into the subepithelium of the endocervix , and the efficiency of GC penetration is regulated by Opa phase variation ., GC enter the subepithelium by disassembling the apical junction and inducing the exfoliation of polarized columnar epithelial cells ., These events are caused by the elevation of the cytoplasmic Ca2+ level and the activation and reorganization of NMII in epithelial cells ., The expression of CEACAM-binding Opa inhibits GC penetration by suppressing NMII activation and redistribution , as well as Ca2+ flux , thereby limiting GC-induced junction disruption and exfoliation of polarized endocervical epithelial cells ., Epithe

Introduction, Results, Discussion, Materials and methods

Colonization and disruption of the epithelium is a major infection mechanism of mucosal pathogens ., The epithelium counteracts infection by exfoliating damaged cells while maintaining the mucosal barrier function ., The sexually transmitted bacterium Neisseria gonorrhoeae ( GC ) infects the female reproductive tract primarily from the endocervix , causing gonorrhea ., However , the mechanism by which GC overcome the mucosal barrier remains elusive ., Using a new human tissue model , we demonstrate that GC can penetrate into the human endocervix by inducing the exfoliation of columnar epithelial cells ., We found that GC colonization causes endocervical epithelial cells to shed ., The shedding results from the disassembly of the apical junctions that seal the epithelial barrier ., Apical junction disruption and epithelial exfoliation increase GC penetration into the endocervical epithelium without reducing bacterial adherence to and invasion into epithelial cells ., Both epithelial exfoliation and junction disruption require the activation and accumulation of non-muscle myosin II ( NMII ) at the apical surface and GC adherent sites ., GC inoculation activates NMII by elevating the levels of the cytoplasmic Ca2+ and NMII regulatory light chain phosphorylation ., Piliation of GC promotes , but the expression of a GC opacity-associated protein variant , OpaH that binds to the host surface proteins CEACAMs , inhibits GC-induced NMII activation and reorganization and Ca2+ flux ., The inhibitory effects of OpaH lead to reductions in junction disruption , epithelial exfoliation , and GC penetration ., Therefore , GC phase variation can modulate infection in the human endocervix by manipulating the activity of NMII and epithelial exfoliation .

Neisseria gonorrhoeae ( GC ) infects human genital epithelium causing gonorrhea , a common sexually transmitted infection ., Gonorrhea is a critical public health issue due to increased prevalence of antibiotic-resistant strains ., Because humans are the only host for GC , a lack of a human infection model has been a major obstacle to our understanding of GC infection ., Here we use a human tissue explant model to examine the mechanism by which GC infect the human endocervix , the primary site for GC infection in women ., We show that GC penetrate into the human endocervix by activating the actin motor myosin and epithelial shedding ., Myosin activation causes the disruption of the endocervical epithelial barrier by inducing apical junction disassembly and epithelial cell shedding , allowing GC penetration into the human endocervical tissue ., GC activate myosin by inducing Ca2+-dependent phosphorylation of myosin light chain ., We further show that GC can enhance and reduce the penetration by expressing pili and the opacity-associated protein that promotes and inhibits myosin activation , respectively ., Our study is the first demonstration of GC penetration into the human endocervix ., Our results provide new insights into the mechanism by which GC manipulate signaling and cytoskeletal apparatus in epithelial cells to achieve penetrating and non-penetrating infection .

phosphorylation, medicine and health sciences, membrane staining, epithelial cells, cytoplasmic staining, molecular motors, actin motors, sexually transmitted diseases, motor proteins, research and analysis methods, specimen preparation and treatment, staining, infectious diseases, contractile proteins, animal cells, proteins, biological tissue, biochemistry, cytoskeletal proteins, cell staining, cell biology, anatomy, post-translational modification, epithelium, biology and life sciences, cellular types, myosins

journal.pgen.1006783

Global analysis of double-strand break processing reveals in vivo properties of the helicase-nuclease complex AddAB

Double-strand breaks ( DSBs ) are a potentially lethal form of DNA damage as incorrectly repaired or unrepaired breaks can lead to the loss of genetic information , chromosomal rearrangements , mutations , or cell death ., Cells have evolved the ability to faithfully repair DSBs via homologous recombination using a sister chromatid or sister chromosome as a template 1–3 ., In all domains of life , homologous recombination requires the processing of DSB ends to produce single-stranded DNA overhangs 3–5 ., In bacteria this processing is carried out by a helicase-nuclease complex , such as AddAB or RecBCD , whereas eukaryotes use multiple complexes including the Rad50/Mre11 complex 3 , 4 ., The single-stranded DNA overhangs produced by the helicase-nuclease complex become bound by a single-stranded DNA-binding recombinase , usually RecA in bacteria , or the homologous Rad51 in eukaryotes ., The RecA/Rad51 filaments that form on ssDNA overhangs can initiate homology search and strand invasion to drive recombination and subsequent repair of the damaged chromosome 1–3 ., Precisely how DSB end processing occurs and how it is regulated to ensure the generation of single-stranded DNA required for recombination without an excessive loss of genomic information is not fully understood ., Biochemical studies have led to two general models ., In one model , the nuclease-helicase complex initially degrades both strands of DNA until it encounters a specific DNA element called chi ( crossover hotspot instigator ) that triggers a switch to a state that drives resection of only one strand , thereby producing the necessary ssDNA overhang needed to initiate homologous recombination 6–13 ., This model has emerged from biochemical studies on E . coli RecBCD , which has two independent helicase domains and a nuclease domain , and B . subtilis AddAB , which contains a helicase and a nuclease domain in AddA along with a nuclease domain in AddB ( which also carries an inactive helicase domain ) 4 , 5 , 14 ., Biochemical studies of AddAB from B . subtilis show that , like RecBCD , recognition of chi sequences on the 3’-terminated strand during degradation converts AddAB from a double-stranded nuclease to a single-stranded DNA nuclease and slows its effective rate of translocation 15–18 ., Structural studies have further suggested that this could be due to a conformational change in the complex following chi recognition by AddB 14 , 19 ., The single-stranded DNA generated by either RecBCD or AddAB is thought to be bound by the RecA filament ., In the case of RecBCD , the loading mechanism for RecA has been well-characterized , showing that RecA binding to single-stranded DNA is facilitated by RecBCD in a chi-dependent manner , via a direct interaction with RecB 8 , 20 , 21 ., How RecA loading occurs in the context of AddAB remains to be determined ., The alternative model for production of single-stranded overhangs posits , at least in E . coli , that RecBCD initially unwinds a DSB end but without any degradation 6 ., Upon activation at a chi site RecBCD then nicks one strand with subsequent helicase activity separating the two strands to create a single-stranded overhang that can load RecA and initiate homologous recombination ., Which of these two models applies in vivo to AddAB is not fully resolved ., As noted , the molecular events underlying the initial processing of DSBs have been extensively studied in vitro , both in bulk and in single-molecule experiments 14–17 , 19 , 22–24 , but assessing these events and measuring the rates of AddAB or RecBCD-dependent processing of a DSB on the chromosome in living cells remains a major challenge ., Genetic assays used to assess RecBCD 25 , 26 , and to some extent AddAB 27–30 activity , in vivo have provided important insights , but direct measurements of DNA processing by these helicase-nuclease complexes has been limited ., The assays used often involve measuring the retention of radioactively labeled nucleotides in chromosomes subjected to UV damage 31 , which produces a large number of lesions and kills most cells , complicating the estimation of degradation rates by a helicase-nuclease complex acting on a single chromosomal DSB ., Techniques such as Southern blotting 32 , 33 have also been used to probe helicase-nuclease activity in vivo , but cannot easily be used to examine DNA processing on a global level ., Advances in whole genome DNA sequencing in combination with the development of systems for the controlled introduction of single DSBs 32 , 34–36 in bacterial chromosomes now offer the ability to probe the in vivo activity of helicase-nuclease complexes like AddAB with higher resolution and more precision ., Caulobacter crescentus is a useful organism for probing the in vivo dynamics and mechanisms underlying DSB repair ., Unlike many bacteria , Caulobacter exhibits once-and-only-once replication of its chromosome under all growth conditions , and large populations of cells are easily synchronized , enabling the isolation and study of cells with a single chromosome ., DSB repair has been previously visualized at the single-cell level in Caulobacter 37 ., Here , we examine the in vivo processing of site-specific DSBs introduced in the Caulobacter chromosome , which is thought to require AddAB 37 , 38 , using the endonuclease I-SceI 34 , 37 ., Using a deep sequencing-based assay we measure the extent of DNA processing by AddAB around a break site and provide evidence that AddAB initially degrades both strands , but is then triggered , by putative chi sites , to resecting a single strand ., We show that putative chi sites in Caulobacter attenuate the rate of AddAB-mediated DNA processing in vivo , but only with ~20% efficiency , similar to in vitro estimates for B . subtilis AddAB 16 ., We find that , in the absence of RecA , AddAB translocation rates inferred in vivo are comparable to the previously measured in vitro rate of ~400 bp/s for B . subtilis AddAB 16 , 22 , 23 , 39 ., Further , our results suggest that , in the presence of RecA , AddAB translocation after chi recognition is reduced ~4-fold ., Successful attenuation of degradation requires the formation of a RecA filament , but not the SOS response or recombination ., Collectively , our results indicate that RecA likely downregulates the translocation rate of AddAB after chi , possibly through a direct protein-protein interaction ., This regulation of AddAB by RecA helps to limit DNA degradation around a break site , thus constraining the impact on transcription to a more limited region of the genome ., To assess DNA processing around a DSB induced on the chromosome , we used the I-SceI system previously developed in Caulobacter 37 ( Fig 1A ) ., Briefly , a single I-SceI site was introduced +780 or +3042 kb from the origin of replication and the I-SceI enzyme , which recognizes and cleaves the I-SceI site to generate a DSB , was placed under a vanillate-regulated promoter on the chromosome 37 , 40 ., This promoter is repressed by the protein VanR; addition of vanillate releases VanR from the Pvan promoter and induces gene expression 40 ., DnaA , the replication initiator , was expressed from an IPTG inducible promoter to control the replication state of cells ., To isolate the initial step of DSB processing from later events of homologous recombination , we conducted our experiments primarily in cells with a single chromosome , the swarmer cells of a C . crescentus population ., To isolate these swarmer cells and to prevent subsequent rounds of replication , cells were grown without IPTG for 1 . 5 h to deplete DnaA and arrest cells in a G1 state ., These G1-arrested cells were then isolated using Percoll density gradient centrifugation 41; flow cytometry analysis verified that this procedure produced a population of G1-phased swarmer cells ( S1A Fig ) ., I-SceI was then induced by adding vanillate for 0 . 5 , 1 , 2 , or 4 h and genomic DNA isolated and sequenced ., As a control , genomic DNA from swarmer cells to which vanillate was not added was also isolated and sequenced ., The fold difference in reads per kilobase per million ( rpkpm ) in the DSB-induced sample relative to the control was plotted as a function of genomic position , hereafter referred to as a DSB processing profile ., In cells with a single chromosome , induction of a DSB with 500 μM vanillate at +780 kb from the origin resulted in bidirectional loss of DNA around the break site , with an ~30% and ~40% drop in reads at the DSB site after 0 . 5 h and 1 h respectively ( Fig 1B , S1B and S1C Fig ) ., After 2 h , there was an ~60% drop in reads at the location of the DSB site ., Similar decreases in reads were also observed upon induction of a DSB +3042 kb from the origin ( Fig 1C , S1D Fig ) ., In this case , 1 h of vanillate induction resulted in an ~60% drop in reads at the site of the DSB with 2 h of induction resulting in an ~80% drop in reads ., The DSB processing profiles were highly reproducible , with independent repeats yielding r values of 0 . 94 ( S1E Fig ) ., To confirm that the troughs observed in the profiles ( Fig 1B and 1C ) result from DSB processing by AddAB , we repeated our assay in ΔaddAB cells ., These cells did not show any significant drop in reads near the DSB site , or elsewhere in the genome ( Fig 1D ) , indicating that AddAB is , in the growth conditions tested here , the only helicase-nuclease complex that processes a DSB ., We also generated a DSB processing profile for an unsynchronized , actively replicating population of cells ., We first treated an asynchronous population of cells with 500 μM vanillate ( as with the synchronized G1/swarmer cells in Fig 1B ) to drive maximal induction of I-SceI , which is sufficient to drive cleavage of both chromosomes in nearly all cells with two chromosomes 37 , thereby preventing homologous recombination-based repair ., However , even with maximal induction of I-SceI , a very small percentage of cells could experience only a single DSB that can be repaired ., Treatment with 500 μM vanillate produced a profile almost identical to that observed with swarmer cells alone ( Fig 1E ) ., This effect was not because cells entered a G1 arrest ( S1G Fig ) , indicating that AddAB activity is not significantly influenced by the replication status of the cells ., We also measured the DSB processing profile for cells in which I-SceI was induced with 2 μM vanillate ( Fig 1F ) ., These cells likely experience only a single DSB and thus can repair the damaged chromosome through homologous recombination-based repair , as judged by the fact that cells treated with 2 μM vanillate showed no major change in their flow cytometry profile ( S1G Fig ) , did not lose viability , and were previously shown to engage in homology-based repair 37 ., The profile for these cells was similar in shape to that of synchronized swarmer cells or asynchronous cells treated with 500 μM vanillate , but the magnitude of differences was substantially reduced ( Fig 1F ) , likely because some fewer cells experience DSBs and because cells can repair a single cut chromosome ., The library preparation procedure used should , in principle , only result in the sequencing of double-stranded DNA ., However , DSB resection could , in principle , result in the production of some single-stranded DNA ., To ensure that we were not sequencing single-stranded DNA , we treated genomic DNA extracted from a DSB-induced sample with Mung bean nuclease , which degrades single-stranded DNA ., The resulting degradation profile was indistinguishable from that of a sample not treated with the nuclease ( S1F Fig ) , indicating that our method only produces reads for double-stranded DNA ., Thus , our profiles likely represent total DNA loss due to the degradation of one or both strands from the DSB ., This interpretation would also fit with prior biochemical studies indicating that AddAB can degrade double-stranded DNA and resect a single strand ., However , as noted before , an alternative model for E . coli RecBCD posits that the helicase-nuclease complex initially unwinds the two strands and then , at chi sites will nick and again unwind but not degrade the DNA ., If the initially unwound DNA reannealed , it would form double-stranded DNA that would be sequenced ., But if that were occurring , we would not have seen any loss of reads near the DSB site ., Alternatively , the initially unwound DNA may not anneal , remaining single-stranded , which is not captured in our sequencing ., Hence , to distinguish between these possibilities , we performed quantitative PCR ( qPCR ) , which can report on total DNA , including any single-stranded DNA that may be missed in our sequencing assay ., If AddAB were only unwinding the DNA flanking a DSB , then qPCR using primer pairs at loci adjacent to a DSB should yield significantly more product than a primer pair that spans the DSB site itself ., However , the qPCR values for loci immediately adjacent to a DSB site at +780 kb were comparable to the value for the DSB site itself , and to the values measured by our sequencing-based profiling , supporting the notion that AddAB initially degrades both strands of DNA ( Fig 1G and 1H ) ., The qPCR values increased at sites further from the DSB site , relative to the qPCR value at the DSB and relative to the values measured by our sequencing approach , likely reflecting the presence of some single-stranded DNA not detected in our sequencing assay ., Thus , taking together prior biochemical studies and our own sequencing and qPCR data , we favor a model in which AddAB initially degrades both strands and then , in response to chi sites ( see below ) , switches to resecting a single strand ., There is also a formal possibility that AddAB does initially just unwind the DSB end and that other nucleases in the cell degrade each strand , but such a model is less parsimonious as it invokes additional components that are not necessary in vitro ., In the 1 h DSB processing profile for swarmer cells with a DSB induced at +780 kb ( Fig 1B ) the read counts were lowest at the site of cleavage and then increased progressively in both directions until they matched the read counts of the control profile ., These profiles can , therefore , be used to estimate an upper bound on the speed of AddAB-dependent processing ., After 1 h , the first point of separation between the DSB-induced and control profiles was ~450 kb to the left of the DSB and ~1300 kb to the right ., Thus , the rate of processing ( degradation and resection ) in vivo can be , at most , ~100 bp/s to the left and ~200 bp/s to the right ., However , because the profiles are not step functions and instead feature a gradual decrease from +450 and +1300 kb toward the DSB site , it implies that most cells degrade DNA more slowly than 100–200 bp/s or initiate DSB processing at different times ., To test this latter possibility , we measured cell viability as a function of time after adding vanillate to induce I-SceI and a DSB ., Because we induce DSBs in swarmer cells containing a single chromosome , recombination-based repair cannot occur and a DSB is lethal ., Thus , if all cells experienced a DSB immediately after the addition of vanillate , we would expect a precipitous drop in viability post-induction ., Instead , we found a gradual decrease in viability suggesting that cells likely experience a DSB at variable times ( S1H Fig ) ., Heterogeneity in a population of cells may also arise if DNA degradation proceeds at different rates in individual cells or if DSB processing is slowed or stopped at different frequencies , possibilities explored further below ., For DSBs at either +780 or +3042 kb , the global processing profile was clearly asymmetric around the break site ( Fig 1I and 1J ) ., In each case , read loss extended further toward the terminus than the origin ., Prior studies have shown that DNA degradation by helicase-nuclease complexes such as AddAB or RecBCD is negatively regulated by chi sequences that are highly abundant in bacterial genomes , and often with a much higher frequency on the lagging-strand template , with respect to DNA replication 2 , 42 ., Because B . subtilis AddAB directionally recognizes chi sites 4 , the distribution of these sequences may underlie the asymmetry seen in our degradation profiles ., The putative chi sequence in Caulobacter was previously predicted computationally to be 5’-GCGGTGGT-3’ 43 ( Fig 2A ) ., To test whether this sequence element is responsible for degradation asymmetry , we first overlaid on the DSB processing profile of cells with a DSB induced at +780 kb the putative chi sequences on the leading ( Figs 2B and S2A ) and lagging strands ( Figs 2C and S2B ) that may affect AddAB translocation and degradation in the 3’->5 direction ., Leading and lagging strands are defined with respect to DNA replication , which is presumed to proceed bidirectionally from the origin ( 0 kb ) to the terminus ( ~2000 kb ) ; for the sake of simplicity , we use leading and lagging strands to refer to the lagging- and leading-strand templates , respectively ( Fig 2A ) ., Additionally , we note that the chi sequences shown in Fig 2 and throughout our study are those that match the computationally predicted chi sequence and are in an orientation that would , in principle , allow them to affect AddAB ., The presence of putative chi sequences was inversely correlated with the extent of degradation , with more degradation occurring on the side of the DSB where the density of putative chi sequences was substantially less ., This pattern was also observed when a DSB was induced +3042 kb from the origin ( S2C and S2D Fig ) suggesting that the asymmetry observed in the DSB processing profiles results from an effect of chi site frequency on AddAB activity ., To more directly determine whether 5’-GCGGTGGT-3’ is the Caulobacter chi sequence and whether the presence of this sequence explains the asymmetry of our profiles , we inserted an array of 15 such sequences on the chromosome either +30 kb or +100 kb from the DSB site at +780 kb ., This chi array was inserted either in the correct orientation for recognition by AddAB ( chifor ) or in the opposite orientation as a control ( chirev ) ., After inducing a DSB , the chirev construct had no effect on degradation ( Fig 2D and 2E , S2E and S2F Fig ) ., In contrast , the chifor construct significantly reduced degradation beyond the location it was inserted when compared to the control or wild-type profiles ( Figs 2D , 2E , S2E and S2F ) ., Although degradation beyond the inserted array of chi sites was reduced , it was not completely eliminated ., Given prior studies suggesting that chi sites switch AddAB to a mode in which it degrades only one strand to produce resected DNA 4 , 17 , we infer that ssDNA degradation likely occurs more slowly than the initial double stranded degradation ., Alternatively , it is possible that the chi recognition frequency is low ., These data also support the conclusion that 5-GCGGTGGT-3 is likely the chi sequence in Caulobacter , though we have not , of course , shown whether they are sites where homologous recombination preferentially occurs ., Importantly for our purposes though , these sites clearly affect DSB processing and likely explain the asymmetric degradation by AddAB from a DSB site ., Notably , the chifor construct produced a clear difference in the profile , relative to the wild type and cells harboring the chirev construct ., This difference was evident within ~2–5 kb of the site of insertion ( Fig 2F and 2G ) , demonstrating that our assay has a resolution of at least 5 kb and likely better ., Collectively , the results presented thus far suggest that the recognition of chi sequences by AddAB results in an attenuation of AddAB-mediated processing of DNA around a break site ., Next , we wondered whether the attenuation of DSB end processing after chi recognition may result , in part , from the recruitment of RecA to the ssDNA formed after AddAB encounters a chi site ., To examine the effect of RecA on DSB processing , we measured the DSB processing profile of cells lacking RecA ( Fig 3A , S3C and S3D Fig ) ., In sharp contrast to the wild type , the profile for ΔrecA cells exhibited significantly more extensive degradation and/or processing in both directions after a DSB ( Fig 3A ) ., Additionally , the asymmetry of the degradation profile was no longer apparent in cells lacking RecA ( Fig 3B and 3C ) ., After 1 h , the first point of separation between the DSB-induced and control profiles was approximately the same on both sides of the DSB , yielding an upper bound of ~400 bp/s for the rate of DNA processing in both directions in ΔrecA cells ., Note that we also detected more total DNA via qPCR than in our DSB processing profiles of ΔrecA cells , again with a progressively increasing ratio of qPCR to degradation profile values away from the DSB site ( S3A and S3B Fig ) ., These results indicate that RecA is important in limiting the extent of DSB end processing ., The effect of RecA on DSB resection could be indirect ., RecA , when bound to single-stranded DNA , can induce auto-cleavage of the transcriptional repressor LexA , resulting in the expression of genes in the SOS regulon , many of which participate in DNA damage response and repair 44 , 45 ., To test whether the effect of RecA on AddAB-dependent degradation around a DSB is indirectly mediated via the SOS response , we introduced a non-cleavable mutant of LexA 46 into our DSB system and measured the profile of cells after a DSB ., The profile for this non-cleavable LexA mutant strain was nearly indistinguishable from the wild-type profile ( Fig 3D ) ., Thus , the effect of RecA on AddAB-dependent degradation is likely not mediated through its effect on the SOS regulon ., Further , the dispensability of the SOS response for AddAB regulation suggests that basal levels of RecA are sufficient to prevent excessive DNA processing at a DSB 45 ., To test whether RecA directly interacts with AddAB , we used a bacterial two-hybrid assay to screen for physical interactions 47 ., Each protein was fused to a subunit of adenylate cyclase and then co-expressed in E . coli ., Interaction between two fusion proteins will reconstitute adenylate cyclase , leading to production of cAMP and the subsequent activation of a reporter gene that turns colonies red on MacConkey agar ., In this assay , we found that RecA interacted with AddA but not with AddB ( Figs 3E , S3E and S3F ) ., We also confirmed an interaction between AddA and AddB , as expected , but not between AddA and a negative control , FtsZ ., RecA or AddA also did not display interaction with empty vector controls , T18 and T25 respectively ., Further , our assay indicated that RecA likely interacts with the N-terminal portion of AddA ( Figs 3F , S3E and S3F ) , where the helicase domain of AddA resides ., This is in contrast to RecA’s interaction with RecB in E . coli , which occurs via the nuclease domain of RecB 20 ., RecA forms a filament on single-stranded DNA that is formed by AddAB after it interacts with chi and begins degrading only one strand of the DNA 2 , 4 , 5 ., To test whether this filament forming activity of RecA is necessary for it to interact with and regulate AddAB , we generated a mutant , RecA ( K83A ) , predicted to abrogate filament formation based on studies of E . coli RecA 48–50 ., This mutant retained an interaction with AddA in the bacterial two-hybrid system and was expressed in vivo at levels comparable to the wild type protein ( S3G Fig ) ., However , the K83A mutant was incapable of regulating AddAB-mediated DNA resection as the degradation profile of the mutant looked similar to that of ΔrecA cells ( Fig 3G ) ., We conclude that RecA likely must form a filament to properly regulate AddAB and attenuate DNA degradation and processing ., To test whether recombination is required for AddAB regulation , or if RecA filament formation is sufficient to slow down AddAB after chi recognition , we constructed a strain producing RecA ( N304D ) 51 , which is predicted to be recombination deficient but still capable of binding ssDNA to form a filament ., This mutant was also expressed in vivo ( S3G Fig ) , but sensitive to DSBs , comparable to ΔrecA cells ( S3H Fig ) ., In asynchronously growing , replicating cells induced with maximal levels of I-SceI ( 500 μM vanillate ) , the profile of this mutant was comparable to wild-type cells , including the same asymmetry around the DSB site ( Fig 3H ) ., In cells treated with only 2 μM vanillate ( a concentration that allows for repair via homologous recombination in wild-type cells ) , an asymmetric global profile was still seen in the recombination-deficient mutant ( Fig 3I ) ., These results suggest that recombination is likely not required for RecA-mediated regulation of AddAB ., To further probe the effect of RecA on AddAB , we sought to assess the rates of AddAB-dependent processing in vivo , both before and after chi recognition ., These rates have been measured previously in vitro for B . subtilis AddAB , although only in the absence of RecA 16 , 22 , 24 , 39; the B . subtilis AddAB translocation rate before chi recognition was reported to be between 400 and 2000 bp/s , with a chi recognition probability of ~0 . 25 , and a post-chi translocation rate only ~15% slower than the pre-chi rate ., To estimate the in vivo rates and chi recognition probability , we ran a simulation of DNA degradation/processing with four parameters:, ( i ) the rate of DSB formation after adding inducer , which produces a distribution of times post-induction when DSB processing begins ,, ( ii ) the degradation rate before chi recognition ,, ( iii ) the probability of recognizing each chi site , and, ( iv ) the DNA processing rate after chi recognition ., Rates of DSB formation were estimated via simulations ( S4A and S4B Fig ) and confirmed by performing qPCR on chromosomal DNA isolated from DSB-induced ΔaddAB cells ( S4C and S4D Fig ) ., Based on previous studies 22 , 39 , it was assumed that AddAB would recognize only one chi sequence and , once bound to chi , the complex would not be affected by further chi sequences encountered ., With a rate of DSB formation of ~0 . 4 DSB / h , we first tested the parameters measured in vitro and found that a pre-chi degradation rate of 400 bp/s , a chi recognition probability of ~0 . 23 , and a post-chi degradation rate of 340 bp/s , produced a close fit to the in vivo symmetric DSB processing profile of cells lacking recA ( Fig 4A ) ., We next considered the case of wild-type cells in which RecA attenuates AddAB-dependent degradation and combines with chi to produce an asymmetric degradation profile ., A reasonable fit to the wild-type profile at 1 and 2 h was produced by simulations with a pre-chi degradation rate of 400 bp/s , a chi recognition probability of ~0 . 22 , and a post-chi processing rate of 51 bp/s ., However , these parameters did not fit the measured profile at 4 h ( S5A Fig ) ., At this later time point , the model predicted more extensive degradation than was observed ., In fact , the profile at 4 h was not substantially different than that observed at 2 h ., This could indicate that cells are dead after 4 hours , although DNA degradation can likely still occur even when cells are no longer viable as judged by plating assays ., Thus , the similarity between the 2 and 4 h profiles could suggest that AddAB dissociates from the DNA at long time points ., We therefore added a fifth parameter to the model , the rate of AddAB dissociation and considered two possible models: Model 1 where we, ( i ) fixed the post-chi processing rate to be 15% less than the pre-chi rate , as measured in vitro in the absence of RecA 16 , 24 , and, ( ii ) varied the rate of AddAB dissociation after chi recognition; Model 2 where we varied both the post-chi processing rate and the dissociation rate for AddAB ( Fig 4B ) ., For each model we identified parameters that produced good fits to the degradation profiles at each time point measured ( Fig 4C and 4D , S5B and S5D Fig ) ., In each case the rate of degradation pre-chi recognition was 400 bp/s and the probability of chi recognition was ~0 . 23 ., In Model 1 where the post-chi processing rate was 340 bp/s , the dissociation rate for AddAB was 0 . 102 / min ., In Model 2 , the post-chi processing rate was 96 bp/s with a dissociation rate for AddAB of 0 . 021 / min ., To distinguish between these models , we sought to directly measure the post-chi processing rate , which differs more than 3-fold between the two models , by examining the time it takes AddAB to degrade two loci positioned at specific distances from a DSB site on the arm where chi site density is highest ., For these experiments we used a strain with a DSB site located +30 kb from the origin , which enabled us to label the endogenous parS locus ~38 kb from the DSB site by expressing a fusion of the protein MipZ and YFP ., MipZ binds ParB , which forms a large nucleoprotein complex at parS; thus , MipZ-YFP forms a fluorescent focus in vivo that marks the cellular position of parS 52 ., We also inserted an orthogonal parS site from the plasmid pMT1 either 130 or 230 kb from the break site; expressing the cognate ParBpMT1 fused to CFP enables the in vivo tracking of this locus 53 ., Using time-lapse microscopy we then measured the timing of disappearance of the MipZ-YFP and ParBpMT1-CFP foci after inducing a DSB ., We infer that the disappearance of each focus reflects the degradation of either one or both strands that correspond to a given locus , as occurs during DSB processing ., Mere translocation of a protein , such as AddAB , past a locus would not lead to the permanent losses in fluorescent foci seen here; for instance , MipZ foci are well known to be maintained after the passage of the replisome 52 ., The model with a post-chi degradation rate of 340 bp/s ( Model 1 in Fig 4B ) predicted an interval between loss of the two fluorescent foci of ~4 or 9 min , respectively , whereas the model with a post-chi degradation rate of 96 bp/s ( Model 2 in Fig 4B ) predicted an interval of ~15 or 30 min , respectively ( Fig 4E–4J ) ., Our measurements , using fluorescence time-lapse microscopy , revealed mean intervals of ~12 and 29 min ( Fig 4E–4H ) , respectively , depending on whether the second locus was 130 or 230 kb from the break site ., As predicted from the above results , we also found that the frequency of loss of a marker -130 kb from the break site was higher than a marker -230 kb away ( Fig 4J ) ., Thus , we favor a model in which AddAB initially drives DSB ends processing at a rate of ~400 bp/s , with an ~23% chance of recognizing each chi site during translocation , and that chi recognition in combination with RecA loading on the single-stranded DNA produced by AddAB slows subsequent processing or translocation ~4-fold , with an additional , modest rate of AddAB dissociation ., Taken together , our results indicate that DSB ends are often subject to extensive degradation and processing , as the AddAB complex may not slowdown at the first chi site encountered or may slowdown but continue degrading one strand of the DNA ., Thus , AddAB-dependent processing of DSB ends could affect the transcription of genes flanking a break site , as shown recently in yeast 54 ., To test this possibility in Caulobacter , we performed RNA-seq on swarmer cells subjected to a single DSB ., We compared the expression levels of individual genes to untreated swarmer cells ., A set of genes associated with the DNA damage response in Caulobacter that are found throughout the chromosome increased significantly following a DSB ., In addition , we observed a clear decrease in the RNA levels of genes nearest the DSB site ( Fig 5A and 5B ) ., These transcriptional profiles correlated well with the DNA processing profiles seen after inducing a DSB ., The asymmetry observed in the DNA profiles was also observed in the transcriptional profiles , with larger decreases in transcription on the arm with fewer chi sequences .,

Introduction, Results, Discussion, Materials and methods

In bacteria , double-strand break ( DSB ) repair via homologous recombination is thought to be initiated through the bi-directional degradation and resection of DNA ends by a helicase-nuclease complex such as AddAB ., The activity of AddAB has been well-studied in vitro , with translocation speeds between 400–2000 bp/s on linear DNA suggesting that a large section of DNA around a break site is processed for repair ., However , the translocation rate and activity of AddAB in vivo is not known , and how AddAB is regulated to prevent excessive DNA degradation around a break site is unclear ., To examine the functions and mechanistic regulation of AddAB inside bacterial cells , we developed a next-generation sequencing-based approach to assay DNA processing after a site-specific DSB was introduced on the chromosome of Caulobacter crescentus ., Using this assay we determined the in vivo rates of DSB processing by AddAB and found that putative chi sites attenuate processing in a RecA-dependent manner ., This RecA-mediated regulation of AddAB prevents the excessive loss of DNA around a break site , limiting the effects of DSB processing on transcription ., In sum , our results , taken together with prior studies , support a mechanism for regulating AddAB that couples two key events of DSB repair–the attenuation of DNA-end processing and the initiation of homology search by RecA–thereby helping to ensure that genomic integrity is maintained during DSB repair .

Double-strand breaks ( DSBs ) are a threat to genome integrity and are faithfully repaired via homologous recombination ., The initial processing of DSB ends that prepares them for recombination has been well-studied in vitro , but is less well characterized in vivo ., We describe a deep sequencing-based assay for assessing the early steps of DSB processing in bacterial cells by the helicase-nuclease complex AddAB ., We find that a combination of chi site recognition and RecA loading is required to attenuate AddAB activity ., In the absence of RecA , the chromosome is excessively degraded with a concomitant loss in transcription ., Our results , along with prior studies , support a model for how chi recognition and RecA together regulate AddAB to maintain genome integrity and facilitate recombination .

sequencing techniques, medicine and health sciences, caulobacter, nucleases, enzymes, dna-binding proteins, enzymology, surgical and invasive medical procedures, dna, molecular biology techniques, bacteria, homologous recombination, research and analysis methods, chromosome biology, proteins, molecular biology, surgical resection, biochemistry, hydrolases, cell biology, nucleic acids, genetics, biology and life sciences, dna repair, dna recombination, dna sequencing, organisms, chromosomes

journal.pgen.1000176

Somatic Pairing of Chromosome 19 in Renal Oncocytoma Is Associated with Deregulated ELGN2-Mediated Oxygen-Sensing Response

Cellular adaptation to changes in oxygen tension is vital for the integrity , maintenance and survival of cells ., Hypoxia-inducible factor ( HIF ) , the major transcription factor of the ubiquitous oxygen-sensing pathway , is a heterodimer composed of α and β subunits 1 ., While HIFβ is constitutively expressed and stable , HIFα is oxygen-labile by the virtue of the oxygen-dependent degradation ( ODD ) domain , which undergoes rapid oxygen-dependent ubiquitin-mediated destruction 2–5 ., Thus , the stability of HIFα dictates the transcriptional activity of HIF 6 ., Critical regulators of HIFα stability are the prolyl-hydroxylase domain-containing enzymes ( PHD/EGLNs ) that hydroxylate HIFα on conserved prolines within the ODD domain in the presence of oxygen 7 , 8 ., Hydroxylated HIFα is recognized by the von Hippel-Lindau ( VHL ) protein ., VHL is the substrate-conferring component of an E3 ubiquitin ligase called ECV ( Elongins/Cul2/VHL ) that specifically polyubiquitinates prolyl-hydroxylated HIFα for subsequent destruction via the 26S proteasome ., Deregulation of HIFα regulatory proteins has been strongly associated with cancer development ., Germline inheritance of a faulty VHL allele on chromosome 3p25 is the cause of VHL disease , characterized by a high frequency of clear cell renal cell carcinoma ( RCC ) , cerebellar hemangioblastoma , pheochromocytoma , and retinal angioma 9 ., Inactivation of the remaining wild-type VHL allele in a susceptible cell leads to tumor formation ., Somatic biallelic inactivation of VHL is also responsible for the development of sporadic clear-cell RCCs , the predominant form of adult kidney cancer 10–12 ., Cells that are devoid of functional VHL show elevated expression of numerous hypoxia-inducible genes due to a failure to degrade HIFα ., In addition to VHL , deregulation of the PHD/EGLN family of prolyl-hydroxylases have also been associated with abnormal cell growth ., Development of erythrocytosis , characterized by an excess of erythrocytes , has been associated with inactivating germline mutations in PHD2/EGLN1 13 , 14 ., Pheochromocytoma , a neuroendocrine tumor of the medulla of the adrenal glands , is linked with deregulation of PHD3/EGLN3 15 ., While biallelic inactivation of VHL is found in the majority of clear cell RCCs , kidney cancer is a heterogeneous disease that can be divided into several subtypes based on morphological and cytogenetic features 16 , 17 ., Chromophobe RCC and renal oncocytoma are two related kidney tumors that together account for approximately 10% of all renal masses ., In contrast to clear cell RCC , VHL mutations and/or increased expression of hypoxia-inducible genes are not found in these tumor subtypes and molecular genetic defects that are associated with tumor development remain unclear ., Identification of molecular genetic defects in renal oncocytoma is particularly challenging as these cells are often described as karyotypically normal and the presence of cytogenetically abnormal regions in which to search for tumor modifying genes is rare in this tumor subtype ., To identify molecular defects associated with renal tumor development , we analyzed gene expression data from a variety of kidney tumors ., This analysis revealed that renal oncocytoma and chromophobe RCC have a striking transcriptional disruption along chromosome 19 ., While in chromophobe RCC the disruption reflected a chromosome 19 amplification , in the renal oncocytoma cells the disruption reflected the close association , or pairing , of chromosome 19q in interphase ., EGLN2 located within the paired region was dramatically overexpressed in renal oncocytoma cells and was associated with the deregulation of numerous hypoxia-inducible genes including a pro-death BNIP3L ., Thus , chromosome 19q pairing in renal oncocytoma unveils a unique mechanism of disrupting oxygen homeostasis via altering the expression of EGLN2 ., Gene expression profiling data derived from renal oncocytomas and chromophobe RCCs was scanned for regional increases or decreases in RNA production , which often indicate the presence of chromosomal amplifications or deletions 18–24 ., Consistent with previous cytogenetic studies , the renal oncocytoma cells were largely devoid of transcriptional abnormalities that would reflect a DNA amplification or deletion ., In contrast , losses of chromosomes 1 , 2 , 6 , 10 , and 17 are frequently found in chromophobe RCC ., In our chromophobe RCC samples , these well-established chromosomal losses were strongly reflected in the gene expression profiling data ( Figure 1A ) ., In addition , a transcriptional abnormality involving genes mapping to chr 19 was frequently identified in both the renal oncocytomas and the chromophobe RCCs but not other subtypes of RCC ( Figure 1A and Figure S1 ) ., In renal oncocytomas , the transcriptional abnormality primarily involved the q arm of chromosome 19 , while in chromophobe RCC the abnormality involved the entire chromosome ( Figure 1A , B ) ., Regional increases in overall RNA production often indicate the presence of an underlying DNA amplification ., As gain of chromosome 19 has not been previously reported as a recurrent abnormality in either renal oncocytoma or chromophobe RCC , DNA copy number analysis was performed on a subset of these samples using high-density single nucleotide polymorphism ( SNP ) arrays ., From the SNP data , an amplification of the entirety of chromosome 19 was detected in the chromophobe RCC samples ( Figure 1C , D ) ., This whole-chromosome amplification was confirmed by fluorescence in-situ hybridization ( FISH ) using locus-specific probes that mapped to the p and q arms of chromosome 19 ( Table S1 ) ., In contrast , no change in DNA copy number was detected in the renal oncocytoma samples ( Figure 1C , D ) ., As a positive control for the DNA copy number analysis , only oncocytoma ( ON ) samples derived from female patients were examined , and a relative gain of the X chromosome was clearly detected in these samples ( Figure 1C ) ., To determine the status of chromosome 19 in more detail in the renal oncocytoma cells , this chromosome was evaluated further using a panel of FISH probes ., Two distinct and well-separated FISH signals , typical of diploid cells in interphase , were frequently observed when probes specific to the chr 19p arm were used ( Figure 2 and Table S2 ) ., In contrast , a single , large FISH signal ( singlet ) or two FISH signals that were in close proximity ( proximal doublet ) were frequently observed when probes specific to the chr 19q arm were used ., Approximately 35% of cells examined contained the singlet signal , while an additional 18% of cells contained proximal doublets ( Table S2 and data not shown ) ., Semi-quantitative image analysis was used to examine the characteristics of the large FISH singlet ( Figure 2B ) ., This analysis demonstrated that the size of the singlet FISH signal was on average 1 . 5-fold larger than the size of two well-separated 19q FISH signals ( P\u200a=\u200a0 . 02 ) ., This large signal was observed using multiple probes directed against the q arm of the chromosome , including centromeric and telomeric probes ( Figure 2C , E ) ., The large FISH singlet had striking similarities to the FISH signals observed in studies of somatically paired chromosomes 25–27 ., Somatic pairing refers to the close association of homologous chromosomes and is typically associated with chromosomes in meiotic prophase ., However , somatic pairing has also been observed in interphase in normal human cells and some tumor cells 26 , 28–32 ., The presence of a large FISH singlet reflects the overlapping FISH signals generated from two chromosomal regions in very close proximity 26 , 27 ., The lack of evidence for a DNA copy number change coupled with the presence of large FISH singlets and proximal doublets using multiple locus-specific probes , suggested that chr 19q was somatically paired ., To confirm that the q arms of chr 19 were somatically paired in the renal oncocytoma cells , the p and q arms of chr 19 were visualized simultaneously using whole-arm chromosome painting ( WCP ) ., Using this approach , two distinct p arms , typical of diploid cells in interphase , were frequently observed in renal oncocytoma cells ( Figure 2G , H and Table S2 ) ., However , the majority of cells contained a single q-arm signal that was located proximal to the two p-arm signals ., While the diffuse nature of the WCP prevented the quantification the fluorescence signal , this pattern is consistent with the locus-specific FISH analysis and further indicates that the q arms of the chromosomes are in close proximity or are paired in these cells ., The changes in gene expression that accompanied the somatic pairing suggested that deregulation of a gene , or multiple genes , associated with tumor development mapped within the paired chr 19q region ., As deregulation of the oxygen-sensing network is a common event in other types of sporadic renal cell carcinomas , genes associated with HIF regulation and that mapped to chr 19q were identified from the Entrez Gene database and tested for expression defects ( see Materials and Methods ) ., We also identified additional genes that were related to kidney-cancer via additional literature searching ( Table S3 ) ., Both analyses identified EGLN2/PHD1 as a possible candidate gene in this region ., To verify that the prolyl-hydroxylase EGLN2/PHD1 was significantly deregulated in renal oncocytoma cells , the level of EGLN2 protein was evaluated in these tumors ( Figure 3A , B ) ., Analysis of matched oncocytoma-normal tissue pairs revealed a dramatic increase in the level of EGLN2 in the oncocytoma tumors versus the level observed in corresponding normal tissue ., Higher expression of EGLN2 was also observed in 2 of 3 chromophobe RCCs examined ( Figure S2 ) ., These results are in contrast to the EGLN2 levels found in clear cell RCC ., Consistent with the gene expression data , virtually no EGLN2 protein was detected in patient-derived clear cell RCC samples , while low basal amounts of EGLN2 were visualized by Western blot analysis in the matched normal samples ( Figure 3 A , C ) ., EGLN2 is one of three prolyl-hydroxylases known to post-translationally modify HIFα , which is required for VHL-mediated destruction of HIFα ., To address whether increased expression of EGLN2 influenced the binding and ubiquitination of HIF-1αODD via VHL , in vitro translated 35S-labeled HA-VHL and in vitro translated unlabeled Gal4-HA-HIF-1αODD were mixed in extracts in which EGLN2 was enriched ( see Materials and Methods ) ., Enrichment of EGLN2 led to an increased association of VHL to the wild-type ODD , but not to a mutated ODD in which a proline residue critical for VHL binding was changed to an alanine ( P546A ) ( Figure 3D ) ., In addition , an in vitro HIF-1αODD ubiquitination assay was performed to determine whether the increased VHL-HIF-1αODD association led to increased HIF-1αODD ubiquitination ., Increased levels of EGLN2 resulted in a dose-dependent increase in VHL-mediated HIF-1αODD ubiquitination ( Figure 3E ) ., These results suggest that overexpression of EGLN2 in oncocytoma could further decrease the level of HIFα below the level observed in normal tissue ., In clear cell RCC , an increase in HIFα due to functional inactivation of VHL induces a transcriptional program that mimics cellular exposure to hypoxic conditions ., In contrast , in the renal oncocytoma , the functional effects of increased expression of EGLN2 would be to decrease HIFα levels ., To examine the cellular effects of decreased HIFα , we re-evaluated previously published data that measured HIF-1 DNA-binding activity , HIF-1α protein levels , and HIF-1β protein levels in cells exposed to hypo- and hyper-oxygenated conditions 6 ., Normoxic conditions in the kidney cortex is estimated to be 3–5% oxygen 6 ., Induction of a hypo-oxygenated condition was associated with a significant increase in HIFα and HIF activity levels ( Figure 4A ) ., Specifically , a six-fold decrease in oxygen concentration ( 3% to 0 . 5% oxygen ) resulted in approximately a four-fold increase in HIF-1α levels ( 2 . 5 to 9 . 8 densitometry units ) ., Further , we noted that HIF-1α levels change in an analogous manner upon induction of hyper-oxygenated conditions: a six-fold increase in oxygen concentration ( 3% to 18% oxygen ) results in greater than a three-fold decrease in HIF-1α levels ( 2 . 5 to 0 . 75 densitometry units ) ., The association between decreased HIF-1α and hyper-oxygenated conditions is easier to evaluate if the HIF dose-response data is plotted on a log-log scale rather than a linear-linear scale ( Figure 4B ) ., The log-log transformed data follow a straight line , indicating that HIFα level and oxygen concentration follow a power-law relationship ( i . e . , f ( x ) =\u200aaxk ) , rather than an exponential relationship ( i . e . , f ( x ) =\u200akax ) ., The biological implications of the power-law relationship is that an n-fold change in oxygen concentration leads to a proportional n-fold change in HIF-1α levels and HIF activity ( Figure S3 ) ., Moreover , these results demonstrate that while increases in HIF-1α are associated with hypo-oxygenated conditions , decreases in HIF-1α are associated with hyper-oxygenated conditions ., To determine whether EGLN2 overexpression is inducing a HIF-mediated hyperoxic cell response in the renal oncocytoma cells , the expression pattern of several known HIF target genes were examined in the renal oncocytoma cells and , for comparison , in clear cell RCC 33 ., Consistent with VHL defects present in the clear cell RCC , gene set enrichment analysis revealed a significant up-regulation of the HIF-1 target genes in clear cell RCC ( P\u200a=\u200a0 . 0001; Figure 4C ) ., Notable up-regulated genes included carbonic anhydrase IX ( CA9 ) , ferroxidase ( CP ) , vascular endothelial growth factor A ( VEGFA ) , and glucose transporter ( GLUT1 ) ., However , in the renal oncocytoma cells , a distinct population of HIF-target genes were significantly down-regulated ( P\u200a=\u200a0 . 01; Figure 4D ) ., Specifically , the HIF-target genes heme oxygenase 1 ( HMOX1 ) , enolase 1 ( ENO1 ) , and Cbp/p300-interacting transactivator ( CITED2 ) were significantly down-regulated , but genes such as CA9 , VEGFA , and GLUT1 were not ., In addition , the recently identified tumor suppressor BNIP3L is downregulated three-fold in the renal oncocytoma cells ( Figure 4E ) ., BNIP3L is an oxygen-regulated member of the Bcl-2 family ( Figure S4 ) ., BNIP3L is a pro-death gene ( induces features of apoptosis , necrosis and autophagy ) and knockdown of this gene is sufficient to convert non-tumorigenic cell lines into tumorigenic lines in xenograft studies 34–36 ., In support , while hypoxia mimetic treatment significantly induced the expression of BNIP3L , HMOX1 , ENO1 , and CITED2 ( Figure 5A , right panel and Figure S5 ) , ectopic transient expression of EGLN2 under physiologic hypoxia ( cyclical 0–7% oxygen exposure 37 ) was associated with reduced level of expression of these genes in comparison to cells transfected with empty plasmid ( Figure 5 and Figure S5 ) ., These results demonstrate that over expression of EGLN2 can downregulate HIF1 responsive factors , such as BNIP3L ., Moreover , while up-regulation of HIF-target genes such as VEGFA are associated with the development of clear cell RCC , these results suggest that down-regulation of distinct subset of HIF-target genes are associated with the development of renal oncocytomas ., A proper oxygen-sensing response is vital to the maintenance of normal cellular functions ., Deregulation of HIF , the principal driver of the adaptive response to hypoxia , is associated with the pathogenesis of several diseases , including cancer ., While the hypoxic tumor microenvironment - by the virtue of the ubiquitous oxygen-sensing pathway - results in modulation of HIF activity , loss-of-function mutations in a growing list of tumor suppressor genes also can affect HIF function ., Mutations in PTEN , PML , TSC , and VHL have been identified in tumor cells that result in the deregulation of HIF via multiple distinct mechanisms involving Akt/PI3K , mTOR and the ubiquitin pathway ., Emerging evidence now implicates cancer-causing mutations that directly impinge on EGLNs ., For example , mutations in succinate dehydrogenase ( SDH ) result in the cytosolic accumulation of succinate , which inhibits EGLNs , leading to the stabilization and activation of HIF-1α 38 , 39 ., Inactivating germline mutations in EGLN1 have been identified to cause erythrocytosis 13 , 14 and deregulation of EGLN3 has been linked to the development of pheochromocytoma , a neuroendocrine tumor of the adrenal glands 15 ., In this study , we reveal somatic pairing of chr 19q as a recurrent cytogenetic abnormality in renal oncocytoma that results in dramatic changes in transcription within the paired region ., The functional consequence of chromosome joining is formally unknown but it is may disrupt chromatin structure causing the juxtaposition of cis and trans regulatory regions that modulate the transcription of a large set of genes ., The identification of EGLN2 as a significantly deregulated gene that maps within the paired chr 19q region directly implicates defects in the oxygen-sensing network to the pathobiology of renal oncocytoma ., These results suggest that in addition to numerical and structural chromosomal abnormalities , somatic pairing should be considered as a chromosomal event that associates with tumorigenesis ., Although the loss of EGLN2 does not lead to decreased HIF1α accumulation , perhaps due to the compensatory activity of EGLN3 , the data from this study suggest that overexpression of ELGN2 leads to decreased HIF1 levels ., More recently , an E3 ubiquitin ligase called Siah2 was identified to target EGLN2 for ubiquitin-mediated destruction and thereby revealing another level of HIF regulation 40 ., The activity of Siah2 is induced under physiologic hypoxia ( <10% oxygen ) , resulting in reduced levels of EGLN2 and stabilization of HIF-1α ., The present findings suggest that the overexpression of EGLN2 via somatic pairing is sufficient to counteract the suppressive activity of Siah2 under physiologic hypoxia ., Under hyper-oxygenated conditions ( 21% oxygen; frequently used as experimental normoxia ) , Siah2 activity is attenuated via a yet-defined mechanism , resulting in the increased abundance of EGLN2 and concomitant reduction in the level of HIF-1α 40 ., The ectopic expression of EGLN2 under 21% oxygen did not result in further diminution of HIF-target gene expression ( data not shown ) , which is likely due to the fact that endogenous EGLN2 is highly abundant or that every available EGLN2 is already activated under hyper-oxygenated conditions ., HIF-regulated genes are involved in many physiological processes including angiogenesis , metabolism , cell proliferation , survival , and apoptosis ., As such , disruption in the regulation of HIF may affect several regulatory pathways that contribute to the transformation of normal cells into cancer cells ., Evasion of apoptosis is one of the hallmark features of cancer cells and represents a key oncogenic event ., BNIP3L is a regulator of p53-dependent apoptosis and silencing of BNIP3L has been associated with enhanced tumorgenicity and reduced apoptotic response 36 ., We show here that BNIP3L is one of several HIF-responsive genes governed , in part , by EGLN2 ., Therefore , we propose that the downregulation of BNIP3L is the result of chromosome-pairing induced upregulation of EGLN2 and that downregulation of BNIP3L contributes to the inhibition of apoptosis to facilitate oncocytoma cell survival and growth ., The disruption of HIF activity has been associated with kidney cancer related to VHL disease , sporadic clear cell RCC , and hereditary papillary RCC 38 , 41 , 42 ., The present study reveals deregulation of the oxygen-sensing response in renal oncocytoma , as well as chromophobe RCCs ( which display DNA amplification mediated up-regulation of EGLN2 ) and thereby supporting the dysfunction of HIF pathway as a common and perhaps central theme in the pathogenesis of kidney cancer ., Single-color expression profiles were generated using the HG-U133 Plus 2 . 0™ chipset ( Affymetrix , Santa Clara , CA ) from renal oncocytoma ( n\u200a=\u200a10 ) , chromophobe RCC ( n\u200a=\u200a10 ) , and nondiseased kidney ( n\u200a=\u200a12 ) samples as described 43 ., The gene expression data can be obtained at the Gene Expression Omnibus ( GSE8271 and GSE7023 ) ., Analysis was performed using BioConductor version 2 . 0 software ., Data preprocessing was performed using the RMA method as implemented in the affy package and using updated probe set mappings such that a single probe set describes each gene 44 , 45 , 46 ., Chromosomal abnormalities were predicted using the comparative genomic microarray analysis ( CGMA ) method as implemented in the reb package 47 ., Briefly , for each measured gene , the gene expression value was normalized such that the average gene expression value in the nondiseased samples was subtracted from the tumor-derived gene expression value ., A Welshs t-test was applied to the relative gene expression values that mapped to each chromosome arm ., For the smoothing curve , the normalized expression values derived from genes mapping to chromosome 19 were replaced by a summary score that comprised a running two-sided t-test statistic using window sizes of 61 , 245 , and 611 ( representing 5% , 20% , and 50% of the length of the chromosome ) ., The results of the three smoothing curves were averaged ., To identify HIF-interacting genes , the Entrez Gene database ( http://www . ncbi . nlm . nih . gov/sites/entrez ) was searched using the search string ‘ ( “HIF” or “VHL” ) and “19”chr and “homo sapiens”orgn’ ., Differentially expressed genes were identified using a two-sided t-test ., For HIF target gene analysis , 36 known HIF-responsive genes identified in Maynard et al . were isolated 33 ., Enrichment of up- and down-regulated genes in the HIF target gene set was performed by comparing differences in the expression level ranks between HIF target gene set to the results of 10 , 000 randomly generated 36-gene sets ., Ranks were based on tumor versus normal expression comparisons as implemented in the limma package 48 ., SNP allele calls were generated using the GeneChip Mapping 100 K Set™ ( Affymetrix , Santa Clara , CA ) according to the manufacturers supplied protocol ., Image quantification was performed with a GeneChip Scanner 3000 and the resulting data was processed using GCOS 1 . 4 ( Affymetrix , Santa Clara , CA ) with default analysis settings ., Allele calls were generated using GTYPE 4 . 0 ( Affymetrix , Santa Clara , CA ) with a confidence threshold set at 0 . 25 ., Raw copy numbers in log2-transformed format ( non-paired reference and test samples ) were exported from the CNAG version 2 . 0 ( Affymetrix , Santa Clara , CA ) software using normal references downloaded from Affymetrix ( http://www . affymetrix . comccnt_reference_data ) ., DNA copy number changes were visualized by data smoothing in which raw copy number values were replaced by a summary score that comprised a running 1-sided t-test statistic with window size set to 31 , where each SNP probe along with 15 5′ SNPs and 15 3′ SNPs were included in the window ., DNA copy number data can be obtained at the Gene Expression Omnibus ( GSE8271 ) ., Bacterial artificial chromosomes ( BACs ) RP11-157B13 ( 19p12 ) , RP11-1137G4 ( 19p13 . 3 ) , RP11-15A1 ( 19q13 . 31 ) were obtained from the Childrens Hospital Oakland Research Institute ( http://bacpac . chori . org ) and BAC CTC-429C10 ( 19q13 . 41 ) was purchased from Invitrogen ( Invitrogen Corporation , Carlsbad , CA ) ., These clones were labeled with either SpectrumGreen or SpectrumOrange ( Abbott Molecular Inc , Des Plaines , IL ) by nick translation and applied to tissue touch preps of oncocytoma samples as described 49 , with the exception that slides were counterstained with VECTASHIELD ( Vector Laboratories , Inc . Burlingame , CA ) anti-fade 4′ , 6-diamidino-2-phenylindole ( DAPI ) ., Telomere-specific DNA probes , the chr 1 , 5 , 19 alpha satellite probe , and the arm-specific paints were purchased from Q-BIOgene ( MP Biomedicals , Solon , OH ) ., FISH was performed using these probes according to the manufacturers supplied protocol ., As the alpha satellite probe cross-hybridizes to chromosome 1 and chromosome 5 , in all studies chromosome 19 was co-labeled with a probe that maps distal to the centromere , RP11-157B13 ( 19p12 ) ., In addition , analysis of the centromeric probe on the metaphase spreads of control cells revealed that hybridization to chromosome 1 resulted in a significantly brighter signal ( data not shown ) ., These hybridization characteristics allowed the discrimination between chr 1 and 5 cross-hybridization ., For image quantification , three separate photomicrographs containing five , six , and three cells , respectively , in which the 19q31 . 31 FISH signals were in the same image plane were obtained ., Photomicrographs were processed using the rtiff package for the R environment 50 ., The fluorescent FISH signals were automatically segmented from background using the method of Ridler and Calvard 51 , individual spots were identified using the connected component algorithm 52 , and the number of pixels per feature were calculated ., Twelve doublet FISH signals and eight singlet FISH signals were compared ., Differences in size were evaluated using a one-sided Students t-test ., U2OS osteosarcoma cell and CAKI renal clear-cell carcinoma cell lines were obtained from the American Type Culture Collection ( Rockville , MD ) and maintained in Dulbeccos modified Eagles medium supplemented with 10% heat-inactivated fetal bovine serum ( Sigma , Milwaukee , WI ) at 37°C in a humidified 5% CO2 atmosphere ., Cyclic hypoxia treatment of cells were performed in humidified chambers at 37°C and flushed with 5% CO2 balance N2 for 30 min , followed by 5% CO2 and 7% O2 balance N2 for 30 min as one cycle ., Cells were grown in these chambers for 16 hours 53 ., Polyclonal anti-EGLN2 and anti-BNIP3L antibodies were obtained from Bethyl Laboratories ( Montgomery , TX ) and Sigma ( Milwaukee , WI ) , respectively ., Polyclonal HIF1α and monoclonal HIF2α antibodies were obtained from BD Biosciences ( San Jose , CA ) and Novus ( Littleton , CO ) , respectively ., Monoclonal anti-vinculin antibody was obtained from Abcam ( Cambridge , MA ) ., Mammalian expression plasmids pcEglN2 was generated by PCR from Flag-EglN2 , a kind gift from Dr . Mircea Ivan , using primers 5′-GACGACGGATCCATGGACAGCCCGTGCCAGC-3′ and 5′-GACGACGAATTCCTAGGTGGGCGTAGGCGGC -3′ ., The PCR product was then ligated into the BamHI and EcoRI sites in pcDNA3 ( + ) ., Plasmid was confirmed by direct DNA sequencing ., Western blotting were performed as described previously 54 ., For first-strand cDNA synthesis , 1 µl of oligo ( dT ) 23 primer ( Sigma ) was incubated with 5 µg of RNA and distilled H2O ( total reaction volume of 20 µl ) for 10 min at 70°C in a thermal cycler ( MJ Research , Boston , MA ) ., The mixture was cooled to 4°C , at which time 4 µl of 5× first-strand reaction buffer , 2 µl of 0 . 1 M DTT , 1 µl of a 10 mM concentration of each deoxynucleoside triphosphate , and 1 µl of Superscript II reverse transcriptase ( Invitrogen ) were added ., cDNA synthesis was performed for 1 . 5 h at 42°C , followed by 15 min at 70°C in the thermal cycler ., Human genomic DNA standards ( human genomic DNA was obtained from Roche , Mannheim , Germany ) or cDNA equivalent to 20 ng of total RNA were added to the quantitative PCR ( qPCR ) reaction mixture in a final volume of 10 µl containing 1× PCR buffer ( without MgCl2 ) , 3 mM MgCl2 , 0 . 25 units of Platinum Taq DNA polymerase , a 0 . 2 mM concentration of each deoxynucleoside triphosphate , 0 . 3 µl of SYBR Green I , 0 . 2 µl of ROX reference dye , and a 0 . 5 µM concentration of each primer ( Invitrogen ) ., Amplification conditions were as follows: 95°C ( 3 min ) , 40 cycles of 95°C ( 10 s ) , 65°C ( 15 s ) , 72°C ( 20 s ) , and 95°C ( 15 s ) ., qPCR was performed using the ABI Prism 7900HT Sequence Detection System ( Applied Biosystems , Foster City , CA ) ., Gene-specific oligonucleotide primers designed using Primer Express ( Applied Biosystems ) were as follows: BNIP3L primer set ( 5′- CTGCACAAACTTGCACATTG-3′ and 5′- TAATTTCCACAACGGGTTCA-3′ ) , HMOX1 primer set ( 5′-GAATTCTCTTGGCTGGCTTC-3′ and 5′- TCCTTCCTCCTTTCCAGAGA-3′ ) , ENO1 primer set ( 5′- CAGCTCTAGCTTTGCAGTCG-3′ and 5′-GACACGAGGCTCACATGACT-3′ ) , CITED2 primer set ( 5′-ACTGCACAAACTGCCATCTC-3′ and 5′-CAGCCAACTTGAAAGTGAACA-3′ ) , beta-actin primer set ( 5′- GGATCGGCGGCTCCAT-3′ and 5′- CATACTCCTGCTTGCTGATCCA-3′ ) , GLUT-1 primer set ( 5′- CACCACCTCACTCCTGTTACTT-3′ and 5′-CAAGCATTTCAAAACCATGTTTCTA-3′ ) ., SYBR Green I fluoresces during each cycle of the qPCR by an amount proportional to the quantity of amplified cDNA ( the amplicon ) present at that time ., The point at which the fluorescent signal is statistically significant above background is defined as the cycle threshold ( CT ) ., Expression levels of the various transcripts were determined by taking the average CT value for each cDNA sample performed in triplicate and measured against a standard plot of CT values from amplification of serially diluted human genomic DNA standards ., Since the CT value is inversely proportional to the log of the initial copy number , the copy number of an experimental mRNA can be obtained from linear regression of the standard curve ., A measure of the relative difference in copy number was determined for each mRNA ., Values were normalized to expression of beta-actin mRNA and represented as the mean value experiments performed in triplicate±standard deviations ., Extracts containing enriched EGLN2 were purified from rabbit reticulocyte lysate as previously described 8 ., Briefly , approximately 1 L of rabbit reticulocyte lysate ( Green Hectares , Oregon , WI ) was diluted to 5 L in 50 mM Tris-HCl ( pH 7 . 4 ) , 0 . 1 M KCl , and 5% ( vol/vol ) glycerol and then was precipitated with 0 . 213 g/ml ( NH4 ) 2SO4 ., After centrifugation at 16 , 000×g for 45 min at 4°C , the resulting supernatant was precipitated with an additional 0 . 153 g/ml ( NH4 ) 2SO4 ., After centrifugation at 16 , 000×g for 45 min at 4°C , the pellet was resuspended in Buffer A ( 40 mM HEPES-NaOH pH 7 . 4 and 5% ( vol/vol ) glycerol ) , dialyzed against Buffer A to a conductivity equivalent to Buffer A containing 0 . 2 M KCl , and applied at 0 . 5 L/h to a 0 . 5 L phosphocellulose ( Whatman , P11 ) column equilibrated in Buffer A containing 0 . 2 M KCl ., The phosphocellulose column was eluted stepwise at 1 L/h with Buffer A containing 0 . 5 M KCl , and 100-ml fractions were collected ., Proteins eluting in the phosphocellulose 0 . 5 KCl step were pooled and precipitated with 0 . 4 g/ml ( NH4 ) 2SO4 ., After centrifugation at 16 , 000×g for 45 min at 4°C , the pellet was resuspended in 4 ml of Buffer A . Following centrifugation at 35 , 000×g for 30 min at 4°C , the resulting supernatant was applied at 2 ml/min to a TSK SW3000 HPLC column ( Toso-Haas , Montgomeryville , PA; 21 . 5×600 mm ) equilibrated in Buffer A containing 0 . 15 M KCl ., The SW3000 column was eluted at 2 ml/min , and 4 ml fractions containing enriched EGLN2 were collected ., An in vitro binding assay was performed as described previously 3 ., TNT reticulocyte lysate ( Promega ) translation products were synthesized in the presence or absence of 35S-methionine ., HIF1α- ( ODD ) translation products were incubated with cellular extract fractions containing enriched EGLN2 , where indicated , for 30 min at 37°C ., Gal4-HA-HIF-1α ( 10 µl ) and HA-VHL ( 10 µl ) translation products were incubated with the indicated antibodies and protein A-Sepharose in 750 µl of EBC buffer ( 50 mM Tris

Introduction, Results, Discussion, Materials and Methods

Chromosomal abnormalities , such as structural and numerical abnormalities , are a common occurrence in cancer ., The close association of homologous chromosomes during interphase , a phenomenon termed somatic chromosome pairing , has been observed in cancerous cells , but the functional consequences of somatic pairing have not been established ., Gene expression profiling studies revealed that somatic pairing of chromosome 19 is a recurrent chromosomal abnormality in renal oncocytoma , a neoplasia of the adult kidney ., Somatic pairing was associated with significant disruption of gene expression within the paired regions and resulted in the deregulation of the prolyl-hydroxylase ELGN2 , a key protein that regulates the oxygen-dependent degradation of hypoxia-inducible factor ( HIF ) ., Overexpression of ELGN2 in renal oncocytoma increased ubiquitin-mediated destruction of HIF and concomitantly suppressed the expression of several HIF-target genes , including the pro-death BNIP3L gene ., The transcriptional changes that are associated with somatic pairing of chromosome 19 mimic the transcriptional changes that occur following DNA amplification ., Therefore , in addition to numerical and structural chromosomal abnormalities , alterations in chromosomal spatial dynamics should be considered as genomic events that are associated with tumorigenesis ., The identification of EGLN2 as a significantly deregulated gene that maps within the paired chromosome region directly implicates defects in the oxygen-sensing network to the biology of renal oncocytoma .

Together , renal oncocytoma and chromophobe renal cell carcinoma ( RCC ) account for approximately 10% of masses that are resected from the kidney ., However , the molecular defects that are associated with the development of these neoplasias are not clear ., Here , we take advantage of recent advances in genetics and computational analysis to screen for chromosomal abnormalities that are present in both renal oncocytoma and chromophobe RCC ., We show that while chromophobe RCC cells contain an extra copy of chromosome 19 , the renal oncoctyoma cells contain a rarely reported chromosomal abnormality ., Both of these chromosomal abnormalities result in transcriptional disruptions of EGLN2 , a gene that is located on chromosome 19 and is critical for the cellular response to changes in oxygen levels ., Defects in oxygen sensing are found in other types of kidney tumors , and the identification of EGLN2 directly implicates defects in the oxygen-sensing network in these neoplasias as well ., These findings are important because the chromosomal defect present in renal oncocytomas may also be present in other tumor cells ., In addition , deregulation of EGLN2 reveals a unique way in which perturbations in oxygen-sensing are associated with disease .

urology/renal cancer, genetics and genomics/cancer genetics, genetics and genomics/bioinformatics, cell biology/cell signaling

journal.pgen.1005609

Virus Satellites Drive Viral Evolution and Ecology

Satellites are defined as viruses which have a life cycle dependent on a helper virus , but lack extensive nucleotide sequence homology to the helper virus and are dispensable for helper virus proliferation 1–4 ., These infectious elements , present both in eukaryotic and prokaryotic cells , have far-reaching consequences ., First , they can play a major role in the population dynamics of viruses and their hosts , with satellite viruses able to greatly limit the proliferation of their helper viruses ., For example , the presence of Staphylococcus aureus pathogenicity islands ( SaPIs ) , a type of satellite virus , reduces phage proliferation 5 , 6 ., Given the crucial role of viruses in shaping microbial communities 7 , satellite viruses may themselves be a key driver of microbial community structure and function ., Second , satellite viruses can have a dramatic role in virulence by controlling the symptoms induced by their helper viruses or by encoding relevant virulence genes ., For example , Hepatitis B virus ( HBV ) is a major health problem of global impact ., Among the HBV chronically infected patients , many are co-infected with the Hepatitis delta virus ( HDV ) , a satellite virus that needs the HBV for propagation ., HDV is the smallest virus known to infect humans and is clinically relevant because it causes a fulminant hepatitis or a more rapid progression of liver disease in the setting of chronic HBV infection 8 ., Satellite prokaryotic viruses ( satellite phages ) are also relevant both in the virulence and in the emergence of novel bacterial pathogens ., In addition to the SaPIs , which have a relevant role in bacterial evolution and pathogenesis by encoding relevant virulence factors 9 , 10 , Vibrio cholerae phage satellites control not only the expression of the clinically relevant CTX phage-coded cholera toxin , but also the transmission of their helper CTX phage 11 , 12 ., Interactions between hosts and their parasites frequently result in antagonistic coevolution , with host evolving defence and parasites evolving counter defence 13 ., Given that satellite viruses typically have negative consequences for their helper viruses , while the satellite viruses require ‘susceptible” viruses for their proliferation , antagonistic coevolution is a feasible outcome ., Antagonistic coevolution can have major impacts on the ecology and evolution of viruses and their hosts ., Specifically , the degree of resistance of helpers to their satellites will determine the spread of both helper and satellite viruses between their prokaryotic or eukaryotic hosts , which in turn affects host population dynamics and evolutionary trajectories ., While the existence of satellite viruses clearly shows adaptation of satellites to helper viruses , it is currently unclear if satellite viruses drive significant evolutionary change in helper virus resistance and , if so , whether satellite viruses in turn evolve to overcome helper virus resistance ., Here we address these questions for the interaction between the SaPIs and their inducing phages ., The SaPIs are the prototypical members of a widespread family of highly mobile pathogenicity islands , the PICIs ( phage-inducible chromosomal islands ) , that exploit the life cycle of their helper phages with elegant precision to enable their rapid replication and promiscuous spread 4 , 10 ., In the absence of helper phage lytic growth , the island is maintained in a quiescent prophage-like state by a global repressor , Stl , which controls expression of most of the SaPI genes 14 ., Following infection by a helper phage or induction of a helper prophage , SaPI de-repression is effected by specific , non-essential “moonlighting” phage proteins that bind to Stl , disrupting the Stl-DNA complex and thereby initiating the excision-replication-packaging ( ERP ) cycle of the island 15 , 16 ., Different SaPIs encode different Stl proteins , so each SaPI commands a specific phage protein for its induction 15 , 16 ., Since SaPIs require phage proteins to be packaged 17 , 18 , this strategy couples the SaPI and phage cycles , but imposes a very significant transmission cost on the helper phages ., In previous work , we observed that different helper phages encoded allelic variants of the inducing genes with different affinity for the SaPI-encoded repressors 15 ., Moreover , we also observed that phage mutants capable of forming plaques on SaPI-positive strains had mutations in the phage-coded inducing genes 15 ., Here , we experimentally show that phages that fail to induce SaPIs as a result of spontaneous mutations of the inducing proteins are strongly favoured by selection , but that these mutants carry fitness cost in the absence of SaPIs ., Propagation of SaPIs on these non-inducing phages results in strong selection of spontaneous SaPI stl-mutants that can be packaged and transferred by the evolved non-inducing phages , imposing a large transmission cost on the helper phages ., Furthermore , bioinformatics data supports the view that SaPIs are an important selective pressure driving the diversity of both genes and gene content in S . aureus phages ., Finally , to show the generality of this result we report similar experimental and bioinformatic results for Enterococcus faecalis phages ., Taken together , our results suggest that helper and satellite viruses undergo extensive antagonistic coevolution ., To determine if SaPIs play an obvious role in phage evolution , we initially analysed the phage sequences deposited in GenBank and identified allelic variants of the phage-coded SaPIbov1 , SaPI1 and SaPIbov2 inducing proteins , corresponding to the dUTPase ( Dut ) , Sri and 80α ORF15-like proteins , respectively 15 ., Representative examples of the different SaPI inducers are shown in S1 Fig . We tested the different selective forces that may have been shaping these proteins during their evolution in vivo by calculating and comparing the dN−dS values of the representative SaPI inducer genes ( S1 Table ) ., The dN−dS , which measures the difference in substitutions rates between non-synonymous site ( dN ) and synonymous site ( dS ) , is classically used as an indicator of selective pressure acting on a protein-coding gene ., As is summarised in Table 1 and shown in S1 Table , all the dN−dS comparisons were significantly lower than 0 ( p < 0 . 005 ) , indicating that the SaPI inducers are under purifying selection ., It is assumed that the main consequence of the purifying selection is a reduction in the level of variation present in the locus under selection , produced by the removal from the population of less-adapted variants ., However , the existence of multiple alleles in the phage-coded SaPI inducers suggests the existence of an evolutionary force operating in opposite direction that maintains the diversity observed in the SaPI inducer proteins ., In previous studies , we demonstrated that variants of the SaPIbov1 and SaPIbov2 derepressing proteins differentially induce the SaPIbov1 and SaPIbov2 cycles , respectively 15 ., Moreover , we also demonstrated that the highly divergent region present in the Dut proteins ( motif VI; S1 Fig ) determines the capacity of the phages to induce the SaPIbov1 cycle by controlling the affinity between the SaPIbov1 Stl repressor and the Dut protein 15 , 16 ., These results suggest that SaPIs could favour certain alleles in the phage population because they have reduced capacity to induce the SaPI cycles ., To test the hypothesis that phages are under strong selection to resist SaPIs , we experimentally determined if the interaction with the SaPIs resulted in the evolution of phages carrying variants in the SaPI inducing proteins ., Phage 80α was used as a model because it induces three different SaPIs: SaPIbov1 , SaPIbov2 and SaPI1 ., Strains RN4220 ( SaPI-negative; a control ) or JP1996 ( RN4220 derivative carrying SaPIbov1 ) were initially infected with phage 80α ( 1:1 ratio , see scheme in S2 Fig ) ., The resulting lysates were then used to infect again the same strains and after the third passage phages were phenotypically characterised ., The phage lysates obtained after the third passage in strain JP1996 ( SaPIbov1-positive ) were used to infect strain JP2129 , an RN4220 derivative carrying SaPIbov2 ., After the third passage done in strain JP2129 , the evolved phages were then used to infect JP2966 , an RN4220 derivative carrying SaPI1 ., As a control , phages only infecting RN4220 were propagated and analysed through the experiment ( see scheme in S2 Fig ) ., We first determined growth of the ancestral and evolved phages ., As observed in Fig 1A , while SaPIs blocked plaque formation by the ancestral 80α phage or by the phages evolved on the SaPI negative strain , they did not obviously interfere with the reproduction of the evolved phage mutant ., These results demonstrate that phages evolved in the presence of SaPI no longer suffer reduced costs of SaPI parasitism ., The most likely explanation of this reduction in cost is that the evolved phages were resistant to the SaPIs ., This was investigated by generating lysogens from two evolved phages , which incidentally carried mutations in all three SaPI inducers ( see below and S2 Table ) ., Next we introduced into the different lysogens derivatives of SaPI1 , SaPIbov1 or SaPIbov2 carrying a tetM marker , which facilitates transfer studies ., The different SaPI-positive strains were then SOS ( mitomycin C ) induced and the capacity of the different phages to induce the SaPIs cycle was analysed ., As shown in Fig 1B and S3 Table , none of the phage mutants induced the SaPIs ., Moreover , uniquely the titre of the ancestral 80α phage , but not that from the evolved phages , was reduced by the presence of the islands ( S3 Table ) ., These experiments show that culturing phages with SaPIs results in the evolution of phage resistant to SaPIs ( i . e no longer induce the SaPI cycle ) , and this resistance results in greatly increased phage proliferation on susceptible bacterial hosts ., From the aforementioned experiment , five 80α phages evolved after the third passage on strain JP1996 ( SaPIbov1-positve ) and 5 from the third passage on the RN4220 ( SaPI-negative ) branch were completely sequenced and analysed ., Only phages that interacted with SaPIbov1 contained mutations in their genomes , which were in all cases located in the SaPIbov1 inducer gene dut ( dUTPase ) ., This result was further confirmed by sequencing the dut gene from other 120 evolved phages ( 60 infecting RN4220 and 60 infecting JP1996 ) , obtained from 3 independent experiments ., As summarised in Table 2 and shown in S2 Table , 100% of the phages infecting the SaPIbov1-positive strain showed mutations in the SaPIbov1-inducing gene dut ., By contrast , no mutations were observed either in the other SaPI inducer genes , corresponding to sri and ORF15 , or in the phages infecting the SaPIbov1-negative strain ( Tables 2 and S2 ) ., To determine the genetic basis of resistance to the other SaPIs , SaPIbov2 and SaPI1 , the SaPI inducer genes from 120 evolved phages ( from 3 independent experiments , 60 after interacting with SaPIbov2 and 60 after interacting with SaPI1 ) were analysed ., The SaPI inducer genes were also sequenced from 60 phages that had only infected the SaPI-negative RN4220 strain ( S2 Fig ) ., As occurred with SaPIbov1 , interaction with SaPIbov2 and SaPI1 selected for phages carrying missense and nonsense mutations in the SaPI inducer genes ( Tables 2 and S2 ) ., In previous work , we demonstrated that expression of the cloned SaPI inducing genes in a SaPI-containing strain was sufficient to induce the SaPI cycles 15 ., As shown in Fig 1C , the cloned dut genes from the evolved phages did not induce SaPIbov1 , while the wild-type gene did ., As the Dut protein levels produced from these constructs are comparable ( Fig 1C ) , this result confirms that the mutations present in the inducing genes are the cause of the inability of the evolved phages to de-repress the SaPI cycles ., Given the high cost imposed by SaPIs on helper phages and the apparent ease at which they can evolve resistance , why isn’t resistance to SaPI exploitation ubiquitous ?, Part of the explanation might be that there are costs associated with resistance ., Indeed , in the absence of the SaPIs both the phage titres and the phage plaque sizes were slightly but consistently reduced in the phage mutants , compared with the wt phage ( S3 Table ) ., Moreover , the number of phages carrying the wild-type versions of the SaPIbov1 and SaPIbov2 inducing genes increased in absence of the interference ( Table 2 ) ., This putative cost was confirmed by competition experiments ( in duplicate ) among the ancestral 80α and two different evolved phages on the SaPI-negative host ( RN4220 ) ., Two thousand p . f . u . of a mixed population ( ratio 1:1 ) of the wt and one of the evolved phages was used to infect a plate containing 1 x 106 RN4220 cells ., Confluent phage plaques were collected , the lysate filtered and the procedure repeated four more times ., After the fifth passage , 20 independent plaques from each of the different experiments were selected and the percentage of the phages under competition was evaluated by PCR and sequencing analyses of SaPI inducing genes ., While the 80α wt and the evolved 80α phages were present in equal numbers in the mixed initial population , passages through the SaPI-negative RN4220 strain selected for the wt phage ( p < 0 . 01; Table 3 ) , confirming there is obvious cost to being resistant to single or multiple SaPIs in this experimental context ., We next determined whether SaPIs can in turn adapt to the presence of the experimentally evolved non-inducing phages ., We made use of two different phage mutants that had evolved resistance to two SaPIs ( SaPIbov1 and SaPIbov2 ) in the previous experiments ( Table 4 ) ., The SaPIbov1 tst::tetM and SaPIbov2 bap::tetM islands , carrying a tetM marker that facilitates the SaPI transfer analyses , were introduced both in the two mutants and in the wt 80α phages ., The different lysogenic SaPI positive strains were SOS ( mitomycin C ) induced and the islands transferred to the cognate recipient strains carrying the same phage that was present in the donor strain ., After the transfer , the SaPI-positive strains were recollected and the procedure repeated 7 more times ., After the eighth passage , the SaPI titre obtained was compared with that obtained with the original SaPIs ., Remarkably , at the end of the experiment the SaPIs that interacted with the mutant phages increased their titres more than 104-fold ( Table 4 ) , suggesting that the SaPIs had adapted to the presence of the SaPI insensitive phages ., By contrast , the titres of those SaPIs interacting with the wt phage 80α did not change significantly through time ., Note that these experiments were done four independent times and the obtained results were consistent in the parallel experiments ., To determine the genetic basis of SaPI adaption to “resistant” phage , 21 different colonies , randomly chosen from the different replicates , were individually analysed and the evolved SaPIs sequenced ., As shown in S4 Table , the analysis of the individual colonies confirmed that the SaPIs had evolved in the presence of the mutant phages , but not in presence of the wt phage 80α ., Importantly , the evolved SaPIs all had mutations in the stl gene ., These mutations , located in the coding or in the promoter region of the stl gene ( S4 Table ) , generated in all the cases an stl- mutant genotype ., Thus , all the evolved SaPIs replicated autonomously in absence of any inducing phage ., Previous work has shown that stl mutant SaPIs can be transferred by non-helper phages 14 , and this is presumably why stl mutations massively increased SaPI transfer rates in the presence of the evolved “resistant” phages ., Finally , we analysed if the coevolved SaPIs blocked reproduction of the evolved phages ., As shown in Fig 2 , this was the case ., Thus , while the evolved phages were resistant to the presence of the original islands , the evolved SaPIs reduced phage reproduction ., Given the likely importance of SaPI-imposed selection , we speculated simple amplification of phages to obtain high titers of phage stock may have itself resulted in significant SaPI-imposed evolution ., Phage collections have been traditionally used to type S . aureus strains ., These collections are generated , maintained and amplified by infecting different propagating strains with specific phages ., Interestingly , and as occurs in nature , most propagating strains carry uncharacterised prophages and SaPIs ., In view of this , we hypothesised that the phage populations used for typing could contain mixed populations that have evolved in response to the SaPIs present in the propagating strains ., To test this , we obtained 3 phage samples ( ϕ29 , ϕ52A and ϕ55 ) from a reference laboratory , and isolated , from each of these samples , five single phages , which were amplified using the non-lysogenic RN4220 strain ., We used both the amplified phages obtained from the single plaques as well as the original phage populations to infect the non-lysogenic strain RN4220 , as well as derivatives of this strain carrying SaPI1 , SaPIbov1 or SaPIbov2 ., The rationale for this experiment was to compare the interference observed with these different phage samples ., We hypothesised that if both samples had the same plating efficiency ( interference ) rate when infecting any of the SaPI-positive strains , both phage populations would be genetically homogenous ( related to the SaPI inducers ) ., By contrast , if a different behaviour was observed , and one of the samples infected the SaPI-positive strain better than the other sample , this would imply that the original population contained a mixed phage population that probably had evolved in response to the SaPI interference ., Although the analysis of the ϕ29 and ϕ52A phage populations did not reveal any difference between the purified phages and those present in the samples from the reference laboratory , 1% of the original ϕ55 phage population generated plaques in the SaPI1-positive strain ., By contrast , only 0 . 001% ( 1000 x reduction ) of the purified ϕ55 phages generated plaques in this strain ., This result suggested that the original phage lysate contained at least two different phage populations , evolved from a common ancestor , carrying variants of the SaPI1 inducing gene ., To test this , one of the previously purified phages infecting RN4220 but showing interference to the SaPI-positive strain was completely sequenced ( ϕ55–2 ) ., One phage having no interference to SaPI1 was also purified and sequenced ( ϕ55–3 ) ., The genome length of ϕ55–2 is 41 , 898 bp , containing the information for approximately 81 ORFs of 50 or more codons , and is deposited in GenBank under accession number KR709302 ., The genome length of ϕ55–3 is 42 , 309 bp , with approximately 83 ORFs , and is deposited in GenBank under accession number KR709303 ., Both ϕ55–2 and ϕ55–3 belong to a class of related staphylococcal Siphoviridae 19 ., Overall , both phages are >99 . 9% identical except for a divergent region of ∼1800 bp that contains the gene coding for the SaPI1 inducer ( Fig 3A ) ., In the ϕ55–2 phage , this region encodes 3 ORFs , the last one being the SaPI1 inducer ., By contrast , phage ϕ55–3 encodes 5 different ORFs , including a variant of the SaPI1 inducer ( Fig 3B ) ., Since the ORFs present in phage ϕ55–3 were also contained in other S . aureus phages , one of many plausible explanations for the differences between these two phages is that recombination occurred between ϕ55–2 and a prophage residing in the propagating strains ., To verify that the aforementioned changes observed in phage ϕ55–3 were not generated during the purification and amplification of the phage , PCR experiments with specific oligonucleotides for each of the phages were performed , using DNA samples obtained from the original phage population ( without amplification ) ., To confirm that the mutations present in phage ϕ55–3 conferred an advantage for the phage in the presence of a SaPI1-positive strain , competition experiments ( in triplicate ) were performed in which a SaPI1-negative or a SaPI1-positive strain were infected ( phage:bacteria ratio 1:3 ) with a mixed population ( 1:1 ) of the ϕ55–2 and ϕ55–3 phages ., The lysates obtained from each experiment were used to infect again the same strains , and after the third passage , the number of the ϕ55–2 and ϕ55–3 phages was evaluated by PCR using oligonucleotides that specifically recognise the different allelic variants of the phage coded SaPI1 inducers ., While both phages were present in equal numbers after infecting the SaPI-negative strain ( ϕ55–2: 55%; ϕ55–3: 45% ) , passages through the SaPI1-positve strain selected for ϕ55–3 ( >95% ) ., The previous results suggested that it would be possible to find closely related phages encoding different alleles of the SaPI inducers as a consequence of the phage interaction with the SaPIs ., To address this , we initially performed a phylogenetic analysis of 33 randomly selected staphylococcal phages ( S3A Fig ) ., Next , we compared the SaPI inducer sequences from closely related phages ., As hypothesised , the genes coding for the SaPI inducers’ proteins represent a source of variation among closely related phages ., S3B Fig shows representative examples of these comparisons ., Moreover , since distantly related phages encode the same SaPI derepressing protein , our analysis revealed that SaPI inducer diversity is independent of phage phylogeny ( S1A , S1C , S1E and S3 Figs ) ., Interestingly , this analysis also revealed one additional strategy by which phages might avoid SaPI repression , namely , losing the genes encoding for the SaPI inducers ., Thus , the SaPI1 inducer sri was absent in phages ϕ11 , ϕPVL or ϕNM3 and the SaPIbov2 inducer was not present in phages ϕ11 , ϕNM2 , ϕPVL-CN125 , ϕPVL , ϕNM3 or ϕ52a , although closely related phages coded for the missing inducers ( S3A Fig ) ., With regards to the SaPIbov1 inducer ( trimeric Dut ) , it is absent in phages ϕ69 , ϕNM1 , ϕNM2 , ϕPVL108 , and ϕ55 ., Surprisingly , instead of the trimeric form , these phages code for a dimeric Dut , which based on the structure of some homologue proteins deposited in the protein data bank ( PDB ) , we predict to be functionally related but structurally completely different ., Why some phages encode a dimeric or a trimeric Dut is under study ., This analysis suggests SaPI-imposed selection can drive significant and rapid evolutionary change in natural phage populations ., Since our laboratory passage experiments suggested that only a few mutations are required to generate phages that escape from SaPI interference , we hypothesised that a similar process will have occurred in nature ., To test this , we looked for proteins with high ( but not complete ) similarity to the 80α or ϕ11 Duts ., Two promising candidate were the Dut proteins encoded by the prophages ϕSaov3 and B2 ( accession numbers YP_005736587 and ERS400827 ) , which have only 5 amino acid changes compared with the 80α and ϕ11 Duts , respectively ( Fig 3C ) ., As shown in Fig 3D , the ϕSaov3 and B2 Dut variants were unable to induce the SaPIbov1 cycle , validating the results obtained with the in vitro evolved phages ., These variants , however , are not widespread in nature ., Since the SaPI inducers are moonlighting proteins with a relevant role in the phage biology 20 , we hypothesised that these variants have probably also affected their function for the phage ., This was analysed by testing the enzymatic activity of 3 Dut variants ( the natural Dut B2 and the evolved Dut 80α I75N and Dut 80α G164S ) ., This analysis revealed that the B2 Dut protein is insoluble and completely inactive , while the two evolved variants had significantly reduced ( p < 0 . 01 , Student’s t-test ) their dUTPase activity ( S4 Fig ) ., In addition , of note is the existence in the evolved phages of some dut genes carrying nonsense and frameshifts mutations , which encode non-functional proteins ( S2 Table ) ., This loss of function probably explains why these variants do not exist or are not widespread in nature ., To demonstrate that phage-inducible chromosomal islands are important for phage evolution and ecology in general , we analysed the interaction between the enterococcal pathogenicity island EfCIV583 and its inducing phage ϕ1 21 ., To do this , we initially demonstrated that the EfCIV583 element interferes with the phage ϕ1 reproduction ( S5 Fig ) ., Next , strains JP10983 or JP10982 ( JP10983 derivative carrying EfCIV583 ) were infected with phage ϕ1 , as previously reported in the analysis of the SaPI-phage interaction ., After the third passage , 6 phages ( 3 infecting JP10983 and 3 infecting JP10982 ) were completely sequenced and analysed ., Only those phages that had interacted with EfCIV583 contained mutations in their genomes , always located in the ϕ1 xis ( EF0309 ) gene ( S5 Table ) ., Remarkably , and in a parallel study , we have demonstrated that the ϕ1 xis gene is the inducer for the EfCIV583 island 22 ., Next , to test if this process is relevant in vivo , we analysed whether related enterococcal phages encoded allelic variants of the EfCIV583 inducer , and if these variants are under purifying selection ., As shown in Fig 4 and Tables 1 and S1 , this was the case , confirming that satellite phages are a major force driving phage evolution ., The significance of coevolution between prokaryotes and their viruses 23 , 24 , and between viruses and their associated viral defective interfering particles 29 is well established ., Here , we investigate coevolution between helper and satellite viruses of S . aureus ., We confirm previous results that parasitism by SaPIs , which exploit phages for their own transmission , impose a massive growth rate cost on the phages 23–25 ., We then show real-time evolution of phage resistance against SaPIs , and in turn the evolution of SaPI exploitation of “resistant” phages ., We identify the genetic basis of experimentally evolved resistance and exploitation , and confirm the importance of SaPI-imposed selection on phage evolution in both natural populations of S . aureus phages through bioinformatic analyses and in laboratory populations of S . aureus phages used in phage typing ., Finally , we report comparable experimental and bioinformatics results in E . faecalis ., We have previously demonstrated that one of the key features of the SaPIs is to interfere with helper phage reproduction , using a variety of mechanisms ., These include, i ) blocking of phage DNA packaging by expression of the SaPI-coded Ppi protein , which interferes with the phage coded TerS protein 5;, ii ) diversion of phage proteins to produce the SaPI-specific particles 5;, iii ) expression of the PtiA homologs 6 , which block phage growth by binding to the helper phage Ltr proteins 26 , 27 , directly inhibiting their ability to activate phage late gene transcription; or, iv ) carriage by the SaPIs of the phage cos or pac sites in order to compete with the inducing phages to be packaged in the phage particles 28 , 29 ., In this work we describe two complementary strategies by which the phages evolve to overcome the SaPI interference: one involves the generation of allelic variants in the SaPI de-repressing proteins with lower affinity for SaPI coded Stl repressor; the other , even more drastic , involves complete loss of the phage-encoded SaPI inducing genes ., Since SaPI interference depends on the induction of the SaPI cycle , both strategies select for non-inducing phages that are not affected by the presence of a quiescent SaPI integrated in the bacterial chromosome ., Crucially , we show that SaPIs in turn adapt to resistant helper phage by loss of function of the global Stl repressor that removes the need for specific phage proteins for induction , allowing transmission of SaPIs by any infecting phages rather than specific helper phages 14 ., While our results show that coevolution between satellite and helper viruses can occur very rapidly , it opens up a number of key questions ., Specifically , how is the intimate association between phages and SaPIs maintained given that SaPI adaptation resulted in the loss of the need of specific helper phages for induction ?, First , there are massive costs associated with loss of function of the SaPI global repressor ., As previously reported , mutations in the stl gene severely affects bacterial physiology and growth , probably because of the uncontrolled replication of the stl mutant SaPIs 14 ., This explains why the SaPIs characterised to date encode a functional Stl protein that block the SaPI cycle in the absence of the helper phage , although it is entirely feasible that de-repressed SaPIs can be favoured by selection in natural populations , at least for short periods of time ., Second , there are costs associated with loss or alteration of the phage inducing proteins , as apparent from the increase in the number of phages carrying the wild-type versions of the SaPIbov1 and SaPIbov2 inducing genes in the absence of the interference caused by these islands , as well as the results from competition experiments between wildtype and mutant phage ., As a result of such costs , wild-type phages are likely to be maintained in the population , further weakening selection for SaPI stl mutants ., These costs of SaPI resistance presumably arise because the phage-coded SaPI inducers are proteins that perform their functions through protein-protein interactions with other phage- or bacterial-coded proteins 30 , and that the Stl repressors have merged the structure of the partners to which the SaPI inducers interact in order to be targeted 16 ., Although this has not been demonstrated yet for the SaPIbov1 ( dut ) and SaPIbov2 ( 80α ORF15 ) inducers , the Sri protein ( SaPI1 inducer ) interact with the cellular DnaI protein inhibiting staphylococcal replication 20 ., Based on this and previous in vitro results discussed above , one type of dynamic of continual phage-SaPI coevolution in nature may therefore be cycles of the following specific events:, i ) Phages evolve SaPI resistance by alteration or loss of the inducing protein , assuming that the short term benefits of SaPI resistance outweigh fitness costs ( if any ) associated with changes in the inducing protein;, ii ) SaPIs respond by loss of the need to be induced by a helper phage , again assuming benefits to the SaPI of being transmitted to new hosts outweigh the costs of reducing the fitness of hosts they infect;, iii ) As a result of the mutant SaPIs being able to exploit both the original and mutant “resistant” phages , phages with the original unaltered SaPI inducing proteins are able to outcompete mutant phages because of the costs associated with altered inducing proteins which no longer confer resistance; and, iv ) SaPIs that require induction by helper phage proteins can now outcompete SaPIs that do not require an inducer because there are large numbers of inducing phage present , starting the cycle again ., This type of coevolutionary dynamic can be described as range fluctuating selection 31 , and can arise when increased resistance and infectivity ranges are associated with increased fitness costs 31 ., Note that ranges here refer to the number of SaPI and phage genotypes that can be resisted and infected , respectively ., The high diversity of phage inducing protein alleles suggests however alternative coevolutionary dynamics are operating in nature in addition to or instead of the model described above ., Specifically , SaPI-imposed selection may cause diversifying selection if SaPIs adapt to changes in inducing proteins by switching to exploit the modified or an alternative protein , rather than losing the need for specific helper phages , and hence evolving more general infectivity ., Consistent with this model , SaPIs , encoding different Stl repre

Introduction, Results, Discussion, Materials and Methods

Virus satellites are widespread subcellular entities , present both in eukaryotic and in prokaryotic cells ., Their modus vivendi involves parasitism of the life cycle of their inducing helper viruses , which assures their transmission to a new host ., However , the evolutionary and ecological implications of satellites on helper viruses remain unclear ., Here , using staphylococcal pathogenicity islands ( SaPIs ) as a model of virus satellites , we experimentally show that helper viruses rapidly evolve resistance to their virus satellites , preventing SaPI proliferation , and SaPIs in turn can readily evolve to overcome phage resistance ., Genomic analyses of both these experimentally evolved strains as well as naturally occurring bacteriophages suggest that the SaPIs drive the coexistence of multiple alleles of the phage-coded SaPI inducing genes , as well as sometimes selecting for the absence of the SaPI depressing genes ., We report similar ( accidental ) evolution of resistance to SaPIs in laboratory phages used for Staphylococcus aureus typing and also obtain the same qualitative results in both experimental evolution and phylogenetic studies of Enterococcus faecalis phages and their satellites viruses ., In summary , our results suggest that helper and satellite viruses undergo rapid coevolution , which is likely to play a key role in the evolution and ecology of the viruses as well as their prokaryotic hosts .

Satellites are defined as viruses that have a life cycle dependent on a helper virus ., Thus , they can be considered as parasites of parasites ., In addition to their fascinating life cycle , these widespread infectious elements , present both in eukaryotic and prokaryotic cells , have a dramatic role in virulence by controlling the symptoms induced by their eukaryotic helper viruses or by encoding key bacterial virulence genes ., While satellites can play an important role in the ecology of the viruses they parasitise , the evolutionary impact on their helper viruses is unclear ., Here we show that staphylococcal pathogenicity islands ( SaPIs ) , an example of a virus satellite , are a major selective force on the viruses ( bacteriophages ) they parasitise ., Using both bioinformatic and experimental evolution data we have been able to confirm that pathogenicity islands are a major selective pressure enhancing the diversity of both genes and gene content in Staphylococcus aureus phages ., Since SaPIs exploit the life cycle of their helper phages to enable their rapid replication and promiscuous spread , these strategies are mechanisms that reduce SaPI interference , thus facilitating the infectivity and dissemination of the helper phages in nature .

journal.pcbi.1000003

Systematic Analysis of Pleiotropy in C. elegans Early Embryogenesis

The phenomenon of pleiotropy highlights the fact that some genes in the genome perform multiple biological functions ., Although individual examples of pleiotropic genes have been discovered 1–4 , pleiotropy remains a poorly understood genetic phenomenon and there have been very few systematic studies ., In S . cerevisiae , the collection of mutant strains for nearly all genes has enabled high-throughput tests of growth fitness under a variety of environmental conditions 5 , 6 ., The degree of pleiotropy has been estimated based on the number of conditions under which mutant strains showed abnormal fitness 6 ., In multi-cellular organisms , the availability of high-throughput RNAi techniques may lead to the opportunity for systematic analysis of pleiotropic genes ., However , when multiple phenotypic effects are present , it is not obvious whether the phenotypic effects should be attributed to the loss of a single function or to multiple functions ., For example , a phenotypic effect at earlier stages of animal development may accumulate during cell divisions and migrations , resulting in many defects at later stages of development ., In this case , although many defects are observed , they can all be accounted for by the loss of a uniform gene function ., Therefore , it is not clear how pleiotropic genes should be identified in practice and what mechanisms lie behind pleiotropy ., C . elegans is especially amenable to genome-wide loss-of-function analyses because of well-characterized anatomy , short life cycle , and the convenience of RNAi techniques ., The C . elegans early embryo is a model system for studying mitotic cell divisions ., Piano et al screened a set of ovary-enriched genes by RNAi and systematically described early embryonic defects for 161 genes in terms of RNAi-associated phenotypes 7 ., Using the RNAi data , they grouped these genes into “phenoclusters” , which correlated well with functional annotations of these genes ., Sonnichsen et al . performed whole-genome RNAi experiments to search for genes involved in early embryogenesis 8 ., They defined a series of cellular defects occurring in the first two cell divisions , and identified 661 genes that showed at least one of these defects ., These genes were manually grouped into functional classes ., For example , genes involved in cell polarity were grouped together since the RNAi of these genes resulted in symmetric cell divisions; genes involved in DNA damage checkpoints were grouped together since the RNAi of these genes resulted in delayed P1 cell division ., Multiple defects during early cell divisions can be scored when a single gene is perturbed ., All the scored defects happen in the first approximately 50 minutes of embryonic development , up to a four-cell stage embryo ., This short time window ensures that most observed defects are direct rather than secondary ., These data and information provide an excellent biological context to systematically explore the phenomenon of pleiotropy ., In this paper , we address several open questions regarding pleiotropy using C . elegans early embryogenesis as the model system ., First , how can complex phenotypes be decomposed and be linked to the loss of specific biological functions ?, Second , how can we systematically identify pleiotropic genes ?, Third , does pleiotropy exist commonly in a biological system ?, Finally , what potential mechanisms underlie pleiotropy ?, We find that sets of cellular defects ( or “signatures” ) are well correlated with losses of certain biological functions , and these signatures can be used to decompose complex phenotypic profiles so as to provide functional annotations ., Approximately half of the genes involved in early embryogenesis are found to be pleiotropic , suggesting the prevalence of pleiotropy in biological systems ., By integrating phenotypic profiles with protein-protein interaction networks , we observe that highly pleiotropic genes tend to show a higher network “betweenness” 9 than other genes involved in early embryogenesis , suggesting that pleiotropic genes play an important role in connecting various biological pathways ., Systematic RNAi screens have identified genes involved in early embryogenesis and have characterized their phenotypic profiles , which are composed of a series of cellular defects 8 ., As has been described previously 8 , phenotypic data can be visualized in a matrix where rows index genes and columns index defects ., A gene is given a score of either zero ( absence ) or a positive value ( presence ) for each of the 45 defects 8 ., We plotted the distribution of the percentage of genes involved in early embryogenesis against the number of defects for which the genes have positive scores ( Figure 1 ) ., By randomly permuting the values among genes while keeping each column sum fixed ( i . e . , fixing the total number of genes each defect is associated with ) , we generated random control datasets and observed that significantly more genes in the real data set exhibit a large number of loss-of-function defects than those in random control sets ., In the real dataset , 57 out of 661 genes show 15 or more defects , whereas on average only 1 gene is expected to show this number of defects in a randomly permuted dataset ( P-value<0 . 001 , see Methods ) ., Genes exhibiting a large number of defects in their phenotypic profiles may be candidates for pleiotropic genes ., However , should the degree of pleiotropy be solely determined by the number of defects ?, It is possible that occurrences of some cellular defects are highly correlated with one another ., The highly correlated defects are likely caused by the perturbation of a single-function gene rather than a pleiotropic gene ., In order to investigate how strongly cellular defects correlate with each other , we analyzed the occurrence of each individual defect and the co-occurrence of each pair of defects ., We then computed the ratio of the observed co-occurrence of each defect pair to the expected co-occurrence as if the two defects occurred independently ( see Methods ) ., We plotted the ratios as a correlation map ( Figure 2 ) and found that some defects co-occur much more frequently than expected , while some never co-occur in the same phenotypic profile , suggesting that not all defects occur independently from each other ., For example , P1/AB nuclear separation—cross-eyed ( Defect 23 ) and four-cell stage nuclei—size/shape ( Defect 34 ) co-occur at very high frequency , suggesting that embryos showing defects in nuclear separation at the two-cell stage are very likely to be abnormal in nuclear size and shape at the four-cell stage ., P1/AB nuclear separation—cross-eyed also co-occurs with P0 cytokinesis—furrow specification ( Defect 20 ) and several other defects , and four-cell stage nuclei—size/shape also co-occurs with P0 spindle rocking ( Defect 17 ) and several other defects ., We also analyzed the occurrence of cellular defects by both linear principal component analysis ( PCA ) and logistic principal component analysis ( LPCA ) 10 ., Although LPCA appears to be more appropriate for 0-1 type of data , PCA is more appealing in terms of its interpretability because the dimensions of LPCA are not orthogonal and the eigenvalues of LPCA cannot be used to rank the importance of principle components ., As dimensional reduction tools , both PCA and LPCA gave similar results for this dataset–the projection of the defects onto the plane spanned by the first and second principal components ( PCs ) reveals very similar pattern ( Figure 3 , for LPCA ) ., For example , P0 cytokinesis—furrow specification ( Defect 20 ) , P1/AB nuclear separation—cross-eyed ( Defect 23 ) , four-cell stage nuclei—size/shape ( Defect 34 ) , and P0 spindle rocking ( Defect 17 ) show high co-occurrence in the correlation map , and they are positioned close to one another in the LPCA plot as well ., The observation of closely related defects suggests that the degree of pleiotropy cannot be readily measured by simply counting the number of defects ., In order to study pleiotropy , we need to identify combinations of defects , or “phenotypic signatures , ” which describe the effects of losing individual biological functions ., Cell divisions in early embryogenesis involve a number of biological functions such as chromosome segregation , cytokinesis , and cell polarity ., Sonnichsen et al . manually grouped genes identified in the RNAi screen into 23 mutually exclusive classes according to their phenotypic profiles 8 ., Among these , 22 classes have functional annotations and the remaining one is composed of genes whose phenotypic profiles contain a large number of defects and do not resemble profiles of any functionally characterized genes ., We designed a computational approach to determine phenotypic signatures for each of the 22 functional classes and to identify additional genes potentially belonging to the given class ( Figure 4 ) ., The phenotypic signature of a class is defined as a collection of cellular defects significantly enriched in that class as compared to the whole dataset ., More specifically , for each class as defined in 8 , we computed the P-value for the enrichment of each defect according to the hypergeometric distribution ., This class phenotypic signature is then composed of all defects whose enrichment P-values are no greater than 0 . 05 after correcting for multiple comparisons ., As a result , we found phenotypic signatures for 18 of the 22 functional classes ., For the remaining 4 classes , no significantly enriched defects could be identified , because these classes all contained too few genes ( 5 or fewer ) for any defect to pass our statistical threshold ., The above procedure can be illustrated for the cell polarity class ( Figure 5 ) ., Originally , a total of 12 genes , including some genes previously known to be involved in cell polarity , were assigned to this class ., We identified 7 defects significantly enriched in this class as its phenotypic signature ., Among those defects , P1/AB asynchrony of division and four-cell stage configuration are the characteristic defects of asymmetric cell divisions ., Defects in P0 pronuclear meeting , P0 spindle positioning , P0 spindle poles , P1 nuclear migration/rotation , and AB spindle orientation are the ones that are likely to accompany the loss of asymmetry ., We searched the rest of the dataset for additional genes with phenotypic profiles matching the signature ( see Methods ) and identified RGA-3 , a putative Rho GTPase activating protein ., This gene was originally classified as involved in cortical structure ., Our search for phenotypic signatures did not rule out its functional involvement in cortical structure , but suggested its additional roles in cell polarity ., A recent paper reported that knocking down RGA-3 along with its paralog RGA-4 resulted in changes in the boundary of anterior and posterior domains of PAR proteins in the early embryo 11 ., This experiment confirmed our prediction for RGA-3s involvement in cell polarity ., Such functional assignment of genes based on phenotypes may seem obvious , since genes sharing similar phenotypes should share similar functions ., However , without the in-depth analysis of phenotypic signatures , additional roles of the genes are often neglected ., Another example of phenotypic signature is shown for the chromosome function class , which is a relatively large class consisting of 64 genes originally ., Its phenotypic signature included P1/AB nuclear separation—cross-eyed , P1/AB nuclei—size/shape , four-cell stage cross-eyed , four-cell stage nuclei—size/shape , and so on ( Figure 6 ) ., Using the phenotypic signature , we identified 8 additional genes for this class ., The phenotypic profiles of these 8 genes all contain defects other than those included in the chromosome function signature , and thus were originally assigned to other classes ., Interestingly , 5 of these 8 genes are known to be involved in nuclear transport functions , suggesting potential connections between nuclear transport and chromosome functions ., Evidence supporting their roles in chromosome function has been reported in recent literature ., NPP-8 , which is part of the nuclear pore complex , was found to be recruited to the chromatin after anaphase onset in the early embryo 12 ., NPP-19 , another nuclear pore complex protein , along with F10C2 . 4 , an uncharacterized gene , were both found to be tightly co-expressed with a group of genes involved in chromosome maintenance 13 ., By determining phenotypic signatures and identifying additional genes as belonging to each functional class , we allow genes playing multiple roles in early embryogenesis to be assigned to multiple classes ., We define Pleiotropy Index as the number of classes a gene is assigned to ., More than half of the genes involved in early embryogenesis are pleiotropic ( i . e . , with Pleiotropy Index ≥2 ) , suggesting that pleiotropy occurs extensively ( Figure 7 ) ., Genes that were not assigned to a functional class in the original screen are mostly pleiotropic ( Table S1 ) ., Although the profiles of these genes do not resemble those of any other known genes , they now can be decomposed into several phenotypic signatures that lead to functional discoveries ., For example , F25H2 . 4 , an uncharacterized gene , is assigned to the classes of cytoplasmic structure , mitochondrial function , meiotic cell cycle progression , and meiosis chromosome segregation ., Although pleiotropy is relatively common , only 3% of the genes involved in early embryogenesis are highly pleiotropic ( i . e . , with Pleiotropy Index ≥5 ) ., Many signaling proteins show a very high Pleiotropy Index ( Table S2 ) , probably because signaling proteins can be part of various molecular machines functioning in early embryogenesis ., For example , of all the 19 kinases involved in early embryogenesis , 18 are pleiotropic ( 95% compared to 59% of all genes involved in early embryogenesis ) , and 5 are highly pleiotropic ( 26% compared to 3% of all genes ) ., The biochemical reaction that kinases catalyze is phosphorylation , and a single kinase can catalyze phosphorylation in multiple contexts and with different protein targets ., Eliminating a kinase may thus result in multiple sets of defects because a variety of protein targets in different contexts cannot be phosphorylated properly ., Since the defects in consideration are not independent of each other , it is possible that the foregoing definition of Pleiotropy Index , although biologically meaningful , can be biased ., To resolve this issue , we take the top 33 principal components ( PCs ) of the data matrix , which can account for 90% of the total variation , and regard them as “mega-defects . ”, Then , for a gene G , we define its influence from a functional class K as the average of the correlations of this genes loading vector with those of all the genes in this class ( see Methods ) ., A gene Gs Relative Pleiotropy Score is the sum of its influences from all functional classes ., The Relative Pleiotropy Score does not have direct functional implications as Pleiotropy Index does , but it gives a relative value of how complex a phenotypic profile is and avoids over-counting highly correlated defects ., We observe that the Relative Pleiotropy Score such defined is highly correlated with Pleiotropy Index ( Figure S1 ) , indicating that both are reasonable proxies to the concept of pleiotropy ., Recent work has revealed a modular organization of genes and proteins in model organisms 13–18 ., Here a module refers to a group of genes or proteins acting in concert to achieve a certain biological function ., However , it is not yet clear how these modules are connected and coordinated ., An immediate implication from our finding of pleiotropic genes is that gene modules overlap instead of being separate from one another ., We hypothesized that pleiotropic genes act as “connectors” between different modules ., The few most highly pleiotropic kinases , for instance , connect most of the major modules in early embryogenesis ( Figure 8 ) ., Many cellular events in early development are mediated by protein-protein interactions ( PPIs ) ., Complexes or pathways in PPI networks can be the molecular identities of modules ., According to our hypothesis , the highly pleiotropic proteins we have identified should reside in central positions in the C . elegans PPI network 13 , 19 ., We tested our hypothesis by studying the relationship between a proteins “betweenness” and its Relative Pleiotropy Score or Pleiotropy Index ., The betweenness of a given node is defined as the number of times that node is on the shortest paths connecting any two nodes in a network 9 ( see Methods ) ., It is a network property that measures the extent to which a node is topologically in a central position between sub-graphs of a network 9 , and it has been applied to characterize modularity of biological networks 20 , 21 ., We ranked the betweenness values for early embryogenesis genes that involve two or more interactions in the network , and found that the rank of betweenness is significantly correlated with the Relative Pleiotropy Score ( P-value\u200a=\u200a0 . 004 ) ( Figure 9 ) ., Furthermore , this statistical significance of the correlation appears to be contributed mostly by a few genes with the highest Relative Pleiotropy Scores ., For example , the sum of betweenness ranks for the 12 genes with the highest Relative Pleiotropy Scores is 1123 , whereas the sum of betweenness ranks for 12 randomly sampled early embryogenesis genes is 1794 on average ( P-value\u200a=\u200a0 . 01 ) ., Similarly , we found that the sum of betweenness values for the 11 genes with the highest Pleiotropy Indices ( Pleiotropy Index≥5 ) is significantly higher than that for 11 early embryogenesis genes chosen at random ( 454701 vs . an average of 179400 , P-value\u200a=\u200a0 . 03 ) ( see Methods ) ., The betweenness property of highly pleiotropic genes presents supporting evidence to our hypothesis that pleiotropic genes act more as connectors between gene modules ., In this paper , we presented the first systematic investigation of pleiotropic genes in a multi-cellular organism ., Using pre-defined functional classes as seeds , we identified phenotypic signatures associated with these classes , and then assigned genes based on their matches to the signatures ., We annotated many uncharacterized genes with complex phenotypic profiles by decomposing their profiles into signatures that are indicative of biological functions ., We also identified additional functions which were previously unknown for some characterized genes ., Our approach can potentially be generalized and applied to many other phenotypic datasets ., For example , Gene Ontology categories can be used in place of pre-defined functional classes in order to obtain phenotypic signatures ., Furthermore , the reproducibility of detecting defects in RNAi experiments may also be used to define signatures from large amount of phenotypic profiles ., Although each gene identified as required for early embryogenesis was assigned to only one class in the original RNAi screen , we found that nearly half of these genes are pleiotropic ., Some genes , in particular those encoding signaling molecules , are highly pleiotropic ., We examined evolutionary rates of highly pleiotropic genes by comparing sequences from C . elegans and C . briggsae ., We found that highly pleiotropic genes evolved at similar rates to other early embryogenesis genes ( data not shown ) , suggesting that pleiotropy may not constitute severe constraints for protein evolution ., Our finding is consistent with a previous report that pleiotropic and non-pleiotropic genes evolve at similar rates in yeast 22 ., We also assessed the possibility that abundantly expressed genes are more likely to be highly pleiotropic ., We retrieved the expression levels of early embryogenesis genes from a SAGE ( Serial Analysis of Gene Expression ) dataset 23 , and correlated with Pleiotropy Index ., By performing linear regression we found a significant negative correlation between expression level and Pleiotropy Index ( P-value<0 . 01 ) ( Figure S2 ) ., The highly pleiotropic genes tend to be less abundantly expressed than genes assigned with only one or two phenotypic signatures ., This is consistent with our observation that signaling molecules such as kinases are enriched in the set of highly pleiotropic genes ., The genes involved in cell signaling are often only expressed at a low level but play very important regulatory roles ., Finally , we proposed a mechanistic interpretation of pleiotropy from the perspective of functional modules in cellular networks ., Since pleiotropic genes are multi-functional , we reasoned that they are likely to coordinate distinct functions involved in early embryogenesis ., Consistent with this notion , we found that highly pleiotropic genes exhibit higher betweenness in PPI networks than randomly selected genes ., However , there are examples of non-pleiotropic genes showing high betweenness and high pleiotropic genes showing low betweenness ., A potential reason is that current PPI data is neither comprehensive nor precise ., False positives and false negatives exist in the datasets of genome-wide yeast two-hybrid screens ., Consequently , the estimation of centrality based on betweenness may not accurate for every protein in the network ., Another possible reason is that mechanisms other than centrality in PPI networks may contribute to pleiotropy ., Hodgkin discussed possible underlying mechanisms of pleiotropy and classified them into seven different types 24 ., “Combinatorial pleiotropy” , the situation that a protein plays various roles through its various binding partners , is only one type of mechanism ., This mechanism is important for the pleiotropy in early embryogenesis , probably because many protein complexes mediate this process ., It is not clear yet what mechanisms underlie pleiotropy in other biological processes in multi-cellular organisms ., We combined results from two genome-wide RNAi screens 25 , 26 which scored maternal sterility , embryonic lethality , and a limited number of post-embryonic defects with the C . elegans PPI networks ., We found 7 genes that exhibited 8 or more of the scored defects and had 2 or more interactions ., These 7 genes had a higher sum of betweenness values than that of 7 randomly selected genes , though the P-value of the difference is marginal ( P-value\u200a=\u200a0 . 09 ) ., This result indicates that PPI networks may contribute to pleiotropy in a broader context , but other mechanisms of pleiotropy probably apply as well ., Currently , few datasets that score a large number of phenotypes in detail are available for multi-cellular organisms ., The mechanisms underlying pleiotropy are worth further investigations once we have more comprehensive and accurate phenotypic profiles as well as other types of functional genomic data ., Phenotypic profiles were represented as a binary matrix where rows indexed genes and columns indexed defects ., Each entry in the matrix was either zero or a positive number , indicating the absence or presence of defects ., We obtained control datasets by randomly permuting values among genes for each column while keeping the number of positive cells in each column fixed ., We calculated the frequency of occurrence for each individual defect ( F, ( i ) ) and the frequency of co-occurrence for each pair-wise combination of defects ( F ( i , j ) ) ., For each pair of defects , we calculated the ratio ( R ( i , j ) ) of the observed co-occurrence frequency over the expected frequency as if the two defects occurred independently: R ( i , j ) =\u200aF ( i , j ) / ( F, ( i ) ×F, ( j ) ) ., We generated a map of R ( i , j ) using the heatmap function in the statistical language R . There were 22 manually assigned functional classes in the phenotypic dataset ., We used genes originally assigned in a class as seeds to identify defects enriched in that class ., The collection of enriched defects was defined as the phenotypic signature of the given class ., We used the cumulative hypergeometric distribution to determine whether a defect was significantly enriched in a class compared to the whole dataset ., In a given class , if the phenotypic profiles of x genes contained a given defect , the P-value was calculated as the following:In this formula , N represents the total number of genes in the dataset; K represents the total number of genes for which phenotypic profiles contain the given defects; n represents the number of genes in the given class ., For each functional class , we examined whether any additional genes can be assigned to the given class by matching phenotypic profiles to the identified signature of that class ., First , we obtained phenotypic profiles of genes originally assigned to the given class and calculated the average number ( “A” ) of defects matching the signature of that class ., Second , we obtained phenotypic profiles of genes not originally belonging to that class and scored them by the number of defects matching the signature ., If a gene scored equal to or higher than A , this gene was assigned to the given class ., This procedure does not require a perfect match , but it does make the enrichment of defects in the signatures even more enriched in each individual class ., In the procedure , we allowed genes to be assigned to multiple classes besides their original assignment , since some genes might play more than one role in early embryogenesis ., Phenotypic signatures of different classes contain different sets of defects ., In a few cases , the signature of one class ( X ) contains all the defects from the signature of another class ( Y ) ., In other words , the defects in the signature of class Y are a subset of that of class X . Thus , a phenotypic profile containing all the defects of the signature for class X automatically contains all the defects of the signature for class Y . In order not to overestimate the degree of pleiotropy , genes with phenotypic profiles matching the signature of X are only assigned to class X , instead of both X and Y . For example , the signature of the protein synthesis class contains all of the defects from the signatures of the cytoplasmic structure , meiosis chromosome segregation , chromosome segregation , and mitochondrial function classes ., It can be speculated that blocking protein synthesis results in a number of deleterious effects that resemble perturbing cytoplasmic structure , meiosis chromosome segregation , chromosome segregation , and mitochondrial functions ., Thus genes assigned to the protein synthesis class were not considered for assignment to any of the above classes ., LPCA is a dimensionality reduction method for binary data 10 ., We applied LPCA to the phenotypic profiles of early embryogenesis genes and projected all the defects onto the first two principal components for visualization ., The MATLAB code of LPCA was downloaded from www . cis . upenn . edu/~ais/software/lpca_code . tar ., We applied PCA to the phenotypic profiles which consist of 661 genes in rows and 45 defects in columns ., Eigenvalue diagnosis indicated that 33 principle components accounted for 90% of the variation in the dataset ., We calculated an average of Pearson correlation coefficients between the gene of interest and any genes from a given functional class ., The relative pleiotropy score is defined as the sum of average Pearson correlation coefficients of all the functional classes ., The betweenness of a node is defined as the number of shortest paths running through the node of interest 9 ., We computed the shortest paths between all pairs of nodes in the largest component of C . elegans PPI networks 13 , 19 ., For each pair of nodes , we enumerated all possible paths in between the chosen pair and increased the betweenness score of the nodes on the shortest paths by one ., If there were N alternative shortest paths on route , we split the credit and assigned partial score 1/N to the nodes on the shortest paths ., We computed betweenness values for proteins that interact with at least two other proteins , because a protein with only one interacting partner could not be on any shortest paths except for the paths involving the protein itself ., We calculated the sum of betweenness values for the early embryogenesis proteins with Pleiotropy Index of 5 or higher ., The P-value of significance was estimated by randomly selecting the same number of early embryogenesis genes that had betweenness values and by calculating the sum of their betweenness values ., The simulation was repeated 1 , 000 , 000 times .

Introduction, Results, Discussion, Methods

Pleiotropy refers to the phenomenon in which a single gene controls several distinct , and seemingly unrelated , phenotypic effects ., We use C . elegans early embryogenesis as a model to conduct systematic studies of pleiotropy ., We analyze high-throughput RNA interference ( RNAi ) data from C . elegans and identify “phenotypic signatures” , which are sets of cellular defects indicative of certain biological functions ., By matching phenotypic profiles to our identified signatures , we assign genes with complex phenotypic profiles to multiple functional classes ., Overall , we observe that pleiotropy occurs extensively among genes involved in early embryogenesis , and a small proportion of these genes are highly pleiotropic ., We hypothesize that genes involved in early embryogenesis are organized into partially overlapping functional modules , and that pleiotropic genes represent “connectors” between these modules ., In support of this hypothesis , we find that highly pleiotropic genes tend to reside in central positions in protein-protein interaction networks , suggesting that pleiotropic genes act as connecting points between different protein complexes or pathways .

In a biological system , some genes play single roles while others perform multiple functions ., How can we determine which genes are multi-functional ?, An informative way for probing gene functions is to eliminate the expression of a given gene and observe the phenotypic consequences ., RNAi techniques have enabled the generation of genome-wide phenotypic data ., Conventionally , genes are clustered into mutually exclusive categories according to the observed defects following RNAi ., However , assigning genes that may play multiple roles exclusively into a single category is arbitrary ., This paper works out a computational approach that categorizes genes while allowing assignment of genes with complex phenotypes into multiple categories ., We apply this approach to genes involved in cell divisions of C . elegans early embryos , and find that about half of these genes can be assigned to more than one functional category ., This approach has allowed the identification of previously undiscovered gene functions ., We also find that genes playing many roles in early embryos tend to reside in central positions in protein networks ., Our approach can be used to perform functional annotations based on phenotypic data in other systems and to identify genes that coordinate multiple biological functions .

developmental biology, computational biology/systems biology

journal.pgen.1003661

The Conditional Nature of Genetic Interactions: The Consequences of Wild-Type Backgrounds on Mutational Interactions in a Genome-Wide Modifier Screen

Fundamental to the logic of genetic analysis is that the observed variation in a phenotype for a genetically mediated trait is causally linked to one or more DNA lesions/variants ., However , it is well known that the phenotypic effects of many individual mutant alleles are context dependent , with respect to environmental influences , as well as the “wild-type” genetic background in which the mutation is observed ., Indeed , genetic background has long been known to influence observed phenotypic expression across traits , organisms , and a range of allelic effects , including hypomorphs , amorphs/nulls and neomorphs 1–9 ., These results make it clear that the phenotypic effects of a mutation ( i . e . penetrance and expressivity ) are themselves “complex traits” , subject to environmental and polygenic influences 1 ., Far beyond being a minor curiosity in genetics , the background dependent effects of a number of mutations have been at the heart of debates over the conclusions and the ability to replicate key findings from several studies , including the genetics of life span 10–14 , stress tolerance 15–17 and pigmentation 18–20 ., Although the basic influence of genetic background on the expressivity of mutations is well documented , the wider consequences of such influences are poorly understood 21 ., In particular , the extent to which wild-type background influences the magnitude and sign of genetic interactions remains unclear ., Research to date addressing this question 4 , 22 , 23 , has largely focused on a small set of mutations , and defined genetic backgrounds ., Recent work has demonstrated that the magnitude of genetic interactions can be influenced by environmental factors 24 , and even ploidy level 25 ., Yet the generality of such findings remains uncertain ., Thus this remains an essential , but poorly explored area of fundamental genetics , as our understanding of epistasis , and our inferences of the topology of genetic networks are often derived from studies of genetic interactions 26–33 ., In addition , modifier screens have been extremely important , and have identified large numbers of genes that interact to influence the visible expression of the phenotype of the focal mutation , even when the modifier may not have a visible phenotype by itself 34 , 35 ., We have previously shown that the phenotypic effects of an allele of the scalloped gene ( sdE3 ) in Drosophila melanogaster is profoundly influenced by wild-type genetic background ( Figure 1B ) , with effects extending to wing disc transcriptional profiles 36 ., One gene that was transcriptionally regulated in a background-dependent matter , optomotor blind/bifid ( omb/bi ) , was then examined in a double mutant combination with sdE3 ., We demonstrated that the phenotypic consequence of the interaction between these mutations was markedly influenced by wild-type genetic background ., In one wild-type background the double mutant combination resembled the individual sdE3 phenotype , while in the other wild-type background , the omb mutation behaved as a strong synthetic enhancer of sd 36 ., Our findings clearly demonstrate the influence of wild-type genetic background on this genetic interaction , but an important challenge is to determine whether such context dependent effects are widespread ., To address this question we performed a genome wide-screen for dominant modifiers of sdE3 using two wild-type genetic backgrounds ., Our results suggest that the majority ( ∼74% ) of all modifiers are background-dependent ., The background-dependence of the modifier alleles are in part due to the wild-type strains differing in overall sensitivity to mutational perturbations ., Using a subset of the deletions spanning the range of phenotypic effects of modifiers , we observed that the interaction effects were consistent using an additional allele , sdETX4 ., Furthermore , we show that the deletion effects are a result of the interaction with mutations at the sd locus , and not a simple consequence of haplo-insufficiency in the genomic region of the deletion ., We also demonstrate that the background-dependent interactions of modifiers with sdE3 are linked to the same genomic regions that contribute to the background-dependent effects of the allele itself ., We argue that the phenotypic expressivity of mutations can be considered a quantitative trait , and a more comprehensive , context-dependent view of the effects of mutations needs to emerge ., Genetic modifier screens are powerful tools to both identify interacting factors that contribute to signaling networks , as well as to infer their topology ., This approach has shaped our understanding of the genetic basis of many traits , across numerous organisms ., However little is known about how wild-type genetic background influences genetic interactions ., We previously demonstrated that the genetic interaction between mutations in two genes , sd and omb , is dependent on genetic background 36 ., To determine if such an effect is a general phenomenon we performed an analysis of genome-wide genetic interactions between the sdE3 mutation and deletions generated in otherwise isogenic backgrounds spanning the autosomes of Drosophila ., We first verified that deletions spanning a number of putative candidate genes ( Dll , wg , vg ) previously demonstrated to interact with sd modify the sdE3 phenotype ., In each of these instances the deletions confirmed previous expectations for the interaction ( Figure S1B ) ., We then screened the autosomes , with two independent sets of genomic deletions , DrosDel 37 and Exelixis/BSC 38 , 39 , each generated in an independent isogenic progenitor background ( Figure 1B ) ., In total 723 deletion-bearing strains ( spanning ∼90% of the autosomal genome ) were crossed to sdE3 in each wild-type background ., F1 males hemizygous for the sdE3 mutation and heterozygous for the deficiencies were scored ., For the 198 deletion strains that consistently modified the sdE3 wing phenotype , ∼74% of the observed effects were dependent on wild-type ( Oregon-R vs . Samarkand ) genetic background ( Table 1 ) ., Frequently , the background contingency was a result of severe effects in one wild-type genetic background , with modest or no effects in the other ( Figure 1A and 2 , Figure 3A ) ., A complete list of modifier regions , and putative candidate genes can be found in Table S1 ., An example of the physical location and contribution of these effects is illustrated using the left arm of chromosome 3 ( Figure 3 , Figure S4 ) , where background-independent and -dependent effects are illustrated , including some deletions with opposing effects in terms of modifying the sdE3 phenotype ., We confirmed these results using a linear model ( ANOVA ) , by asking what proportion of all “significant” modifiers also had a “significant” interaction effect between genetic background and the deletion ., Based upon these criteria ∼79% of modifiers demonstrated background dependence ., While each cross was carried out independently , there were a large number of crosses performed , and each deletion bearing genotype was compared to a common set of controls from within each block of crosses ( see methods ) ., Therefore we utilized several methods that adjust for multiple comparisons ., While these methods will decrease the number of deletions deemed modifiers using standard comparisons ( i . e . α\u200a=\u200a0 . 05 ) , we are primarily interested in the proportion of such modifiers that are due to background dependent effects ., Using False Discovery Rate ( FDR ) we observed a similar frequency ( ∼78% ) as with unadjusted p-values , while with the sequential Bonferroni ( Holm ) it was ∼68% ., Regardless of the exact approach used , it is clear that the vast majority of modifiers recovered are background dependent ., We performed this screen using two different sets of deletions , each of which varied in the size of the deletion ., We observed little association between deletion size and severity of phenotypic modification ( Samarkand: correlation-0 . 09 & -0 . 08 using Exelixis & DrosDel respectively; Oregon: −0 . 061 & −0 . 067 using Exelixis & DrosDel deletions respectively , Figure S5 ) ., The lack of association between size of deletion and magnitude of effect suggests that it is unlikely that the observed effects are due to the number of genes perturbed in each deletion ., These key results suggest that at least in sensitization screens , and possibly for many studies of genetic interaction , wild-type genetic background will have profound influences on the range of phenotypes observed and the modifiers that are identified , with only a subset of modifiers being background-independent ., Using Flymine and Droid 40 , 41 as well as literature mining we examined all of the previously identified genes that act as genetic modifiers , protein-protein interacting partners , or are targets of transcriptional regulation by SD ., From these sources we collated evidence for 19 genes that were covered by deletions in this screen ( i . e . excluding genes on the X ) , and all but one ( sens ) were recovered as genetically interacting with sdE3 ( Figure 3B ) ., However , more than 50% of these specific loci demonstrated background-specific interactions with sdE3 , including vg , which is known to physically interact with SD to form a heterodimer , and is transcriptionally regulated by this complex ., Several well-known genetically or physically interacting genes ( such as salm and yki ) showed surprisingly mild enhancement of the phenotype , which may be a result of the particular wild-type backgrounds used in this study ., These findings suggest that even for well-characterized interacting genes , the influence of genetic background can be substantial , consistent with the flexible nature of genetic interactions ., An important caveat to this interpretation is that many of these deletions may contain more than one gene ., This could potentially mean that the interaction is due to both the deletion of the focal gene as well as other loci nearby ., Yet , as described above , we observed no evidence for a relationship between deletion size and magnitude of effect , suggesting that this may be a minor contributing factor ., To further validate , refine , and extend our analysis we quantified a subset of 44 of the Exelixis deletion lines that spanned the range of modifier phenotypes across both severity and background-dependence ., Interestingly ( Figure 4 ) , the background-dependent interactions are clearly a result of both specific differences with respect to the nature of sensitizing mutational effects in each background , as well as to the degree of sensitivity to mutational perturbation ., Indeed , the sdE3/Y; Deletion/+ combinations in the Oregon-R wild-type background demonstrated considerably more variation between deletion strains , compared to the same genotypes in Samarkand ( Figure 4 ) ., Despite the fact that the sdE3 mutation in the Oregon-R background had more severe loss of wing tissue ( Figure 1 , Figure S1 ) , the range of both enhancement and suppression exceed that of the same mutation in the Samarkand background ( Figure 4 ) ., The between deletion co-efficient of variation ( CV ) for wing size in the Oregon-R background is approximately double that ( 0 . 34 ) of the Samarkand background ( 0 . 15 ) ., These results were confirmed using a Levenes test with a non-parametric bootstrap ., Despite the differences in both degree and spectrum of sensitivity , there was still a moderate correlation of effects of the sdE3/Y; Deletion/+ combinations ( 0 . 66 , CI ( 0 . 46 , 0 . 8 ) ) across the two wild-type backgrounds ., These data indicate many of the modifiers are acting in the same direction , although vary for magnitude of effect ., Interestingly , even the non-genetic component of phenotypic variation observed for Oregon-R sdE3/Y; +/+ in crosses to the wild-type deletion progenitor shows considerably greater phenotypic variation for wing size compared to Samarkand ( Figure 4 ) , although it is unclear if this is related to the changes in within strain variation ( robustness ) ., While the semi-quantitative measure of wing size used for the initial screen , and quantitative measure described above are highly correlated ( see methods ) , a few putative modifier regions failed to replicate in the tertiary validation cross with quantitative measures ., Similarly a few deletion lines that were expected to not have an effect ( based on the initial screen ) , did have one with the quantitative measure ., However these potential false positives and negatives are few , of similar numbers , and thus are not expected to influence the overall conclusions ., One possible explanation for these results would be that the deletions influenced wing size , per se , and the results were not a specific consequence of the interaction between sd and the deletion ., To investigate this we quantitatively examined females who were heterozygous for the sdE3 mutation and for the deletions ( i . e . sdE3/+ ; Deletion/+ ) across each genetic background ., These females have qualitatively “wild-type” wings , and previous work did not observe an effect of sdE3 on wing size in females as heterozygotes 42 ( although it did influence wing shape ) ., Therefore we quantified these females across the same set of deletions as described above ., If the deletions were not generally acting as modifiers of the “sensitized” sd mutant phenotype in hemizygous males , but as general modulators of size , then we would expect a strong positive correlation between the effects on size in males and females ( sdE3/+ ; Deletion/+ vs . sdE3/Y ; Deletion/+ ) ., The correlation between Samarkand and Oregon-R sdE3/+ ; Deletion/+ females was ∼0 . 8 , suggesting that the effects of the deletions on overall wing size is similar across backgrounds ., However the correlations within each background ( i . e . sdE3/+; Deletion/+ vs . sdE3/Y ; Deletion/+ ) were 0 . 22 , ( CI −0 . 08 , 0 . 49 ) , and 0 . 21 , ( CI −0 . 08 , 0 . 48 ) respectively , and neither case was significantly different from 0 ., The lack of a correlation indicates that the influence of the deletions in sdE3 hemizygous males is largely independent of any effects on overall wing size ., More importantly the CV for wing size in females ( across deletions ) for both backgrounds was ∼0 . 03 , which is 5× and 10× less than that observed for sdE3 hemizygotes in Samarkand and Oregon-R respectively ( Figure S6 ) ., This suggests that most of the phenotypic variation for wing size due to the deletion is observed when the backgrounds are “sensitized” with the sd mutation , while having relatively little influence on wild-type wing size ., Are the loci influencing the background-specific genetic interactions the same as those that modulate phenotypic expressivity for wing size of the focal sdE3 mutation ?, To address this question we generated a set of backcross lines between Oregon-R and Samarkand ( both fixed for sdE3 ) , where “long” wings were selected in the backcross to the Oregon-R background , and “short” wings in backcrosses to the Samarkand background ( Figure S3 ) ., Using ∼30 SNPs polymorphic across backgrounds , we verified that these backcross lineages showed expected genotypes for more than 90% of markers ( i . e . phenotypically short wings but with Samarkand genotypes ) ., Among the molecular markers that did introgress , include those tightly linked to the unknown causal loci on 2R near cytological band 48 and at the centromere of 3L 36 ., If the loci modulating the magnitude of the genetic interactions were caused by genes other than those influencing the background-specific disruption of wing development , we would predict weak correlations between sdE3/Y; Deletion/+ in Oregon-R and the equivalent genotype from the “short” backcross ( with an otherwise Samarkand background ) ., Similar logic prevails for the Samarkand and the “long” phenotype ., However , even using semi-quantitative measures , it is clear that these are highly correlated; 0 . 82 ( CI 0 . 66–0 . 91 ) and 0 . 86 ( CI 0 . 73–0 . 93 ) respectively ., These results are consistent with the loci influencing the background-dependent genetic interactions being the same as those influencing the background-dependent effects on the phenotypic expressivity of the focal sdE3 mutation ., The results described above demonstrate that the loci that influence the background dependent nature are linked to those influencing phenotypic expressivity of the mutation itself ., However , it was unclear if the observations were due to some particular properties of the sdE3 allele , or a more general function of perturbation at the sd locus ., To address this , we retested a subset ( 29 ) of the deletions spanning the range of phenotypic effects with sdE3 , using an additional allele sdETX4 , across each genetic background ., The phenotypic consequences of sdETX4 , while background-dependent , are somewhat weaker than sdE3 ( Figure S7A ) ., Despite these phenotypic differences , there was a moderate to high correlation across the modifiers effects on these two alleles ., In the Oregon-R and Samarkand wild-type genetic backgrounds respectively , the correlation between the effects of the deletions on the phenotypes of the sdE3 and sdETX4 allele was 0 . 66 ( CI 0 . 38–0 . 82 ) , and 0 . 76 ( CI 0 . 55–0 . 88 ) ., In addition the general pattern of greater sensitivity to mutational perturbation by modifiers of the sd phenotype appears to be generally maintained ( Figure S7B ) ., These results demonstrate that even across multiple alleles , the background dependence of the modifiers is maintained ., Although the primary goal of this study was to explore the flexibility in genetic interactions , not to identify candidate genes , for confirmatory purposes , we examined several genomic regions that demonstrated background-dependent or -independent modifiers ( Table S2 ) ., Interestingly , one region , 49E1 , contained vg , which encodes a SD-regulated transcriptional factor that forms a heterodimer with SD ., Fine mapping , followed by the use of candidate insertional mutants ( co-isogenic to the Exelixis deletions ) confirmed that the vgF02736 allele behaved as a background-dependent enhancer with strong enhancement in Samarkand , but very weak enhancement in Oregon-R ., We followed this up by introgressing this allele into both the Samarkand and Oregon-R background ., Again we observed background-specific enhancement of the sd phenotype ., Other fine mapping regions suggest several candidate genes , although for at least one region , no obvious candidate gene could be determined ( Table S2 ) ., There are outstanding questions that our study is unable to address ., The background dependent nature of the genetic interactions could be the result of a “third-order” effect between the sd mutation , the hemizygous allele uncovered over the deletion and other loci across each wild-type genetic background ., An alternative , and perhaps simpler explanation would be of differential quantitative complementation uncovered by the deletion 46 ., In such cases , the variation in the degree of the modification of the focal mutation ( sd ) is a direct result of the alleles that differ across backgrounds uncovered by the deletion ., While we expect that our results are a combination of both explanations , it is likely that without very high resolution mapping of the genomic regions , or test of specific polymorphisms will we be able to determine the relative contribution of each type of interaction ., However the previous work that motivated this current study , namely the background dependent interaction between sd and Omb was clearly due to a third order effect 36 ., Understanding the degree to which increasingly higher order epistasis contributes to phenotypic variation is under-explored but of great importance 47 ., One curious finding of our study was that the background ( Oregon-R ) that demonstrated the higher degree of phenotypic expressivity of the focal sd mutations , showed increased sensitivity to mutational perturbation ( both enhancers and suppressors ) as well as greater phenotypic variation within strain ., Recent work has demonstrated that loci can influence trait variability ( “noise” ) directly 48–50 , including naturally occurring variants in the Hsp90 gene of Drosophila 51 ., Indeed even cell-to-cell variation , and variation in penetrance appears to have a complex genetic architecture 48 influenced by variability in gene expression 52 ., It is unclear whether the loci that contribute to increased phenotypic “noise” also contribute to the amplified sensitivity to mutational perturbation as seen in the Oregon-R vs . Samarkand wild-type backgrounds ., In previous work Oregon-R does have higher levels of phenotypic variation in quantitative measures of wing shape , but no increased sensitivity to weak ( heterozygous ) mutational perturbation 42 ., However the focal mutations used in the current study ( sdE3 and sdETX4 ) represented more severe perturbations to wing development , so this may not provide an adequate comparison ., Regardless , this remains an unanswered question , and a potential link between so-called variance controlling genes and sensitivity to perturbation would have important implications for the genetic architecture of canalization and robustness 5 , 53 ., One constraint of the current study is that we utilized a hypomorph of moderate phenotypic effect , as opposed to a null allele ., While a formal definition of functional epistasis ( sensu 54 ) requires the use of null alleles , most interaction screens utilize alleles of comparable ( hypomorphic ) effect to allow the recovery of both enhancers and suppressors ., Nevertheless , previous work has demonstrated that null alleles can also show background-dependence effects in the primary effect of the mutation , including on development , growth and viability 1 , 2 , and our results demonstrate that these conditional effects are likely to be reflected in the genetic interactions between mutations as well ., In addition we demonstrated that the quantitative effects we observed with the interaction between sdE3 and segmental deletions in each wild-type genetic background were correlated when observed across another ( weaker ) allele , sdETX4 , suggesting that such effects are not due to a particular allele ., We also demonstrated that the effects of these interactions are tightly linked to the same genomic regions that contribute to the primary background-dependent phenotypic effects of the mutations ., Thus for our system , the genetic variants influencing the phenotypic expressivity of the focal mutation appear to be the same as those modulating both the magnitude , and potentially the sign of genetic interactions between mutations ., While the positive and negative implications for modifier ( and other genomic ) screens is clear , the potential flexibility of genetic networks given segregating variation in a population needs to also be considered ., In particular an allele entering a population ( either as a new mutation , or as a result of introgression from another population or species ) may not have a “fixed” effect on fitness; instead the genetically contingent effects of the allele result in a distribution of phenotypic effects , including a possible change in sign ( i . e . from deleterious to beneficial ) ., The Oregon-R strain was originally obtained from the Bloomington stock center , while Samarkand was obtained from the lab of Dr . Trudy Mackay ., For both strains , we further inbred them to near isogenicity , and tested via a panel of 30 polymorphic markers to confirm there was no contamination or residual heterozygosity ., A combination of sequencing and PCR-based genotyping suggests that these two strains have an approximately 2% divergence from one another , and that all sequenced regions examined to date are a subset of variation from natural populations ., The X-linked sdE3 mutant allele ( obtained from the Drosophila stock center , Bloomington IN ) , used in this study is caused by a P{wE ry1t7 . 2\u200a=\u200awE} transposon located in the third intron of the sd gene 55 ., This mutant allele was introgressed into two lab wild-type strains , Oregon-R and Samarkand , both marked with white ( w ) , by repeated backcrosses involving homozygous mutant female and the wild type male for over 20 generations 36 ., These lines have been subjected to extensive genotyping to verify the extent of the introgression , and to avoid contamination ., The sdETX4 and vgF02736 alleles were also obtained from the Bloomington stock center , and were introgressed for 20 generations into each wild-type strain ., To validate the primary findings of this study , we repeated crosses , and quantified wing size for a subset of 44 deletions , spanning the direction and magnitude of effects ( background dependent-independent , suppressor-enhancer , as well as negative controls ) observed in the genome-wide screen ., A single wing from each of 5 male flies ( w sdE3/Y; Deletion/+ ) was dissected and mounted in glycerol , for both backgrounds ., For the isogenic wild-type control strain , 30 individuals were used from each background-specific set of crosses to better ascertain the degree of variability ., Images of the wings were captured using an Olympus DP30BW camera mounted on an Olympus BW51 microscope ., Six landmarks ( Figure S2 ) were digitized using tpsDIG software 58 and centroid size was used as a measure of wing size ., The landmarks were specifically chosen as they could be discerned on all wings ( Figure S2 ) ., To quantitatively verify the background-dependent effects of a given deletion on wing size ( Figure 4 ) the following model was used:where Y is the Centroid Size , B is the background and D is the deletion ., The analysis was performed using the lm function in R ( V 2 . 12 ) and 95% confidence intervals were constructed using confint ., Significance was determined by non-overlapping confidence intervals with controls ., The quantitative measure of wing size used for this analysis , correlates well with the semi-quantitative method and results used for the initial screening ( r\u200a=\u200a0 . 82 , CI:0 . 69–0 . 9 in Oregon-R , r\u200a=\u200a0 . 78 , CI:0 . 63–0 . 87 in Samarkand ) ., This suggests high repeatability of the initial screen , as well as the semi-quantitative measure of wing size ., To ascertain whether there was a commensurate effect of the genomic deletions in “wild-type” wings ( as opposed to the mutant phenotype caused by sd mutants ) , we quantified wing size in females heterozygous for the focal sdE3 mutation with each deletion ( w sdE3/w sd+; Deletion/+ ) digitizing the same 6 landmarks on the wing ., Potentially the genomic regions ( from the wild-type strains ) that influence the genetic interaction between the deletions and sdE3 could be independent of those regions that influence the variation for phenotypic expressivity of the sdE3 mutation itself ., To test this we generated lines that had “high expressivity” sdE3 phenotypes in an otherwise “low expressivity” background ( Figure S3 ) ., A backcross-selection procedure was used to introgress the modifiers that contribute to the “large wing” phenotype from the Samarkand background into the “small wing” background of Oregon-R and vice-versa ( Figure S3 ) ., Upon generation of these lines , we repeated the dominant modifier screen as described above using a subset of 32 of the 44 confirmed modifiers and negative controls ., These lines were used in identical crosses to those outlined above , with sdE3/Y; Deletion/+ individuals examined ., To narrow down several genomic regions to a set of a few candidate genes we utilized an additional set of overlapping deletions in DrosDel , Exelixis and BSC strains followed by use of P-element insertional mutations co-isogenic with the Exelixis panel of lines ., We utilized this approach for four genomic regions ( 49E1 , 57B3-B5 , 63F2-F7 , and 86E13-E16 ) detailed in Table S2 .

Introduction, Results, Discussion, Materials and Methods

The phenotypic outcome of a mutation cannot be simply mapped onto the underlying DNA variant ., Instead , the phenotype is a function of the allele , the genetic background in which it occurs and the environment where the mutational effects are expressed ., While the influence of genetic background on the expressivity of individual mutations is recognized , its consequences on the interactions between genes , or the genetic network they form , is largely unknown ., The description of genetic networks is essential for much of biology; yet if , and how , the topologies of such networks are influenced by background is unknown ., Furthermore , a comprehensive examination of the background dependent nature of genetic interactions may lead to identification of novel modifiers of biological processes ., Previous work in Drosophila melanogaster demonstrated that wild-type genetic background influences the effects of an allele of scalloped ( sd ) , with respect to both its principal consequence on wing development and its interactions with a mutation in optomotor blind ., In this study we address whether the background dependence of mutational interactions is a general property of genetic systems by performing a genome wide dominant modifier screen of the sdE3 allele in two wild-type genetic backgrounds using molecularly defined deletions ., We demonstrate that ∼74% of all modifiers of the sdE3 phenotype are background-dependent due in part to differential sensitivity to genetic perturbation ., These background dependent interactions include some with qualitative differences in the phenotypic outcome , as well as instances of sign epistasis ., This suggests that genetic interactions are often contingent on genetic background , with flexibility in genetic networks due to segregating variation in populations ., Such background dependent effects can substantially alter conclusions about how genes influence biological processes , the potential for genetic screens in alternative wild-type backgrounds identifying new loci that contribute to trait expression , and the inferences of the topology of genetic networks .

Examining the consequences of how one mutation behaves when in the presence of a second mutation forms the basis of our understanding of genetic interactions , and is part of the fundamental toolbox of genetic analysis ., Yet the logical interpretation of such mutational interactions depends on the generality of such findings ., A small number of studies have demonstrated that factors such as the wild-type background in which the mutations are studied can have a profound impact on the observed phenotype of both specific effects of the mutation and the interactions between mutations ., However , whether such findings are a common property of genetic interactions was unknown ., We tested the generality of the background dependence of interactions between mutations and observed that the vast majority of the interactions were highly dependent on the wild-type background in which they are observed ., We demonstrate that the same regions of the genome that contribute to the differences observed in the degree of severity of the mutational effect appear to also be responsible for the background dependence of the interaction .

epistasis, mutation, genetic mutation, mutation types, phenotypes, genetic screens, heredity, genetics, genetic suppression, population genetics, biology, complex traits, gene function

journal.pcbi.0030161

Elucidating the Altered Transcriptional Programs in Breast Cancer using Independent Component Analysis

Microarray technology is enabling genetic diseases like cancer to be studied in unprecedented detail , at both transcriptomic and genomic levels ., A significant challenge that needs to be overcome to further our understanding of the relation between the quantitative transcriptome of a sample/cell and its phenotype is to unravel the complex mechanism that gives rise to the measured mRNA levels ., The amount of a given mRNA transcript in a normal sample/cell is determined by a whole range of biological processes , some of which ( e . g . , transcription repression and degradation ) act to reduce this number , while others ( e . g . , transcription factor induction ) act to increase it ., Therefore , it is natural to model the level of a given mRNA transcript as the net sum of a complex superposition of cooperating and counteracting biological processes , and , furthermore , to assume that disease is caused by aberrations in the activation patterns of these biological processes that upset the delicate balance between expression and repression in otherwise healthy tissue ., Many distinct biological mechanisms that underlie the aberrations observed in human cancer have been identified , most notably copy-number changes 1 and epigenetic changes 2 , yet it is the effect that these changes have downstream on the functional pathways that ultimately dictates whether these changes are pathological or not ., While several studies have recently characterised the altered functional pathways and transcriptional regulatory programs in human cancer , they have done so either by interrogating the expression data directly with previously characterised pathways , regulatory modules 3–6 , and functionally related gene lists 7 , or by interrogating derived “supervised” lists of genes for enrichment of biological function 8 ., Hence , these studies have not attempted to infer the altered biological processes , which putatively map to alterations of known functional pathways and transcriptional regulatory programs ., Thus , an unsupervised method that first infers the underlying altered biological processes and then relates these to aberrations in pathways or regulatory module activity levels is desirable ., A necessary property of such an algorithm is that it allows “gene-sharing , ” so that a specific gene can be part of multiple distinct pathways ., In this regard , it is worth noting that popular approaches for analysing transcriptomic data , such as hierarchical or k-means clustering , do not allow for genes to be shared by multiple biological processes , since they place a gene in a single cluster 9 , and so they are not tailored to the problem of inferring altered pathways ., Algorithms that allow genes to be part of multiple processes/clusters have also been extensively applied 10–12 ., Among these , Singular Value Decomposition ( SVD ) or Principal Components Analysis ( PCA ) provides a linear representation of the data in terms of components that are linearly uncorrelated 12 ., While this linear decorrelation of the data covariance matrix can uncover interesting biological information , it is also clear that it fails to map the components into independent biological processes , since there is no requirement for PCA components to be statistically independent ., Mapping the data to independent biological processes , whereby independence is measured using a statistical criterion , should provide a more realistic representation of the data , since it explicitly recognises how the data was generated in the first place ., This assumption , which is to be tested a posteriori , underlies the application of Independent Component Analysis ( ICA ) to gene expression data 13 , 14 ., Specifically , ICA decomposes the expression data matrix X into a number of “components” ( k = 1 , 2 , . . K ) , each of which is characterised by an activation pattern over genes ( Sk ) and another over samples ( Ak ) ( Figure 1 and Materials and Methods ) ,, in such a way that the gene activation patterns ( S1 , S2 , . . . , SK ) are as statistically independent as possible while also minimising the residual “error” matrix E ( in the above , ⊗ denotes the Kronecker tensor product ) ., It is worth noting that while ICA also provides a linear decomposition of the data matrix , the requirement of statistical independence implies that the data covariance matrix is decorrelated in a non-linear fashion , in contrast to PCA where the decorrelation is performed linearly ., Many studies have shown the value of ICA in the gene expression context as a dimensional reduction and gene-functional discovery tool 15–20 and also as a potential tool for classification and diagnosis 21 , 22 ., To validate the ICA model , most of these studies used the Gene Ontology ( GO ) framework 23 ., However , GO does not provide the best framework in which to evaluate the ICA paradigm , since many genes with the same GO term annotation may not be part of the same biological pathway or may not be under the control of the same regulatory motif , and vice versa ., In fact , to date no study has evaluated the ICA paradigm in the explicit context of biological pathways and regulatory modules ., In this work we apply various popular ICA algorithms to six of the largest available microarray cancer datasets ., We focus on breast cancer for two reasons ., First , for this type of cancer many large patient cohorts that have been profiled with microarrays are available ., Second , breast cancer is a highly heterogeneous disease and hence it provides a more challenging ( and hence suitable ) arena in which to compare and evaluate different methodologies ., We also use two large microarray datasets from two other cancer types to show that our results are valid more generally ., The aim of our work is 2-fold ., First , to test the ICA paradigm by showing that it significantly outperforms both a gene-sharing method that does not use the statistical independence criterion ( PCA ) and a traditional ( “non–gene-sharing” ) clustering method ( k-means ) ., We achieve this by using a pathway and regulatory module–based framework for validation ., The second aim is to find the most frequently altered pathways and regulatory modules in human breast cancer and to explore their relationship to breast cancer phenotypes ., The main modelling hypothesis underlying the application of ICA to gene expression data is that the expression level of a gene is determined by a linear superposition of biological processes , some of which try to express it , while other contending processes try to suppress it ( Figure 1 ) ., It is assumed that these biological processes correspond to activation or inhibition of single pathways or sets of highly correlated pathways , and that each of these pathways only affects a relatively small percentage of all genes ., Because of the statistical independence assumption inherent in the ICA inference process , we would expect the identified independent components to map more closely to known pathways than an alternative linear decomposition method , like PCA , that does not use the statistical independence criterion ., Similarly , we would expect ICA components to map closer to pathways than clusters derived from popular clustering algorithms such as k-means or hierarchical clustering ., To test the modeling hypothesis of ICA for expression data , we first asked how well the inferred components mapped to known pathways , as curated in the MSigDB pathway database 24 ( Materials and Methods , Table S1 ) ., This strategy was initially applied to a total of six breast cancer microarray datasets ( “Perou” 25 , “JRH-1” 26 , “Vijver” 27 , “Wang” 28 , “Naderi” 29 , “JRH-2” 30 ) , summarised in Table 1 , and for four different implementations of the ICA algorithm ( “fastICA” , “JointDiag” , “KernelICA” , and “Radical” ) 31–34 as well as for ordinary PCA and two versions of k-means clustering ( PCA-KM and MVG-KM ) ( Materials and Methods and Protocol S1 ) ., For each of the ICA algorithms and PCA , we inferred ten components and selected the genes based on their weights in the corresponding column of the source matrix S ( Materials and Methods ) ., The average number of genes selected per component ranged from 50 to 200 depending on the cohort ( Table S2 ) ., For the two k-means clustering algorithms , ten gene clusters were inferred on subsets of most variable genes to ensure that the average number of genes per cluster was similar to that of the PCA and ICA components ., This step was necessary to ensure an objective comparison of the different algorithms ., In what follows we also use the term component to denote clusters ., To evaluate how close the inferred components of a given algorithm in a particular cohort mapped to existing pathways , we defined a pathway enrichment index , PEI , as follows ., For each component i and pathway p , we first evaluated the significance of enrichment of genes in that pathway in the selected feature set of the component by using the hypergeometric test ( see Materials and Methods ) ., This yielded for each component i and pathway p a p-value Pip ., Correction for multiple testing was done using the Benjamini-Hochberg procedure to obtain an estimate for the false discovery rate ( FDR ) ., A component i was then declared enriched for a pathway p if the Benjamini-Hochberg corrected p-value was less than 0 . 05 ., Hence , we would expect approximately 5% of significant tests to be false positives ., Finally , we counted the number of pathways enriched in at least one component and defined the PEI as the corresponding fraction of enriched pathways ., The PEI for each of the seven methods ( “PCA” , “MVG-KM” , “PCA-KM” , “fastICA” , “JointDiag” , “KernelICA” , “Radical” , and “PCA” ) and the four largest breast cancer sets ( “Vijver” , “Wang” , “Naderi” , “JRH-2” ) are shown in Figure 2A ( the results for all six breast cancer cohorts are presented in Figure S1 ) ., This showed that across the four major cohorts the PEI was higher for ICA algorithms when compared with PCA and the clustering-based methods ., Interestingly , for the two largest cohorts ( “Vijver” and “Wang” ) , the degree of improvement in the PEI of ICA over PCA , MVG-KM , and PCA-KM was highest ., In contrast , for the smaller cohorts ( e . g . , “Perou” and “JRH-1” ) , the degree of improvement of ICA over PCA or KM was less marked ., Hence , since we found that cohort size had a significant impact on the inferred components , we restricted all subsequent analysis to the four major breast cancer cohorts ., It is also noteworthy that when comparing the various ICA algorithms with each other we didnt observe any appreciable difference in their respective PEI ., To investigate this further , we next compared the algorithms on the subset of nine cancer-signalling pathways from the curated resource NETPATH ( http://www . netpath . org ) and five oncogenic pathways 35 ., These are pathways that are frequently altered in cancer and hence we would expect many of these to be captured by the ICA algorithm ., Thus , for each method and study we counted the number of pathways that were enriched in any of the components ( Figure 2B ) ., This showed that in the three largest breast cancer studies ( “Vijver” , “Wang” , and “Naderi” ) , PCA and the KM-methods captured the least number of pathways ., In the two largest cohorts ( “Vijver” and “Wang” ) , for example , the “RADICAL” ICA algorithm captured ten and six of the 14 pathways , while PCA captured eight and two pathways , respectively ., As a further validation that ICA outperforms PCA , we investigated the relation of the derived components with regulatory modules ., Specifically , we tested the selected gene sets from each component for enrichment of genes with common regulatory motifs in their promoters and 3′ UTRs 36 ., Under the ICA paradigm we would expect genes that are under the common regulatory control of a transcription factor to appear in the same ICA component ., Thus , for each breast cancer cohort and method we counted the number of regulatory motifs whose associated genes were overrepresented in components ( Figure 2C ) , using as before the hypergeometric test to test for significant enrichment ( Materials and Methods ) ., This showed that PCA performed worst out of all algorithms ., In two cohorts ( “Wang” and “Naderi” ) , none of the PCA components was associated with any of the 173 distinct regulatory motifs ., In contrast , the components derived by ICA algorithms were consistently associated with regulatory motifs ., Interestingly , the improvement of ICA over KM-based methods was less marked with only study ( “Wang” ) showing a substantial improvement ( Figure 2C ) ., The results above show that ICA provided a more biologically meaningful decomposition of breast cancer expression data than PCA or KM-based methods ., This suggested to us that similar results would hold in other types of cancer ., To check this , we analysed two additional large microarray datasets , one profiling 221 lymphomas 37 ( “Hummel” ) and another profiling 132 gastric cancers 38 ( “Chen” ) ( see Table 1 ) ., The same analysis on these two additional datasets confirmed that the PEI was higher for ICA when compared with PCA or KM-clustering methods ( Figure 2A ) , and that ICA components also mapped closer to known regulatory motifs ( Figure 2C ) ., To investigate the robustness of the algorithms , we next compared the ability of the algorithms to identify pathways and regulatory modules that were differentially activated independent of the breast cancer cohort used ., Two important observations that were independent of the ICA algorithm and cohort used could be derived from the heatmaps of differential activation of pathways and regulatory modules ( Figures S2–S5 ) ., First , ICA identified many more pathways that were consistently differentially activated across all four breast cancer cohorts ( Figure 3A ) ., This further confirmed that the associations between components and pathways as picked out by ICA were more robust and consistent between cohorts than those identified through PCA , MVG-KM , or PCA-KM ., Among the pathways that were found to map most frequently and consistently to components were those related to estrogen signalling as well as to other important breast cancer–signalling pathways such as the EGFR1 and TGF-β pathways ( Figures 3B and S2–S5 ) ., We also found cell-adhesion , immune-response , cell-cycle , and metabolic pathways to be commonly differentially activated across the cohorts ., While breast cancer studies have found study-specific gene clusters associated with cell-cycle , estrogen-response , cell-adhesion , and immune-response functions , our results show that expression variation across breast tumours can be understood in terms of single pathways ( i . e . , a fixed common set of genes for all studies ) that relate to these biological functions ., Second , we also observed that ICA outperformed PCA , MVG-KM , and PCA-KM in identifying regulatory modules that were consistently differentially activated across cohorts ( Figure 3C ) ., Specifically , the KernelICA algorithm identified the regulatory modules TATA , AACTTT , NFAT , IRF , and NF1 , while MVG-KM only picked out TATA , with PCA and PCA-KM failing to capture any regulatory module ., Among the motifs with regulatory gene modules that were most frequently captured by independent components , we found several with important general ( e . g . , TATA ) and specific transcription factors ( e . g . , NF1 and ETS2 ) ( Figures 3D and S2–S5 ) ., We next asked whether components mapping into the various pathways/modules were associated with breast cancer phenotypes ., Specifically , we considered three categorical phenotypes: estrogen receptor ( ER ) status ( 0 , 1 ) , histological grade ( 1 , 2 , 3 ) , and outcome ( 0 , 1 ) ., To evaluate statistical significance of any association between a component k and phenotype , we considered the distribution of weights from the corresponding row of the mixing matrix , i . e . , Ak ( Materials and Methods ) , across the different categories ., We used the Wilcoxon rank-sum test for the two binary phenotypes and the Kruskal-Wallis test for histological grade ., Because of the clustering nature of the MVG-KM and PCA-KM algorithms , in these two cases we first applied k-means over the genes in the cluster to partition the samples into two groups and subsequently used Fishers exact test to determine whether the phenotype distribution across the two groups was significantly different from random or not ., This revealed a complex pattern of significant associations with several components differentiating breast tumours according to ER status and histological grade ( Figures S2–S5 ) ., It is notable that in all cohorts ICA components associating with clinical outcome were also found , while PCA generally did not ., Another feature was the fact that more and stronger phenotype associations were uncovered by using ICA as compared with PCA ., On the other hand , MVG-KM performed as well as ICA in mapping to ER , grade , and outcome phenotypes ., Since we characterised each component in terms of the differential activation pattern of cancer-related pathways and regulatory modules , for those components associated with a phenotype we were able to link the corresponding pathways and regulatory motifs with the phenotype ( Figure 4 ) ., This led to several well-known but also novel observations ., First , as expected , ICA components that were strongly associated with ER status were frequently mapped to the estrogen signalling pathway ., Second , ICA components that mapped to the CR ( cancer related ) cell-cycle pathway 39 were frequently associated with either grade or outcome ., The association between cell-cycle genes and grade or outcome is well-known 26 , 30 , 40 , and our finding further shows that an independently characterised cell-cycle pathway associates with these clinical variables across multiple studies ., Third , we observed that pathways relating to immune response functions and the classical complement pathway were frequently correlated with ER status , grade , and , although less frequently , with clinical outcome ., For example , we found in each of the four major breast cancer cohorts an ICA component that mapped to the CR immune response pathway 39 , and which was consistently overactivated in ER− relative to ER+ tumours ( Figure 5A and Table 2 ) ., We note that the same set of genes , when viewed over the measured expression matrix also separated the samples according to ER status ( Figure 5B and Table 2 ) ., Fourth , in all studies where grade information was available , an ICA component mapping to either matrix-metalloproteinases ( MMP ) or the cell-adhesion pathway was found to be associated with histological grade ., In three studies ( “Wang” , “Vijver” , and “Naderi” ) , the MMP pathway was also found to be associated with outcome ., Another interesting pathway we found to be associated with histological grade was an epithelial–mesenchymal transition ( EMT ) signalling pathway characterised in 41 ., Specifically , ICA revealed a component driving upregulation of genes involved in EMT in poorly differentiated tumours relative to low-grade tumours across the three studies where grade information was available ( Figure 6A and Table 3 ) ., When the same set of genes defining the EMT pathway was viewed over the measured expression matrix , their pathway coherence was less evident , although the association with grade was still revealed by k-means clustering ( Figure 6B and Table 3 ) ., The parallel analysis for regulatory motifs and breast cancer phenotypes provided direct links between the associated transcription factors and clinical variables ( Figure 4B ) ., Strikingly , we found that the interferon regulatory factor ( IRF ) showed the strongest associations with both the ER and grade phenotypes ., The regulatory module associated with the TATA box was also frequently associated with ER , grade , and outcome ., Interestingly , we found differential activation of the regulatory modules associated with the neurofibromin-1 ( NF1 ) , NFAT , and ETS2 transcription factors to be associated with clinical outcome , which is significant in view of the results of several recent studies linking these transcription factors with the metastatic and cell-growth properties of breast cancer cells 42–46 ., It is important to point out that ICA facilitated the identification of many of the biological associations in comparison with PCA , MVG-KM , and PCA-KM ( Figure 7 ) ., Thus , for example , we can see that the association between immune response and ER status was found in all cohorts by any one of the four ICA algorithms , whereas PCA and the KM methods were generally not as robust ( Figure 7A ) ., A similar observation could be made for the associations between the EMT pathway and grade , and that of the IRF module and ER status ( Figure 7B and 7C ) ., For the case of NF1 and clinical outcome , this association was not identified by PCA or the KM-based methods ( Figure 7D ) ., Finally , we verified that in many cases the identified associations were independent , in the sense that the component ( s ) or genes linking a pathway with a phenotype could be different from the one ( s ) linking another pathway with the same phenotype ., For example , we noted that this was the case for the associations of the cell-adhesion and estrogen-signalling pathways with grade ( see Figures S2 and S4 ) ., Similarly , the associations of the immune response pathway and IRF module with ER status ( Figure 7A and 7C ) could not be attributed to a common gene subset selection , since the pathway and module gene sets shared no genes in common ., Networks are a useful tool for graphically representing relational structures between many layers of organisation ., In our application , we sought to construct a network of associations , linking breast cancer phenotypes , pathways , and regulatory modules with each other as the nodes in the network ., To represent only the most salient and robust features , we focused attention on those pathways and regulatory modules with most phenotypic associations ( Figure 4 ) and on those associations that were most consistently predicted across cohorts ., Thus , we constructed an average network over the networks for each study by defining a link between any two nodes in the network if there were at least three studies in which there was a link between the two nodes , as predicted by ICA ( Figure 8 ) ( KernelICA was used but the other ICA algorithms gave similar networks ) ., This revealed a complex network of associations between transcription factors , pathways , and breast cancer phenotypes ., Strengthening the association of immune response with ER status further , we found triangular relationships involving the NF-κβ , ETS2 , and IRF transcription factors ( Figure 8A ) , which is plausible in view of their role in regulating immune response pathways 47–49 ., The corresponding network for clinical outcome showed that apart from the cell-cycle and estrogen-signalling pathways , only the EGFR1 and TGF-β pathways were consistently associated with outcome ( Figure 8B ) ., In our view , it is most natural to analyse gene expression data in the context of a generative model , however approximate this model is to the true underlying mechanism that gives rise to the measured expression levels ., ICA provides such a generative model since it explicitly recognises how the data was generated in the first place ., By comparing ICA with PCA and clustering-based methods , we have shown that a more realistic representation of the data is obtained by allowing “gene-sharing” and using the statistical independence criterion ( non-linear decorrelation ) in the inference process ( ICA ) , as opposed to not allowing gene-sharing ( MVG-KM , PCA-KM ) and only using a linear decorrelation criterion ( PCA ) ., We showed this on a total of six cancer microarray datasets , using existing pathway knowledge and gene regulatory module databases for evaluation ., Specifically , we found that ICA components mapped closer to cancer-related pathways as well as to gene modules that are under the control of a common regulatory motif ., It is worth pointing out though that the improvement of ICA over KM methods was less marked in the case of regulatory motifs , as we would expect , since a clustering method is partially tailored to finding co-regulatory structure ., Importantly , when comparing the results across cohorts , we found that ICA algorithms were much more robust than PCA or KM-based methods , in the sense that pathways that were found to be differentially activated through ICA in one cohort were also consistently differentially activated in the other cohorts ., A similar observation could also be made for the regulatory motifs and their regulatees ., For example , using PCA or PCA-KM , no regulatory module was found to be differentially activated across all four major breast cancer studies , while the ICA algorithms found an average of four modules ., The most likely explanation for the relatively smaller number of regulatory modules found in common across the four studies , as compared with pathways , is that many regulatory modules important to breast cancer have yet to be elucidated ., Of note , we also performed the enrichment analysis of the independent components for chromosomal bands ( using the MSigDB database ) , which confirmed that the independent components were not capturing transcriptional programs localised to specific chromosomal regions ., Instead , we believe that the inferred independent components encapsulate “net” transcriptional programs that act globally and downstream of the epigenetic and genetic modifications underlying cancer ., We also found that ICA components were associated more often with known breast cancer phenotypes , including clinical outcome , and that these associations were also much stronger for ICA than for PCA ., While this result is to be expected , since ICA components map closer to pathways that have been characterised using phenotypic information , one should also bear in mind that these pathways were derived from independent experiments; hence , the stronger associations between components , pathways , and phenotypes as revealed by ICA provides a validation , not only of the algorithm itself , but also of the characterised pathways ., Another important observation was the presence of multiple components showing an association with a particular pathway , regulatory module , or phenotype ., This suggests that a significant proportion of pathways are part of multiple biological processes ., Alternatively , the presence of multiple components enriched for a given pathway may reflect distinct gene subset selection , which in turn suggests that the pathways in MSigDB and NETPATH may need to be refined further ., In the context of phenotypes , the presence of multiple components correlating with ER status , grade , or outcome , is suggestive of tumour heterogeneity , since , more often than not , the differential distribution of the phenotype across samples is dependent on the precise component ., Hence , the fingerprint patterns of pathway activation derived from ICA could potentially form the basis for further clinically relevant definitions of breast cancer subtypes ., In an exploratory analysis , ICA revealed many interesting associations between pathways and phenotypes that can form the basis for future investigations ., While all methods were able to identify the expected relationships of the estrogen-signalling pathway with ER status and cell-cycle pathway with histological grade , ICA clearly outperformed PCA and KM-clustering in identifying many other biologically relevant associations ( Figure 7 ) ., For example , ICA consistently found an expression mode involving immune response pathways that was upregulated in ER− versus ER+ tumours ., Thus , while the relation between immune response and ER status is still poorly understood 50 , our results clearly point at an important link between the immune response and estrogen signalling in breast cancer , which needs to be explored further ., ICA also revealed interesting associations of the EMT-signalling , cell-adhesion , and MMP pathways with histological grade and clinical outcome ., Specifically , we found a component upregulating EMT genes in high-grade versus low-grade tumours , and which was statistically significant in three major cohorts ., The association between the activity level of the cell-adhesion and MMP pathways with clinical outcome as revealed by ICA is also noteworthy given that supervised approaches tend to only find genes related to cell-cycle pathways , as these are the strongest predictors of grade and outcome ., While the association of cell-adhesion genes with outcome has been noted before in breast cancer 29 and to a lesser extent in gastric cancer 51 , here we show that this result holds for a specific pathway and across several breast cancer cohorts ., ICA , in contrast to PCA and KM-clustering , also identified interesting associations between transcription factor modules and phenotypes ( Figure 7 ) ., For instance , it found strong associations between the IRF and ER status and between NF1 and clinical outcome , as well as an association between NFAT and outcome ( Figure 4 ) ., These associations are plausible given that changes in NFAT have been shown to alter the metastatic and growth properties of breast cancer cells 42–44 , and given the important role NF1 and IRF play in breast cancer generally 52–56 ., It could be argued that both IR- and cell-adhesion pathways are differentially activated across tumours merely as a result of lymphocytic or stromal contamination , respectively ., However , microarray studies profiling breast cancer cell lines ( BCL ) have shown that genes associated with IR- and cell-adhesion functions are also differentially regulated across cell lines 25 , 57 ., In particular , it was shown that genes related to cell-adhesion functions were overexpressed in ER− compared with ER+ cell-lines 57 ., While the study in 57 did not explicitly mention the differential expression of immune response genes , we verified , by applying ICA to this set of only 31 breast cancer cell lines ( BCL ) , that an independent component enriched for immune response genes was present and that it correlated with the ER status of the cell lines ( Figure S6 ) ., This provided further validation of the link between differential regulation of immune response pathways with the ER status of breast cancer cells , while also simultaneously confirming that the differential regulation of these genes across the tumour set is not necessarily related to varying degrees of lymphocytic infiltration ., Generally , we found that genes selected in the same independent component showed a relatively strong co-expression pattern ( Figure 5B ) ., It follows that ICA components can often be given a biological interpretation similar to that of clusters inferred through , say , hierarchical or k-means clustering ., To illustrate this with another example , we considered the case of estrogen signalling and ER status ., This showed that clustering over the genes selected in an IC that was associated with estrogen signalling and ER status yielded similar heatmaps for the measured expression matrix and the IC submatrix , and , furthermore , for both heatmaps the association with the phenotype was evident ( Figure S7 ) ., On the other hand , ICA also found “non-trivial” associations , such as the association of the EMT pathway with grade ( Figure 6A ) , where the functional relationship of the genes in the same pathway was not as evident from the gene expression matrix ( Figure 6B ) ., Given that genes are shared by multiple pathways , the functional relationship of the genes may indeed not manifest itself as a strong co-expression pattern ., Thus , it would appear that ICA , through the statistical independence criterion , which effectively uses non-linear correlation measures ( as op

Introduction, Results, Discussion, Materials and Methods

The quantity of mRNA transcripts in a cell is determined by a complex interplay of cooperative and counteracting biological processes ., Independent Component Analysis ( ICA ) is one of a few number of unsupervised algorithms that have been applied to microarray gene expression data in an attempt to understand phenotype differences in terms of changes in the activation/inhibition patterns of biological pathways ., While the ICA model has been shown to outperform other linear representations of the data such as Principal Components Analysis ( PCA ) , a validation using explicit pathway and regulatory element information has not yet been performed ., We apply a range of popular ICA algorithms to six of the largest microarray cancer datasets and use pathway-knowledge and regulatory-element databases for validation ., We show that ICA outperforms PCA and clustering-based methods in that ICA components map closer to known cancer-related pathways , regulatory modules , and cancer phenotypes ., Furthermore , we identify cancer signalling and oncogenic pathways and regulatory modules that play a prominent role in breast cancer and relate the differential activation patterns of these to breast cancer phenotypes ., Importantly , we find novel associations linking immune response and epithelial–mesenchymal transition pathways with estrogen receptor status and histological grade , respectively ., In addition , we find associations linking the activity levels of biological pathways and transcription factors ( NF1 and NFAT ) with clinical outcome in breast cancer ., ICA provides a framework for a more biologically relevant interpretation of genomewide transcriptomic data ., Adopting ICA as the analysis tool of choice will help understand the phenotype–pathway relationship and thus help elucidate the molecular taxonomy of heterogeneous cancers and of other complex genetic diseases .

The amount of a given transcript or protein in a cell is determined by a balance of expression and repression in a complex network of biological processes ., This delicate balance is compromised in complex genetic diseases such as cancer by alterations in the activation patterns of functionally important biological processes known as pathways ., Over the last years , a large number of microarray experiments profiling the expression levels of more than 20 , 000 human genes in hundreds of tumor samples have shown that most cancer types are heterogeneous diseases , each characterized by many different expression subtypes ., The biological and clinical goal is to explain the observed tumor and clinical heterogeneity in terms of specific patterns of altered pathways ., The bioinformatic challenge is therefore to devise mathematical tools that explicitly attempt to infer these altered pathways ., To this end , we applied a signal processing tool in a meta-analysis of breast cancer , encompassing more than 800 tumor specimens derived from four different patient cohorts , and showed that this algorithm significantly outperforms popular standard bioinformatics tools in identifying altered pathways underlying breast cancer ., These results show that the same tool could be applied to other complex human genetic diseases to better elucidate the underlying altered pathways .

oncology, homo (human), genetics and genomics, computational biology

journal.pgen.1005117

Asymmetric Transcript Discovery by RNA-seq in C. elegans Blastomeres Identifies neg-1, a Gene Important for Anterior Morphogenesis

Asymmetric cell divisions produce daughter cells of different size , molecular content , or developmental potential ., These events promote tissue-type diversity in developing embryos , specify terminal differentiation , and allow for the maintenance of adult tissues 1 , 2 ., Asymmetric cell divisions trigger divergent cell fates through the unequal distribution of cell fate determinants or by moving daughter cells into different morphogen fields 3 , 4 ., Searches to identify intrinsic cell fate determinants and the mechanisms that guide their asymmetric distribution have been difficult to adapt to high-throughput strategies ., A key challenge is separating daughter cells with sufficient purity and yield for genome-wide and proteome-wide assays ., We sought to overcome this challenge in Caenorhabditis elegans by coupling a low-input RNA-seq protocol 5 , 6 with hand-dissection of blastomeres , which ensures absolute purity in each pool of isolated cells ( Fig 1A ) ., In C . elegans , an asymmetric cell division cleaves the recently-fertilized zygote into a larger anterior AB cell and a smaller posterior P1 cell , each of which has distinctive characteristics and fates ( Fig 1B ) 7 ., The AB cell undergoes a series of rapid , symmetric cell divisions to eventually produce hypodermal , muscle , and neuronal tissue 8–10 ., The smaller daughter cell , P1 , is rich in perinuclear bodies of ribonucleoparticles called P granules ., Unlike the AB cell that undergoes symmetric cell division , the P1 cell initiates a series of successive asymmetric cell divisions in which the smaller cell progressively inherits P granules and primordial germ cell fate and the larger cell becomes somatic tissues such as hypodermal , intestine , neuronal , and muscle tissues 8 , 11 , 12 ., The somatic branches of the AB- and P1-lineages are transcriptionally active but the germ cell branch of the P1 lineage remains largely transcriptionally quiescent with some notable exceptions 13–16 ( Fig 1B ) ., Four features make the first division of C . elegans embryogenesis an excellent model for studying the apportionment of mRNA through asymmetric cell division ., First , the cell divisions of the early embryo are invariant and cell fates are precisely mapped 8 , 13 , 15–19 , allowing one to connect any mRNA asymmetries to functional consequences later in embryogenesis ., Second , many of the mechanisms and proteins responsible for polarity and asymmetry have been identified , ultimately allowing the mechanisms that drive mRNA partitioning to be placed into an established framework 20 ., Third , zygotic transcription does not initiate until the 4-cell stage and becomes widespread at the 16-cell stage 13 , 15 , 21 , meaning that RNA segregation , stabilization , and degradation can be observed independent of de novo transcription ., Fourth , a few transcripts have been previously identified as asymmetrically abundant at this stage using in situ hybridization ( mex-3 , pos-1 ) allowing for verification by independent methods 22–24 ., A previous study 16 identified 14 AB-enriched mRNA transcripts and 4 P1-enriched transcripts using a single-cell RNA-seq approach , in which only one cell is measured in each sample ., Because our goal was to obtain as comprehensive a set of asymmetrically patterned genes as possible and to guard against cell-to-cell variability and noise , each of our samples consisted of a pool of 20 individual hand-dissected cells ., By pooling AB and P1 cells , we were able to achieve lower variance in our samples ( S2 Fig ) , allowing us to identify 80 AB-enriched and 201 P1 enriched transcripts ., This is consistent with other studies showing that pooling cells to quantities of 20-cells or more buffers against cell-to-cell variation and yields more reliable and reproducible quantification 25 ., The larger number of asymmetric transcripts we discovered allowed us to derive common properties of mRNAs that are asymmetrically partitioned in the early embryo ., We complemented our transcriptome profiling approach with quantitative microscopy to verify our findings and further resolve patterns of mRNA distribution ., We also used our dataset to identify neg-1 as a gene newly discovered to be important for anterior morphology ., We developed a low input RNA-seq protocol ( RNA-amp-seq ) that maintains relative transcript abundance and minimizes the potential for contamination ., Total RNA was amplified by in vitro transcription ( Eberwine amplification ) of a low input cDNA pool 26 , and amplified RNA was subjected to RNA-seq ., Transcript abundance determined by RNA-amp-seq had a high correlation to standard RNA-seq ( r2 = 0 . 84; S1A Fig ) and preserved calls of enriched and depleted transcripts ( S1B Fig ) ., Although there is high concordance between amplified and unamplified samples , some transcripts may not be captured by the amplification procedure , resulting in false negatives ., These results agree with previous studies demonstrating that in vitro transcription amplifies whole transcriptomes linearly , preserving relative transcript abundances 6 , 16 , 27 ., To obtain AB and P1 specific transcriptomes , we isolated AB and P1 from 2-cell stage embryos ., Two-cell stage embryos were removed from their eggshells with chitinase and chymotrypsin treatment and were extracted from the remaining envelope through mechanical sheering ., AB and P1 cells were separated using a hollow-tipped glass needle attached to a mouth aspirator 28 , 29 ., Once separated , the two blastomeres were clearly distinguishable by their relative sizes ., Twenty matched AB and P1 cells were pooled for each of three replicates ( Fig 1A ) ., We performed RNA-amp-seq and identified transcripts with statistically significant differential abundances 30–33 ., Eighty of these RNAs were enriched in the anterior AB cell and 201 in the posterior P1 cell ( Fig 1C–1G ) with a Benjamani-Hochberg-adjusted P value of less than 0 . 10 ., For some analyses , these were ranked by adjusted P value ( S1 Dataset , S1 Table ) ., We used several independent methods to confirm subsets of our identified asymmetrically abundant mRNA transcripts ., First , we compared asymmetric transcripts to an existing database of C . elegans RNA in situ hybridization images 34 ., Note that the in situ database was created systematically and was not designed or optimized to detect transcript asymmetries at the two-cell stage ., Therefore , a lack of a discernable positive signal was not evidence of a false positive in our dataset ., We queried in situ hybridization entries for our 80 AB-enriched , 201 P1-enriched , and the 80 symmetric transcripts with the most uniform distribution ( of 7664 ) ( Fig 2A–2D , S2 Dataset ) ., Many transcripts were absent from the online database , showed no staining , or were uninterpretable ., Entries that had successful staining were scored in a blind survey that was used to generate a symmetry score ., We found a strong association between entries that were identified as AB-enriched or P1-enriched by our RNA-seq analysis and those that yielded either high or low symmetry scores , respectively ( Fig 2A–2D , S2 Dataset ) ., Rates among all in situ images with positive staining were less than 3% AB-enriched and less than 1% P1-enriched ( 97% symmetric ) when we queried a random set of 100 genes present at the 2-cell stage ., Given that the hybridizations were not optimized for detecting cell-to-cell variation at the 2-cell stage of development , it is remarkable that so many of our transcripts were validated by this resource ., Second , we performed qRT-PCR on pools of five blastomeres to measure the abundance of individual RNA transcripts ., We selected transcripts to test from each set of genes: AB-enriched , P1-enriched , and symmetric ., The ratio of transcript abundance ( P1/AB ) fold change determined by qRT-PCR was highly correlated with that determined by RNA-seq ( r2 = 0 . 86 ) ( Fig 2D ) ., One caveat to this technique is that reliable quantification requires a linear standard dilution series for each primer set ., Only transcripts with mean RNA-seq abundance values over 15 , 000 passed this requirement and were quantifiable by this method ., We also compared our data to the few transcripts whose mRNA localization had been previously characterized at the 2-cell stage of development ., The mex-3 mRNA transcript is preferentially abundant in the AB cell 23 , pos-1 is P1-enriched 24 , and cey-2 appears initially uniform in early 2-cell stage but becomes P1-enriched in late 2-cell stage 22 ., Of these transcripts , mex-3 appeared in our set of statistically significant AB-enriched transcripts ., However , neither cey-2 nor pos-1 appeared on our list of P1-enriched transcripts ., Though cey-2 and pos-1 were enriched 1 . 4 fold and 1 . 2 fold in the P1 cell in our dataset , they did not meet the conservative FDR-adjusted P value threshold we used ., The transcripts for ama-1 , eft-4 , dpy-3 , act-1 , and tba-1 were previously characterized as having uniform distribution at the 2-cell stage 22 and appear among our symmetrically patterned transcripts ., Further , a recent study reported that a transcript we identified as AB-enriched , W02F12 . 3 , was quantifiable as AB-enriched by microscopy 35 ., There was high concordance between the AB and P1-enriched transcripts reported in Hashimshony et al . 16 and those identified by our study ( S2B and S2C , Fig ) ., Five of the 14 genes previously identified as AB-enriched were also classified as AB-enriched in our study , and 3 of 4 P1-enriched genes previously identified were also in our set of 201 P1-enriched genes ( S2B and S2C , Fig ) ., Both Hashimshony et al . and our study failed to identify cey-2 and pos-1 transcripts as P1-enriched , which had been previously shown by in situ hybridization 22 , 24 ., The limitations of qRT-PCR and the publicly available in situ hybridization datasets motivated us to employ an alternative method of measuring mRNA transcript abundance ., We performed quantitative in situ hybridization , also known as single-molecule FISH ( smFISH ) ., In separate experiments , we used probes designed to hybridize to three AB-enriched transcripts ( mex-3 , rank #4; neg-1 , rank #42; tes-1 , rank #77 of 80 total ) , three P1-enriched transcripts ( chs-1 , rank #1; pgl-3 , rank #32; bpl-1 , rank #170 of 201 total ) , and three symmetric transcripts ( gpd-2 , set-3 , and B0495 . 7 ) ., We chose mex-3 because it has been previously reported to be AB-enriched and neg-1 because of our interest in this gene specifically ., Other candidates were chosen to span a range of expression levels and to represent asymmetric transcripts at the top and bottom of our RNA-seq-based P value ranked lists ., For each probe set , in each cell of the 2-cell embryo , both the number of fluorescent particles and the total fluorescence generated by the particles were quantified ( Fig 3A–3F , S4A and S4B Fig ) ., We report the ratio of “particle density” , where “particle density” is the volume-normalized count of fluorescent particles in the P1 cell as compared to the AB cell ( Fig 3A–3F ) ., In the case of mex-3 , which is known to be enriched in AB , and neg-1 , which was discovered to be AB-enriched by RNA-seq in this study , quantitative analysis confirmed AB enrichment ( Fig 3A and 3B ) ., We next performed multiplex FISH with dual probe sets , which allowed us to compare an asymmetric transcript to a symmetric transcript within the same embryo ., This approach provided an internal control and buffered against variations in hybridization efficiency ., We tested for significant differences in the ratios of particle densities ( P1/AB ) of tes-1 ( AB-enriched ) and chs-1 , pgl-3 , and bpl-1 ( P1-enriched ) relative to gpd-2 , set-3 , or B0495 . 7 ( symmetric transcripts ) ., The three symmetric transcripts were nearly always within a 2-fold range of abundance in the AB and P1 cells ( Fig 3C–3F ) ., In contrast , tes-1 was up to 8-fold enriched in the AB cell relative to P1 ( Fig 3C ) , confirming AB enrichment ., This is important because tes-1 is a low-abundance transcript that is not reliably detectable by qRT-PCR ., It is the 77th of 80 AB-enriched asymmetric transcripts ranked by P value ., Thus , we have observed that even transcripts near the bottom of our asymmetrically abundant lists can exhibit reproducible , quantitative , and microscopically verifiable asymmetric patterns ., Three transcripts that were P1-enriched by RNA-seq were tested by smFISH ( chs-1 , pgl-3 , and bpl-1 ) ., Of these , chs-1 and bpl-1 transcripts were P1-biased relative to internal controls ( Fig 3D–3F ) ., chs-1 and bpl-1 are ranked at the top and the bottom of the RNA-seq P1-enriched list of transcripts respectively ., pgl-3 was also tested but did not show statistically significant differences between an internal control ( Fig 3E ) ., Instead , pgl-3 showed high variability in P1-enrichment ranging from marginal AB-enrichment to 4-fold higher P1–enriched particle density ., We noticed that cells with higher ratios of P1 particle density were often later 2-cell stage embryos ( Fig 3H ) indicating that variability may be due to a greater degree of asymmetry in older 2-cell embryos , and a lesser degree in 1-cell embryos ., We also noted a qualitative difference in the chs-1 hybridization signal ., The fluorescence particles appeared much larger than signal produced from other transcripts ., This granular pattern was suggestive of P granule localization ( Fig 3D–3I ) ., Because of their granular nature , we were not able to quantitate these transcripts with reliable single molecule resolution ., For this reason we also measured total fluorescence for all our experiments ., However , even in the case of chs-1 , which had the most notable granular patterning , the ratios and statistics calculated from total fluorescence density measurements closely paralleled measurements for particle density ( S4 Fig ) ., smFISH allowed us to observe RNA abundance patterns in embryos throughout development ., The chs-1 , pgl-1 , and bpl-1 transcripts exhibited uniform transcript abundance in 1-cell stage embryos whereas transcripts in 2-cell stage embryos were localized asymmetrically ( Fig 3G and 3H , S5 Fig ) ., This suggests that differential transcript degradation or stabilization may contribute to pattern these particular mRNAs ., Cell-specific distributions continued for chs-1 and bpl-1 transcripts , with particles concentrated in one or two posterior cells over the course of several cell divisions ( Fig 3H ) ., We hypothesized that genes encoding asymmetrically abundant transcripts might have lineage-specific roles in the development of the early embryo ., At least one known example supports this idea ., MEX-3 mRNA and protein are both distributed preferentially to the AB cell and mex-3 is required for proper AB lineage specification ., We used RNAi to survey the knockdown phenotypes of 33 of our asymmetrically abundant transcripts ( Fig 4A ) ., Though RNAi assessments have been performed in the early embryo for some of these genes as part of systematic studies 36–42 , we re-tested these 33 genes and scored and ranked relative embryonic lethality under uniform and controlled conditions ., RNAi corresponding to 9 of the 33 genes tested yielded greater than 5% embryonic lethality in either wild type ( N2 ) or sensitized ( rrf-3 ) worms 40 ( Fig 4A ) ., We next tested whether any of these 9 genes with embryonic lethality phenotypes were involved in lineage-specific functions by performing timecourse microscopy after dsRNA injection ., RNAi to neg-1 yielded the most dramatic anatomical defects , so we selected this gene for further study ( Fig 4B–4D ) ., neg-1 encodes a small protein of unknown function predicted to contain an unstructured N-terminus and a potentially structured and positively charged C-terminus 43 ., The only known homologs to this protein occur in C . elegans and C . brenneri ., F32D1 . 6 has recently been named neg-1 ( Negative Effect on Gut development 1 ) ., We found that neg-1 RNA was enriched and highly abundant in the anterior AB cell ( Fig 1D , Fig 2E , Fig 3A ) ., Disruption of neg-1 by RNAi led to partially-penetrant embryonic lethality , with a median lethality rate of 96% by injection at 15°C , 81% at 20°C and 30% at 25°C ( Fig 4B ) , and thus was cold-sensitive ., Lethality was lower by RNAi feeding ( 30% at 20°C ) , which is consistent with previously published studies of neg-1 RNAi feeding , which reported 40% lethality 44 ( Fig 4A ) ., To achieve a more detailed characterization of the cause of embryonic lethality , we performed time-lapse microscopy on embryos from mothers that either were or were not injected with neg-1 dsRNA ( Materials and Methods ) ( Fig 4C and 4D ) ., These experiments were conducted at 20°C ., Among neg-1 depleted embryos , 22 . 2% arrested without apparent signs of elongation , 50% arrested with partial elongation and 27 . 7% elongated ., In embryos undergoing partial elongation , we noticed a failure of the cells in the anterior of the embryo to enclose , which likely caused the elongation defect ., During this time , development of the posterior continued normally ( Fig 4C and 4D ) ., This is reminiscent of hammerhead ( hmr-1 ) mutation in that the hypodermis failed to enclose the anteroventral regions of the embryo leading to a failure of elongation 45 ., It is also similar to humpback ( hmp-1 , hmp-2 ) phenotypes although we did not observe the classic dorsal rippling or bulging that is typical of those mutants 8 , 45 , 46 ., We suspected that the primary defect in neg-1 depleted embryos was a failure of the hypodermis to fully enclose ., During elongation , hypodermal cells encircle the embryo by extending from the dorsal half of the embryo and expanding to the ventral side , meeting and forming junctions at the ventral midline 47 ., Once the ventral enclosure is complete , circumferential constriction of seam cells ( a file of mid-body cells ) and hypodermal cells powers the extension of the body into an elongated shape 48 ., To more precisely monitor hypodermal cell behavior in neg-1 knock-downs , we performed time-lapse microscopy in a dlg-1::GFP strain that marks apical adherens junctions of epithelial cells of the hypodermis and gut 49 ., We found that in 55% of neg-1 RNAi-treated embryos ( 18/33 ) , hypodermal cells were misshapen prior to ventral enclosure ( Fig 4E and 4F ) ., Within this group , 9 embryos failed to complete ventral enclosure and did not undergo elongation ( Fig 4E and 4F ) ., The other 9 did not complete ventral enclosure on the anterior end and only partially underwent elongation ., In these embryos , elongation led to an organized posterior region of the worm but forced internal cells out through the unenclosed anterior leading to the hammerhead-like phenotype ., Interestingly , 18% ( 6/33 ) of embryos exhibited hypodermal cell enclosure that was largely normal , but mistimed ., Typically , two actin-rich “leader” cells on flanking sides of the body are the first to meet at the ventral midline and are then followed by the more-posterior hypodermal cells ., In the subset of embryos with defective timing , the leader cells and posterior cells met synchronously at the ventral midline ., Of these 6 mistimed-enclosure embryos , 4 failed to complete elongation , whereas the other two hatched ., The remainder of embryos ( 9/33 , 27% ) exhibited normal hypodermal morphogenesis , and of these , 7 of 9 hatched ( Fig 4E and 4F ) ., While there was a strong relation between abnormal behavior of hypodermal cells in neg-1 compromised embryos and the likelihood of successful hatching ( Fig 4F ) , the correlation was not perfect , suggesting that processes other than hypodermal cell behavior are also defective when neg-1 is lacking ., mRNA abundance usually , but not always , correlates with protein production ., To test whether anterior neg-1 mRNA enrichment correlated with NEG-1 protein enrichment , we compared smFISH staining for neg-1 mRNA to fluorescence in worms harboring a NeonGreen::neg-1 protein fusion ., As visualized by smFISH , neg-1 mRNA appeared uniform in the 1-cell fertilized embryo , became localized to the anterior in the AB cell and its daughters , and then declined dramatically in signal during the 6–10-cell stages ( Fig 4G ) ., We observed NeonGreen fluorescence in the ABa and ABp nuclei at the 4-cell stage ., NeonGreen persisted in the anterior nuclei of the 6-cell and 8-cell stage embryos , but then decreased by the 10–32-cell stages ., Therefore , anterior enrichment of neg-1 mRNA preceded fluorescent protein accumulation by one cell cycle , and the pattern of anterior-localization between mRNA and protein was correlated ., Having identified an anterior transcript required for embryogenesis and required for full function in many AB-derived hypodermal cells , we asked whether asymmetric transcripts had functions associated with the lineage in which they were enriched ., Transcripts that accumulated in AB were enriched in GO biological process categories 50 that were distinct from the categories enriched in P1 transcripts ( Fig 5A ) ., Categories associated with the AB transcripts included organelle organization , cellular organization , chromosome organization , and cell-cycle processes , locomotion , and morphogenesis ( Fig 5A , S5 Dataset ) ., The AB cell lineage undergoes more rapid cell cycle progression than the P1 lineage 8 , 9 , 49 , 50 , which explains the enrichment for genes involved in cell cycle processes ., Transcripts preferentially abundant in the posterior P1 cell were associated with translational control , embryo development , proton transport , and cell death ( Fig 5A , S5 Dataset ) ., Although translational control is prevalent throughout C . elegans embryogenesis , germ line development is especially dependent on translational control due to the transcriptional quiescence of this lineage until later stages of embryogenesis 12 , 52 , 53 ., Competition and cooperation among RNA-binding proteins , some of which are asymmetrically distributed in the AB and P1 lineages , control translational repression and activation of maternally inherited mRNAs 54 ., For example , MEX-3 is required for full translational repression of PAL-1 , NOS-2 , GLP-1 , and ZIF-1 in anterior blastomeres 23 , 55–57 , and POS-1 and GLD-1 account for translational repression of key transcripts in posterior blastomeres 24 , 55 , 58 ., Maternally loaded mRNA transcripts of the C . elegans early embryo have been previously categorized into classes according to the dynamics of their distribution ., Whereas Class I mRNAs are maintained in the embryo over time , Class II mRNAs are progressively degraded in somatic blastomeres ., Known Class II mRNAs are limited to a few examples such as nos-1 and nos-2 , which degrade in somatic cells beginning at the 4-cell stage but co-localize with the P granules in the P lineage 59 ., Baugh et al . identified 1749 genes that produced “Maternal Degradation” MD transcripts by microarray time-course assays on whole embryos ., MD transcripts are hypothesized to contain Class II mRNAs as well mRNAs that are degraded in other spatial patterns 60 ., AB-enriched and P1-enriched transcripts were almost twice as likely to be categorized as MD maternal mRNAs when compared to symmetrically distributed transcripts ( 43 . 9% 26/66 of AB-enriched , 51 . 0% of P1 enriched 25/147 , and 26 . 1% 737/2815 of symmetric transcripts were MD; Fig 6A–6C ) ., This suggests that asymmetrically distributed transcripts were more likely to decrease in relative abundance as embryogenesis progressed compared to symmetrically distributed transcripts ., This decline in abundance could be due to a uniform degradation of the transcript across the whole embryo or a relative reduction in the amount of transcript as it becomes restricted to a specific cell lineage ., Indeed , by in situ hybridization ( Fig 3H , S4 Fig ) transcripts asymmetrically abundant in the P1 cell often exhibited P2-specific localization at the 4-cell stage ., Transcription is largely quiescent in the P1 cell and its descendants , so it is possible that maternal mRNAs are specifically sequestered in that lineage for later use in germ line development ., The transcriptomes of the Z2/Z3 progenitor germ cells isolated from early embryos have been previously characterized by sorting them using Ppie-1::GFP::PGL-1 fluorescent markers 61 ., We did not find a statistically significant over-representation of P1-enriched transcripts among Z2/Z3-enriched transcripts ( Fig 6B and 6C ) ., This indicates that P1-enriched transcripts are not set aside for retention in the Z2/Z3 germ line precursors at a higher rate than those found in the AB-enriched or symmetric sets at the 2-cell stage ., However , individual P1-enriched transcripts may be of particular interest in germ line precursor biology ., Indeed , transcripts that are both enriched in P1 and in Z2/Z3 include pgl-3 , which encodes one of the two key scaffolding proteins of the P granules 65 ( Fig 6B ) , and plk-3 , a component whose presence is correlated with P granule development 66 ., To determine whether asymmetric transcripts were associated later in development with a specific cell or tissue type , we asked whether AB- or P1-enriched transcripts were over-represented in any specific embryonic cell type 61 ., We found significant associations between AB transcripts and hypodermal transcripts , AB transcripts and pharyngeal transcripts , and between P1 transcripts and pharyngeal transcripts ( Fig 6C , S6 Dataset ) ., However , we saw no clear association between AB-enriched transcripts and cell types that were exclusive to the AB-lineage or between P1-enriched transcripts and cell types that were exclusive to the P1-lineage ., This suggests that the primary purpose of asymmetric transcript abundance following the first embryonic division is not to retain transcripts for cells that arise later in each lineage ., Together these results suggest that mRNAs asymmetrically abundant at the 2-cell stage tend to decline rapidly in relative abundance as embryogenesis progresses , but that only a small fraction of the corresponding transcripts are tissue-specific or lineage-specific later in development ., These findings are consistent with the observation that the transcriptome undergoes waves of change throughout development 60 ., These waves are shaped through RNA degradation and zygotic transcription 67 , 68 ., RNA-binding proteins recognize sequences and topologies in their RNA targets to affect localization , stabilization , polyadenylation , sequestration or degradation ., We asked whether transcripts enriched in the AB or P1 cells shared RNA sequence features that could account for their patterning ., Though we found no associations between 5′ splice leader usage , 5′ UTR length , 5′ UTR nucleotide composition , 3′ UTR length , or 3′ UTR sequence composition and cell-specific categories ( S6A and S6B Fig ) , we found that short gene models ( both spliced and unspliced ) were associated with P1 enrichment , and longer gene models with AB-enrichment ., The median AB-enriched transcript length was longer than the median length of symmetric transcripts , which in turn was longer than the median of P1-enriched transcripts ( Fig 6D , S6C Fig ) ., A large number of P1-enriched transcripts were very short ( 250–750 bp ) , a length class that was virtually non-existent in the AB set ., Of the 73 genes whose models yielded spliced lengths of less than 750 bp and were P1-enriched , 13 were neuropeptide-like proteins ( nlp and flp ) and 14 were ribosomal protein components ( rpl or rps ) ., We speculated that asymmetrically abundant transcripts might contain different RNA-binding protein associated sequence motifs or miRNA target sites ., We searched de novo for RNA sequences that distinguished the AB-enriched or P1-enriched 3′ UTRs from those of the symmetric set ., We identified two motifs over-represented in the 3′ UTRs of P1-enriched genes: UUUAUUGCAU and polyC ( 10-mer C ) but none that were over-represented in the AB-enriched set of genes ( Fig 6E ) 62 , 69 , 70 ., The UUUAUUGCAU motif is an Adenine and Uracil rich element ( ARE ) that contains features similar to known POS-1 and MEX-3 recognition sequences ., The UAUU sequence is similar to the zinc-finger coordinated UAUU sequence found in the recognition sequence of the CCCH-type zinc finger protein POS-1 ( UA ( U2-3 ) RD ( N1-3 ) G ) and its human homolog , tristetraprolin ( TPP-1 , UAUUUAUU ) ., The UUUAUUGCAU motif also resembles two stretches of UUUAUUGA within the nos-2 3′ UTR that are perfectly conserved in three Caenorhabditis species , are required for MEX-3 binding in vitro , and are required for preventing NOS-2 protein accumulation in anterior blastomeres 52 , 56 ., POS-1 and MEX-3 are two RNA binding proteins that are asymmetrically distributed in the 2-cell stage embryo and whose target sequence motifs have been described ( Fig 6F ) ., We noticed a striking similarity between the POS-1 target sequence and the P1-enriched sequence we identified ( Fig 6E ) suggesting that POS-1 or another CCCH-type zinc finger may function in the retention of P1-enriched transcripts ., We have expanded the knowledge of which mRNA molecules are asymmetrically distributed during the first cell division in C . elegans , a powerful model for asymmetric cell division and early development ., Our approach led to the identification of neg-1 ., a gene that produces anterior mRNA and protein and is important for ventral enclosure and elongation , processes that involve hypodermal cells derived from the AB-lineage ., We identified a transcript , neg-1 , that is more abundant in the anterior cell following the first cell division of the C . elegans zygote ., The protein product of this transcript is also preferentially localized to anterior nuclei , starting in the following cell division ., Disruption of neg-1 by RNAi led to defective anterior morphogenesis due to failures in hypodermal cell ventral enclosure and elongation ., The hypodermal leader cells ( ABpraappap , ABpraapppa , ABplaappap , and ABplaapppa ) , the ventral cells posterior to the leader cells ( ABpraapppp , ABplaapppp , and the P cells ) , and the seam cells ( H0 , H1 , V1–V6 , and T ) are all derived from the AB lineage and are all actively involved in hypodermal cell enclosure along the ventral midline ., The C founder cell yields other hypodermal cells but these are posterior and dorsally located and do not play as large a role in ventral enclosure 8 , 71 ., Thus , the data presented here show that the asymmetrically abundant neg-1 mRNA is associated with subsequent asymmetric protein abundance , and that loss of neg-1 function has consequences on anterior morphogenesis ., It remains an open question as to how a protein whose anterior patterning is most striking at the 4-cell stage yields an RNAi phenotype 300 minutes later ., NEG-1 may alter or regulate gene expression in the AB lineage , which would be consistent with its positive charge and concentration in the nucleus ., The functional characterization of neg-1 in this manuscript indicates that cell-specific RNA profiling may be a fruitful way to identify transcripts and proteins that contribute to lineage-specific processes ., We identified neg-1 based on its preferential abundance in the AB cell ., With this in mind , other transcripts identified in our study warrant special attention with respect to their potential for lineage-specific functions ., For example , cdc-25 . 3 was AB-enriched ., Its homolog cdc-25 . 1 regulates relative cell-cycle rate in other lineages 9 ., cdc-25 . 1 and cdc-25 . 3 may work together to regulate lineage-dependent cell-cycle rates ., Many of the AB-enriched transcripts , such as erm-1 and hmp-2 , are associated with epithelial adherens junctions , possibly foreshadowing the important role of hypodermal cell function among the progenitors of the AB lineage ., Cytoskeletal regulatory genes enriched

Introduction, Results, Discussion, Materials and Methods

After fertilization but prior to the onset of zygotic transcription , the C . elegans zygote cleaves asymmetrically to create the anterior AB and posterior P1 blastomeres , each of which goes on to generate distinct cell lineages ., To understand how patterns of RNA inheritance and abundance arise after this first asymmetric cell division , we pooled hand-dissected AB and P1 blastomeres and performed RNA-seq ., Our approach identified over 200 asymmetrically abundant mRNA transcripts ., We confirmed symmetric or asymmetric abundance patterns for a subset of these transcripts using smFISH ., smFISH also revealed heterogeneous subcellular patterning of the P1-enriched transcripts chs-1 and bpl-1 ., We screened transcripts enriched in a given blastomere for embryonic defects using RNAi ., The gene neg-1 ( F32D1 . 6 ) encoded an AB-enriched ( anterior ) transcript and was required for proper morphology of anterior tissues ., In addition , analysis of the asymmetric transcripts yielded clues regarding the post-transcriptional mechanisms that control cellular mRNA abundance during asymmetric cell divisions , which are common in developing organisms .

At key moments in development , asymmetric cell divisions give rise to daughter cells of differing characteristics , a process that promotes cell-type diversity in complex organisms ., The first cell division of the C . elegans early embryo is a powerful model for understanding asymmetric cell division because the timing of divisions and the placement of their division planes are precise and reproducible ., We surveyed the mRNA content of each daughter cell in the C . elegans 2-cell embryo using low-input RNA sequencing ., We identified several hundred asymmetric transcripts and tested them for functions in development ., We found that the gene neg-1 produced mRNA and protein preferentially on the anterior ( head-side ) of 2-cell and 4-cell stage embryos and that loss of neg-1 led to consequences in anterior morphogenesis later in development ., We also analyzed the asymmetric transcripts using quantitative microscopy , bioinformatics comparisons with previously existing datasets , and RNA sequence motif discovery to gain insight to the mechanisms by which asymmetric abundance patterns arise .

journal.pgen.1005993

Parental Origin of Interstitial Duplications at 15q11.2-q13.3 in Schizophrenia and Neurodevelopmental Disorders

Recurrent duplications of ~4Mb at 15q11 . 2-q13 . 3 , overlapping the Prader-Willi syndrome /Angelman syndrome ( PWS/AS ) region between breakpoints 2 and 3 or 1 and 3 ( BP2-BP3 or BP1-BP3 ) are recognised risk factors for developmental delay ( DD ) and autism spectrum disorders ( ASD ) 1–4 ., More recently , these duplications were implicated as risk factors for schizophrenia ( SZ ) 5–8 , however data are limited ., The 15q11 . 2-q13 . 3 region contains a cluster of imprinted genes , which are expressed from one parental allele only as a consequence of germline epigenetic events ( Fig 1 ) ., Within this cluster there are several paternally expressed genes , including SNRPN , MKRN3 , MAGEL2 and NECDIN , and two maternally expressed genes , namely ATP10A , and UBE3A ., The genes within the 15q11 . 2-q13 . 3 interval are mostly canonical imprinted genes , in that expression is robustly monoallelic , although the imprinting status of ATP10A appears to be polymorphic and influenced by gender 9 ., As a consequence of the presence of both maternally and paternally expressed imprinted genes , CNVs at this interval may be expected to have different phenotypes depending on their parent of origin ., Indeed , most studies that have tested the parental origin of 15q11 . 2-q13 . 3 duplications show that those of maternal origin are usually responsible for the disease phenotypes ., However , the penetrance of 15q11 . 2-q13 . 3 duplications has not yet been estimated ., Moreover , whilst rare duplications of paternal origin have generally been regarded as benign 1 , 10 , this has not been studied systematically ., The assumption that duplications of paternal origin are benign comes from small numbers of observations of healthy mothers who carry duplications of paternal origin ( and have transmitted them , making them maternal in the affected offspring ) , as well from the fact that they are much rarer in cohorts of DD/ASD children 1 , 10 ., However , this pattern can also be explained by a lower penetrance of the paternal duplications and a lower prevalence in the general population ., Only very large studies on patients and healthy controls can provide an accurate estimate of their role ., The observations of paternal duplications occurring de novo , their extreme rarity in controls and their large size and high gene content , made us suspect that they are under selection pressure , like all other similar CNVs for which we have calculated the selection pressure 11 and therefore are likely to be pathogenic , although with a lower penetrance ., The aim of this study was to conduct the largest and most detailed assessment of 15q11 . 2-q13 . 3 interstitial duplications to date ., By examining large cohorts of DD , ASD and SZ , along with large numbers of controls , we were able to estimate the prevalence , penetrance , and selection coefficients of 15q11 . 2-q13 . 3 interstitial duplications of maternal and paternal origin for SZ and for other neurodevelopmental disorders , and to identify clinical features common to SZ carriers of these duplications ., We clearly implicate 15q11 . 2-q13 . 3 interstitial duplications of paternal origin in the aetiology of DD , but do not find them at increased rates in SZ , which is significantly associated only with duplications of maternal origin ., These data clarify the contribution of imprinted genes within the PWS/AS interval to psychopathology , and have important , tangible benefits for patients with 15q11 . 2-q13 . 3 duplications by aiding genetic counseling ., The prevalence rates of 15q11 . 2-q13 . 3 interstitial duplications in SZ , other neurodevelopmental disorders , and controls are shown in Table 1 ., Among 28 , 138 SZ probands , there were 25 individuals with 15q11 . 2-q13 . 3 interstitial duplications , of whom 24 were of maternal and only one of paternal origin ., Prevalence estimates in SZ were therefore 0 . 085% ( 95%CI = 0 . 057–0 . 13% ) for maternal and 0 . 0036% ( 95%CI = 0 . 00064–0 . 02% ) for paternal duplications ., Among 51 , 001 probands with DD/ASD/MCA from two large studies on referrals to clinical genetics clinics with DD/ASD/MCA 18 , 21 , two from ASD cohorts and the current study 5 , 17 , 53 ( 0 . 1% ) had an interstitial 15q11 . 2-q13 . 3 duplication ., Isodicentric chr15 ( idic15 ) and interstitial triplications reported in these 51 , 001 probands ( N = 6 ) were excluded from analysis , while no triplications were observed in SZ or control subjects ., Only a small proportion of the previously published duplications had been reported for parental origin ., In order to arrive at a more precise estimate of the ratio between maternal and paternal interstitial duplications in DD/ASD/MCA cohorts , we analysed an additional 13 individuals from the BBGRE database ( 20 , 260 individuals analysed for the current study ) and one new ASD case from Iceland , and included 37 DD/ASD/MCA subjects from three studies that estimated the parental origin of duplications in such patients , but reported no population prevalence data 10 , 19 , 20 ( Table 1 ) ., This gave us a total of 60 DD/ASD/MCA subjects with established parental origin: 50 ( 83 . 3% ) maternal and 10 ( 16 . 7% ) paternal ., Using these proportions , we extrapolated the prevalence rates of maternal and paternal duplications for the 53 DD/ASD/MCA systematically ascertained carriers at 0 . 087% ( 95%CI = 0 . 065–0 . 12% ) for maternal and 0 . 017% ( 95%CI = 0 . 009–0 . 033% ) for paternal duplications , respectively ( Table 1 ) ., Among 149 , 780 controls , there were four individuals with maternal and four with paternal duplication origin , giving identical rates of 0 . 0027% for both parental types ( 95%CI = 0 . 001–0 . 0069% ) ., Analysis of bipolar disorder ( BD ) datasets was not among the aims of this study ., Just for the record , our review of 8 , 084 BD probands 22 found no 15q11 . 2-q13 . 3 duplication carriers ., Although we reported one such case in our original publication 5 , the available data suggests that these duplications do not play any significant role in BD ., Table 2 shows the estimates for the prevalence of 15q11 . 2-q13 . 3 duplications in different population groups , and the estimated penetrance for SZ and other neurodevelopmental disorders ., We estimated the general population frequency of 15q11 . 2-q13 . 3 duplications of maternal origin to be about two times higher than those of paternal origin ( 0 . 0069% vs . 0 . 0033% ) ., The penetrance of maternal duplications for DD/ASD/MCA is very high ( 50 . 5% ) and is about 2 . 5 times higher than those of paternal origin ( 20 . 7% ) ., Maternal duplications also have high penetrance for SZ ( 12 . 3% ) but in contrast , paternal duplications appear not to increase risk for SZ ( penetrance of 1 . 1% ) , although we cannot completely exclude their role in SZ , as this estimate is based on a single observation in a SZ patient and therefore has large 95%CIs ., We describe 10 relatives of probands that carried 15q11 . 2-q13 . 3 duplications ( Fig 2 and S1 Table ) ., Five of those were affected with psychosis or DD/ASD and all had 15q11 . 2-q13 . 3 duplications of maternal origin ., Of the 5 unaffected relatives , only two had duplications of maternal origin ( one of these , 50320–1 , had depressive disorder ) ., The three remaining unaffected individuals with paternal duplications had no reported neuropsychiatric phenotypes ( but had not been specifically assessed ) ., Six of the relatives were transmitting mothers and two were transmitting fathers ., Five of the six mothers had offspring affected with SZ , while the two transmitting fathers had one healthy and one Attention Deficit Hyperactivity Disorder ( ADHD ) offspring ( Fig 2 ) ., The remaining affected individuals in Fig 2 , all marked with no fill ( white ) , were not tested , as we had no DNA from them , but they are compatible with having maternal duplications ., We do not have sufficient data on offspring of 15q11 . 2-q13 . 3 duplication carriers to make direct estimates of reproductive fitness and therefore present results for selection coefficients based on the ratio between de novo and inherited duplications: de novo/ ( de novo+inherited ) , following our previous work 11 ( Table 3 ) ., The estimated selection coefficients appear similar: 0 . 55 for maternal and 0 . 58 for paternal duplications ( but small numbers of paternal duplications preclude accurate estimates ) ., Using the formula discussed in the Methods ( μ = qs ) , we estimated the mutation rates as follows: maternal duplications , 0 . 000069×0 . 55 ≈ 1 per 27 , 000 newborns and paternal duplications , 0 . 000033×0 . 58 ≈ 1 per 50 , 000 newborns ., Here we use population rates , rather than allele frequencies ( q ) , and mutation rates per newborn , rather than per gamete ( μ ) , as the formula suggests , simplifying the presentation ., We point out that the estimates for both s and μ are less reliable compared to those for other CNVs 11 , and that we refer to mutation rates in newborns , rather than in germ cells , which differ by orders of magnitude ( Discussion ) ., Our collaboration compiled a series of 29 duplication carriers with SZ or schizoaffective disorder ( SZA ) , including the affected relatives ( S1 and S2 Tables ) , of which only a small proportion have been reported before 5 , 7 , 8 ., The 20 SZ/SZA cases with data on age at onset had a relatively early mean age at onset of 18 . 1 ( SD = 6 . 9 ) years and in five of those the illness started during childhood ( <13 years ) ., Developmental and/or cognitive data were available for 21 cases and 76% of them had recorded learning/developmental problems ., These ranged from mild ID ( n = 3 ) to borderline ID ( n = 8 ) with the rest having unspecified or nonverbal learning difficulties ., The median IQ score was 75 ( range 62–89 ) among the 11 cases with formal IQ tests ., Specific psychiatric symptoms included catatonia ( n = 6 ) , disorganized behaviour ( n = 5 ) and prominent antisocial traits ( n = 5 ) ., Epilepsy was reported in only one case ., Among the 54 cases included in this study ( S1 and S2 Tables ) , there were three carriers where ADHD was listed as a phenotype ., One of these was of paternal origin , from a study that tested 727 children with ADHD 23 ., The only case with a paternal duplication among the 12 cases in the study of Aypar et al . 10 also had ADHD ., Still , these numbers are clearly too small to conclude that paternal duplications have a specific role in ADHD ., It should be noted that several of the population controls who carried duplications ( all from Iceland ) were not specifically subjected to formal neuropsychiatric assessment ( S1 Table ) ., However , they had not been registered as psychiatric patients and there was no information to indicate that they had developmental delay ., Interestingly , two of the controls ( control 2 and control 4 ) recently participated in a neuropsychiatric test battery project conducted in Iceland conducted by the Icelandic authors of the current paper ., This revealed that Control 2 ( paternal origin duplication ) has an IQ of 100 and Global Assessment of Functioning ( GAF ) score of 81 and also is registered as having dyslexia , while Control 4 ( maternal origin duplication ) has an IQ of 84 and GAF score of 90 ., A third “unaffected” control ( Control 3 ) had Alzheimer’s disease at the age of 64 ., These observations indicate that even population controls who do not suffer with our target diagnoses , could have some subtle cognitive phenotypes , highlighting the variable penetrance of this CNV ., Seventeen individuals analysed for this study ( probands with SZ/SZA , their affected relatives and one control ) , had extended clinical/physical data available ( S2 Table ) ., Congenital anomalies were rare: one with cleft palate and one with cardiomegaly ., Dysmorphic features were more common , recorded in 10 carriers , and included micro- , macro- or dolichocephaly , high palate , facial asymmetry and others ., Six cases ( 35% ) had urological conditions including urethral stricture , polyuria , urge incontinence , nephrectomy and recurrent urinary tract infections ., Endocrinology problems included hypocalcemia , hypercholesterolemia , diabetes mellitus and hypothyroidism , however we cannot state whether they are more common than in any population of SZ patients , or had been caused by medication ., Maternal duplications of the PWS/AS critical region at chromosome 15q11 . 2-q13 . 3 are known to be pathogenic , causing DD and intellectual disability 24 , and are also among the most common single genetic risk factors for ASD 1 , 25 ., More recently they were also implicated as risk factors for SZ 5–8 , although with a much lower estimated penetrance than for DD/ASD 26 and based on very few observations ., In contrast , paternal duplications have not been considered to be pathogenic 1 , 10 ., Here we conducted the largest and most detailed assessment of interstitial duplications at 15q11 . 2-q13 . 3 to date ., We were able to estimate the prevalence , penetrance , and selection coefficients of 15q11 . 2-q13 . 3 duplications of maternal and paternal origin for SZ and other neurodevelopmental disorders , and to identify clinical features common to SZ carriers of these duplications ., We clearly implicate 15q11 . 2-q13 . 3 duplications of paternal origin in the aetiology of DD , but not for SZ , where only maternal duplications increased risk ., Our data confirm that maternal interstitial duplications have a high penetrance ( 62 . 4% ) for any neurodevelopmental disorder ., Much of this was accounted for by the DD/ASD/MCA group , and only 12 . 3% by SZ ( Table 2 ) ., This CNV is found in about 1:1176 patients suffering with SZ ( 95%CI = 1:769–1:1754 ) ., In contrast , paternal duplications do not appear to increase risk for SZ , ( penetrance of 1 . 1% ) , suggesting that only maternal duplications have a specific effect on psychosis ( although a small role for paternal duplications cannot be confidently excluded due to the wide confidence intervals ) ., The importance of epigenetic status of duplications at this interval was further underlined by analysis of a number of families ( Fig 2 ) ., Duplications in two unaffected mothers had a DNA-methylation pattern indicative of being paternally derived , whereas their offspring , who possessed a maternally derived duplication , suffered from psychotic illness ., Although they appear to have no role in SZ , we show for the first time that paternal duplications are pathogenic , increasing the risk for DD/ASD/MCA with a penetrance of 20 . 7% ( 95%CI = 4 . 4–100 ) ., One reason why paternal duplications have been regarded as non-pathogenic in the past is their rare occurrence in patients ., Here we demonstrate that they are also rare in the general population as a whole , with prevalence rates of 0 . 0033% for paternal and 0 . 0069% for maternal duplications ( Table 2 ) ., Their pathogenicity is supported by the strong selection pressure operating against them ( s = 0 . 58 , Table 3 ) ., We tested if some of these results could be due to an overestimation of the population prevalence of DD/ASD/MCA or SZ , as those referred for testing or taking part in SZ studies might constitute a more severely affected sub-group ., However , even if we use lower population rates of 0 . 5% for SZ and 2% for the combined group of ASD/DD/MCA , our penetrance estimates remain very high ( S3 Table ) ., The penetrance might even be higher , if we include subtle cognitive phenotypes , as described above for those population controls who took part in formal neurocognitive testing ., The question arises as to why paternal duplications are rarer than maternal ones in the general population , despite their lower pathogenicity ., One explanation is a lower mutation rate in males ., Indeed we estimate ( with great approximation ) that 1:27 , 000 and 1:50 , 000 newborns have a mutation that arose on maternal and paternal chromosomes , respectively ., This refers to the rate in newborns with de novo mutations , while the mutation rate in sperm is much higher , observed in about 1:400 sperm cells 27 , indicating a strong negative selection against such embryos before birth ., Differences in the mutation rates according to the parental origin have been shown for other CNVs as well 28 , so this may be one explanation of the observed difference ., The ( possibly ) lower rate of de novo mutations of paternal origin among newborns cannot , on their own , explain their extreme rarity in the population ., Firstly , paternal duplications should be less efficiently eliminated from the population by negative selection pressure , due to their lower penetrance for neurodevelopmental disorders ., Secondly , some maternal duplications will change to paternal when transmitted from male carriers ., We now suggest one further explanation for their rarity: male patients with SZ and other neurodevelopmental disorders have lower fecundity ., Indeed , men suffering with SZ have only half the number of offspring compared to women with SZ 29 ., This effect was demonstrated for another high-penetrance CNV , the 22q11 . 2 deletion , where male carriers had on average three times fewer offspring than female carriers with the same deletion ( 0 . 3 vs . 0 . 9 per carrier ) 30 ., This parental bias could reduce substantially the number of inherited paternal 15q11-q13 duplications , compared to maternal ones ., We did not observe any trend for SZ patients to have a particular size of duplication , identifying individuals with all four possible combinations of breakpoints between BP1 and BP4 ( Fig 1 ) ., Within the region common to all these duplications ( BP2-BP3 ) , the most likely candidate gene casing the neurodevelopmental phenotypes is UBE3A ( Fig 1 ) ., It is only expressed from the maternal allele in neurons 31 and so maternal , but not paternal , duplications spanning this gene would lead to an over-dosage of expression in the brain ., ATP10A is also maternally expressed , but the polymorphic nature of the imprinting of this gene 9 makes the canonically imprinted UBE3A the main causal candidate from an epigenetic perspective ., Moreover , UBE3A also has a strong neural pedigree as it encodes E6-AP ubiquitin ligase , which is important in the degradation of proteins such as p53 and the ubiquitins 32 , and has been shown to influence the glutamatergic system via its action on the synaptic protein Arc 33 ., The role of this gene is supported by a recent clinical case with a micro-duplication encompassing only UBE3A , pointing to this gene as being key to the neuropsychiatric phenotypes 34 ., The most consistent phenotypic characteristic of SZ carriers of these duplications is their cognitive deficit ., Features of DD or intellectual deficit were recorded in 76% of carriers of maternal duplications on whom data was available ., The median IQ score was 75 ( range 62–89 ) among those who had a test ( although this IQ result might be biased towards lower scores , as patients with more obvious learning problems are more likely to be referred for such testing ) ., Congenital anomalies were fairly rare but mild dysmorphic features were observed in 10/17 cases with available clinical data ., The interest in the clinical presentation of psychosis among carriers of 15q11 . 2-q13 . 3 duplications of maternal origin started following reports that individuals with Prader-Willi syndrome who had maternal uniparental disomy ( two maternally inherited copies and no paternal copy ) might present with cycloid psychoses ( acute polymorphic clinical pictures with prominent affective or motor symptoms ) 35 , 36 ., In our original paper 5 we noted that one of our cases had SZA and one had prominent affective symptoms ., Here we present more cases with extended clinical descriptions ., The rate of SZA disorder was 20% ( 4 out of 20 ) for cases with more detailed clinical records ., SZ/SZA patients tended to have an early age at onset ( mean of 18 . 1 years , SD = 6 . 9 ) ., Catatonia was recorded in six patients and disorganised or aggressive behaviour in eight patients ., Antisocial traits were noted in all five cases from Canada ., Although no single clinical picture emerges , it appears that psychotic patients with maternal 15q11 . 2-q13 . 3 duplications are more likely to present with disorganised , aggressive , antisocial and/or catatonic features ., We did not find cases with cycloid psychoses , suggesting this may be restricted to Prader-Willi syndrome patients with maternal uniparental disomy as a consequence of the combined effect of both the increased dosage of maternally expressed genes , and the loss of paternally expressed genes ., We acknowledge certain limitations in our study ., SNP arrays or array CGH can accurately determine extent and copy number of unbalanced chromosome regions ( S2 Fig shows a selection of duplications and triplications tested with Illumina SNP arrays ) , but give no information on the structural arrangement and position of the material ., This can only be established by examining chromosome preparations , in conjunction with FISH probes ., As we only had DNA material , this was not possible for this study ., However , the presence of three copies is generally indicative of an interstitial duplication , whilst four copies ( triplication ) suggests the presence of a supernumerary idic15 ., We excluded triplications from our study ., Some rare idic15 cases can also represent only duplications and be indistinguishable from interstitial duplications by array testing ., Therefore , while the vast majority of duplications in this study are likely to be interstitial , a small number could be supernumerary chromosomes , but still representing only duplications of the genetic material ., As the main factor for pathogenicity is likely to be the copy number of the genes in the region , we feel that this limitation does not affect our conclusions ., Interstitial triplications/idic15 are not the subject of our work , but we can report that there were no triplications in SZ subjects ., This could be due to higher pathogenicity , leading to early onset neurodevelopmental phenotypes ., Another limitation of our study concerns the role of paternal duplications in SZ , were we cannot completely exclude a small role , due to the wide confidence intervals for the prevalence and penetrance estimates ., Finally , as discussed above , several unaffected controls had not had formal testing and although we can be confident that they do not suffer with severe neurodevelopmental disorders , we cannot exclude subtle phenotypes , and indeed have detected certain problems among the tested controls ., This is not surprising , as a large study from Iceland has already shown that “healthy”carriers of pathogenic CNVs have lower cognitive performance 39 ., In conclusion , our study clarifies the distinct roles of maternal and paternal interstitial duplications at 15q11 . 2-q13 . 3 in neuropsychiatric disorders , underlining the importance of maternally expressed imprinted genes in this interval to the incidence of psychotic illness ., We also show that paternal duplications are pathogenic , increasing risk for DD/ASD/MCA with a penetrance of 20 . 7% ., Defining the parent-of-origin of duplications at 15q11 . 2-q13 . 3 , which does not require parental DNA , may allow the refinement of genetic counselling and/or therapeutic intervention for individuals carrying these CNVs ., The CLOZUK study has UK National Research Ethics approval ( Ethics Committee WALES REC 2 , Study ID: 10/WSE02/15 ) ., The CLOZUK samples were collected anonymously from across the UK ( thus without express consent ) , consistent with the UK Human Tissue Act and with the approval of the above ethics committee ., We collated the available clinical and molecular data from large ( >400 cases ) , systematically ascertained CNV studies of SZ from the literature , or known to us via our collaborations ., We similarly collected data on other neurodevelopmental disorders such as DD/ASD and multiple congenital anomalies ( MCA ) , focusing on two large studies based on referrals to genetic clinics and on three studies that specifically determined the parental origin of these duplications and reported on the presence of triplications/idic15 ( Table 1 ) ., We used the reported information on parental origin of the 15q11 . 2-q13 . 3 duplications or , where possible , obtained DNA samples to establish this ( details in S1 Text ) ., Most teams that we approached responded to our requests , but some had no access to patient DNA and could not take part ., The control cohorts included 149 , 780 individuals ( Table 1 ) ., The largest sample was from Iceland ( ~115 , 000 genotyped individuals at the time writing ) , a population-based cohort containing related individuals and those affected with medical and/or neuropsychiatric conditions ., An ideal control cohort would only have one individual per family to avoid biases from over-sampling within population lineages ., However , the Icelandic sample is designed to achieve total population coverage; therefore it is impractical to single out unrelated individuals ., The rate of 15q11 . 2-q13 . 3 duplications in this population was assumed to be unbiased without excluding relatives of individuals with or without 15q11 . 2-q13 . 3 duplications ., In fact , this population approaches the ideal general population ascertainment , that should give the best estimates for the various analyses performed here , therefore we decided to retain the two pairs of relatives who were carriers in this population ., Penetrance was estimated according to the formula originally proposed by Vassos et al . 37 , updated for the joint effect of DD/ASD/MCA and for SZ , as we proposed earlier 26 ., Briefly , we first estimated the rate of the duplications in the general population ., We assumed that the control ( healthy ) population comprises 95% of the population , as explained in our previous work 11 ., The rest of the population is made up of approximately 1% SZ and 4% DD/ASD/MCA cases ., From this we estimated the proportion of 15q11 . 2-q13 . 3 duplication carriers in the general population that develop SZ or DD/ASD/MCA ( the penetrance ) ., Although many duplication carriers might have both SZ and DD/ASD/MCA , we made the simplifying assumption that they would usually be ascertained only once ., As the DD/ASD/MCA patients referred for genetic testing and the SZ patients recruited for studies might represent a more severely affected sub-group , we repeated the estimates after reducing by half these population rates ( down to 0 . 5% for SZ and 2% for DD/ASD/MCA ) and show these results in the S1 Text , S3 Table ., Estimating 95% confidence intervals for the frequencies in each population followed the method we used in our previous paper 26 ., Briefly , we first estimated the binomial CIs for the frequencies of CNVs in each population , including controls and the general population , using the Wilson score interval ., Upper and lower 95% bounds for penetrance were estimated from the upper or lower bounds of CNV frequencies in patients and the lower or upper bounds of the frequencies in the general population ., According to the mutation-selection balance theory , pathogenic mutations in the general population are found at low frequencies , where the addition of de novo mutations is balanced by the selection pressure against them ( q = μ/s ) , where q is the allele frequency in the general population , μ is the mutation frequency and s is the selection coefficient 11 , 26 ., In order to estimate the selection coefficients ( s ) of 15q11 . 2-q13 . 3 duplications of maternal and paternal origin , we used the methods outlined in our previous work 11 , 26 as follows: The selection coefficient of a CNV can be approximated as the proportion of de novo CNVs out of the total number of CNVs ( de novo and inherited ) that are observed in an unbiased sample of CNV carriers ., This is because the number of CNVs filtered out by natural selection is approximately equal to those introduced in the population as de novo mutations , after making the simplifying assumption that the frequency of the CNV does not change from generation to generation ., We then estimated the mutation rate of the duplications , using the above formula ., We should point out that the actual differences between maternal and paternal mutation rates are prone to error , e . g . due to the flipping of maternal and paternal duplications according to the gender of transmitting parents and possible differences in fecundity between affected male and female carriers ( Discussion ) ., We established the parental origin of 15q11 . 2-q13 . 3 duplications for carriers from studies where it was previously unknown or not reported ( Table 1 and S1 Table ) ., These were the studies/datasets by Gawlick , Alexic , the BBGRE database 18 ( https://bbgre . brc . iop . kcl . ac . uk/ ) , and new carriers in the Icelandic population and three published datasets 12 , 16 , 23 ., Parental origin was established by one of several methods ., For DNA available to the Cardiff laboratory , we used a methylation-sensitive high-resolution melt curve analysis , as described previously 8 , 19 , 38 , ( S1 Text and S1 Fig ) ., The same method was used for the samples from Canada 7 ., In the Icelandic sample parental origin was either determined with methylation-sensitive Southern analysis 5 or by long-range haplotype analysis ( S1 Text ) ., Microsatellite analysis was used for the BBGRE dataset 18 .

Introduction, Results, Discussion, Methods

Duplications at 15q11 . 2-q13 . 3 overlapping the Prader-Willi/Angelman syndrome ( PWS/AS ) region have been associated with developmental delay ( DD ) , autism spectrum disorder ( ASD ) and schizophrenia ( SZ ) ., Due to presence of imprinted genes within the region , the parental origin of these duplications may be key to the pathogenicity ., Duplications of maternal origin are associated with disease , whereas the pathogenicity of paternal ones is unclear ., To clarify the role of maternal and paternal duplications , we conducted the largest and most detailed study to date of parental origin of 15q11 . 2-q13 . 3 interstitial duplications in DD , ASD and SZ cohorts ., We show , for the first time , that paternal duplications lead to an increased risk of developing DD/ASD/multiple congenital anomalies ( MCA ) , but do not appear to increase risk for SZ ., The importance of the epigenetic status of 15q11 . 2-q13 . 3 duplications was further underlined by analysis of a number of families , in which the duplication was paternally derived in the mother , who was unaffected , whereas her offspring , who inherited a maternally derived duplication , suffered from psychotic illness ., Interestingly , the most consistent clinical characteristics of SZ patients with 15q11 . 2-q13 . 3 duplications were learning or developmental problems , found in 76% of carriers ., Despite their lower pathogenicity , paternal duplications are less frequent in the general population with a general population prevalence of 0 . 0033% compared to 0 . 0069% for maternal duplications ., This may be due to lower fecundity of male carriers and differential survival of embryos , something echoed in the findings that both types of duplications are de novo in just over 50% of cases ., Isodicentric chromosome 15 ( idic15 ) or interstitial triplications were not observed in SZ patients or in controls ., Overall , this study refines the distinct roles of maternal and paternal interstitial duplications at 15q11 . 2-q13 . 3 , underlining the critical importance of maternally expressed imprinted genes in the contribution of Copy Number Variants ( CNVs ) at this interval to the incidence of psychotic illness ., This work will have tangible benefits for patients with 15q11 . 2-q13 . 3 duplications by aiding genetic counseling .

The genetic interval 15q11 . 2-q13 . 3 on human chromosome 15 contains several so-called “imprinted genes” which are subject to epigenetic marking leading to activity from only one parental copy ., This is in contrast to non-imprinted genes , whose activity is independent of their parent-of-origin ., Deletions affecting the 15q11 . 2-q13 . 3 interval cause Prader-Willi and Angelman syndromes ( PWS/AS ) , depending on whether the deletions are paternally or maternally derived respectively ., Duplications at the PWS/AS interval region may also lead to neurodevelopmental disorders , including developmental delay ( DD ) , autism spectrum disorder ( ASD ) and schizophrenia ( SZ ) ., Due to presence of imprinted genes within the region , the parental origin of these duplications may be key to the pathogenicity ., We show , for the first time , that paternal duplications lead to an increased risk of developing DD/ASD/multiple congenital anomalies ( MCA ) but , unlike maternal duplication , do not appear to increase risk for SZ ., This study refines the distinct roles of maternal and paternal duplications at 15q11 . 2-q13 . 3 , underlining the critical importance of maternally active imprinted genes in the contribution to the incidence of psychotic illness ., This work will have tangible benefits for patients with 15q11 . 2-q13 . 3 duplications by aiding genetic counseling .

neuropsychiatric disorders, medicine and health sciences, pathology and laboratory medicine, adhd, prader-willi syndrome, angelman syndrome, social sciences, developmental psychology, neuroscience, disorders of imprinting, autism spectrum disorder, developmental neuroscience, neurodevelopmental disorders, gene expression, schizophrenia, pathogenesis, mental health and psychiatry, clinical genetics, psychology, neurology, genetics, biology and life sciences

journal.pcbi.1002460

Genome-Scale Modeling of Light-Driven Reductant Partitioning and Carbon Fluxes in Diazotrophic Unicellular Cyanobacterium Cyanothece sp. ATCC 51142

Cyanothece spp ., are unicellular , diazotrophic cyanobacteria that temporally separate light-dependent oxygenic photosynthesis and glycogen accumulation from N2 fixation at night 1 ., When grown under nutrient excess , Cyanothece sp ., strain ATCC 51142 ( thereafter Cyanothece 51142 ) cells can accumulate significant amounts of storage polymers including glycogen , polyphosphates , and cyanophycin 2 ., The inter-thylakoid glycogen granules are significantly larger in size than those found in other cyanobacteria , which points at an unusual branching pattern and packaging of this compound ., From a biotechnological perspective , this presents an intriguing theoretical possibility to accumulate substantially higher amounts of polyglucose without any significant increase in the number of granules 3 ., Cyanothece 51142 is also of interest for bioenergy applications due to its ability to evolve large quantities of H2 ., Remarkably , H2 production in this organism can occur under light conditions in the presence of O2 and is mediated by nitrogenase 4 , 5 Sequencing of the Cyanothece 51142 genome 6 has enabled application of high-throughput genomic approaches to study the unique physiological and morphological features of this organism ., Transcriptomic and proteomic studies have been conducted to analyze global gene expression patterns under a variety of environmental conditions and infer regulatory pathways that govern the organisms diurnal growth 7 , 8 ., The availability of genomic information also provides means to construct genome-scale constraint-based models of metabolism , which are powerful tools for systems-level analysis and prediction of biological systems response to environmental cues and genetic perturbations 9 , 10 ., Such models have been developed for a variety of biological systems 9 but only in a few studies has this approach been applied to photosynthetic microorganisms , including Synechocystis sp ., PCC 6803 11– , Rhodobacter sphaeroides 14 , and Chlamydomonas reinhardtii 15 , 16 ., However , the modeling of metabolism in oxygenic photoautotrophs is an intriguing problem due to the complexity of photosynthetic and respiratory electron transport chains , and the potential effects of two distinct photosystems upon the generation and fate of reductant and energy that drives the remainder of metabolism ., In this work , we developed the first genome-scale metabolic model of Cyanothece 51142 and used a combination of computation and experimental approaches to investigate how photosynthetic and respiratory fluxes affect metabolism ., Discrete representation of PS II and PS I and their integration with multiple respiratory pathways enabled modeling of photon fluxes and electron flux distributions under conditions of variable light quality and intensity ., The predicted changes in growth rates of Cyanothece 51142 in response to changes in light input were experimentally tested using a photobioreactor with controlled sources of monochromatic 630 and 680 nm light ., We also carried out computational and experimental analyses of light- and nitrogen-limited chemostat growth of Cyanothece 51142 and used mRNA and protein expression data to constrain model-predicted flux distributions ., Both in silico and experimental data suggest that respiratory electron transfer plays a significant role in balancing the reductant ( NADPH ) and ATP pools in the cells during photoautotrophic growth ., This study is a first step towards a systems-level analysis of cyanobacterial metabolism , as it integrates information into a genome-scale reconstruction to understand metabolism qualitatively and quantitatively through a constraint-based analysis 9 ., We also discuss strategies for improving internal flux distributions through integration of in silico simulations and data ., To build a constraint-based metabolic model of Cyanothece 51142 , a genome-scale metabolic network was reconstructed using the genome annotation and data from NCBI 6 , SEED 17 , KEGG 18–20 , and CyanoBase 21 , 22 ., The resulting iCce806 network contains 806 genes and 667 metabolic and transport reactions ( see Dataset S1 and Tables S1 , S2 , S3 for network details ) ., Most of the 42 reactions without genes associated with them were added to complete metabolic pathways needed for biomass production ., The final reconstruction encompasses central metabolic pathways such as the Calvin-Benson cycle , the pentose phosphate pathway ( PPP ) , reactions within the tricarboxylic acid ( TCA ) cycle , as well as , the complete set of anabolic pathways involved in biosynthesis of glycogen , cyanophycin , amino acids , lipids , nucleotides , vitamins , and cofactors ., Pathways for glycolate synthesis ( via ribulose-1 , 5-bisphosphate carboxylase/oxygenase , i . e . , photorespiration ) , glycolate conversion to serine , and glycerol catabolism are also included ., Photosynthetic electron transfer associated with the thylakoid membrane is represented as a set of four separate reactions , including light capture by photosystem II ( PS II ) and photosystem I ( PS I ) , electron transfer between the two photosystems , and cyclic electron transfer around PS I . Similarly , respiratory electron transfer is represented by reactions catalyzed by terminal cytochrome c oxidase ( COX ) , quinol oxidases ( QOX , both bd- and bo-types ) , NADH dehydrogenases ( NDH , type 1 and 2 ) , and succinate dehydrogenase ., In addition , two reactions ( NADP+- and ferredoxin- requiring ) for flavin-dependent reduction of O2 ( i . e . , Mehler reactions ) were included ., A simplified scheme of the photosynthetic and respiratory electron transfer reactions in iCce806 is shown in Figure 1 ., For initial testing , we examined the ability of the constraint-based model of iCce806 to predict growth under photoautotrophic ( using light and fixing CO2 ) , heterotrophic ( using glycerol in the dark ) , and photoheterotrophic ( using glycerol and light ) conditions with different nitrogen sources ., In silico calculated biomass yields , which simulated carbon or light- limited growth ( Figure S1 ) , qualitatively agreed with previously reported growth data for Cyanothece 51142 1 , 2 , 23 ., Other non-growth conditions that were simulated with the model , included nitrogen fixation as occurs during the dark phase of Cyanotheces ciracadian cycle 1 ., In this case , the oxidation of glycogen provides reductant and ATP for nitrogenase , and we examined the models ability to quantitatively predict the amount of nitrogen ( N2 ) that could be fixed and stored in the dark , by maximizing cyanophycin production from glycogen ., Although H2 is an obligate co-product of the nitrogenase reaction , no H2 was produced in the initial simulations under dark N2-fixing conditions , contradicting experimental observations ., Model examination revealed that all of the nitrogenase-generated H2 was utilized by hydrogenases to reduce NAD ( P ) and ferredoxin , which ultimately increased cyanophycin production ., When the three hydrogenase reactions ( HDH_1 , HDH_2 , and UPHYDR ) were eliminated from the model , the predicted ratio of fixed N2 to consumed glycogen depended on the non-growth associated ATP requirement ( NGAR ) , and was estimated to be 0 . 3 ( NGAR\u200a=\u200a2 . 8 ) or 0 . 67 ( NGAR\u200a=\u200a0 ) mole N2/mole glycogen , which was in accordance with an experimentally measured value of 0 . 51 2 ., Under this condition , the model predicted that H2 production would have same yields as fixed N2 ( 0 . 3 to 0 . 67 mole H2/mole glycogen ) due to the stoichiometry of the nitrogenase reaction ., We also evaluated how fluxes through electron transfer reactions are affected by the nitrogenase flux under N2-fixing dark conditions ., With glycogen being the sole source of reductant for both ATP-generating oxidative phosphorylation and N2 reduction , a balance between fluxes through respiratory pathways and nitrogenase reaction is needed ., In the absence of the hydrogenase reactions , the model predicted that O2 reduction via COX , QOX , or Mehler reactions are required to consume NADH resulting from glycogen catabolism ( Figure S2 ) ., The model predicts that the COX reaction is required to achieve the maximum N2 fixation rate since it generates more ATP than the QOX or Mehler pathways ( ∼9 O2 are needed per N2 fixed ) ., This is consistent with the results from recent proteomic studies showing the CoxB1 ( cce_1977 ) subunit of COX is more predominant during the dark 24 , 25 ., These results suggest terminal oxidases are important under dark N2-fixing conditions not only to generate an intracellular anaerobic environment for nitrogenase , but also to provide ATP for nitrogenase activity ., As photosynthesis and respiratory electron transport chains are interconnected in cyanobacteria 26 , these pathways were allowed to interact in the iCce806 model ., To perform model robustness analysis , we computationally explored the impact of key photosynthetic and respiratory pathways on growth rate and intracellular flux distributions under varying photon uptake flux for PS I , while the photon uptake flux for PS II was fixed at 20 mmol·g−1 AFDW·h−1 ( Figure 2 ) ., First , the model was evaluated assuming only linear photosynthetic electron transfer ., In this case , all alternative reductant sinks including the proton and O2 reduction as well as cyclic photosynthetic reactions around PS I were eliminated from the model ( Figure 2A ) ., Under this condition , growth only occurred at one value of photon uptake flux for PS I and extracellular organic products ( ethanol , lactate and/or alanine with trace amounts of formate ) would have to be secreted in order to generate enough ATP to support biomass production ., Second , when cyclic photosynthetic reactions were added back , the photon uptake flux for PS I could vary with a fixed photon uptake flux for PS II , but significant amounts of extracellular products were still formed until the photon uptake flux for PS I exceeded ∼85 mmol·g−1 AFDW·h−1 ( Figure 2B ) ., No growth occurred unless PS I photon uptake flux was greater than or equal to the photon uptake flux for PS II ., Only when the model was allowed to use both cyclic photosynthesis and O2 reduction reactions were no extracellular products predicted and the photon uptake flux for PS I could be less than that for PS II ( Figure 2C ) ., Since experimental data does not indicate that any by-products including H2 or organic acids are produced by Cyanothece 51142 at a detectable level during photoautotrophic growth with excess ammonium , a plausible mechanism for balancing growth through the generation of additional ATP may involve activity of the cytochrome oxidases ., The discrete representation of PS II- and PS I-mediated reactions and their interactions with multiple respiratory reactions in iCce806 enabled further in silico analysis of growth and electron flux distributions under photoautotrophic conditions of variable light quality and intensity ., In this case , the complete model was used to explore which reactions would be used to support maximal photoautotrophic growth rates for different levels of PS II and PS I photon uptake fluxes ., To predict the corresponding growth rates under light-limited conditions , we constrained the photon uptake fluxes ( ranging from 0 to 60 mmol·g−1 AFDW·h−1 ) through each photosystem ., The resulting phenotypic phase plane ( PhPP ) contained three distinct regions ( Figure 3A ) : in two regions growth was limited only by fluxes through PS II ( region 1 ) or PS I ( region 3 ) , while in region 2 growth was limited by both PS II and PS I photon uptake fluxes ( i . e . , increases in either flux would improve growth rate ) ., By adding artificial ATP or NADPH generating reactions ( ADP+HPO4+H→ATP+H2O and NADP+H→NADPH ) to the model and analyzing changes in predicted maximal growth rates , we were able to identify that in regions 1 and 3 growth was NADPH/reductant-limited , while in region 2 it was limited by energy supply ( Figure 3A ) ., To analyze the effect of photon uptake rates on electron flux distributions , we calculated the flux ranges using flux variance analysis ( FVA ) for all photosynthetic and respiration reactions within each PhPP region ( Figure 3B ) ., In this instance , PhPP FVA was run with constraints that restrict the model to a given region and to the maximum growth for each point in the region ( in contrast , standard FVA is used at a single point in a region ) ., Using PhPP FVA , we identified active ( both minimum and maximum flux values are positive or negative ) , inactive/blocked ( minimum and maximum fluxes are both zero ) , and optional ( which could have at least one zero and one non-zero flux value somewhere in the region ) reactions leading to optimal solutions in each PhPP region ., This new analysis technique allowed classification of reaction usage across entire regions of the PhPP and is not restricted to fixed points within a region ., While linear photosynthesis was active and Mehler reactions were blocked across the entire PhPP , there were differences in the usage of photosynthetic and respiratory reactions observed within all three regions ( Figure 3B ) ., Surprisingly , while generation of NADPH from reduced ferredoxin via linear photosynthesis is the key source of reductant , ferredoxin-NADP+ oxidoreductase ( FNR ) was predicted to be active in region 2 , but optional in regions 1 and 3 ., Closer examination of in silico calculated electron flux distributions revealed that , in addition to FNR , the model utilized a cycle involving glutamine synthetase , glutamate synthase and transhydrogenase , resulting in ATP-driven NADPH production ., In regions 1 and 3 , the model predicts there is excess ATP , and so this cycle can be used instead of FNR to move electrons from ferredoxin to NADPH ., However , this cycle is unlikely to be of any physiological relevance since there has been no experimental data supporting this route for making NADPH , and FNR is essential for photoautotrophic growth in unicellular cyanobacteria such as Synechococcus 7002 27 ., Differences in the predicted usage of respiratory reactions were also found ., In region 1 , where growth is limited by the flux through PS I , at least one of the COX and QOX reactions must be active to oxidize excess electron carriers ( Pc , Cyt c6 , or Pq ) generated from PS II ., Similarly , in region 3 under PS II flux limitation , excess electron carriers ( Pq , Fd ) must be reduced via NDH-1 or –2 or ferredoxin-dependent cyclic electron transfer ( FdPq ) ., Conversely , due to ATP limitation in region 2 , the model favored reactions with higher proton pumping capacities and so both the QOX and FdPq reactions were inactive ., The usage of COX was optional in region 2 and depended on photon uptake rates ( e . g . , COX reaction was inactive at the boundary between regions 2 and 3 ) ., The model predictions ( Figure 3A ) were compared to batch growth experiments in the LED-photobioreactor which allowed instantaneous measurements of initial growth and photon uptake rates by Cyanothece 51142 cells exposed to different intensities and ratios of 630 and 680 nm light ( Table 1 ) ., When Cyanothece 51142 cultures were illuminated with both 630 nm and 680 nm light , initial growth rates generally correlated with the total photon flux through PS II and PS I , with higher growth rates observed at 80 mmol·g−1 AFDW·h−1 total photon flux and 630 nm∶680 nm light ratio of 2∶1 ., When cultures were exposed to only a single wavelength of light ( batch experiments 6–10 ) , i . e . , either 630 or 680 nm , Cyanothece 51142 cells displayed a similar trend with higher growth rates observed at higher photon flux intensities ., The predicted growth rates were within 7% of the experimentally measured values , except for the two cases where single 630 nm wavelength irradiances were used ( Table 1 ) ., The reasons for this are unclear but may be due to other physiological and/or biochemical phenomena such as state transitions that are not contained within the model but are operating in vivo ., Data from these batch experiments ( batch experiments 1–5 , Table 1 ) were also used to estimate the growth ( GAR ) and non-growth ( NGAR ) associated ATP requirements ., NGAR is the amount of energy spent to maintain the cell ( i . e . , maintenance energy ) ., GAR is defined as energy expenditures used on protein and mRNA turnover or repair , proton leakage , and maintenance of membrane integrity; it does not include ATP spent on polymerization reactions , which are already accounted for in the macromolecular synthesis pathways of the network ., The time-averaged growth and photon uptake rates were used to constrain the model and the maximal amount of ATP hydrolysis was calculated ( Figure S3 ) for each batch experiment ., A plot of growth rate versus maximum ATP hydrolysis flux was generated and a linear fit used to estimate the GAR and NGAR values 28 ., Specifically , the slope of the fitted line is the GAR ( 544 mmol·g−1 AFDW·h−1 ) , and the y-intercept is NGAR ( 2 . 8 mmol·g−1 AFDW·h−1 ) ., The estimated GAR value is significantly higher than those reported from other bacteria 29; however , these model estimates assume that all absorbed photons lead to photosynthetic fluxes ( 100% quantum efficiency ) and that the overall efficiency of ATP production via all electron transfer reactions ( photosynthetic and respiratory ) are accurate ., Depending on the growth condition the quantum yields can change , and for Cyanothece 51142 this value was reported to be between ∼70–100% for photoautotrophic growth 23 ., Upon further analysis , we found the estimated Cyanothece ATP requirements were most sensitive to reductions in quantum efficiency and the amount of ATP generated by photosynthesis and respiration ( Table S4 ) ., Since neither quantum efficiency nor combined photosynthetic and respiratory ATP production were experimentally measured for Cyanothece 51142 , the original estimates , GAR\u200a=\u200a544 and NGAR\u200a=\u200a2 . 8 were used in all growth simulations ., Chemostat cultures grown under light and ammonium limitations were used to calculate metabolic fluxes and further understand reductant partitioning pathways in Cyanothece 51142 ., The differences in biomass composition between these growth conditions indicated a major shift in carbon partitioning pathways ( Figure 4; and Table S5 ) ., In ammonium limited cultures , carbohydrates comprised almost half of cell biomass; in contrast , under light limitation , Cyanothece 51142 cells contained higher amounts of protein , nucleic acids , and approximately 10% cyanophycin ., The quantitative biomass composition measurements were used to generate two separate biomass equations for the metabolic model; experimentally measured growth rate , photon uptake rates , and O2 production rates were included as additional model constraints ( Table S6 , in this case no mRNA or protein expression data is used by the model ) ., Using FBA and through minimization of the overall flux magnitude , we calculated representative flux distributions under light and ammonium limitations ( values listed in Table S1 ) ., As expected , changes in flux values were consistent with differences in measured biomass compositions used in the simulations: under light limitation , fluxes increased for reactions involved in biosynthesis of amino acids , nucleotides and cyanophycin , while ammonium limitation resulted in flux increases for glycogen biosynthesis ., Comparisons of global transcriptome profiles displayed by Cyanothece 51142 during ammonium- and light-limited chemostat growth also reflected the rewiring of cellular metabolism ( Table S7 ) ., Under ammonium limitation , significant increase in relative mRNA abundances was observed for genes involved in N2 fixation ( cce_0198 , cce_0545–0579 ) , iron acquisition ( cce_0032–0033 , cce_1951 , cce_2632–2635 ) , respiratory electron transport ( cce_1665 , cce_3410–3411 , cce_4108–4111 , cce_4814–4815 ) as well as peptide transport , synthesis , and protein repair ( cce_0392 , cce_1720 , cce_3033 , cce_3054–3055 , cce_3073–3075 ) ., Among the most highly expressed genes in ammonium-limited Cyanothece 51142 cells was the one encoding 6-phosphogluconate dehydrogenase ( cce_3746 ) , a key PPP enzyme ., Under light limitation , the major changes in the transcriptome of Cyanothece 51142 included upregulation of genes encoding: components of the photosynthetic apparatus and electron transport chain ( cce_0776 , cce_0989–0990 , cce_1289 , cce_2485 , cce_2959 , cce_3176 , cce_3963 ) ; pigment biosynthesis ( cce_0920 , cce_1954 , cce_2652–2656 , cce_2908 , cce_4532–4534 ) ; CO2 uptake and fixation machinery ( cce_0605 , cce_3164–3166 , cce_4279–4281 ) ; ATP synthase ( cce_2812 , cce_ 4485–4489 ) , and protein synthesis machinery ( cce_ 4016–4030 ) ( Table S7 ) ., Global proteome profiles of Cyanothece 51142 corroborated the shifts in gene expression ( Table S8 ) ., The abundance of proteins from central metabolism ( glycolysis , TCA , and pentose phosphate pathway ) all had significant differences between cells grown under ammonium- and light-limited conditions ., Enzymes of the oxidative PPP branch , namely glucose-6-phosphate dehydrogenase ( cce_2535–2536 ) , 6-phosphogluconolactonase ( cce_4743 ) and 6-phosphogluconate dehydrogenase ( cce_3746 ) , showed increased abundances under ammonium limited conditions ., Similarly , two-fold increase in abundance levels was observed for gluconeogenesis proteins , including fructose 1 , 6-bisphosphatase ( cce_4758 ) , glucose-6-phosphate isomerase ( cce_0666 ) , glyceraldehyde-3-phosphate dehydrogenase ( cce_3612 ) , and phosphoglycerate kinase ( cce_4219 ) ., In contrast , relative abundances of proteins catalyzing the conversion of glycerate-3P to pyruvate ( cce_1789 and cce_2454 ) were unchanged or up-regulated ( pyruvate kinase cce_3420 ) in light-limited cells ., Consistent with the results from global mRNA profiles was the up-regulation of Cyanothece 51142 proteins involved in photosynthesis and carbon fixation under light-limited conditions ( Table S8 ) ., Notably , two key components involved in the electron transfer to PS I , namely plastocyanin ( cce_0590 ) and cytochrome b6 ( cce_1383 ) , displayed elevated peptide abundances in light-limited cells ., Since there may be more than one flux distribution that is consistent with the experimentally measured rates of growth , photon uptake , and O2 production we used FVA to identify required ( flux must be non-zero ) , optional ( flux may or may not be zero ) , or inactive ( flux must be zero ) reactions under light- and ammonium-limited growth conditions ., As our initial simulations ( Table 2 ) produced a large number of optional reactions ( 170 out of 667 for both growth conditions ) , that represent uncertainty regarding usage , we subsequently used the transcriptome and proteome data ( TPD ) to further constrain the model ., Using a modification to a previously developed approach 30 , we obtained a flux distribution that was consistent with measured rates and TPD while reducing the overall flux magnitude ( Table S1 ) ., In this analysis , flux was favored through reactions for which proteins were detected and disfavored through reactions associated with undetected proteins and transcriptome data less than a given threshold ( e . g . , log2 of mRNA expression level is less than 8 ) ., The model constrained by TPD predicted that the majority of reactions in central metabolism would be active under both chemostat conditions ( Figure 5 ) ., In addition , we subsequently applied FVA employing additional constraints arising from the TPD ., Comparison between FVA results with and without TPD constraints demonstrated a significant decrease in the number of ambiguities ( the optional reaction set ) when TPD is used ( Table 2 ) ., While the number of optional reactions was reduced by incorporating TPD into the model , the flux spans ( difference between maximum and minimum values ) of individual fluxes was still large ( >30 mmol·g−1 AFDW·h−1 for some central metabolic reactions , Table S1 ) ., These large flux spans could arise from cycles or alternative pathways in the model , and deleting these features from the model could subsequently reduce the flux spans ., FVA was repeated using measured growth , photon uptake , and O2 release rates under light-limited conditions as constraints and with optional reactions were deleted ( similar results were found for ammonia limited conditions , data not shown ) ., Flux spans for reactions in central metabolism ( Figure 5 ) were then calculated for a series of single or double reaction deletions in silico ., The purpose of this analysis was to identify those reactions that exert the greatest impact on the flux span in central metabolism ( Figure 6A ) ., Single deletions of glyceraldehyde-3-phosphate dehydrogenase ( GAPD or GAPD_NADP ) or hydrogenase ( HDH_1 ) reduced the average central metabolic flux span the most ( from 74 to 22 mmol·g−1 AFDW·h−1 ) ., Other single deletions with significant effects included FNR and NDH-1 , which are involved in photosynthesis and respiration ., The reaction deletions shown in Figure 6A all had a larger impact on reducing average central metabolic flux span than did imposition of constraints based on TPD ., There were cases where single deletions had large effects on other specific reactions , but only modest effects on overall central metabolic flux spans ., For example , a single deletion in phosphogluconate dehydrogenase ( PGDHr ) reduced the span for glucose-6-phosphate isomerase flux ( PGI ) to 0 ( Figure 6B ) , but only reduced the average central metabolic flux span by ∼0 . 7 mmol·g−1 AFDW·h−1 ., The in silico analysis of double reaction deletions did not yield any new double deletions that would reduce the average central metabolic flux span significantly ., However , some double deletions strategies did reduce flux spans of individual reactions ., Several cyanobacterial metabolic models ( all for Synechocystis PCC 6803 ) have been published , which represented photosynthesis as two lumped reactions 12 , 31 for linear ( PSII , Cyt b6f , PSI , and FNR ) and cyclic ( PS I and Cyt b6f ) pathways ., In this study , we modeled photosynthesis as a larger set of separate reactions 13 as this structuring allowed analysis of the effects of different illumination on the production and partitioning of reductant through photosynthetic and respiratory reactions , as well as the contribution of different electron transfer pathways to growth ., Our PhPP FVA results showed how different photosynthetic and respiratory electron transport chain components are used to maximize biomass production under different lighting regimes ., It was not surprising that linear photosynthesis was active in all three regions because the cell needs photons from both PSI and PSII to grow under photoautotrophic conditions ., However , the Mehler reactions were inactive in all three regions when we only consider maximal growth rate solutions ., In regions 1 and 3 , reducing equivalents ( e . g . , NADPH ) limit growth and the Mehler reactions would lower the amount of reducing equivalents available for growth ., The Mehler reactions are less energetically efficient than NADH dehydrogenase and cytochrome oxidase so the model would not use them in region 2 , where ATP is limiting ., So while the Mehler reactions can carry flux in the model , using these reactions lowers the maximum growth rate making them inactive ( blocked reactions ) in our PhPP analysis ., A recent study showed that the Mehler reactions are operational in Synechocystis sp ., PCC 6803 , serving as a sink for excess electrons 32 ., These reactions are also likely to be active in Cyanothece 51142 , since the associated proteins were detected in the proteomic data ( Table S8 ) ., As a result the model only predicted non-zero Mehler fluxes when the proteomic data were used to constrain the model ( Table S1 ) ., In the absence of cyclic photosynthesis , other products including water ( produced by COX , QOX or Mehler reactions ) , H2 ( via hydrogenase ) , or small organic compounds ( alanine , ethanol , lactate and formate ) were predicted to be necessary in order to balance the electrons and ATP needed to support growth ., In the presence of linear and cyclic photosynthesis reactions , these products must also be produced unless significant amounts of cyclic photosynthesis occurs ( >3 times the amount of linear photosynthesis ) ., Since H2 and small organic compounds are not generally produced under photoautotrophic conditions with excess ammonium , any additional energy is most likely supplied by cytochrome oxidase activities that reduce photosynthetically produced O2 ., Interestingly , in the absence of cytochrome oxidase activities in the model , the PS I fluxes must always be greater than or equal to the PS II fluxes ., It was shown that the marine cyanobacteriium Synechococcus has a PS I/PS II protein ratio >1 , which has been explained as a mechanism to protect PS II from photo-damage 33 ., Under conditions with high levels of PS II activity , cytochrome oxidase activity may ensure an adequate supply of oxidized plastoquinone ( needed for PS II ) and reduce O2 concentrations to limit photorespiration ., Similarly , cyclic electron flow via NADH dehydrogenase- or ferredoxin-dependent routes have also been experimentally demonstrated to play important roles in balancing the amount of NADPH and ATP produced via photosynthesis ., Synechocystis 6803 mutants lacking ndhD genes ( encoding subunits of NDH-1 ) had significantly lower cyclic photosynthesis activity 34 ., Although the mechanism of electron transfer from ferredoxin to the plastoquinone pool ( without using NDH ) is still unclear , its activity has been demonstrated in green algae 35 and higher plants 36 ., Our computational simulations also showed that , under light-limited photoautotrophic conditions , cyclic electron transfer involving NADH dehydrogenase ( NDH-1 ) is needed for maximal growth if ATP ( rather than NADPH ) is limiting ., In an environment where PS I photon availability is high relative to PS II , cyclic electron transport is needed ( Figure, 2 ) to increase availability of PS I substrates ( reduced PC or Cyt c6 ) and protect against photo-damage ., Cyclic electron flow has been experimentally shown to help protect the photosynthetic apparatus from photo-damage 37–39 In addition to studying the interactions between components of the photosynthetic and respiratory components computationally , we also experimentally evaluated cells grown under continuous light conditions in light- and ammonia-limited chemostats ., The measured 630 nm and 680 nm photon uptake and O2 production rates suggests that reductant was being directed towards O2 via the Mehler , QOX , and/or COX reactions ., In both chemostat conditions , the model predicted that steady-state growth rate could have been achieved using lower photon uptake rates by decreasing the amount of reductant generated by PS II that was predicted to reduce O2 ., A limitation to flux balance analysis is that a wide range of flux values may be consistent with the constraints in the computational model ., An iterative application of computational and experimental methods is an important strategy to improve the comprehensive understanding of cyanobacterial metabolism ., We have begun to apply this iterative approach , by including mRNA and protein expression datasets as additional constraints beyond biomass composition and physiological rate measurements ., Experimentally-measured TPD were successfully used to further constrain the model , and thereby reduce uncertainty and increase the number of required ( that is , metabolically active ) reactions ( Table 2 ) ., However , there remained discrepancies in that the model did not predict flux through all reactions for which proteins were experimentally detected ., Such discrepancies can be used to subsequently improve the model with previously developed approaches 40–42 ., For example , an earlier version of the m

Introduction, Results, Discussion, Material and Methods

Genome-scale metabolic models have proven useful for answering fundamental questions about metabolic capabilities of a variety of microorganisms , as well as informing their metabolic engineering ., However , only a few models are available for oxygenic photosynthetic microorganisms , particularly in cyanobacteria in which photosynthetic and respiratory electron transport chains ( ETC ) share components ., We addressed the complexity of cyanobacterial ETC by developing a genome-scale model for the diazotrophic cyanobacterium , Cyanothece sp ., ATCC 51142 ., The resulting metabolic reconstruction , iCce806 , consists of 806 genes associated with 667 metabolic reactions and includes a detailed representation of the ETC and a biomass equation based on experimental measurements ., Both computational and experimental approaches were used to investigate light-driven metabolism in Cyanothece sp ., ATCC 51142 , with a particular focus on reductant production and partitioning within the ETC ., The simulation results suggest that growth and metabolic flux distributions are substantially impacted by the relative amounts of light going into the individual photosystems ., When growth is limited by the flux through photosystem I , terminal respiratory oxidases are predicted to be an important mechanism for removing excess reductant ., Similarly , under photosystem II flux limitation , excess electron carriers must be removed via cyclic electron transport ., Furthermore , in silico calculations were in good quantitative agreement with the measured growth rates whereas predictions of reaction usage were qualitatively consistent with protein and mRNA expression data , which we used to further improve the resolution of intracellular flux values .

Cyanobacteria have been promoted as platforms for biofuel production due to their useful physiological properties such as photosynthesis , relatively rapid growth rates , ability to accumulate high amounts of intracellular compounds and tolerance to extreme environments ., However , development of a computational model is an important step to synthesize biochemical , physiological and regulatory understanding of photoautotrophic metabolism ( either qualitatively or quantitatively ) at a systems level , to make metabolic engineering of these organisms tractable ., When integrated with other genome-scale data ( e . g . , expression data ) , numerical simulations can provide experimentally testable predictions of carbon fluxes and reductant partitioning to different biosynthetic pathways and macromolecular synthesis ., This work is the first to computationally explore the interactions between components of photosynthetic and respiratory systems in detail ., In silico predictions obtained from model analysis provided insights into the effects of light quantity and quality upon fluxes through electron transport pathways , alternative pathways for reductant consumption and carbon metabolism ., The model will not only serve as a platform to develop genome-scale metabolic models for other cyanobacteria , but also as an engineering tool for manipulation of photosynthetic microorganisms to improve biofuel production .

biology, computational biology

journal.ppat.1003591

Chikungunya Virus 3′ Untranslated Region: Adaptation to Mosquitoes and a Population Bottleneck as Major Evolutionary Forces

Genetic change , which can lead to adaptation to new hosts or vectors , is a major cause of the emergence or re-emergence of arthropod-borne viral ( arboviral ) and other RNA viral diseases 1 , 2 ., However , compared to the numerous investigations of point mutations within viral genomic open reading frames , the evolution and determinants of fitness of untranslated genome regions ( UTRs ) have received far less attention ., The 3′ UTRs of arboviral genomes exhibit large size variations , ranging from ∼100 nt to more than 700 nt , and involving extensive substitutions , insertions and deletions even within viral species ., This length variation suggests that the heterogeneous regions may not be essential for replication , a view supported by experimental studies with genetically engineered viruses lacking a large part of the 3′UTR that remain viable , albeit with different levels of attenuation 3–8 ., However , these seemingly redundant sequences must play some role favored by natural selection , because otherwise longer genomes should theoretically be less efficiently replicated ., Improved understanding of the forces driving the evolution of the arboviral 3′UTR is needed to provide important insights on its role on pathogenesis and host/vector adaptation ., An interesting observation is that the variable region in the 3′UTR often contains direct repeats ( DRs ) in the arboviral genera Alphavirus 9 , 10 and Flavivirus 11 ., These DRs can be relatively conserved in closely related viruses , indicating that repeat duplication may serve as a major evolutionary mechanism for the 3′UTR , and that these DRs may have functional significance ., Indeed , sequence comparisons of flaviviruses suggest that duplication of long repeat elements ( LREs ) and extensive deletions are the main evolutionary mechanisms of the 3′UTR 12 , 13 ., Despite their high level of sequence diversity , secondary structure predictions suggest that the flavivirus 3′UTR comprises enriched stem-loop structures , with some conserved structural motifs observed in all species , suggesting functional selection 14 ., Furthermore , sequence comparisons of different eco-groups suggest that mosquito-borne flaviviruses , which usually use multiple invertebrate and/or vertebrate host species , have a more diverse 3′UTR than tick-borne or non-vector-borne flaviviruses , which have more limited host ranges and/or transmission dynamics 11 ., This raises the hypothesis that the DRs may interact with host/vector factors to maintain efficient replication in multiple hosts , and to facilitate the adaptation to new hosts ., An interaction between viral 3′UTR and host proteins has been indicated by several kinds of data ., First , the 3′UTRs in both alphaviruses 15–18 and flaviviruses 19 interact with cellular proteins ( in both mosquito and mammalian cells ) to directly or indirectly facilitate genome replication ., It has been observed that several alphaviruses usurp the cellular HuR protein , which enhances mRNA stability and therefore inhibits viral RNA decay 16–18 ., Additionally , arboviral 3′UTRs can encode microRNAs ( miRNAs , such as observed in West Nile virus ) which regulate cellular gene expression to enhance viral replication 20 ., Finally , flaviviruses generate one or more small subgenomic flavivirus RNAs ( sfRNAs ) , which are essential for pathogenicity in vertebrate cells and in mice 21 ., These sfRNAs are collinear with the 3′UTR and produced by incomplete viral RNA degradation by the host 5′-3′ exonuclease XRN1 , mediated by pseudoknot ( PK ) structures upstream of the 3′UTR 22 , 23 ., Their observed functions include:, 1 ) regulation of antigenome synthesis 24 ,, 2 ) inhibition of the cellular exoribonuclease XRN1 and alteration of host mRNA stability 25 ,, 3 ) evasion of the Type I Interferon response 26 , and, 4 ) RNA inference suppression in both mammalian and insect cells by inhibiting Dicer-mediated in vitro cleavage of double-stranded RNA 27 ., Despite these advances in understanding the functional roles of arboviral 3′UTRs , there is no solid evidence to relate the occurrence of indels , which appear to occur frequently during their evolution , with any particular adaptation to a given host or vector ., The extensive within-species diversity in the 3′UTR of the alphavirus chikungunya virus ( CHIKV ) , especially lineage-specific DR patterns ( revealed in this study; Fig . 1 ) , is unique within this genus of mainly mosquito-borne viruses ., Together with prior reconstructions of CHIKV evolutionary history 28 , as well as the relative sequence conservation and comparability among lineages , this diversity in 3′UTR sequences provides a unique opportunity for understanding their evolution and functional importance ., Chikungunya virus ( Togaviridae: Alphavirus ) is transmitted among nonhuman primates and humans via Aedes spp ., mosquitoes ., It causes chikungunya fever , a febrile illness associated with debilitating arthralgia and rash 29 ., Chikungunya virus has a single-stranded , positive sense RNA genome of ∼12 kb , including a notably long 3′UTR ranging from ∼500 to 700 nt ., Enzootic in tropical and subtropical regions of Africa , CHIKV has emerged several times into a human-mosquito urban cycle to cause major epidemics both within and outside of Africa ., Phylogenetic analyses suggest that the currently circulating CHIKV strains form three major geographic lineages , namely the entirely enzootic West African lineage , the East , Central and South African ( ECSA ) enzootic lineage , which includes the recently emerged epidemic strains responsible for Indian Ocean basin and Asian outbreaks , and the Asian lineage 28 , which has been circulating in an Aedes aegypti-human cycle for over 50 years ., Interestingly , CHIKV genome comparisons suggest lineage-specific 3′UTR structures , with the Asian lineage exhibiting a unique pattern of mutation , duplication , and insertion ( Fig . 1 ) ., Although the most recent common ancestor of the Asian lineage is estimated to have occurred in the early 1950s , just before the 1956 Thailand outbreak , it is not clear when this lineage was introduced from eastern Africa into Asia or whether the distinct mutations and structural rearrangement in the Asian 3′UTR occurred before or after this introduction and establishment of the urban cycle ., It is also unknown whether this novel Asian 3′UTR structure is the result of adaptation to the urban transmission cycle there Although CHIKV antibodies have been detected in nonhuman primates in Asia 30 , spillback from human transmission cycles is difficult to rule out such as to the urban vector A . aegypti implicated in all Asian outbreaks prior to 2007 ., To address these questions related to evolution of the 3′UTR and its potential influence on the epidemic potential of the Asian CHIKV strains , we dissected the inferred structural changes in the 3′UTR of the Asian CHIKV lineage and explored their effects on the replication in vertebrate hosts and vectors ., Our findings provide important insights into the functional role of the mosquito-borne arbovirus 3′UTR ., To explore the repeat structure in the CHIKV genome , DNA matrix comparisons were conducted based on representative strains from each of the three major lineages ( West Africa , ECSA and Asian ) ., The results suggested that the 3′UTR , but not other genome regions , contains multiple DRs , with the Asian lineage having a distinct pattern ( Fig . S1 ) ., Imposing these repeat patterns onto the rough sequence alignment generated from the guide tree based on the complete open reading frame sequences led to a refined and reliable alignment with striking lineage-specific structures and minor indels within each lineage ., The complete sequence alignment is available upon request , with a simplified version shown in Figure S2 ., As illustrated in Fig . 1 , the CHIKV 3′UTR contains two DR elements consistent in the West African and ECSA lineages , namely DR1 ( 39 nt , two copies ) and DR2 ( 62 nt , 3 copies ) ., However , the Asian 3′UTR is distinct , including, 1 ) a long insertion ( 193 bp ) near the 3′ end , which is the result of the direct duplication of its 5′-adjacent region ( Fig . 1 , shaded blue ) , and, 2 ) accumulated mutations ( point mutations and insertions ) around DR2a , including the DR1a region , and the duplication of this entire region hereafter designated as DR ( 1+2 ) to replace the DR1b/DR2b region , or vice versa ., Previous studies annotated the alphavirus 3′UTR into three repeat sequence elements ( RSEs ) and a 19 nt conserved sequence element ( CSE ) at the 3′ end 9 ., The DR2 found in our study corresponds to RSEs defined previously 18 ., However , DR1 and DR3 have not been described previously , and we found the 19 nt CSE is not strictly conserved , with occasional mutations that change its length observed among sequences ., Interestingly , DR3 is immediately adjacent to the 19 nt CSE which is believed essential in viral replication 31 ., To determine if these DRs form any structural/functional units , as suggested in flaviviruses 11 , 14 , RNA secondary structures were predicted via Mfold 32 , STAR 33 , and Vienna 34 , 35 , at both 37°C ( typical primate host temperature ) and 28°C ( typical mosquito vector temperature in the tropics ) ., These programs generated slightly different secondary structures ( data not shown ) ., In most predictions the folds of different copies of DR1 and DR 2 differed , and the overall structures generally differed at 28° and 37°C ( except those estimated by Vienna ) ., Figure S3 shows a sample of top ranked structures produced by Mfold ., In summary , there are many short stem-loop structures distributed throughout 3′UTR , and they form the basis for a higher-level secondary structure ., Despite the many stem-loop structures in DR1 and DR2 , the repeating elements themselves may not necessarily correspond to a specific uniform structure ., This interpretation is consistent with previous analyses of flaviviruses 11 ., According to Mfold , this region may also fold into different structures at 37°C and 28°C , with the former more compact and the latter looser ., In contrast , the DR3 region is relatively conserved and contains a distinct Y-shaped structure of 80 nt ( Fig . S3 , dark blue ) conserved in all three CHIKV lineages at both 37°C and 28°C , suggesting its functional importance ., Notably , duplication of DR3 in the Asian lineage added another copy of this Y-shaped structure ., To evaluate the effect of the 3′UTR on CHIKV replication , we engineered a series of mutant viruses based on two wild-type ( wt ) CHIKV strains , the Mal06 strain ( MY002IMR/06/BP; GenBank Acc . No . EU703759 . 1 ) , a representative of the Asian lineage , and the SL07 strain ( SL-CK1; GenBank Acc . No . HM045801 . 1 ) , representing the ECSA lineage ., Fig . 2A illustrates the genetic organization of the engineered viruses , including, 1 ) the wt Mal06 and SL07 ,, 2 ) modified version of each including synonymous mutations as genetic markers ,, 3 ) chimeras of each strain with swapped 3′UTRs , and, 4 ) Mal06 variants with either 1 or 2 copies of DR ( 1+2 ) or DR3 deleted ., Their fitness levels were first compared through replication kinetics in both Vero ( African green monkey ) and C6/36 ( A . albopictus ) cells , and then further evaluated through competition tests in vivo ., Comparing the replication kinetics of these CHIKV strains provided interesting insights into the role of the 3′UTR in mammalian and insect cells ( Fig . 2B ) ., First , viruses from both the ECSA and Asian lineages replicated rapidly , reaching peak titers 24–48 h post-infection ., Variants derived from the ECSA SL07 strain showed significantly ( ∼3–4 log difference ) higher replication in both C6/36 and Vero cells ., These results were consistent with the observation that the IOL lineage strains have been rapidly replacing the local Asian lineage strains in Southeast Asia since 2006 36 ., Our finding that chimeric viruses with 3′UTRs derived from a different lineage exhibit no significant fitness change in Vero cells and only a slight fitness change in C6/36 also suggested that the ORF , rather than the 3′UTR , is the main determinant of CHIKV replication efficiency ., Strikingly , the Asian Mal06 strain variants with swapped 3′UTRs exhibited significantly different replication kinetics in C6/36 cells , with RNA copies per ml ranging from 8 . 2×102 Mal06 with deletion of 2 copies of DR ( 1+2 ) to 1 . 2×106 ( wt ) at 12 h post-infection , and 3 . 5×104 Del-DR ( 1+2 ) ab to 9 . 2×106 ( wt ) at 24 h post-infection , respectively ., In contrast , no significant difference was observed for any ML06 variants at either 24 ( 2 . 4–4 . 7×107 copies/mL ) or 48 h post-infection ( 4 . 8–8 . 9×107 copies/mL ) in Vero cells , indicating that the CHIKV 3′UTR may play a more important role in interacting with cell factors in mosquito than mammalian hosts ., These results also suggest that genetic variation in the 3′UTR does not have a major effect on its interaction with viral proteins and/or RNA ., Consistent with reports from other arboviruses 3–8 , CHIKV with only a partial 3′UTR was still viable in cell cultures , but with altered replication kinetics , especially in mosquito cells ., In C6/36 cells , deleting either one or both copies of DR ( 1+2 ) led to a significant reduction in the replication rate , with the deletion of both copies having the most severe effect ., These data indicate that the role of DR ( 1+2 ) does not rely on the presence of two copies , such as in the formation of a dimer proposed previously 12 ., Similarly , deleting both copies of DR3 in the Asian Mal06 strain also led to a severe reduction in its replication rate in mosquito cells ( Fig . 2 ) ., The fitness loss from deletion of both DR3 copies was intermediate between that resulting from deleting one copy of DR ( 1+2 ) and deleting both DR ( 1+2 ) copies ., Interestingly , deleting only one copy of DR3 from the Mal06 strain did not significantly change its replication kinetics in C6/36 cells ., Due to the intrinsic experimental error caused by, 1 ) small variations in the viral titer of initial inocula ,, 2 ) variation in cell density among triplicate test samples , and, 3 ) RNA quantification , competition tests were conducted to more sensitively compare fitness levels between virus pairs ., To determine the underlying reason for the fixation of Asian lineage CHIKV 3′UTR , we evaluated its fitness for infection and dissemination in the main Asian mosquito vector ( 1950s–2007 during evolution of the Asian lineage ) , A . aegypti , and viremia in the surrogate vertebrate host , CD1 mice infected at 11–12 days of age 37 using competition experiments ., The Mal06 variants containing a deletion of one copy of either DR ( 1+2 ) or DR3 were competed against the genetically marked wt strain , and the results are shown in Fig . 3 ., Control competitions indicated that the synonymous genetic marker did not significantly influence viral fitness in mosquitoes 1 or CD1 mice ( Fig . 3B ) ., Strikingly , deleting DR3a in the Asian Mal06 strain produced contrasting effects in mosquito versus mammalian hosts ., In mosquitoes , only wt virus was detected from the majority ( 23/30 ) of the mosquito heads 10 dpi after a mixed bloodmeal , whereas virus with only one copy of DR3 was found only in 5/30 samples , with two of them showing a mixture of both competitors ( Fig . 3C ) ., In contrast , Mal06 with a deletion of DR3b outcompeted the wt virus in CD1 mice , as indicated by virus ratios in all the blood samples taken 1–2 days post-infection ( dpi; Fig . 3C ) ., This result was consistent with our hypothesis that the direct effects of the 3′UTR can outweigh the potential detrimental effects of genome length to determine CHIKV fitness ., Similarly , deleting one copy of the DR ( 1+2 ) led to a significant CHIKV fitness loss in mosquitoes , and a slight advantage in mice ., Specifically , following mosquito infection and dissemination , and assay at 10 dpi , 14 of 15 infected mosquito heads contained only the wt Mal06 virus , whereas only one was infected by the mutant Mal06/ΔDR ( 1+2 ) a ( Fig . 3D ) ., Despite the nearly equal RNA ratio between the two viruses during the first two days after infection of mice , the mutant virus with only one copy of DR ( 1+2 ) , and thus shorter genome , showed a significantly higher prevalence at 3 dpi , indicating its selective advantage at later stages of infection ( Fig . 3D ) ., The less dramatic effect of deleting DR ( 1+2 ) a compared to DR3a may be due to its shorter length ( 155 vs . 193 nt ) ., In addition , the initial inoculum ratio of Mal06/ΔDR ( 1+2 ) a was slightly lower in the competition test in CD1 mice , which may also have influenced the outcome ., In conclusion , retaining two copies of DR ( 1+2 ) provided a selective advantage to CHIKV in mosquitoes but not in vertebrates , compared to only one copy ., Despite the selective advantage for the infection and dissemination in mosquitoes of having two DR3 copies , it is not clear whether the fixation of the current CHIKV Asian lineage 3′UTR was due to an improved fitness level compared to its ECSA ancestor , which parsimony analysis predicted shared the 3′UTR seen in extant ECSA strains ., To address this question , chimeric viruses were generated with backbones from the Mal06 strain ( Asian lineage ) and SL07 strain ( ECSA lineage ) and swapped 3′UTRs , and their relative fitness levels were compared using competition tests ., Surprisingly , the chimeric virus Mal06/SL07 3′UTR outcompeted the Mal06 wt strain in both mosquitoes and mice ( Fig . 3E ) ., In contrast , the chimeric virus with the SL07 backbone and the Mal06 3′UTR exhibited lower fitness than its wt ECSA counterpart ( Fig . 3F ) ., Investigations of the function and evolution of 3′UTRs in arboviruses , including repeat identifications 9 , 10 , 13 and secondary structure predictions 12 , 14 , 38 , as well as experimental studies 3–8 , 39 , 40 , have taken place for many years ., However , ongoing positive selection on the 3′UTR has never been observed in ‘real-time , ’ and the role of the 3′UTR remains poorly understood ., A distinct 3′UTR sequence pattern was observed in all Asian lineage CHIKVs sampled from 1958 to 2009 ., As shown in Fig . 1 , this Asian lineage 3′UTR contains an insertion of 193 nt at the 3′ end , the result of a direct duplication of the 5′end of the adjacent UTR region ., In addition , both copies of direct repeats of DR ( 1+2 ) contain the same accumulated mutations found in the corresponding ECSA and West African UTRs ., No intermediate 3′UTR form has been observed in CHIKV sequences from other lineages , raising the interesting questions: When and what caused the unique pattern of the Asian 3′UTR , and why did it become fixed in Asia ?, Was the current Asian lineage 3′UTR formed before or after its introduction into Asia ?, If it was formed before the introduction , why has it apparently disappeared from Africa ?, Or if it was formed after the introduction into Asia , what fitness advantage did it provide over the ancestral UTR ?, Our results offer some insights into these questions ., First , the 3′UTR from the ECSA lineage has significantly higher fitness than that of the Asian lineage in both A . aegypti and mice when placed into either the ECSA or Asian genetic backbone ., This suggests that the fixation of the Asian 3′UTR was not due to an increased fitness level compared to its ancestor , and was not likely a result of directional ( positive ) selection ., Next , the duplication of the Asian DR3 imparts increased fitness in mosquitoes , indicating its selective advantage in transmission ., Similarly , the deletion of one copy of the DR ( 1+2 ) region leads to reduced fitness in mosquitoes but slightly higher fitness in mice ., Finally , in contrast to the compact stem-loop structures observed in West African and ECSA 3′UTRs , part of the Asian DR ( 1+2 ) region contains a fragment of linear sequence that is not predicted to form a stable stem-loop structure , indicating that the mutations in the DR ( 1+2 ) region may have been tolerated due to its lack of or loss of a structural/functional constraint ., Based on this , we propose an evolutionary path of the Asian CHIKV 3′UTR illustrated in Fig . 4 ., First , a deletion occurred that resulted in the loss of one copy each of DR1 and DR2 ., Compared to its inferred ECSA ancestor , this mutant strain was presumably debilitated in its fitness for infection and dissemination in its principal vector , A . aegypti although it may have had a slight fitness increase for replication in humans based on the murine model ., In the large enzootic CHIKV populations that exist in Africa , this mutant could disappear quickly due to its low frequency ., The rapid fixation of such a mutant in Asia can only be explained by a population bottleneck where stochastic events can facilitate the fixation of a beneficial allele , and even allow a mutant with reduced fitness to circumvent selection ., It is possible that this mutation accompanied the intercontinental transmission from Africa to Asia , which probably involved one or a few infected persons; it is also possible that a CHIKV population bottleneck was influenced by a mosquito eradication campaign in Southeast Asia ( 1955–1969 ) ., Although this effort was designed to eliminate malaria 41 , 42 , it included the use of DDT inside homes , which also reduced populations of A . aegypti responsible for urban CHIKV transmission ., The use of DDT was also instrumental in the eradication of A . aegypti in many parts of the Americas during the 1950s to1960s 43 , 44 ., The coincidence of our estimated year of the most recent common ancestor of currently circulating Asian CHIKV lineage ( 1948–1956 ) with this malaria eradication campaign suggests a possible link ., Second , due to the breakdown of previous structural/functional constraints on the now deleted 3′UTR region , many neutral mutations accumulated in the DR ( 1+2 ) region of the Asian lineage ., Because the formation of a stem-loop structure in a viral RNA genome can facilitate polymerase slippage 45 , a duplication eventually occurred in both the DR ( 1+2 ) and DR3 regions ., These duplications improved the fitness of the Asian CHIKV strain in A . aegypti to an extent that outweighed possible fitness loss in humans , and the duplicated mutant therefore rapidly replaced the previous 3′UTR to become fixed in Asia ., This adaptation to the mosquito vector may have facilitated the initiation of Asian epidemics in 1958 ., Studies in flaviviruses have provided important insights on the structure , evolution and functional importance of arboviral 3′UTRs ., Basically , sequence duplications and deletions , in contrast to point mutations that are predominant in ORFs , are the major evolution mechanisms of flavivirus 3′UTRs 12 ., Interestingly , the ORF region adjacent to the flavivirus 3′UTR 13 , as well as 5′UTR panhandle structure 46 , may also have originated from duplications of 3′ long repeat sequences ( LRS ) ., Furthermore , conserved secondary structures have been observed in the 3′UTRs of all eco-groups of flaviviruses 14 ., However , despite the conservation of these DRs , there is no obvious relationship between them and secondary RNA structures; thus it is not clear why they are preserved , why they retain their double or triple copy numbers , and what exactly are their biological roles ., Similar to most previous studies with other viruses 3–8 except those of the flavivirus genome cyclization motif 47 , 48 , CHIKVs with deletions of different 3′UTR DRs remain infectious , although they exhibit a spectrum of replication reduction in C6/36 cells ., The significant fitness differences in C6/36 cells but similar replication kinetics of these deletion mutants in Vero cells suggests that the 3′UTR plays a more important role in interacting with mosquito cell factors , and adaptation to vectors may be a major driving force for the evolution of the CHIKV 3′UTR ., The importance of the 3′UTR in mosquito transmission is also supported by our findings of a strong impact on mosquito infection and dissemination caused by viruses with swapped 3′UTRs ., In addition , although competition tests between different virus groups in CD1 mice suggested only minor fitness differences caused by the 3′UTR , CHIKVs with shorter genome lengths consistently outcompeted those with longer genomes in the vertebrate model ( Fig . 3 ) ., Therefore , it is not clear if the higher fitness in the vertebrate host is due to an enhanced functional role of the 3′UTR or simply faster replication rates of shorter genomes ., Taken together , our results suggest that adaptation to mosquitoes is a major factor driving evolution of the CHIKV 3′UTR ., This conclusion is in agreement with those form studies of flaviviruses ., Deletion of the entire variable 3′UTR region of tick-borne encephalitis virus , but not the core element at its end , has no effect on BHK cell replication or murine virulence 6 ., Interestingly , the longer 3′UTR favored for replication in arthropods has been taken to an extreme by Kamiti River ( KRV ) viruses , a flavivirus found only in mosquitoes and which cannot infect vertebrates ., KRV , which contains a 3′UTR of 1208 nt that apparently resulted from self-duplication 49 , suggests a major role for the 3′UTR for replication in insect cells ., Likewise , the alphavirus Eilat , which also is restricted to insect cell infection , has a large 3′UTR of 520 nt 50 ., In contrast , alphaviruses not known to be transmitted by vectors have very short 3′UTRs , including salmon pancreas disease virus ( 89 nt ) , sleeping disease virus ( 87 nt ) 51 , and salmonid alphavirus-3 with 87 nt 52 ., However , the role of the 3′UTR in vertebrate cells should not be neglected entirely ., For example , it is known that the 3′UTR affects alphavirus RNA stability in both mammalian and mosquito cells 17 , 18 ., Short deletions of different parts of the SINV 3′UTR lead to host-dependent fitness changes in mammalian , chicken and mosquito cells , suggesting that they are involved in interactions with different host-specific cellular factors 4 ., In many cases a SINV 3′UTR deletion mutant is more severely impaired in mosquito than in chicken cells , but the inverse phenotype has also been observed 3 ., A similar pattern is seen in dengue-4 virus , where deletion of a long upstream region ( ∼120 nt ) of the 3′UTR leads to increased replication in simian LLC-MK2 cells but similar antibody responses in Rhesus monkeys , while other 3′UTR deletions reduce infectivity in both systems 5 ., The balance between functional gain and reduced replication efficiency due to genome size may be key in determining the evolution of the 3′UTR ., What remains obscure is the exact nature of the molecular interaction mechanisms between arboviral 3′UTRs and cellular proteins , which have been proposed to be mediated by the stem-loop RNA 12 ., Flavivirus studies 53 suggested that the level of perturbation of these secondary RNA structures rather than the size of deletions might affect viral replication ., Our RNA secondary structural estimations suggest that duplication of CHIKV DR3 provides additional secondary structure , including the 80 nt conserved Y-shaped structure , without significantly changing other 3′UTR structures ., Also , the enhanced replication in mosquitoes of CHIKV with this insertion suggests that this Y-shaped structure interacts with mosquito factors ., However , the repeated elements DR1 and DR2 do not correspond strictly to structural units ( Fig . S3 ) , although the two copies of DR1 in the West Africa lineage retain the same predicted structure ., Rather , duplication is predicted to result in the formation of new , local stem-loop structures , and more complicated secondary structures on a larger scale at 37°C ., The interaction of the cellular HuR protein with different alphaviruses ( SINV , CHIKV , and Ross River virus ) via different 3′UTR binding sites , probably all through AU rich sequences 18 , suggests that the DR1 and DR2 region may also interact with cellular factors via primary sequence ., Moreover , the stem-loop-rich structures in arboviral 3′UTRs may encode viral miRNAs ., The recent discovery of an miRNA generated from the West Nile virus 3′UTR in infected mosquito cells , as well as the discovery of its host cellular target 20 , provides evidence that viral miRNA can be important determinants of virus-host interactions ., To explore the possibility of CHIKV-produced miRNA , we estimated the potential pri-miRNA sites in its genome using Vmir 54 and found some , including several in the 3′UTR ( data not shown ) ., Further experimental studies should be carried out to confirm these predictions ., The significant effect of genetic change in CHIKV 3′UTR on the fitness level in mosquito and mosquito cells , but not in vertebrate cells , could reflect an interaction between CHIKV and insect-specific genes or proteins , such as those in the antiviral RNA interference ( RNAi ) pathway , the major insect innate immune mechanism 55 ., This hypothesis is supported by the flavivirus sfRNAs role in RNA inference suppression in both mammalian and insect cells by inhibiting Dicer-mediated in vitro cleavage of double-stranded RNA 27 ., Another possibility is that the 3′UTR may fold into different structures in vertebrate vs . mosquito cells maintained at different temperatures , as suggested by our Mfold results ., This structural difference could affect protein binding ., In addition , the presence of a miRNA ( WNV ) generated in mosquito but not mammalian cells suggests that the miRNA processing may differ between these cell types 20 ., Finally , mammalian cells may have more redundant gene expression regulation systems where the effect of down-regulation in one signal transduction pathway can be compensated via other intertwined pathways , making them more robust in their viral regulation of gene expression than insect cells ., In conclusion , we observed for the first time lineage-specific evolution of the 3′UTR in an arbovirus , and our results suggest that the CHIKV 3′UTR plays an important role in adaptation to the mosquito vector ., The founder effect that our results suggest was apparently responsible for the establishment of an inferior CHIKV 3′UTR in Asia before or during the 1950s ., This reinforces our previous findings , which demonstrated epistatic mutations in this CHIKV lineage that probably resulted from the same founder effect , and which limited the fitness and adaptation of Asian strains 56 ., This study was carried out in strict accordance with the recommendations in the Guide for the Care and Use of Laboratory Animals of the National Institutes of Health ., The protocol # 02-09-068 was approved by the Institutional Animal Care and Use Committee of the University of Texas Medical Branch ., All available complete genome sequences of CHIKV were downloaded from the GenBank library ., A maximum Likelihood tree was constructed based on the complete coding sequences ( CDS ) using PAUP* v4 . 0b 57 , utilizing the best-fit model estimated by MODELTEST 58 ., This ML tree was then used as a guide to generate a sequence alignment of 3′UTR of CHIKV utilizing MAFFT 59 ., Strains with incomplete 3′UTRs and those with unique indel patterns were excluded , leading to a dataset of 108 sequences ., Sequence repeats were identified using the DNA matrix analysis in MacVector® based on representative strains of each lineage , followed by manual adjustments based on sequence alignments ., To determine whether DRs form structural units , the RNA secondary structure of the CHIKV 3′UTR from each lineage was estimated using several programs with different advantages , including Mfold 32 , STAR ( STructure Analysis of Rna; 33 ) and Vienna RNA Secondary Structure Package 34 , 35 , based on either representative strains ( Mfold

Introduction, Results, Discussion, Materials and Methods

The 3′ untranslated genome region ( UTR ) of arthropod-borne viruses is characterized by enriched direct repeats ( DRs ) and stem-loop structures ., Despite many years of theoretical and experimental study , on-going positive selection on the 3′UTR had never been observed in ‘real-time , ’ and the role of the arbovirus 3′UTR remains poorly understood ., We observed a lineage-specific 3′UTR sequence pattern in all available Asian lineage of the mosquito-borne alphavirus , chikungunya virus ( CHIKV ) ( 1958–2009 ) , including complicated mutation and duplication patterns of the long DRs ., Given that a longer genome is usually associated with less efficient replication , we hypothesized that the fixation of these genetic changes in the Asian lineage 3′UTR was due to their beneficial effects on adaptation to vectors or hosts ., Using reverse genetic methods , we examined the functional importance of each direct repeat ., Our results suggest that adaptation to mosquitoes , rather than to mammalian hosts , is a major evolutionary force on the CHIKV 3′UTR ., Surprisingly , the Asian 3′UTR appeared to be inferior to its predicted ancestral sequence for replication in both mammals and mosquitoes , suggesting that its fixation in Asia was not a result of directional selection ., Rather , it may have resulted from a population bottleneck during its introduction from Africa to Asia ., We propose that this introduction of a 3′UTR with deletions led to genetic drift and compensatory mutations associated with the loss of structural/functional constraints , followed by two independent beneficial duplications and fixation due to positive selection ., Our results provide further evidence that the limited epidemic potential of the Asian CHIKV strains resulted from founder effects that reduced its fitness for efficient transmission by mosquitoes there .

The 3′ untranslated genome region ( UTR ) of arbovirus ( arthropod-borne virus ) RNA genomes is characterized by enriched direct repeats ( DRs ) and stem-loop structures , which are thought to play a specific role in maintaining efficient transmission in multiple hosts ., However , this hypothesis is vague and has little experimental support ., Based on our observation of a distinct , lineage-specific sequence structure in the chikungunya virus ( CHIKV ) Asian lineage 3′UTR , we tried to understand the underlying driving forces on the arboviral 3′UTR evolution ., Specifically , we sought to determine whether the dramatic genetic changes in the Asian lineage 3′UTR were the result of adaptation into the new host or vector populations ., Using reverse genetic methods , we obtained results that suggested that the DRs have a significant effect on the viral fitness level in mosquitoes but not in mammals ., Interestingly , instead of a directional selection from its ancestral state , our results suggest that the evolution of the CHIKV Asian lineage 3′UTR involved a population bottleneck with a deleterious deletion of DRs , followed by accumulation of mutations due to the loss of structural/functional constraints ., Later , the duplication of two regions that are beneficial in mosquitoes led to the fixation of this 3′UTR sequence in Asian lineage , possibly facilitating the 1958 Thailand outbreak .

evolutionary biology, genetics, biology, microbiology

journal.pcbi.1001041

Determinants of Heterogeneity, Excitation and Conduction in the Sinoatrial Node: A Model Study

The sinoatrial node ( SAN ) is a complex heterogeneous tissue and its function may depend on this complexity 1 ., Measurements from intact rabbit SAN have shown heterogeneity of electrophysiological properties from the center to the border of the atrium including gradual morphological changes in action potentials ( AP ) , a decrease in maximum diastolic potential ( MDP ) , an increase in peak overshoot potential ( POP ) , an increase in upstroke velocity ( UV ) and a decrease in pacemaker potential slope 2 , 3 ., Some studies of the SAN describe a discrete-region model of SAN organization 4 , comprising a central region of small primary pacemaker cells surrounded by a zone of larger transitional cells ., Kodama et al . 5 observed AP variability in small balls of tissue isolated from SAN , and suggested a transition in ion channel expression as the cause ., An additional series of articles in rabbit have reported that AP characteristics , current density , Ca2+ handling and connexin density are cell-size dependent 1 , 6 , 7 ., More recently , Lyashkov et al . 8 identified three morphologically distinct SAN cells ., However , experiments on enzymatically dissociated cells of all three types revealed no significant variations in APs , cycle length ( CL ) , Ca2+ cycling or channel expression ., Other studies have also failed to detect size-dependent ( i . e . cell type dependent ) differences in isolated SAN cells 9 , 10 ., From these disparate camps , two distinct hypotheses have arisen to explain intact SAN heterogeneity ., The first is that the SAN has two specific cell types , central cells and peripheral cells , each with distinct electrophysiological characteristics 1 , 6 ., The second hypothesis suggests that all observed heterogeneity in the intact SAN results from electrotonic coupling effects - cells in the SAN near the atria will be strongly affected and modified by the atrium ., Here we used a computational modeling approach to build distinct models based on the existing contrasting data sets that support the two hypotheses , and attempted to simulate experimentally measured properties of isolated SAN cells and characteristics of intact SAN tissue ., We then used the computational model to probe other anatomical factors that likely contribute to the observed function and heterogenetity in the SAN ., It has been observed that fibroblasts constitute a larger fraction of the SAN , than atrial or ventricular tissue 11 ., Anatomical studies of the rabbit SAN suggest a disorganized “mesh” of SAN cells arranged around “islands” of fibroblasts 11 ., Fibroblasts form functional gap junctions with myocytes in vitro and in vivo 11 , 12 , 13 ., Recent in vitro experiments suggest that electrically coupled fibroblasts alter impulse SAN conduction 12 , 13 and that fibroblasts may affect the spontaneous activity of the SAN cells 14 ., Fibroblast density increases with age and may play a role in ageing-induced bradycardia or sick sinus syndrome 15 , 16 ., Atrial cells are also dispersed throughout the SAN 11 , 17 and it is unclear how these cells may influence SAN excitability ., Verheijck et al . 11 , 17 hypothesized that the gradual increase in density of atrial cells from the SAN center toward the atria causes a gradual increase in atrial electrotonic influence that underlies the transition from nodal to atrial action potentials ., Our computational modeling approach allowed us to examine how sources of heterogeneity in the SAN including cellular differences , gradients in coupling , fibroblasts and atrial myocytes in the SAN affect excitation and conduction ., Our tissue simulations suggest electrotonic effects as plausible to account for SAN heterogeneity ., Uncoupled fibroblasts act as obstacles to conduction in the SAN and , when coupled slow conduction by acting as current sinks , or shunt electrical activity between regions , depending on the orientation , density and coupling strength ., Our model simulations also revealed only minor effects of atrial cells in the SAN ., The Kurata rabbit SAN central cell model 18 was used as the base model for cell and tissue simulations in this study because it incorporates:, 1 ) intracellular Ca2+ dynamics and a subsarcolemmal Ca2+ diffusion compartment ,, 2 ) the novel pacemaker current Ist ,, 3 ) Ca2+ dependent inactivation of L-type Ca2+ channel, 4 ) accurate activation kinetics of IKr ,, 5 ) revised kinetic formulations for 4-AP-sensitive currents ( Ito and Isus ) ., The Kurata model reproduces AP waveforms , ionic currents , effects of ion channels blockers and differential effects of BAPTA and EGTA on pacemaker frequency 18 ., Please refer to Text S1 for details ., Effects of vagal stimulation were incorporated into the model in order to investigate the experimentally observed pacemaker shift in response to ACh application as we have done previously 19 ., The concentration of ACh was chosen to approximate the experimentally observed effect of ACh on SAN cells , where maximum diastolic potential is hyperpolarized by 10 mV 20 ., This concentration ( 1×10−5 M ) results in effects on ICa , L and If , and IK ., All tissue level simulations are described in full detail in the online Text S1 ., We modeled the sick sinus syndrome associated P1298L Na+ channel mutation , by modifying INa properties according to experimental data 21 ., We incorporated a 13 . 5% negative shift of the inactivation curve and implemented the same percentage changes of the fast and slow inactivation time constants ( τfast increased by a factor of 2 , τslow increased by a factor of 3 . 6 ) as observed experimentally ., The maximal Na+ channel conductance was reduced by 44% to reproduce the reduction in INa as recorded experimentally ., Simulations were carried out with a passive electrophysiological model of atrial fibroblasts as described previously 22 ., Details are in Text S1 ., Simulations were encoded in C/C++ and run on a Sun Fire X4440 ×64 Server and multiple Apple Intel based Mac Pros 3 . 0 GHz 8-Core using OpenMP with the Intel ICC compiler version 11 . 1 ., Numerical results were visualized using MATLAB R2009a by The Math Works , Inc ., An explicit Euler method with a time step of 0 . 01 ms and Neumann no flux boundary conditions are used ., All source code used in this paper is available by request to ceclancy@ucdavis . edu ., First , we constructed a “non-uniform” radially symmetric tissue model ( Figure 1A ) , containing central and peripheral model cells based on experimental data that support this view ( see Methods ) ., We incorporated a linear gradient of cell-to-cell coupling from the center to the periphery of the SAN based on histological studies that show spatial gradients of gap junctions , resulting in a 10-fold increase in density from SAN center to periphery 2 , 23 ., A schematic is shown in Figure 1A , where low coupling is white and the gradient of increasing coupling toward the periphery is indicated by degree of shading ., We tested a range of linear gradients spanning the experimental estimate ( 10-fold ) , from 5-fold to 15-fold 23 ., We used the measured average intercellular coupling value 24 in the SAN center of 7 . 5nS ( white ) ., Figure 1B shows simulations of SAN excitation in the non-uniform model under conditions of shallow ( from 7 . 5 nS in the center to 37 . 5 nS in periphery - 5-fold larger GGap in periphery , left ) and steep ( from 7 . 5 nS to 112 . 5 nS -15-fold larger GGap , right ) gradients ., In both cases we observed initiation of pacemaking in the periphery – a result that is not consistent with experimental observations , where central pacemaking is observed ( Figure 1B left , pacemaker site is bold ) ., We were not completely surprised by this finding since in our single cell simulations the peripheral cell has a faster intrinsic excitation frequency ( See Figure 2C in Text S1 ) ., We postulated that electrotonic coupling to the atrium might make peripheral cells less excitable by acting as a large current sink ., However , our simulations suggested this is not the case , and that no degree of “physiological” coupling shifted pacemaking to the SAN center in the non-uniform model ( Figure 1B and Figure 2B , open circles ) ., Moreover , the CL in the non-uniform model was 234 ms , which is markedly shorter than the experimentally observed values of 361±38 ( see Table 1 ) ., The upstroke velocity ( UV ) of action potentials in the periphery ( 60 . 7 V/s ) was too large and maximum diastolic potential ( MDP ) in the periphery was too negative and outside of the measured range ( see Table 1 ) ., In order to be sure that the results presented above did not depend on the specific conductance values for each current , we performed a sensitivity analysis of pacemaker location to 10% perturbations ( encompassing the experimental measurement ranges ) in all ionic conductances ., The pacemaker location was robust to increases or decreases in any conductance – peripheral pacemaking was always observed for the non-uniform model ., The methods and results are contained in Text S1 ., We next used the uniform model incorporating data that supports the hypothesis that no intrinsic differences exist between central and peripheral cells 8 , 9 , 10 and that observed differences in the intact SAN derive from proximity of cells to the atrium ., This is supported by data in single cells where no differences in AP properties in large versus small cells isolated from the SAN were observed 8 ., The 2D “uniform” model contains only central cells connected to the atrium ( Figure 2A ) ., Figure 2B illustrates the pacemaker site for values of GGap gradients for the non-uniform ( shown in Figure 1 ) and the uniform model ., Electrical initiation in the first 15 cells is central pacemaking ., Although the non-uniform model did not simulate central pacemaking for any values tested , the uniform model simulated central pacemaking over a wide range ( Figure 2B , filled circles ) , beginning with a coupling gradient of 5× ., Simulations are shown in Figure 2C using the uniform model with a GGap gradient of 10× 2 , 23 ., We performed the same sensitivity analysis as described above and found that central pacemaking was robust to changes in all conductances and always observed for the uniform model ., One of the important manifestations of the heterogeneous SAN is the shift of the pacemaker site in response to different innervations ., In the case of vagal stimulation , the pacemaker shifts towards the periphery of the SAN 25 ., Therefore , in order to mimic vagal stimulation in both our non-uniform and uniform models , we incorporated the recorded changes to ACh-activated K+ current ( IK , ACh ) , If and ICa , L as we have done previously 19 ., In the rabbit SAN , fine nerve processes form a basket around the pacemaker cells at the normal leading pacemaker site , but there are few or no visible fibers in the periphery of the SAN 26 ., Following these data , we have applied the effects of vagal innervation only in SAN “central cells” ( i . e . cells #1–15 ) ., Figure 2D shows that simulated vagal stimulation resulted in a shift in the pacemaker site in the uniform model to the periphery ( cell #20 ) compared with control ( Figure 2C , pacemaker site at cell #6 ) , consistent with experiments ., In the non-uniform model , on the other hand , simulated vagal stimulation resulted in a slight shift of the pacemaker site within the periphery of the SAN ( from cell #19 in control to cell #22 under vagal stimulation , not shown ) ., The mechanism for the shift of the pacemaker site under vagal stimulation is as follows: During vagal stimulation , an increase in IKACh results in a 10 mV hyperpolarization of the MDP in the central region of the SAN ., ACh also partially inhibits ICa , L , and shifts the activation curve of the hyperpolarization-activated current , If , towards more negative potentials ., This leads to a slowing of diastolic depolarization and consequently pacing frequency ., Cells in the periphery , which are unaffected by vagal stimulation have a faster intrinsic frequency and thus drive pacemaking ., Studies suggest that in addition to increased gap junction density in peripheral SAN , gap junctions in the SAN periphery are distinct isoforms with larger conductance 2 , 23 , 24 , 27 , 28 , 29 ., In the SAN center , Cx30 . 2 , small conductance gap junctions form 30–40 pS channels with Cx40 and Cx45 28 ., In the periphery of the SAN 60–120 pS ( 2–4 fold increase in coupling compared to center ) Cx43 and Cx45 are expressed 27 , 30 ., We thus assessed effects of increased peripheral coupling on the SAN by incorporating an additional multiplicative factor for peripheral coupling between 1 and 4 in cells #15–30 ., Additional peripheral coupling was applied to the uniform model with the 10× GGap gradient ( from Figure 2C ) ., Steady-state APs of a peripheral cell ( cell #27 ) are shown in Figure 3A ., As peripheral coupling was increased , peak overshoot potential ( POP ) increased ( from 17 . 9mV in control to 20 . 9mV in 4× peripheral coupling ) , maximum diastolic potential ( MDP ) and take-of-potential ( TOP ) became more negative ( from −66 . 7mV to −68 . 8mV and −44 . 1mV to −51 . 4mV ) and diastolic depolarization ( DD ) slope was reduced ( from 0 . 2mV/ms to 0 . 1mV/ms ) ., Note also that action potential duration ( APD ) is reduced as peripheral coupling is increased , owing to the voltage dependent reduction in cellular input resistance in the neighboring atrium as rectification of IK1 currents is alleviated 31 ., Figure 3D shows the effect of increased intercellular coupling in the periphery to reproduce observed increased upstroke velocity ( UV ) ( cell #27 ) 1 , 2 ., Further investigation revealed that an increase in ICa , T ( from −1 . 14 pA/pF to −2 . 9 pA/pF ) causes increased UV ( Figure 3D ) ., Increased peripheral coupling results in a reduction in DD slope and leads to lesser inactivation of ICa , T , which increases the peak current of ICa , T ( due to more channel availability ) during the TOP phase of the AP ( Figure 3B ) ., Although ICa , L is the primary current activated during AP upstroke , it remained unchanged ( Figure 3C ) because the simulated decrease in DD primarily affects ICa , T and not ICa , L ., Central cells are not affected by the increase in peripheral coupling ., Peripheral cells are increasingly affected according to their proximity to the atrium – those that are closest to the border of the atrium are most affected ., This is because the increase in coupling “allows” cells in the periphery to “feel” more electrotonic influence of the atrium ., Electrotonic coupling of cardiac myocytes and fibroblasts has been observed in vitro and in vivo in the rabbit SAN 11 , 12 , 13 ., SAN tissue has fibroblasts interspersed in islands that occupy about 50% of SAN volume 32 ., Because atrial fibroblasts are distinct from ventricular fibroblasts and lack large K+ currents observed in ventricular fibroblasts 14 , 33 , we incorporated an atrial fibroblast in the model 14 ( schematic in Figure 4A and B ) , which allowed us to test the effects of fibroblast density ( 10% , 25% and 50% of the SAN ) , fibroblast island size ( 2×2 , 4×4 and 6×6 , Figure 4A ) and the distribution of fibroblasts ., Fibroblast distributions assumed no fibroblast island touched another island ( as this would change the island size ) ., We first tested fibroblasts as obstacles to electrical propagation with various distributions and densities of uncoupled fibroblasts ( GGap\u200a=\u200a0 nS ) and observed slowed conduction ( SAN to atrium conduction time ( SACT ) ) as a function of fibroblast density ( Figure 4C ) ., Neither island distribution ( filled squares in each column denote five different fibroblast distributions as shown in Figure 3 in Text S1 ) nor fibroblast island size ( islands composed of 2×2 ( small ) , 4×4 ( medium ) and 6×6 ( large ) fibroblasts are shown as indicated in Figure 4C , respectively ) largely affected SACT ., 50% fibroblast density with 2×2 islands could not be obtained within the constraint that islands not touch islands ( asterisk in panel A ) ., With large plentiful fibroblast islands ( 6×6 ) and clustered near the atrium ( 50% fibroblast density in right panel ) , the atrium failed to excite ( Figure 4C ) ( see Figure 3 in Text S1 for fibroblast distributions – here , distributions 3 and 5 failed to excite the atrium ) ., A fibroblast barrier near the atrium caused a mismatch between availability of depolarizing charge ( source , the SAN ) and charge required for excitation ( sink , the atrium ) ., The intrinsic excitation frequency of the SAN was unaffected by the presence of fibroblasts ( 302ms ) in all simulations at GGap\u200a=\u200a0 nS ( not shown ) ., Next , we explored the effect of the strength of coupling between myocytes and fibroblasts on impulse propagation in the SAN by setting GGap between myocytes and fibroblasts to 1 , 3 and 6 nS , from the measured myocyte-fibroblast coupling range 34 ., We simultaneously varied fibroblast density , island size and island distribution ( Figure 5 ) ., As observed experimentally 12 , 13 , 35 , our simulations predict fibroblasts induced slowing of conduction and increased fibroblast density increased SACT ., With a fibroblast density of 10% , conduction was slowed , but additionally varying island size ( compare panels A , B and C ) , island distribution ( compare columns in individual panels ) or coupling between myocytes and fibroblasts ( note clustering of symbols ) did not have large additional effects on SACT ( Figure 5 , A–C ) ., However , when we increased fibroblast density to 25% ( Figure 5 , D–F ) , island distribution affected conduction time ( SACT ) ., In particular , distributions #3 and #5 , where islands of fibroblasts were clustered near the edges of the SAN , sped conduction , while distribution #4 with centralized islands slowed conduction ., Increasing fibroblast density to 50% revealed how coupling ( GGap ) affects conduction ( Figure 5 , G–H ) ., In most distributions with 50% fibroblast density , when GGap is large ( 6 nS , circle ) SACT is shorter ( compared to GGap\u200a=\u200a3 nS , denoted by “X” or 1 ns , filled triangle ) ., Electrical impulses pass through a tightly coupled fibroblast island faster than when weakly coupling ., As in the uncoupled simulations ( for GGap\u200a=\u200a0 nS , Figure 5C ) , distributions #3 and #5 ( see Figure 3 in Text S1 ) failed to excite the atrium for weak coupling ( GGap\u200a=\u200a1 nS , Figure 5H ) ., Distributions with more fibroblasts at the border of the atrium fail to allow sufficient current to pass for excitation of neighboring atrial cells ., Figure 6 shows the impact of fibroblast density alone on excitation frequency ( cycle length - CL ) ., CLs at Ggap\u200a=\u200a3 nS for 2×2 , 3×3 and 4×4 size islands and 10% , 25% and 50% fibroblast density are shown ., As fibroblast density increased , CL increased ., This simulation may explain the reduced frequency observed in the SAN of ageing and diseased hearts 15 , 16 ., Verheijck E . et al . 10 have observed atrial cells interspersed in the SAN and suggested a “mosaic” model of SAN and atrial cells for SAN organization ., In the mosaic model , the percentage of atrial cells varies from 63% in the periphery to 22% in the center ., We incorporated a random distribution of atrial cells with a density of 22% in the center increasing gradually to 63% in the periphery 10 ( schematic in Figure 7B , atrial cells in blue ) connected via Ggap as in the neighboring nodal cells ( there is no evidence for atrial specific gap junction channel ( Cx43 ) presence in the SAN ) ., Our simulations suggest that atrial cells have minor effects on electrical properties and behavior of the SAN ( Figure 7A ) ., CL and SACT in our mosaic model are longer ( 322 ms , 32 . 6 ms , respectively ) compared with control ( 302 ms , 13 . 9 ms ) because MDP is more hyperpolarized when atrial cells are present in the SAN ., UV in the center is slower in the mosaic ( 4 . 2 mV/ms compared with 6 mV/ms in control ) ., In contrast , UV is faster in the periphery ( 18 mV/ms compared with 13 . 8 mV/ms ) , consistent with experimental observations ( see Table 1 in Text S1 ) ., Interspersed atrial cells also shorten APD in both central and peripheral regions of the SAN owing to the influence of IK1 – repolarization alleviates IK1 rectification leading to reduced resistivity , which increases the electrotonic influence of the atria during repolarization and during the diastolic interval ( see also Table 1 in Text S1 ) ., Figure 7C shows the propagation of an impulse generated in the central region of the SAN through a peripheral region of increased coupling ( as described in Figure 2 ) into the atrium as generated by the mosaic model ., A simulation showing the effects of vagal stimulation in the central region ( first 15 cells ) of the mosaic model is shown in Figure 5 in Text S1 ., Consistent with our previous results , vagal stimulation in the mosaic model results in a peripheral shift of pacemaking and a slowing in CL ., Finally , we tested the effect of a familial sick sinus syndrome mutation that results in altered inactivation kinetics leading to a loss of Na+ current 21 ., Figure 8 depicts the simulation predictions after incorporation of the P1298L sodium mutation with and without vagal stimulation in the mosaic model ( Figure 8A and B , respectively ) ., We observe mutation induced slowing of beating frequency and conduction velocity from the SAN to the atria , which becomes dramatic in the presence of ACh ., Because no Na+ current is present in the SAN cells , the mutation effect results from the loss of Na+ channel function in atrial cells only ., Interspersed atrial cells within the SAN that are affected by the mutation are less excitable and consequently draw more current from neighboring SAN cells ( left panels of A and B , show effect of mutation on interspersed atrial cells within the SAN alone ) ., This results in a slowing of pacemaking ., Due to mutual entrainment in the SAN region we observed a slowing in heart rate in all cells ., The presence of vagal stimulation dramatically exacerbates the mutation effects – by additionally slowing diastolic depolarization ( and thus pacing frequency ) in SAN cells ( panel B of Figure 8 ) ., Note that when the mutation is also incorporated into atrial cells ( right panels of A and B ) , atrial action potential duration is increased ., This is a result of the mutation-induced reduction in the peak overshoot potential in the atrial cells ( 38 . 35 mV versus 24 . 29 mV with the mutation ) that reduces the driving force of repolarizing K+ currents ., When we began this study , we found that the widely used central and peripheral cell models reproduced measured single cell properties from experiments that supported the existence of two distinct cell populations 7 ., But , in our hands , without a number of unjustified parameter modifications these cell models could not be coupled together and reproduce observed widely agreed upon tissue properties of the SAN , including central pacemaking 2 ., Close inspection of the existing literature led us to suspect that a number of other groups that have attempted a heterogeneous reconstruction of the SAN with central and peripheral cells also failed ., Zhang et al . 7 first developed mathematical models of central and peripheral SAN cells and a 1D gradient model of the SAN , which were subsequently modified by Garny et al . 37 in tissue simulations ., The existing models that contain both cell types are rife 27 , 37 , 41 with parameter modifications ( by starkly isolating peripheral cells and altering their conductance properties to make them more “central” like ) to shift pacemaking to the center and “make it work” ., These modifications of SAN single cell model parameters made them less representative of experimentally determined single cell activity ., When we considered that cellular properties from the SAN were more uniform than had been previously reported and built a model based on those data , we could reproduce measured cell AND tissue properties in the SAN without any parameter modification ., We hope that this study will put to rest much of the “wiggly jiggly” with parameters that has been done with SAN tissue models over the years – we show that a simple model composed only of central cells can reproduce measured SAN properties – both in single cells and in tissue ., Most importantly , our uniform model reproduced experimental tissue properties without arbitrary parameter modifications ., A very recent study by Butters et al . appears to be largely consistent with the results that we present here 41 ., Although distinct “peripheral” pacemaking cells are included in their 2D SAN tissue model , these cells are present at very low density , surrounded by atrial cells or non-excitable regions ( block zone ) , and do not appear to be present in the conduction pathway of the excitable impulse generated in the SAN center ., In the Butters model , peripheral cells have apparently been “uncoupled” from the pacemaker by isolating very small low-density clusters within large atrial or inexcitable sinks ., This seems consistent with our findings – a heterogeneous model of distinct central and peripheral cells will fail to evoke central pacemaking ., We speculate that removing the peripheral cells from the simulation would have no effect on the results from the Butters paper ., In an early study to probe propagation in the SAN , Joyner and van Capelle 42 constructed a radially symmetric simplified 2D single cell type SAN model and adjoining atrial tissue ., Their model predicted that driving the atrium required an SAN five times larger than reported 23 ., In our model , SAN drove the atrium regardless of size ( not shown ) ., Joyner and van Capelle also reported that atrial excitation required partial uncoupling of the SAN from the atrium ., Again , no such modifications were required with our model ., Joyner and van Capelle 42 noted that a gradual increase in coupling allowed the SAN to successfully drive the atrium ., Here , our simulations support the notion that an increase in intercellular coupling from center to periphery is a prerequisite ., In future studies , different geometric configurations ( such as the block zone , interdigitations between the SAN and atrial tissue border , interweaving cells in the center etc . ) could be incorporated into the model ., Although mechanosensitive currents in fibroblasts have been described in the SAN 22 , the intrinsic feedback mechanisms controlling the currents are still controversial and so we did not apply descriptions of mechano-electrical feedback in our model fibroblasts ., A very recent study in atrial fibroblasts observed TRPM7 channels that account for Ca2+ influx in atrial fibroblasts and are markedly upregulated in patients with atrial fibrillation where they may underlie pathological fibrosis 43 ., This new finding is not accounted for in our study ., Finally , in this study we have neglected the effects of sympathetic stimulation of the SAN ., Clearly , sympathetic innervation will constitute an important component of future studies ., In conclusion , we showed that a relatively simple model is sufficient to represent many observed properties in the SAN ., Our tissue model simulations suggest that the atrial load is a primary determinant of heterogeneity of the SAN , sequence and rate of propagation ., In addition , we show fibroblasts as obstacles , current sinks , or shunts to SAN conduction depending on distribution , density and coupling .

Introduction, Methods, Results, Discussion

The sinoatrial node ( SAN ) is a complex structure that exhibits anatomical and functional heterogeneity which may depend on:, 1 ) The existence of distinct cell populations ,, 2 ) electrotonic influences of the surrounding atrium ,, 3 ) the presence of a high density of fibroblasts , and, 4 ) atrial cells intermingled within the SAN ., Our goal was to utilize a computer model to predict critical determinants and modulators of excitation and conduction in the SAN ., We built a theoretical “non-uniform” model composed of distinct central and peripheral SAN cells and a “uniform” model containing only central cells connected to the atrium ., We tested the effects of coupling strength between SAN cells in the models , as well as the effects of fibroblasts and interspersed atrial cells ., Although we could simulate single cell experimental data supporting the “multiple cell type” hypothesis , 2D “non-uniform” models did not simulate expected tissue behavior , such as central pacemaking ., When we considered the atrial effects alone in a simple homogeneous “uniform” model , central pacemaking initiation and impulse propagation in simulations were consistent with experiments ., Introduction of fibroblasts in our simulated tissue resulted in various effects depending on the density , distribution , and fibroblast-myocyte coupling strength ., Incorporation of atrial cells in our simulated SAN tissue had little effect on SAN electrophysiology ., Our tissue model simulations suggest atrial electrotonic effects as plausible to account for SAN heterogeneity , sequence , and rate of propagation ., Fibroblasts can act as obstacles , current sinks or shunts to conduction in the SAN depending on their orientation , density , and coupling .

It is well known that a small structure in the atrium called the sinoatrial node ( SAN ) is the pacemaker for the heart ., However , the complexity and heterogeneity intrinsic to this structure has made it difficult to determine some aspects of sinoatrial node function ., Here we use a computational approach , based on experimental data , to tease out the individual contributions of cellular and tissue heterogeneities and the effect of fibroblasts and atrial cells on sinoatrial node function ., The computational models suggest that the complex features of the intact sinoatrial node can be reconstructed with a relatively simple model ., Our simulations also predict that the presence of non-cardiac cells in the node likely contribute to its function .

cardiovascular disorders/arrhythmias, electrophysiology, and pacing, biophysics, computational biology

journal.pcbi.0030247

Comparative Genomics Search for Losses of Long-Established Genes on the Human Lineage

It is intuitive to think that changes leading to increased complexity , adaptation , and intelligence are achieved by the gain and improvement of genetic components such as genes and regulatory elements ., However , in certain scenarios , a loss of function can also bring a selective advantage ., The best-known examples are losses of cell surface receptors to confer pathogenic resistance , such as the inactivation of the DUFFY gene contributing to malaria resistance 1 and homozygosity for a null allele of chemokine receptor CCR5 conveying resistance to infection by various pathogens , including HIV 2 ., In addition , the loss of an existing biological component can open new developmental opportunities ., The human-specific loss of a myosin heavy chain isoform expressed in the masticatory muscles has been linked to the weakening of human jaw muscles , possibly allowing the increase of cranial capacity in humans , although this is still quite speculative 3 ., Adaptive gene loss is the type of genetic change that leads to better fitness for an organism by inactivating a functional gene ., As argued by the “less-is-more” hypothesis , gene losses may be an important engine of evolutionary innovation 4 ., In addition to adaptive evolution , gene losses can play an important role in human diseases where conditionally advantageous mutations improve fitness in a particular environment ., For example , deleterious mutations affecting hemoglobin and other red blood cell proteins are common in many human populations due to a heterozygote advantage in malaria epidemic environments ., This improved fitness comes at a cost for those born with deleterious mutations on both alleles , since the homozygous state causes anemia including sickle cell disease 5–7 ., Other human diseases such as glucose-6-phosphate dehydrogenase deficiency 7 and cystic fibrosis 8 , 9 have also been associated with the heterozygote advantage ., Despite the apparent importance of adaptive gene loss , we know surprisingly little about its contribution and significance at the genomic level and over a broad time scale ., Most research on adaptive evolution in mammals focuses on new genes or regulatory elements as well as on modifications to known genes , such as amino acid substitutions 10 , 11 ., With the complete genomes of human and several other mammals including chimp , rhesus , mouse , rat , and dog 12–16 , it is now feasible to systematically identify adaptive gene losses in the human lineage through the course of mammalian evolution ., A claim for adaptive genetic change typically requires evidence of DNA signatures indicating directional selection , and is accompanied by the identification of selective pressures acting on the organisms that are consistent with DNA , fossil , or historical evidence ., Methods for detecting amino acid or DNA signatures left by natural selection are not generally applicable for identifying adaptive gene loss 17 , 18 ., An inactivated gene is no longer maintained through the forces of natural selection , and secondary mutations begin to accumulate at the neutral rate ., Therefore , methods based on sequence conservation or ratio of synonymous versus nonsynonymous mutations are not suitable to detect adaptive gene losses 11 , 19–21 ., Recent adaptive losses can be detected by the distinct DNA signatures left by positive selection; however , those signatures only persist for a narrow evolutionary window of at most 250 , 000 years 22–24 ., To detect adaptive gene losses further back into the evolutionary past , it is reasonable to assume that a nonredundant gene that was functional for a long time and then inactivated is a good candidate for adaptive gene loss ., While not every loss of a well-established gene is adaptive , searching for those candidates can be used to enrich for adaptive gene losses ., Gene loss normally leaves behind a pseudogene ., However , the vast majority of pseudogenes in a genome did not bring a selective advantage to the organism ., Most pseudogenes arise through a gene copying operation of either retrotransposition ( reverse-transcribing a processed mRNA back to DNA , which is reinserted in the genome at a different location ) 25 , or by segmental or tandem duplication of a genomic region 26 ., These are called processed or unprocessed pseudogenes , respectively ., While processed pseudogenes in general have a single exon and a polyadenine tail , unprocessed pseudogenes typically have multiple exons and preserve the intron–exon structures of the parental gene ., The vast majority of processed pseudogenes are “dead on arrival , ” due to the lack of complete coding regions or necessary transcription and translation signals in the new genomic location ., Even when a functional gene is formed by segmental duplication , one copy often becomes silenced by degenerative mutations due to functional redundancy 27 ., In contrast , adaptive gene losses arise from degradation of genes with well-established functions , which often do not have close homologs in the genome ., Taking advantage of the genomic signatures left behind by retrotransposition or gene duplication , several genomic surveys identified tens of thousands of pseudogenes in the human genome using sequence homology to a functional parental gene 28–32 ., However , because they lack close homologs , many losses of well-established genes were missed by these studies ., More importantly , these analyses focused on cataloging pseudogenes in the human genome , but not on addressing whether the pseudogenizations played a role in evolution ., This study identified losses of well-established protein-coding genes in the human lineage since the common ancestor of euarchontoglires ( primates , lemurs , tree shrews , rodents , and lagomorphs such as rabbits ) ., We applied a novel comparative genomic method to identify pseudogenes by syntenic mapping of gene structures between the human–mouse–dog trio of genomes ., This approach is able to systematically detect the sequence signature left by losses of well-established genes , distinguishing true losses from mere loss of redundant genes following duplication or retrotransposition ., Our analysis was able to differentiate the losses of well-established genes from the large background of human pseudogenes ., Twenty six losses of well-established genes were identified in the human lineage since the common ancestor of euarchontoglires , approximately 75 million years ago ( Mya ) ., Sixteen of those were previously uncharacterized gene losses in the human genome , such as the loss of acyltransferase 3 during great ape evolution ., After a mutation inactivates a functional gene , the signature of the intron–exon structure can still be detected for some time before neutral decay erases it from the genome ., Based on the observation that mammalian gene structures are typically conserved between species , a gene prediction program called TransMap was developed that exploits the large-scale conservation of gene order and orientation on mammalian chromosomes to map gene structures between genomes ., TransMap is essentially a cross-species mRNA alignment grogram ( Text S1 ) that relies upon the “syntenic” alignments produced by the BLASTZ program 33 ., TransMap is highly sensitive in detecting gene structures for both genes and pseudogenes ( Table S2 ) ., Unlike most existing pseudogene detection methods 30 , 31 , 34–36 , TransMap does not rely on sequence homology to a parental gene from the same genome; therefore , it is well-suited for detecting losses of well-established genes whose functional precursor has not been recently duplicated ., To identify gene losses , the mapped coding region is conceptually translated and scanned for ORF-disrupting mutations ., A TransMap prediction is a candidate for gene loss if any of the following ORF-disrupting mutations are detected in the mapped coding regions: stop codons , frameshifts , a splice junction that is not GT-AG , or when less than 50% of the coding region can be mapped to the target ( Figure S1 ) ., To decrease the number of false positives , a valid conceptual translation is required in an outgroup genome ., For example , if a mouse mRNA TransMaps from mouse to the dog genome with a valid conceptual translation , but fails to do so from mouse to the human genome , the gene is a candidate for a human gene loss ( Figure 1 ) ., Using mouse mRNAs as queries and the dog genome as an outgroup , we identified gene losses in the human lineage since the common ancestor of euarchontoglires ., We chose the RefSeq database because it is one of the most comprehensive collections of mouse transcripts 37 ., Based on the differential prediction status in the dog and human genomes , 1 , 008 of the 19 , 541 mouse RefSeq transcripts were identified as potential human losses ( Table S3 ) ., The candidates were reduced to 90 after filtering out overlaps with GenBank human mRNAs in order to identify pseudogenes where no transcriptional activity in the form of mature mRNA has been observed in humans , and to remove the false positive pseudogenes predicted by TransMap ( ( Text S1 ) ., A visual examination of an alignment of the mouse mRNA sequence , mouse , human , and dog genomic sequences and their three-frame translations further reduced the number of candidates to 72 , eliminating 18 that we are not confident represent true pseudogenes in the human genome ., The pipeline is illustrated in Figure 1 ., Of the 72 candidates , 27 are predicted to be olfactory receptors ( ORs ) and ten are members of large gene families , such as keratins ., These gene families are organized as tandem gene clusters that have experienced copy number changes and/or complex local rearrangements since the common ancestor of euarchontoglires ., The dynamics of gene clusters make it difficult to unambiguously discern ortholog/paralog relationships among species and analyze the evolutionary history of the lost genes ., Therefore , we focused on the remaining 35 ( non-OR , non-cluster ) candidates that were confirmed by visual inspection , which we referred to as the “definite” losses in the human lineage ., Among them , 21 gene losses are annotated with some biological functions in mouse and 14 have not been characterized functionally ( Table 1 ) ., The large number of ORs found by this method is consistent with observations that human OR genes experienced a rapid acceleration of pseudogene formation 38 , 39 ., Previous studies have shown evidence that human genes involved in olfaction have a significant tendency to be under positive selection , indicating ORs have undergone directional selection in humans , including by pseudogenization 18 , 39 ., Many previously identified non-olfactory gene losses were confirmed as well ., These include gulonolactone ( L- ) oxidase ( GULO ) , a vitamin C biosynthesis enzyme that is the genetic basis for scurvy 40 , and urate oxidase ( UOX ) , an enzyme converting uric acid to allantoin 41 ., In addition , a human-specific nonsense mutation was confirmed in an orphan chemoattractant G protein–coupled receptor 33 ( GPR33 ) ., Additionally , confirmed gene losses included the human-specific loss of cardiotrophin-2 ( CTF2 ) due to a 8 bp deletion 42 , cytochrome c oxidase subunit VIIIb ( COX8B ) with only 40% mapping to the human genome yielding an ORF of eight amino acids 43 , and others listed in Table 1 , note a ., Our analysis also identified 23 previously uncharacterized losses , of which 21 belong to the definite loss group and two are members of complex gene clusters ., One example of a previously unknown definite loss is acyltransferase 3 ( ACYL3; NM_177028 ) , identified by the Riken mouse cDNA project 37 , 44 and whose function in mammals has not been characterized experimentally ., We conducted protein profile analysis and determined that this gene has a highly conserved acyltransferase 3 domain ( hence we annotated the gene ACYL3 ) 45 ., Further structural modeling ( SAM , TMHMM , SignalP ) revealed mouse Acyl3 is a multipass transmembrane protein with its C-terminal domain forming a helix bundle ., The N-terminal is extracellular and hydrophilic with conserved cysteine residues able to form disulfide bonds 46–49 ., The extracellular domain might be involved in cellular response to external signals ., Thus , Acyl3 might be a membrane protein with acyltransferase activity or a multipass transporter to pass molecules across the membrane upon external signals 50 , 51 ., Phylogenetically , ACYL3 is ancient and conserved in archaea , bacteria , fungi , worms , flies , and mammals ., While numerous copies of ACYL3 are encoded in fly and worm genomes , mammalian genomes have only one copy ., A nonsense mutation ( TGG to TGA ) located in one of the transmembrane helices is shared by the human and chimpanzee genomes ., However , the ancestral TGG codon is present in two orangutan trace sequences , and a valid conceptual translation is present in the rhesus genome ., To narrow down the timing of the inactivation , we sequenced a PCR product amplified from the corresponding region in a gorilla DNA sample ., The sequencing result showed the TGG ( W ) codon is present in the gorilla genome ., Based on this evidence , it appears that the nonsense mutation inactivated ACYL3 after the divergence of gorillas from the human lineage , and before the divergence of humans and chimpanzees ( Figure 2 ) ., It is intriguing that the last copy of such an ancient enzyme as ACYL3 was lost during the evolution of great apes ., Although we do not know the precise evolutionary impact of the loss , its expression pattern in the mouse pituitary gland and developmental abnormalities observed in Drosophila null mutants suggests the loss might be related to development or hormonal regulation 52–54 ., As shown in Table 1 , other previously unknown gene losses include CETN4 , a mammalian centrin expressed in ciliated cells including those present in the cerebellum 55 , NEPN ( nephrocan ) , an inhibitor of TGF-β signaling pathway 56 , and NRADD , a death domain containing membrane protein involved in mediating apoptosis in response to ER stress 57 ., It is worth noting that ER-mediated apoptosis triggers a cascade leading to the activation of Caspase 12 ( CASP12 ) , a gene that is also lost in humans ., The loss of CASP12 is still polymorphic in humans and has been shown to have experienced a recent selective sweep 58 , 59 ., The timing of the gene losses was determined by finding the branch interval that encloses the earliest shared ORF-disrupting mutations between humans and other mammals on a phylogenetic tree ., The branch intervals on the human lineage for the 35 definite losses are illustrated on a mammalian phylogeny ( Figure 3 ) ., Using complete genome sequences of human , chimp , rhesus , mouse , and dog , six genes were determined to be human-specific losses , i . e . , lost after the divergence of humans and chimpanzees ., Ten genes were found to be lost during the period between the human–chimp split and the divergence of old world monkeys from the human lineage ., Among the ten genes , seven were observed to have independent ORF-disrupting mutations in the rhesus lineage that are not shared with humans or chimps ., Seventeen genes were determined to be lost prior to the divergence of old world monkeys from the human lineage and after the common ancestor of euarchontoglires ., Due to insufficient sequence information in the rhesus genome , two genes could only be determined to be lost at some point during the 70 million years ( My ) prior to the human–chimp split and after the common ancestor of euarchontoglires ., To refine the timing of the gene losses , we extracted trace sequences from several additional primates ( orangutan , marmoset , tarsier , galago ) and tree shrew ., Using these trace sequences , we were able to narrow down 50% of gene losses to a much more precise branch on a phylogenetic tree ., For example , the timing of gene losses for GUCY2D , NEPN , and others ( number 19 to 22 ) was narrowed down to the period of approximately 25 to 40 Mya , between the dates when old world and new world monkeys split off from the human lineage ( Figure 3 ) ., In addition to identifying previously unknown gene losses in the human genome , our analysis refined the timing of several previously known gene losses ., For example , SULT1D1 , a sulfotransferase , and GSTA4 , a glutathione S-transferase alpha 4 isozyme are known to be pseudogenes in human while their mouse orthologs remain functional 60 , 61; however , it was unclear when the inactivation occurred ., Our analysis discovered that the pseudogenization of GSTA4 and SULT1D1 occurred approximately 14 to 25 Mya , between the dates when orangutans and old world monkeys split off from the human lineage ., GULO is known to be inactive in primates with the inactivation dating to some time prior to the separation of apes and old world monkeys ( >25 Mya ) 62 ., Frameshift indels , nonsense mutations , and genomic deletions are observed in the human GULO sequence , indicating an older pseudogene that has experienced numerous secondary mutations ., The shared mutation analysis has shown that human , chimp , rhesus , and marmoset share a nonsense mutation , while galago , mouse , and dog share the TGG tryptophan codon ., Therefore , the inactivation of GULO occurred before the separation of new world and old world monkeys ( >40 Mya ) ., A significant contribution of this analysis is to differentiate losses of well-established genes from the large background of pseudogenes caused by retrotransposition or formed shortly after segmental or tandem duplication ., The method of syntenically mapping gene structures to both a target and outgroup genome is likely to filter out almost all processed pseudogenes ., However , TransMap does not fully eliminate those genes that were silenced soon after duplication , which we referred to as duplication-induced pseudogenes ., To identify duplication-induced gene losses , we need to determine when the duplication occurred ., Recent segmental duplications can be detected by within-genome sequence homology ., If there is a self-alignment chain in the UCSC human genome browser 63 enclosing the gene loss region , the region is determined to have been recently duplicated ., We determined when the duplications had occurred by tracing along the human lineage through a seven-species syntenic alignment ( human , chimp , rhesus , mouse , rat , dog , opossum ) to determine the origin of each duplicate in the best self-alignment ( the one with the highest alignment score recorded in the UCSC genome browser ) ., If a gene and its duplicate trace back to a single region in an outgroup genome , the duplication was determined to have occurred on the branch immediately after the outgroup split off from the human lineage ., If the gene and its duplicate consistently traced back to different regions through the series of outgroups all the way back to opossum , the duplication was determined to occur prior to the common ancestor of human and opossum ., Figure 4A is a schematic illustration of this procedure ., The branch of gene duplication is the branch of gene birth ( by duplication ) ., In many cases , there are no detectable self-alignments , indicating an ancient duplication had formed the functional precursor to the pseudogene ., We presume the functional precursor of the pseudogene existed prior to the earliest common ancestor of human and the species whose genomic sequence can be aligned to the human exons , therefore providing a lower bound timing of the gene birth event ., To narrow down the timing of gene birth on the long branch between dog and opossum , we included scaffold assemblies of the elephant , tenrec , and armadillo genomes ., To infer gene birth events that occurred further back in the evolutionary past , we included the chicken genome in the analysis ( Figure 4B ) ., Using the above method , the gene birth by duplication branch for the 35 non-OR , non-cluster definite gene losses was determined and is shown in Table 2 ( Gene Birth Branch ) ., Subsequently , we estimated the length of time ( in My ) a gene remained functional before its pseudogenization using the separation of the gene birth and death branches ., Since the timings of both events are estimated as branch intervals , an upper and lower bound estimation of the separation was obtained ., Using a 50 My threshold , we classified the candidates based on their functional time lengths as losses of well-established genes , duplication-induced pseudogenes , or undetermined ., If the lower estimation of functional time length is greater than 50 My , the candidate is classified as a loss of a well-established gene ., If the upper estimation of functional time length is smaller than 50 My , the gene loss is classified as a duplication-induced pseudogene ., The gene loss is classified as undetermined if its functional time length overlaps the 50 My threshold ., Table 2 gives the estimated functional time length for the 35 definite losses ., Among them , 26 are classified to be losses of well-established genes , which accounted for the majority ( 74% ) of the definite losses ., Five are classified as duplication-induced losses—CYP2G1 , SORD , S100a15 , CXCL7 , and UNC93a— ( labeled “**” in Tables 1 and 2 ) ., The remaining four are undetermined ., Of these 26 losses of well-established genes , 16 have not been previously characterized as human pseudogenes in the literature ., Among these 16 , four have been functionally characterized in mouse , which are NRADD , NEPN , CETN4 , and GUCY2D ., Table S5 describes various subsets constructed using the 35 “definite” losses ., All four candidates do not have detectable homologs in the human genome ., Most strikingly , NRADD , NEPN , and CETN4 remained functional for more than 300 My before being inactivated ., This study presents the first attempt to systematically identify adaptive gene losses in the human genome since the common ancestor of euarchontoglires , approximately 75 Mya ., Using losses of well-established genes as the proxy for adaptive gene losses , we focused on identifying a class of pseudogenes that were once functional and retained this function through tens of millions of years of evolution ., We confidently identified 26 losses of well-established genes , including 16 that were not previously known in the literature ., The highlight of this analysis is the ability to automatically detect losses of genes bearing no significant homology to any functional paralog in the human genome ., Their functional precursors had an ancient origin , but enough evolutionary time has elapsed to erase any significant homology with other genes in the human genome ., These genes were functioning for hundreds of millions of years and silenced recently within the past 75 My ., It has been proposed that the majority of pseudogenes are either dead-on-arrival 58 or inactivated quickly after duplication 27 ., Therefore , it is not surprising that we have identified a much smaller number of pseudogenes as compared to the thousands identified by previous whole genome analysis that aimed to catalog the human genome for unprocessed pseudogenes 30 , 31 , 36 , 64 ., We overlapped our results with two well-known pseudogene databases , Yale pseudogene database , composed of mostly various computational predictions 64 , and VEGA pseudogene collection , compiled by manual curation 65 ., We found limited overlap between the losses identified in Table 1 with both pseudogene sets ( Table S4 ) ., Only two out of 31 annotated , zero out of 14 hypothetical , and five out of 27 ORs were found by all three analyses ., Neither database has GULO , Cardiotropin 2 , or many others listed in Table 1 ( see note b ) ., A recent genome scan identified 67 human-specific gene losses , including 36 ORs 58 ., Excluding ORs , only one out of the six human-specific gene losses identified in Table 1 , Gpr33 , was also discovered in that study 58 ., Another possible overlap is Ugt2b1 , which belongs to a tandem cluster of Ugt2B genes on Chromosome 4 ., The limited overlap in part reflects the difference in methodology used to identify the pseudogenes , but also makes apparent that none of these methods in their present state are able to form the complete set of losses of genes with ancient origins ., It also confirms that we have identified some unprocessed pseudogenes derived from functional precursors of ancient origin , where evolution has erased any significant homology to their current functional paralogs ., The gene loss candidates shown in Table 1 are by no means a complete list of losses of well-established genes in the human lineage during the past 75 My ., TransMap gene model prediction methodology is not perfect , many factors can introduce prediction errors including uncertainties in sequence alignments , errors generated by the gene model prediction and evaluation procedures , and evolutionary changes of the gene structures across mammalian species ( Text S1 ) ., For example , the well-known human specific loss of CMAH ( a CMP-sialic acid hydroxylase ) 66 was not found by this analysis due to the strictness of TransMap gene model predictions , causing a valid CMAH gene model in the dog genome to be excluded because it featured a noncanonical GC-AG splice junction ., However , the use of an outgroup genome and the mRNA filter makes the analysis far more likely to produce false negatives than false positives ., Several other factors also contribute to this incompleteness ., First , our method using human–mouse–dog comparison relied upon well-defined mouse genes to seed the search and valid dog predictions for outgroup confirmation ., Problems in either one will return a false negative result ., Our analysis missed MYH16 because it is not in mouse RefSeq , which could be due to an independent loss or a misannotation ., We further investigated its absence and found that the MYH16 syntenic region is not present in the mouse genome , indicating an independent loss in mouse via genomic deletion ., Our analysis required a valid conceptual translation in the dog genome , which may fail to occur due to TransMap prediction errors , sequencing gaps , or an independent loss in dog ., However , the chance of producing a valid mapping increases if multiple outgroups , such as the opossum genome 67 or a computationally reconstructed ancestral genome 68 , were used and the resultant gene loss predictions were combined ., For example , the previously documented human specific loss of Htr5b 69 can be identified using a reconstructed boreoeutherian genome as the outgroup ( Haussler lab , unpublished data ) ., Our analysis can also be improved by extending our seed mRNAs to include those from other species and by using multiple outgroup genomes ., For example , using chimpanzee MYH16 mRNA as a seed could have found this pseudogene in human ., Our analysis may not identify human polymorphic gene losses ., For example , the human-specific loss of CASP12 58 , 59 was not identified by our analysis because the latest human genome assembly ( NCBI release 36 ) has the functional allele ., Several other human polymorphic losses were also missed by our analysis for the same reason 70 , 71 ., These polymorphic null alleles are potentially crucial to human diseases , e . g . , CASP12 in sepsis and CCR5 in HIV infection ., Incorporating human EST and mRNA information , as was done by Hahn et al . 70 , 71 , or the human SNP dataset 72 , could help our method identify human polymorphic gene losses ., Overlapping those alleles with human disease loci , such as those documented in OMIM database 73 or identified by genetic association studies , might lead to the identification of new human disease associated genes ., Another factor that may cause the method to overlook gene losses is related to segmental duplication ., After a gene is duplicated , both the ancestral copy ( the copy in the original genomic context ) and the daughter copy ( the copy duplicated in the new genomic context ) are equally subject to degenerative mutations ., Since our analysis evaluates based on the status of the ancestral copy , if evolution silences the daughter copy , it will not be identified by our method ., However , this type of false negative is quite limited in our results because it only applies when a segmental duplication occurred after the boreoeutherian common ancestor ., Treating the daughter copy in the same way as the ancestral copy will solve this problem , except in the case of a tandem segmental duplication , where it is difficult to distinguish the ancestral copy from the daughter copy ., Among the 26 losses of well-established genes , six were identified to be lost independently in the human and old world monkey lineages ( numbers 8 , 11 , 12 , 13 , 15 , 25 in Table 1 ) ., This can be interpreted as a confirmation for adaptive evolution , if we believe that a common selection pressure forced these genes to be lost in separate clades ., Other known independent losses such as Caspase15 and Gpr33 seem to confirm this hypothesis 74 , 75 ., An alternative interpretation is that the gene function is no longer needed , such as the loss of GULO in guinea pigs and humans 40 ., However , it is also quite probable that the original loss did not occur independently on different lineages , but rather a common mutation that was missed by the analysis might have occurred earlier on a shared ancestor to inactivate the gene ., This might have been a mutation in a noncoding region , or a mutation that was erased by secondary mutations such as genomic deletions ., For example , a prior , noncoding mutation in any of the six cases we found could have disrupted the transcription , translation , or regulatory signals of the gene in the common ancestor of old world monkeys and apes , rendering the gene effectively inactive at the time that these lineages split ., Since the gene is no longer under selective pressure to maintain its integrity , secondary ORF-disrupting mutations could follow , occurring independently in the separate lineages , as observed by our analysis ., To identify genes that are truly lost , we have focused on regions lacking any reported mRNA evidence , including in cell lines derived from cancer cells ., A large number of candidates with differential mutational status in the human and dog gene predictions ( 918 out of 1 , 008 ) were filtered out because they overlap with some mRNA evidence in humans ., The majority of these are likely to be TransMap prediction errors ( Text S1 , Table S1 ) ., However , some pseudogenes still generate transcripts if the transcription signal is intact , and these would be overlooked by our method ., An example of a transcribed pseudogene in the human genome that appears on this list is CATSPER2 ( chr15: 41815434–41825788 ) , represented by GenBank mRNA BC066967 , and BC047442 ., The mammalian gene collection annotates it as a transcribed pseudogene ., If a pseudogene is transcribed and spliced , its mRNA transcript with ORF-disrupting mutations ( i . e . , premature stop codon ) is targeted and degraded by the cells RNA surveillance pathway of nonsense mediated decay 76 , although this process may not be complete ., Only with time will these pseudogenes will be completely silenced at the level of transcription ., In addition , studies have shown that occasionally a pseudogene , like Makorin1 , not only transcribes but also plays a vital biological role in stabilizing the mRNA of its homologous coding gene 77 ., Thus it is difficult to prove that a transcribed pseudogene is completely nonfunctional ., Theories of molecular evolution suggest three outcomes for new genes arising from gene duplication: degeneration due to functional redundancy , evolution into a new function , or function sharing by both copies 27 ., The expected time that elapses before a gene is inactivated is thought to be relatively short 27 ., Lynch and Conery estimated the half-life of a new duplicate

Introduction, Results, Discussion, Materials and Methods

Taking advantage of the complete genome sequences of several mammals , we developed a novel method to detect losses of well-established genes in the human genome through syntenic mapping of gene structures between the human , mouse , and dog genomes ., Unlike most previous genomic methods for pseudogene identification , this analysis is able to differentiate losses of well-established genes from pseudogenes formed shortly after segmental duplication or generated via retrotransposition ., Therefore , it enables us to find genes that were inactivated long after their birth , which were likely to have evolved nonredundant biological functions before being inactivated ., The method was used to look for gene losses along the human lineage during the approximately 75 million years ( My ) since the common ancestor of primates and rodents ( the euarchontoglire crown group ) ., We identified 26 losses of well-established genes in the human genome that were all lost at least 50 My after their birth ., Many of them were previously characterized pseudogenes in the human genome , such as GULO and UOX ., Our methodology is highly effective at identifying losses of single-copy genes of ancient origin , allowing us to find a few well-known pseudogenes in the human genome missed by previous high-throughput genome-wide studies ., In addition to confirming previously known gene losses , we identified 16 previously uncharacterized human pseudogenes that are definitive losses of long-established genes ., Among them is ACYL3 , an ancient enzyme present in archaea , bacteria , and eukaryotes , but lost approximately 6 to 8 Mya in the ancestor of humans and chimps ., Although losses of well-established genes do not equate to adaptive gene losses , they are a useful proxy to use when searching for such genetic changes ., This is especially true for adaptive losses that occurred more than 250 , 000 years ago , since any genetic evidence of the selective sweep indicative of such an event has been erased .

One of the most important questions in biology is to identify the genetic changes underlying evolution , especially those along the lineage leading to the modern human ., Although counterintuitive , losing a gene might actually bring a selective advantage to the organism ., This type of gene loss is called adaptive gene loss ., Although a few cases have been characterized in the literature , this is the first study to address adaptive gene losses on a scale of the whole human genome and a time period of up to 75 million years ., The difficulty of identifying adaptive gene losses is in part the large number of pseudogenes in the human genome ., To circumvent this problem , we used two methods to enrich the process for the adaptive candidates ., The first is a novel approach for pseudogene detection that is highly sensitive in identifying single-copy pseudogenes that bear no apparent sequence homology to any functional human genes ., Second , we used the length of time a gene is functional before loss as a proxy for biological importance , which allows us to differentiate losses of long-established genes from mere losses due to functional redundancy after gene duplication .

primates, computational biology, evolutionary biology, homo (human), mammals

journal.pcbi.1000959

Individualization as Driving Force of Clustering Phenomena in Humans

Many biological systems exhibit collective patterns , which emerge through simple interactions of large numbers of individuals ., A typical example is agglomeration phenomena ., Such clustering dynamics have been found in systems as different as bacterial colonies 1 , gregarious animals like cockroaches 2 , fish schools 3 , flocks of birds 4 , and animal groups 5 ., Similar phenomena are observed in ecosystems 6 and human populations , as examples ranging from the formation of pedestrian groups 7 to the formation of urban agglomerations demonstrate 8 , 9 ., Recently , numerous studies on the structure of human interaction networks 10–12 demonstrated that clustering is not restricted to physical or geographical space ., For instance , clustering has been extensively studied in networks of email communication 13 , phone calls 12 , scientific collaboration 14 and sexual contacts 15 ., It is much less understood , however , how and what conditions clustering patterns emerge in behavioral or opinion space ., Empirical studies suggest that opinions differ globally 16 , 17 , while they cluster locally within geographical regions 18 , socio-demographic groups 19 , or Internet communities 20 ., In addition , research on dynamics in work teams demonstrates that even groups of very small size often show high opinion diversity and can even suffer from opinion polarization 21 , 22 ., Opinion clustering is defined as the co-existence of distinct subgroups ( clusters ) of individuals with similar opinions , while opinions in different subgroups are relatively large ., The gaps in our theoretical understanding of opinion clustering are pressing since both local consensus and global diversity are precarious ., On the one hand , cultural diversity may get lost in a world where people are increasingly exposed to influences from mass media , Internet communication , interregional migration , and mass tourism , which may promote a universal monoculture 23 , 24 , as the extinction of languages suggests 25 ., On the other hand , increasing individualization threatens to disintegrate the social structures in which individuals are embedded , with the possible consequence of the loss of societal consensus 26 , 27 ., This is illustrated by the recent debate on the decline of social capital binding individuals into local communities 28 ., Early formal models of social influence imply that monoculture is unavoidable , unless a subset of the population is perfectly cut off from outside influences 29 ., Social isolation , however , appears questionable as explanation of pluralism ., In modern societies , distances in social networks are quite short on the whole , and only relatively few random links are required to dramatically reduce network distance 10 ., Aiming to explain pluralism , researchers have incorporated the empirically well-supported observation of “homophily” , i . e . the tendency of “birds of a feather to flock together” 30 , 31 , into formal models of social influence 32 ., These models typically assume “bounded confidence” ( BC ) in the sense that only those individuals interact , whose opinions do not differ more than a given threshold level 33 , 34 ., As Fig . 1A illustrates , BC generates opinion clustering , a result that generalizes to model variants with categorical rather than continuous opinions 32 , 35 ., However , clustering in the BC-model is sensitive to “interaction noise”: A small random chance that agents may interact even when their opinions are not similar , causes monoculture again ( see Fig . 1B ) ., To avoid this convergence of opinions , it was suggested that individuals would separate themselves from negatively evaluated others 19 , 36 , 37 ., However , recent empirical results do not support such “negative influence” 38 ., Scientists also tried to avoid convergence by “opinion noise” , i . e . random influences , which lead to arbitrary opinion changes with a small probability ., Assuming uniformly distributed opinion noise 39 leads to sudden , large , and unmotivated opinion changes of individuals , while theories of social integration 26 , 27 , 40 , 41 and empirical studies of individualization 42 , 43 show a tendency of incremental opinion changes rather than arbitrary opinion jumps ., Incremental opinion changes , however , tend to promote monoculture , even in models with categorical rather than continuous opinions 44 ., Fig . 1 demonstrates that adding a “white noise” term ( ) to an agents current opinion in the BC model fails to explain opinion clustering ., Weak opinion noise ( ) triggers convergence cascades that inevitably end in monoculture ., Stronger noise restores opinion diversity , but not clustering ., Instead , diversity is based on frequent individual deviations from a predominant opinion cluster ( for ) ., However , additional clusters cannot form and persist , because opinion noise needs to be strong to separate enough agents from the majority cluster—so strong that randomly emerging smaller clusters cannot stabilize ., In conclusion , the formation of persistent opinion clusters is such a difficult puzzle that all attempts to explain them had to make assumptions that are difficult to justify by empirical evidence ., The solution proposed in the following , in contrast , aims to reconcile model assumptions with sociological and psychological research ., The key innovation is to integrate another decisive feature into the model , namely the “striving for uniqueness” 42 , 43 ., While individuals are influenced by their social environment , they also show a desire to increase their uniqueness when too many other members of society hold similar opinions ., We incorporate this assumption as a white noise term in the model ., However , in contrast to existing models we assume that noise strength is not constant but adaptive ., To be precise , we assume that the impact of noise on the opinion of an individual is the stronger the less unique the individuals opinion is compared to the other members of the population ., Consumer behavior regarding fashions illustrates the adaptability of opinion noise: When new clothing styles are adopted by some people , they often tend to be imitated by others with similar spirit and taste ( the “peer group” ) ., However , when imitation turns the new style into a norm , people will seek to increase their uniqueness ., This will sooner or later lead some individuals to invent new ways to dress differently from the new norm ., Adaptive noise creates a dynamic interplay of the integrating and disintegrating forces highlighted by Durkheims classic theory of social integration 26 ., Durkheim argued that integrating forces bind individuals to society , motivating them to conform and adopt values and norms that are similar to those of others ., But he also saw societal integration as being threatened by disintegrating forces that foster individualization and drive actors to differentiate from one another 27 , 40 , 41 ., The “Durkheimian opinion dynamics model” proposed in the following can explain pluralistic clustering for the case of continuously varying opinions , although it incorporates all the features that have previously been found to undermine clustering: ( 1 ) a fully connected influence network , ( 2 ) absence of bounded confidence , ( 3 ) no negative influence , and ( 4 ) white opinion noise ., From a methodological viewpoint , our model builds on concepts from statistical physics , namely the phenomenon of “nucleation” 45 , illustrated by the formation of water droplets in supersaturated vapor ., However , by assuming adaptive noise , we move beyond conventional nucleation models ., The model also resembles elements of Interacting Particle Systems 46 like the voter model and the anti-voter model 47–50 which have been used to study dynamics of discrete opinions ( “pro” and “contra” ) ., However , we focus here on continuous opinions like the degree to which individuals are in favor of or against a political party ., Computational simulation experiments reveal that , despite the continuity of opinions in our model , it generates pluralism as an intermediate phase between monoculture and individualism ., When the integrating forces are too strong , the model dynamics inevitably implies monoculture , even when the individual opinions are initially distributed at random ., When the disintegrating forces prevail , the result is what Durkheim called “anomie” , a state of extreme individualism without a social structure , even if there is perfect consensus in the beginning ., Interestingly , there is no sharp transition between these two phases , when the relative strength of both forces is changed ., Instead , we observe an additional , intermediate regime , where opinion clustering occurs , which is independent of the initial condition ., In this regime , adaptive noise entails robust pluralism that is stabilized by the adaptability of cluster size ., When clusters are small , individualization tendencies are too weak to prohibit a fusion of clusters ., However , when clusters grow large , individualization increases in strength , which triggers a splitting into smaller clusters ( “fission” ) ., In this way , our model solves the cluster formation problem of earlier models ., While in BC models , white noise causes either monoculture or fragmentation ( Fig . 1C ) , in the Durkheimian opinion dynamics model proposed here , it enables clustering ., Therefore , rather than endangering cluster formation , noise supports it ., In the following , we describe the model and identify conditions under which pluralism can flourish ., The model has been elaborated as an agent-based model 51 addressing the opinion dynamics of interacting individuals ., The simulated population consists of agents , representing individuals , each characterized by an opinion at time ., The numerical value for the opinion varies between a given minimum and maximum value on a metric scale ., We use the term “opinion” here , for consistency with the literature on social influence models ., However , may also reflect behaviors , beliefs , norms , customs or any other cardinal cultural attribute that individuals consider relevant and that is changed by social influence ., The dynamics is modeled as a sequence of events ., Every event the computer randomly picks an agent and changes the opinion by the amount ( 1 ) The first term on the rhs of Eq ., 1 models the integrating forces of Durkheims theory ., Technically , agents tend to adopt the weighted average of the opinions of all other members of the population ., Implementing homophily , the social influence that agent has on agent is the stronger , the smaller their opinion distance is ., Formally , we assume ( 2 ) The parameter represents the range of social influence of agents ., For small positive values of , agents are very confident in their current opinion and are mainly influenced by individuals who hold very similar opinions , while markedly distinct opinions have little impact ., The higher is , however , the more are agents influenced by individuals with considerably different opinions and the stronger are the integrating forces in our Durkheimian theory ., The disintegrating forces on the opinion of agent are modeled by a noise term ., Specifically , the computer adds a normally distributed random value ( “white noise” ) to the first term on the rhs of Eq ., 1 ., While we assume that the mean value of the random variable is zero , the standard deviation has been specified as ( 3 ) The larger the standard deviation , the stronger are the individualization tendencies of an agent ., Following Durkheims theory , equation 3 implements noise in an adaptive way: Accordingly , an agents striving for individualization is weak , if there are only a few others with similar opinions ., Under such conditions , there is no need to increase distinctiveness ., However , if many others hold a similar opinion , then individuals are more motivated to differ from others ., By including the focal agent in the sum of Eq ., 3 , we assume that there is always some degree of opinion noise , even when agent holds a perfectly unique opinion ., These fluctuations may have a variety of reasons , such as misjudgments , trial-and-error behavior , or the influence of exogenous factors on the individual opinion ., Furthermore , this assumption reflects Durkheims notion that the seeking for uniqueness is a fundamental feature of human personality , which cannot be suppressed completely 26 , 52 ., We use the parameter of Eq ., 3 to vary the strength of the disintegrating forces in society ., The higher the value of , the higher is the standard deviation of the distribution , from which is drawn , and the stronger are the disintegrating forces ., Finally , to keep the opinions of the agents within the bounds of the opinion scale , we set the value of to zero , if the bounds of the opinion space would be left otherwise ., We have studied the Durkheimian opinion dynamics model with extensive computer simulations , focusing on relatively small populations ( ) , because in this case it is reasonable to assume that all members may interact with each other ., For bigger populations one would have to take into account the topology of the social interaction network as well ., Such networks would most likely consist of segregated components ( “communities” ) , which are not or only loosely connected with each other 12–15 ., Existing social influence models can explain how under such conditions each community develops its own shared opinion ( see Fig . 1A ) ., However , according to these models opinion clustering is only stable when there is no interaction between communities 29 , 33 , an assumption that appears not to be empirically correct in an increasingly connected world ., Therefore , we focus on a setting for which the lack of connectedness is guaranteed to be excluded as explanation of clustering and study model dynamics in relatively small and complete interaction networks ., To illustrate the model dynamics , Fig . 2 shows three typical simulation runs for different strengths of disintegrating forces , while the strength of the integrating force is kept constant ., In each run , all agents start with an opinion in the middle of the opinion scale ( ) , i . e . conformity ., This is an initial condition for which the classical BC-model does not produce diversity ., Fig . 2A shows typical opinion trajectories for a population in which the integrating forces are much stronger than the disintegrating forces ., Consequently , the population develops collective consensus , i . e . the variation of opinions remains small , even though not all agents hold exactly the same opinion ., Triggered by the random influences , the average opinion performs a characteristic random walk ., When the disintegrating force prevails , the pattern is strikingly different ., Fig . 2B shows that for large noise strengths , the initial consensus breaks up quickly , and the agents opinions are soon scattered across the entire opinion space ., Simulation scenarios A and B are characteristic for what Durkheim referred to as states of social cohesion and of anomie ., Interestingly , however , pluralism arises as a third state in which several opinion clusters form and coexist ., Fig . 2C shows a typical simulation run , where the adaptive noise maintains pluralism despite the antagonistic impacts of integrating and disintegrating forces—in fact because of this ., In the related region of the parameter space , disintegrating forces prevent global consensus , but the integrating forces are strong enough to also prevent the population from extreme individualization ., This is in pronounced contrast to what we found for the BC-model with strong noise ( Fig . 1C ) ., Instead , we obtain a number of coexisting , metastable clusters of a characteristic , parameter-dependent size ., Each cluster consists of a relatively small number of agents , which keeps the disintegrating forces in the cluster weak and allows clusters to persist ., ( Remember that the tendency of individualization according to Eq . 3 increases , when many individuals hold similar opinions . ), However , due to opinion drift , distinct clusters may eventually merge ., When this happens , the emergent cluster becomes unstable and will eventually split up into smaller clusters , because disintegrating forces increase in strength as a cluster grows ., Strikingly , the state of diversity , in which several opinion clusters can coexist , is not restricted to a narrow set of conditions under which integrating and disintegrating forces are balanced exactly ., Fig . 3 demonstrates that opinion clusters exist in a significant area of the parameter space , i . e . the clustering state establishes another phase , which is to be distinguished from monoculture and from anomie ., To generate Fig . 3 , we conducted a simulation experiment in which we varied the influence range and the strength of the disintegrating force ., For each parameter combination , we ran 100 replications and measured the average number of clusters that were present after 250 , 000 iterations ., To count the number of clusters in a population , we ordered the agents according to their opinion ., A cluster was defined as a set of agents in adjacent positions such that each set member was separated from the adjacent set members by a maximum of 5 scale points ( =\u200aopinion range/ ) ., Fig . 3 shows that , for large social influence ranges and small noise strengths , the average number of clusters is below 1 . 5 , reflecting monoculture in the population ., In the other extreme , i . e . for a small influence range and large noise strengths , the resulting distribution contains more than 31 clusters , a number of clusters that cannot be distinguished from purely random distributions ., Following Durkheim , we have classified such cases as anomie , i . e . as the state of extreme individualism ., Between these two phases , there are numerous parameter combinations , for which the number of clusters is higher than 1 . 5 and clearly smaller than in the anomie phase ., This constitutes the clustering phase ., Fig . 3 also shows that , for each parameter combination , there is a small variance in the number of clusters , which is due to a statistical equilibrium of occasional fusion and fission processes of opinion clusters ( see Fig . 2C ) ., The same results were found , when starting the computer simulations with a uniform opinion distribution ., This demonstrates that the simulations were run long enough ( 250 , 000 iterations ) to obtain reliable results ., It also suggests that clustering is an attractor in the sense that the model generates clustering independent of the initial distribution of opinions ., In addition , we performed additional statistical tests with the simulation outcomes to make sure that the existence of clusters in our model indeed indicates pluralism and not fragmentation , a state in which a population consists of one big cluster and a number of isolated agents ( see Fig . 4 ) ., To illustrate , Fig . 4A plots the size of the biggest cluster in the population versus the number of clusters ( see the blue areas ) ., For comparison , the yellow area depicts the corresponding distribution for randomly fragmented opinion distributions ., The figure shows that the distributions hardly overlap and that the Durkheimian model generates clustering rather than fragmentation ., In clear contrast , Fig . 4B reveals that the opinion distributions generated by the noisy BC-model are fragmented and not clustered ., Finally , to exclude that results have been influenced by floating point inaccuracies 53 we conducted simulation experiments with the restriction that influence weights could not adopt values smaller than ., All results could be replicated ., The phenomenon of self-organized clustering phenomena in biological and social systems is widespread and important ., With the advent of mathematical and computer models for such phenomena , there has been an increasing interest to study them also in human populations ., The work presented here focuses on resolving the long-standing puzzle of opinion clustering ., The emergence and persistence of pluralism is a striking phenomenon in a world in which social networks are highly connected and social influence is an ever present force that reduces differences between those who interact ., We have developed a formal theory of social influence that , besides anomie and monoculture , shows a third , pluralistic phase characterized by opinion clustering ., It occurs , when all individuals interact with each other and noise prevents the convergence to a single opinion , despite homophily ., Our model does not assume negative influence , and it behaves markedly different from bounded confidence models , in which white opinion noise produces fragmentation rather than clustering ., Furthermore , our model does not rely on the problematic assumption of classical influence models that agents are forevermore cut-off from influence by members of distinct clusters ., In order to demonstrate this , we studied model predictions in a setting where all members of the population interact with each other ., However , empirical research shows that opinion clustering tends to coincide with clustered network structures 20 and spatial separation 18 ., It would therefore be natural to generalize the model in a way that it also considers the structure of real social networks ., Such a model is obtained by replacing the values by , where are the entries of the adjacency matrix ( i . e . , if individuals and interact , otherwise ) ., Then , the resulting opinion clusters are expected to have a broad range of different sizes , similar to what is observed for the sizes of social groups ., Our model highlights the functional role that “noise” ( randomness , fluctuations , or other sources of variability ) plays for the organization of social systems ., It furthermore shows that the combination of two mechanisms ( deterministic integrating forces and stochastic disintegrating forces ) can give rise to new phenomena ., We also believe that our results are meaningful for the analysis of the social integration of our societies ., According to Durkheims theory of the development of societies 26 , traditional human societies are characterized by “mechanical solidarity” ., In these societies , individuals are strongly integrated in very homogeneous communities which exert strong influence on the behavior and opinions of individuals ., According to Durkheim , however , these regulating social structures dissolve as societies turn modern ., In addition , Durkheim 26 and contemporary social thinkers 27 argue that in modern and globalized societies individuals are increasingly exposed to disintegrating forces , which foster individualization 26 ., As a consequence , the social forces which let individuals follow societal norms may lose their power to limit individual variation ., Durkheim feared that the high diversity could disintegrate societies as they modernize 26 ., That is , extreme individualization in modern societies may obstruct the social structures that traditionally provided social support and guidance to individuals ., Today , modern societies are highly diverse , but at the same time they are far from a state of disintegration as foreseen by Durkheim ., He argued that this is possible if societies develop what he called “organic solidarity” ., In this state societies are highly diverse but at the same time the division of labor creates a dense web of dependencies which integrate individuals into society and generate sufficient moral and social binding 26 ., Strikingly , our formal model of Durkheims theory revealed another possibility which does not require additional integrating structures like the division of labor: Besides monoculture and anomie , there is a third , pluralistic clustering phase , in which individualization prevents overall consensus , but at the same time , social influence can still prevent extreme individualism ., The interplay between integrating and disintegrating forces leads to a plurality of opinions , while metastable subgroups occur , within which individuals find a local consensus ., Individuals may identify with such subgroups and develop long-lasting social relationships with similar others ., Therefore , they are not isolated and not without support or guidance , in contrast to the state of disintegration that Durkheim was worried about ., We have seen , however , that pluralism and cultural diversity require an approximate balance between integrating and disintegrating forces ., If this balance is disturbed , societies may drift towards anomie or monoculture ., It is , therefore , interesting to ask how the current tendency of globalization will influence society and cultural dynamics ., The Internet , interregional migration , and global tourism , for example , make it easy to get in contact with members of distant and different cultures ., Previous models 24 , 35 suggest that this could affect cultural diversity in favor of a monoculture ., However , if the individual striving for uniqueness is sufficiently strong , formation of diverse groups ( a large variety of international social communities ) should be able to persist even in a globalizing world ., In view of the alternative futures , characterized by monoculture or pluralism , further theoretical , empirical , and experimental research should be performed to expand our knowledge of the mechanisms that will determine the future of pluralistic societies .

Introduction, Model, Results, Discussion

One of the most intriguing dynamics in biological systems is the emergence of clustering , in the sense that individuals self-organize into separate agglomerations in physical or behavioral space ., Several theories have been developed to explain clustering in , for instance , multi-cellular organisms , ant colonies , bee hives , flocks of birds , schools of fish , and animal herds ., A persistent puzzle , however , is the clustering of opinions in human populations , particularly when opinions vary continuously , such as the degree to which citizens are in favor of or against a vaccination program ., Existing continuous opinion formation models predict “monoculture” in the long run , unless subsets of the population are perfectly separated from each other ., Yet , social diversity is a robust empirical phenomenon , although perfect separation is hardly possible in an increasingly connected world ., Considering randomness has not overcome the theoretical shortcomings so far ., Small perturbations of individual opinions trigger social influence cascades that inevitably lead to monoculture , while larger noise disrupts opinion clusters and results in rampant individualism without any social structure ., Our solution to the puzzle builds on recent empirical research , combining the integrative tendencies of social influence with the disintegrative effects of individualization ., A key element of the new computational model is an adaptive kind of noise ., We conduct computer simulation experiments demonstrating that with this kind of noise a third phase besides individualism and monoculture becomes possible , characterized by the formation of metastable clusters with diversity between and consensus within clusters ., When clusters are small , individualization tendencies are too weak to prohibit a fusion of clusters ., When clusters grow too large , however , individualization increases in strength , which promotes their splitting ., In summary , the new model can explain cultural clustering in human societies ., Strikingly , model predictions are not only robust to “noise”—randomness is actually the central mechanism that sustains pluralism and clustering .

Modern societies are characterized by a large degree of pluralism in social , political and cultural opinions ., In addition , there is evidence that humans tend to form distinct subgroups ( clusters ) , characterized by opinion consensus within the clusters and differences between them ., So far , however , formal theories of social influence have difficulty explaining this coexistence of global diversity and opinion clustering ., In this study , we identify a missing ingredient that helps to fill this gap: the striving for uniqueness ., Besides being influenced by their social environment , individuals also show a desire to hold a unique opinion ., Thus , when too many other members of the population hold a similar opinion , individuals tend to adopt an opinion that distinguishes them from others ., This notion is rooted in classical sociological theory and is supported by recent empirical research ., We develop a computational model of opinion dynamics in human populations and demonstrate that the new model can explain opinion clustering ., We conduct simulation experiments to study the conditions of clustering ., Based on our results , we discuss preconditions for the persistence of pluralistic societies in a globalizing world .

computational biology

journal.pgen.1001110

Incremental Genetic Perturbations to MCM2-7 Expression and Subcellular Distribution Reveal Exquisite Sensitivity of Mice to DNA Replication Stress

In late mitosis to early G1 phase of the cell cycle , DNA replication origins are selected and bound by the hexameric origin recognition complex ( ORC; 1 ) ., ORC then recruits the initiation factors CDC6 and CDT1 , which are required for loading MCM2-7 , thereby forming the “pre-replicative complex” ( pre-RC ) ., The formation of pre-RCs is termed origin “licensing” and this gives origins competency to initiate a single round of DNA synthesis before entering S phase ., MCM2-7 is a hexamer of six distinct but structurally-related minichromosome maintenance ( MCM ) proteins ( reviewed in 2–5 ) ., In vivo and in vitro evidence indicates that the MCM2-7 complex is the replicative helicase 6–8 ., MCM2-7 proteins are abundant in proliferating cells 9 , and are bound to chromatin in amounts exceeding that which is present at active replication origins or required for complete DNA replication 10–14 ., Although these and other studies showed that drastic decreases in MCMs are tolerated by dividing cells , there are certain deleterious consequences ., In Xenopus extracts and mammalian cells , excess chromatin-bound MCM2-7 complexes occupy dormant or “backup” origins that are activated under conditions of replication stress , compensating for stalled or disrupted primary replication forks 11 , 15–16 ., The depletion of these backup licensed origins was associated with elevated chromosomal instability and susceptibility to replication stress , factors that might predispose to cancer ., In previous work , Shima et al found that a hypomorphic allele of mouse Mcm4 ( Mcm4Chaos3 ) caused high levels of GIN and extreme mammary cancer susceptibility in the C3HeB/FeJ background 17 ., This provided the first concrete evidence that endogenous mutations in replication licensing machinery may have a causative role in cancer development ., The ethylnitrosourea ( ENU ) -induced Mcm4Chaos3 point mutation changed PHE to ILE at residue 345 ( Phe345Ile ) ., This amino acid is conserved across diverse eukaryotes and is important for interaction with other MCMs 18 ., Budding yeast engineered to bear the orthologous mutation exhibit DNA replication defects and GIN 17 , 19 ., Surprisingly , MEFs from Mcm4Chaos3 mice not only had reduced levels of MCM4 , but also MCM7 17 , suggesting that the point mutation might destabilize the MCM2-7 complex ., Subsequently , it was reported that mice containing 1/3 the normal level of MCM2 succumbed to lymphomas at a very young age , and had diverse stem cell proliferation defects 20 ., These mice also had 27% reduced levels of MCM7 protein , and their cells exhibited decreased replication origin usage when under replication stress ( treatment with hydroxyurea ) conditions 21 ., These studies imply that relatively modest decreases in any of the MCMs may be sufficient to cause cancer susceptibility , developmental defects , and GIN 20 ., Here , we report that genetically-induced reductions of MCM levels in mice , achieved by breeding combinations of MCM2-7 alleles , caused several health-related defects including increased embryonic lethality , GIN , cancer susceptibility , growth retardation , defective cell proliferation , and hematopoiesis defects ., Remarkably , genetic reduction of MCM3 , which mediates nuclear export of excess MCM2-7 complexes in yeast 22 , rescued many of these defects , presumably attributable to observed increases in chromatin-bound MCM levels ., These data suggest that relatively minor misregulation or destabilization of MCM homeostasis can have serious consequences for health , viability and cancer susceptibility of animals ., To extend previous findings that Mcm4Chaos3Chaos3 cells exhibited decreases in MCM4 and MCM7 protein , and to determine if the decreased levels were differentially compartmentalized in the cell , we quantified soluble and chromatin-bound MCM2-7 levels in mouse embryonic fibroblasts ( MEFs ) by Western blot analysis ., As shown in Figure 1A , all MCMs were decreased in both compartments by at least 40% compared to WT cells ., Because Mcm4Chaos3/Chaos3 MEF cultures have slightly decreased proliferation and G2/M delay ( Figure 1A and 17 ) , it is possible that the lower MCM levels in mutant MEFs are entirely attributable to growth defects ., To test this , we assessed the levels of nuclear MCM2 in S-phase cells by flow cytometry ( Figure 1B ) ., Although MCM2 levels in WT and Mcm4Chaos3/Chaos3 G1 nuclei were essentially the same ( P\u200a= . 65; t-test ) , mutant cells transitioned from G1 to S with 40% less nuclear MCM2 content than in WT ( P< . 02; t-test ) ., The levels of nuclear MCM2 in WT decreased through S phase more sharply than in mutants , which transitioned to G2 with only ∼23% less than controls ( Figure 1B ) ., This differential decline is apparent in the flow plots , where WT cells exhibit a greater downward slope in the S compartment ( Figure 1B ) ., The decreases in MCM2 from early to late S were 51% in WT and 38% in mutants ., The MCM2 intra-S modulation phenomenon is also addressed in subsequent experiments ., The marked differences in nuclear MCM2 concentration between actively proliferating ( S-phase ) WT and mutant cells indicates that a biochemical or regulatory basis , rather than a population skewing , underlies the differences in protein levels ., Another possible explanation for the coordinated decrease in MCMs is that the mutant MCM4Chaos3 protein destabilizes the MCM2-7 hexamer and causes subsequent degradation of uncomplexed MCMs ., Other groups reported that knockdown of Mcm2 , Mcm3 , or Mcm5 in human cells decreased the amount of other chromatin-bound MCMs 15–16 , leading to a similar proposition that the cause was MCM2-7 hexamer destabilization 16 ., If true , then we would expect mRNA levels to be unchanged in mutant cells ., To test this , we performed quantitative RT-PCR ( qRT-PCR ) analysis of Mcm2-7 , and several control housekeeping genes in Mcm4Chaos3/Chaos3 MEFs ., Analysis of 5 littermate pairs of primary MEF cultures revealed that transcript levels for each of these genes in mutant cells was 51–65% of WT , similar to the protein decreases ( Figure 1C ) ., Levels of mRNA in the 7 housekeeping genes analyzed were not altered significantly ( Figure 1C , right panel ) ., This data suggest that either reduced MCM4 levels per se , or defects resulting from the Mcm4Chaos3 allele , cause a decrease in the levels of all Mcm mRNAs ., Interestingly , the mRNA reduction appears to occur post-transcriptionally , a phenomenon that is currently under investigation ( Chuang and Schimenti , unpublished observations ) ., The Mcm4Chaos3 allele was identified in a forward genetic screen for mutations causing elevated micronuclei ( MN ) in red blood cells , an indicator of GIN 17 ., While the altered MCM4Chaos3 protein may cause DNA replication errors as does a yeast allele engineered to contain the same amino acid change 19 , it is also possible that the decrease in overall MCM levels in Mcm4Chaos3 mutants contributes to , or is primarily responsible for , elevated S-phase DNA damage and GIN as is seen in various cell culture models ( see Introduction ) ., To test this possibility , we generated mice from ES cells bearing gene trap insertions in Mcm2 , Mcm3 , Mcm6 , and Mcm7 ( Figure 2A; alleles are designated as Mcm#Gt ) ., These gene traps are designed to disrupt gene expression by fusing the 5′ end of the endogenous mRNA ( via use of a splice acceptor ) to a vector-encoded reporter , resulting in a fusion protein lacking the C-terminal portion of the endogenous ( MCM ) protein ., As with a previously-reported Mcm4 gene trap 17 , each of these alleles proved to be recessive embryonic lethal ( Figure S1 ) ., Furthermore , each allele appeared to be a null , since mRNA levels in heterozygous MEF cultures were ∼50% lower than WT controls ( Figure 2B ) ., To determine if heterozygosity for various Mcms caused pan-decreases in Mcm mRNA levels as does homozygosity for Mcm4Chaos3 , mRNA levels for each of the Mcm2-7 genes were also quantified ., Whereas Mcm2Gt/+ cells did show ∼20% decreases in the other Mcms , the Mcm3 , Mcm4 , Mcm6 and Mcm7 gene trap alleles did not ( Figure 2B ) ., Thus , it appears that the marked Mcm pan-decreases in Mcm4Chaos3/Chaos3 cells are not due to decreased Mcm4 RNA per se , but rather a response to replication defects cause by the mutant protein ., Notably , the pan Mcm2-7 downregulation in Mcm2Gt/+ cells is consistent with the observation that MCM7 is decreased in Mcm2IRES-CreERT2/IRES-CreERT2 mice , although mRNA levels were not evaluated in that study 20 ., After breeding the gene trap alleles into the C3HeB/FeJ genetic background for at least 2 generations ( Mcm4Chaos3/Chaos3 females get mammary tumors in this background ) , blood MN levels were measured ., Heterozygosity for each allele caused an increase in the fraction of cells with MN ( Figure 2C ) ., Compound heterozygosity further increased MN on average , as did heterozygosity for 3 or more gene traps ( Figure 2C ) , indicating that genetically-based decreases in any of the MCMs precipitate GIN ., As outlined above , previous studies showed that reductions of particular MCMs in cells or mice reduces the levels of other MCMs , causing GIN , cancer , and developmental defects ., However , the reduction in MCM levels required to precipitate these consequences , and whether there is a threshold effect , is unclear ., To explore the consequences of incremental MCM reductions on viability and cancer in mice , we crossed the Mcm4Chaos3 and gene trap alleles into the same genome ., In the case of Mcm2 , there was a striking and highly significant shortfall of Mcm4Chaos3/Chaos3 Mcm2Gt/+ offspring at birth ( Figure 3A; Figure S2 ) ., Heterozygosity for Mcm2Gt itself was not haploinsufficient , as indicated by Mendelian transmission of Mcm2Gt in crosses of heterozygotes to WT ( 119/250; χ2\u200a=\u200a0 . 448 ) ., These results demonstrate that there is a synthetic lethal interaction between Mcm4Chaos3 and Mcm2Gt that is related to MCM2 levels ., Additionally , the surviving Mcm4Chaos3/Chaos3 Mcm2Gt/+ offspring were severely growth retarded; males weighed ∼50% less than Mcm4Chaos3/Chaos3 siblings ( Figure 3B; this genotype causes disproportionate female lethality ) ., Another indication of a quantitative MCM threshold effect is that C3H-Mcm4Chaos3/Chaos3 mice are developmentally normal , but Mcm4Chaos3/Gt animals die in utero or neonatally ( Figure 3A ) 23 ., The synthetic interaction between Mcm4Chaos3 and Mcm2Gt might be specific , or it may reflect a general consequence of reduced replication licensing ( and consequent elevated replication stress ) ., We therefore tested whether hemizygosity for Mcm3 , Mcm6 or Mcm7 would also cause synthetic phenotypes in the Mcm4Chaos3/Chaos3 background ., The Mcm4Chaos3/Chaos3 Mcm6Gt/+ genotype caused highly penetrant embryonic lethality; only 10% of the expected number of such animals survived to birth ( Figure 3A; Figure S2 ) ., The Mcm4Chaos3/Chaos3 Mcm7Gt/+ genotype caused both embryonic and postnatal lethality ., The number of liveborns was ∼50% of the expected value , and only 8% of those ( 5/62 ) survived to weaning ( Figure 3A; Figure S2 ) ., Additionally , as with Mcm2 , hemizygosity for Mcm6Gt and Mcm7Gt in the Mcm4Chaos3/Chaos3 background caused growth retardation ( Figure 3B ) ., The decrease in male weight was ∼20% and ∼80% respectively , compared to Mcm4Chaos3/Chaos3 siblings at the oldest age measured ( Mcm4Chaos3/Chaos3 Mcm7Gt/+ animals died before wean , so the oldest weights were taken at 10 dpp ) ., In contrast to the synthetic phenotypes with Mcm2 , 4 , 6 and 7 , there was no significant decrease in viability ( Figure 3A ) or weight ( not shown ) in Mcm4Chaos3/Chaos3 Mcm3Gt/+ mice ., This seeming inconsistency is addressed in the following section ., As mentioned earlier , mice with ∼35% of WT MCM2 protein , but not 62% , showed early latency ( 10–12 week ) lymphoma susceptibility 20 ., To identify if there is a critical MCM threshold for cancer susceptibility , we aged a cohort of Mcm2Gt/+ mice , representing approximately intermediate MCM2 levels ., As shown in Figure 4A , these animals did not show a dramatic cancer-related mortality in the first 12 months of life ., However , we did find that ∼3/4 of these animals had tumors at death or necropsy by 18 months of age ( data not shown ) ., These combined data are suggestive of a potential gradient of susceptibility , but that there is a critical minimum threshold of MCM levels , between ∼35 and 50% in the case of MCM2 , required to avoid early cancer and other developmental defects ., To further resolve this phenomenon , surviving Mcm4Chaos3/Chaos3 Mcm2Gt/+ mice were aged and monitored ., They began dying at 2 months of age , and all were dead ( or sacrificed when they appeared moribund ) by 7 months ( Figure 4A ) ., Gross necropsy and histopathological analyses revealed or suggested lymphomas/leukemias in 20 of these animals ( summarized in Table S1 with histological examples in Figure S3; detailed histopathology analysis of a T cell leukemic lymphoma is presented in Figure 4B ) ., Six of these had chest tumors that were likely thymic lymphomas ., The cause of death for the remaining 7 animals was undetermined ., Consistent with previous studies 17 , most Mcm4Chaos3/Chaos3 mice hadnt yet succumbed from tumors or other causes by 12 months of age ., Additional animals of these genotypes are incorporated in Figure 6 , but histopathological analyses werent conducted ., These data show clearly that removing a half dose of MCM2 from Mcm4Chaos3/Chaos3 cells is sufficient to produce greatly elevated cancer predisposition to the already-underrepresented survivors at wean ., Mcm4Chaos3/Chaos3 Mcm2Gt/+ MEFs had 45% the amount of Mcm2 mRNA as Mcm4Chaos3/Chaos3 cells ( Figure 7C ) , which already had a 38% reduction compared to WT ( Figure 1 ) ., Thus , Mcm2 RNA was reduced to ∼17% of WT ., To determine if elevated GIN might be responsible for the cancer susceptibility phenotype , we measured erythrocyte MN ., Whereas the percentage of micronucleated RBCs in Mcm4Chaos3/Chaos3 mice was 4 . 18±0 . 26 ( mean±SEM , N\u200a=\u200a12 ) , Mcm4Chaos3/Chaos3 Mcm2Gt/+ mice averaged 5 . 85±0 . 47 ( N\u200a=\u200a16 ) , indicating a synergistic increase ( P<0 . 01 ) ., Overall , the data support the notion that in whole animals , reduction of MCMs to under 50% of WT causes severe developmental and physiological problems ., The data reported here and elsewhere 17 , 20 support a model where phenotypic severity is proportionally related to MCM concentrations ., However , our genetic experiments uncovered one notable exception: hemizygosity for Mcm3 did not cause any severe haploinsufficiency phenotypes ( increased lethality and decreased weight ) as did Mcm2/6/7 in the Mcm4Chaos3/Chaos3 background , or Mcm4Gt in trans to Mcm4Chaos3 ( Figure 3A; Figure S2 ) ., Since extreme reductions of MCM3 in cultured human cells caused GIN and cell cycle arrest 16 , the absence of synthetic effects with McmChaos3 led us to hypothesize that either mice are more tolerant to lower levels of this particular MCM , or that MCM3 is present in a stoichiometric excess compared to the other MCMs , at least in a subset of cell types ., To explore these issues we performed additional phenotype analyses , and also sought to uncover potential effects of MCM3 reduction by reducing other MCMs simultaneously ., Strikingly , rather than exacerbating the synthetic lethality in Mcm4Chaos3/Chaos3 Mcm2Gt/+ mice , Mcm3Gt heterozygosity significantly rescued their viability to 72 . 5% from 29 . 7% ( Figure 5A and Fig S3 ) ., Not only was viability rescued , but also growth ( weight ) of Mcm4Chaos3/Chaos3 Mcm2Gt/+ Mcm3Gt/+ survivors compared to Mcm4Chaos3/Chaos3 Mcm2Gt/+ animals produced from the same matings ( Figure 5B ) ., Mcm3 hemizygosity also significantly rescued the near 100% lethality of Mcm4Chaos3/Gt animals ( nearly 6 fold increased viability ) , and doubled the viability of Mcm4Chaos3/Chaos3 Mcm6Gt/+ mice ( Figure 5A; Figure S3 ) ., Rescue of Mcm4Chaos3/Chaos3 Mcm7Gt/+ was not observed ( not shown ) ., The rescue of the reduced growth phenotype by Mcm3 hemizygosity led us to evaluate the proliferation of compound mutant cells ., Whereas Mcm4Chaos3/Chaos3 and Mcm4Chaos3/Chaos3 Mcm3Gt/+ primary MEFs proliferated at identical rates , Mcm4Chaos3/Chaos3 Mcm2Gt/+ MEFs showed a severe growth defect beginning ∼5 days in culture ( Figure 5C ) ., As with whole animals , MEF growth was partially but significantly rescued by Mcm3 hemizygosity ., Since the Mcm4Chaos3 and Mcm2Gt alleles causes elevated GIN ( micronuclei in RBCs ) , we considered the possibility that the Mcm3 rescue effect might be related to an attentuation of GIN ., Accordingly , we measured MN levels in Mcm4Chaos3/Chaos3 mice with different combinations of other Mcm mutations ., As shown in Figure 5D , hemizygosity for Mcm2 and Mcm7 caused a significant elevation in MN levels , unlike Mcm3 ., However , the increased MN in Mcm4Chaos3/Chaos3 Mcm2Gt/+ was not rescued by Mcm3 hemizygosity ., This suggests that the synthetic lethality and mouse/cell growth defects are not related to GIN per se ., However , in the course of measuring MN in enucleated peripheral blood cells , we noticed that the ratio of CD71+ cells was significantly higher in both Mcm4Chaos3/Chaos3 Mcm2Gt/+ and Mcm4Chaos3/Chaos3 Mcm7Gt/+ mice ( 3 . 3 and 6 . 2 fold , respectively; Figure 5E ) ., This increase in the ratio of reticulocytes ( erythrocyte precursors; immature RBCs ) to total RBCs is characteristic of anemia ., Hemizygosity for Mcm3 , which alone had no effect on CD71 ratios of Chaos3 mice , corrected completely this abnormal phenotype in Mcm4Chaos3/Chaos3 Mcm2Gt/+animals ( Figure 5E ) ., Because MCM2-depleted mice were reported to have stem cell defects 20 , and Mcm4Chaos3/Chaos3 Mcm#Gt/+ mice had clear developmental abnormalities , we examined the efficiency of reprogramming mutant MEFs into induced pluripotent stem cells ( iPS ) ., The efficiency was quantified using either : 1 ) iPS-like colony formation , or, 2 ) cells counts of SSEA1 and LIN28 positive cells by flow cytometry ., Both gave similar results ., Mcm4Chaos3/Chaos3 Mcm2Gt/+ cells were severely compromised in the ability to form iPS cells compared to Mcm4Chaos3/Chaos3 ( ∼200 fold less efficient; Figure 5F ) ., However , additionally reducing Mcm3 by 50% increased iPS formation from both Mcm4Chaos3/Chaos3and Mcm4Chaos3/Chaos3 Mcm2Gt/+ MEFs by ∼2 . 5 and 10 fold , respectively ., Finally , we found that reduced MCM3 levels could rescue the cancer susceptibility of two different Chaos3 models ., As shown earlier ( Figure 4 ) , Mcm4Chaos3/Chaos3 Mcm2Gt/+ mice were highly cancer-prone with an average latency of <4 months ., When a dose of Mcm3 was removed from mice of this genotype , lifespan was extended dramatically in both sexes as a consequence of delayed cancer onset , and the cancer spectrum shifted from lymphoma/thymoma towards mammary tumors ( Figure 6A ) ., Additionally , hemizygosity of Mcm3 delayed ( or eliminated ) the onset of mammary tumorigenesis in Mcm4Chaos3/Chaos3 females by ∼4 or more months ( Figure 6B ) ., However , although Mcm3 hemizygosity rescued viability of Mcm4Chaos3/Gt mice ( Figure 5A ) , these animals were cancer prone with a shorter latency ( by ∼6 months ) and different spectrum ( primarily lymphomas ) than Mcm4Chaos3 homozygotes ., We considered two possibilities to explain the surprising phenotypic rescues of reduced MCM genotypes ( Mcm4Chaos3/Chaos3 ; Mcm4Chaos3/Chaos3 Mcm2/6Gt/+ ; Mcm4Chaos3/Gt ) by additional MCM3 reduction ( Mcm3Gt/+ ) ., One is that the phenotypes are related to altered stoichiometry of MCM monomers , and that disproportionally high amounts of MCM3 relative to MCM4 and MCM2/6/7 have a dominant negative effect ., However , as demonstrated above , levels of MCM3 are proportionally reduced in Mcm4Chaos3/Chaos3 cells ( Figure 1 ) ., The second possibility is that decreased levels of MCM3 leads to a favorable change in the amounts or subcellular localization of MCMs ., Various experiments have indicated that MCM2-7 hexamers or subcomplexes must be assembled in the cytoplasm before nuclear import in yeast 4 , and in mice , nuclear import appears to require MCM2 and MCM3 24 ., MCMs shuttle between the nucleus and cytoplasm during the cell cycle in S . cerevisiae ., Although in most other organisms MCMs are reported to be predominantly and constitutively nuclear localized throughout the cell cycle , dynamic redistribution between the nucleus and cytoplasm has been observed in hormonally-treated mouse uterine cells 25 ., In budding yeast , nuclear export is dependent upon Mcm3 , which has a nuclear export signal ( NES ) that is recognized by Cdc28 to promote export of MCM2-7 22 ., Analysis of mouse and human MCM3 using NES prediction software ( www . cbs . dtu . dk/services/NetNES/ ) 26 revealed the presence of homologously-positioned , leucine-rich potential NESs ( Figure 7A ) ., Therefore , we hypothesized that the rescue of phenotypes by Mcm3 hemizygosity is due to decreased MCM protein export from the nucleus , or alternatively , increased nuclear import or stabilization that allows greater access of all MCMs for licensing chromatin ., To explore this hypothesis , we performed Western blot analysis of MCM levels in Mcm4Chaos3/Chaos3 MEFs with or without the Mcm3Gt and/or Mcm2Gt alleles , and examined the effects of Mcm3 dosage on the levels of nuclear and chromatin-bound MCM2 and MCM4 ., The results are presented in Figure 7B ., In all cases , the genetic reductions of Mcm2 and Mcm3 led to corresponding decreases in the cognate mRNA levels ( Figure 7C ) , with only minor additional decreases of other MCM mRNAs ( beyond that already caused by homozygosity for Mcm4Chaos3 ) occuring in the context of Mcm2 hemizygosity ( similar to Mcm2Gt/+ MEFs in Figure 2B ) ., The overall levels of total , nuclear , and chromatin-bound MCM2 and MCM4 were unaffected by hemizygosity of Mcm3 in Mcm4Chaos3/Chaos3 cells ( Figure 7B ) ., When Mcm2 levels were genetically reduced by half , a condition causing the severe phenotypic effects described earlier , this caused a marked decrease in the level of chromatin-bound MCM3 and MCM4 ( in addition to MCM2 itself ) , although total and nuclear MCM3/4 levels were affected to a lower degree or not at all ., Strikingly , the decreased levels of chromatin-bound MCM2/3/4 in Mcm4Chaos3/Chaos3 Mcm2Gt/+ MEFs were reversed by Mcm3 heterozygosity , but levels of total MCM2 and MCM4 were not restored ., The increase of chromatin-bound MCMs occured despite the presence of less MCM3 , suggesting that MCM3 is present at levels in excess of that needed to bind chromatin , presumably for pre-RC formation in the context of the MCM2-7 hexamer ., In conclusion , a 50% reduction in total MCM3 increases MCM2/4 loading onto chromatin when MCM2 is otherwise limiting , and this rescue is associated with amelioration of several phenotypes ., We found that elevation of nuclear MCMs in the Mcm3Gt/+ MEFs was often ( as shown in Figure 7B ) , but not consistently elevated across samples by Western analysis ( not shown ) ., Therefore , we quantified MCM2 during the cell cycle by flow cytometric analysis of nuclei from 7 replicate MEF cultures ., Similar to WT MEFs ( examples in Figure 1B ) , NIH3T3 cells showed a decrease of nuclear MCM2 during S phase progression ( Figure 7D , left panel ) ., However , all genotypes with in the Mcm4Chaos3/Chaos3 background had a reduced decline ., Thus , for comparative quantitation across genotypes , we compared the levels of MCM2 levels at the beginning of G1 vs . that in S phase ( regions used for these calculations are indicated in the left panel ) , using the calculation described in the Figure 7 legend ., The data are graphed in the right panel ., The data revealed that regardless of genotype , the difference in average amounts of nuclear MCM2 at the beginning and end of G1 ( ΔG1 ) did not vary ., Compared to Mcm4Chaos3/Chaos3 , cells lacking 1 dose of Mcm2 had relatively lower levels of S phase MCM2 ( ΔS ) compared to early G1 ., Additional removal of an Mcm3 dose partially rescued the ΔS value , indicating that these cells had ∼16% more nuclear MCM2 in S phase compared to Mcm4Chaos3/Chaos3 cells hemizygous for Mcm2 alone , despite overall reduced MCM2 levels in the cell ( Figure 7B , left panel ) ., MCM2-7 proteins exist abundantly in proliferating cells and are bound to chromatin in amounts exceeding that required to license all replication origins that initiate DNA synthesis 9–12 , 14 ., The role of excess chromatin-bound MCM2-7 has been a mystery referred to as the “MCM paradox” 27 , perpetuated by observations that drastic MCM reductions in certain systems can be compatible with normal DNA replication or cell proliferation 13 , 28–30 ., However , these circumstances are not universal , and reductions are not entirely without consequences ., Early studies showed that a reduction in MCMs resulted in decreased usage of certain ARSs 12 and conferred genome instability 31 in yeasts ., In cell culture systems , depletion of certain MCMs have been found to cause cell cycle defects , checkpoint abberations and GIN 13 , 16–17 , 29 , 32 ., Recent work has shed light on aspects of the MCM paradox ., Using Xenopus egg extracts attenuated for licensing by addition of geminin ( an inhibitor of CDT1 , which is required for MCM loading onto origins ) , one study proposed that excess chromatin-bound MCM2-7 complexes license “dormant” origins that can be activated to rescue stalled or damaged replication forks , a situation that can become important under conditions of replication stress 11 ., Similar results were subsequently reported for human cells depleted of MCMs by siRNA 15–16 , and for replication stressed MCM2-deficient MEFs 21 ., Our finding that nuclear MCM2 levels decrease as S-phase progresses , and moreso in WT than in Mcm4Chaos3/Chaos3 MEFs , is consistent with the dormant origin hypothesis ., The decrease may reflect displacement of dormant hexamers by active replisomes , followed by subsequent degradation or nuclear export ., If WT nuclei have more dormant licensed origins than Chaos3 mutants , then WT cells would be expected show a greater loss of MCMs ., The isolation of Mcm4Chaos3 provided the first demonstration that mutant alleles of essential replication licensing proteins can cause GIN and cancer 17 ., Diploid budding yeast containing the same amino acid change in scMcm4 as the mouse Mcm4Chaos3 exhibited Rad9-dependent G2/M delay ( Rad9 is a DNA damage checkpoint protein ) , elevated mitotic recombination , chromosome rearrangements , and intralocus mutations 19 ( Li , X . and Tye , B . , personal communication ) ., One explanation for these outcomes is that the Chaos3 mutation impairs MCM4 biochemically in a manner leading to elevated replication fork defects , and that these defects lead to the GIN and cancer phenotypes ., Alternatively , and/or in addition , the observed associated pan-reductions of MCMs in mouse cells 17 raised the possibility that decreased replication licensing might be the primary or ancillary cause for the mouse phenotypes ., The subsequent finding that mice ( Mcm2IRES-CreERT ) containing ∼1/3 the normal level of MCM2 had GIN and and cancer lent support for the idea that reductions in MCMs contribute to the Chaos3 phenotypes 20 ., Although amounts of all MCMs were not investigated in Mcm2IRES-CreERT/IRES-CreERT mice , 65% reduction of MCM2 caused a reduction of dormant replication origins in MEFs that were replication stressed by hydroxyurea 21 ., In Mcm4Chaos3/Chaos3 mice , we hypothesize that in the context of Mcm2 , 6 or 7 heterozygosity , which further reduces overall and chromatin-bound MCM levels below that already caused by Mcm4Chaos3 ( measured to be <20% of WT mRNA levels for Mcm2 ) , MCMs are reduced to a degree that compromises cell proliferation ., This then translates into the various developmental defects and increased cancer susceptibility we observed ., Whatever the exact mechanistic cause of these phenotypes , it is clear that the phenotypes are related to reduction of one or more MCMs below a threshold level that is <50% ., The severe developmental consequences of MCM depletion in mice suggests that certain cell types in the developing embryo are highly sensitive to the effects of replicative stress , and/or that relatively minor cell growth perturbations of such cells are not well-tolerated in the context of complex , rapidly-occuring developmental events ., The molecular basis for these phenotypes does not appear to be directly related to GIN , because whereas Mcm3 hemizygosity rescued several phenotypes , and delayed cancer latency in Mcm4Chaos3/Chaos3 mice , it did not concommitantly decrease MN ., This suggests that phenotypes such as decreased proliferation and embryonic death are caused by genetically-induced replication stress , moreso ( or in addition to ) than GIN alone ., Our genetic studies indicate that there is a quantitative MCM threshold required for embryonic viability , as demonstrated by the synthetic lethalities we observed when combining homozygosity of Mcm4Chaos3 with Mcm2Gt , Mcm6Gt or Mcm7Gt heterozygosity , but not in the heterozygous single mutants ., Additionally , the Mcm4Chaos3/Gt genotype , which reduced MCM levels below 50% , caused embryonic and neonatal lethality 17 ., Underscoring the exquisite sensitivity of whole animals to subtle perturbations in the DNA replication machinery were the remarkable phenotypic rescues ( viability , growth , iPS efficiency , etc . ) by Mcm3 hemizygosity ., The decreased MCM dosage led to increases in S phase nuclear MCMs and chromatin-bound MCMs , presumably reflecting increased replication origin formation ., The various single and compound mutants described here and elsewhere 20 , which show that 50% reductions of any one MCM is well-tolerated but decreases of ∼2/3 are not , supports the idea of a threshold effect , and suggests that the threshold lies somewhere between 1/3 and 1/2 of normal MCM levels ( at least in the cases of MCM2 , MCM6 and MCM7 ) ., These results also emphasize the importance of relevant physiological models , both in general and with respect to the MCMs ., RNAi knockdown of MCM3 in human cells to ∼3% normal levels was still compatible with normal short-term proliferation , although the cells had GIN and high sensitivity to replication stress 16 ., It is doubtful such a drastic situation would be recapitulated in vivo ( it would likely result in embryonic lethality as in Mcm3Gt/Gt mice ) ., Nevertheless , it is noteworthy in that study that MCM3 depletion was better tolerated than knockdowns of any other member of the replicative helicase ., The finding that reductions in MCM3 rescued MCM2/4/6 depletion phenotypes lends insight into dynamics and regulation of mammalian DNA replication ., In budding yeast , MCMs shuttle between the nucleus and cytoplasm during the cell cycle ., MCM2-7 multimers must be assembled in the cytoplasm before being imported into the nucleus during G1 phase 4 ., The MCM2-7 importation is dependent upon synergistic nuclear localization signals ( NLS ) on Mcm2 and Mcm3 22 ., In order to prevent over-replication of the genome , MCMs are exported from the nucleus during S , G2 and M 4 ., This export is dependent upon Mcm3 , which has a nuclear export signal ( NES ) that is recognized by Cdc28 to promote MCM2-7 export in a Crm1-dependent manner 22 ., In contrast to budding yeast , MCMs that have been studied ( MCM2/3/7 ) are primarily nuclear-localized throughout the cell cycle in metazoans and in fission yeast 4 ., Upon dissociation from chromatin during S phase , MCM2-7 complexes are reported to remain in the nucleus but are sequestered via attachement to the nuclear envelope or other nuclear structures 24 , 33–35 ., Interestingly , mcm mutations in fission yeast that disrupt intact MCM2-7 heterohexamers triggers active redistribution of MCMs to the cytoplasm 36 ., Additi

Introduction, Results, Discussion, Materials and Methods

Mutations causing replication stress can lead to genomic instability ( GIN ) ., In vitro studies have shown that drastic depletion of the MCM2-7 DNA replication licensing factors , which form the replicative helicase , can cause GIN and cell proliferation defects that are exacerbated under conditions of replication stress ., To explore the effects of incrementally attenuated replication licensing in whole animals , we generated and analyzed the phenotypes of mice that were hemizygous for Mcm2 , 3 , 4 , 6 , and 7 null alleles , combinations thereof , and also in conjunction with the hypomorphic Mcm4Chaos3 cancer susceptibility allele ., Mcm4Chaos3/Chaos3 embryonic fibroblasts have ∼40% reduction in all MCM proteins , coincident with reduced Mcm2-7 mRNA ., Further genetic reductions of Mcm2 , 6 , or 7 in this background caused various phenotypes including synthetic lethality , growth retardation , decreased cellular proliferation , GIN , and early onset cancer ., Remarkably , heterozygosity for Mcm3 rescued many of these defects ., Consistent with a role in MCM nuclear export possessed by the yeast Mcm3 ortholog , the phenotypic rescues correlated with increased chromatin-bound MCMs , and also higher levels of nuclear MCM2 during S phase ., The genetic , molecular and phenotypic data demonstrate that relatively minor quantitative alterations of MCM expression , homeostasis or subcellular distribution can have diverse and serious consequences upon development and confer cancer susceptibility ., The results support the notion that the normally high levels of MCMs in cells are needed not only for activating the basal set of replication origins , but also “backup” origins that are recruited in times of replication stress to ensure complete replication of the genome .

Proper replication of the genome is essential for maintenance of the genetic material and normal cell proliferation ., DNA replication can be compromised by exogenous factors and genetic disruptions ., Such compromise can lead to disease such as cancer , which is characterized by genomic instability ( an elevated mutation rate ) ., Because the DNA replication apparatus is essential , relatively little is known about how genetic variants impact the health of whole animals ., In this report , we studied mice bearing combinatorial mutations in a component of the replication apparatus , the MCM2-7 helicase ., MCM2-7 is a complex of 6 proteins that are essential for initiating DNA replication along chromosomes , and to unwind the DNA during DNA replication ., We find that although cells have excess amounts of MCM2-7 to support proliferation under normal circumstances , that incremental MCM depletions have multiple drastic effects upon the whole animal , including embryonic lethality , stem cells defects , and severe cancer susceptibility ., Additionally , we report that mouse cells regulate and coordinate the levels of usable MCM proteins , both at the level of synthesis and also by regulating access to chromatin ., The implication is that genetic variants that impact MCM levels , even to a minor degree , can translate into disease .

biochemistry/replication and repair, genetics and genomics/cancer genetics, genetics and genomics/animal genetics, genetics and genomics/chromosome biology

journal.pcbi.1004686

Hydrodynamic Radii of Intrinsically Disordered Proteins Determined from Experimental Polyproline II Propensities

Many proteins , and protein domains , that perform critical biological tasks have disordered structures under normal solution conditions 1–3 ., These proteins are referred to as intrinsically disordered 4 and , accordingly , molecular models of disordered protein structures are needed to understand the physical basis for the activities 2 , 3 , roles regulating key signaling pathways 5 , and relationships to human health issues 6–9 that have been linked to intrinsically disordered proteins ( IDPs ) ., The properties of disordered protein structures are often associated with conformational propensities for polyproline II ( PPII ) helix 10–12 and charge-based intramolecular interactions 13–15 ., PPII propensities are locally-determined 16 and intrinsic to amino acid type 17–19 , while charge-charge interactions seem to be important for organizing disordered structures owing to both long and short range contacts 13–15 , 20 , 21 ., Since chain preferences for PPII increase the hydrodynamic sizes of IDPs 22 , 23 , and Coulombic interaction energies are distance-dependent , it could be argued that charge effects on IDP structures are modulated locally by intrinsic PPII propensities ., A number of issues with that hypothesis , however , are apparent ., First , it has not been established if PPII propensities measured in short peptide models of the unfolded states of proteins 17–19 translate to IDPs ., It could be that PPII propensities are negligible and unimportant in IDP systems ., Second , methods capable of separating the impact of weak to possibly strong local conformational propensities and charge-charge interactions in the context of flexible and disordered protein structures have not been demonstrated , but are required for testing any potential interdependence ., To investigate such issues , a computer algorithm 22–24 based on the Hard Sphere Collision ( HSC ) model 25 was developed for parsing the contributions of intrinsic PPII propensities and charge to the structures of IDPs , as represented by the hydrodynamic radius ( Rh ) ., A HSC model was chosen since PPII propensities and charge effects could be added separately and in steps , to isolate contributions to simulated IDP structures ., Rh was chosen since experimental values are available for a wide range of IDP sequences , allowing direct comparisons to model-simulated Rh ., Here we demonstrate that Rh for disordered proteins trend with chain propensities for PPII structure by a simple power-law scaling relationship ., Using experimental PPII propensities for the common biological amino acids from Kallenbach 17 , Creamer 18 , and Hilser 19 , this relationship was tested against experimental Rh from 22 IDPs 23 , 26–42 ranging in size from 73 to 260 residues and net charge from 1 to 43 ., We observed that the power-law scaling function was able to reproduce IDP Rh with good agreement when using propensities from Hilser , while the Kallenbach and Creamer scales consistently overestimated Rh ., The ability to describe Rh from just intrinsic PPII propensities associated with a sequence was supported by simulation results showing that charge effects on IDP Rh are generally weak ., Relative to the effects of PPII propensities , charge effects on IDP Rh were substantial only when charged side chains were separated in sequence by 2 or fewer residue positions and if the sequence had higher than typical bias for one charge type ( i . e . , positive or negative ) ., Overall , these results demonstrated that two seemingly disparate experimental datasets , IDP Rh and intrinsic PPII propensities , are in qualitative agreement; providing evidence for considerable sequence-dependent conformational preferences for PPII structure in the disordered states of biological proteins ., Rh for IDPs are sensitive to site-specific and general structural perturbations such as amino acid substitutions 23 , changes in net charge 13 , 14 , charge rearrangements 15 , and temperature changes 22 , 43 , 44 ., Fig 1 shows that IDP Rh differ substantially from Rh for folded proteins 22 , 45 , 46 that have similar residue length , N . Rh from modeling proteins with no strongly preferred conformations 22 , which is referred to as a random coil 47 , is also provided for comparison to the experimental values ., The solid line representing coil Rh was determined from computer simulation of randomly configured polypeptide chains using a HSC model 22 ., Owing to favorable native contacts that promote stable globular structures , folded proteins have Rh that are compacted relative to the Rh of simulated random coils ., In contrast , the data in Fig 1 indicate that Rh from IDPs are generally larger than random coil estimates ., The dependence of Rh on N for chemically denatured proteins follows a power-law scaling relationship ,, Rh=Ro⋅Nv ,, ( 1 ), where Ro is 2 . 2 Å and v is 0 . 57 45 ., To understand changes in Ro and v that are required for modeling the dependence of Rh on N for IDPs , it is useful to recognize that unfolded proteins in aqueous solutions absent high concentrations of guanidine hydrochloride or urea show Rh compaction 48 with a concomitant decrease in v 49 ., Consistent with that observation , Marsh and Forman-Kay demonstrated that Rh and N scale with v = 0 . 509 for IDPs under normal conditions 49 ., Ro for IDPs was found to depend on PRO content and net charge by ,, Ro= ( 1 . 24⋅fPRO+0 . 904 ) ⋅ ( 0 . 00759⋅|Q|+0 . 963 ) ⋅2 . 49 ,, ( 2 ), where fPRO is the fractional number of PRO residues and |Q| the absolute net charge determined from sequence 49 ., Since PRO residues have strong propensities for PPII helix , which is an extended structure 50 , and repulsive interactions between charged groups likewise favor extended conformations to minimize unfavorable energetics , a simple molecular interpretation of Eq ( 2 ) can be offered whereby the Rh dependence on N for IDPs follows a baseline trend of Rh = ( 2 . 17 Å ) ∙N0 . 509 ( i . e . , Ro with fPRO and |Q| set to zero ) with sequence-dependent increases in Rh from this baseline owing to chain propensities for PPII and repulsive charge-charge interactions ., Simulated Rh for random coils were observed to trend with N by Rh = ( 2 . 16 Å ) ∙N0 . 509 22 , supporting this hypothesis ( and reproduced in Fig 1 ) ., The effects of ALA to GLY substitutions on IDP Rh also indicated that chain propensities for PPII structure modulate IDP Rh and not simply PRO content 23 ., To model the effects of PPII propensities on coil Rh , a sampling bias for PPII structure was applied to random coil simulations and the relationship between Rh , N , and fractional number of residues in the PPII conformation , fPPII , was determined 22 , 23 ., This is shown in Fig 1 by stippled lines to demonstrate that increases in fPPII cause increases in coil Rh ., These results were generated from simulations that modeled PPII bias by applying an identical sampling bias for PPII structure at each residue position in a polypeptide chain and , accordingly , did not include effects that could be caused by position-specific variations in PPII propensity ., To test for effects on coil Rh owing to PPII propensity variations within a polypeptide chain , conformational ensembles for N = 15 , 25 , 35 , 50 , and 75 were generated for poly-ALA with the algorithm modified to allow position-specific sampling rates for PPII structure ., It was shown previously that the effects of N on Rh were mostly insensitive to amino acid sequence in HSC model simulations of disordered proteins 22 and thus poly-ALA was chosen as a computational simplification ., Variations in PPII propensity among residue positions were simulated by applying a sampling bias for PPII structure ( SPPII ) at every position , every second position , every third position , every fourth position , or every fifth position in the poly-ALA chains ., SPPII at values of 0 . 1 , 0 . 2 , 0 . 3 , 0 . 4 , 0 . 5 , 0 . 6 , 0 . 7 , 0 . 8 , and 0 . 9 were tested at the indicated residue locations ., This PPII sampling strategy resulted in 225 separate simulated ensembles ( 5 N lengths X 5 patterns X 9 SPPII values ) ., A set of simulations using randomly determined position-specific bias for PPII structure was also modeled using poly-ALA chains ., These additional simulations used N = 15 , 25 , and 35 , with each residue position assigned a different random value for SPPII ., Position-specific random assignments were repeated 3 times for SPPII ranging from 0 to 1 , 0 to 0 . 5 , 0 . 25 to 0 . 75 , and 0 . 5 to 1 , resulting in an additional 36 simulated ensembles ( 3 N lengths X 3 distributions of random position-specific PPII biases X 4 applied ranges in PPII sampling bias ) ., The ensemble-averaged fractional number of residues in the PPII conformation ( i . e . , the propensity ) can be different from SPPII in these simulations since randomly generated structures containing van der Waals contact violations are removed from the calculation ., Differences between the applied sampling rate ( i . e . , SPPII ) and the observed ensemble-averaged rate ( i . e . , fPPII ) at SPPII-targeted positions followed the same Gaussian relationship that was established previously for whole-chain SPPII and fPPII comparisons 22 and thus straight-forward conversion between applied and observed bias rates was available ( S1 Fig ) ., fPPII determined from simulation for residue positions with no applied SPPII was 0 . 012 ± 0 . 004 ., Cumulative results from the >250 separate ensemble simulations were analyzed in terms of the power-law scaling relationship given by Eq ( 1 ) ., Previously , we demonstrated that the exponential term , v , was dependent on SPPII while Ro was mostly independent of SPPII with an averaged value of 2 . 16 Å 22 ., Fig 2A shows v , determined from ln ( Rh/2 . 16 ) /ln ( N ) , for each simulated ensemble and plotted as a function of fPPII calculated for the whole chain ., Rh for each simulated ensemble was calculated as ,, Rh=〈L〉/2 ,, ( 3 ), and fPPII , chain as ,, fPPII , chain=〈NPPII〉/N⋅, ( 4 ), In Eq ( 3 ) , <L> = ∑ Li∙Pi , where Li is the maximum Cα-Cα distance calculated for state i , Pi is the Boltzmann probability for state i , and the summation was over all states i of an ensemble ., In Eq ( 4 ) , <NPPII> = ∑ NPPII , i∙Pi , where NPPII , i is the number of residues in the PPII conformation for state i ., The distinction of “chain” given to fPPII in Eq ( 4 ) was provided to limit confusion between fPPII calculated for a whole chain versus fPPII calculated for specific residue positions ., The relationship between v and fPPII , chain for all simulations followed a logarithmic trend that was fit to the equation ,, v ( fPPII , chain ) =vo+β⋅ln ( 1−fPPII , chain ) ,, ( 5 ), using the Levenberg-Marquardt method of nonlinear least squares 51 , 52 ., The parameters vo and β were found to be 0 . 503 ± 0 . 002 and -0 . 11 ± 0 . 003 , respectively ., Fig 2B shows that Rh determined from fPPII , chain ( Eq ( 4 ) ) and N by combining Eqs ( 1 ) and ( 5 ) ( see Eq ( 6 ) below ) correlated strongly with Rh calculated directly from a simulated ensemble ( Eq ( 3 ) ) ., All possible patterns of position-specific PPII bias were not tested in our computer trials ., Results in Fig 2 predict , however , that in general a quantitative relationship exists for disordered proteins between Rh , N , and the ensemble-averaged per-residue chain propensity for PPII structure ( fPPII , chain ) ., Results from HSC model simulations that are summarized in Figs 1 and 2 can be interpreted as an ideal relationship between Rh and N that includes the general effects of sterics and PPII propensities but is absent other intrinsic and intramolecular factors ., Contributions from Coulombic interaction energies to IDP Rh will be discussed below and added to this model ., First , the simulation-derived relationship between Rh , N , and fPPII , chain is tested by applying experimental PPII propensities to the sequences of IDPs in Fig 1 . The identity , sequence , and experimental Rh for each IDP are given in Supporting Information ( S1 and S2 Tables ) ., This dataset includes 22 IDPs containing 3016 total residue positions ., Amino acids represented at rates greater than 0 . 05 in this dataset were , in rank order and listed by their three letter codes , SER ( 0 . 104 ) , GLU ( 0 . 100 ) , LEU ( 0 . 083 ) , PRO ( 0 . 080 ) , ASP ( 0 . 074 ) , GLY ( 0 . 073 ) , ALA ( 0 . 073 ) , THR ( 0 . 061 ) , LYS ( 0 . 055 ) , GLN ( 0 . 053 ) , and VAL ( 0 . 053 ) ., Amino acid PPII propensities reported by Kallenbach 17 , Creamer 18 , and Hilser 19 for disordered proteins are reproduced in Table 1 and were used for testing the relationship ,, Rh=2 . 16⋅N0 . 503−0 . 11⋅ln ( 1−fPPII , chain ) ⋅, ( 6 ), These propensity scales were chosen since weak correlations are observed among the group ( S2 Fig ) , indicating a potential for yielding different results when each set is used separately with Eq ( 6 ) for a given IDP sequence ., A physical explanation for the different PPII propensity values reported for the amino acids is not given here ( e . g . , the reported ALA PPII propensities are very different when compared ) , other than to note that their measurements used host peptide sequences that were also very different ( Table 1 ) ., Kallenbach measured PPII propensities in the background of a GLY-rich host peptide , whereas the scale reported by Creamer was determined for positions flanked on both sides by PRO residues ., The propensity scale from Hilser was measured for positions located in between PRO and valine ( VAL ) ., Other PPII propensity scales were not included in these tests due to similarities to the Kallenbach , Creamer , or Hilser reported values ., For example , a PPII propensity scale from Zondlo 53 correlated with the Creamer values ( coefficient of determination , R2 , gave a correlation of 0 . 58 ) , likely owing to the use of a host peptide that also flanked the guest position with PRO residues ., Inspection of Table 1 shows that PPII propensities for tryptophan ( TRP ) and tyrosine ( TYR ) were not reported by Creamer ., For these amino acids , we used the averaged Creamer-reported value calculated from the 18 other amino acids ( 0 . 58 ) ., In the Hilser set , TRP and TYR had lower than average PPII propensity ., In contrast , TRP and TYR had higher than average PPII propensity in the Kallenbach set ., Using the Creamer average was a compromise that likely had low significance in our tests since TRP and TYR had very low representation among the IDP sequences; 0 . 008 and 0 . 012 , respectively ., PPII propensities were not reported for PRO and GLY by Kallenbach ., Here , we used 1 for PRO since it is generally accepted that PRO has the highest propensity for PPII structure 10 , 12 , 17–19 ., This gave PRO a larger value than ALA ( 0 . 818 ) , which was the amino acid with the highest reported propensity in the Kallenbach set ., GLY was given a propensity of 0 . 50 , which is lower than the Kallenbach average ( 0 . 626 ) but higher than the lowest value ( 0 . 428 ) ., This also was a compromise from observing that GLY had the lowest value in the Hilser set ( 0 . 13 ) , but an average value in the Creamer set ( 0 . 58 ) ., fPPII , chain was calculated for each IDP by using the amino acid PPII propensity given in Table 1 , summing over the IDP sequence , and dividing by N . Fig 3A shows the experimental scales predict different chain propensities for PPII structure for each IDP sequence ., The scale from Kallenbach gave fPPII , chain ranging from 0 . 746 to 0 . 628 , whereas the Creamer and Hilser scales gave fPPII , chain from 0 . 609 to 0 . 579 and 0 . 489 to 0 . 283 , respectively ., Eq ( 6 ) was then used to predict Rh from fPPII , chain for comparison to experimentally observed Rh , which is shown in Fig 3B ., The average prediction error ( |Rh , predicted−Rh , observed| ) and the correlation between predicted and observed Rh is given in Table 2 . To assess contributions from the amino acid scales for predicting Rh , a null model was included by assigning each amino acid the PPII propensity of 0 . 012 , the background fPPII calculated from HSC simulations when no sampling bias for PPII structure was applied ( i . e . , SPPII = 0 ) ., Accordingly , the null model represents random coil values ., Different values of fPPII , chain predict different Rh for a given IDP sequence , as expected from Eq ( 6 ) ., For example , the null model , which used the smallest fPPII , chain values , predict Rh that are smaller than observed for each IDP ., In contrast , PPII propensities from Kallenbach and Creamer , which report relatively large fPPII , chain values , predict Rh that are larger than observed for each IDP ., Experimental propensities from Hilser predict Rh that trend with the identity line , showing good agreement , but also showing scatter relative to that line ( average error was 2 . 5 Å ) ., In an attempt to reduce prediction error , a composite PPII propensity scale that used the Hilser values by default but the Kallenbach values for residues located between GLY ( i . e . , GLY-X-GLY ) and Creamer values for residues located between PRO ( i . e . , PRO-X-PRO ) was tested ., This context-specific composite propensity scale ( identified as “Composite” in Table 2 and Fig 3B ) caused only small changes in predicted Rh , with no significant improvement in prediction capabilities relative to using only the Hilser reported PPII propensities ., Since Rh increases with N ( Fig 1 ) , prediction error was normalized for peptide length by ,, normalized\xa0error\xa0= ( predicted\xa0Rh\xa0−\xa0observed\xa0Rh ) / ( random\xa0coil\xa0Rh ) ⋅, ( 7 ), Random coil Rh was calculated using Eq ( 6 ) with fPPII , chain = 0 . 012 , the null model value ., Average normalized error is given in Table 2 for each propensity scale ., Fig 4 shows trends in the normalized error with N and net charge density , determined as the absolute net charge normalized for peptide length ,, net\xa0charge\xa0density\xa0=\xa0|Q|/ ( random\xa0coil\xa0Rh ) ⋅, ( 8 ), S1 Table gives net charge and N for each IDP ., No obvious bias with peptide length ( i . e . , N ) was observed in the normalized error for the Hilser and composite propensity scales ., Normalized error clearly increased with N when using Kallenbach and Creamer values , indicating that these PPII propensities may be over-estimated when applied to IDP sequences to predict Rh ., Since the exponent in Eq ( 6 ) becomes larger with increasing fPPII , chain , a set of propensity values that systematically are too large would cause normalized errors that increase with N . It is interesting to note that normalized error correlated with net charge density for each experimental propensity scale ( Fig 4B and Table 2 ) , suggesting that prediction error was caused partially by charge effects on Rh that were not included in the model ., This is not surprising since Marsh and Forman-Kay demonstrated that increases in net charge correlate with increases in IDP Rh 49 and the trend we observed of decreasing normalized error with increased net charge density is consistent with their conclusions ., Extrapolating this trend to zero net charge density for the Hilser and composite propensity scales yields positive normalized errors suggesting that , in the background of no net charge contributions to Rh , the PPII propensities reported by Hilser may also be slightly too large when using Eq ( 6 ) to predict Rh ., While this analysis of experimental PPII propensities indicated that one of the scales was capable of reproducing experimental Rh with good agreement for a set of IDPs , it is important to recognize that comparative tests based on Eq ( 6 ) may not be suitable for affirmation ., Since Rh in this model depends only on N and chain averaged propensity for PPII structure , contrived scales that predict IDP Rh with similar agreement in terms of the average prediction error are simple to generate ., For example , each IDP could be given a sequence-independent fPPII , chain value of 0 . 364 , which was determined by converting experimental Rh to an apparent fPPII , chain using Eq ( 6 ) and then averaging over the IDP dataset ., Using this static fPPII , chain to predict IDP Rh gives an average prediction error ( identified as “Static” in Table 2 ) that is close to the error obtained when using the experimental scale from Hilser ., Correlations between predicted and observed Rh and between normalized error and net charge density for the contrived static scale , however , decreased relative to the correlations that were observed with the experimental scales , suggesting that static representations of fPPII , chain may not fully capture some molecular dependencies that are inherent to IDP Rh ., To further investigate the capabilities of Eq ( 6 ) for relating IDP Rh and PPII propensity , random sets of amino acid scales were generated following a two-step protocol and analyzed ., First , a random number between 0 and 1 was used to target an average propensity for a scale ., Then , random scales were generated , where each amino acid was assigned a different random value between 0 and 1 , until a set was found whose average for the 20 amino acids matched the target determined in the first step ( ±0 . 05 ) ., The goal from using two steps to generate scales was to ensure that chain averaged propensities in the high , medium , and low range were evenly sampled ., This sampling scheme was repeated until 100 , 000 random scales were generated ., Each propensity scale was then used to predict Rh from Eq ( 6 ) and the results are summarized in Fig 5 ., It was observed that randomly generated scales gave average prediction errors for the IDP dataset ranging from 1 . 9 to 239 . 8 Å , correlations between predicted and observed Rh ranging from 0 . 02 to 0 . 88 , and correlations between normalized error and net charge density from 0 to 0 . 81 ., Optimal values for these metrics ( i . e . , highest correlations coupled with lowest average error ) , seem to focus toward values of R2 and average error that are obtained when using experimental PPII propensities from Hilser ., This result shows that experimental Rh of the IDP dataset are in good qualitative agreement with experimental PPII propensities reported by Hilser , and vice versa , giving evidence that the molecular properties of IDPs that link Rh , N , and fPPII , chain are well-approximated by the simple power-law scaling relationship of Eq ( 6 ) ., In the HSC model used for this study , a computer algorithm generates polypeptide structures by random conformational search until Rh ( Eq ( 3 ) ) converges to a stable ensemble-averaged value 22 ., A structure-based energy function parameterized to solvent-accessible surface areas that has been tested extensively 54–62 is used to population-weight each randomly generated structure ., To approximate charge effects on ensemble populations , the energy function was modified to include Coulombic interaction energies by ,, ΔGCoulomb=332DH2O⋅2⋅∑iZi⋅ ( ∑jZjRij⋅eκ⋅Rij ) ,, ( 9 ), where the constant 332 converts the energy into units of kilocalories per mole at 25°C , DH2O is the dielectric of water , Z is the charge at site i or j , Rij is the distance between two charged sites i and j ( in Å ) , κ ( the Debye parameter ) accounts for screening from solution ionic strength , and the sums are over all charge-bearing sites ., The Debye parameter was calculated as ,, κ=2 . 913⋅I/DH2O ,, ( 10 ), where I is ionic strength ( in molarity , M ) ., DH2O used was 78 . 3 63 and I was 0 . 1 M to represent normal conditions ., Since the simulations used poly-ALA chains , charged residues were modeled with a positive or negative charge located at the coordinates of the Cβ atom to denote the approximate location for flexible and charged side chains ., Coordinates for the backbone N and O atoms of the first and last residues were used to assign positive and negative charge , respectively , to N- and C-termini ., Simulations were limited to 25 residue poly-ALA chains to establish trends for the effects of charge on Rh in this model ., For each ensemble , an identical SPPII was applied at each residue position ., SPPII was varied among the different simulations to target ensemble-averaged fPPII , chain ranging from 0 . 1 to 0 . 92 ., Fig 6A shows that introducing charge at N- and C-termini had no effect on simulated Rh for poly-ALA chains ., Modeling negative charge at the Cβ position of each residue , or positive charge ( S3 Fig ) , caused large increases in Rh from repulsive electrostatic intramolecular interactions ., Identical charge at every other residue position caused smaller increases in Rh , while identical charge at every third position gave Rh that were mostly similar to Rh of poly-ALA modeled with no charges ., These data predict that the effects of charge on IDP Rh should weaken as charged residues separate in sequence , as expected ., Fig 6B shows the ensemble-averaged distance between “charged” Cβ atoms that were closest in sequence for each ensemble in panel A , indicating repulsive charge-charge interactions at distances ≥9 Å had only minor effects on Rh ., The Debye length for the modeled conditions ( i . e . , 1/κ ) was 9 . 6 Å , which is the distance where interactions between charged groups become negligible at a given ionic strength ., The simulation results thus trend with expected outcomes for fully solvated charges ., It was also observed that , for polypeptides with each residue position charged , fPPII , chain calculated for an ensemble was larger than expected based upon the applied SPPII ( Fig 6A inset ) ., This result predicts that repulsive charge-charge interactions between side chain groups preferentially select for the extended PPII structure to minimize unfavorable interaction energies ., To test the effects of clusters of charge on Rh , polypeptides with patterns consisting of three consecutively charged residues were also simulated ( Fig 7 ) ., Similar trends were observed , whereby the effects of charge on Rh weaken as charged groups ( i . e . , clusters ) were separated in sequence ., Charge clusters , however , affected Rh when modeled with 4 intervening non-charged residues , with weaker effects persisting at even larger separation distances between the clusters ., This contrasts with the simulation results for non-clustered charged residues that exhibited negligible effects on Rh when charges were separated by as little as 2 intervening uncharged residue positions ( Fig 6A ) ., Since IDPs , in general , contain both positive and negative charges , simulations with opposite charge at adjacent residue positions were also performed ., Fig 8A shows that repeating patterns of opposite charge had minimal effects on Rh in these simulations , even when each residue position was charged ., This was mostly the case for charge clusters too ( Fig 8B ) with the exception that the simulation would sporadically generate ensembles with compacted Rh , whereby “compacted” is used to indicate Rh smaller than what was observed for non-charged poly-ALA coils of identical N . Overall , the amount of Rh compaction owing to favorable interactions between oppositely charged residues ( or clusters ) was small when compared to increases in Rh that were observed owing to unfavorable interactions between identically charged residues ( or clusters ) ., The results in Figs 6–8 from modeling charge effects on Rh indicate that , in general , the strongest effects on Rh should occur owing to identical charges at sequentially-adjacent residue positions ( Figs 6 and 7 ) and for polypeptides with the least amount of mixing of positive and negative charge types ( Fig 8 ) ., To test these two general observations , the IDP dataset was analyzed to determine the net number of adjacent charges in each IDP sequence ., This was calculated by first summing the number of ASP residues that had GLU or ASP immediately next or prior in sequence with the number of GLU residues that had GLU or ASP immediately next or prior in sequence to determine the total number of negative charges with an adjacent negatively charged neighbor ., A similar calculation was performed using LYS and ARG to determine the number of positive charges with an adjacent positively charged neighbor ., The net number of adjacent charges for an IDP was then the absolute value in the difference between the positive and negative adjacent charge numbers ( provided in S1 Table ) ., Fig 9A shows that normalized error in predicted Rh for the IDP dataset trends with the net adjacent charge density ( i . e . , net adjacent charge normalized for peptide length ) , similar to the correlation that was observed between normalized error and net charge density ( Fig 4B ) ., This should be expected since net charge and net adjacent charge correlate with R2 = 0 . 64 in the dataset ., The set of IDPs was also split according to the amount of mixing of positive and negative charge types in a given sequence ., To do this , a “charge bias” was calculated for each IDP as the simple ratio of total negative charges ( sum of ASP and GLU residues ) to total positive charges ( sum of LYS and ARG residues ) , or vice versa , depending on which ratio gave a value greater than 1 . As a metric for separating IDPs with “high” and “low” charge bias , a “typical” charge bias was calculated for the entire dataset by the concatenated sequence and found to be 1 . 9 ., The average IDP charge bias , found to be 4 . 2 , was not used to separate IDPs since: 1 ) ratio-based distributions are skewed , 2 ) only 7 IDPs would have been in the “high” charge bias set , and 3 ) 4 of these 7 were sequences derived from the p53 protein ., Using the charge bias of the concatenated sequence gave 12 IDPs in the high charge bias set and 10 IDPs in the low charge bias set ., Fig 9B shows that correlations between net adjacent charge density and normalized error in predicted Rh persisted in the set of IDPs with high charge bias and mostly disappeared for IDPs with low charge bias , seeming to agree with the simulation prediction that significant mixing of positive and negative charge types in a sequence should reduce charge effects on Rh ., Applying this analysis to net charge density gave different results ( S4 Fig ) ., Correlations between net charge density and normalized error in predicted Rh decreased for both the high and low charge bias sets ., This could be owing to trends shown in Fig 6 , whereby net charge effects on Rh depended strongly on the distance between the charged groups ., Overall , these results seem to indicate that charge effects on IDP structures are highly dependent on sequence , however , charge effects on Rh can be weakened substantially by mixing negative and positive charge types or by slight increases in the distances between charged groups in sequence ., The hypothesis that charge effects on Rh may be generally weak for IDPs is supported by data in Fig 3B showing that Rh could be predicted without specific consideration of charges when provided an appropriate amino acid scale for intrinsic PPII propensities ., Fig 1 shows that experimental Rh for IDPs are much larger than computational predictions based on random coil modeling of the Rh dependence on N . Numerous studies have demonstrated the importance of Coulombic effects for regulating IDP structural preferences 13–15 ., Thus , it could be surprising to note that sequence effects on IDP Rh can be predicted with good agreement from sequence differences in PPII propensity , even when other intramolecular factors are ignored ., Rh predicted from IDP sequence and Eq ( 6 ) seemed to work best when using an experimental PPII propensity scale from Hilser and colleagues 19 , or a composite scale that combined the Hilser , Kallenbach 17 , and Creamer 18 propensities , giving an average error of ~2 . 5 Å for an IDP dataset covering a wide range of residue lengths , net charge , and sequence composition ., As examples of sequence differences in this dataset , the fractional number of PRO residues ( fPRO = ( # PRO residues ) /N ) varied from 0 to 0 . 24 , SER from 0

Introduction, Results, Discussion, Methods

The properties of disordered proteins are thought to depend on intrinsic conformational propensities for polyproline II ( PPII ) structure ., While intrinsic PPII propensities have been measured for the common biological amino acids in short peptides , the ability of these experimentally determined propensities to quantitatively reproduce structural behavior in intrinsically disordered proteins ( IDPs ) has not been established ., Presented here are results from molecular simulations of disordered proteins showing that the hydrodynamic radius ( Rh ) can be predicted from experimental PPII propensities with good agreement , even when charge-based considerations are omitted ., The simulations demonstrate that Rh and chain propensity for PPII structure are linked via a simple power-law scaling relationship , which was tested using the experimental Rh of 22 IDPs covering a wide range of peptide lengths , net charge , and sequence composition ., Charge effects on Rh were found to be generally weak when compared to PPII effects on Rh ., Results from this study indicate that the hydrodynamic dimensions of IDPs are evidence of considerable sequence-dependent backbone propensities for PPII structure that qualitatively , if not quantitatively , match conformational propensities measured in peptides .

Molecular models of disordered protein structures are needed to elucidate the functional mechanisms of intrinsically disordered proteins , a class of proteins implicated in many disease pathologies and human health issues ., Several studies have measured intrinsic conformational propensities for polyproline II helix , a key structural motif of disordered proteins , in short peptides ., Whether or not these experimental polyproline II propensities , which vary by amino acid type , reproduce structural behavior in intrinsically disordered proteins has yet to be demonstrated ., Presented here are simulation results showing that polyproline II propensities from short peptides accurately describe sequence-dependent variability in the hydrodynamic dimensions of intrinsically disordered proteins ., Good agreement was observed from a simple molecular model even when charge-based considerations were ignored , predicting that global organization of disordered protein structure is strongly dependent on intrinsic conformational propensities and , for many intrinsically disordered proteins , modulated only weakly by coulombic effects .

journal.pgen.1002451

A High Density SNP Array for the Domestic Horse and Extant Perissodactyla: Utility for Association Mapping, Genetic Diversity, and Phylogeny Studies

Horses have held a valued place in human civilization for over 5 , 000 years through service in war , agriculture , sport and companionship 1 ., Over the last several centuries , more than 400 distinct horse breeds have been established by genetic selection for a wide number of desirable phenotypic traits 2 ., In contrast to other large domestic animal species including cattle , chickens , sheep , swine , goats and camelids that are selectively bred mainly for production of food ( meat , milk , eggs ) or fiber , the domestic horse is primarily a utilitarian animal - bred for endurance , strength , speed , and metabolic efficiency 1 ., The horses use as a work animal and means of transport required selection for individuals that were able to perform daily physical activity even when feedstuffs were scarce ., The natural athleticism of horses and their enforced intensive exercise regimes makes them outstanding models for study of the musculoskeletal , cardiovascular and respiratory systems , while their natural susceptibility and resistance to infectious agents is useful in studies of the immune system ., Understanding the genetic basis for within and among breed variation in equine health , disease and performance traits will continue to provide important information on mammalian biology and genetic mechanisms of disease ., The horse was selected by the National Human Genome Research Institute ( NHGRI ) for whole genome shotgun sequencing as a representative of the order Perissodactyla ., The genome of the female Thoroughbred Twilight was sequenced to 6 . 8 fold coverage at the Broad Institute of Harvard and MIT accompanied by paired-end sequences from over 150 , 000 BAC clones performed at the Helmholtz Centre for Infection Research , and the University of Veterinary Medicine Hannover , Germany ., This project has produced the EquCab2 . 0 assembly with a total contig length of 2 . 43 Gb , 96% of it assigned to chromosomes , and a predicted genome size of 2 . 67 Gb ( http://ncbi . nlm . bih . gov/genome/guide/horse ) ., A significant SNP discovery component within the NHGRI project identified ∼750 , 000 SNPs from Twilight and ∼400 , 000 SNPs from seven horses of different breeds , enabling an estimate of the overall frequency of SNPs within the equine genome ( ∼1/1500 bp ) , and providing sufficient markers to construct a whole genome SNP panel for use in the domestic horse and related species 3 ., This report describes the overall properties and several uses of an equine whole genome SNP array termed the EquineSNP50 BeadChip ., Similar to other important domestic animal species , such as the dog , pig , chicken , sheep and cow , this resource has positioned the domestic horse as a viable large animal model for genetic research ., Equine researchers are now in an excellent position to evaluate the structure of the genome within and across horse breeds as well as closely related species ., Data from this assay will yield important information about selection and population history , and facilitate association mapping studies to allow for the identification of loci associated with both valuable and deleterious traits ., 60 , 000 SNPs from the EquCab2 genome assembly that gave suitable design scores for the Illumina Infinium II assay were selected in an attempt to provide even coverage of the genome ., SNPs observed in discovery horses ( in reference to the Twilight genome assembly ) , or in both discovery horses and Twilight , were utilized ( Table S1 ) ., Of the 354 horses from 14 different breeds selected for genotyping , 3 individuals failed to genotype ( Table S2 ) ., Analysis of 8 pairs of replicate samples ( Twilight and the seven SNP discovery horses ) resulted in perfect replication of 868 , 820 genotypes ( replication frequency of 1 . 0 ) ., Mendelian inheritance was confirmed in 15 of 18 trios ( Table S3 ) ., 54 , 602 SNPs provided genotype data , and 53 , 524 SNPs were validated ( defined as having at least one heterozygous genotype call ) , indicating overall assay conversion and validation rates of 0 . 910 and 0 . 980 , respectively ., The validation rates were highest for SNPs that were observed in a single discovery breed ( 0 . 990 ) when compared to Twilight , or observed in any two discovery breeds ( 0 . 989 ) ( Table S1 ) ., 53 , 066 of the validated SNPs ( 99 . 1% ) were polymorphic ( defined as minor allele frequency ( MAF>0 . 01 ) in the entire sample set ., The average spacing between functional SNPs on the 31 autosomes was 43 . 1 kb ., There were 12 gaps greater than 500 kb across the 31 autosomes , with the largest gap being 1 , 647 . 5 kb on ECA6 ( Table S4 ) ., Coverage on ECAX ( average inter-SNP spacing of 48 . 88 kb ) was lower than the rest of the genome ( Table S4 ) ., The number of polymorphic SNPs ( MAF≥0 . 01 ) within a breed ranged from 43 , 287 to 52 , 085 ( 79% to 95% ) ; 26 , 473 ( 48 . 5% ) SNPs were polymorphic in every breed ( Table S5 ) ., 90% of informative SNPs ( MAF>0 . 05 across breeds ) were less than 110 kb apart , and 95% of informative SNPs were less than 150 kb apart ( Figure S1 ) ., The discovery breed source of the SNPs did not greatly affect their informativeness ( MAF>0 . 05 ) in the 14 analyzed breeds individually , or as a whole ( Table S6 ) ., Genotyping was attempted in 53 individuals from 18 species evolutionarily related to the domestic horse ( Table S7 ) ., The extant Perissodactyla ( odd-toed hoofed mammals ) include three families: the Equidae ( horses , asses and zebras ) , the Rhinocerotidae ( rhinos ) , and the Tapiridae ( tapirs ) , divided into two suborders , the Hippomorpha ( horses , asses and zebras ) and the Ceratomorpha ( rhinos and tapirs ) 4 ., Of the 53 individuals genotyped , one single zebra ( Equus zebra hartmannae ) completely failed to genotype ., Individual genotyping rates were slightly lower across the Hippomorpha ( mean 0 . 959 ) , and dramatically lower across the Ceratomorpha ( mean 0 . 246 ) when compared to the domestic horse ( mean 0 . 996 ) ( Table S7 ) ., Quality scores ( GC10 , see Materials and Methods ) in the Hippomorpha ( mean\u200a=\u200a0 . 705 ) were similar to those in the domestic horse ( mean\u200a=\u200a0 . 730 ) , however mean GC10 scores were much lower in Ceratomorpha ( mean\u200a=\u200a0 . 236 ) ( Table S7 ) ., Due to lower quality scores and lower genotyping rates in some species , the genotypes in the Perissodactyla were further filtered based on raw intensity scores , individual genotyping rates , and SNP genotyping rates ( see Materials and Methods and Text S1 for details ) ., The number of loci called after filtering for signal intensity and genotyping rates are presented in Table S8 ., In the Hippomorpha our filtering criteria had little impact on genotyping rates , decreasing the call rate by only 1 to 3% ., In contrast , filtering criteria decreased the call rates by 45 to 57% in the Ceratomorpha , suggesting that a large portion of the initial genotyping calls were unreliable ., Further , mean GC10 scores for the remaining SNPs after filtering were 0 . 721 across all Hippomorpha and 0 . 389 across all Ceratomorpha respectively ( Table S8 ) , suggesting that data quality in the Ceratomorpha was questionable even after additional filtering ., Thus only the Hippomorpha data were analyzed further ., In the Hippomorpha , conversion rates ranged from 0 . 891 in the Przewalskis Horse ( Equus przewalskii ) to 0 . 834 in the Hartmanns Mountain Zebra ( Equus zebra hartmannae ) ., The number of validated loci ranged from 265 ( 0 . 8% ) in the Somali Wild Ass ( Equus asinus somalicus ) to 26 , 859 ( 50 . 8% ) in the Przewalskis Horse ( Table S8 ) ., The average observed heterozygosity in the Hippomorpha ( excluding the domestic horse ) ranged from 0 . 003 in the Domestic Ass , Somali Wild Ass , Grevys zebra and Hartmanns Mountain zebra to 0 . 168 in the Przewalskis Horse ( Table S8 ) ., Mean MAF in the nine Przewalskis Horses was 0 . 126 ., The number of informative SNPs within breeds ranged from 37 , 053 ( 68% ) in the Norwegian Fjord to 47 , 669 ( 87% ) in the Quarter Horse ( Table S5 ) ., Mean MAF within breeds also ranged from 0 . 180 to 0 . 232 in the Norwegian Fjords and Quarter Horses , respectively ., 17 , 428 SNPs were informative ( MAF≥0 . 05 ) in every breed and 49 , 603 SNPs were informative within the entire sample set ( across all breeds ) ., The overall MAF across all breeds was 0 . 236 ( SD\u200a=\u200a0 . 139 ) , and the median MAF was 0 . 224 ., The Mongolian breed displayed the highest genetic diversity , HE\u200a=\u200a0 . 292 , whereas genetic diversity was the lowest in the Thoroughbred HE\u200a=\u200a0 . 247 ( Table S5 ) ., Genotypes for all SNP pairs less than 4 Mb apart were evaluated to estimate genome-wide linkage disequilibrium ( LD ) ( as r2 ) within and across breeds ., As expected , LD was higher within a breed than across breeds ., Initial LD declined rapidly across all horses with mean r2 dropping below 0 . 2 by 50 kb ( Figure 1 and Figure S2 ) ., Within breed r2 values dropped most rapidly in the Mongolian , however , r2 was below 0 . 2 within 100 to 150 kb in the majority of breeds ., LD was initially highest in the Thoroughbred , where r2 does not drop below 0 . 2 until 400 kb , and remained higher than other breeds until approximately 1 , 200 kb ., The extent of long-range LD was the highest in the Standardbred and French Trotter ( Figure 1 ) ., Mean individual inbreeding coefficients ( F ) were highest in the Thoroughbred and Standardbred ( 0 . 15 and 0 . 12 , respectively ) , and lowest in the Hanoverian , Quarter Horse and Mongolian ( 0 . 06 , 0 . 04 , and 0 . 02 , respectively ) ( Table S9 ) ., The average genetic distance ( D ) between pairs of individuals from different breeds was 0 . 270 ( sd\u200a=\u200a0 . 014 ) , while the mean distance between pairs of individuals from the same breed was 0 . 240 ( sd\u200a=\u200a0 . 020 ) ., As seen in Figure 2a , the distribution of D between individuals drawn from different breeds is relatively smooth; however , the distribution of D within breeds is distinctly tri-modal ., To further investigate this tri-modal distribution , the mean D was calculated for each breed separately ( Table S10 ) ., D was lowest in the Norwegian Fjord and Icelandic horses ( 0 . 21 ) which accounted for a large proportion of the left peak in Figure 2a , whereas D was highest in the Hanoverian , Quarter Horse and Swiss Warmblood ( 0 . 25–0 . 26 ) which accounted for a large proportion of the right peak ., Metric multidimensional scaling ( MDS ) of pair-wise genetic distances was used to visualize the relationships among the 335 horses from 14 breeds ., Plotting dimension 1 versus dimension 2 resulted in tight clustering by breed , with the exception of the Quarter Horse , Hanoverian and Swiss Warmblood ( Figure 3 ) ., The 7 SNP discovery horses and Twilight were outliers relative to other members of their breeds ( Figure 3 ) ., MDS plots and breed relationships in dimensions 3 through 6 are provided in Figure S3 ., Parsimony analysis with a set of 40 , 697 autosomal SNPs across all Hippomorpha ( horses , asses and zebras ) placed the modern horse as sister taxa to Equus przewalski , and both the modern horse and E . przewalski as a sister clade to all the other equids , which fell out into species groups ( Figure 4 ) ., Pair-wise genetic distances were also calculated with all domestic horse breeds , and the Przewalskis Horses ( n\u200a=\u200a9 ) ( Figure 2 ) ., MDS revealed tight clustering of the Przewalskis Horse ( n\u200a=\u200a9 ) with the Mongolian and Norwegian Fjord horse samples when dimension 1 was plotted against dimensions 2 , 3 or 4 ( Figures S4 and S5 ) ., The Przewalskis Horse samples were not completely separated from the Mongolian and Fjord samples until dimension 6 ( Figure S5 ) ., The average genetic distance ( D ) between Przewalskis Horses and domestic horses was greater than the average D between pairs of individuals drawn from any 2 different domestic horse breeds ( Figure 2b ) , however there was significant overlap in the distribution of D values in the Przewalskis-domestic horse pairs and the domestic horse-different breed pairs ., To investigate this overlap , the distances between Przewalskis Horses and each breed were calculated ( Table S11 ) ., The results show that D between Przewalskis Horse and other breeds ranged from 0 . 25–0 . 31 , and was smaller between Przewalskis Horse and Mongolians , Norwegian Fjords , Belgians and Icelandics than between Przewalskis Horse and Thoroughbreds ., The relationships between the domestic horse breeds and the Przewalskis Horse are also demonstrated by parsimony analysis in Figure 5 , where the Przewalskis Horse falls out into a strongly supported , monophyletic clade that is basal to the remainder of the modern breeds ., Parsimony analysis also supports most associations among the domestic horse breeds suggested by MDS ( Figure 3 ) ., To demonstrate the utility for GWAS , within and across breed mapping was performed for 3 coat color loci ., Phenotypes were inferred either from the genotypes of all 9 known coat color loci with consideration of known interactions , or from genotype only at the locus of interest to model a simple Mendelian trait ., The three most common alleles in our data set included the recessive chestnut coat color locus ( MC1R ) on ECA3 5 , the recessive black coat color locus ( ASIP ) ( agouti ) 6 on ECA22 , and the dominant gray locus ( STX17 ) on ECA25 7 ., Both basic chi-square case-control allelic association and Cochran-Mantel-Haenszel ( CMH ) association analyses identified the chestnut and black loci across breeds regardless of how phenotype was inferred ( Figure 6a and 6b , Tables S12 and S13 , and Figures S6 and S7 ) ., Gray was not mapped across all breeds using allelic association , CMH , ( Figure 6c , Tables S12 and S13 , Figure S8 ) , or structured association mapping using principal components or mixed-model analyses to control for underlying population structure ( data not shown ) ., Within breeds the chestnut locus was successfully mapped in Quarter Horses ( 22 cases and 24 controls ) and Thoroughbreds ( 11 cases and 26 controls ) ( Table S14 ) ., The ASIP locus was successfully mapped in the Andalusian ( 6 cases and 10 controls ) , when black was considered as a simple recessive trait ( ignoring epistatic interactions ) ., The gray phenotype was not successfully mapped within the two breeds attempted ., The assay conversion rate was lower on this equine array when compared to a similar assay designed for cattle or pigs ( 91 . 0% versus 92 . 6% and 97 . 5% respectively ) ; however , SNP validation rates were slightly higher in the horse than in bovine or porcine ( 98 . 0% versus 95 . 1% and 94% , respectively ) 8 , 9 ., SNPs discovered in any two breeds were somewhat more likely to be validated than SNPs discovered in a single breed ., Regardless of the discovery source , a large proportion of SNPs were validated and informative in breeds not represented in the SNP discovery effort ., In the array design , SNPs from the “across breed” SNP discovery resource rather than SNPs discovered in the genome assembly process were used ., This strategy was predicted to increase the utility of the SNP resource in non-Thoroughbred breeds ., The high success rate of the validation justifies the approach of using representatives of global breed groups to generate a SNP resource 3 ., The 54 , 000 polymorphic SNPs are distributed across the autosomes with few large gaps ( >500 kb ) ., One large gap on ECA6 was the result of a misplaced contig in a pre-release of the sequence assembly from which the array was designed; even though it was correctly placed in the released assembly , no SNPs were selected from this region ., Coverage was slightly lower on ECAX , likely reflecting fewer SNPs to choose from for assay design ., The SNP discovery algorithm rejects sequences that align equally to multiple locations ., The repetitive nature of the X chromosome in most mammals means that this limitation rejects a large number of potential SNPs that would not be positionally informative ., The number and mean MAF of polymorphic SNPs varied between breeds ., On average the number of SNPs informative in any given domestic horse breed was higher than informativeness of similar assays within given cattle and dog breeds 8 , 10 ., Mean MAF across all samples ( 0 . 24 ) was slightly lower than the mean MAF reported for bovine ( 0 . 26 ) , ovine ( 0 . 28 ) or porcine assays ( 0 . 27 ) 8 , 9 ( Illumina Data Sheets at http://www . illumina . com/applications/agriculture/livestock . ilmn#livestock_overview ) ., Breeds with recent or ongoing admixture , such as the Quarter Horse , Hanoverian and Swiss Warmblood , had the highest mean MAF and the largest numbers of informative SNPs , while the lowest mean MAF were in the Norwegian Fjord , Belgian and Icelandic horse ., The relatively high number of informative SNPs in the Icelandic horse may reflect its use in SNP discovery ., Despite low genetic diversity and high levels of inbreeding , the mean MAF in the Thoroughbred was higher than any other non-admixed breed , and the fraction of informative SNPs exceeded that of any other breed included in the SNP discovery effort with the exception of the Quarter Horse ., The high level of SNP informativeness in the Thoroughbred breed likely reflects bias due to its use in both SNP discovery and as the reference genome sequence ., We attempted to use the EquineSNP50 BeadChip to genotype a limited number of individuals from 18 other Perissodactyl species ., Due to the SNP discovery design , it was unlikely that a large proportion of the markers in this assay would be polymorphic in other species; however , the identification of even several hundred useful markers in any of these species would provide a dramatic increase in the number of autosomal markers available for conservation genetics applications ., A variable number of genotypes were produced across species , with higher genotyping rates and better quality scores in the more closely related Hippomorpha ( horses , asses and zebras ) than in the Ceratomorpha ( rhinos and tapirs ) ., After data filtering , the assay conversion rate of the remaining SNPs was fairly high , and quality scores in Hippomorpha species were similar to those in the domestic horse , suggesting it may be a useful tool for certain applications in species other than Equus caballus ., SNP validation rates in Equus species other than Equus przewalski were low , which may reflect species divergence as well as the very limited number of individuals genotyped in most species; genotyping a larger cohort within each species would be necessary to determine the true polymorphism rates ., Further work is also necessary to determine the accuracy of genotyping calls in Equus sp ., by reproducibility , concordance with other genotyping methods and confirmation of Mendelian inheritance with parent-offspring trio data 11 ., Lastly , low quality scores , even after data filtering and inconsistent genotyping rates in the Ceratomorpha , suggest that the EquineSNP50 BeadChip will likely have much more limited utility in these species ., Measurements of genetic diversity , inbreeding and LD all reflect population demographic history ., Our measurements of genome-wide LD within and across breeds agreed well with previous work based on ten randomly selected 2 Mb genome segments 3 ., Due to population subdivision , the extent of LD within a given breed was greater than LD across breeds ., LD in the domestic horse is lower than in dogs , which does not decline nearly as rapidly over the first 100 kb and has a very slow decline over the next 1–2 Mb 12 ., Not surprisingly , within breed patterns of LD in horses were similar to those observed in domestic cattle , which typically share a similar system of mating using popular sires and at times extensive line breeding 13 ., LD declined most rapidly in the Quarter Horse and Mongolian horse , with r2 values dropping below 0 . 2 within the first 50–100 kb ., The short extent of LD in the Mongolian and Quarter Horse reflects the low level of inbreeding and high genetic diversity in both breeds ., The short extent of LD in the Mongolian horse is likely a result of its age and large population size ., This breed has been bred in domestication since approximately 2000 BC , and the current population size is ∼3 million individuals 2 ., High diversity in the Mongolian horse is in concordance with previous studies based on microsatellite loci that demonstrated that the Mongolian horse had the highest heterozygosity and genetic diversity in a study of 13 domestic horse populations 14 ., Unlike the Mongolian horse , other breeds with long histories had a moderate decline in LD ., These include the Icelandic horse , which originated from stock imported to Iceland in ∼900 AD , the Norwegian Fjord horse thought to have been selectively bred for at least 2 , 000 years , and the Belgian draft horse believed to be descended from the war horse of the Middle ages 2 ., Somewhat longer LD in these old breeds likely reflects the fact that their population histories have included severe population bottlenecks ., An Icelandic horse bottleneck has been associated with the 1783 eruption of the volcano Lakagigar , in which an estimated 70% of the population was destroyed from volcanic ash poisoning 2 , and a population bottleneck in the Belgian and other draft horse breeds arose due to their disappearance as a utilitarian animal after World War II ., It has been postulated that all present day Norwegian Fjords are descendants of a single stallion foaled in 1891 , however previous studies using microsatellite markers have not yet corroborated this assumed bottleneck 15 ., Short LD in the Quarter Horse , a recently established breed with a registry less than 100 years old , is likely a result of a very large population size ( ∼4 million individuals ) , rapid population expansion and population admixture since the breeds formation 16 , 17 ., In contrast , LD was clearly the highest in the Thoroughbred , reflecting the breeds low diversity , high inbreeding , and closure of the studbook to outside genetic influence for more than 300 years ., Previous work has demonstrated that approximately 78% of Thoroughbred alleles are derived from 30 founders , and that a single founder stallion is responsible for approximately 95% of paternal lineages 18 ., The long extent of LD in this breed also reflects the high level of inbreeding which has been shown to have an even greater impact on the extent of LD than diversity 17 ., The impact of low diversity and high inbreeding on LD can also be seen in the Standardbred and the French Trotter , both breeds which , while having a more rapid decline in LD than the Thoroughbred , have long-range LD that persists further than the Thoroughbred ., The mean pair-wise genetic distance between individuals within a breed was 0 . 24 , which is higher than reported in cattle , but lower than reported in sheep ( 0 . 21 and 0 . 25 respectively ) 19 ., However , D was not normally distributed in horses , displaying three distinct peaks ., When the distance matrix was partitioned by breed , the pair-wise distances were largest within the Quarter Horse , Swiss Warmblood and Hanoverian , all breeds with admixture and low to moderate levels of inbreeding , while the pair-wise distances were the smallest within the Norwegian Fjord and Icelandic horse , which may reflect their previous population bottlenecks ., There is also substantial overlap between the within and across breed distributions , which was likely the result of high genetic diversity in admixed breeds , as well as close relationships between breeds such as the Standardbred and French Trotters ., MDS plots demonstrated that individuals within most breeds were tightly clustered in relation to other breed groups ., This was true even for the Thoroughbred population where two geographically distinct sample origins were represented ( United Kingdom , Ireland , and United States ) ., The exceptions to this were the three breeds with recent and/or ongoing admixture; the Quarter Horse , Hanoverian and Swiss Warmblood ., In addition , the Hanoverian and Quarter Horse , and to a lesser extent the Swiss Warmblood , had larger variation along dimension 1 than other breeds , suggesting that the admixture may be resulting in significant population substructure ., The Andalusian breed was not tightly clustered in dimension 6 , suggesting population substructure as well ., This is consistent with the practice of some American breeders crossing Andalusians ( from Spain ) with closely related Lusitano horses ( from Portugal ) in their breeding programs ., Close relationships between some breeds were also visualized , including the clustering of the Standardbred and the French Trotter apart from the other breeds in dimension 3 ., This may be the result of the influence of the Standardbred on the French Trotter , or similar selective pressures for the trotting phenotype in both breeds ., The Norwegian Fjord , Icelandic , Mongolian , and Belgian clustered together in the first 3 dimensions , and Icelandic and Norwegian Fjord horses clustered tightly together in all 6 dimensions ., This may reflect the suggested influence of Mongolian genes in the development of the Norwegian Fjord and subsequent development of the Icelandic horse from Scandinavian stock imported to Iceland 15 , 20 ., However the close clustering of the Belgian horse with these older breeds does not fit this history and its clustering may also reflect the low MAF and lower number of informative SNPs in the Belgian , Icelandic and Norwegian Fjord ., Ten horses are outliers relative to their breed: a Norwegian Fjord , a Mongolian , the seven SNP discovery horses , and Twilight ., Increased heterozygosity due to SNP discovery bias likely accounts for the outlier status of Twilight and the seven SNP discovery horses ., We expect to observe greater diversity in all SNP discovery breeds because observations of diversity in other breeds rely on across-breed allele sharing rather than direct allelic observation ., Parsimony analysis supports many relationships suggested by MDS ., For instance , breeds in which individuals cluster tightly in MDS , such as the Thoroughbred and Arabian , are represented in the cladogram as monophyletic clades with high bootstrap support; whereas breeds that have continuing admixture , such as the Quarter Horse , Swiss Warmblood , and Hanoverian , do not show monophyly and share a branch of the clade with the Thoroughbred ., In some instances , relationships that were not clear from the MDS plot are demonstrated in the tree , such as the close placement of the Saddlebred and Arabian ., In parsimony analysis of only Equus spp ., using over 40 , 000 SNPs , high bootstrap support distinguishes Equus caballus from Equus przewalskii while also making a clear distinction between those species and the zebras and asses ., With further work , the use of random nuclear SNPs in equid phylogeny studies should prove superior to the existing studies that use either mitochondrial SNPs , or SNPs from just a few nuclear genes 21–23 ., The horse is thought to have been domesticated from the now extinct Tarpan ( also known as the European wild horse Equus ferus ) 1 ., The close clustering of the domestic horse and the Przewalskis Horse is consistent with the hypothesis that the Przewalskis Horse ( also known as the Asiatic wild horse Equus przewalskii ) is a sister species to the Tarpan ., This close relationship between the domestic horse and the Przewalskis Horse is also likely a result of relatively recent gene flow between these lineages since divergence from a common ancestor ., While Equus przewalskii and Equus caballus have a different number of chromosomes ( 2n\u200a=\u200a66 and 2n\u200a=\u200a64 , respectively ) , they can interbreed and produce viable offspring ., Since their discovery by the western world in the late 1880s , the question of admixture of the Przewalskis Horse and domestic horse has remained a topic of debate and controversy ., Known introgressions took place in the early years of the propagation program that prevented the extinction of the species 24 and , more recently with the offspring of the last wild-caught mare at the Askania Nova breeding center 25 ., In addition , there was likely interbreeding of Equus przewalskii and Equus caballus in the wild , as the range of the Przewalskis Horse and the domestic horse overlapped in China , Russia and Mongolia 26 ., Gene flow from the domestic to Przewalskis Horse in our study is supported by the tight clustering of the Przewalskis Horse and several of the horse breeds in MDS , most notably the Mongolian horse and related breeds ., This relationship is reiterated by parsimony analysis where the Mongolian , Icelandic , and Norwegian Fjord are in close association with the Przewalskis Horse ., The pair-wise genetic distances between Przewalskis Horses and some domestic horse breeds falls within the range of within breed pair-wise differences in domestic horse breeds , which corroborates earlier findings 3 ., Thus , while Equus ferus and Equus przewalskii are considered different species based on chromosomal number differences , surviving Przewalskis Horses today are truly Equus przewalskii and Equus caballus hybrids 1 ., A major application of this genotyping technology will be in genome-wide association mapping of traits in the domestic horse 27–31 ., The success of such studies will depend upon LD within the mapping population , properties of the loci themselves , population structure , and the mode of inheritance ., Our attempt to map three known Mendelian coat color traits in a sample set not specifically designed for that purpose , met with varying success ( Tables S12 , S13 , S14; Figures S6 , S7 , S8 ) ., The MC1R locus was successfully mapped both across breeds and within several breeds ., This is a result of good informative SNP density in this region , larger sample sizes for several breeds in which the chestnut allele is segregating , and extended homozygosity surrounding the locus ., The centromeric location of the MC1R locus that limits recombination , as well as selection for the chestnut trait in many breeds , resulted in a conserved haplotype within breeds ranging from 1 . 2–4 . 2 Mb and a 750 kb minimally conserved haplotype across breeds ( Figure S9 , Table S15 ) ., The length of this conserved haplotype is nevertheless surprising given the presence of the MC1R chestnut allele since at least the fifth millennium before present 32 On the other hand , the mapping of ASIP , while successful across breeds , suffered from lower numbers of relevant samples within many individual breeds and low SNP density at the ASIP locus itself ., Mapping the STX17 gray locus was unsuccessful due to confounding by population substructure , sparse marker density in the region , and poor power to detect a dominant locus due to low sample sizes both within and across breeds ., Nevertheless , our results demonstrate the utility of whole genome mapping within breeds when studies are sufficiently powered , although power clearly varies among breeds , and the rate of false positives increases with small sample sizes ., Further , due to across-breed haplotype sharing in the horse 3 , across-breed mapping of certain traits that are clearly conserved across breeds is possible if proper consideration is given to confounding population substructure ., Ideally , increased genome coverage with additional , highly informative SNPs would be more effective for mapping studies , particularly in admixed and/or breeds with low LD ., We have constructed and validated a 54 , 000 SNP genotyping assay that will enable mapping of loci associated with equine health and performance , as well as the study of breed diversity and relationships ., The array will also likely have many uses in the study of the population genetics of other equid species ., SNPs assayed on the

Introduction, Results, Discussion, Materials and Methods

An equine SNP genotyping array was developed and evaluated on a panel of samples representing 14 domestic horse breeds and 18 evolutionarily related species ., More than 54 , 000 polymorphic SNPs provided an average inter-SNP spacing of ∼43 kb ., The mean minor allele frequency across domestic horse breeds was 0 . 23 , and the number of polymorphic SNPs within breeds ranged from 43 , 287 to 52 , 085 ., Genome-wide linkage disequilibrium ( LD ) in most breeds declined rapidly over the first 50–100 kb and reached background levels within 1–2 Mb ., The extent of LD and the level of inbreeding were highest in the Thoroughbred and lowest in the Mongolian and Quarter Horse ., Multidimensional scaling ( MDS ) analyses demonstrated the tight grouping of individuals within most breeds , close proximity of related breeds , and less tight grouping in admixed breeds ., The close relationship between the Przewalskis Horse and the domestic horse was demonstrated by pair-wise genetic distance and MDS ., Genotyping of other Perissodactyla ( zebras , asses , tapirs , and rhinoceros ) was variably successful , with call rates and the number of polymorphic loci varying across taxa ., Parsimony analysis placed the modern horse as sister taxa to Equus przewalski ., The utility of the SNP array in genome-wide association was confirmed by mapping the known recessive chestnut coat color locus ( MC1R ) and defining a conserved haplotype of ∼750 kb across all breeds ., These results demonstrate the high quality of this SNP genotyping resource , its usefulness in diverse genome analyses of the horse , and potential use in related species .

We utilized the previously generated horse genome sequence and a large SNP database to design an ∼54 , 000 SNP assay for use in the domestic horse and related species ., The utility of this SNP array was demonstrated through genome-wide linkage disequilibrium , inbreeding and genetic distance measurements within breeds , as well as multidimensional scaling and parsimony analysis ., Association mapping confirmed a large conserved segment containing the chestnut coat color locus in domestic horses ., We also assess the utility of the SNP array in related species , including the Przewalskis Horse , zebras , asses , tapirs , and rhinoceros ., This SNP genotyping tool will facilitate many genetics applications in equids , including identification of genes for health and performance traits , and compelling studies of the origins of the domestic horse , diversity within breeds , and evolutionary relationships among related species .

animal types, genetics, biology, genomics, population biology, genetics and genomics, veterinary science, agriculture

journal.pcbi.1006209

Evolution of chemokine receptors is driven by mutations in the sodium binding site

Directed cell migration is fundamental for life because this process is involved in key biological processes such as embryonic development , organogenesis , immune surveillance , host defense , and wound repair ., Leukocyte migration and tissue localization during homeostatic and inflammatory conditions depend directly upon chemokines ( or chemotactic cytokines ) , a family of small secreted proteins ., Chemokines implement their functions by acting through specific receptors belonging to the family of class A ( rhodopsin-like ) G-protein-coupled receptors ( GPCRs ) ., The human chemokine-receptor system is composed of forty-five chemokines and twenty-two receptors , with complex specificity/promiscuity pattern 1 ., Some chemokine-receptor pairs are highly specific but most chemokines and receptors can be involved in different pairings ., This system has a highly positive developmental and protective role in physiological conditions but it is also implicated in a broad array of pathologies , including autoimmune and inflammatory diseases , allergies , cancer metastasis , and HIV infection ., Therefore , the chemokine-receptor system is an attractive target for drug development 1 , 2 ., Among the chemokine receptors , CXCR4 and CCR5 have been extensively studied because of their role as co-receptors of HIV for virus entry ., Chemokines are structurally classified as CC , CXC , C3XC , and C chemokines , based on the arrangement of the N-terminal disulfide forming cysteines ., Chemokines can also be classified according to their main function 3 ., The homeostatic chemokines are involved in homing of lymphocytes in physiological conditions , whereas inflammatory chemokines are involved in attracting lymphocytes in inflammatory area ( note that some chemokines have dual functions ) ., Chemokine receptors can be classified by phylogeny into two groups ., The oldest group , which appeared in jawless fishes , binds mainly homeostatic chemokines while the most recent group , which appeared in jawed vertebrates , bind mainly inflammatory chemokines 3 , 4 ., The “atypical” chemokine receptors with promiscuous chemokine binding are phylogenetically related to either one of these groups ., Previously called decoys or scavengers , these atypical receptors usually act as β-arrestin biased receptors that do not promote migration but rather shape chemokine gradients to permit migration induced by conventional chemokine receptors 5 ., Several structures of chemokine receptors , in inactive or pseudo-active forms , bound to chemical ligands or chemokines , have been resolved 6–11 and have provided invaluable information on the mechanism of action of these receptors ., Details of the mechanism of chemokine binding to cognate receptors are emerging with the analysis of the recent structures of chemokine-receptor complexes 6 , 9 ., The structure of these complexes corroborates the insertion of the chemokine N-terminus into the receptor helical core and the plasticity of the chemokine receptors to adapt to different ligands ., Because of the wide variety of diseases in which chemokine receptors are implicated , chemokine receptors constitute very attractive targets for the pharmaceutical industry ., However , despite important investments , only two drugs targeting chemokine receptors have received Food and Drug Administration approval for clinical use: maraviroc , which targets CCR5 in HIV/AIDS treatment , and plerifaxor , which targets CXCR4 for hematopoietic stem cell mobilization ., Difficulties in targeting chemokine receptors for anti-inflammatory therapy may arise from inappropriate target selection and ineffective dosing or from the redundancy of the chemokine system 12 ., Recent advances in the understanding of chemokine signaling have shown that this apparent redundancy hides biased signaling ., The activation of the same receptor by different chemokines may induce different cellular issues 13 ., This observation indicates a system more complex than initially thought ., In addition , the effect of a ligand may depend on the presence of different chemokines and of the cellular system or tissue under investigation 1 ., Finally , the chemokine/receptor system is species-specific and may lead to different results in mouse/rat trials compared to humans ., Taken together , these additional levels of complexity make pharmacodynamics and pharmacology studies very difficult for therapeutic applications ., Understanding the molecular determinants involved in functional specificity of chemokine receptors could help the rational design of drugs targeted towards this important receptor sub-family ., Evolutionary information , based on analysis of multiple sequence alignment ( MSA ) can be used to gain structural and functional information on protein families ., Previously , we have used evolutionary information to successfully predict the kinked structure of the transmembrane helix 2 ( TM2 ) in chemokine receptors prior to their crystallization 14 ., These receptors are part of a larger sub-family , the chemotaxic ( CHEM ) sub-family in Fredriksson’s classification , which includes different chemotaxic and vasoactive receptors 15 ., We have shown that the CHEM sub-family , along with the PUR sub-family of purinergic receptors , evolved by divergence from the somatostatin/opioid ( SO ) receptor sub-family in vertebrates , and that the latter sub-family evolved from the deletion of one residue in TM2 in an ancestral receptor 14 , 16 ., Receptors from these three sub-families possess a characteristic P2 . 58 pattern ( Ballesteros’ numbering ) which corresponds to one of the main GPCR evolutionary pathways 16 , 17 ., In the present study , we investigate the evolutionary determinants of chemokine receptors using principal component analysis of sequence covariations in nested GPCR sequence sets ., This approach highlights three residues whose mutations were crucial for the emergence of chemokine receptors and their subsequent divergence into homeostatic and inflammatory receptors ., These key residues are located at the binding site of a sodium ion which is thought to be a general feature amongst class A GPCRs 18 , 19 ., To further define the structural/functional role of these residues , we carried out molecular dynamics ( MD ) simulations of the chemokine receptors CCR5 and CXCR4 , chosen as prototypes of homeostatic and inflammatory chemokine receptors ., We show that the evolution of chemokine receptors was driven , at least in part , by dramatic changes in the sodium binding mode ., Most ancient receptors , which appeared in jawless fishes , have a highly constrained sodium binding site ., These constraints were subsequently loosened during the divergence of this receptor family ., We discuss the implications of these findings in terms of evolution of the chemokine receptor functions and mechanisms of action ., To highlight residues characterizing chemokine receptors , we applied a hierarchical approach to search residues characteristic of the four nested sets of human sequences that lead from class A receptors to the chemokine receptor sub-family ., These sets correspond to: ( 1 ) class A GPCRs , ( 2 ) the P2 . 58 receptors ( SO , CHEM and PUR sub-families ) , ( 3 ) the CHEM sub-family and ( 4 ) the chemokine receptor sub-family ., Sequence sets were prepared as described in Methods ., They are visualized on the Neighbor Joining ( NJ ) tree of human receptors shown in Fig 1A ., In a previous study 17 , we have analyzed the sequence covariation in the multiple sequence alignment ( MSA ) of human class A GPCRs ., The network representation of the top pairs with highest covariation scores highlighted the central role of position 2 . 58 as an evolutionary hub ., This representation provides information on positions that covary with the P2 . 58 pattern ., However , this arrangement is dependent on the number of top pairs selected ., To obtain a representation of the covariation data independent of a user selected parameter , we carried out the principal component analysis ( PCA ) 20 of the double-centered covariation matrix ( specifically , an eigen-decomposition of this matrix ) , obtained from the MSA of human class A GPCRs ( S1 File ) ., Fig 1B shows the positions in the MSA plotted in the plane formed by the first two components of the PCA ., This analysis highlights a few positions clearly separated on the first and the second axes ., The position with the highest coordinate on the first component is 2 . 58 ., Next are positions 2 . 57 and 2 . 59 , then positions 1 . 46 , 3 . 35 , 4 . 46 , and 2 . 45 ., These positions have the top covariation scores with position 2 . 58 17 ., On the second dimension , residues with highest coordinates are positions 7 . 49 , then 6 . 48 and 1 . 53 ., These positions correspond to hallmark residues that led to the divergence of the PUR sub-family ., Indeed , positions 7 . 49 and 6 . 48 are highly conserved Asn and Trp in most class A GPCRs , but are Asp and Phe in the PUR sub-family ., Likewise , position 1 . 53 is usually Val in most GPCRs but Ala in PUR receptors ., Proline 2 . 58 is the hallmark residue of the SO , CHEM , and PUR sub-families ., This pattern results from the deletion of one residue located two positions upstream of the TM2 proline in an ancestral P2 . 59 receptor 14 ., The covariation of positions 2 . 57 and 2 . 59 with position 2 . 58 is a consequence of the indel mechanism ., Logo representation of amino acid distribution ( Fig 1D ) indicates an increase in the frequency of Gly and Asn at positions 1 . 46 and 2 . 45 , respectively , and a small polar residue instead of an aliphatic residue at position 4 . 46 in P2 . 58 receptors ., In addition , position 3 . 35 is Asn in most P2 . 58 receptors while this amino acid is absent at this position in the complementary set ., The next step was to apply the same approach to the MSA of the human CHEM sub-family ( S2 File ) ., This analysis ( Fig 1C ) highlights residues associated with the divergence of chemokine receptors on the first component and the split between homeostatic and inflammatory receptors on the second component ., Position 3 . 57 , at the limit between TM3 and ICL2 , is an alanine in chemokine receptors and a proline in other CHEM receptors ( Fig 1E ) ., However , we can note that the presence of Pro or Ala at this position is a common feature of human class A GPCRs ( Fig 1D ) , a pattern which suggests a role in the interaction with G proteins ., Most interestingly , position 7 . 45 in TM7 is either His or Arg in chemokine receptors , which is very infrequent in other human GPCRs ( 2% ) ., Position 7 . 45 is preferentially Asn in class A receptors ( 67% ) ., The second component highlights positions 2 . 49 and 3 . 35 ., We have previously shown that position 2 . 49 differentiates homeostatic and inflammatory receptors ( A2 . 49 and S2 . 49 , respectively ) 17 ., This position is strongly correlated with position 3 . 35 , which is preferentially Asn and Gly in homeostatic and inflammatory receptors , respectively ( Fig 1F ) ., The position of these hallmark residues in the structure of CXCR4 and CCR5 , as prototypes of homeostatic and inflammatory chemokine receptors , is displayed in Fig 1G and 1H ., Polar positions 2 . 45 and 4 . 46 are located on the external surface of the receptor at the interface with the membrane ., These two positions face each other and may be involved in polar interactions ., Indeed , in the crystal structure of CCR5 , S4 . 46 is involved in H-bonding with N2 . 45 ., Most interestingly , positions 2 . 49 , 3 . 35 , and 7 . 45 are clustered in the receptor core and line the allosteric sodium binding pocket ., Among them , positions 3 . 35 and 7 . 45 can be directly involved in the coordination of the sodium ion 18 ., In the two crystal structures of sodium bound P2 . 58 receptors , the δ-opioid receptor ( OPRD , PDB entry 4N6H ) 21 and the proteinase activated receptor 1 ( PAR1 , PDB entry 3VW7 ) 22 , positions 3 . 35 and 7 . 45 participate in sodium binding , either directly ( N3 . 35 in OPRD ) or through a water molecule ( N7 . 45 in OPRD and N3 . 35/S7 . 45 in PAR1 ) ., This ion acts as a negative modulator of GPCRs and stabilizes the inactive structure 18 ., Mutations of N3 . 35 to smaller residues ( Ser or Ala ) in CXCR4 23 and CXCR3 24 yield constitutively active mutants ., In the closely related angiotensin II receptor AT1 , the N3 . 35G mutant has also high constitutive activity 25 ., The presence of a glycine residue at position 3 . 35 in inflammatory chemokine receptors is thus surprising , and this prompted us to investigate the history of position 3 . 35 ., Covariation can result either from phylogenetic history with the correlated residues already present in the common ancestor ( or in an early step of subsequent evolution ) and maintained throughout evolution or from an epistasis mechanism in which several correlated mutations led to functional divergence in the receptor family ., Differentiation between the two mechanisms requires the analysis of the GPCR repertoires from different species covering several animal phyla ., The CHEM and PUR sub-families are specific to vertebrate species 14 , 16 ., The N3 . 35 pattern is present in vertebrate SO receptors as exemplified by the opioid receptors ., Thus , we analyzed the amino acid distribution at position 3 . 35 in the SO sub-family from different species: H . sapiens , D . rerio , B . floridae , C . elegans , N . vectensis and T . adhaerens ( S3 File ) and reported it on the NJ tree of these receptors ( Fig 2A ) ., The N3 . 35 pattern is not present in the receptors from non bilaterian species ( N . vectensis and T . adhaerens ) , but polar residues ( S , T ) at this position are observed in sequences from N . vectensis ., The N3 . 35 pattern is present in a few sequences from C . elegans , and in almost all chordates sequences ., Interestingly , orthologs of the urotensin II receptor ( UR2R in Uniprot nomenclature ) are present in B . floridae ., This is the first observation of UR2R in an invertebrate species ., The small sub-family containing UR2R ( Fig 2A ) is characterized by the T3 . 35 pattern and is more closely related to invertebrate SO receptors , as an intermediate between chordate and non-chordate SO members ., This analysis indicates that the N3 . 35 pattern , which strongly covaries with the P2 . 58 pattern in human GPCRs , is a hallmark of chordate SO receptors ., The presence of N3 . 35 is correlated with the evolutionary drift of SO receptors observed by multidimensional scaling 16 , a pattern suggesting that this residue might have contributed to the evolution of the SO sub-family and its subsequent divergence ., This residue , directly involved in the binding of the allosteric sodium ion in the δ-opioid receptor 21 , is present in most vertebrate SO , CHEM , and PUR receptors ., This study strongly supports the assumption that the N3 . 35 pattern is secondary to the deletion in TM2 and might have been important for the evolutionary drift of P2 . 58 receptors in vertebrates ., We also analyzed position 7 . 45 in the same set of SO receptors ( Fig 2A ) ., In most receptors , position 7 . 45 is polar ., This polar residue is usually Asn in chordates SO receptors ( B . floridae , D . rerio , H . sapiens ) , with a single observation of His in a sequence from D . rerio ., This is not the case in sequences from non-chordate species in which position 7 . 45 is more variable with several examples of His in T . adhaerens and N . vectensis and an example of Arg in C . elegans ., Finally , we analyzed positions 3 . 35 and 7 . 45 in the human CHEM sub-family ( Fig 2B ) ., The H7 . 45 pattern is a hallmark of chemokine receptors , indicating that the mutation of this position may have been crucial for the emergence of chemokine receptors ., H7 . 45 is found in 20 out of 23 chemokine receptors , while R7 . 45 is found in only three homeostatic receptors ( CXCR6 , CCR7 , and CCR10 ) ., Among other CHEM receptors , position 7 . 45 is usually Asn ( 84% ) and the H7 . 45 pattern is observed only in the orphan GPR182 , closely related to ACKR3/CXCR7 and in C5aRL ., In chemokine receptors , position 3 . 35 is Asn in 10 out of 12 homeostatic receptors and Gly in 8 out of 10 inflammatory receptors ., In the other CHEM receptors , position 3 . 35 is either Asn ( 72% ) or Ser/Thr ( 24% ) and Gly is observed only in the orphan GPR33 receptor ., To analyze the consequences of these mutations on the mechanism of sodium binding , we carried out molecular dynamics simulations of CXCR4 and CCR5 in the presence of a sodium ion which was initially positioned in the receptor models in the vicinity of D2 . 50 ( see Methods ) ., After insertion of the models within a hydrated POPC bilayer , MD simulations were carried out for 420 ns ., In both cases , the root mean square deviations ( RMSD ) of the Cα atoms of the transmembrane ( TM ) domain underwent a very fast increase of about 1 Å within the first nanosecond , followed by a slower phase that reached a plateau at about 1 . 7 Å after 20 to 40 ns ( Fig 3A ) ., The root mean square fluctuations ( RMSF ) indicated similar magnitude of fluctuations for both receptors ., As usual in GPCRs , the RMSF of the loops and the termini could reach 3–4 Å , whereas the residues in the central part of the TM helices had fluctuations below 1 Å ( Fig 3B ) ., We can note that ( 1 ) the presence of three glycine residues in TM3 of CCR5 , at positions 3 . 30 , 3 . 35 and 3 . 39 , does not alter the fluctuations of this helix as compared to CXCR4 , and ( 2 ) the positions lining the sodium binding pocket ( residues 2 . 50 , 3 . 35 , 3 . 39 , and 7 . 45 ) have similar low fluctuations in both receptors ., However , in spite of these similarities , striking differences were observed in the behavior of the sodium ion bound to CXCR4 and CCR5 during the simulations ( Fig 3C ) ., In CXCR4 , after fast motion during the first nanosecond , the sodium ion remained stable with an average RMSD of 1 . 3 ± 0 . 3 Å ., Similar results were seen for the three CXCR4 replicates ., By contrast , in CCR5 , the RMSD of the sodium ion did not converge but indicated exchanges between ( at least ) two positions with RMSD of approximately 1 . 2 and 2 . 5 Å ., These exchanges provided different RMSD patterns for the five CCR5 replicates carried out ., Typical snapshots of the sodium ion bound to CXCR4 and CCR5 are displayed in Fig, 4 . In CXCR4 , the ion is coordinated to four protein atoms ( D2 . 50:OD1 , N3 . 35:OD1 , S3 . 39:OG , N7 . 45:NE2 ) and to the oxygen atom of one water molecule ., The position of the H7 . 45 ring is maintained by face-to-edge interaction with W6 . 48 in the g+ rotameric state ., The second closest water molecule links the D2 . 50:OD2 atom to the backbone distortion of TM7 ( H7 . 45:O and N7 . 49:N ) , while the third one links the D2 . 50:OD2 atom to the N1 . 50:OD1 and N7 . 49: ND2 atoms ., Two different binding modes of the sodium ion to CCR5 , obtained from classical MD simulations , are shown in Fig 4B and 4C ., In Fig 4B , the sodium ion interacts with the OD1 atom of D2 . 50 , the NE2 atom of H7 . 45 in the g- conformation and four water molecules , an interaction resulting in a coordination number of six ., In Fig 4C , H7 . 45 is now in the trans conformation ., The sodium ion has a bivalent coordination with the OD1 and OD2 atoms of D2 . 50 , it also interacts with the OD1 atom of N7 . 49 and with two water molecules , resulting in a coordination number of five ., To better characterize the environment of the sodium ion in CXCR4 and CCR5 , we measured the distances between the ion and the putative protein coordinating atoms ( Fig 5 ) ., Receptor coordinating atoms include oxygen atoms from carbonyl , carboxyl , and hydroxyl groups , and the nitrogen atom with lone-pair electrons from imidazole rings ., This nitrogen corresponds to the NE2 atom since histidine residues have been modeled in the most frequent tautomeric form with the hydrogen atom on the ND1 atom ., In CXCR4 , the sodium ion remained within coordination distance of the D2 . 50:OD1 , N3 . 35:OD1 , S3 . 39:OG and N7 . 45:NE2 atoms for at least 98% of the trajectories ( Table 1 ) ., No contact was observed with the D2 . 50:OD2 or the N7 . 49:OD1 atoms ., In contrast to CXCR4 , the coordinating atoms of the sodium ion in CCR5 included the OD1 and OD2 atoms of D2 . 50 , the NE2 atom of N7 . 45 and the OD1 atom of N7 . 49 ., Contacts with these residues could last several tenths of nanoseconds but were not stable on the sub-microsecond timescale ., The ion moved between several sub-sites and its coordination was reorganized within and between trajectories ., The sodium ion could have monovalent or bivalent coordination with the OD1 and/or the OD2 atoms of D2 . 50 ., It could also be coordinated with the NE2 atom of H7 . 45 and with the OD1 atom of N7 . 49 ( these two interactions were mutually exclusive ) ., In addition to the contacts displayed in Fig 4B and 4C , other modes of interaction were observed , for example , with water molecules bridging the sodium ion and the D2 . 50 side chain , but these modes usually involved at least one contact with D2 . 50 , H7 . 45 or N7 . 49 ., Finally , the N3 . 35G and A2 . 49S mutations created a cavity in which the G3 . 35:O and S2 . 49:OG atoms might act as an additional binding site when S2 . 49 was in the trans orientation ., However , only transient contacts with these atoms were observed in the CCR5 trajectories ( Fig 5 ) ., Analysis of the contact frequencies highlights the variability in the sodium binding mode of CCR5 ( Table 1 ) ., For both CXCR4 and CCR5 , during the contacts , the distances between the sodium ion and the coordinating atoms were similar to those observed in the crystal structures ( Table 2 ) ., The analysis of the number of protein atoms coordinated to the sodium ion during the MD simulations ( Fig 6 ) corroborates the diversity of the sodium binding modes in CCR5 ., This number usually varied between 1 and 3 with similar weights of about 30% but , in about 10% of the frames , no direct contact was observed ., In contrast with CCR5 , the sodium ion in CXCR4 was coordinated to four protein atoms in 97 ± 2% of the trajectory frames ., We also investigated the number of water molecules in the vicinity of the sodium ion ., In approximately 85% of the CXCR4 trajectory frames , the coordination of the sodium ion was completed by the oxygen atom from a single water molecule ( Fig 4A ) ., In the remaining frames , a second water molecule was present in the first coordination shell of the ion ., This water molecule , which was hydrogen-bonded to N3 . 35:ND2 and L2 . 26:O upon interaction with the sodium ion , was located between TM2 , TM3 , and TM4 ., For CCR5 , the number of water molecules in the first shell of the sodium ion varied from 2 to, 5 . The total coordination number ( Fig 6C ) did not display such variability with an average value of 5 . 4 ± 0 . 2 for CCR5 , to be compared to 5 . 1 ± 0 . 1 for CXCR4 ., These values are consistent with data mining analysis of the sodium environment in proteins 26 ., Finally , to further characterize the sodium binding site , we calculated the radial distribution function of water around the sodium ion in CXCR4 and CCR5 ( Fig 6D ) ., Comparison of these distribution functions highlights the differences between the CXCR4 and CCR5 sodium binding pockets ., For CXCR4 , in addition to the water molecules in the first coordination shell of the ion at a distance of about 2 . 5 Å , only two water molecules could be present at a distance of about 5 and 6 . 5 Å to the sodium ion , as observed in the snapshot displayed in Fig 4A ., For CCR5 , eight to nine water molecules were present in the first two shells ., These differences can be explained by the difference in the sizes of the internal pocket in the vicinity of D2 . 50 in CXCR4 and CCR5 ., Indeed , the size increased from 90 Å3 in CXCR4 to 236 Å3 in CCR5 , a pattern which is consistent with the changes in side chain volume upon the N3 . 35G ( 54 Å3 ) and S3 . 39G ( 29 Å3 ) mutations ., The wider pocket in CCR5 can accommodate more water molecules than CXCR4 and does not constraint the sodium ion which can move by up to 3–4 Å during the simulations ( Fig 3 ) ., These changes in the size of the sodium pocket result in static and dynamic sodium binding modes in CXCR4 and CCR5 , respectively ., It is worth noting that , in spite of the high mobility of the sodium ion in CCR5 , an egress of the ion was not observed during the simulations ., The split between homeostatic and inflammatory chemokine receptors is characterized by the A2 . 49S mutation , which lines the sodium binding pocket ., In CCR5 , when S2 . 49 is in the trans rotameric state , it faces the sodium binding site at a distance of 2 . 8 Å from the carbonyl group of G3 . 35 ., This geometry could provide an additional binding site to the sodium ion ., However , we observed only transient escapes of the sodium ion toward this putative site ( Fig 5 ) ., We extended this simulation to 1 . 0 microsecond but failed to observe stable binding of the ion to the putative site , albeit the trans orientation of S2 . 49 was stable ( Fig 7 ) ., In order to obtain a larger sampling of the receptor conformational spaces , we carried out accelerated molecular dynamics ( aMD ) simulations 27 of CCR5 ., In these accelerated trajectories , the RMSD of the sodium ion , 3 . 0 ± 0 . 9 Å , indicated high fluctuations of the sodium ion within the binding pocket on the nanosecond time scale ., Frequent interactions of the ion with both G3 . 35:O and S2 . 49:OG were observed and could last several nanoseconds , after rotamerization of W6 . 48 to the trans conformation ( Fig 7B ) ., In order to determine whether the alternative binding site reached during aMD simulations could remain stable during classical MD simulations , a snapshot of the aMD trajectory with the sodium ion interacting with the G3 . 35:O and S2 . 49:OG atoms was selected ., The system was energy minimized and used as starting coordinates for subsequent classical MD simulations ( Fig 7C ) ., In four out of five replicates , during several tens of nanoseconds ( from 18 to 58 ns ) , the sodium ion remained at this position , and then could experience exchanges between the alternative and canonical sites that are distant of 3–4 Å ( Fig 7C ) ., In the alternative site ( Fig 4D ) , the sodium ion is coordinated to the carbonyl oxygen of G3 . 35 , the hydroxyl oxygen of S2 . 49 and three water molecules ., In addition , S2 . 49 is H-bonded to S3 . 38 , providing further stability to this configuration ., D2 . 50 is now located in the second coordination shell and interacts with the sodium ion through one or occasionally two water molecules ., We can note that W6 . 48 , in the trans conformation , forms a trap that closes the sodium binding pocket ., In this conformation , it cannot form the face-to-edge interactions with H7 . 45 that favor the interaction of the latter residue with the sodium ion ., The rotameric state of W6 . 48 might explain the differences observed between the simulations restarted from the aMD snapshot and the initial MD simulations in which W6 . 48 remained in the crystal structure conformation ( Fig 7 ) ., In this article , we seek to identify the key residues that drove the evolution of chemokine receptors ., Nested PCA analysis of sequence covariation matrices ( Fig, 1 ) highlighted three positions ( i . e . , 3 . 35 , 7 . 45 , and 2 . 49 ) that are part of the allosteric sodium binding pocket 18 ., Mutation of at least one of these positions was crucial at each hierarchical step that led from class A receptors to the split between homeostatic and inflammatory chemokine receptors ., This observation prompted us to investigate the history of these positions and their structural and functional roles in prototypical chemokine receptors ., Sodium has been shown to be an important regulator of a wide variety of class A GPCRs , acting as a negative allosteric modulator 18 ., This allosteric role has been confirmed by the presence of a sodium ion bound in a conserved position in several high resolution structures of GPCRs 21 , 22 , 28 , 29 ., The overall binding cavity is conserved within class A GPCRs and involves highly conserved residues , especially D2 . 50 ( fully conserved ) , but also S/T3 . 39 ( 80% conserved ) , and N7 . 45 ( 67% conserved ) , a pattern suggesting that sodium binding may be a general property of class A GPCRs 18 ., A recent MD investigation of the free energy profiles and kinetics of sodium binding to 18 GPCRs revealed a conserved sodium binding mechanism 19 ., In addition to its documented role as a negative allosteric modulator , the sodium ion might contribute to the mechanisms of receptor activation 30 , 31 , to voltage sensing 32 , and to biased signaling 21 , 33 ., Moreover , presence of a sodium binding site might have contributed to the evolutionary success of class A GPCRs 18 ., In view with this latter role , it is noteworthy that expansion of P2 . 58 receptors in vertebrates with the emergence of the CHEM and PUR sub-families is subsequent to the appearance of the N3 . 35 pattern in the SO receptors of chordates ( Fig 2 ) ., In the sodium bound crystal structures of two P2 . 58 receptors , OPRD and PAR1 , N3 . 35 is involved in the first or in the second coordination shell of the sodium ion 21 , 22 ., The divergence that led to the chemokine receptors is characterized by a specific mutation at position 7 . 45 ( Fig 1 ) ., This position is preferentially an Asn residue in class A receptors ( 67% ) but a His , or in a few cases , an Arg residue , in chemokine receptors ., Oldest chemokine receptors , CXCR4 and ACKR3/CXCR7 , present in the lamprey 3 , possess a histidine at this position , indicating that the divergence to chemokine receptors involved the N7 . 45H mutation ., Our molecular dynamics simulations of CXCR4 and CCR5 indicate that H7 . 45 participates in sodium binding ., Indeed , in the neutral state , one nitrogen atom of the imidazole ring ( usually NE2 ) has a lone-pair of electrons that allow His to act as a Lewis Base to form coordination complexes ., Coordination to divalent ions ( e . g . , Fe++ , Zn++ , Ni++ , Cu++ ) is frequently found in proteins 26 ., Coordination to the sodium ion is less frequent but is also observed either in model systems 34 or in proteins 26 ., The distance of about 2 . 5 Å ( Table, 2 ) that we observed is in agreement with statistical analysis of the Cambridge Structural Database 35 ., In chemokine receptors ( Fig 2B ) , H7 . 45 can only be substituted by Arg , which allows direct salt bridge interaction with D2 . 50 to stabilize inactive structure ., We and others previously noted that the split between homeostatic and inflammatory chemokine receptors is characterized by an Ala to Ser mutation at position 2 . 49 17 , 36 ., This position strongly covaries with position 3 . 35 ( second component in Fig 1C ) ., Position 3 . 35 , which is a conserved Asn in 70% of P2 . 58 receptors , is Gly in the small subset of inflammatory chemokine receptors ., This latter mutation is striking because of the importance of N3 . 35 in sodium binding and its role in the stability of the receptor inactive state ., In the homeostatic receptors CXCR4 23 and CXCR3 24 , the mutation of N3 . 35 to Ser or Ala induces constitutive G protein and β-arrestin activity ., Among inflammatory chemokine receptors , the direct effect of sodium ions on receptor activity has been experimentally verified on CCR3 37 , which possesses a Gly residue at position 3 . 35 , a configuration indicating that these receptors maintain the ability to bind sodium ions ., With this regard , the presence of a His residue at position 7 . 45 , involved in sodium binding , might explain the maintained sodium binding ., Nevertheless , MD simulations highlight dramatic differences in the sodium binding sites of CXCR4 and CCR5 ., In CXCR4 , the tightly bound sodium ion is

Introduction, Results, Discussion, Methods

Chemokines and their receptors ( members of the GPCR super-family ) are involved in a wide variety of physiological processes and diseases; thus , understanding the specificity of the chemokine receptor family could help develop new receptor specific drugs ., Here , we explore the evolutionary mechanisms that led to the emergence of the chemokine receptors ., Based on GPCR hierarchical classification , we analyzed nested GPCR sets with an eigen decomposition approach of the sequence covariation matrix and determined three key residues whose mutation was crucial for the emergence of the chemokine receptors and their subsequent divergence into homeostatic and inflammatory receptors ., These residues are part of the allosteric sodium binding site ., Their structural and functional roles were investigated by molecular dynamics simulations of CXCR4 and CCR5 as prototypes of homeostatic and inflammatory chemokine receptors , respectively ., This study indicates that the three mutations crucial for the evolution of the chemokine receptors dramatically altered the sodium binding mode ., In CXCR4 , the sodium ion is tightly bound by four protein atoms and one water molecule ., In CCR5 , the sodium ion is mobile within the binding pocket and moves between different sites involving from one to three protein atoms and two to five water molecules ., Analysis of chemokine receptor evolution reveals that a highly constrained sodium binding site characterized most ancient receptors , and that the constraints were subsequently loosened during the divergence of this receptor family ., We discuss the implications of these findings for the evolution of the chemokine receptor functions and mechanisms of action .

The chemokine-receptor system is involved in a broad array of pathologies , including autoimmune and inflammatory diseases , allergies , cancer metastasis , and HIV infection ., It is an attractive , but difficult , target for drug development and a deeper understanding of the structure-function relationships of the chemokine receptors is required to help design drugs targeted against these receptors ., To gain information on the mechanism of action of the chemokine receptors , we developed an evolutionary approach based on the global analysis of co-mutations in receptor sequences ., This approach drew attention to a few residues whose mutation was crucial for the evolution of the chemokine receptor family ., To understand the role of these residues , we have carried out molecular dynamics simulations that revealed that these mutations dramatically modified the binding mode of a sodium ion involved in receptor regulation ., These changes accompanied the divergence of chemokine receptor functions between immune surveillance and inflammation ., They indicate unanticipated roles of the sodium ion in the mechanism of action of the chemokine receptors .

cell motility, medicine and health sciences, ccr5 coreceptor, molecular dynamics, pathology and laboratory medicine, immunology, homeostatic mechanisms, sodium, physiological processes, signs and symptoms, homeostasis, coreceptors, g protein coupled receptors, research and analysis methods, sequence analysis, inflammation, bioinformatics, proteins, chemistry, transmembrane receptors, immune response, chemotaxis, biochemistry, signal transduction, diagnostic medicine, cell biology, physiology, database and informatics methods, chemokines, biology and life sciences, physical sciences, computational chemistry, chemical elements

journal.pgen.1006588

Recruitment of Fkh1 to replication origins requires precisely positioned Fkh1/2 binding sites and concurrent assembly of the pre-replicative complex

Replication of genomic DNA in budding yeast ( Saccharomyces cerevisiae ) is initiated from hundreds of origins throughout the S phase ., Replication origins can be characterized by their efficiency , which refers to the probability that a particular origin will fire in a given cell cycle , and by the timing of their firing in the S phase ., In general , early firing origins are also efficient , i . e . replication is initiated from these origins in almost every S phase ., However , the determinants of early origin firing are not fully understood ., It has been shown that origins located in euchromatic regions close to centromeres fire early in the S phase , while origins found in sub-telomeric heterochromatin are generally late-firing 1 , 2 ., Relocation of several origins into ectopic loci has revealed that some origins adjust their firing time according to the local chromatin context , while a set of chromosomal localisation-independent origins retain their early-firing pattern in the new location 3 ., Both origin relocations and genome-wide DNA replication initiation studies have shown that Forkhead transcription factor family members Fkh1 and Fkh2 are required to ensure early firing of chromosomal localisation-independent origins ., These findings were further confirmed by the fact that these origins contain at least two consensus binding sites for Fkh1/2 proteins , as well as by observations that disruption of these sites leads to a loss of the origin’s early firing signature 3 , 4 ., The consensus binding sequence for Forkhead family proteins , RYMAAYA , is rather loosely defined and allows many variations in the actual DNA sequence 5 ., Therefore , approximately 46 , 000 Fkh1/2 consensus sequences can be found throughout the budding yeast genome , but only about 1650 of them are actually bound by Forkhead factors 6 ., Remarkably , overexpression of Fkh1 leads to its recruitment to multiple new loci that were not occupied at the normal expression level of Fkh1 7 , indicating that availability of free Forkhead proteins might be limiting in cells ., This also suggests that in the presence of great excess of potential binding sites the Forkheads are stably recruited only to the loci that either are more accessible , or where their binding is supported by other proteins ., It is not clear how the binding of Fkh proteins is regulated in replication origins ., However , mutation of one of the two Fkh sites present at ARS305 , ARS607 and ARS737 significantly reduces binding of Fkh1 to these origins 3 ., This suggests that multiple Fkh1/2 consensus sites in close proximity to each other are required for efficient binding of Fkh1 ., In addition , due to the asymmetrical nature of the Fkh1/2 consensus binding sequence , the efficient binding of Forkhead factors may be influenced by orientation of the sites ., Interestingly , three chromatin-independent early-firing replication origins ARS305 , ARS607 and ARS737 , contain two Fkh1/2 sites in divergent orientation relative to each other and separated by 72 base pairs , suggesting that the overall configuration of the Forkhead binding motifs at early replication origins is carefully conserved ., To elucidate the role of the precise orientation and spacing of Fkh1/2 sites in regulation of early-firing replication origins , we tested the efficiency of Fkh1 binding to origins with altered patterns of Fkh1/2 consensus sites , as well as the firing profile of such origins ., We show that only wild-type configuration of Forkhead binding sites leads to efficient Fkh1 recruitment to an origin in G1 and to early firing of the origin in the S phase ., We also demonstrate that even when the consensus sites are present in their wild-type configuration , Fkh1 fails bind to the origin if recruitment of the Mcm2-7 complex is disrupted ., These results suggest that efficient DNA binding of Forkhead family proteins is strongly influenced by cooperative interactions with other DNA binding factors and is not determined merely by their consensus DNA binding sequence ., Our previous study has revealed that two Fkh1/2 binding sites were required for early firing of replication origins ARS305 , ARS607 and ARS737 3 ., At all these origins , one of the Fkh1/2 binding sites is located close to the ARS consensus sequence ( ACS ) and another is found 72 base pairs away ., To test whether the distance between the Fkh1/2 sites is important for efficient binding of Fkh1 and for origin regulation , we made a panel of yeast strains with modified ARS607 origins inserted into a GAL-VPS13 locus ., In all modified ARS607 sequences , the native ACS-proximal Fkh1/2 site ( partially overlapping with ACS ) was left undisturbed , while the distal Fkh1/2 site was mutated ( Fig 1A ) ., Mutation of the distal consensus sequence led to a significant drop in Fkh1 occupancy , although it remained higher than at loci that do not contain any Fkh1/2 binding sites ( Fig 1C ) ., We then introduced a new Fkh1/2 site at various distances from the proximal site and determined the efficiency of Fkh1 binding to these origins in G1-arrested cells ., When the Fkh1/2 sites were separated by 10 base pairs , binding of Fkh1 protein to the origin was detected at slightly reduced levels compared to the wild-type ( wt ) sequence ( Fig 1C ) ., In contrast , binding of Fkh1 to all other origins was indistinguishable from that seen at an origin where only a single Fkh1/2 site was present , suggesting that precise distance between Fkh1/2 binding sites in ARS607 is critical for efficient recruitment of Fkh1 protein to the origin ( Fig 1C ) ., To finely probe the tolerance of Fkh binding to altered size of the gap between Fkh1/2 sites , we made 5 bp , 10 bp and 15 bp insertions and two different 10 bp deletions between the Fkh1/2 binding sites in GAL-VPS13-ARS607 ( Fig 1B and S4 Fig ) ., In these constructs , modifications of ARS607 were minimal and importantly , all modified loci retained the original Fkh1/2 sites in their immediate surrounding sequences ., This minimized the possibility that recruitment of Fkh1 was affected by local DNA sequence context rather than by the change in gap size between the Fkh1/2 sites ., We found that all introduced modifications , even insertion of 5 bp into the locus , caused significant drop of Fkh1 binding to the origin ( Fig 1C ) ., Concordant with their impaired ability to bind the Fkh1 protein , origins ARS305 , ARS607 and ARS737 with mutated Fkh1/2 binding sites also lose their early firing pattern in vivo 3 ., To determine whether the distance of Fkh1/2 sites in ARS607 is crucial also for early firing of the origin , we arrested cells in G1 and released them synchronously into S phase in the presence of hydroxyurea ( HU ) , thus enabling the firing of early but not late origins ., The dynamics of the relative copy number of the GAL-VPS13-ARS607 locus following the release from G1 block were determined by qPCR analysis of extracted genomic DNA ., If the origin could fire early in S phase , the locus initiated replication in the presence of HU and the relative amount of its DNA was expected to increase during the experiment ., This assay revealed that only the strain with wt ARS607 was able to support early replication of the GAL-VPS13-ARS607 , while in all other strains the locus was not replicated in the presence of HU ( Fig 1D and S1 Fig ) ., Interestingly , the origin where the two Fkh1/2 sites were separated by 10 bp did not support early replication of the locus , despite the fact that Fkh1 occupancy at that origin was relatively high ( Fig 1C ) ., This suggests that the proper spacing of Fkh1/2 binding sites rather than the mere binding of Fkh1 is critical for the early firing of the origin ., To show that the alterations in ARS607 sequence that affected its early firing did not render it inactive , we confirmed that the Mcm2-7 complex was recruited to all modified GAL-VPS13-ARS607 loci , indicating their proper licensing ( Fig 1E ) ., In early firing origins ARS305 , ARS607 , and ARS737 the asymmetrical Fkh1/2 consensus sequences are found in divergent orientation , suggesting that directionality , or symmetry of Fkh1/2 sites may be important for efficient binding of Forkhead factors to replication origins ., To test this hypothesis , we reversed the orientations of Fkh1/2 consensus sites in ARS305 ( Fig 2A ) and tested whether this affected the efficiency of Fkh1 binding and early firing of the origin ., Fkh1 binding was nearly lost in all Fkh1/2 binding site reversal mutants , including the one in which both sites were rotated to form a convergent conformation , implying that the proper orientation of Fkh1/2 sites is critical for Fkh1 binding to ARS305 ( Fig 2B ) ., As expected , none of the above mutants were able to fire early in S phase when cells were released into HU-containing media ( Fig 2C and S2 Fig ) , although all of them were properly licensed ( Fig 2D ) ., As the Forkhead consensus sequence RYMAAYA allows numerous variations in the actual DNA sequence , these sites can be found frequently throughout the genome ., Our results with modified origins ARS607 and ARS305 suggest that the two Fkh1/2 sites must be present in proper orientation and with correct spacing between them for Forkhead-dependent regulation of these origins ., To find out how many double Fkh1/2 sites are present in the yeast genome and how many of them co-localize with early replication origins , we searched the yeast genome for locations of Fkh1/2 consensus binding sites and plotted those onto a genome-wide early DNA replication initiation profile , based on BrdU incorporation into DNA in the presence of HU 8 ., Specifically , we sought loci where two Fkh1/2 consensus sites were separated by 62–88 base pairs and oriented in three different patterns–divergently , convergently , or unidirectionally ., After analyzing the entire yeast genome , we found 5023 double Fkh1/2 sites that were separated by 62 to 88 bp and oriented in one of the three possible patterns ( S1 Table ) ., Approximately 3% of these patterns co-localized with early replication origins ., However , when both the distance and the orientation of sites were taken into account , the sites in divergent orientation separated by 71–79 bp were almost three times more likely to co-localize with early origins than sites with alternate configurations ( Fig 3A ) ., When similar analysis was performed using late replication origins , no overrepresentation of any pattern of Fkh1/2 sites was found ., Moreover , divergently oriented sites separated by 71–79 bp were visibly underrepresented in late origins ( Fig 3B ) ., Next , we mapped the locations of ARS consensus sequences in early origins that overlapped with divergent Fkh1/2 sites separated by 71–79 bp ., The ACS was found in close proximity ( up to 100 bp ) to Forkhead binding sites at 20 origins , and alignment of these revealed several common features ( Fig 3C and S3 Fig ) : First , there was no clear preference for position of the ACS within the Fkh motif–it could be found between or outside of the two sites; however , very often ACS partially overlapped with one of the Forkhead consensus sequences ., Secondly , the ACS and its proximal Fkh1/2 consensus site were located on complementary strands , i . e . ARS consensus on the T-rich strand and the proximal Forkhead site on the A-rich strand ., Thirdly , continuous adenine nucleotide tracks were present between the Fkh1/2 sites ., All analysed origins were found to contain either at least one continuous A-track of at least 5 bases , or multiple 4-bp A-tracks ., We speculate that these sequences facilitate initial melting of DNA strands during origin activation ., As Fkh1/2 sites are located very close to the ACS in early replicating origins , we tested whether the formation of the pre-RC influences the efficiency of Fkh1 recruitment to these loci ., We mutated the ACS sites in VPS13-ARS305 and VPS13-ARS607 , as well as in native ARS305 and ARS737 loci ( Fig 4A ) , and measured Fkh1 binding to these origins ., Although the sequence , spacing and orientation of Fkh1/2 sites were unchanged , the binding of Fkh1 was nearly lost in all ACS-mutated loci ( Fig 4B ) ., This indicates that Fkh1 does not bind non-functional replication origins and suggests that recruitment of Fkh1 to early origins may be coupled with origin licensing ., Licensing of replication origins begins with the recruitment of ORC to the ACS motif and is completed during G1 phase by Cdc6- and Cdt1-mediated loading of MCM double hexamer complex onto the origins 9 , 10 ., In addition , several early firing origins are also pre-loaded with the Cdc45 protein that is required during the subsequent S phase for activation of MCM helicase 11 , 12 ., To determine which step of origin licensing is necessary for efficient binding of Fkh1 to origins , we monitored its recruitment to the GAL-VPS13-ARS607 locus in a re-licensing assay in a set of strains expressing temperature sensitive mutants of Cdc6 , Mcm2 or Cdc45 proteins ., The re-licensing assay using GAL-VPS13-ARS607 is based on the fact that all pre-RC components can be removed by active transcription over the replication origin and , if the cell remains continuously arrested in G1 , these origins become re-licensed rapidly upon shut-down of transcription 13 ., The general outline of the assay is shown in Fig 4C ., First , cells were arrested in G1 and kept arrested throughout the rest of the experiment ., Next , transcription of GAL-VPS13-ARS607 was activated , leading to displacement of all pre-RC components and Fkh1 proteins from the locus ., At the same time , the cells were shifted to a non-permissive temperature to inactivate Cdc6 , Mcm2 , or Cdc45 proteins ., After two hours of incubation , transcription of GAL-VPS13-ARS607 was repressed , which in turn enabled re-licensing of the locus up to the step where the temperature-sensitive component of the pathway was required ., As expected , MCM was reloaded to the locus in wt and cdc45-ts strains , but not in cdc6-ts and mcm2-ts strains ( Fig 4D ) ., Similarly , Fkh1 failed to rebind the origin in cdc6-ts and mcm2-ts strains , while it was efficiently re-recruited in wt and cdc45-ts strains ( Fig 4E ) , indicating that Fkh1 was recruited to origins during replication licensing together with or shortly after the loading of MCM complex ., These results also suggest that Fkh1 binds replication origins in cell cycle dependent manner , i . e . in G1 when pre-RCs are present , but not in G2/M phase when DNA replication is finished and new pre-RCs are not formed yet ., To confirm this , we compared the recruitment of Fkh1 to origins in G1 and M phases of the cell cycle ., As expected from earlier results , Fkh1 was bound to ARS305 , ARS607 , ARS737 and VPS13-ARS305 in G1 , but not in M phase , while Fkh1 binding to its recognition sequence in the CLB2 promoter was not affected by the cell cycle ( Fig 4F ) ., In eukaryotic cells , DNA replication is initiated from numerous origins throughout the S phase ., Temporal activation of origins is regulated by a variety of factors including the origin’s location and local chromatin context ., However , some origins appear to be immune to effects of local chromatin structures and fire early even when transposed to new naturally late-replicating genomic loci ., Recent studies have shown that Forkhead family transcription factors are responsible for early activation of many origins ., Forkhead-regulated origins typically contain multiple Fkh1/2 consensus binding sites near the ACS , and at least two of these are required for their early activation 3 , 4 ., Interestingly , at origins where Fkh1 binding was studied in greater detail ( ARS305 , ARS607 and ARS737 ) , the consensus sequences are arranged in an identical pattern: the two sites are oriented divergently and separated by 72 base pairs ., Additionally , one of the sites is located very close to the ACS , overlapping it at ARS607 and ARS737 ( Fig 4A and S3 Fig ) ., In order to determine whether the exact spacing and orientation of Fkh1/2 sites are critical for Fkh1 recruitment and early activation of these origins , we constructed a panel of yeast strains with altered Fkh1/2 consensus site configuration ., At ARS607 , we altered the spacing between the two sites , while at ARS305 we modified their relative orientation ., Consensus site reversal could not be done in ARS607 as the proximal site overlaps the ACS and its rotation would have inactivated the origin ., On the other hand , multiple Fkh1/2 consensus sites present near the ARS305 locus left very limited possibilities to change the distance between the two key sites at this origin ., By contrast , ARS607 has no other Fkh1/2 sites within 400 bp of the ACS in 3’ direction ., To avoid the possible influence of other DNA replication origins and Fkh1/2 consensus sites near their native loci , the modified ARS305 and ARS607 were inserted into the ectopic naturally late-replicating GAL-VPS13 locus ., We have shown previously that both origins are fully functional and fire early in GAL-VPS13 if their two Fkh1/2 consensus sites remain intact 3 , 13 ., Our results show that Fkh1/2 sites are very precisely arranged near the early replication origins and that no alterations in their configuration are tolerated ., Changing the spacing between the sites or reversing their orientation leads to significant decreases in Fkh1 binding to the origins ( Fig 1C and 2B ) ., Accordingly , such modified origins also fail to fire early in the S phase ( Fig 1D and 2C ) ., The only exception to this general pattern was observed when the distance between the two sites was reduced to 10 bp ., However , while this change had only a modest effect on Fkh1 binding , the altered origin failed to fire early in S phase ., This observation underscores the delicate nature of the mechanism behind Fkh1’s role in replication regulation , indicating that mere binding of this protein is insufficient for proper regulation of an origin’s firing time ., Our findings are also supported by genome-wide replication initiation data ., We observed that the 72 base-pair gap and divergent orientation of Fkh1/2 sites was present at 48 locations throughout the budding yeast genome , with 7 of those overlapping early-firing origins ( S1 Table ) ., Relaxing the search criteria by allowing a 71–73 bp gap between the sites increased the number of total hits but did not change the fact that divergent Fkh1/2 consensus sequences preferentially co-localized with early replicating origins ( Fig 3A ) ., Combined data from our genome-wide analysis of replication origins revealed that divergently oriented Fkh1/2 consensus sites separated by 71–79 bp overlapped with early origins more frequently than was expected from random distribution of such sites ., However , this result may overestimate the tolerance in the gap size between Fkh1/2 sites , given our finding that increasing the gap from 72 bp to 77 bp at ARS607 results in loss of its ability to bind Fkh1 and to fire early in S phase ( Fig 1C and 1D ) ., Overall , our results with modified ARS607 and ARS305 indicate that even minor rearrangements of the Fkh1/2 sites are not tolerated and suggest that binding of Fkh1 to replication origins requires precise arrangement of Fkh1/2 binding sites in the locus ., Previous studies have revealed that several sequence elements within ARS305 and ARS607 are essential for full activity of these origins ., Deletion analysis of ARS305 demonstrated that the region including the ACS-proximal Fkh1/2 site was required for the origin’s function 14 ., Moreover , systematic mutational analysis of ARS305 identified three short regions , in addition to the ACS , that influenced the stability of plasmids carrying this ARS as a sole replication origin ., Mutation of nucleotides immediately adjacent to the 11-bp ACS lead to a complete loss of the origin’s activity , while disruption of either Fkh1/2 site lead to a significant decrease in plasmid retention during exponential cell growth 15 ., In addition , yeast DNA replication origins contain nuclease hypersensitive A/T-rich sequences near the ACS , termed DNA unwinding elements 16 ., In early origins , these sequences contain one or several continuous stretches of adenine nucleotides ( S3 Fig ) that may be required for efficient opening of DNA strands by the MCM helicase ., Disruption of Fkh1/2 sites or A-tracks within ARS607 leads to decreased mitotic stability of plasmids , demonstrating the contribution made by these elements to the full activity of the origin 17 , 18 ., These results support the view that Forkhead proteins enhance replication origins’ efficiency by marking them as ‘first to fire’ when DNA synthesis begins ., If Forkhead binding is disturbed , the origin remains functional but loses its early-firing properties and concordantly , becomes less efficient ., Complexity of Fkh1 binding to DNA was further supported by the discovery that formation of the pre-RC at origins was necessary for efficient recruitment of Fkh1 ., Fkh1 binding to ACS-mutated origins was severely reduced despite the fact that all such loci contained correctly oriented and spaced Fkh1/2 sites ( Fig 4B ) ., This suggests either that Fkh1 binding is enhanced by interactions with pre-RC components , or that pre-RC-directed chromatin reorganisation is required for Fkh1 to gain access to the Fkh1/2 consensus sites ., Previous studies have shown that replication origins are flanked by strongly positioned nucleosomes and that the ORC complex is needed for this arrangement in vivo and in vitro 19 , 20 ., Therefore , accessibility of Fkh1/2 sites may be compromised without ORC-dependent nucleosome positioning ., On the other hand , one Fkh1/2 site often overlaps the ACS in early origins , suggesting that binding of the ORC and Forkhead are mutually exclusive ., However , recent in vitro studies demonstrate that in addition to the ACS , ORC also binds other ACS-like sequences within origins , and that its selectivity towards different binding sites is partially regulated by its interaction with Cdc6 during origin licensing ., Moreover , after MCM loading is completed , ORC is removed from the ACS 21 , 22 ., These results suggest that the ACS is an essential entry point for sequential loading of different pre-RC components onto origins ., However , it is not necessarily the final binding site for all recruited proteins complexes ., Therefore , redistribution of pre-RC components around the ACS may provide an opportunity for the Forkhead proteins to bind their recognition site within the ACS ., To determine which steps in pre-RC formation are critical for Fkh1 recruitment , we used the origin re-licensing assay 13 that allowed us to monitor re-binding of Fkh1 to the origin in conditions where different pre-RC components were inactivated by temperature-sensitive mutations ( Fig 4C ) ., We observed that Fkh1 was not recruited to the origin in cdc6-ts or mcm2-ts strains , while it was successfully reloaded in a cdc45-ts strain ( Fig 4E ) ., This indicates that Fkh1 is recruited to origins at the same time or shortly after the pre-RC is fully formed and the Mcm2-7 complex is loaded ., This model was further supported by the observation that Fkh1 occupancy at origins decreases significantly in M phase , where origins are not licensed ( Fig 4F ) ., By contrast , the next step–recruitment of Cdc45 –is not required for Fkh1 binding ., Once recruited to origins , the binding of Fkh1 is presumably stabilised by the ORC complex , as Forkheads interact directly with ORC proteins 4 ., However , apparently the ORC alone is not sufficient for efficient recruitment of Forkheads , as the formation of entire pre-RC is required for successful binding of Fkh1 to the origin ( Fig 4E ) ., Interestingly , the 71–79 bp gap between Fkh1/2 sites in early origins corresponds very closely to the footprint of the Mcm2-7 double hexamer , which covers about 70–80 bp DNA when loaded onto origins 23 , 24 ., Therefore , it is possible that on licensed origins the Mcm2-7 complex is stabilised by Forkhead proteins that flank the helicase on both sides ., Reciprocally , loading of the Mcm2-7 complex may be necessary to fully expose the Fkh1/2 binding sites ., We also noticed that in several early origins one of the Fkh1/2 sites is ‘doubled’–it contains two consensus sequences that overlap partially ( S3 Fig ) ., This provides some flexibility of the gap size between Fkh1/2 sites , which might help fine-tune the loading of the pre-RC and Forkhead proteins ., Overall , these results suggest that binding of Fkh1 to replication origins , and possibly to other genomic locations , is a finely regulated process that requires precise arrangement of Fkh1/2 binding sites and the presence of supporting protein complexes in the locus ., All Saccharomyces cerevisiae strains were congenic with strain W303 and are listed in S2 Table ., The GAL-VPS13-ARS strains contain different versions of ARS305 and ARS607 in the GAL-VPS13 locus , at 3220 bp downstream from the VPS13 start codon ., The following ARS sequences were used for construction of GAL-VPS13-ARS strains ( sequence coordinates from the Saccharomyces Genome Database , http://www . yeastgenome . org ) : ARS305 ( Chr3 , nucleotides 39529–39800 ) ; ARS607 ( Chr6 , nucleotides 199392–199779 ) ., To change the distance between Fkh1/2 binding sites in GAL-VPS13-ARS607 , the distal Fkh1/2 site was mutated ( GTAAATA to GATCCTA ) and then a new Fkh1/2 site ( GTAAATA ) was inserted at various distances ( 10 , 30 , 60 , 90 , 120 , 150 , 180 , 240 , or 300 bp ) away from the ACS-proximal Fkh1/2 site ., For finely mapping the tolerance of Fkh binding for altered gap size between Fkh1/2 binding sites within ARS607 , insertions of 5 , 10 , or 15 bp were introduced between the two sites in GAL-VPS13-ARS607 locus ., All insertions were located at a distance of 7 bp from the distal Fkh1/2 binding site ., 10 bp deletions between the Fkh1/2 sites were made in two different positions in ARS607 , one located 24 bp and the other 2 bp away from the distal Fkh1/2 site ., In GAL-VPS13-ARS305 , one or both Fkh1/2 consensus binding sequences were reversed ( 5’ site: TGTTTAT to ATAAACA; 3’ site: GTAAATA to TATTTAC ) ., In strains AKY956 and AKY952 , ACS of GAL-VPS13-ARS305 , or GAL-VPS13-ARS607 was mutated ( in ARS305: TTTATATGTTTT to TTTATATGggTT; in ARS607: GTTTATATTTAG to GTTTATATccAG ) ., ACS of ARS305 and ARS737 ( TTTTAATATTT to TTTTAATAccc ) were mutated in their native loci in strains AKY1121 and AKY1122 , respectively ., Sequences of all modified origins are shown in S4 Fig . All modified origins were inserted into genomic loci by two step gene replacement protocol ., First , URA3 gene was inserted into the desired locus and then replaced with ARS sequence by homologous recombination and counter-selection on 5-FOA plates ., Strains carrying temperature sensitive alleles cdc6-1 , mcm2-td , or cdc45-td 25–27 were used to construct strains AKY1061 , AKY1143 and AKY1144 for the origin re-licensing assay ., For efficient α-factor arrest , the BAR1 gene was deleted in all strains ., For ChIP assays , the Fkh1 protein was tagged with C-terminal triple E2-tag recognized by a 5E11 antibody ( Icosagen ) , while Mcm4 was tagged with C-terminal triple myc-tag recognized by a 9E10 antibody ., Cells were grown in yeast extract-peptone-dextrose ( YPD ) medium containing 2% glucose as a carbon source before fixation with 1% formaldehyde for the ChIP assay ., Cell cycle arrest in G1 was achieved by addition of α-factor-mating pheromone ( Zymo Research ) to the growth media to a final concentration of 100 nM and by further incubation for 3 hours ., Cell cycle arrest in M phase was achieved by incubating the cells with nocadazole ( Sigma ) with final concentration 20 μg/ml for 60 minutes ., ChIP assays were performed as described previously 28 ., Shortly , whole-cell extract from 107 cells was used for ChIP assays with 0 . 5 μg of anti-E2 tag antibody ( 5E11 ) or 1 μg of anti myc-tag antibody ( 9E10 ) ., Co-precipitated DNA was analysed by quantitative PCR ( qPCR ) using Roche Lightcycler 480 real-time PCR system under standard conditions ( 40 cycles; 95°C for 15 s , 58°C for 20 s , 72°C for 20 s ) ., Maxima SYBR Green/ROX qPCR Master Mix ( Thermo Scientific ) was used ., qPCR was done with primer pairs covering the relevant regions of VPS13 as well as native origins ARS607 , ARS305 , ARS737 and ARS522 ., Signals were normalized to the high copy-number telomeric PAU1 gene ( in GAL-VPS13-ARS607 strains to the native ARS607 origin ) ., Presented results show the average of three independent experiments , error bars indicate standard deviations ., Sequences of qPCR primers are shown in S3 Table ., Yeast strains were arrested in G1 for 3 hours with α-factor and then released into YPD media containing 200mM hydroxyurea ( HU ) at 24°C ., Samples were collected 45 and 75 minutes later by fixing approximately 2x107 cells in 80% ethanol on ice ., Subsequently , cells were washed twice with water and disrupted with 0 . 5mm glass beads in lysis buffer ( 2% Triton X-100 , 1% SDS , 100 mM NaCl , 10 mM Tris-HCl pH 8 . 0 , 1 mM EDTA pH 8 . 0 ) ., Cell lysate was incubated at 40°C for 15 min , genomic DNA was extracted with phenol-chloroform , precipitated with ethanol and dissolved in water ., The relative amount of DNA was determined by qPCR with primers specific for ARS305 , ARS607 , ARS522 and VPS13 loci ., The late-replicating locus ARS522 was used to normalize the data and to calculate the relative increase of the DNA amount at other loci during the experiment ., Presented results show the average of three independent experiments , error bars indicate standard deviations ., Yeast strains were grown at 24°C in YP-raffinose media for up to 48 hours to obtain culture densities of approximately 1×107 cells/ml ., Cells were arrested in G1 for 3 hours with α-factor , then washed once with water and transferred to YP-galactose ( containing α-factor ) to induce transcription of the GAL-VPS13-ARS607 cassette ., Additionally , during the galactose treatment , temperature was shifted to 37°C to activate the degron system utilized to inactivate Mcm2 or Cdc45 proteins ., In order to ensure destruction of Mcm2 and Cdc45 , as well as of the temperature-sensitive version of Cdc6 , all subsequent steps were carried out at 37°C ., Cultures were grown in YP-galactose for 2 hours , following which cells were washed with water and transferred to YPD ( pre-warmed to 37°C ) containing α-factor ., Cultures were incubated at 37°C for 40 minutes , samples were cross-linked with formaldehyde and processed for ChIP analysis ., ChIP data were normalized on the native GAL10 gene , which was regulated by carbon source changes in parallel with the GAL-VPS13-ARS607 cassette ., Saccharomyces cerevisiae genome was scanned for single and double Fkh1/2 consensus binding sites ( RYMAAYA ) ., For tandem sequences , all possible orientations of the sites: divergent ( ‘head-to-head’ ) , convergent ( ‘tail-to-tail’ ) , unidirectional ( ‘head-to-tail’ ) were included and gaps of 62 to 88 bp between the sites were allowed ., Midpoint coordinates of discovered double Forkhead binding motifs were plotted against the genome-wide dataset of early DNA replication initiation profile ( as determined by BrdU incorporation in the presence of HU ) 8 ., On each chromosome , the highest BrdU signal that did not overlap with any of the confirmed replication origins was set as threshold value ., All BrdU peaks over the threshold value were considered to represent genuine early replication origins ., Midpoint coordinates of double Forkhead binding sites that were found within 200 bp from BrdU peaks maximum signal were considered as overlapping with the origin ., Random overlap was calculated as average overlap between the peaks of early origins and scrambled Fkh1/2 consensus sequences ( YAAYMAR , MAARYAY , AAYMYAR ) in all orientations , separated by 50–100 bp ., All combinations of scrambled sequences were found uniformly over the replication origins with no preference for any particular sequence or orientation (

Introduction, Results, Discussion, Materials & methods

In budding yeast , activation of many DNA replication origins is regulated by their chromatin environment , whereas others fire in early S phase regardless of their chromosomal location ., Several location-independent origins contain at least two divergently oriented binding sites for Forkhead ( Fkh ) transcription factors in close proximity to their ARS consensus sequence ., To explore whether recruitment of Forkhead proteins to replication origins is dependent on the spatial arrangement of Fkh1/2 binding sites , we changed the spacing and orientation of the sites in early replication origins ARS305 and ARS607 ., We followed recruitment of the Fkh1 protein to origins by chromatin immunoprecipitation and tested the ability of these origins to fire in early S phase ., Our results demonstrate that precise spatial and directional arrangement of Fkh1/2 sites is crucial for efficient binding of the Fkh1 protein and for early firing of the origins ., We also show that recruitment of Fkh1 to the origins depends on formation of the pre-replicative complex ( pre-RC ) and loading of the Mcm2-7 helicase , indicating that the origins are regulated by cooperative action of Fkh1 and the pre-RC ., These results reveal that DNA binding of Forkhead factors does not depend merely on the presence of its binding sites but on their precise arrangement and is strongly influenced by other protein complexes in the vicinity .

In this study , we explore the mechanisms that determine activation of DNA replication origins in early S phase ., It has been shown that a subset of replication origins is regulated by Forkhead family transcription factors that ensure their firing at the beginning of S phase ., However , the recruitment of Forkhead factors to replication origins is not a straightforward process–there are thousands of Forkhead binding sites in the genome and their presence does not guarantee that Forkheads actually bind these sites ., We show that recruitment of Fkh1 protein to DNA replication origins requires precise arrangement of Forkhead binding sites and depends on formation of pre-replicative complexes at the origins ., These results clarify the mechanisms of Forkhead-dependent regulation of early DNA replication origins and also reveal that mere presence of consensus binding sites is not sufficient for recruitment of Forkhead proteins to their target loci .

insertion mutation, cell cycle and cell division, cell processes, dna-binding proteins, mutation, fungi, model organisms, dna replication, experimental organism systems, sequence motif analysis, dna, synthesis phase, research and analysis methods, saccharomyces, sequence analysis, bioinformatics, proteins, genetic loci, yeast, biochemistry, cell biology, nucleic acids, database and informatics methods, genetics, biology and life sciences, yeast and fungal models, saccharomyces cerevisiae, organisms

journal.pcbi.1000685

Computational Complementation: A Modelling Approach to Study Signalling Mechanisms during Legume Autoregulation of Nodulation

Legumes are one of the largest families of flowering plants that occupy about 15% of Earths arable surface; yet they provide 27% of the worlds primary crop production and more than 35% of the worlds processed vegetable oil 1 , signifying their cropping potential ., Legumes are also the major natural nitrogen-provider to the ecosystem , contributing roughly 200 million tons of nitrogen each year 2 equivalent to over 200 billion dollars worth of fertiliser replacement value ., Underlying this powerful fixation capability is a plant developmental process termed “nodulation” , which results from the symbiosis of legume roots and soil-living bacteria broadly called rhizobia ., Yet for a legume plant itself , excessive nodulation may cause over-consumption of metabolic resources and disproportional distribution of internal growth regulators 3 , and may interfere with developmentally related lateral root inception and function ., Legume plants have evolved a long-distance systemic signalling regulatory system , known as autoregulation of nodulation ( AON ) , to maintain the balance of nodule formation 3–7 ., It has been hypothesised that the induction of the nodule primordium produces a translocatable signal Q , which moves through a root-shoot xylem pathway to the leaves ., This Q signal , or an intermediate , is detected in the phloem parenchyma of leaf vascular tissue by a transmembrane leucine-rich repeat ( LRR ) receptor kinase 8 related in structure to CLAVATA1 in Arabidopsis ., This kinase is referred to as GmNARK in soybean 9 , 10 , HAR1 in Lotus 11 , and SUNN in Medicago 12 ., Q is presumed to be a CLV3/ESR-related ( CLE ) peptide 13 , 14 ., The perception of the Q signal by the LRR receptor kinase triggers production of a hypothetical shoot-derived inhibitor ( SDI ) that is transported to the root to inhibit further nodule initiation ., SDI can be extracted from wild-type leaves , re-fed via petiole feeding into loss-of-function mutants , resulting in restoration of the wild-type phenotypes 15 ., It is a small , water-soluble , heat-stable and inoculation-dependent molecule ., However , other mechanisms involved in AON signalling remain largely unknown , though the pre-NARK events ( those setting up the signal transmission and then Q signal transduction ) as well as the post-NARK events ( firstly KAPP phosphorylation , ensuing transcriptional changes , and then SDI production ) are being investigated 10 , 15 , 16 ., To help understand such biological complexities , system modelling has been broadly applied 17–19 ., From a systematic view , behind the signalling mechanisms is a network of components connected by intricate interfaces , with activities such as “assembly , translocation , degradation , and channelling of chemical reactions” occurring simultaneously 20 ., These components and their interactions – also responding to the temporally and spatially changing environment – frame dynamic and complex systems at multiple scales to orchestrate plant development and behaviour ., As a full understanding of system properties emerging from component interactions cannot be achieved only by “drawing diagrams of their interconnections” 17 , computational techniques become indispensable for processing massive datasets and simulating complex mechanisms 21 ., Although computational approaches have been progressing rapidly for modelling plant signalling , such as for signal transport 22 , 23 , canalization 24 and signalling network 25 , most efforts have focused on cellular or tissue levels ., Since AON is in essence a long-distance inter-organ regulatory network , our investigation required modelling at the whole-plant scale ., Functional-structural plant models 26 , such as those developed for resource allocation 27–29 and shoot signalling 30–34 , can take inter-organ communication into account and use plant architecture as a direct reporter of underlying processes ., Functional-structural modelling allowed us to simulate the hypothesised AON signalling and integrate it with nodulation ., Yet the major difficulty was not how to model the hypotheses but how to test them through modelling ., To meet this challenge , we have developed a new approach – Computational Complementation – for AON study ., Following description of the computational complementation method , we will present its first application in investigating whether wild-type cotyledons participate as an SDI producer in the AON system ., Previous studies have indicated that mRNA for GmNARK , which , if translated , is responsible for perceiving the Q signal and triggering the SDI signal , exists in wild-type unifoliate and trifoliate leaves ., It is expressed in all vascular tissue 8 of the plant ( including the root ) , but its product is functional only as a nodulation control receptor in the leaf 35 ., Thus the RNA expression pattern does not match biological function in AON ., Relevant to the investigation here , the vasculature of the cotyledon also expresses RNA for GmNARK; whether this is functional in AON signalling was unclear ., Therefore we used computational complementation to test two opposing hypotheses:, ( a ) cotyledons function as part of the root , incapable of perceiving Q and producing SDI; or, ( b ) cotyledons function as part of the shoot , involved in regulating root nodules ., Genetic complementation 36 is a classical approach to define genetic cause-and-effect relations ., For example , assuming two mutant organisms exhibit the same phenotype caused by loss-of-function ( recessive ) mutations , then their hybrid will be wild-type , if the mutations are in different genes ( called cistrons ) ; conversely the hybrid will be mutant if the mutations are in the same cistron ., In other words , the wild-type ( functional ) allele complements the deficiencies of the mutant ., Genetic complementation is also used in transgenic analysis of organisms , as a loss-of function mutation in a candidate wild-type gene is deemed causal for a mutant phenotype if that mutant is effectively complemented by the transfer of a dominant wild-type allele ., The complementation approach introduced here does not cross one genotype with another , but will use computational modelling to complement the deficiency ( in an empirical model ) of a mutant to determine if this recovers the virtual wild-type phenotype ., We use two well-characterized soybean ( Glycine max L . Merrill ) genotypes: the wild-type soybean Bragg and its loss-of-function mutant nts1116 37 ., Wild-type soybean Bragg performs AON to keep its nodulation balance well-maintained ( Fig . 1A and C ) , leading to characteristic crown nodulation in upper root portions ., In its near-isogenic mutant nts1116 , the Q signal generated from early nodule proliferation cannot induce SDI due to the lack of GmNARK activity in leaves ( Fig . 1B ) ., Reduced SDI in GmNARK-deficient plants leads to a phenotype with many more nodules than wild-type , called “supernodulation” or “hypernodulation” ( Fig . 1D ) 5 ., Compared with Bragg , the only deficiency of nts1116 plants is the significantly reduced capacity of producing SDI ., The key idea of our complementation approach comes from this point ., We “add” hypothetical components of AON signalling , including those of signal production , transport , perception and function ( see also Text S3 ) , into the empirical model that depicts the growth behaviours of nts1116 plants to see if a wild-type phenotype can be restored ., The flowchart of methodology for this approach is given in Fig . 2 , including the following steps: The architectural and functional-structural models mentioned in steps, ( i ) and, ( ii ) have been built with context-sensitive L-systems 31 ., The empirical data used for building architectural models of Bragg and nts1116 plants were collected every second day from growth experiment under the same conditions until the 16th day post-sowing ( all plants were inoculated on the 2nd day ) ., Materials and methods for this glasshouse experiment are given in supporting Text S1 ., The growth data , algorithms and techniques used for model construction are described in supporting Text S2 ., The remaining steps of the flowchart , including, ( iii ) ,, ( iv ) ,, ( v ) and, ( vi ) , are implemented for hypotheses testing and prediction ., In this initial application of our computational complementation approach , two opposing hypotheses were tested:, ( a ) cotyledons function as part of the root , incapable of perceiving Q and producing SDI ( abbreviated as “cotyledon-root” hypothesis ) ;, ( b ) cotyledons function as part of the shoot , involved in regulating root nodules ( abbreviated as “cotyledon-shoot” hypothesis ) ., Since GmNARK is expressed in all organs 8 ( including cotyledons ) and since cotyledons are short-term terminal organs ( as they are degraded 7–14 days after germination ) , neither the cotyledon-root nor the cotyledon-shoot hypothesis was favoured a priori ., Theoretically speaking , if all other AON mechanisms ( such as signal production , transport , perception and function ) had been confirmed and used as basis for this application , the tested hypothesis leading to a wild-type nodulation pattern could be the correct one ., However , the actions of many other signalling components also remain unclear ., One or two virtual experiments are obviously insufficient to allow conclusions ., Implementing too many experiments ( to test all mechanisms together ) , however , would miss the emphasis and undermine efficiency ., With these concerns , our strategy was to adjust parameters for signal production , transport , perception and function within a limited range , and use them as different conditions for different virtual experiments ., Among all these experiments , if the complementation results ( nts1116+AON ) based on the cotyledon-root hypothesis are always or in most cases closer to Bragg than those based on the cotyledon-shoot hypothesis , then the cotyledon-root hypothesis would be considered plausible; otherwise , the cotyledons are more likely to function as general-sense leaves to regulate root nodulation ., According to this specific strategy , 27 virtual experiments ( varying three rates of transport for both Q and SDI and three levels of nodulation inhibitory threshold ) were designed for each of the two hypotheses: CRH_1∼CRH_27 for cotyledon-root testing and CSH_1∼CSH_27 for cotyledon-shoot testing ., The only difference between CRH_i and CSH_j , if i\u200a=\u200aj , is whether cotyledons can function for AON signalling or not ., Details of the virtual-experiment assumptions and conditions are described in the supporting Text S3 ., To quantify the comparison between complementation results and Bragg phenotype , we define their similarity degree Scp as ( 1 ) where Nnt , Nbr and Ncp are the nodule numbers generated respectively by the architectural model of nts1116 plants , the architectural model of Bragg , and the functional-structural model of nts1116+AON ., This can be understood as the ratio of the number of nodules inhibited by the virtual experiment to the number of nodules inhibited by a real Bragg plant ., The similarity degrees of overall nodule number produced by virtual experiments on the 10th and the 16th day after sowing are listed in Fig . 3 and Fig . 4 , where Rq and Rsdi represent the transport rates of Q and SDI signals ( mm/day ) ., These data indicated that the similarity degrees resulting from cotyledon-shoot hypothesis were generally much higher than those from cotyledon-root hypothesis , supporting the former hypothesis ., Considering that values of Scp greater than 100% may mean over-regulation and might not be optimal , the criterion for further evaluating Scp is defined in Fig . 5 ., According to this criterion , the virtual experiments based on cotyledon-root hypothesis produced unsatisfactory results on the 10th day ( Fig . 3 , left-hand column ) , in sharp contrast to the cotyledon-shoot experiments ( Fig . 3 , right-hand column ) ., Although there were good results derived from virtual experiments CRH_1 , CRH_2 , CRH_11 and CRH_13 on the 16th day ( Fig . 4 , left-hand column ) in terms of nodule number , the nodule size and density from these experiments were all far from similar with the Bragg pattern ( Fig . 6 ) ., In comparison , the nodule distribution generated by CSH_1 ( Fig . 6D ) – the opposite of CRH_1 – was quite close to that of the Bragg architectural model ., We predicted from these complementation experiments that the cotyledons should be part of the shoot and participate as an SDI producer in wild-type soybean plants ., To confirm the above prediction and also to evaluate the effectiveness of this approach , a “real-plant” grafting experiment was conducted ., The critical experiment was to graft – between Bragg and nts1116 plants – the shoot of one genotype with cotyledons to the root of the other genotype without cotyledons , and also to graft the shoot of one genotype without cotyledons to the root of the other genotype with cotyledons , forming four graft combinations: Ns+Nc+Br , Ns+Bc+Br , Bs+Bc+Nr and Bs+Nc+Nr ( Table 1 ) ., Materials and methods for this graft experiment are given in the supporting Text S1 ., The collected empirical data for nodule number were not only classified by each graft type but also according to each plants cotyledon retention status ( Table 2 ) ., According to the experimental results , the nodule number from the Ns+Nc+Br graft type was much higher than that from the Ns+Bc+Br ( Fig . 7A ) ., For the Ns+Bc+Br graft type alone , its plants with fallen cotyledons had more nodules than those with persisting cotyledons , and the plants with yellow cotyledons had more nodules than those with green cotyledons ( Fig . 7C ) ., These differences suggest Bragg cotyledons were the only leaves to regulate nodulation in Ns+Bc+Br plants , because unifoliate and trifoliate leaves of nts1116 plants were unable to do so ., Data of another graft type with Bragg cotyledons – the Bs+Bc+Nr ( Fig . 7D ) also suggested that the Bragg cotyledons participated in providing SDI ., However , more nodules were found in the Bs+Bc+Nr plants than in the Bs+Nc+Nr plants that had no Bragg cotyledons ( Fig . 7B ) ., An explanation for this observation is that the Bs+Nc+Nr allowed more nodules to be formed at early stages than the Bs+Bc+Nr , leading to more Q signal moving from root to shoot ., As the cotyledon biomass declined greatly at later stages of seedling growth ( resources are unloaded for plant growth and the “spent” cotyledon is eventually discarded ) , the difference in shoot between Bs+Bc+Nr and Bs+Nc+Nr became insignificant ., Therefore larger amounts of Q triggered more SDI , which finally inhibited more nodules in Bs+Nc+Nr ., To better understand this nonlinear characteristic brought out by real-plant experiments , we returned to the virtual-experiment models and visualised the dynamic signal allocation during CRH_1 and CSH_1 ( Fig . 8 ) ., As demonstrated by the visualisation , the SDI concentration ( in the root ) of CRH_1 was lower than that of CSH_1 on the 5th day but became higher from the 10th day on , in agreement with the above analysis of the nodulation difference between Bs+Bc+Nr and Bs+Nc+Nr ., Thus , we conclude that the testing result from our initial application of computational complementation is confirmed: the cotyledons “belong” to the shoot and function as a source of the nodulation regulator in wild-type soybeans ., The computational complementation approach introduced here is an original contribution to the study of legume autoregulation of nodulation ., Compared with conventional biological technologies with broader implications to plant development , one of the major advantages of this approach is its capability to complement the deficiency of a mutant plant at an organ scale with totally hypothetical and concept-derived physiological components ., It is also able to make hypothetical signalling details manipulable and visible ., For example , as demonstrated in the above case , signal transport rates can be modified as hypothesised and the allocation of signal can be dynamically visualised ., These functionalities not only enable AON researchers to test hypotheses or make predictions using time- and resource-saving virtual experiments , but also bring out possible underlying details that are unobservable through real-plant experiments ., Moreover , the application of this approach is not only limited to AON research , but also potential to other plant signalling studies such as those on branching regulation ( e . g . , 38 ) , flowering control ( e . g . , 39 ) and lateral root initiation ( e . g . , 40 ) ., This approach contributes a new idea to the domain of computational plant modelling – computational complementation ., From a classic modelling point of view , one can formulate a model based on empirical data and then verify the model against the data , which has been used for development of crop ( e . g . , 41 ) and architectural ( e . g . , 42 ) models ., However , what we investigate is a largely unclear internal signalling system – most of the detailed mechanisms remain unknown , which determines there is no direct parameterisation-and-verification data to evaluate the modelled signalling hypotheses ., Using an indirect strategy , functional-structural modelling allows us to use the observable structure as a reporter for estimation of the unobservable function ., But for this study , we have to link the structure of one genotype with the function of another genotype ., The reason for this is: the wild-type Bragg nodulation has already been regulated , thus incorporating AON to Bragg architecture would double the regulation and have no reasonable comparison target for validation; in contrast , the nts1116 is a non-AON plant and this is its only difference with Bragg , therefore activating AON in nts1116 plant could result in system behaviours comparable with the wild type ., Another feature of this approach resides in the level of complexity for simulation of structural and signalling processes ., We captured root details for studying shoot-root signalling rather than oversimplifying the root system ., And the signalling pathways are constructed with sub-modules of which the size and number can be manipulated without limitation , which allows future modelling work to be extended to lower-scale mechanisms ( such as tissue and cellular scale ) ., We also created a synchronisation algorithm for coordination of multi-rate procedures to enhance the precision of signalling-development interactions ., A description of these modelling techniques is given in the supporting Text S2 ., The approach also has some limitations ., For example , due to the nature of complementation , it can only be used for a single mutation at a time , though leaky mutants can be handled by parameter optimization ., Another drawback is that it cannot distinguish between different mutations in the same pathway that result in the same phenotype in the first instance ., In other words , if the hypothesised mechanisms used to complement the mutant are the same in both cases , and so is the phenotype of the two mutants , computational complementation cannot be used to say which gene component of the regulatory network has been mutated ., Our first application of this approach was to test whether wild-type soybean cotyledons are involved in production of SDI ., Also but more importantly , we expected this application to evaluate whether the computational complementation idea is effective ., The virtual-experiment results suggested the wild-type cotyledons can produce SDI , which was further confirmed by a graft experiment on real plants ., This demonstrates the feasibility of computational complementation and shows its usefulness for future applications ., The next step is to apply this approach to support research for the identification of Q and SDI ., Candidate signals , such as CLE peptide for Q 13 , 14 and auxin for SDI 43 , will be tested to see if they play the roles in AON as hypothesised ., In addition , environmental factors , such as soil nitrogen status , that have effects on the process could also be tested with this approach ., Furthermore , the finding that wild-type soybean cotyledons act as an SDI producer in AON opens the door for testing physiological transgenerational effects , such as altered nodulation patterns influenced by the Bradyrhizobium infection status of mother plant through presence of SDI in cotyledons .

Introduction, Methods, Results, Discussion

Autoregulation of nodulation ( AON ) is a long-distance signalling regulatory system maintaining the balance of symbiotic nodulation in legume plants ., However , the intricacy of internal signalling and absence of flux and biochemical data , are a bottleneck for investigation of AON ., To address this , a new computational modelling approach called “Computational Complementation” has been developed ., The main idea is to use functional-structural modelling to complement the deficiency of an empirical model of a loss-of-function ( non-AON ) mutant with hypothetical AON mechanisms ., If computational complementation demonstrates a phenotype similar to the wild-type plant , the signalling hypothesis would be suggested as “reasonable” ., Our initial case for application of this approach was to test whether or not wild-type soybean cotyledons provide the shoot-derived inhibitor ( SDI ) to regulate nodule progression ., We predicted by computational complementation that the cotyledon is part of the shoot in terms of AON and that it produces the SDI signal , a result that was confirmed by reciprocal epicotyl-and-hypocotyl grafting in a real-plant experiment ., This application demonstrates the feasibility of computational complementation and shows its usefulness for applications where real-plant experimentation is either difficult or impossible .

Endogenous signals , such as phytohormones , play a vital role in plant development and function , controlling processes such as flowering , branching , disease response , and nodulation ., However , the signalling mechanisms are so subtle and so complex that details about them remain largely unknown ., In this study , we develop a “Computational Complementation” approach for the investigation of long-distance signalling networks during legume autoregulation of nodulation ( AON ) ., The key idea is to use computational modelling to complement the deficiency of an empirical model of an AON deficient mutant with hypothesised AON components ., If the complementation restores a wild-type nodulation phenotype , the modelled hypotheses would be supported as reasonable ., To evaluate the feasibility of this approach , we tested whether wild-type soybean cotyledons participate in AON , commonly controlled by “real” leaves ., The test gave an affirmative result ( i . e . , cotyledons do have AON activity ) , which was subsequently confirmed by a graft experiment on real plants ., Future applications of this approach may be to test candidate AON signals such as auxins , flavones , and CLE peptides , and other plant signalling networks .

plant biology, genetics and genomics, computational biology

journal.pcbi.1003991

The Pharmacodynamics of the p53-Mdm2 Targeting Drug Nutlin: The Role of Gene-Switching Noise

The p53 gene is an important oncosuppressor gene , and its product is heavily involved in the control of both cell proliferation and cell differentiation ., It is well known that p53 triggers cell cycle arrest and even apoptotic pathways in response to moderate and , respectively , strong stress signals 1 ., Concerning cell differentiation , p53 suppression induces a strong increase of the probability of symmetric division in breast stem cells 2 , and drug-driven activation of p53 induces rapid differentiation of human embrionic stem cells 3 ., Underexpression of p53 is often observed in tumors carrying wild-type p53 1 , 4 ., This phenomenon is caused by overexpression of Mdm2 protein , the main competitor of p53 1 , 4 ., For example , this may occur when the gene p14 , which inhibits Mdm2 by sequestering it into the nucleus , is deleted , as observed in breast , brain and lung cancers 1 ., Another cause leading to the underexpression of wild-type p53 is given by Mdm2 amplification 1 , 4 ., Moreover , binding to viral proteins in infected cells causes underexpression of p53 in chronic infection-related cancers 1 , 4 ., Underexpressed wild-type p53 is seen as a primary candidate target for antitumor therapies based on chemical molecules 1 or siRNA 5 ., Among the p53-targeting drugs , a prominent role is played by Nutlins 6 , a family of small molecules able to bind Mdm2 exactly in the binding pockets where p53 binds , so impeding the formation of p53-Mdm2 complexes and inducing a rapid p53 level increase ., Since the activation of p53 may cause the triggering of both the apoptotic pathways and the differentiation of tumor stem cells , Nutlins are regarded as potentially important antitumoral agents ., In their study , Vassilev et al . 6 showed a potent antitumor activity of Nutlins on wild-type p53 tumor cell lines , such as HCT116 , RKO and SJSA-1 cells , whereas only a marginal effect on mutant p53 cell lines ( such as SW 480 , MDA-MB-435 , PC3 ) was observed ., The same research group 7 later found that different Nutlins subtypes may have a differential action on different tumor cell lines ., A number of other preclinical studies reported that Nutlin is an effective antitumor drug for important types of cancers carrying dysfunctional wild-type p53 ., The antineoplastic action of Nutlin on chronic B-cell lymphocytic leukemia with wild-type p53 has been shown 8 , documenting a series of synergies with doxorubicin ., Nutlin is active against prostate cancer cells retaining wild-type p53 and androgen receptor signaling 9 , and works by inhibiting their proliferation via cell cycle arrest and apoptosis ., Nutlin-3a is also active in Hodgkin lymphoma , where p53 is rarely mutated 9 ., In Ewings sarcoma cells , Nutlin-3 restores wild-type p53 functions , with cancer growth inhibition and apoptosis induction , whereas no effect was observed for cells with mutated p53 ( the mutation , however , affects only of those tumors ) ., In addition , Nutlin is active against human glioblastoma multiforme 10 , where of patients carry amplifications of Mdm2 ., In this case , Nutlin was active in the wild-type p53 glioblastomas , where it also caused cell senescence ., In 10 , it has been explicitly noticed that cell lines can significantly differ in their apoptotic response to similar levels of p53 activation ., We may observe that the Nutlin-mediated restoration of p53 levels does not automatically guarantee beneficial effects if other modules of p53-related pathway are dysfunctional ., For example , Ma et al . 11 showed that Nutlin-3 is unable to induce p53-related apoptosis in cells where p53-Ser46 phosphorylation is defective ., In retinoblastoma p53 is intact , but it is silenced by MDMX overexpression 12 , 13 ., A preclinical study 13 has reported strong activity of locally-administered Nutlin-3a against retinoblastoma , and synergy with topotecan ., Recently , it has been shown that Nutlin overcomes resistance to Vemurafenib in melanoma lines 14 , and to Cisplatin in ovarian cancer cells 15 ., Finally , in both the above-mentioned studies concerning the role of p53 in the differentiation of stem cells 2 , 3 , Nutlin was the drug used for p53 activation ., The above experimental findings on the effect of Nutlin on wild-type p53 tumors can be roughly summarized as follows: the binding of Nutlin to Mdm2 , by inactivating the main antagonist of p53 , leads to increasing the p53 level , which negatively influences the tumor growth , in part because of the onset of cell arrest and apoptosis , in part – for stem cell-based tumors – by establishing in cancer stem cells a more physiological pathway of asymmetric cell division ., However , the design and implementation of efficacious therapies requires going beyond a mere descriptive approach , which disregards the kinetics and the quantitative features of the phenomena ., Valid tools can be provided by Systems Biology , which is able to integrate information from multiple sources in a coherent quantitative model by using mathematics and bioinformatics 16 ., Indeed , the role of p53 has elicited the interest of many computational biologists since the seminal experimental/modeling work conducted by Alon et al . 17 , where the onset of oscillations in p53 concentration during the response to radiation stress was shown ., A major contribute was given by Ciliberto et al . 18 , who stressed the role of both negative and positive feedbacks in p53/Mdm2 interplay ., Other authors 19 , 20 , 21 , 22 stressed the role of delays in the p53/Mdm2 network , although recently it has been noticed 23 , 24 , 25 that the use of explicit delays to explain p53 oscillations ( and other dynamical features ) may be avoided by including in the model the complexes formed by p53 and Mdm2 ., Sturrock et al . 26 , 27 and Dimitrio et al . 28 proposed models where the intra-cellular spatial diffusion of p53 is represented and used as causative agent for the onset of the observed oscillations ., Laise et al . 29 recently proposed a model of the hypoxia-related apoptotic pathway p53/HIF-1/p300 networks ., Apart from 29 , all the above-mentioned mathematical models have focused only on the p53/Mdm2 network ., However , PTEN protein plays a major role in p53 regulation 30 , 31 and should not be neglected ., A stochastic model of p53/Mdm2/PTEN interplay during environmental stresses was proposed by Puszynski et al . 32 , and that model forms the basis of our work ., Zhang and colleagues in 33 included the p53/PTEN/Akt/Mdm2 positive feedback in their deterministic model , although not directly including PTEN among the state variables , showing its relevance in the interplay between p53/Mdm2/PTEN network and p21 network ., Moreover , in 34 they more directly analyzed the role of the delicate trade-off between the p53/PTEN/Akt/Mdm2 and the ATM/p53/Wip1 feedbacks during the process of DNA damage response ., The present work is aimed at building – and , to some extent , comparing with data – a quantitative model of Nutlin pharmacodynamics at the single-cell level that explicitly includes the main biochemical network regulating p53 , along with the transcriptional feedbacks ., These chemical reactions , mostly following the mass-action law , are converted into a hybrid stochastic model , i . e . , a model including both differential equations and birth-and-death stochastic processes ., The model , besides Mdm2 , includes PTEN and ubiquitins as two other major players shaping the dynamics of the p53 network ., Indeed p53 is a transcription factor for PTEN , which in turn ( through PIP and Akt ) induces Mdm2 phosphorylation by means of a positive feedback 30 , 31 ., Since these processes are enacted with timescales not substantially different to those typical of Mdm2-p53 interactions , PTEN cannot be eliminated from the model via a quasi-steady state approach ., On the contrary , since we are interested in analyzing the dynamics of cell response to time-varying Nutlin concentration , PTEN is a primary actor and its subnetwork has to be explicitly represented ., The role of p53 ubiquitination in the dynamics of p53 has been emphasized in 18 ., Moreover , we trace p53-Mdm2 complexes , as suggested in 23 , 24 ., Concerning the pharmacodynamics of Nutlin , which is the key issue of our study , the competition of Nutlin with p53 for Mdm2 binding is coupled in our model with a simple linear cell uptake ., We show by simulations that the stochasticity of gene-switching may account for the observed inter-cell variability of the response ., Moreover , our simulations suggest that dose-splitting could reduce the anti-tumoral effect of Nutlin in vivo ., Considerations on the limits of the model , the clinical applicability of the drug , and the future research direction conclude this work ., As in 32 , we follow the experimental evidence 37 , 38 , 39 that transcription factors regulate the probability that a given gene is ON or OFF , rather than the mRNA transcription rate ., The ON/OFF random gene-switching results in a burst production of mRNA molecules , and introduces ( also in the idealized case of the absence of downstream sources of randomness ) a large level of stochasticity in the dynamics of cell regulation networks 40 , 41 , 42 , 43 ., Denoting by the number of gene copies of a generic gene , the number of copies of gene active at time is ., We assume that the deactivation rate of the single gene copy is constant , and that the deactivation events are independent , so that: As far as the activation rates are concerned , we recall that the p53 protein is a transcription factor for both Mdm2 and PTEN 44 , and that p53 phosphorylation enhances p53 activity in transcription 45 ., Moreover , p53 is involved in co-translational dimerization and in post-translational dimerization of dimers 46 ., Although such tetramerization appears to be rather inefficient in solution , p53 dimers exhibit high cooperativity in DNA binding , with a Hill coefficient of 1 . 8 47 , and mutated p53 with impaired tetramerization binds DNA with an affinity six-fold less than the affinity of the wild-type protein ., Thus , we assume that p53 in the cell is mainly present as a dimer , and that its activity in transcription requires tetramerization at the level of DNA binding ., These assumptions yield towhere ., We assume that when a gene copy is active , transcription proceeds at a constant rate ., We remark that although all the other chemical reactions of the model are described by ordinary differential equations , the time-courses of all the chemical species will be actually given by stochastic processes since all the reactions are ultimately driven by gene activation ., Finally , note that the random fluctuations of the gene activation may be seen as a bounded stochastic process 48 perturbing the system constituted by proteins and transcripts , some of which , in turn , feedback on the dynamics of this peculiar kind of noise ., As mentioned above , Nutlin perturbs the p53-Mdm2 system by binding to Mdm2 and occupying the p53 binding pocket on the Mdm2 molecule ., As a consequence , Mdm2 cannot form complexes with p53 , and p53 ubiquitination is impaired 6 ., So , in our model , we assume that each of the Mdm2 forms ( unphosphorylated in cytoplasm , and phosphorylated in cytoplasm and nucleus ) can be in two states ., The first is Mdm2 free from Nutlin , which can bind to p53 and then is called active ., The second one is Mdm2 coupled to Nutlin , so that it cannot form p53-Mdm2 complexes and it is called inactive ., Note that both such states can be phosphorylated or unphosphorylated , and that , when phosphorylated , they can be translocated to or from the nucleus ., The accumulation of cytoplasmic inactive Mdm2 is caused by Nutlin binding as well by dephosphorylation of Nutlin-bound phospho-Mdm2 ., Conversely , its loss derives by Nutlin dissociation , phosphorylation and degradation ., These processes yield the equation ( 1 ) where denotes the number of free Nutlin molecules in the cell , the number of phosphorylated AKT molecules , and the parameters have an obvious meaning ., The dynamics of the amount of cytoplasmic inactive phosphorylated Mdm2 is ruled by similar processes , to which nuclear import and export must be added: ( 2 ) In the nuclear compartment , similarly , Nutlin binds to nuclear-phosphorylated Mdm2 which is inactivated according to the following equations: ( 3 ) The dynamic equations for the Nutlin-free cytoplasmic unphosphorylated and phosphorylated Mdm2 , as well as for nuclear phosphorylated Mdm2 , are reported in S1 Text ., Although cell uptake of Nutlin appears saturable , the saturation seems to be achieved for rather high extra-cellular concentrations ( in HCT116 cells , no clear saturation up to extra-cellular concentrations of 50 microM has been found 57 ) ., In view of the moderate values of the extra-cellular concentration of free Nutlin usually attained in the experiments , we assume a linear uptake ., Concerning Nutlin efflux , the export rate is assumed linear for simplicity ., Binding of Nutlin to Mdm2 and the dissociation of their complexes also contribute to the change of the intra-cellular amount of free Nutlin ., Taking into account all these processes , for the cell amount of free Nutlin we can write the following equation: ( 4 ) where denotes the extra-cellular concentration of free Nutlin ., To simulate in vivo Nutlin treatments , we exploited the pharmacokinetics data for oral delivery in mice reported in 58 to compute the extra-cellular Nutlin concentration ., In particular , we have chosen parameters that fit the measured Nutlin concentration in retina , since the time profile of such a concentration is similar to those in plasma and spleen 58 , and then such profile can be taken as an approximation of the pharmacokinetics in the whole organism ., It is important to remark that a substantial binding of Nutlin to plasma proteins has been demonstrated 58 , so that the free Nutlin concentration in plasma is only a small fraction of the total Nutlin concentration ., In 58 , the binding data were fitted to the equilibrium equation ( 5 ) where denotes the protein-bound Nutlin concentration , is the concentration of total plasma protein binding sites , and is the equilibrium association constant ., From their data , Zhang et al . 58 estimated M , and M . Denoting by the concentration of total Nutlin , we haveand can be expressed in terms of , obtaining: ( 6 ) Protein binding is likely to occur also in the retina , and , as suggested in 58 , we may assume that in this tissue the binding is the same that in plasma ., Therefore , assuming that, ( i ) drug distribution occurs in a single compartment ,, ( ii ) only free Nutlin is eliminated ,, ( iii ) elimination is linear , and, ( iv ) protein binding is in quasi-steady state , the simplest pharmacokinetic equation for Nutlin reads: ( 7 ) where is the drug dose rate ( in , e . g . , mg Kg−1 sec−1 ) , is the initial time of delivery , is a factor accounting for the conversion from mg Kg−1 to moles , divided by the distribution volume , and is given by ( 6 ) ., will be the input of Eq ., ( 4 ) for the intra-cellular Nutlin ., Usually , in representing oral delivery , the gastro-enteric release is assumed exponential so that Eq ., ( 7 ) , in case of administration of a single dose at time , can be rewritten as ( 8 ) where is the dose ( in mg Kg−1 ) ., An easy modification of the above equation accounts for the case of repeated administrations ., In 6 , Vassilev et al . reported experimental in vitro measurements of cell viability as a function of different concentrations of Nutlin ., Different tumor cell lines ( HCT116 , RKO , SJSA-1 ) were exposed to Nutlin for five days , and thereafter cell viability was assessed by MTT assay ., Since the MTT assay measures the activity of intra-cellular enzymes that reduce the tetrazolium dye , and therefore in a broad sense it measures the cellular metabolic activity , the loss of viability according to this assay indicates either cell cycle arrest or cell death ., Successive investigations 59 , 60 with different experimental techniques demonstrated that Nutlin induces cell arrest in all the considered cell lines , whereas it induces substantial apoptosis ( revealed by Annexin V positivity ) only in SJSA-1 cells , even though apoptosis is not absent in the other cell lines , particularly in RKO cells ., These findings suggested that Nutlin-dependent activation of p53 leads to different outcomes ( cell arrest or apoptosis ) because of different downstream alterations 59 ., The data in 6 show a decrease of the viability when the drug concentration increases , the viability being suppressed at M Nutlin concentration ( see Fig . 2 ) ., Surprisingly , such a dose-response pattern is quantitatively quite similar for all the considered cell lines despite the fact that the Mdm2 gene is 25-fold amplified in SJSA-1 cells and not amplified in HCT116 and RKO cells 59 , 61 ., Our stochastic simulations , performed with the parameter values reported and discussed in S3 Text ( see Tables 1 and 2 in S3 Text ) show that the proposed model is nicely able to reproduce these experimental curves , as illustrated in Fig . 2 , by assuming that the loss of viability is caused by the rising of p53 level over a certain threshold 62 ., More precisely , we assume that if the amount of nuclear-phosphorylated p53 exceeds a threshold value for at least a time , then cell arrest or apoptosis is triggered , and the viability of that cell is lost ., The adequacy of this strict assumption in fitting the data in 6 might be questioned when the response of a substantial fraction of cells consists only in cell cycle arrest ( as for HCT116 and RKO cells ) , since there are several evidences that the Nutlin-induced cell cycle block is reversible after the end of the stimulus , and that the kinetics of this recovery is different in different cell lines 60 , 63 , 64 , 65 ., Concerning RKO cells , it has been suggested that in cells not undergoing apoptosis the cell cycle block may be quite long 60 ., On the contrary , contrasting results on the action of Nutlin on HCT116 cells have been found 60 , 64 , 65 , with evidences that the time needed to recover proliferation after treatment may be very short ( full proliferation was observed three days after the removal of Nutlin 60 ) or rather long ( colony formation was totally suppressed by Nutlin treatment for seven days after drug removal 65 ) ., Therefore , taking into account the lack of consensus on the recovery kinetics of arrested HCT116 cells , we restricted ourselves to fit only the data from RKO and SJSA-1 cells ., To obtain Fig . 2 , we tried and 1 h with few changes in the numerical results , and selected h for the final fitting ., To predict the fraction of viable cells at each concentration , the individual response of 500 cells was simulated ( see S2 Text for details on the simulation algorithm ) ., The number of Mdm2 gene copies was assumed equal to 2 when data from RKO cells were fitted , and equal to 50 in the case of data from SJSA-1 cells ., Different values of the p53 threshold were allowed for Mdm2-amplified and non-amplified cells ., The values of the parameters of Table 2 of S3 Text , together with the threshold values , were adjusted by a trial-and-error procedure ., As expected , in the case of SJSA-1 cells , a threshold value much lower than the value set for RKO cells ( vs . molecules/cell ) was needed , to compensate for the lower p53 levels imposed by the abundance of Mdm2 molecules ., Actually , other causes such as differences in the p53 transcriptional activity or in the abundance of downstream molecules can contribute to set the threshold value , but for sake of simplicity and since there are no clear experimental evidences , they are not included in the present model ., Note that the dose-response data reported in 6 are given as a function of the total Nutlin concentration in the medium ., Some degree of Nutlin binding to the culture medium proteins , however , has been demonstrated in 58 , and from such measurements we could estimate the equilibrium association constant and the concentration of medium binding sites ( see Fig . 1 and Table 3 of S3 Text ) ., Supposing that the binding capability of the medium employed in 6 and of the medium employed in 58 be the same , we computed for each total concentration the corresponding concentration of free Nutlin by means of ( 6 ) ., These values were used in Eq ., ( 4 ) to calculate the intra-cellular free Nutlin amount ., We also predicted the cell response when the PTEN feedback was disabled ( keeping all the other parameters unchanged ) , to mimic tumor cells in which PTEN is not expressed ., The expected reduction of Nutlin efficacy occurs only when no amplification of Mdm2 is present , when , instead , the effect of PTEN deletion is very limited ., It is interesting to compare the dynamics of nuclear-phosphorylated p53 and nuclear-phosphorylated Mdm2 after the exposure to different Nutlin concentrations among those of Fig 2 ., In Fig . 3 , we show stochastic simulations of RKO cells for the exposure to total concentrations of M and M ( panels A–D ) ., The median number of p53 molecules grows after the start of Nutlin exposure and tends to stabilize after some oscillations to a value higher than the baseline value ., When the PTEN feedback is disabled , the increase of p53 amount is reduced ., In the panels E–H of Fig . 3 , showing the simulation of SJSA-1 cells , we may note that the number of nuclear Mdm2 molecules is larger than in the case of RKO cells , and the number of p53 molecules is smaller , in agreement with the presence of a robust Mdm2 gene amplification ., Fig . 4 shows the corresponding dynamics of total and free intra-cellular Nutlin ., We recall here that molecules in a cell of volume equal to m3 correspond to a concentration of M . Note that most of the intra-cellular Nutlin is bound to the Mdm2 molecules ., In Vassilevs experiments 6 , the extra-cellular Nutlin concentration was maintained constant ., This is not the case of in vivo delivery , when the drug is given as boli , i . e . , computationally speaking , in impulsive doses ., By fitting available data of Nutlin pharmacokinetics 58 , we identified the parameters of the pharmacokinetic model ( 8 ) ( see Fig . 5 , panels A and B , and Table 3 of S3 Text ) ., By means of that model , we have simulated realistic oral deliveries of Nutlin in mice ., Since during bolus delivery cells are transiently exposed to the drug , the possible cell recovery from cycle arrest is expected to have a remarkable influence on the fraction of cells still blocked at the assessment time ., Some exploratory simulations ( see S4 Text ) , where recovery is allowed to occur a random time since active p53 level drops below the threshold , confirm the extent of this impact , showing the great importance of the mean recovery time and of the time at which viability is assessed ., Thus , on the basis of our simple hypothesis on the cell response to Nutlin , we can predict the fraction of cells that do not respond during the time of simulation , whereas we cannot predict in principle the fraction of cells that are blocked ( or are in apoptosis ) at the end of simulation ., Of course , these quantities coincide if cells preferentially undergo apoptosis ( SJSA-1 cells ) , or if the recovery time is larger than the interval between the start of treatment and the assessment time , as it should be for RKO cells ., In Fig . 6 , we show the simulated dose-response curves in the case of a dose given as a single bolus ( solid lines ) , and when the dose is split in four boli ( dashed curves ) , administered with 24 h ( panel A ) , 12 h ( panel B ) , and 6 h breaks ( panel C ) ., The dose-response curves are deeply affected by the splitting ., Indeed:, i ) both in RKO cells and in SJSA-1 cells , splitting the dose causes a larger viability up to about 200 mg/kg;, ii ) in SJSA-1 cells , doses larger than 200 mg/kg guarantee almost zero viability for both the single and the split dose delivery;, iii ) in RKO cells , for doses larger than 200 mg/kg split doses are more effective than the single dose , which keeps a residual fraction of non-responding cells of about at 400 mg/kg;, iv ) when the fractionated doses are delivered with intervals of 6 h , the viability is generally larger than in the case of 24 h intervals , i . e . , the therapeutic response is disadvantaged;, v ) after a single dose , SJSA-1 cells appear more responsive than RKO cells ., Some insights into the above behavior can be obtained by analyzing the dynamics of extra-cellular free Nutlin and intra-cellular Nutlin , both total and free , and the time-courses of nuclear p53/Mdm2 ., Figs ., 7 and 8 report such profiles in RKO cells when the total Nutlin dose is 50 mg/Kg and 400 mg/Kg , respectively ., In Figs ., 7B and 8B , the response to the single doses is shown by plotting the profiles of nuclear-phosphorylated p53 and of nuclear-phosphorylated Mdm2 ., Note that although immediately after the dose delivery the Mdm2 amount reduces close to zero , after a short time-lag the number of molecules rapidly recovers and a high peak is reached 10 hours after the drug administration ., Concerning p53 , the peak is reached before Mdm2 regrows over the baseline value , and ultimately also p53 is restored to its pre-delivery value ., Note , moreover , that the p53/Mdm2 response is initiated by the first small peak of the total intra-cellular Nutlin amount ( well visible in Fig . 8A ) corresponding to the peak of extra-cellular free Nutlin concentration , and not by the delayed and dominant peak of intra-cellular Nutlin ., Due to the rapid drug elimination , the splitting with 24 h breaks results in four almost independent dynamics ., In such a case , there is only a slight accumulation of the total intra-cellular Nutlin , more visible at 400 mg/kg ( see Fig . 8C ) , which is mirrored in the nuclear p53 peaks ( see Fig . 8D ) ., When the interval among split doses is 6 h , instead , there is a clear accumulation of the total intra-cellular Nutlin both at doses of 50 and 400 mg/Kg ., Quite surprisingly , the p53 peaks , although rather merged together , have heights on average smaller than the peaks achieved by 24 h breaks ( compare Fig . 7D and F , and Fig . 8D and F ) ., The figures also evidence a nonlinear relationship between the dose injected and the total amount of intra-cellular Nutlin , and between the dose injected and the peak of nuclear p53 ( measured from the baseline value ) ., When the dose of 400 mg/Kg is split with 24 h breaks , the first median p53 peak is over of the median p53 peak after a single dose , whereas the dose injected is only ( compare Fig . 8B and D ) ., The nonlinearity is present also at the dose of 50 mg/kg but it is less pronounced: after splitting with 24 h breaks , the median p53 peaks are about of the peak after the single dose ( compare Fig . 7B and D ) ., The complex p53 regulation network , may be the sources of this nonlinearity ., The inefficacy of dose-splitting at low doses and the opposite behavior at high doses ( found in simulating RKO cells ) can be explained by the contrasting effects of the following factors:, a ) the existence of a threshold triggering the cell-arrest/apoptotic response penalizes the dose fractionation , unless the dose is very high ., In fact , if the p53 level exceeds the threshold after a single dose delivery , the same is not granted for split doses;, b ) the nonlinearity between dose and intra-cellular Nutlin amount ( above described ) rewards fractionated schedules by advantaging small doses ., The necessity that the level of crosses a threshold to elicit the cell response also explains the general increase of viability observed in our simulations with 6 h breaks , since in this case the p53 peaks were smaller than the p53 peaks found with 24 h-break splitting ., A finer inspection of the simulated response shows that in the case of splitting with 6 h breaks , the PTEN amount reaches the largest value ( not shown ) , likely because the level of active p53 is rather sustained over the whole 24 h period of Nutlin administration ( we remind that , according to the model , the overall transcription activity of p53 is substantially related to the time integral of its concentration ) ., The high level of PTEN causes a reduced Mdm2 phosphorylation and then accumulation of Nutlin-bound non-phosphorylated Mdm2 in the cytoplasm ., In this way the accumulation of total intra-cellular Nutlin during drug delivery with 6 h breaks may not translate into a corresponding increase of the peak level of nuclear active p53 ., Although the response of RKO and SJSA-1 cells to a continuous Nutlin exposure is quite similar ( see Fig . 2 ) , SJSA-1 cells are predicted to be more responsive than RKO cells after a single bolus delivery ( Fig . 6 ) ., We may advance an explanation based on the role of PTEN ., First , note that the relationship between the dose-response curves of the different cell lines with PTEN OFF in Fig . 2 is similar to that of Fig . 6 , single dose , i . e . , SJSA-1 cells result more responsive ., With PTEN ON and continuous exposure to the drug , the slow positive PTEN feedback loop has time to play its role and , as a result , the active p53 level increases in the nucleus ., This favors the RKO mortality but not that of SJSA-1 cells ., Indeed , the strong Mdm2 overexpression makes the positive feedback promoted by PTEN less important ., Thus , we can see a viability difference between PTEN ON and OFF for RKO , but not for SJSA-1 cells ., In the case of a dose delivered with a single bolus , the input signal does not last long enough to trigger the positive PTEN feedback , so RKO cells do not exhibit Mdm2 blocking in cytoplasm and the consequent increase of nuclear p53 ., Their viability thus remains greater than that of SJSA-1 cells ., Although deterministic models give valuable information on the average behavior of a biochemical system , they are by definition unable to reproduce statistical behavior differences , both intra-cellular ( i . e . , possible random changes in the response of single cells when observed for a long time ) and inter-cellular ( i . e . , different responses of two identical cells ) ., The experimentally observed dose-response curves mirror this inter-cellular variability of the response to drug delivery ., In recent years a vast body of research has focused on the randomness affecting biomolecular networks ., Two kinds of stochasticity are usually considered ., The first kind is caused by the interplay between cells and their microenvironment ., This stochasticity is termed extrinsic noise ., Another kind of randomness comes from the intrinsic stochastic nature of chemical reactions , and its effect becomes more evident when the number of transcripts or proteins is low ., In such a case , differential equations do not allow an accurate representation of the dynamics of those transcripts and/or proteins ., Interestingly , even when a differential equation model appears to be feasible , in some conditions the average behavior of its stochastic counterpart can diverge significantly from the deterministic prediction ( see e . g . , 66 ) ., However , another internal source of noise is often neglected: the randomness of the process of gene activation/deactivation ., Actually this kind of noise might be one of the major sources of random fluctuations in intra-cellular protein concentrations ., If the switching rates of the genes are very large , one can neglect this noise because it is filtered by the network itself ., Still , this is not possible in many cases as it has been experimentally shown 37 , 38 , 39 and theoretically confirmed 40 , 41 , 42 , 43 ., In reproducing the original Vassilevs experiments 6 in-silico , our numerical simulations have shown that the stochasti

Introduction, Models, Results, Discussion

In this work we investigate , by means of a computational stochastic model , how tumor cells with wild-type p53 gene respond to the drug Nutlin , an agent that interferes with the Mdm2-mediated p53 regulation ., In particular , we show how the stochastic gene-switching controlled by p53 can explain experimental dose-response curves , i . e . , the observed inter-cell variability of the cell viability under Nutlin action ., The proposed model describes in some detail the regulation network of p53 , including the negative feedback loop mediated by Mdm2 and the positive loop mediated by PTEN , as well as the reversible inhibition of Mdm2 caused by Nutlin binding ., The fate of the individual cell is assumed to be decided by the rising of nuclear-phosphorylated p53 over a certain threshold ., We also performed in silico experiments to evaluate the dose-response curve after a single drug dose delivered in mice , or after its fractionated administration ., Our results suggest that dose-splitting may be ineffective at low doses and effective at high doses ., This complex behavior can be due to the interplay among the existence of a threshold on the p53 level for its cell activity , the nonlinearity of the relationship between the bolus dose and the peak of active p53 , and the relatively fast elimination of the drug .

P53 is an antitumor gene regulating vital cellular functions such as repair of DNA damage , cellular suicide , and cell proliferation: in many tumors p53 is lowly expressed and/or mutated ., Drugs targeting the biomolecular network of p53 are becoming important ., The network includes the key proteins Mdm2 and PTEN , whose production is regulated by p53 , and which , in turn , enact positive and negative feedbacks on p53 ., Drug Nutlin , inhibiting the p53 inhibitor Mdm2 , might be important for tumors where p53 is underproduced but unmutated ., We investigate the cellular mechanism of action of Nutlin ., The basic concept of our mathematical model is that the experimentally observed cell-to-cell variability of Nutlin efficacy is caused by the randomness of gene activation/deactivation of Mdmd2 and PTEN ., Indeed , the abundance/scarceness of p53 regulates the probability that the relative genes are active or inactive ., The model reproduced the experimental cell-specific response to different doses of Nutlin ( dose-response curves ) in some types of tumor cells ., Much clinical research focus on metronomic drug delivery regimens , where instead of giving large doses with long intervals , smaller doses are frequently delivered ., In our simulations , dose-splitting of Nutlin produced a response generally worse than the response to a single dose .

systems biology, biochemistry, biochemical simulations, medicine and health sciences, clinical medicine, pharmacodynamics, biology and life sciences, pharmacology, computational biology, pharmacokinetics

journal.pcbi.0030055

Dynamic Simulations on the Arachidonic Acid Metabolic Network

Nonsteriodal anti-inflammatory drugs ( NSAIDs ) ( e . g . , aspirin ) are widely used for the treatment of musculoskeletal pain and other conditions ., In the US , more than 1% of the population uses NSAIDs daily 1 , and the market for NSAIDs now amounts to more than $6 billion annually worldwide 2 ., Although NSAIDs do alleviate the aches and pains , these drugs have undesirable side effects on the gastrointestinal tract and the central nervous system in addition to the potential exacerbation of conditions such as asthma 1 ., The findings that cyclooxygenase-2 ( COX-2 ) plays a major role in inflammation , and that inhibition of COX-1 causes gastrointestinal toxicity and mild bleeding diathesis 3 , had suggested that selective COX-2 inhibitor would be an effective anti-inflammatory drug with low gastrointestinal side effects 4 ., Ironically , the unexpected cardiovascular side effects of selective COX-2 inhibitors have surfaced 5 , 6 ., Thus , on September 30 , 2004 , Merck & Company announced a voluntary withdrawal of the companys COX-2 inhibitor , VIOXX ( rofecoxib ) 7 ., Other FDA-approved COX-2 inhibitors , such as celecoxib ( Celebrex ) and valdecoxib ( Bextra ) , are being re-evaluated 8–10 ., Despite years of studies , safe anti-inflammatory drug design remains a great challenge ., Failures in anti-inflammatory drug design illustrate the limitations of the current drug discovery paradigm ., A steady waning in the productivity of the pharmaceutical industry in the past decade has been observed ., This decline coincides with the introduction of target-based drug discovery 11 ., Recently , medicinal chemists have started to think about drug discovery from a systems biology perspective 12 , 13 ., Studying the cross-talks between biological responses rather than one by one may provide a better understanding of disease development and achieve accurate evaluation on drug efficacy and toxicity 14 , 15 ., This new approach has been applied to safe drug design 16 , 17 ., For example , the former SmithKline Beecham ( now GlaxoSmithKline , http://www . gsk . com ) focused on the blood coagulation cascade biochemical network 18 , 19 ., Armed with a good understanding of the disease from the regulatory network level , the company used model predictions to develop a fully humanized anti–Factor IX antibody that has entered clinical trials ., Rajasethupathy et al . have recently reviewed advances in the practical applications of systems biology to drug discovery 20 ., These researchers promote the development of network-based drug design , which devises drug-treatment strategies from the level of the disease system using computational models and high-throughput experiments ., In this paper , we study the dynamic properties of the arachidonic acid ( AA ) metabolic network in human polymorphonuclear leukocytes ( PMNs ) in the hope of gaining more insights into anti-inflammatory drug design ., An ordinary differential equation ( ODE ) model of the AA metabolic network was developed ., Flux analysis and simulation on the addition of exogenous AA were performed to study the network balance ., The therapeutic effects of anti-inflammatory inhibitors were simulated , and the difference between dual functional COX-2 and 5-lipoxygenase ( 5-LOX ) inhibitors and the mixture of these two types of inhibitors was studied ., Corresponding experiments on the introduction of exogenous AA , COX-2 , and 5-LOX inhibitors were performed and were found to be consistent with model predictions ., Our work shows that flux balance is important for the efficacy and safety of the drugs ., Compared with traditional single-target drugs , drugs against multiple targets can control the network balance and lead to safer treatment ., Inflammation is a type of nonspecific immune response to infection , irritation , or other injury ., It is characterized by redness , swelling , pain , and sometimes loss of function ., When harmful agents invade the human body , inflammatory mediators are released by immune cells ., This release causes vasodilation , emigration of neutrophils , chemotaxis , and increased vascular permeability ., AA is the precursor of inflammatory mediators , including prostaglandins ( PGs ) and leukotrienes ( LTs ) ., Extensive researches on the metabolism of AA in human PMNs have been performed ., Thus , based on the Kyoto Encyclopedia of Genes and Genomes ( KEGG ) 21 and a survey of the literature ( Figure 1; see details in Protocol S1 ) , a computational model of the AA metabolic network in human PMNs was constructed ( Dataset S1 ) ., The production of inflammatory mediators is initiated by inflammatory stimuli that activate phospholipase A2 ., This enzyme catalyzes the hydrolysis of the sn-2 position of the membrane glycerophospholipids to release AA ., Two separate pathways produce inflammatory mediators ., One is initiated by COX-2 and produces PGs , while the other is initiated by 5-LOX and produces LTs ., PGE2 , LTA4 , and LTB4 are the major inflammatory mediators in our model ., Once PMNs have been activated by inflammatory stimuli , the cells release inflammatory mediators and cause accumulation and activation of other cells ., A series of ODEs was established to simulate unicellular behavior ( see details in Materials and Methods ) , which included 24 initial concentrations and 45 reaction constants ( see details in Protocol S1 ) ., A total of 23 reaction constants was taken from experimental values , while the others were obtained by fitting the calculated production of LTB4 and its ω-oxidation products ( ω-LTB4 ) to experimental data 22 ( Figure 2 and Tables S1–S2 ) ., The parameter set that fit the experimental data well was chosen for further studies ., Flux analysis was performed on the main network pathways , which included the 5-LOX , 15-LOX , and COX-2 pathways ., The result of the 12-LOX pathway analysis was not shown here due to its weak effect on other pathways in the current network 23 ., Figure 3A shows simulations of 30 min ., In the first 5 min , following the activation by inflammatory stimuli , the flux of the 5-LOX pathway was the largest and was responsible for the major metabolism of AA ., As the result of negative feedback regulations ( e . g . , the suicide inhibition of LTA4H by LTA4 ) , the flux of the 5-LOX pathway decreased along with time , while the flux of the 15-LOX pathway increased ., Ultimately , the 15-LOX pathway flux became the largest after the first 5 min ., During the 30-min simulation , the flux of the COX-2 pathway remained small and played the least important role in the AA metabolic network in human PMNs ., The first 10 min was critical for the production of LTs , while PGs continued to accumulate with time ., These findings are consistent with previous experimental data 24 , 25 that showed that LTs rather than PGs are the main inflammatory mediators produced in human PMNs ., Further simulations with different exogenous AA concentrations were performed ., It seems logical to postulate that more AA would produce more inflammatory mediators because AA was the only source of LTs and PGs in our model; however , the simulation results interestingly showed that the output of LTs decreased with the concomitant increase of exogenous AA ( Figure 4A and 4D ) ., This decrease was the result of the negative-feedback mechanisms in the 5-LOX pathway ., The additional AA is mainly metabolized through the 15-LOX pathway ., Further experiments were performed to validate this assumption ( see details in Materials and Methods ) ., A decrease of LTs was observed when more than 0 . 5 mM AA was added to PMNs ., The output of ( 15S ) -hydroxy-5Z , 8Z , 11Z , 13E-eicosatetraenoic acid ( 15-HETE ) increased at the same time ( Figure 4C and 4B ) ., Thus , the simulation predictions are qualitatively consistent with the experimental results ., Simulations on widely used anti-inflammatory inhibitors , including 5-LOX inhibitor , COX-2 inhibitor , and a combination of the two , were performed ( Figure 3 ) ., The COX-2 inhibitor increased the flux through the 15-LOX pathway , while the flux through the 5-LOX pathway remained almost constant ., The reason is that the effect of COX-2 pathway in the current model is weak in the early stage of AA metabolism and becomes more evident with time ., The 5-LOX inhibitor induced the peak flux immediately after initiating the metabolism in both the COX-2 and the 15-LOX pathways ., When both the COX-2 and 5-LOX inhibitors were introduced , the COX-2 and 5-LOX pathways were shut off , and the majority of the flux went through the 15-LOX pathway ., These simulation results showed that single-target anti-inflammatory drugs cannot stop the production of all inflammatory mediators , and that the combination of the 5-LOX and COX-2 inhibitors would likely yield better therapeutic results ., All of the inhibitors increased the production of 15-HETE , which was consistent with our experimental results ( Figure 5 ) ., We then did further simulation on the combination of 5-LOX inhibitor and COX inhibitor ., There are two strategies for this combination: developing dual functional COX-2 and 5-LOX inhibitors , or using a mixture of these two types of inhibitors ., A few papers have been published on the development of these two anti-inflammatory strategies , and some drug candidates have already been in clinical tests 26–31 ., When using a combination of inhibitors , two issues need to be considered: one is the mixing ratio ( MR ) of different single-functional inhibitors , which makes great contributions to the efficacy and safety of the mixture; the other is the relative inhibition constant to different enzymes ( DR ) , which decides the therapeutic effect of the dual-functional inhibitor ., The efficacy of the dual-functional COX-2 and 5-LOX inhibitor and the mixture with different concentrations and DR/MR values was investigated and compared ., To ensure the equality in the comparison , the same total inhibition ability ( KiCOX-2 × Ki5-LOX; see details in Materials and Methods ) and the same total concentration of inhibitors were used in the simulations ., The inhibition intensity on the production of LTs and PGs was calculated to evaluate the efficacy of inhibitors ( see details in Materials and Methods ) ., As shown in Figure 6A , for the mixture , the inhibitors had the largest effective concentration region when MR was close to the relative activity ( ER ) of the two enzymes ( see definition in Materials and Methods ) ; that is , the enzymes can be inhibited to more than 90% if the total inhibitor concentration ( CIt ) was larger than approximately 1% of the total concentration of 5-LOX and COX-2 ( CEt ) ., When CIt was less than 1% of CEt , the enzymes cannot be effectively inhibited ., When MR deviated from ER , the effective concentration region of the inhibitors became much smaller ( e . g . , when MR deviated from ER by an order of 104 ) , strong inhibition ( >90% ) was achieved only when CIt was about ten times larger than the CEt ., Davies et al . 32 have reviewed the clinical pharmacokinetics of a COX-2–selective inhibitor , meloxicam ., The maximum plasma concentration of this compound is from 0 . 531 to 5 . 35 mg/l after application of clinical dosage ( 5–30 mg ) to volunteers , which is about 0 . 3 to 3 times the CEt in our model ., Thus , in general clinical treatments , the mixture of compounds might be effective in a quite broad region of MR/ER ( about 0 . 001 to 1 , 000 ) ., If MR deviates too much from ER , then the clinical dosage will not be effective ., For the dual-functional inhibitor , the compound also had the largest effective concentration region when DR was close to ER ( Figure 6B ) ., However , the low concentration limit was extended to as low as 10−4 ( CIt/CEt ) ; that is , the dual-functional inhibitor was much more effective compared with the mixture of inhibitors when the concentration was low ( Figure 6C ) ., If the dual-functional inhibitors and the mixture have similar pharmacokinetic properties , and the same drug dose is applied to patients , the dual-functional inhibitors will be effective for a longer time than the mixture , as they are active at low concentration ., When DR deviated from ER , the effective concentration region of the inhibitor also became smaller ., There was a similar trend when DR was larger than ER ., When DR was smaller than ER , noticeable inhibition could be observed at very low inhibitor concentration ., Development of safe anti-inflammatory drugs has been an especially tough problem for a long time ., Failures in selective COX-2 inhibitors have provided a good lesson on the drug safety problem ., Thus , network-based drug design , following the development of systems biology , should be useful for finding safe therapeutic strategies from the level of the disease network ., In the current study , we used the AA metabolic network in human PMNs as an example for the network-based drug design study ., A mathematical model for the AA metabolic network in human PMNs was built using ODEs to describe enzymatic reactions and feedback loops ., Some of the parameters were taken from published experimental studies , and other parameters and initial conditions were determined by fitting to experimental curves of LTB4 and derivative production ., The model was validated by experiments on the influence of introducing exogenous AA or inhibitors and was found to explain experimental data well ., However , as the network is complex , its robustness and reliability need to be further validated in future studies ., Nevertheless , our AA metabolic network model can enhance the understanding of the production time course of inflammatory mediators , including LTs and PGs , and can be used to assist anti-inflammatory drug design ., Network dynamic properties were found to be important for anti-inflammatory drug design targeting the AA network ., Results of flux analysis , simulations , and experiments on the effect of exogenous AA showed the complexity of a compounds behavior in a system ., Due to the effect of feedback regulations and other pathways in the network , the effect of a molecule in a system may not be same as its effect on a single point reaction ., Thus , designing drugs from the network system level is necessary ., Performing model studies combined with experiments would be an effective way to find safe drugs ., Single-target anti-inflammatory inhibitors were found to have limitations based on simulations and experiments in the current work ., Single-target inhibitors cannot achieve full success in anti-inflammatory treatment because they cannot control the production of both LTs and PGs at the same time ., Results in our studies suggest that the combination of the COX-2 inhibitor and the 5-LOX inhibitor would have better treatment ., In fact , a dual COX-2/5-LOX inhibitor , licofelone ( ML3000 ) 33 , 34 , has been successfully completed phase III trials and is demonstrated to be superior in safety and equally efficacious for standard treatments of osteoarthritis ., Furthermore , multitarget anti-inflammatory drug efficacy was investigated ., We simulated and compared the efficacy of dual COX-2/5-LOX inhibitors and the mixture of single-functional COX-2 and 5-LOX inhibitors ., The mixing ratio and the relative inhibition constant were found to be important to the efficacy of the mixture and the dual-functional inhibitor , respectively ., Generally speaking , the dual-functional inhibitor was effective in a larger concentration range , making it more robust towards concentration fluctuations ., Further studies are necessary to achieve a better understanding of the difference between the dual-functional inhibitor and the mixture of single-functional inhibitors ., The mathematical model of the AA metabolic network in PMNs ., Based on KEGG and a survey of the literature , a group of ODEs were devised to develop the mathematical model of the AA metabolic network in human PMNs ( see details in Protocol S1 and Dataset S1 ) ., Michaelis–Menten equations are used to describe the catalysis in the network:, where S is the concentration of substrate , Et is the total concentration of enzyme , Kcat is turnover number , and Km is the Michaelis–Menten constant ., If competitive reversible inhibitors are involved in the catalysis , the equation is:, where I is the concentration of inhibitor and Ki is the inhibition constant , which is defined as:, If the inhibitors are irreversible , we assume the enzymes would decay according to the following equation:, where K is a constant ., When activators are involved in the catalysis , we use the following equation:, where A is the concentration of activator and KI is a constant ., PGE2 can upregulate 15-LOX through transcription in this network ., Based on the experimental data , we describe its effect with the following equation:, where g is the concentration of PGE2 and K is a constant ., The ode15s routine of Matlab 6 . 5 ( Mathworks , http://www . mathworks . com ) was used to solve the ODEs ., LTB4 and ω-LTB4 metabolic curves under different exogenous AA concentrations were calculated ., These calculated curves were fit to the experimental data by empirically modulating parameters that had no direct values from published experiments , while the other parameters remained fixed to their experimental values ., The parameter set that fit the experimental data well was chosen for further studies ., Simulating the therapeutic effects of the dual functional inhibitor and the mixture ., The inhibition behavior on different enzymes is assumed to be independent and can be calculated by the following equations ( only competitive reversible inhibitors are studied here ) :, where I is the concentration of inhibitor , and Ki is the dissociation constant ., We use these equations with the consideration that the binding affinity of drugs is usually strong and the necessary concentration of inhibitors is in the same magnitude with the concentration of the enzyme ., Then the enzyme-inhibitor complex cannot be neglected , and the above equations are required ., To evaluate the efficacy of inhibitors , inhibition intensity on the production of inflammatory mediators is defined as:, where PGs1 is the concentration of PGs after taking drugs , PGs0 is the concentration of PGs before treatment , LTs1 is the concentration of LTs after taking drugs , and LTs0 is the concentration of LTs before treatment ., We use the same value of total inhibition ability and total concentration of the dual functional inhibitor and the mixture to ensure the equality in the comparison ., The total inhibition ability is defined as the product of inhibit constant to COX-2 and 5-LOX ( KiCOX-2 × Ki5-LOX ) and is fixed to 1 × 10−14 in all simulations ., ER is defined as:, where A is the activity of enzyme , and C is the concentration ., The ER value in the current model is 0 . 02 ., Materials ., LTB4 , 20-OH-LTB4 , 20-COOH-LTB4 , 5-HETE , 15-HETE , PGB2 , MK886 , CAY10404 , and AA were purchased from Cayman Chemical ( http://www . caymanchem . com ) ., The calcium ionophore , A23187 , was obtained from Acros Organics ( http://www . acros . com ) ., Concentrations of the LTs and hydroxyeicosatetraenoic acids were determined by UV spectroscopy at 270 nm and 236 nm , respectively ., PGB2 was used as an internal standard , and its concentration was determined by UV spectroscopy at 278 nm ɛ ( PGB2 ) = 26 , 000 ., Following the reported procedure 35 , human PMNs were isolated from the venous blood of healthy volunteers who had not ingested any aspirin-like compounds in the preceding week ., Blood was collected in the anticoagulant Na-EDTA and was centrifuged at 100g for 10 min ., The platelet-rich plasma was discarded ., The blood was then mixed with 6% dextran T-500 in 0 . 01 M PBS ( T500/PBS = 1:1 ) ., After 45 min , the upper layer that contained the leukocytes was layered gently over lymphocyte separation medium and centrifuged at 550g for 30 min to pellet the cells ., The residual erythrocytes were removed by osmotic hemolysis with ice-cold water for 20 s ., The mixture was washed twice by centrifugation in 0 . 01 M PBS at 250g for 5 min and then resuspended in PBS ( >95% PMNs , <5% monocytes and lymphocytes ) ., PMNs were preincubated with MK886 36 ( an inhibitor of 5-LOX ) , CAY10404 37 ( an inhibitor of COX-2 ) , and a combination of MK886 and CAY10404 at 37 °C for 10 min , respectively ., To stimulate the PMNs , 10 μM A23187 , 2 mM Ca2+ , and 2 mM Mg2+ were added for 1 h at 37 °C ., Incubations were terminated by the addition of ethyl acetate ( containing 160 μl acetic acid/40 ml ) ., PGB2 ( 100 ng ) was added as an internal standard ., The upper solvent was evaporated under a stream of nitrogen , and the residue was dissolved in 30 μl methanol ., Reverse-phase high-performance liquid chromatography ( HPLC ) was used to assay 20-OH-LTB4 , 20-COOH-LTB4 , and 15-HETE 38 ., HPLC was performed with an Agilent 1100 series instrument ( http://www . agilent . com ) ., A column ( Retasil C18 , 4 . 6 × 200 mm cartridge , 5 μm particle size; Elite , http://las . perkinelmer . com ) was used for separation of the samples ., Solvents A and B consisted of methanol–water–acetic acid ( 10:90:0 . 05 ) and methanol–acetonitrile–acetic acid ( 30:70:0 . 05 ) , respectively ., Lipids were eluted at a rate of 1 . 0 ml/min with continuous monitoring for UV absorbance at 235 nm and 270 nm for detection of 15-HETE and LTs , respectively ., The retention times for 20-COOH-LTB4 , 20-OH-LTB4 , PGB2 , and 15-HETE were 6 . 399 , 6 . 603 , 11 . 604 , and 17 . 899 min , respectively ., Cells were preincubated with 0 . 1 mM , 0 . 5 mM , and 1 mM AA at 37 °C for 10 min ., To stimulate PMNs , 10 μM A23187 , 2 mM Ca2+ , and 2 mM Mg2+ were then added for 1 h at 37 °C ., Incubations were terminated by the addition of ethyl acetate ( containing 160 μl acetic acid/40 ml ) ., PGB2 ( 100 ng ) was added as an internal standard ., The upper solvent was evaporated under a stream of nitrogen , and the residue was dissolved in 30 μl methanol ., Again , reverse-phase HPLC was used to assay 20-OH-LTB4 , 20-COOH-LTB4 , and 15-HETE as described above .

Introduction, Results, Discussion, Materials and Methods

Drug molecules not only interact with specific targets , but also alter the state and function of the associated biological network ., How to design drugs and evaluate their functions at the systems level becomes a key issue in highly efficient and low–side-effect drug design ., The arachidonic acid metabolic network is the network that produces inflammatory mediators , in which several enzymes , including cyclooxygenase-2 ( COX-2 ) , have been used as targets for anti-inflammatory drugs ., However , neither the century-old nonsteriodal anti-inflammatory drugs nor the recently revocatory Vioxx have provided completely successful anti-inflammatory treatment ., To gain more insights into the anti-inflammatory drug design , the authors have studied the dynamic properties of arachidonic acid ( AA ) metabolic network in human polymorphous leukocytes ., Metabolic flux , exogenous AA effects , and drug efficacy have been analyzed using ordinary differential equations ., The flux balance in the AA network was found to be important for efficient and safe drug design ., When only the 5-lipoxygenase ( 5-LOX ) inhibitor was used , the flux of the COX-2 pathway was increased significantly , showing that a single functional inhibitor cannot effectively control the production of inflammatory mediators ., When both COX-2 and 5-LOX were blocked , the production of inflammatory mediators could be completely shut off ., The authors have also investigated the differences between a dual-functional COX-2 and 5-LOX inhibitor and a mixture of these two types of inhibitors ., Their work provides an example for the integration of systems biology and drug discovery .

Inflammation is a basic way in which the body reacts to infection , irritation , or other injury ., When it is uncontrolled and misdirected , it causes diseases such as rheumatoid arthritis , inflammatory bowel disease , asthma , and others ., In the United States , more than 1% of the population uses nonsteroidal anti-inflammatory drugs , such as aspirin , ibuprofen , or naproxen , daily to relieve aches and pains ., However , these drugs have undesirable side effects ., The withdrawal of VIOXX ( rofecoxib; Merck , http://www . merck . com ) in 2004 has given a good lesson on safety problems ., To assist the design of safe anti-inflammatory drugs , we have constructed a computational model of the arachidonic acid ( AA ) metabolic network in human polymorphous leukocytes ., By analyzing the flux changes upon drug treatment in this metabolic network , drugs against multiple targets were found to be capable of reducing toxicity as they exhibited balanced control of the system ., The model of the AA metabolic network provides helpful information for anti-inflammatory drug discovery ., This work sets an example for the integration of systems biology and drug discovery .

homo (human), computational biology

journal.pcbi.1001097

Dynamic Conformational Changes in MUNC18 Prevent Syntaxin Binding

Intracellular membrane fusion in eukaryotes is mediated by a well-conserved fusion machinery composed of SNARE ( soluble N-ethylmaleimide-sensitive factor attachment protein receptor ) and SM ( Sec1/munc18-like ) proteins 1 ., In the early studies , munc18a was shown to bind syntaxin , one of the central SNARE members and block ternary SNARE complex formation , suggesting that it plays a negative regulatory role 2 , 3 ., However , genetic and biochemical studies indicated that SM proteins play a positive essential role as demonstrated by their null mutants; studies with mutated worms , flies and mice lacking munc18a , revealed a dramatic decrease in secretory granule fusion , docking and priming 4 , 5 , 6 ., Therefore , the central hypothesis , to date , is that SM proteins play several roles depending on their mode of binding to the SNARE members 7 , 8 ., The first mode of interaction that was discovered 9 relates to the binding of munc18a to a stable closed-conformation of syntaxin ., This mode of interaction allows the specific transfer of syntaxin through the endoplasmic reticulum and the Golgi apparatus to the plasma membrane , keeping syntaxin from engaging to ectopic intracellular SNARE complexes 10 , 11 ., Recent studies demonstrate that SM proteins bind only or additionally to a short peptide present at the N-terminus of syntaxin , designated as the N-peptide 1 , 10 , 12 ., This mode of interaction was intensively investigated in the last few years and its importance is under a strong debate ., One of the main hypotheses for the role of the interaction of munc18a with the N-terminal of syntaxin is that this interaction allows munc18a to bind the SNARE ternary complex suggesting a stimulatory role for munc18a in the last stages of SNARE-mediated fusion 13 ., The rat munc18a , which was structurally resolved as part of the complex with syntaxin-1a 9 , 12 , is an arched-shaped three-domain protein ( Figure 1A ) that embraces syntaxin in a cavity located between domains 3a and 1 ( Figure 1 , A and B ) ., Phosphorylation by protein kinase C ( PKC ) or phosphomimetic mutations in residues 306 and 313 ( S306D , S313D ) of munc18a modulate this interaction by reducing the affinity to form a complex 14 , 15 ., Previous studies have suggested that replacement of the polar serine moieties in domain 3a of munc18a by phosphate groups or negatively charged glutamates disrupts the complex due to electrostatic repulsion between munc18a and the adjacent area of syntaxin ( Figure 1B ) , which contains acidic residues 14 ., In the present study , munc18a dynamics was studied , for the first time , using molecular dynamics ( MD ) simulations under different conditions for several hundred nanoseconds ., We show that in the absence of syntaxin , wild-type munc18a exhibits a dynamic equilibrium between several states , differing in the size of the syntaxin-binding site ( the cavity between domains 3a and 1 ) ., In the next step , we examined the dynamic behavior of the phosphomimetic munc18aS306D , S313D and we show that following in-silico insertion of the mutations into the wild-type structure , munc18a adopted a rigid closed-cavity conformation which makes syntaxin binding less probable ., The closed-cavity conformation is induced specifically by the PKC phosphomimetic mutations and reversible upon dephosphorylation of the protein back to the wild-type form ., In the present study , munc18a dynamics was studied using molecular dynamics ( MD ) simulations , a powerful method in which the dynamics and conformational changes in proteins can be followed in a virtual fashion ., We performed three MD simulations of the wild-type munc18a ( termed 1 , 2 and 3 , Table, 1 ) as described in details in the Methods section ., The simulations were performed under the same conditions; accept for applying two distinct informatics tools; Swiss-Pdb 16 or Rosetta 17 , 18 , 19 for in silico reconstruction and structural modeling prediction of regions in the protein that were not resolved in the crystal structure 9 ., The high resemblance of the basic dynamics characteristics of munc18a in the three simulations ( Text S1 and Figures S1 and S2 ) allowed us to evaluate the general relative inter-domain motions of the protein and attribute them to the activity and function of the protein ., We monitored specifically the changes in the main syntaxin-binding site of munc18a , which is the area of the cavity between domains 3a and 1 9 ., We first measured the change in the distance between the centers of mass of domains 3a and 1 ( Figure 1C ) and of the distance between specific residues ( Gly 26 in domain 1 and Glu273 in domain 3a ) on both sides of the cavity ( Figure 1 , D–E ) during the simulations ., The measurements showed that the distances frequently change , indicating structural fluctuations of these domains and dynamic changes in the size of the cavity , becoming wider or narrower ( Figure 1 , C–E ) ., During the simulations , the main motions of the protein were isolated from its overall movement using an essential dynamics ( ED ) analysis ., ED analysis is a method for isolating the various modes of motion of a protein during the simulation by yielding a set of eigenvectors corresponding to its internal motions namely the amplitudes and the directions of the motions 20 ., The vectors are scaled according to the time scale of the motion from the slowest undulations which generally correspond with motions of large regions in the protein , and up to the fast and high-frequency local fluctuations ., The ED analysis of the wild-type munc18a simulations clearly illustrated that the main motion vectors exhibit opening and closure of the cavity between domains 3a and 1 ( Figure 1 , F–G ) dramatically changing its size ., The high flexibility in the size of the munc18a cavity probably assists in binding or unbinding of syntaxin or other target proteins that bind munc18a in other regions as well ( such as CDK5 for example ) 9 , 21 ., Squid munc18 ( sSec1 ) , a homolog of the rat protein ( munc18a ) , has been crystallized as a free protein , i . e . unbound to the squid syntaxin 22 , and three variations of the structure are available ., The following section examines the similarity between the dynamics behavior of the wild-type munc18a during the simulations and the resolved crystal structures of its squid homolog , sSec1 ., Figure 2 presents a superposition of the three available sSec1 crystal structures ( 1EPU . pdb , 1FVF . pdb and 1FVH . pdb ) and the munc18a crystal structure ( 3C98 . pdb ) ., In the three simulations , domain 3a , and particularly the β-hairpin ( residues 263–280 ) exhibited high structural variability , sampling manifold structures ( Figure 2B ) ., Similarly , the three resolved sSec1 structures exhibit high variability among them in the structure of domain 3a ., In the simulations , domain 1 remarkably preserved its secondary structure and we observed a clear rotational motion of this domain ., Similarly , superposition of the three crystal structures of the squid protein shows that they share the same secondary structure for domain 1 , but domain 1 is positioned in a slightly different angle reflecting a rotation motion of this domain ( Figure 2C ) ., The β-hairpin in domain 3a of munc18a ( residues 261–280 ) plays a prominent role in the interaction of munc18a with syntaxin-1a ., Eight amino-acid residues out of the 19 that compose the β-hairpin are engaged in interactions with the H3 domain of syntaxin , making the hairpin an essential element in the binding of syntaxin , and in keeping syntaxin in its closed ( inactive ) structure ., Therefore , any fluctuations in the position of the β-hairpin might influence the affinity of syntaxin to munc18a and might cause syntaxin to alternate to its open structure ., Comparison of the munc18a structure to the crystal structure of Sly1p , the yeast Golgi homolog of munc18a ( 1mqs . pdb , downloaded from PDB 23 ) indicates that the hairpin of the later , although partially unstructured , resides in a much higher position than in the munc18a crystal structure ( Figure 3A ) ., In this structure , the Golgi resident syntaxin ( sed5p ) is absent from the cavity area and the crystal structure only includes its N-terminal peptide which is bound to the area of domain 1 ., Interestingly , in simulation 2 of munc18a ( Table 1 ) , we traced a prominent motion of the β-hairpin of the protein , moving during the simulation from its original location in the crystal structure outwards and upwards , protruding from the rest of the protein ( Figure 3 , B and C ) ., This motion , resulting in a position similar to that seen in the crystal structure of the Sly1p , confirms the possibility raised before that the motion of the β-hairpin might serve as a mechanism for the release of syntaxin from the cavity area 22 ., To further confirm this notion , we reconstructed the full structure of Sly1p ( see Methods ) and performed a simulation of its dynamics under the same conditions of the munc18a simulations ., We focused on the movement of the β-hairpin in domain 3a ( residues 298 to 327 ) during the simulation ., Indeed , during the 20 ns of the simulation , we observed an extensive rotation-translation movement of this region downwards approaching domain 3a ( Figure 3D ) and consequently narrowing the width of the cavity ., Thus , the β-hairpin might serve as a gate for the cavity , opening and closing the cavity when needed ., In the current study , we show that this motion can occur spontaneously with no interference from any additional factor ( s ) ; although we cannot determine the probability of this type of motion in the free or syntaxin-bound munc18a , these data support the hypothesis that the β-hairpin serves as a switch for syntaxin-binding or unbinding ., After characterizing , in detail , the dynamics of the wild-type munc18a , the next step of our study was to examine the dynamic behavior of the phosphomimetic munc18aS306D , S313D and determine the differences compared to the wild-type dynamics ., Characterizing the differences in the dynamics of munc18aS306D , S313D will assist to determine a mechanism that might explain the reduced affinity of syntaxin to munc18aS306D , S313D/phosphorylated munc18a ., Following in-silico insertion of the mutations ( See Methods ) into the wild-type structure , the mutant was simulated under the same conditions as the wild-type ( ∼35 ns , simulation M1 , Table 1 ) ., Strikingly , analysis of the fluctuations in the distance between the centers of mass of domains 3a and 1 indicated a marked decrease in the distance between the centers of mass of domains 3a and 1 ( Figure 4A ) ., Already in the first ∼3 ns of the simulation , the distance decreased from 3 . 9 nm to 3 . 3 nm , and during the rest of the simulation , the distance stabilized ( ∼3 . 5 nm ) exhibiting only further minor fluctuations ( Figure 4A ) ., Observation of the mutant dynamics showed that the decrease in the distance between the centers of mass of domains 3a and 1 represents a process of closure of the cavity between these domains ., The closure leads to the preferential stabilization of a distinct closed-cavity conformation of munc18aS306D , S313D , a conformation that probably cannot bind syntaxin via the cavity ., Further examination of the closure process shows that the conformational change in the protein includes a structural disruption in the area of the mutations , which is located in domain 3a , on the side opposite to the cavity ( Figure 4 , B–D ) ., Calculation of the average local RMSF ( Root Mean Square Fluctuations ) in the area of the mutations ( residues 306–313 ) showed a large increment of 28% to 120% in the specific RMSF values of these residues with respect to the wild-type values , indicating substantial movements of this region ( Figure 4E ) ., This structural disruption on one side of domain 3a might destabilize its overall structure , allowing the area adjacent to the cavity to move towards domain 1 , located on the other side ., ED analysis performed both using Dynatraj and by GROMACS for the most dominant motions of the munc18aS306D , S313D simulation ( Methods ) , demonstrated that the closing motion of the cavity ( Figure 5A ) occurs as part of the most dominant motion in the simulation ., The GROMACS-based ED analysis shows that the most dominant motion in the munc18aS306D , S313D simulation is twice the size of the main motion of the wild-type munc18a simulation and encompasses 37% of the total movement of the protein during the simulation ( Figure 5B ) ., To examine whether the phosphomimetic mutations can induce the closure of munc18a cavity also when the structure was already well-relaxed , In-silico phosphomimetic mutations were inserted into the well-relaxed structure of munc18a ( the structure obtained after 35 ns simulation of the wild-type , see Methods and Table 1 ) , and the structure was simulated from that point for another 35 ns ( Simulation M2 , Table 1 ) ., The phenomenon of cavity closure was clearly reproduced , but the time course of the process was different ( data not shown ) ., Comparison of the structures in the first and last frames of this simulation clearly illustrates two distinct conformations: the initial open-cavity conformation and the final closed-cavity conformation ( Figure 5 , C and D ) ., For comparison to the wild-type simulation ( Figure 1E ) , the change in the distance between residues Gly 26 in domain 1 and Glu 273 in domain 3a , during this munc18aS306D , S313D simulation , is presented ., The distance between these residues decreased during the last 20 ns of the simulation ( Figure S3 ) demonstrating again the closure of the cavity ., In addition , a video of the trajectory of this simulation , demonstrating the closure process is presented ( Video S1 ) ., To determine the relative stability of the structures that munc18aS306D , S313D samples during the simulation , and to identify the most stable and dominant structure in the mutant simulations , we had used another quantitative analysis tool for the simulations termed , cluster analysis ( 24 , Methods ) ., Briefly , Cluster analysis segments the structures that the protein samples during the simulation into sub-groups ( termed , clusters ) ., The structures are divided to clusters according to an adjustable RMSD ( Root Mean Square Deviations ) cut-off value that defines the variance between structures that populate the same cluster ( 24 , Methods ) ., Comparison of the cluster analyses performed for the phosphomimetic munc18aS306D , S313D and the wild-type munc18a simulations , using the same RMSD cut-off value , showed that munc18aS306D , S313D samples fewer conformations than the wild-type during the simulations , having less distinct clusters , 48 vs . 72 respectively ( Table 2 ) ., Moreover , the three largest clusters in the munc18aS306D , S313D simulation encompass about 27% of the total structures population compared with only 16% as determined in the wild-type munc18a cluster analysis ., Analyzing the size of the syntaxin-binding cavity in the three largest clusters of the munc18aS306D , S313D compared to the wild-type shows that the three largest clusters in the phosphomimetic munc18aS306D , S313D simulation demonstrated a smaller variance in the values of the distances between the centers of mass of domains 3a and 1 exemplifying that the size of the cavity is relatively unchanged compared to the size of the cavity in the wild-type three main clusters ., As detailed in Table 2 , the mean distance between the centers of mass of the two domains is significantly shorter for the phosphomimetic munc18aS306D , S313D illustrating that a closed-cavity conformation predominates in the phosphomimetic munc18aS306D , S313D three largest clusters ( Table 2 ) ., In summary , the cluster analysis shows that munc18aS306D , S313D samples less distinct conformations during the simulations ., The mutant is more rigid in the cavitys region than in the wild-type simulation and a closed-cavity conformation predominates ., In order to identify the driving force for the cavity closure process , we examined the energetic components ( Lennard-Jones LJ and electrostatic ) of munc18aS306D , S313D during the simulation time ., Inspection of the change in the energetic components of munc18aS306D , S313D shows that the closing motion of the protein was correlated with a decrement in the sum of the electrostatic and LJ energy components of the system indicating stabilization of the structure ( Figure 6 , A and B ) , and the formation of extra electrostatic and hydrophobic interactions ., Specifically in the cavity area , we monitored the time-dependent pattern of hydrogen bonds and found that three to five additional hydrogen bonds were formed during the simulations between residues located on both sides of the cavity ( Figure 6D ) ., The later is in contrast to the wild-type simulation , that during the same simulation time , the number of hydrogen bonds between domains 3a and 1 fluctuated between 0–2 ( Figure 6C ) ., The interactions between residues located on both sides of the cavity kept them in proximity and stabilized the closed-cavity conformation ., In addition to the Hydrogen bonds that were formed during the munc18aS306D , S313D simulation , LJ interactions between residues in domains 3a and 1 further stabilized the closed state ( Figure 6B and Figure 7 , A and B , residues in green and yellow ) ., Table 3 summarizes the interactions observed during the simulation between residues located on both sides of the cavity ., It should be noted that Arg39 9 and other munc18a residues that are essential for its interaction with syntaxin , were found to be involved in the inter-domain interactions , bringing both sides of the cavity together ., Similarly , Lys46 ( domain, 1 ) that is involved in the interaction with syntaxin forms an electrostatic interaction during the free munc18aS306D , S313D simulation ( M2 ) with Asp262 located in domains 3a ., Figure 7C depicts the decrease in the distance between Lys46 and Asp262 to ∼2 . 5 nm , as they approach each other during the simulation , forming a stable electrostatic interaction already after 5 ns of the simulation ( Figure 7 , C–E ) ., The analysis of the energetic components of the munc18aS306D , S313D system during the simulation shows that the mutant protein ( munc18aS306D , S313D ) is energetically-stabilized in the closed-cavity conformation in which residues on both sides of the cavity interact with each other ., Therefore , the binding of syntaxin to munc18aS306D , S313D requires breaking several intra-molecular electrostatic bonds and as a result might become energetically unfavorable ., We next investigated whether the closed-cavity conformation is reversible , whether it is induced directly by the insertion of the phosphomimetic mutations and whether the protein can regain its flexibility in the area of the cavity ., We removed the phosphomimetic mutations from the structure of the protein in the last frame of the munc18aS306D , S313D simulation and performed another simulation of 36 ns of this structure mutated back to the wild-type ( D306S , D313S ) ., This simulation showed that the back-mutated wild-type protein gradually regains its dynamic nature in the cavity area and the cavity starts to reopen ( Figure 8 , A and B ) ., The distance between the centers of mass of two regions adjacent to the cavity: residues 35–70 ( domain, 1 ) and residues 260–280 ( domain 3a ) increased from 1 . 8 nm to 2 . 2 nm during the 36-ns back-mutation simulation ( munc18aD306S , D313S , Figure 8A ) ., Next , we extended this analysis by looking at the relative motion of larger sections of the protein; measurement of the distance between the centers of mass of domains 3a and 1 during the munc18aD306S , D313S simulation indicated that the distance gradually increases from 3 . 4 nm up to 3 . 8 nm , reflecting reopening of the cavity ., The opening movement of the cavity was also observed by ED analysis; in Figure 8C , we present a porcupine plot of the fourth eigenvector of the dynamics demonstrating by the direction and length of the ‘needles’ a clear expansion of the cavity ., Finally , a straightforward superposition of domains 3a and 1 from the last frames in the simulations of munc18aS306D , S313D ( t\u200a=\u200a35 ns , red ) and munc18aD306S , D313S ( t\u200a=\u200a36 ns , blue ) indicates that the positions of domains 3a and 1 are further away from each other in the back-mutated munc18aD306S , D313S compared to the phosphomimetic munc18aS306D , S313D and similarly to the wild-type ( Figure 8D ) ., The results suggest that the phosphorylation/phosphomimetic mutations induce a closed-cavity conformation that can be reversed upon dephosphorylation/back-mutations of the protein ., Mutations of Ser 306 and Ser 313 to Ala in munc18a were shown to turn the protein to be non-phosphorylated and had no affect on syntaxin binding 14 , 25 ., To check the specificity of the closed-cavity conformation to the phosphomimetic mutations in these positions , another simulation ( 36ns long ) was performed , following the dynamics of the non-phosphorylated mutant munc18aS306A , S313A under the same conditions as in the previous simulations ., Measurement of the distance between the centers of mass of domains 3a and 1 during the munc18aS306A , S313A simulation shows that the distance was fluctuating between 3 . 5 to 4 . 2 nm ( Figure 8E ) , similarly to the fluctuations that were observed in the wild-type simulation ( Figure 1C ) ., We did not track any substantial movement of domains 3a and 1 towards each other , thus , no closure of the cavity was observed as was depicted in the phosphomimetic munc18aS306D , S313D simulations ., Analysis of the time-dependent change in the number of hydrogen bonds between domains 3a and 1 shows that the number of hydrogen interactions remained 0 or 1 during most frames of the simulation indicating that no new hydrogen bonds were formed during the simulation between residues located in domains 3a and 1 , in contrast to the phosphomimetic munc18aS306D , S313D simulations ( Figure 8F ) ., In summary , the mutant munc18aS306A , S313A did not adopt a closed-cavity conformation ( Figure 8G ) and the dynamics resembled that of the wild-type state ( Figure 8 , E–G ) indicating specificity of the closing phenomenon to the phosphomimetic mutations in these positions ., The current study reveals , for the first time , new conformations that munc18a can adopt when it is unbounded to syntaxin ., Based on a rigorous analysis of a comprehensive set of molecular dynamics simulations we were able to monitor the dynamics of the wild-type free munc18a in comparison to its mutant forms ( phosphomimetic , back-mutated and non-phosphorylated mutants ) , focusing on the structural changes that occur during the trajectories in the main syntaxin-binding site , the cavity between domains 3a and 1 ., We show that munc18a , in its syntaxin-unbounded form , is in a dynamic equilibrium between conformations varying in the size of its syntaxin-binding cavity located between domains 3a and 1 ., Specifically , we found that munc18a can adopt a stable conformation where its cavity , serving as the main syntaxin-binding site , is mostly blocked by inter-domain interactions ., This conformation is induced following in silico insertion of phosphomimetic mutations in positions 306 and 313 ( S306D , S313D ) ., We propose that the observed reduction in affinity of munc18a to syntaxin following phosphorylation or insertion of phosphomiemtic mutations as shown experimentally is a result of preferential stabilization of a conformation of munc18a where the syntaxin-binding site is less accessible for syntaxin ., This conformation of munc18a makes the binding of syntaxin less probable , and energetically and sterically unfavorable ., Our proposed mechanistic explanation is supported by a few studies carried out in the past that already speculated that munc18a might have additional distinct conformations different from the one that was resolved ( bound to syntaxin ) in the published crystal structure 9 , 12 ., Previous studies suggested that the munc18a conformations could be induced by interactions with other proteins , such as Rab , Rab effector or munc13 9 ., However , to the best of our knowledge , there is no other available resolved conformation ( i . e crystal structure ) of munc18a ., Another indication for the existence of several munc18a conformations is the putative binding site of the protein cyclin-dependent kinase 5 ( CDK5 ) in munc18a ., CDK5 has been shown to phosphorylate munc18a and to mediate the disassembly of the munc18a-syntaxin-1a complex , with the assistance of other proteins ., The site of CDK5-mediated phosphorylation in munc18a is located between domains 2 and 3 ( residue Thr574 ) ., In the crystal structure of the munc18a-syntaxin complex , this region of munc18a is buried in the protein and therefore inaccessible , indicating that CDK5 probably interacts with a different conformation of munc18a that was not determined yet 9 , 21 ., The closed-cavity conformation of munc18a is specifically induced by the phosphomiemetic mutations; however it is not exclusively present in this mutated form of the protein ., Molecular dynamics simulation of another mutated form of munc18a - munc18aF115E 13 showed that the introduction of this mutation induced closure of the cavity as well ( Figure 9 , A–B ) ., The closure was initiated by a dominant movement of domain 1 towards domain 3a ., These results suggest that the closed-cavity conformation can be driven by several types of mutations ., The tendency of the protein to form this conformation might be a general mechanism explaining the impaired binding of several mutated forms of munc18a to syntaxin ., The established hypothesis attributes the reduced affinity of the phosphorylated munc18a ( or the phosphomimetic mutant munc18aS306D , S313D ) to syntaxin to the local repulsion of syntaxin by the negative charges of the phosphates ( or glutamates ) in this region of munc18 ., This repulsion was suggested to reduce the compatibility and the overall affinity of the complex 14 ., The hypothesis presented in the current study , based on extensive molecular dynamics simulation and analyses , challenges this paradigm and suggests that the reduced affinity results from closure of the cavity of munc18a , making it inaccessible for syntaxin binding in this area ., Many key biological processes such as the synaptic processes 21 , 26 are regulated by protein phosphorylation ., In order to understand the effects of this process , it is essential to characterize specifically the structural changes induced by phosphorylation , leading to a change in the affinity of proteins to target proteins ., In this study , we followed structural changes that phosphorylation might induce and we were able to provide a novel mechanism for explaining experimental results showing reduced affinity between proteins ., As the potential of phosphorylation to induce substantial conformational changes in proteins was already shown in previous studies 27 , 28 , 29 , 30 , we suggest that the present conventional paradigm , explaining the reduced affinity of the phosphorylated munc18a to syntaxin as merely a local repulsive phenomenon , is rather simplified ., Efforts should be aimed at tracking the global dynamic conformational changes that occur in the phosphorylated munc18a or in other mutated forms of munc18a in attempt to resolve munc18a conformations and in particular the closed-cavity conformation ., All simulations performed were using the coordinates of munc18a crystal structure that were taken from the recently refined crystal structure of the syntaxin-1a-munc18a complex , determined by x-ray crystallography at a resolution of 2 . 6 Å 9 , 12 ., The crystal structure coordinates , taken from the Protein Data Bank ( PDB file: 3C98 . pdb ) , include 556 residues out of the 594 residues of the full sequence of munc18a: 6 residues ( 317–323 ) in domain 3a and 25 residues ( 506–531 ) in domain 2 have not been structurally resolved ., In addition , at both terminals; the first three residues of the N-terminal ( amino-acid residues 1–3 ) and residues 593–594 of the C terminal were not resolved as well ., Three simulations of wild-type munc18a were performed differing in the tools used for completion and structural prediction of the missing regions ., In simulations 1 and 2 , the missing residues were added to the structure and modeled using the Swiss-PDB program ( 16 , http://www . expasy . org/spdbv/ ) and in simulation 3 , the Rosetta software 17 was used for the completion and modeling as detailed below ., In the Swiss-PDB , an energy-minimizing computation was performed by the Swiss-PDB tool using Gromos96 implementation of the Swiss-PDBViewer following the addition of the residues ., All MD simulations presented in this article were performed using the GROMACS 4 . 0 suite of software 31 , using the GROMACS 53a6 force field 32 ., The protein was embedded in a dodecahedron box containing the SPC water molecules 35 , 097 molecules for the Swiss-PDB based structures ( 1 and, 2 ) and 34 , 972 molecules for the Rosetta-based structure ( simulation, 3 ) that was extended to at least 15 Å between the proteins structure and the edge of the box ., Assuming normal charge states of ionizeable groups corresponding to pH 7 , the net charge of munc18a structure is −4e ., Hence , 74 sodium and 70 chloride ions were added to the Swiss-PDB structure trajectory box at random positions , to neutralize the system at a physiological salt concentration of 100 mM ., Similarly , 73 sodium ions and 69 chloride ions were added to the Rosetta structure trajectory box ( simulation 3 ) ., The difference in ion numbers is a result of the difference in the number of water molecules ., Prior to the dynamics trajectory , internal constraints were relaxed by energy minimization ., Following this step , an MD equilibration run was performed under position restraints for 40 ps ., Then , unrestrained MD runs were initiated ., Two runs of 35 ns each were performed for the Swiss-PDB structure ( simulations 1 and, 2 ) and a single run of ∼35 ns for the selected Rosetta structure ( simulation 3 ) ., During the MD runs , the LINCS algorithm 33 was used in order to constrain the lengths of all bonds; the water molecules were restrained using the SETTLE algorithm ., The time step for the simulation was 2 fs ., The simulation was run under NPT conditions , using the Berendsen coupling algorithm to keep the temperature and pressure constant ( P\u200a=\u200a1 bar; τP\u200a=\u200a0 . 5 ps; τT\u200a=\u200a0 . 1 ps; T\u200a=\u200a300 K ) ., Van der Waals ( VDW ) forces were treated using a cut-off of 12 Å ., Long-range electrostatic forces were treated using the PME method ., The coordinates were saved every 1 ps ., Low-pass frequency filtering was performed on the simulations using the g_filter tool of GROMACS ., The amino acid sequence of the protein Sly1p was fed into I-TASSER ( iterative threading assembly refinement algorithm ) , a 3D protein structure prediction tool 34 , 35 , 36 in order to predict the full length structure of the protein ( 671 residues ) ., A partial structure of Sly1p-Sed5p complex crystal structure is available as well ( 1mqs . pdb , 23 ) ., One of the best-scored Sly1p model structure obtained by the I-TASSER was chosen as the starting coordinates for the Sly1p MD simulation ., The simulation was run for 15 ns under the same conditions and procedure as described for the munc18a ., Simulations of the phosphomimetic double-mutant munc18aS306D , S313D were carried out using the same procedure as described for the wild-type simulations ., The Swiss-PDB software was used for in silico replacement of Ser 306 and Ser 313 with glutamates ., The positions of the mutated residues were optimized and the overall structure was subjected to energy minimization performed by the Swiss-PDB software and then by the GROMACS suite ., More detai

Introduction, Results, Discussion, Methods

The Sec1/munc18 protein family is essential for vesicle fusion in eukaryotic cells via binding to SNARE proteins ., Protein kinase C modulates these interactions by phosphorylating munc18a thereby reducing its affinity to one of the central SNARE members , syntaxin-1a ., The established hypothesis is that the reduced affinity of the phosphorylated munc18a to syntaxin-1a is a result of local electrostatic repulsion between the two proteins , which interferes with their compatibility ., The current study challenges this paradigm and offers a novel mechanistic explanation by revealing a syntaxin-non-binding conformation of munc18a that is induced by the phosphomimetic mutations ., In the present study , using molecular dynamics simulations , we explored the dynamics of the wild-type munc18a versus phosphomimetic mutant munc18a ., We focused on the structural changes that occur in the cavity between domains 3a and 1 , which serves as the main syntaxin-binding site ., The results of the simulations suggest that the free wild-type munc18a exhibits a dynamic equilibrium between several conformations differing in the size of its cavity ( the main syntaxin-binding site ) ., The flexibility of the cavitys size might facilitate the binding or unbinding of syntaxin ., In silico insertion of phosphomimetic mutations into the munc18a structure induces the formation of a conformation where the syntaxin-binding area is rigid and blocked as a result of interactions between residues located on both sides of the cavity ., Therefore , we suggest that the reduced affinity of the phosphomimetic mutant/phosphorylated munc18a is a result of the closed-cavity conformation , which makes syntaxin binding energetically and sterically unfavorable ., The current study demonstrates the potential of phosphoryalation , an essential biological process , to serve as a driving force for dramatic conformational changes of proteins modulating their affinity to target proteins .

Protein phosphorylation plays a significant regulatory role in multi-component systems engaged in signal transduction or coordination of cellular processes , by activating or deactivating proteins ., The potential of phosphorylation to induce substantial conformational changes in proteins , thereby changing their affinity to target proteins , has already been shown but the dynamics of the process is not fully elucidated ., In the present study , we investigated , by molecular dynamics simulations , the dynamic conformational changes in munc18a , a protein that is crucial for neurotransmitter release and interacts tightly with the SNARE syntaxin-1 ., We further investigated the conformational changes that occur in munc18a when it is phosphorylated , reducing its affinity to syntaxin-1a ., The results of the simulations suggest that there is a conformational flexibility of the syntaxin-unbounded munc18a that allows changes in the shape of the syntaxin-1a binding cavity ., In silico insertion of phosphomimetic mutations into munc18a led to a reduction in the flexibility and closure of the syntaxin-binding site ., We suggest that the reduced affinity of phosphorylated munc18a to syntaxin-1a stems from the difficulty of syntaxin-1a to bind to the munc18a closed-cavity conformation , induced by the PKC phosphorylation of munc18a .

neuroscience/neuronal signaling mechanisms, neuroscience/theoretical neuroscience, biophysics/theory and simulation, biochemistry/theory and simulation

journal.pgen.1000556

General Rules for Optimal Codon Choice

The genetic code is redundant with most amino acids encoded by several synonymous codons ., In many genomes , some codons are favored over others by selection likely because they are translated more efficiently and accurately 1–5 ., The selectively favored codons tend to correspond to the most highly expressed tRNAs 6–9 ., Selection for the use of favored codons should be stronger for genes that are more highly expressed ., For this reason , highly expressed genes such as ribosomal genes or translation elongation factors use favored codons almost exclusively and exhibit very high levels of codon bias 6 , 10–13 ., In contrast , the identity of the codons used by many genes that are not highly expressed may be determined to a large extent by the nucleotide substitution patterns of the genome that are unrelated to natural selection at the level of translation ., Previous studies have demonstrated that the overall codon usage patterns of genomes can be predicted based solely on the nucleotide composition of their intergenic regions 14 , 15 ., Such studies were interpreted as showing that for most genes selection at the level of translation is only secondary in determining codon usage , as it is too weak to counteract the effects of biases in the patterns of nucleotide substitution that are experienced by the genome in general 14 , 15 ., The identity of selectively favored codons varies among organisms 16–18 ., For example , the favored codon for leucine in Escherichia coli and Drosophila melanogaster is CTG , in Bacillus subtilis TTA , in Saccharomyces cerevisiae TTG , and in Saccharomyces pombe CTT 18 ., The rules governing the identities of favored codons in different organisms remain entirely obscure ., One possibility is that the optimal codons are chosen randomly in evolution in a process akin to the frozen accident hypothesized to have occurred in the evolution of the genetic code 19 ., However , there are some serious difficulties with this possibility ., First , some optimal codon choices appear highly structured and counterintuitive ., For instance , in Drosophila all optimal codons are G or C ending ( majority are C ending ) while the genome is ∼65% AT rich on average 17 ., Even more problematic is the observation that the identity of optimal codons shifts in evolution quite readily ., This implies that the frozen accidents of optimal codon choice can become “unfrozen” at times and then after a period of time become frozen again but in a new state ., Such shifts would seem to require long periods of weak selection given that they would require a large number of genes to change at a large number of sites seemingly against the pressure of natural selection 1 ., One difficulty in gaining insight into this problem is that only few metazoans have clear selection-driven codon bias and the identity of favored codons in other organisms such as bacteria , archea and fungi have not yet been determined ., Here we identify the favored codons in 675 fully sequenced bacterial genomes , 52 archeal genomes and 10 fungal genomes ( Text S1 , S2 , S3 ) ., We demonstrate that , unlike in Drosophila , the identities of favored codons in bacteria , archea , and fungi correspond to the nucleotide content of the intergenic regions of each genome ., Thus , GC rich organisms tend to have GC rich favored codons while AT rich organisms tend to have AT rich favored codons ., This indicates that , unlike previously suggested , selection is not secondary in determining the codon usage patterns of genomes ., Rather , selection consistently acts in the same direction as the nucleotide substitution biases that determine the nucleotide content of genomes in general ., We further use the data in bacteria to demonstrate that once nucleotide substitution patterns are taken into account additional amino-acid specific rules determining the identity of favored codons become apparent ., Finally , our findings allow us to suggest a possible mechanism by which the identity of favored codons can change between genomes without necessitating prolonged periods of weak selection on the efficiency and accuracy of translation ., We begin by considering bacterial genomes ., A straightforward and widely used way to identify the favored codons is to ask which of the codons encoding a particular amino acid increase in frequency as genes become more biased in the choice of codons overall 13 , 17 , 20 , 21 ., Following this reasoning , for each of the 675 bacteria , we calculated the overall degree of codon bias for each gene using the effective number of codons ( Nc ) ( 22 , Materials and Methods ) ., Nc measures codon bias of a gene across all codon families without making any assumptions regarding the identity of optimal codons ., Values of Nc range between 20 , for extremely biased genes that use only one codon per amino acid , to 61 , for genes that use all synonymous codons equally ., A version of Nc , Nc was suggested by Novembre 23 ., Nc takes into account and adjusts for background nucleotide composition ., The intent of Nc is to define codons that are used unusually frequently given the background GC content of the considered protein coding sequence 23 ., For each of the 18 amino acids that are encoded by more than a single codon , we examined the correlation between the frequency of each of its synonymous codons in a gene and the Nc of the gene ., For each amino acid we identified the most favored ( optimal ) codon defined as the codon that showed both the strongest and statistically significant positive Spearman correlation with the overall level of codon bias ( P≤0 . 05/n , where n is the number of codons encoding the amino acid in question , Materials and Methods ) ., For some amino acids in some organisms we could find no favored codons ., The identities of the identified optimal codons , for each of the 18 amino acids , in each of the 675 bacteria are summarized in Table S1 ., Codon bias can be the result not only of selection but also of variation in the patterns of nucleotide substitution ., Thus , in order to demonstrate that the codons identified by our procedure are in fact selectively favored , it is necessary to show that variation in codon bias among genes within most genomes cannot be explained without the involvement of selection ., To do so , we conducted two tests ., First , we examined whether the most codon biased ( MCB ) genes are the most highly expressed genes ., Specifically , we asked whether ribosomal genes and translation elongation factors , which are often among the highest expressed genes 24 , 25 , are statistically significantly ( P<0 . 05 ) over-represented among the 100 MCB genes in each genome ( Materials and Methods ) ., We found that for 658 of the 675 bacterial genomes studied here this is indeed the case ( Table S2 ) ., For most of the bacteria in the study the P-value was much lower than 0 . 05 ( Table S2 ) ., This test might be weakened by imperfect annotations in some genomes ., Nevertheless , it does show that for the vast majority of bacteria the MCB genes are likely under the strongest selection for optimal codon usage ., In order to further demonstrate that codon bias in these genomes is not entirely due to variability in patterns of nucleotide substitutions unrelated to translational selection , we extracted in each genome the first 100 fourfold and twofold degenerate codons of each coding sequence ., We then replaced the third codon positions of these coding segments ( CS ) with 100 randomly selected nucleotides from the intergenic sequences adjacent to them , while maintaining to identity of the encoded amino acids ., This resulted in a set of intergenic control coding segment ( ICCS ) that maintain the protein sequences and nucleotide content patterns of the genome but remove the effects of selection on synonymous sites that we expect to see in the CS ., We calculated the level of codon bias of each of the ICCS and each of the CS and examined for each genome whether the 100 most codon biased CS are significantly more biased than the 100 most codon biased ICCS ( P≤0 . 05 , using a one-tailed Wilcoxon test ) ., We found that this is indeed the case for all but one of the 675 bacteria examined ., As in the previous test P-values were always much smaller than 0 . 05 ( Table S2 ) ., This further suggests that for the vast majority of organisms the optimal codons we identified are indeed likely to be selectively favored ., An examination of the identified optimal codons ( Table S1 ) led us to realize that there appears to be a relationship between the identity of optimal codons and intergenic GC content ., To examine this relationship systematically we classified the codons in each codon family into the most GC rich , the most AT rich , and those with intermediate GC content ( such codons exist only for Leucine and Arginine ) ., We gave a score of 1 to each GC rich codon , a score of −1 to each AT rich codon and a score of 0 to the intermediate codons ( Table S3 ) ., For each genome we summed the scores of its optimal codons and divided the sum by the number of codon-families for which we could identify the optimal codon ., Thus an organism that has only GC-rich optimal codons will receive a score of 1 while an organism that uses only AT-rich optimal codons will receive a −1 ., We plotted these scores against the intergenic GC contents of the genomes ( Figure 1A ) and found a clear correlation between the optimal codon GC score and intergenic GC content ( rspearman\u200a=\u200a0 . 88 , n\u200a=\u200a675 , P≪0 . 00001 ) ., In order to eliminate the possible effects of close taxonomic relationships between some of the analyzed bacteria , we repeated this analysis after randomly selecting a single representative from each bacterial genus ., The correlation between the optimal codon GC score and intergenic GC content ( Figure 1B ) remains highly significant ( rspearman\u200a=\u200a0 . 84 , n\u200a=\u200a263 , P≪0 . 00001 ) ., We repeated this analysis for the 52 archea ( Figure 2A and Table S4 ) and the 10 fungi ( Figure 2B and Table S5 ) ., We found that for both of these groups there are similar correlations between the intergenic GC content and the optimal codon GC score ( rspearman\u200a=\u200a0 . 73 , n\u200a=\u200a52 , P<0 . 00001 for archea , rspearman\u200a=\u200a0 . 74 , n\u200a=\u200a10 , P≤0 . 02023 for fungi ) ., Vicario et al . 17 found that D . melanogaster has only GC-rich optimal codons even though the nucleotide substitution patterns of its genome tend towards AT ., When we plot the optimal codon score for D . melanogaster ( calculated based on the optimal codons identified in Vicaro et al . 17 ) against the background GC content of D . melanogaster ( estimated in the same paper , based on the sequences of short introns 17 , Figure 2B ) , we find that for its low GC content Drosophila appears to be using a higher proportion of GC rich codons than any of the other three groups of organisms ., We also analyzed an additional metazoan , Caenorhabditis elegans , that has a lower optimal codon GC score and a lower GC content 26 than D . melanogaster ( Figure 2B ) ., However , there are not enough fully sequenced metazoan genomes with documented selection-driven codon bias to examine the relationship between optimal codon identity and nucleotide content in Metazoa ., It is important to note that there is no a priori reason why translationally favored codons should match the nucleotide content of intergenic DNA ., Previous studies have demonstrated a relationship between overall codon usage of genomes and their intergenic GC content 14 , 15 ., Because in these studies little attention was given to the inner-genome variation in the patterns of codon usage , these results were thought to indicate that selection makes only a weak contribution to creating codon biases , and that the major contributor to the codon bias phenomenon are genome-wide nucleotide substitution biases ., By identifying optimal codons and showing that their identity also tracks nucleotide content of intergenic regions we demonstrate that it is not that selection weakly affects codon bias , but rather that it appears to be consistently acting in the same direction as the nucleotide substitution biases of genomes ., In order to identify optimal codons , we used Nc , a measure of codon bias that corrects for variation in genomic GC content 23 ., Given our findings it is possible that by using this method we eliminated some of the signal wed expect to find ., For example , based on our findings we expect that the optimal codons in a GC rich genome should be GC rich ., Highly expressed genes will use optimal codons more and will be more codon biased and more GC rich ., Nc is expected to correct some of this effect out even though it is in fact true signal rather than noise ., Indeed when we identify optimal codons in bacteria using Nc , rather than Nc we find an even stronger correlation between the GC richness of optimal codons and the GC richness of intergenic sequences ( Figure S1 , rspearman\u200a=\u200a0 . 91 , n\u200a=\u200a675 , P≪0 . 00001 ) ., Interestingly we find that the same optimal codons are almost always identified using both Nc and Nc for genomes with intergenic GC contents higher than 40% ( Figure 3 ) ., However , for genomes with intergenic GC contents lower than 40% the same optimal codon is identified in only ∼50% of cases ., In addition we found that our ability to identify optimal codons is much reduced in AT rich genomes ., These two findings make sense if selection to use optimal codons is generally weaker for AT rich genomes than for GC rich genomes ., Indeed , many of the AT rich bacteria are endosymbionts that are known to be slow growing and in which selection for translation accuracy and efficiency is thought to be weaker 27 , 28 ., Even if genomes with GC contents below 40% , for which our ability to clearly identify optimal codons appears to be somewhat reduced are removed from consideration , the correlation between intergenic GC content and the optimal codon GC score remains highly significant ( rspearman\u200a=\u200a0 . 73 , n\u200a=\u200a366 , P≪0 . 00001 ) ., It thus appears that our finding of a relationship between intergenic GC content and the identity of optimal codons is robust to the possible misidentification of optimal codons in the AT rich genomes ., To learn more about the rules governing the identity of optimal codons we split all genomes into five groups based on their intergenic GC content ., We summarized the identities of the optimal codons in each group for the fourfold degenerate codon families , the codon families with three or six codons , and the twofold degenerate codon families in Figure 4 , Figure 5 , and Figure 6 respectively ., To be more certain of our assignment of optimal codons , we demanded that the same optimal codon be identified using both correlations with Nc and correlations with Nc ., If for a certain codon family in a certain genome one or both of these correlations resulted in the identification of no optimal codon , or if they both identified different optimal codons we classify the optimal codon as “none” ., Examining these figures allowed us to observe again that GC rich bacteria tend to use GC rich optimal codons while AT rich bacteria tend to use AT rich optimal codons ., However , these figures also demonstrate additional rules governing the identity of optimal codons in bacteria ., For example , among the fourfold degenerate codons ( Figure 4 ) , for high GC organisms , C is strongly preferred over G in the optimal codons of Threonine , and Glycine ., At the same time G appears to be preferred over C in the optimal codons of Proline , and Valine ., Our results are less clear for AT rich genomes , as in such genomes for more codon families in more organisms we could identify no clear optimal codon ., However , in such genomes , T appears to be preferred over A in the optimal codons of all fourfold degenerate codon families other than Proline ., Similarly interesting patterns can be seen for codon families with six members ( Figure 5 ) ., For Leucine , for example , in AT rich genomes the TTA codon is preferred among optimal codons ., This makes sense as this is the most AT rich codon encoding Leucine ., At the same time , for the optimal codons of GC rich bacteria the CTG codon is strongly preferred over the equally GC rich CTC codon ., A similar pattern appears for Arginine ., For AT rich genomes the optimal codon is most frequently the most AT rich codon ( AGA ) ., However , for GC rich genomes CGC is almost always selected over CGG ., Such family specific patterns are intriguing and require further study ., In a previous study 28 Rocha investigated codon bias from the tRNA perspective by analyzing the copy numbers of the tRNAs with different anticodons in different genomes ., Surprisingly , he found that the most frequent anticodons remain constant across different genomes and do not change with GC content ., Rocha observed that generally in the first anticodon position ( which will bind to the third codon position ) of twofold-degenerate amino acids , G is always more frequent than A while T is more frequent than C . He therefore expected to observe a preference for C or A in third codon positions of these codon families over G and T 28 ., We observe that similarly to other codon families the tendency of organisms to use the more AT rich or GC rich optimal codon out of the two possible twofold degenerate codons depends on intergenic GC content ( Figure 6 ) ., However , for codon families that can end in either G or A ( Gln , Glu and Lys ) the shift from using the more AT rich optimal codons to using the more GC rich optimal codons tends to occur at higher GC contents , compared to the codon families that end in either C or T ( Asn , Asp , Cys , His , Phe and Tyr ) ., This means that more organisms use the C or A ending codons as expected from Rochas results ., For many organisms only a single tRNA exists for a certain codon family ., It is therefore clear that tRNA modifications and wobble rules are involved in allowing a single tRNA to bind different codons ., These wobble rules and modifications may be different in different organisms ., Such differences made it difficult for Rocha to define expectations as to which codons would be best recognized by the most frequent anticodons in each organism for codon families with more than two members 28 ., We could therefore not compare the results of Rocha to our results for such codon families ., Our results not only provide a clear set of rules governing the identity of the favored codons , they also provide a possible mechanism by which this identity can shift between organisms ., Variation in GC content across genomes implies shifts in nucleotide content ., The pattern we found implies that such shifts in nucleotide content are accompanied by shifts in the identity of favored codons ., Let us consider a scenario in which a genome begins shifting towards a different global GC content that does not match the GC content of its favored codons ., After a while , genes that are not under strong selection at the level of translation will start using codons that correspond to the new GC content of the genome ., While , individually these genes may not be expressed highly enough to be under strong selection for the use of favored codons , together they may affect the efficiency of translation substantially ., For this reason it may become advantageous for the tRNAs that correspond to these newly frequent codons to increase their expression ., While Rocha has shown that the identity of the tRNA with the highest copy number does not tend to change much between bacteria 28 , this can be achieved by increasing the transcription of a certain anticodon tRNA , or through regulation of tRNA modifications ., Following this increase , the highly expressed genes will be free to start using the codons that correspond more to the GC content of the genome ., This will be encouraged by the new pattern of nucleotide substitutions of the genome and should eventually remove the selection for the high expression of the tRNAs that recognize the old favored codons ., As a result after a time new favored codons may emerge that correspond to the nucleotide content of the genome ., In order to prove such a scenario it will be necessary to carefully examine shifts in nucleotide content and in the identity of optimal codons across a bacterial phylogenetic tree ., In such a way it may be possible to ask whether changes in the identity of optimal codons indeed follow changes in nucleotide content ., This analysis is beyond the scope of this paper and so it is important to note that the scenario we suggest here for shifts in optimal codon usage is hypothetical ., This scenario is intriguing however as , if true , it explains how the identity of favored codons can shift without requiring a prolonged period of weakened selection ., Furthermore , this scenario suggests that while selection for the use optimal codons is strongest for a specific set of highly expressed genes , the identity of the optimal codons is in fact determined largely by the majority of genes , on which selection is much weaker ., The codon bias phenomenon has been studied for decades ., Yet , basic questions regarding this phenomenon remain unanswered ., Here , we provide an insight into one such basic open question: What determines the identity of the codons favored by selection for translation accuracy and efficiency in different genomes ., We show that in all three kingdoms of life the identity of the favored codons matches the nucleotide content of the intergenic regions of each genome ., Furthermore , once the relationship between the identity of favored codons and nucleotide content is taken into account additional amino-acid specific rules determining the identity of favored codons come to light ., We then use our findings to provide a possible answer to a second open question: how can the identity of favored codons shift in evolution and do such shifts require prolonged periods of weakened selection ?, Our findings allow us to suggest a scenario for shifts in the identity of favored codons that does not require a weakening of selection ., The completed genomic sequences and coding sequence annotaions of the 675 bacteria , 52 archea , and 10 fungi were downloaded from the NCBI FTP server ., ( ftp://ncbi . nlm . nih . gov ) ., For each of the fully sequenced bacteria , archea and fungi used in the study ( Text S1 , S2 , S3 ) we extracted the DNA coding sequences of all the annotated proteins ., For each protein in each genome we calculated the effective number of codons ( Nc 22 ) ., Nc , measures the overall codon bias of a gene across all codon families 22 ., The measure does not make any assumptions regarding the identity of the optimal codons ., Values of Nc range between 20 , for extremely biased genes that use only one codon per amino acid , to 61 , for genes that use all synonymous codons equally 22 ., Since the estimation of Nc is problematic for short sequences , we removed from consideration coding sequences shorter than 50 codons ., In order to further account for sensitivity to sequence length , we used the version of Nc supplied by Novembre as part of his ENCprime package that corrects for sequence length 23 ., Nucleotide content is also expected to affect Nc ., We therefore also used a version of Nc , Nc which was developed by Novembre and which corrects for nucleotide content 23 ., In order to identify optimal codons for a specific genome we calculated for each codon its frequency within its codon family in all of the annotated coding sequences in each genome ., We then calculated the correlation between the frequency of each codon within each gene and the overall codon bias ( once using Nc and once using Nc 23 ) of that gene ., We removed from consideration genes in which the codon family appeared less than 10 times ., The optimal codon for each codon family was defined as the codon that showed the strongest and significant negative correlation with the Nc or Nc of the gene ., To be considered significant a correlation had to have a P-value smaller or equal to 0 . 05/n , where n is the number of codons in the codon family ., In such a way we correct for the fact that we performed more comparisons for more degenerate codon families ., Spearman correlations were performed using the R statistical package ., In order to randomly select a single member of each bacterial genus , bacteria sharing a genus name ( i . e . Escherichia , or Mycobacterium ) were grouped and a single member of each group was randomly selected ., For each genome , we counted how many of the 100 most biased ( lowest Nc ) genes are annotated as “ribosomal” or “elongation factor” ., We then randomly selected 100 of the remaining genes in the genome and counted how many of these random genes are annotated as ribosomal genes or elongation factors ., We repeated this randomization 1000 times and calculated the P-value that tells us in how many of these random samples does an annotation of “ribosomal” or “elongation factor” appear as often or more often than for the most biased genes ., We say that ribosomal genes and elongation factors are significantly over represented among the 100 most biased genes if this P-value is lower or equal to 0 . 05 ., To create the intergenic control coding sequences ( ICCS ) we used the following strategy for each of the 675 genomes ., I ) We extracted the first 100 four-fold degenerate and two-fold degenerate codons of each protein coding gene ., We removed from consideration genes that had less than 100 two-fold and four-fold degenerate codons ., II ) For each protein coding gene we extracted its two adjacent intergenic sequences ., We concatenated both adjacent intergenic sequences ( the 5′ and the 3′ intergenic sequences ) and selected a 100 base pair segment of this sequence at random ., We shuffled the order of the nucleotides of these intergenic segments randomly ., We removed intergenic regions shorter than 50 bases and if for a gene there was not at least 100 bases of adjacent intergenic region , we removed that gene from consideration ., III ) We created ICCS using the real coding sequences as a backbone and replacing the third codon positions , based on the shuffled adjacent intergenic sequences , while maintaining the encoded protein sequence ., For example if in the real protein at the tenth position we have a Valine encoded by the four-fold degenerate codon GTG and the shuffled segment of the adjacent intergenic sequence has a T in the tenth position , our ICCS will have a GTT in the tenth codon position ., In the case of a two-fold degenerate codon such as the Lysine codons AA ( A/G ) , we selected AAG if the corresponding intergenic position contained either a G or a C and AAA if the corresponding intergenic position contains an A or a T . At the end of this process we obtained for each genome two sets of coding segments of a consistent length; the “real” coding sequences ( CS ) and the ICCS ., Both of these encode exactly the same proteins ., The third codon positions of the ICCS reflect the composition of the real genes adjacent intergenic regions .

Introduction, Results/Discussion, Materials and Methods

Different synonymous codons are favored by natural selection for translation efficiency and accuracy in different organisms ., The rules governing the identities of favored codons in different organisms remain obscure ., In fact , it is not known whether such rules exist or whether favored codons are chosen randomly in evolution in a process akin to a series of frozen accidents ., Here , we study this question by identifying for the first time the favored codons in 675 bacteria , 52 archea , and 10 fungi ., We use a number of tests to show that the identified codons are indeed likely to be favored and find that across all studied organisms the identity of favored codons tracks the GC content of the genomes ., Once the effect of the genomic GC content on selectively favored codon choice is taken into account , additional universal amino acid specific rules governing the identity of favored codons become apparent ., Our results provide for the first time a clear set of rules governing the evolution of selectively favored codon usage ., Based on these results , we describe a putative scenario for how evolutionary shifts in the identity of selectively favored codons can occur without even temporary weakening of natural selection for codon bias .

Codon bias is a long recognized and long studied biological phenomenon ., Yet several basic questions regarding codon usage remain unresolved ., Here , we address one such basic open question: the identity of the codons that are favoured by selection for translation accuracy and efficiency varies greatly and , at first glance , idiosyncratically among genomes ., What are the rules governing the identity of favoured codons in the different genomes ?, We systematically identified the optimal codons of 675 bacteria , 52 archea , and 10 fungi ., Using these data , we show that universally across all bacteria , archea , and fungi the identity of the favoured codons tracks the nucleotide content of the genome as a whole ., Once the effect of nucleotide content on selectively favored codon choice is taken into account , additional , until now unknown , universal amino acid specific rules governing the identity of selectively favored codons become apparent ., Finally , we use our findings to offer a plausible scenario as to how the identity of optimal codons can shift between genomes by tracking the nucleotide patterns of the genome and without necessitating a reduction in selection .

genetics and genomics/genomics, genetics and genomics/microbial evolution and genomics, evolutionary biology/microbial evolution and genomics, molecular biology/translation mechanisms, microbiology/microbial evolution and genomics, evolutionary biology/genomics, molecular biology/bioinformatics, evolutionary biology/bioinformatics, computational biology/genomics, evolutionary biology, molecular biology/translational regulation, genetics and genomics, genetics and genomics/bioinformatics

journal.pcbi.1000680

Self versus Environment Motion in Postural Control

Our visual system senses the movement of objects relative to ourselves ., Barring contextual information , a car approaching us rapidly while we stand still may produce the same visual motion cues as if we and the car were approaching each other ., The nervous system thus needs to deal with this problem of ambiguity which will be reflected in the way we control our body posture 1–3 ., Consequently , neuroscientists have extensively studied such situations ., In such studies , a subject typically stands in front of a visual display and postural reactions to varied movements of the displayed visual scene are measured 4–11 ., Even in the absence of direct physical perturbations , subjects actively produce compensatory body movements in response to the movement of the visual scene ., This indicates that subjects attribute part of the visual motion to their own body while they resolve the ambiguity in visual stimuli ., Here we constructed a Bayesian attribution model ( Fig . 1A ) to examine how the nervous system may solve this problem of sensory ambiguity ., This model shows that optimal solutions will generally take on the form of power laws ., We found that the results from experiments with both healthy subjects and patients suffering from vestibular deficits are well fit by power laws ., The nervous system thus appears to combine visual and physical motion cues to estimate our body movement for the control of posture in a fashion that is close to optimal ., To test our Bayesian attribution model , we considered data from two published experiments with healthy subjects 4 , 5 as well as a new experiment we performed to cover the range of visual scene velocities that are relevant to the model predictions ., Any purely linear model , for example a Kalman controller , predicts that the gain of the postural response , which is the influence of visual scene motion on the amplitude of postural reactions , remains constant ., For these datasets , however , the gain of the postural response decreased with increasing velocities of visual scene motion ( Fig . 1C , 2A and 2C; slope\u200a=\u200a−0 . 78±0 . 15 s . d . across datasets , p<0 . 005 ) ., At low velocities , the gain was close to one which would be expected if the nervous system viewed the body as the sole source of the visually perceived motion ., At higher velocities though , the gain decreased which would be expected if the nervous system no longer attributed all of the visually perceived motion to the body ., The nervous system thus does not appear to simply assume that visually perceived motion can be fully attributable to the body ., To explain this nonlinear influence of visual scene velocity on the postural response , we constructed a model that describes how the nervous system could solve the problem of sensory ambiguity ( Fig . 1A ) ., The nervous system can combine visual cues with physical motion cues , such as vestibular and kinesthetic inputs , to estimate our body movement 12–16 ., However , our sensory information is not perfect and recent studies have emphasized the importance of uncertainty in such cue combination problems 17–19 ., Visual information has little noise when compared with physical motion cues 20 ., However , it is ambiguous as it does not directly reveal if the body , the environment or both are the source of the visually perceived movement ., In comparison to visual cues , physical motion cues are typically more noisy but they are not characterized by the same kind of ambiguity ., For these reasons , the nervous system can never be certain about the velocity of the body movement , but can at best estimate it using principles of optimal Bayesian calculations 21–25 ., To solve the ambiguity problem , the model estimated the velocity of bodys movement for which the perceived visual and physical motion cues were most likely ., Such estimation is only possible if the nervous system has additional information about two factors: typical movements in the environment and typical uncertainty about body movements 26 ., For example , if a car sometimes moves fast and our body typically moves slowly , then the nervous system would naturally attribute fast movement to the car and slow movement to our body ., Indeed , recent research has indicated that human subjects use the fact that slow rather than fast movements are more frequent in the environment when they estimate velocities of moving visual objects 27–30 ., This distribution , used by human subjects , is called a prior ., Following these studies our model used a sparse prior for movements in the visual environment , that is a prior which assigns high probability to slower movements in the environment and low probability to faster movements in the environment 29 ., We wanted to estimate the form of the prior over body movements from our experimental data ., We found that when subjects maintained an upright body posture while viewing a stationary visual scene , the distribution of their body velocity was best described by a Gaussian ( Fig . 1B ) ., Therefore , we used a Gaussian to represent the prior over body velocity ., The attribution model derives from five assumptions ., We assume the above sparse prior over movements in the environment 29 ., We assume that for the movement of visual environment that is vivid and has high contrast , visual cues provide an estimate of relative movement that has vanishing uncertainty ., We assume a Gaussian for the prior over body movement ( see Methods for details ) ., We also assume a Gaussian for the likelihood of the physical motion cues which indicate that the body is not actually moving and is close to the upright position ., Lastly we assume that visual scene velocities are large in comparison to the uncertainty in our detection of our body movements 31 ., Under these assumptions , we can analytically derive that the best solution has a gain that varies as a power law with the visual scene velocity ( see Methods for details ) ., We thus obtain a compact , two parameter model that predicts the influence of visual perturbations on the estimates of body movement ., Our attribution model calculates how the nervous system should combine information from visual and physical senses to optimally estimate the velocity of body movement ., However , the nervous system does not need to solve its problems in an optimal way , but may use simple heuristics 32 ., We thus proceeded to compare the attribution model with other models in its ability to explain the decrease in the gain of postural reactions ., For this purpose , we compared models using the Bayesian Information Criterion ( BIC ) which is a technique that allows the comparison of models with different numbers of free parameters 33 ., For the gains observed in our experiment ( Fig . 1C ) , the Bayesian model had a BIC of −7 . 5±1 . 84 ( mean BIC±s . e . m . across subjects ) ., We found that a linear model that predicted constant gain of postural reactions could not explain the observed results ( BIC\u200a=\u200a1 . 08±0 . 59 , p<0 . 001 , paired t-test between BIC values ) ., We then considered a model in which the amplitude of postural response increased logarithmically up to a threshold stimulus velocity and then saturated ., This model predicted the response gains observed at higher scene velocities more poorly than the attribution model ( BIC\u200a=\u200a−3 . 45±0 . 99 , p<0 . 05 ) ., We also tested another model in which the gain was initially constant but decreased monotonically with increasing visual scene velocities ., This model did worse at predicting the gain than the Bayesian model ( BIC\u200a=\u200a5 . 82±0 . 04 , p<0 . 001 ) ., Thus , the Bayesian model that estimated the velocity of the body movement best fit the available data ., Another way of applying the attribution model is to human behavior in disease states ., Patients with bilateral vestibular loss have vestibular cues of inferior quality 34 ., The attribution model suggests that these patients postural behavior would be based more strongly on visual feedback and that their gain should decrease less steeply as a function of stimulus velocity ., Indeed , patients tested in previous studies 4 , 5 showed a greater influence of vision on posture and gains that decreased less steeply ( Fig . 2B , 2D slope\u200a=\u200a−0 . 22±0 . 1 s . d . across datasets , p<0 . 005 ) when compared with healthy subjects , a phenomenon that is well mimicked by the attribution model ., The postural behavior of patients showed marked differences from that of healthy subjects 4 ., At low visual scene velocities , patients and healthy subjects had similar gain values ., However , at higher scene velocities , patients exhibited larger gains when compared with healthy subjects ., If the postural responses in patients were only influenced by elevated noise in the vestibular channels , the gain should vary in a similar manner at all visual scene velocities ., That is , the gain of patients should be higher than healthy subjects at all visual scene velocities ., However , increased gain of patients only at higher scene velocities alludes to a change in how patients interact with large movements in the visual environment ., In our model , the best fit to the data of healthy subjects corresponds to a prior of about , while the fit to the patients data corresponds to a prior of ( see Methods for details ) ., It would thus appear that rather than a sparse prior , patients have a prior that is closer to a Gaussian ., It is not surprising that patients interact with the extrinsic environment differently from healthy subjects ., In fact , such patients can develop space and motion phobia particularly in situations where there is a conflict between visual and vestibular cues and may actively avoid such conflicting environments 35–37 ., Our model fits suggest that patients may seek out environments that are devoid of fast movement of large field stimuli ., This is a prediction that can be tested in future research , for example by equipping patients with telemetric devices with cameras that record velocities in their environment ., When we visually perceive displacement between ourselves and the environment , it may be caused by the movement of our body , movement of the environment , or both ., In this paper , we have presented a model that formalizes how the nervous system could solve the problems of both ambiguity ( self vs environment ) and noise in perceived sensory cues ., We suggest that the nervous system could solve these problems by estimating the movement of the body as per the principles of Bayesian calculations ., We found that the model can account for the gain of postural responses when both healthy subjects and patients with vestibular loss viewed movement of a visual scene at various velocities ., Importantly , our model predicts a simple functional form , power laws , as the best cue combination strategy ., This makes it easy to test predictions without having to implement complicated estimation procedures ., Postural stabilization during stance is a two-step process comprised of estimation and control and in this paper we have only focused on estimation ., Computational models in the past have examined how the nervous system implements this two-step process and have explained a wide range of data 5 , 8 , 34 , 38–40 ., In these models , cue combination was implemented as a change in the sensory weights 8 , 14 and incorporation of nonlinear elements 5 , 34 , 39 ., The control aspect was typically implemented by approximating the human body to a single- or double-link inverted pendulum , linearized about the upright position ., These models are powerful tools for describing human behavior as they can describe changes both in amplitude and in phase as stimulus parameters are varied ., As current models already largely separate postural control into an estimation part and an estimation-dependent control part , it would be straightforward to combine our estimation system with a dynamical control system ., When the control strategy is linear then any nonlinearity has to come from the estimation stage ., If control is nonlinear , then there will be interactions between nonlinearities in estimation and control ., Our attribution model exclusively focuses on the source of the nonlinearity inherent in the estimation process ., If control is nonlinear then parts of the effects we describe here may be due to nonlinearities in control and parts due to estimation ., The influence of the nonlinearity in each could be tested by experiments that decouple estimation from control ., Importantly , though past models have assumed nonlinearities in the estimation part of the model 5 , 8 , 14 , 34 , we give a systematic reason for why this nonlinearity should exist and why it should have approximately the form that has been assumed in past studies ., To test our model , we used visual scene velocities that were in all likelihood , larger than the uncertainty in our perception of our body sway ., Our model analytically demonstrates that for these velocities , the gain is proportional to a power law over the visual scene velocity ., This leads us to question how the model would perform over a different range of scene velocities ., There could be two possible solutions to this question ., Firstly , the nervous system may use power laws to estimate the gain of the postural responses at all visual scene velocities ., However , this solution does not make any sense as it would predict infinite gain near zero velocity ., Secondly , at very small scene velocities , the nervous system may adopt a strategy different from power laws ., We argue in favor of the latter possibility ., We predict that at scene velocities that are close to our perceptual threshold of body sway , our attribution model would fail to explain the gain of postural responses ., In this situation , the Taylor series expansion that we use can no longer be truncated after the first term and quadratic elements need to be considered ( see Methods ) ., The attribution model will predict power laws if the prior over visual movements is locally smooth within the range of uncertainty in our perception of body movement ., Ambiguity is a central aspect of various cue combination problems in perception and motor control and here we have characterized its influence on postural control ., The success of the attribution model in predicting human behavior suggests that the nervous system may employ simple schemes , such as power laws , to implement the best solution to the problem of sensory ambiguity ., While recent research indicates how the nervous system could integrate cues that have Gaussian likelihoods 41 or priors 29 , little is known about the way non-Gaussian probability distributions may be represented at the neuronal level ., The nonlinearity in cue combination that we observed here raises interesting questions about the underlying neural basis of these computations in the nervous system ., Ten healthy young adults ( age: 20–34 years ) participated in our experiment ., Subjects had no history of neurological or postural disorders and had normal or corrected-to-normal vision ., Subjects were informed about the experimental procedures and informed consent was obtained as per the guidelines of the Institutional Review Board of Northwestern University ., A computer-generated virtual reality system was used to simulate the movement of the visual environment ., Subjects viewed a virtual scene projected via a stereo-capable projector ( Electrohome Marquis 8500 ) onto a 2 . 6 m×3 . 2 m back-projection screen ., The virtual scene consisted of a 30 . 5 m wide by 6 . 1 m high by 30 . 5 m deep room containing round columns with patterned rugs and painted ceiling ., Beyond the virtual scene was a landscape consisting of mountains , meadows , sky and clouds ., Subjects were asked to wear liquid crystal stereo shutter glasses ( Stereographics , Inc . ) which separated the field sequential stereo images into right and left eye images ., Reflective markers ( Motion Analysis , Inc . ) attached to the shutter glasses provided real-time orientation of the head that was used to compute correct perspective and stereo projections for the scene ., Consequently , virtual objects retained their true perspective and position in space regardless of the subjects movement ., Subjects stood in front of the visual scene with their feet shoulder-width apart and their arms bent approximately 90° at their elbows ., The location of subjects feet on the support surface was marked; subjects were instructed to stand at the same location at the beginning of each trial ., During each trial , subjects were instructed to maintain an upright posture while looking straight ahead at the visual scene ., Subjects viewed anterior-posterior sinusoidal oscillation of the scene at 0 . 2 Hz and 5 peak amplitudes: 1 , 3 , 25 , 100 and 150 cm ., The visual scene thus oscillated at peak velocities of 1 . 2 , 3 . 7 , 31 , 125 and 188 cm/s , respectively ., Subjects viewed each scene velocity once for a period of 60 s in random order ., In addition , subjects experienced a control condition in which they viewed the stationary visual scene ., Reflective markers were placed on the shoulder joints and fifth lumbar vertebra ., A six infra-red camera ( Motion Analysis , Inc . ) system was used to record the displacement of the reflective markers at 120 Hz ., Displacement data of the markers was low pass filtered using a fourth order Butterworth digital filter with a cutoff at 6 Hz ., Trunk displacement , chosen as an indicator of postural response , was calculated using the displacement of the shoulder and spine markers 42 ., Amplitude of the postural response at the frequency of the visual scene motion , that is 0 . 2 Hz , was calculated in a manner adopted in neurophysiological studies 43 , 44 ., A sinusoid of frequency 0 . 2 Hz was chosen ., The amplitude and the phase of this sinusoid were estimated such that the squared error between the trunk displacement and the fitted sinusoid was minimized ., The amplitude of the fitted sinusoid thus indicated the amplitude of the postural response at the frequency of the visual scene motion ., The gain of the trunk displacement was then computed as the ratio of the amplitude of the fitted sinusoid to the amplitude of visual scene motion ., We formalize the ambiguity problem encountered by the nervous system with the help of a graphical model ( Fig . 1A ) ., The visual scene projected on the display sinusoidally oscillates with a velocity , while the velocity of the body movement is ., represents a noisy estimate of body velocity that is sensed by vestibular and kinesthetic signals ., On the other hand , represents the visually perceived velocity of the relative movement between the body and the environment ., Our Bayesian model combines the sensory cues , and , to obtain the best estimate of body velocity , ., As the amplitude of postural reactions are influenced by subjects perceived body movement 2 , 45 , we assume that the nervous system produces body movements proportional to the estimated body velocity ., Using Bayes rule we obtain: ( 1 ) We assume that the visual and physical channels are affected by independent noise ., Therefore , we get: ( 2 ) We estimated the form of the prior over body velocity , , from our data ., In our experiment , subjects experienced a control condition where they maintained upright body posture when viewing a stationary visual scene ., We computed the average velocity of the trunk displacement across all subjects 42 ., We then computed a histogram of the body velocity and observed that a Gaussian best described the distribution of body velocity ( Fig . 1B ) ., We , therefore , assumed that subjects prior over body movements would be represented by a Gaussian ., While the actual body movements during unperturbed stance are large , the more relevant information is the underlying uncertainty in our perception of our body sway ., The uncertainty in our perception of our body sway is much narrower than the width of the distribution of the actual body velocities seen in Fig . 1B 31 ., This is because for small body movements during normal stance , the nervous system may not constrain the body even though it is aware that the body has moved away from the upright position 46 ., As the likelihood of the physical motion cues , , can also be represented by a Gaussian , we define: ( 3 ) Here represents a Gaussian for the combined prior-and-likelihood with variance ., The likelihood of visual motion cues , , is given by: ( 4 ) Humans expect visual objects in their environment to move slowly more often than rapidly ., This bias has been interpreted as a prior in a Bayesian system ., We therefore use a sparse prior of the functional form 29 ., As visual cues are precise when compared with other sensory cues , we assume that the variance of the noise in visual channels is negligible ., Furthermore , in the experimental situations we model here , movement of the visual display is relatively fast in comparison to the typical uncertainty subjects may have about their body velocity 31 ., We therefore marginalize over all possible to obtain: ( 5 ) Substituting Equations 3 and 5 in Equation 2 , we get: ( 6 ) In the situations we model here , subjects stood on a stationary support surface ., Thus , the physical motion cues indicated that the body was close to the upright position; that is ., We therefore get: ( 7 ) For body movements close to the upright position , we can use a Taylor series expansion and drop elements of order 2 and higher to solve the second exponent term in Equation 7 ., We thus get: ( 8 ) Importantly , when visual scene velocities are large in comparison to the typical uncertainty in our perception of our body movements , then the maximum of the ( visual ) environmental prior is far away ., As that is far away and the uncertainty in the perception of body movement is narrow , the approximation that only zero- and first-order terms will be important is well justified ., The resulting estimate represents a Gaussian with a maximum at: ( 9 ) Thus , the best estimate of the body velocity , as long as the environment velocity is large in comparison to the typical uncertainty in our perception of body sway , can be represented as a power law over the environment velocity ., ( 10 ) Our model thus has two free parameters: the variance of the noise in prior-and-likelihood of the physical motion cues; , the parameter associated with the prior over environmental velocities ., We fitted the model ( Equation 10 ) to the experimentally measured gain of healthy subjects tested in our experiment ., We then fitted the model to the experimentally measured gains of healthy subjects and vestibular-deficient patients tested in previous studies 4 , 5 ., We chose the model parameters such that the mean squared error between the model fits and the experimental data was minimized ., For healthy subjects , the values of free parameters were as follows: =\u200a0 . 34 and =\u200a1 . 32 ( for subjects tested in our experiment ) ; =\u200a0 . 37 and =\u200a1 . 28 ( for subjects tested by Peterka et al . ) ; =\u200a0 . 33 and =\u200a1 . 03 ( for subjects tested by Mergner et al . ) ., For vestibular-deficient patients , the values of free parameters were as follows: =\u200a0 . 46 and =\u200a1 . 7 ( for patients tested by Peterka et al . ) ; =\u200a0 . 524 and =\u200a1 . 85 ( for patients tested by Mergner et al . ) ., To test the performance of our attribution model , we compared it with other simple models of postural control ., We first considered a linear model in which the gain of postural response was constant ( Fig . 3A ) ., This model had a single free parameter , the gain , and had a functional form: We then developed a nonlinear model that incorporated the findings of published empirical and modeling studies ., The amplitude of postural reaction is known to increase logarithmically with the visual scene velocity until it saturates 4 ., We tested a model of the functional form ( Fig . 3B ) :Here represents the visual scene velocity at which saturation occurs ., We chose =\u200a2 . 8 cm/s based on the previous findings in the literature 5 ., This model had a single free parameter , the slope , ., We considered another model where the gain of postural reactions is initially constant , but decreases monotonically with increasing visual scene velocities ( Fig . 3C ) ., This model , with three free parameters , has the functional form: We fitted these models to the gain values of each subject tested in our experiment ., We computed the Bayesian Information Criterion for each subject and for each model ., We then performed a paired t-test to determine if there was a significant difference in the BIC values for different models .

Introduction, Results, Discussion, Methods

To stabilize our position in space we use visual information as well as non-visual physical motion cues ., However , visual cues can be ambiguous: visually perceived motion may be caused by self-movement , movement of the environment , or both ., The nervous system must combine the ambiguous visual cues with noisy physical motion cues to resolve this ambiguity and control our body posture ., Here we have developed a Bayesian model that formalizes how the nervous system could solve this problem ., In this model , the nervous system combines the sensory cues to estimate the movement of the body ., We analytically demonstrate that , as long as visual stimulation is fast in comparison to the uncertainty in our perception of body movement , the optimal strategy is to weight visually perceived movement velocities proportional to a power law ., We find that this model accounts for the nonlinear influence of experimentally induced visual motion on human postural behavior both in our data and in previously published results .

Visual cues typically provide ambiguous information about the orientation of our body in space ., When we perceive relative motion between ourselves and the environment , it could have been caused by our movement within the environment , or the movement of the environment around us , or the simultaneous movements of both our body and the environment ., The nervous system must resolve this ambiguity for efficient control of our body posture during stance ., Here , we show that the nervous system could solve this problem by optimally combining visual signals with physical motion cues ., Sensory ambiguity is a central problem during cue combination ., Our results thus have implications on how the nervous system could resolve sensory ambiguity in other cue combination tasks .

neuroscience/behavioral neuroscience, neuroscience/cognitive neuroscience, computational biology/computational neuroscience

journal.pntd.0004578

Improved PCR-Based Detection of Soil Transmitted Helminth Infections Using a Next-Generation Sequencing Approach to Assay Design

Estimated to infect more than one quarter of the world’s total population , the soil transmitted helminths ( STH ) are responsible for profound morbidity and nutritional insufficiency 1 ., Concentrated in the world’s most impoverished locations , the results of widespread infection on economic capacity are equally burdensome ., Yet despite the scope of such disease , and continuing efforts to improve treatment programs and integration strategies , reliable and accurate diagnosis of STH infections remains difficult , and resulting prevalence estimates remain imprecise 1–2 ., In recent years , the interest in molecular diagnostic methods for the detection of gastrointestinal helminths has grown substantially ., Largely , this escalation in interest has occurred in parallel with the belief that standard microscopy-based methodologies for the examination of stool samples are sub-optimal , leading to underrepresentation of infection 3–5 ., Further complicating matters , rates of STH egg/larval excretion have been shown to vary considerably within sequentially collected stool samples originating from a single infected individual 6–7 ., This variability in egg/larval count can result in false negative samples , particularly when non-amplification-based diagnostic methodologies are utilized 7 ., Such underrepresentation of disease complicates programmatic efforts , making the accurate assessment of the effects of intervention difficult , and frequently leaving low-level infections undiagnosed 5 , 8–9 ., Additionally , microscopy-based diagnostic methods have been linked with pathogen misidentification due to the morphological similarities that exist between species 5 , 10 ., Because of such concerns , a number of conventional and real-time PCR-based assays have been developed with the objective of improving both species-specificity and limits of detection 4 , 11–17 ., These assays have proven valuable , and as global efforts to estimate the burden of disease caused by the soil transmitted helminths ( STHs ) continue to increase , the number of studies incorporating such assays has risen in response 3 , 5 , 9 , 18–21 ., To date , the target sequences for such assays have been ribosomal internal transcribed spacer ( ITS ) sequences , 18S or ribosomal subunit sequences , or mitochondrial genes such as cytochrome oxidase I ( COI ) 4 , 11–14 ., Ribosomal sequences have been selected as diagnostic targets because they are typically found as easily identified moderate copy number tandem repeats in nucleated organisms 22–25 ., Similarly , multiple copies of mitochondrial targets are found in the vast majority of eukaryotic cells 26 , making them attractive target choices ., However , while effective , such diagnostic targets are often sub-optimal ., This is particularly true in the case of nematodes and other multi-cellular organisms where species-specific , highly repetitive DNA elements frequently make up a substantial portion of the genome , and are often present at copy-numbers exceeding 1 , 000 per haploid genome 27–29 ., Due to such overrepresentation , non-coding repetitive sequence elements have become the targets of choice for many PCR-based diagnostic assays for the detection of various helminth species 30–31 ., However , the identification of such repeats has historically been complicated and labor intensive ., This identification has relied on techniques such as restriction endonuclease digestion of genomic DNA , followed by gel electrophoresis and Sanger DNA sequencing or polyacrylamide slab gel sequencing 32–34 ., However , the advent of next-generation sequencing ( NGS ) technologies and associated informatics tools has expedited the search for highly repetitive sequence elements 35–39 , and greater confidence can be placed in the accuracy of the results of such searches ., Furthermore , as ribosomal and mitochondrial sequences tend to demonstrate high degrees of conservation between species , species-specificity of detection is also improved through the targeting of unique , highly-divergent , non-coding repeat DNA elements ., Here we describe the development of a multi-parallel real-time PCR assay for the detection of five species of soil transmitted helminths ( Necator americanus , Ancylostoma duodenale , Trichuris trichiura , Strongyloides stercoralis , and Ascaris lumbricoides ) ., Using NGS-generated sequence data and the Galaxy-based RepeatExplorer computational pipeline 38–39 , we have searched the genomes of each organism for highly repetitive , non-coding DNA elements in order to identify diagnostic targets capable of providing optimal limits of detection and species-specificity of detection ., Using these targets to design small-volume , multi-parallel tests 4 , we have created a platform that provides cost-minimizing implementation of only those assays appropriate for a specific geographic region based upon the infections present ., While performing multiplex assays may provide labor and time savings in locations where many parasites are co-endemic , such assays result in considerable waste when used in areas harboring only one or a few of the target species ., In such settings , the “pick-and-choose” nature of multi-parallel assays minimizes reagent waste , and by improving upon limits of detection , the species-specific platform we describe here should facilitate improved STH monitoring and mapping efforts ., Since NGS-based repeat analyses allow for the selection of the most efficacious target sequences , this approach to assay design should be applied to the development of additional diagnostics tests for other eukaryotic pathogens ., For isolation of genomic DNA from N . americanus , A . duodenale , and T . trichiura , extractions were performed on cryopreserved adult worms in accordance with the “SWDNA1” protocol available on the Filarial Research Reagent Resource Center website ( http://www . filariasiscenter . org/parasite-resources/Protocols/materials-1/ ) ., For N . americanus and A . duodenale , DNA extractions were conducted using a pool of approximately 10 adult worms ., Both hookworm species belonged to strains originating in China ., In the case of T . trichiura , extraction was performed using a single adult female worm of Ugandan origin ., For S . stercoralis and A . lumbricoides , previously extracted genomic DNA was received from collaborators ., S . stercoralis DNA was obtained from laboratory-reared worms originating from Pennsylvania , USA , and A . lumbricoides DNA was isolated from worms obtained from Ecuador ., For each parasite analyzed , raw sequencing reads were uploaded to the Galaxy-based RepeatExplorer web server 39 ., Reads were processed according to the workflow in Fig 1 , enabling the identification of high copy-number repeat DNA sequences for each organism ., Promising repeat families were further analyzed using the Nucleotide BLAST tool ( http://blast . ncbi . nlm . nih . gov/Blast . cgi ) available from the National Center for Biotechnology Information ( NCBI ) ., Results from each organism were screened for repetitive DNA elements found to have high degrees of homology with elements of the human genome , common bacteria of the human microbiome , or other parasitic organisms likely to be found within the human gut ., Had such sequences been identified as among the most repetitive , they would have been eliminated from further consideration as they would be expected to cause species-specificity challenges during downstream PCR assay development ., However , no such conserved highly repetitive elements were identified ., Following screening , sequences from each organism , putatively determined to be among the most highly repetitive , were utilized for further assay development ( Fig 2 ) ., Candidate primer and probe pairings for each organism , excluding A . lumbricoides , were designed using the PrimerQuest online tool ( Integrated DNA Technologies , Coralville , IA ) , utilizing the default parameters for probe-based qPCR ., The putative species-specificity of each primer pair was further examined using Primer-BLAST software ( http://blast . ncbi . nlm . nih . gov/Blast . cgi ) ., In the case of S . stercoralis the highest copy-number repeat ( as determined by RepeatExplorer ) was not selected as a target sequence , due to design difficulties associated with the extreme A-T richness of the repeat ( A-T % = 80 . 25 ) ., As a result , a second repeat analysis was performed , selecting only for sequence reads with > 30% G-C content , and a second candidate sequence was selected based on these results ., In the case of A . lumbricoides , RepeatExplorer analyses of two different sequencing runs performed from two distinct libraries both resulted in the identification of ribosomal and mitochondrial sequences as the most highly repetitive ., For this reason , sequences from an existing , proven , primer and probe set targeting the ITS1 region were selected for further analysis 14 , 16 ., With the exception of the previously published A . lumbricoides probe , all probes were labeled with a 6FAM fluorophore at the 5’ end , and were double quenched using the internal quencher ZEN and 3IABkFQ ( IOWA BLACK ) at the 3’ end ( Integrated DNA Technologies ) ., This fluorophore-quencher combination was chosen as comparative testing of each probe revealed improved Ct values and greater ΔRn values using this chemistry when compared to typical TAMRA quenching ( Fig 3 ) ., Primer and probe sets for each organism can be found in Table 1 ., A panel of 79 blindly-coded patient samples , obtained in Timor-Leste as part of a previously described study 42 , was tested using the newly described multi-parallel Smith assays , as well as the previously described , multiplex real-time PCR detection methodology ( QIMR assay ) ( Table 2 , S1 Table ) ., As samples were patient-obtained and no true “gold standard” exists for the detection of the various STH infections examined here , it is difficult to definitively determine whether increased sample positivity is a result of improved assay detection limits or non-specific , off-target amplification ., For this reason , the comparative performances of each assay were assessed through calculations of positive , negative , and overall agreement 43 ., For the detection of N . americanus , a positive agreement ( PA ) of 100% and a negative agreement ( NA ) of 61% were calculated ., This resulted in an overall agreement ( PO ) of 85% ( Kappa 0 . 658 ) ., Use of the Smith assay resulted in the detection of 60 positive samples , while the QIMR assay resulted in the detection of 48 positives ., All 48 QIMR-positive samples were among the 60 positive samples detected using the Smith methodology ., For the detection of A . lumbricoides , a PA of 100% , an NA of 82% , and a PO of 91% ( Kappa 0 . 822 ) were seen ., The Smith assay for A . lumbricoides detection resulted in the identification of 47 positive samples , while the corresponding QIMR assay resulted in 40 positives ., Again , all 40 QIMR-positive samples were among the 47 Smith-positive samples which were identified ., Detection of Trichuris gave a PA of 71% , an NA of 88% and a PO of 85% ( Kappa 0 . 580 ) ., Sample examination using the Smith assay identified 18 positive extracts , while examination with the QIMR assay identified 14 positives ., However , only 10 positives were common to both assays , with 8 samples identified as positive only by the Smith assay , and 4 samples demonstrating the presence of parasite DNA using only the QIMR methodology ., Amplification in control reactions demonstrated that the QIMR assay , but not the Smith assay , would provide for the detection of the closely related parasite Trichuris vulpis , a whipworm species common to canines , but also known to cause zoonotic infection 48–49 ., As Trichuris ssp ., including T . vulpis , Trichuris suis , and Trichuris ovis have a wide geographic distribution with increased prevalence in tropical and sub-tropical locations 50–51 , the four QIMR-positive , Smith-negative samples were sequenced to determine the identity of the Trichuris species present within these samples ., BLAST analysis indicated that two of the samples contained DNA from the ruminant parasite T . ovis ( E values = 0 . 0 ) ., Unfortunately , two independent trials failed to produce usable sequence for the remaining two samples , after which both sample stocks had been exhausted , making further examination impossible ., Examination of all 79 samples for the presence of S . stercoralis resulted in the detection of only a single positive sample ., This single sample was identified using both the Smith and QIMR assays ., Sample examination for the presence of Ancylostoma resulted in the identification of 22 Ancylostoma ssp ., positive samples using the QIMR methodology ., However , not a single A . duodenale-positive sample was identified using the Smith assay ., As the zoonotic parasite Ancylostoma ceylanicum has been suspected of causing human infection in Timor-Leste 52 , a previously described , semi-nested PCR-RFLP assay was employed to discriminate infection with A . duodenale from infection with A . ceylanicum 47 ., In this assay , an MvaI restriction digest of PCR product is indicative of the presence of A . ceylanicum , while digestion with Psp1406I is indicative of A . duodenale ., 21 of the 22 Ancylostoma ssp ., positive samples were digested by MvaI , identifying the infections as A . ceylanicum in origin ., Two independent PCR trials ( four replicates ) failed to amplify the remaining Ancylostoma ssp ., -positive sample , preventing a definitive determination of the identity of the parasite in that sample ., Because a sizeable panel of field-collected samples was analyzed using the two different real-time PCR methodologies discussed here , a comparison of Ct values was conducted for all samples testing positive for a given parasite by both the Smith and QIMR methods ( S1 Table ) ., All 10 samples demonstrating positive results for T . trichiura when tested by both assays showed lower Ct values using the Smith methodology ( mean difference in Ct value = 7 . 86 +/- 2 . 46 ) ., Examination for N . americanus resulted in a similar pattern , with all 48 samples testing positive by both methodologies possessing lower Ct values when tested using the Smith assay ( mean difference in Ct value = 4 . 94 +/- 1 . 22 ) ., In the case of A . lumbricoides , Ct values were lower using the QIMR methodology for 38 of 40 samples demonstrating positive results for both assays ., However , at 0 . 896 +/- 0 . 767 , the mean difference in Ct values was low ., For S . stercoralis testing , only a single positive sample was identified ., This sample possessed a lower Ct value when tested using the Smith assay ., As no samples tested positive for Ancylostoma using the Smith assay ( QIMR-positive samples were demonstrated to be A . ceylanicum ) , a Ct comparison could not be made ., In light of their impact on global health , the importance of optimal and species-specific diagnostic methods for the detection of soil transmitted helminths cannot be overestimated ., While current molecular assays making use of ribosomal and mitochondrial targets have vastly improved the diagnosis of STH infection , these targets are frequently sub-optimal , potentially leaving low-level infections undiagnosed ., Furthermore , such sequences may lack the species-specificity required to discriminate between different species of the same genus ., In contrast , assays targeting high copy-number repetitive sequences improve upon assay detection limits , as many eukaryotic pathogens contain large numbers of such non-coding repeat DNA elements ., Accordingly , by coupling the high throughput nature of NGS with the Galaxy-based RepeatExplorer computational pipeline , a cost effective , accurate , and expedited methodology for the identification of high copy-number repeat DNA elements was developed ., Through the design of real-time PCR primer/probe pairings that uniquely target such repetitive sequences in a species-specific manner , diagnostic accuracy and limits of detection are improved dramatically when compared with microscopy-based diagnostic techniques and PCR-based diagnostics targeting mitochondrial or ribosomal sequences ., Utilizing this strategy , we have successfully identified novel target sequences for the detection of N . americanus , A . duodenale , T . trichiura , and S . stercoralis ., Furthermore , we have demonstrated the consistent detection of genomic DNA from each target organism at quantities of 2 fg or less , and have presented evidence to suggest improved limits of detection and species-specificity relative to an established and validated PCR diagnostic methodology Llewellyn , 2016 ., Although further testing utilizing “spiked” samples containing known quantities of eggs/larvae is currently underway , 2 fg of DNA is far less than the quantity present within a single fertilized egg or L1 larvae of each species 53–55 ( Table 3 ) ., In principle , we have therefore demonstrated the potential of these assays to detect a single egg within a tested patient stool sample ., While the high copy-number nature of non-coding repetitive sequence elements makes them attractive diagnostic targets , such elements also frequently demonstrate rapid evolutionary divergence 56–57 ., This divergence increases the diagnostic appeal of these sequences , as divergence reduces the risk for non-specific , off-target amplification , a characteristic essential for the development of species-specific PCR assays capable of discriminating between closely related organisms ., Accordingly , while additional testing against genomic DNA from a growing panel of closely related parasites will continue to be used to evaluate the species-specificity of each selected primer/probe set , we have successfully demonstrated that each Smith assay does not amplify off-target templates from any other parasite species included within this multi-parallel platform ., Furthermore , by employing a semi-nested PCR-RFLP tool , we were able to successfully demonstrate that our assay for the detection of A . duodenale does not amplify the closely related parasite A . ceylanicum ., In contrast , the previously published primer/probe set employed for comparative testing was unable to distinguish between these two species , resulting in consistent off-target amplification of A . ceylanicum DNA ., Similarly , while our T . trichiura assay failed to amplify four samples containing genetic material from Trichuris ssp ., , the comparative QIMR assay again demonstrated non-specific , off-target amplification for at least two of these samples , as sequence analysis demonstrated the presence of DNA from the ruminant parasite T . ovis ., Taken together , these findings support the notion that improved assay species-specificity results from non-coding , repeat-based PCR assay design ., Of note , to our knowledge , this is the first example of T . ovis potentially serving as a causative agent of zoonotic infection ., However , as sheep are considered a major agricultural commodity of Timor-Leste 58 , the possibility exists that individuals testing positive for T . ovis may have ingested intestinal material from an animal harboring infection , making it conceivable that the T . ovis DNA present was not the result of zoonotic infection ., Given that T . ovis is not known to cause human infection , further exploration of this possible zoonosis is warranted ., Attempting to design a non-coding , repetitive DNA sequence-based assay for the species-specific detection of A . lumbricoides presented a unique set of challenges ., A . lumbricoides , like many species of Ascaridae , discards large portions of its highly repetitive , non-coding genomic DNA during embryonic development ., This process , known as chromosome diminution , eliminates the presence of such DNA elements from post-embryonic somatic cells 59–61 ., Presumably for this reason , two separate repeat analyses , performed on two distinct library preparations , failed to identify any repetitive sequences with copy numbers greater than ribosomal and mitochondrial targets ., Accordingly , a previously described primer/probe set targeting the ITS1 ribosomal region was chosen for inclusion in our multi-parallel platform 14 , 16 ., In order to improve diagnostics for this parasite , further analysis of A . lumbricoides using DNA extracted from eggs alone ( before chromosome diminution ) will be undertaken ., In addition to the potential detection limit improvements and species-specificity gains realized when diagnostically targeting non-coding repetitive DNA sequences , designing multi-parallel assays provides another unique set of advantages over previous design strategies 4 ., By reducing the number of tests required , multiplex assays can provide labor and reagent savings over alternative diagnostic measures when used in environments that harbor the full complement of organisms targeted by the assay 62–63 ., However , as the geographic distribution of STH species is not uniform , the use of multi-parallel assays makes it possible to select only the assays appropriate for a given location , reducing primer/probe costs associated with testing for unnecessary targets 4 ., By running these assays as “small-volume” 7 μl reactions , reagent use is minimized , resulting in cost savings ., Furthermore , as multi-parallel reactions are run independently , this enables the development of new assays for new pathogens and their subsequent addition to the testing platform without the complex re-optimization of assay conditions required for multiplex PCR assays ., While reagent costs associated with performing molecular diagnostic testing are higher than costs associated with conducting traditional microscopy-based diagnostics , expenses associated with molecular techniques are declining as improved reagents and enzymes have allowed reaction volumes to decrease , minimizing reagent needs 4 , 64 ., Furthermore , reagent improvements have increased the practicality of sample pooling , a practice already adopted by many tropical disease surveillance and diagnostic efforts 65–69 ., Such pooling allows for cost-reducing high-throughput screening of stool samples 70–71 ., Thus , while the total cost associated with performing a duplicate Kato-Katz thick smear under field conditions has been estimated at $2 . 06 72 and we estimate the total cost associated with the duplicate testing a single stool sample using all five multi-parallel assays to be approximately $10 , the pooling of as few as five samples would render small volume , multi-parallel PCR testing more cost effective than Kato-Katz testing ., Furthermore , molecular diagnostic accuracy and reliability provide increased clarity of results 64 , allowing for the implementation of more informed and effective treatment and control strategies ., Such improvements in efficiency result in greater programmatic gains , drastically reducing long-term costs and expenses of control or elimination programs ., One profound shortcoming which hampers STH diagnostic development is the lack of a reliable gold standard for detection 8 ., While still used in many clinical , mapping , and research efforts , microscopy-based methodologies are known to lack both adequate limits of detection and species-specificity of detection 3–5 , 10 , 64 ., Similarly , while currently available molecular methods have greatly improved upon many of the shortcomings inherent to microscopy , the use of sub-optimal ribosomal or mitochondrial targets possessing relatively high degrees of conservation can result in both false-negative , and off-target , false-positive results ., Thus , a gold standard of detection is sorely needed ., Unfortunately , without a definitive method for assigning positive/negative status to an unknown sample , distinguishing improved limits of detection from false-positive amplification can be difficult ., Nonetheless , comparative assay testing remains an important aspect of designing any diagnostic test ., As such , we believe the evaluation of Timor-Leste patient samples presented in this paper provides strong evidence for improved limits of detection when utilizing the newly described Smith assays ., While strain-specific genetic differences arising within divergent geographic isolates could present detection challenges , testing on a limited number of patient-derived samples from Argentina and Ethiopia aimed at providing evidence for the global applicability of these multi-parallel assays is currently underway ., Additional studies to further validate these assays on a variety of geographic isolates will continue ., In all instances , and for all parasites excluding Ancylostoma and Trichuris ( where off-target amplification of A . ceylanicum and T . ovis by the QIMR assay was demonstrated ) , each Timor-Leste patient sample that provided a positive QIMR assay result also demonstrated positivity with the corresponding Smith assay ., Furthermore , all N . americanus , T . trichiura , and S . stercoralis samples that were positive by both assays exhibited lower Ct values for the Smith assay results ., These findings strongly suggest improved limits of detection for the Smith assays , and support our contention that samples returning Smith assay positive results , but QIMR assay negative results , are likely low-level positives escaping detection by the sub-optimal PCR platform ., This conclusion is further supported by the finding that the Smith assays do not show off-target amplification of any other STH parasites , human DNA or E . coli DNA ., As both the QIMR and Smith assays for the detection of A . lumbricoides make use of the same previously published primer/probe combination 14 , 16 , comparative assay testing for this parasite provided results which were more difficult to interpret ., As increased reaction volumes are known to frequently improve detection limits for an assay , likely due to the large volume nature of the QIMR assay ( 25 μl vs . 7 μl for Smith ) , 38 of 40 samples returning positive results for both testing platforms demonstrated lower Ct values when examined using the QIMR method ., Interestingly , despite this tendency for QIMR testing to result in lower Ct values , seven samples identified as positive using the Smith assay were found to be QIMR-negative ., In contrast , not a single sample was found to be QIMR-positive and Smith-negative ., As the QIMR assays are multiplexed , one explanation for this apparent contradiction is that the multiplex methodology failed to detect A . lumbricoides in a subset of samples that were positive for multiple STH parasites ( S1 Table ) ., Such failures are known to occur in multiplex reactions , particularly when primer concentrations are suboptimal , as reagents are utilized for the amplification of a more prevalent target , preventing the amplification of the lower copy-number target sequences within the sample 73 ., Alternatively , while the results of our assay specificity testing present compelling evidence to the contrary , the possibility of false positive amplification cannot be definitively ruled out ., Non-coding repetitive DNA elements are found in nearly all eukaryotic organisms ., Such sequences are typically highly divergent , and frequently exist in high copy-number ., These characteristics make them ideal molecular diagnostic targets , particularly for the detection of pathogens such as the STHs , which remain an underdiagnosed , poorly mapped global health concern ., By applying next-generation sequencing technology to the challenge of repeat DNA discovery , we have designed highly specific multi-parallel PCR assays with improved limits of detection over existing diagnostic platforms ., We believe that these assays will greatly aid in the global efforts to map STH infection , facilitating accurate disease prevalence estimates ., Furthermore , we intend to apply this approach to molecular target discovery of other parasitic organisms and NTDs , as optimal limits of detection and species-specificity of detection are vital to all diagnostic efforts ., This is particularly true when implementing diagnostics in climates of decreasing disease prevalence ., Accordingly , as NTD elimination efforts continue to progress , optimized assays will play an increasingly critical role in the detection of sporadic and focal infections and the monitoring for disease recrudescence .

Introduction, Materials and Methods, Results, Discussion

The soil transmitted helminths are a group of parasitic worms responsible for extensive morbidity in many of the world’s most economically depressed locations ., With growing emphasis on disease mapping and eradication , the availability of accurate and cost-effective diagnostic measures is of paramount importance to global control and elimination efforts ., While real-time PCR-based molecular detection assays have shown great promise , to date , these assays have utilized sub-optimal targets ., By performing next-generation sequencing-based repeat analyses , we have identified high copy-number , non-coding DNA sequences from a series of soil transmitted pathogens ., We have used these repetitive DNA elements as targets in the development of novel , multi-parallel , PCR-based diagnostic assays ., Utilizing next-generation sequencing and the Galaxy-based RepeatExplorer web server , we performed repeat DNA analysis on five species of soil transmitted helminths ( Necator americanus , Ancylostoma duodenale , Trichuris trichiura , Ascaris lumbricoides , and Strongyloides stercoralis ) ., Employing high copy-number , non-coding repeat DNA sequences as targets , novel real-time PCR assays were designed , and assays were tested against established molecular detection methods ., Each assay provided consistent detection of genomic DNA at quantities of 2 fg or less , demonstrated species-specificity , and showed an improved limit of detection over the existing , proven PCR-based assay ., The utilization of next-generation sequencing-based repeat DNA analysis methodologies for the identification of molecular diagnostic targets has the ability to improve assay species-specificity and limits of detection ., By exploiting such high copy-number repeat sequences , the assays described here will facilitate soil transmitted helminth diagnostic efforts ., We recommend similar analyses when designing PCR-based diagnostic tests for the detection of other eukaryotic pathogens .

With a growing emphasis on the mapping and elimination of soil transmitted helminth ( STH ) infections , the need for optimal and specific diagnostic methods is increasing ., While PCR-based diagnostic methods for the detection of these parasitic organisms exist , these assays make use of sub-optimal target sequences ., By designing assays that target non-coding , high copy-number repetitive sequences , both the limit of detection and species-specificity of detection improve ., Using next-generation sequencing technology , we have identified high copy-number repeats for a series of STH species responsible for the greatest burden of disease ., Using these repetitive sequences as targets in the design of novel real-time PCR assays , we have improved both the limits of detection and species-specificity of detection , and we have demonstrated this improved detection by testing these assays against an established PCR-based diagnostic methodology ., Accordingly , these assays should facilitate mapping and monitoring efforts , and the generalized application of this approach to assay design should improve detection efforts for other eukaryotic pathogens .

sequencing techniques, invertebrates, animals, necator americanus, ascaris, ascaris lumbricoides, molecular biology techniques, strongyloides stercoralis, research and analysis methods, ancylostoma, sequence analysis, strongyloides, trichuris, artificial gene amplification and extension, necator, repeated sequences, molecular biology, dna sequence analysis, polymerase chain reaction, genetics, nematoda, biology and life sciences, genomics, organisms

journal.pntd.0001980

Distinct Transcriptional Signatures of Bone Marrow-Derived C57BL/6 and DBA/2 Dendritic Leucocytes Hosting Live Leishmania amazonensis Amastigotes

Leishmania ( L . ) amazonensis perpetuates in South and Central America , its main location being the wet forests of the Amazon basin ., The perpetuation of this Leishmania species relies successively on two hosts which cohabit more or less transiently within this ecosystem: blood-feeding sand flies and mammals , including wild rodents and humans ., A broad spectrum of clinical manifestations , ranging from single cutaneous lesions to multiple , disfiguring nodules 1 , 2 , 3 assess the durable establisment of L . amazonensis as intracellular amastigotes in the dermis ., As model rodents , the laboratory mice of different inbred strains can be subverted as hosts by L . amazonensis , the establishment of parasites in the dermis being more or less rapid ., In C3H , BALB/c and C57BL/6 mice high parasite loads , coupled to non healing skin-damages are displayed at site of L . amazonensis inoculation and in multiple skin sites reached by parasites emigrating from the primary inoculation site 4 , 5 , 6 , 7 , 8 ., By contrast , in DBA/2 mice , at the inoculation site , the L . amazonensis population size is rapidly controlled , a process coupled to a controlled inflammatory process with limited parasite dissemination in distant tissue ( s ) , if any 9 ., Knowing that once in the dermis of the mouse , amastigotes are hosted by mononuclear phagocytes including macrophages and dendritic leukocytes ( DLs ) 10 , 11 , 12 , 13 , 14 , 15 , we have addressed the following question: could the DLs harbouring live amastigotes contribute to the distinct phenotypes observed in C57BL/6 and DBA/2 mice ?, Since the frequency of DLs hosting live Leishmania amastigotes within the skin and skin-draining lymph nodes ( DLNs ) remains very low 16 , 17 we decided to first conduct an in vitro study relying on bone marrow-derived DLs ( BMD-DLs ) from C57BL/6 and DBA/2 mice exposed or not to live L . amazonensis amastigotes ., Based on flow cytometry ( FCM ) , genechip ( Affymetrix Mouse GeneChip ) and real-time quantitative PCR ( RT-qPCR ) analyses performed on sorted DLs hosting live DsRed2-expressing L . amazonensis transgenic amastigotes 17 many distinct features have been highlighted ., DBA/2 DLs displayed transcriptional signatures and markers that could be related to the early phenotype observed in vivo , in contrast to live amastigotes-hosting C57BL/6 DLs ., The data are consistent with rapid and sustained immune regulatory functions accounting for the remodeling of the DBA/2 ear as L . amazonensis protective niche ., All together this study provides , for the first time , a solid base for exploring, i ) the inflammatory processes that maintain the amastigote population under control in DBA/2 mice and, ii ) the inflammatory processes coupled to extended parasite dissemination and to poor parasite population control in C57BL/6 mice ., Six week old female DBA/2 , C57BL/6 and Swiss nu/nu mice were purchased from Charles River ( Saint Germain-sur-lArbresle , France ) ., All animals were housed in our A3 animal facilities in compliance with the guidelines of the A3 animal facilities at the Pasteur Institute which is a member of Committee 1 of the “Comité dEthique pour lExpérimentation Animale” ( CEEA ) - Ile de France - Animal housing conditions and the protocols used in the work described herein were approved by the “Direction des Transports et de la Protection du Public , Sous-Direction de la Protection Sanitaire et de lEnvironnement , Police Sanitaire des Animaux under number B75-15-28 in accordance with the Ethics Charter of animal experimentation that includes appropriate procedures to minimize pain and animal suffering . TL is authorized to perform experiment on vertebrate animals ( licence 75-717 ) issued by the Paris Department of Veterinary Services , DDSV ) and is responsible for all the experiments conducted personally or under his supervision as governed by the laws and regulations relating to the protection of animals . DsRed2-transgenic L . amazonensis strain LV79 ( WHO reference number MPRO/BR/72/M1841 ) amastigotes were isolated from Swiss nude mice inoculated 2 months before within a BSL-2 cabinet space as described previously 17 . These amastigotes did not present any antibodies at their surface 18 . Promastigotes derived from amastigotes were cultured at 26°C in complete M199 medium . The metacyclic promastigote population ( mammal-infective stage ) was isolated from stationary phase cultures ( 6 day-old ) on a Ficoll gradient . Ten thousand metacyclic promastigotes in 10 µl of PBS were injected into the ear dermis of C57BL/6 and DBA/2 mice . Increased ear thickness was measured using a direct reading Vernier caliper ( Thomas Scientific , Swedesboro , NJ ) and expressed as ear thickness . DLs were differentiated from bone marrow cells of DBA/2 or C57BL/6 mice according to a method described previously 18 , 19 . Briefly , bone marrow cells were seeded at 4×106 cells per 100 mm diameter bacteriological grade Petri dish ( Falcon , Becton Dickinson Labware , Franklin Lakes , NJ ) in 10 ml of Iscoves modified Dulbeccos medium ( IMDM; BioWhittaker Europe , Verviers , Belgium ) supplemented with 10% heat-inactivated foetal calf serum ( FCS; Dutscher , Brumath , France ) , 1 . 5% supernatant from the GM-CSF producing J558 cell line , 50 U/ml penicillin , 50 µg/ml streptomycin , 50 µM 2-mercaptoethanol and 2 mM glutamine . Cultures were incubated at 37°C in a humidified atmosphere with 5% CO2 . On day 6 , suspended cells were recovered and further cultured in complete IMDM supplemented with 10% of the primary culture supernatant before seeding on day 10 in hydrophobic 6-well plates ( Greiner , St Marcel , France ) at a concentration of 9×105 cells/well in 3 ml complete IMDM . On day 4 post the distribution of DLs in the 6 well plate culture , DLs were exposed or not to freshly isolated DsRed2-LV79 amastigotes or to live BCG at micro-organism-DL ratios of 5∶1 and 10∶1 , respectively . DL cultures were placed at 34°C and sampled at 24 hours post micro-organism addition . Recovered DLs were incubated first in PBS-FCS supplemented with 10% heat-inactivated donkey serum for 15 minutes , second in PBS containing 10% FCS and 0 . 01% sodium azide in presence of antibodies directed against surface antigens . Extracellular staining procedures were performed with specific monoclonal antibodies ( mAbs ) directed against MHC class II molecules ( M5/114 clone ) conjugated to PE-CY5 ( 0 . 2 µg/ml ) and either of the following biotinylated mAbs directed against CD86 ( GL1 clone ) , CD80 ( K-10A1 clone ) , CD54 ( 3E2 clone ) , CD11c ( HL3 ) and IgG control ( B81-3 clone ) at 0 . 5 µg/ml ( eBioscience , San Diego , USA ) . Biotinylated mAbs were revealed using 1 . 5 µg/ml Streptravidin conjugated to Phycoeythrin ( Molecular Probes , Cergy Pontoise , France ) . PE-conjugated mAb directed against CXCR-4 ( 2B11 clone ) was purchased from eBioscience . Analysis was performed on the FACSCalibur . DLs were selected on FSC-SSC parameters ( to excluded debris ) , and on the basis of MHC class II expression to discard the fraction of “contaminating” cells expressing no surface MHC class II molecules ., Intracellular staining of amastigotes was performed after fixation in PBS containing 1% paraformaldehyde ( PFA ) for 20 minutes at 4°C with the 2A3-26 mAb which was shown to strictly bind to the L . amazonensis amastigote 18 ., DLs were washed in Perm/Wash solution from the BD Cytofix/Cytoperm™ Plus Kit ( BD Bioscience ) and incubated with 5 µg/ml of Alexafluor 488- conjugated 2A3-26 mAb in Perm/Wash buffer for 30 minutes at 4°C in the dark ., Then DLs were washed in Perm/Wash buffer and fixed with in PBS −1% paraformaldehyde ( PFA ) ., DLs were exposed or not to freshly isolated DsRed2-LV79 amastigotes at a parasite -DL ratio of 5∶1 ., DL cultures were placed at 34°C and sampled at 5 , 24 and for 48 hours post parasite addition ., Detached DLs were centrifuged on poly-L-lysine-coated glass coverslips and incubated at 34°C for 30 minutes ., Cells were then fixed with 4% PFA for 20 minutes , permeabilised with saponin and incubated with 10 µg/ml of the amastigote-specific mAb 2A3-26-AlexaFluor 488 and 1 µg/ml of biotinylated-mAb ( M5/114 ) directed against MHC class II molecules ., The revelation was performed using 1 . 5 µg/ml streptravidin conjugated to Texas Red ( Molecular Probes , Cergy Pontoise , France ) ., Finally , they were mounted on glass slides with Hoechst 33342-containing Mowiol ., Incorporation of Hoechst into DNA allowed the staining of both host cell and amastigote nuclei ., Epifluorescence microscopy images were acquired on an upright microscope Zeiss Axioplan 2 monitored by the Zeiss Axiovision 4 . 4 software ., DsRed2-LV79 amastigotes were added or not to cultures of C57BL/6 and DBA/2-DLs ., Twenty four hours later , three samples collected from three distinct cultures of either unexposed DLs or DLs exposed to DsRed2-LV79 amastigotes were carefully sorted as previously described by Lecoeur et al . 20 ., Briefly cells were first incubated in PBS-FCS containing 0 . 2 µg/ml of the anti-MHC class II mAb ( M5/114 ) conjugated to PE-Cy5-conjugated mAb ( eBioscience ) ., After two washes , cells were resuspended at 5×106 cells/ml in PBS containing 3% FCS and 1% J558 supernatant ., The cell sorting was performed using a FACSAria ( BD Biosciences , San Jose , CA ) equipped with completely sealed sample injection and sort collection chambers that operate under negative pressure ., PE-Cy5 and DsRed2 fluorescences were collected through 695/40 and 576/26 bandpass filters respectively ., FSC and SSC were displayed on a linear scale , and used to discard cell debris with the BD FACSDiva software ( BD Biosciences ) 17 ., L . amazonensis amastigote-hosting DLs were sorted by selecting cells expressing both surface MHC Class II molecules and DsRed2 fluorescence and immediately collected for RNA extraction by using the RNeasy Plus Mini-Kit ( Qiagen ) as previously described 21 ., Whatever the readout assays-Affymetrix or RT-qPCR - the RNA populations used were prepared from the same samples ., The quality control ( QC ) and concentration of RNA were determined using the NanoDrop ND-1000 micro-spectrophotometer ( Kisker , http://www . kisker-biotech . com ) and the Agilent-2100 Bioanalyzer ( Agilent , http://www . chem . agilent . com ) ., Two hundred ng of total RNA per sample were processed , labelled and hybridized to Affymetrix Mouse Gene ST 1 . 0 arrays , following Affymetrix Protocol ( http://www . affymetrix . com/support/downloads/manuals/expression_analysis_technical_manual . pdf ) ., Three Biological replicates per condition were run ., Following hybridization , the arrays were stained and scanned at 532 nm using an Affymetrix GeneChip Scanner 3000 which generates individual CEL files for each array ., Gene-level expression values were derived from the CEL file probe-level hybridization intensities using the model-based Robust Multichip Average algorithm ( RMA ) 22 ., RMA performs normalization , background correction and data summarization ., An analysis is performed using the LPE test 23 ( to identify significant differences in gene expression between parasite-free and parasite-harbouring DLs , and a p-value threshold of p<0 . 05 is used as the criterion for significant differential expression ., The estimated false discovery rate ( FDR ) was calculated using the Benjamini and Hochberg approach 24 in order to correct for multiple comparisons ., A total of 1 , 340 probe-sets showing significant differential expression were input into Ingenuity Pathway Analysis software v5 . 5 . 1 ( http://www . ingenuity . com ) , to perform a biological interaction network analysis ., The symbols of the modulated genes are specified in the text ( fold change FC values between brackets ) , while their full names are given in additional file 1 ., MIAME-compliant data are available through GEO database http://www . ncbi . nlm . nih . gov/geo/ accession GSE Total RNAs from DLs cultures were reverse-transcribed to first strand cDNA using random hexamers ( Roche Diagnostics ) and Moloney Murine Leukemia Virus Reverse Transcriptase ( Invitrogen , Life Technologies ) ., A SYBR Green-based real-time PCR assay ( QuantiTect SYBR Green Kit , Qiagen ) for relative quantification of mouse target genes was performed on a 384-well plate LightCycler 480 system ( Roche Diagnostics ) ., Crossing Point values ( Cp ) were determined by the second derivative maximum method of the LightCycler 480 Basic Software ., Raw Cp values were used as input for qBase , a flexible and open source program for qPCR data management and analysis 25 ., Relative expression for 8 transcripts ( ccl2 , cl17 , ccl19 , ccr1 , ccr2 , cxcr4 , cd274 , tnfsf4 ) were calculated for sorted LV79-hosting DLs using sorted DLs from Leishmania unexposed cultures as calibrators ., For normalization calculations , candidate control genes were tested ( pgk1 , h6pd , ldha , nono , g6pd , hprt , tbp , l19 , gapdh , rpIIe and ywhaz ) with the geNorm 26 and Normfinder programs 27 ., Tbp and nono were selected as the most stable reference genes for the C57Bl/6 DLs ., RpIIe and tbp were selected for the DBA/2 DLs ., At day 4 and 7 post the inoculation of 104 metacyclic promastigotes , three mice were sacrificed , the abundance of some transcripts being determined by real time RT-qPCR ., Control , naïve mice were analyzed in parallel ., Whole ear pinnas and ears-DLN were removed and fragmented using the Precellys 24 System 21 ., Total RNAs were extracted and processed for RT-qPCR as described above ., Ldha and nono were selected as the most stable reference genes for the C57Bl/6 and DBA/2 ears ., tbp and nono were selected for the as the most stable reference genes for C57Bl/6 DLNs while ywhaz and nono were selected for the DBA/2-DLNs ., The experimental procedure for quantifying Leishmania in tissues was done as previously described by de La Llave et al 21 ., Briefly , serial 10-fold dilutions of parasites ( from 108 to 101 ) were added to either ears or ear-DLN recovered from C57BL/6 or DBA/2 naive mice ., Total RNAs were extracted and processed for RT-qPCR as described above ., The primers for Leishmania gene target ( ssrRNA ) to quantify the number of parasites were F- CCATGTCGGATTTGGT and R- CGAAACGGTAGCCTAGAG 28 ., A linear regression for each standard curve was determined: number of parasites against the relative expression of ssrRNA values ., Two-sided Students paired t-tests were used to compare FCM experiments ( 4<n<6 ) ., A Mann-Whitney test was used to compare ear thickness measurements and number of parasites ., C57BL/6 and DBA/2 mice were given into the ear pinna dermis a low number ( 104 ) of L . amazonensis ( LV79 strain ) metacyclic promastigotes ., The monitoring of ear macroscopic features up to 100 days post inoculation ( PI ) has evidenced mouse inbred strain-specific features ( Figure 1 ) ., C57BL/6 mice did not display any significant inflammatory signs during the early phase ( ranging from day 0 to day 22 PI , phase 1 ) , whereas they later display sustained inflammatory signs ( after 22 days , phase 2; figures 1A , 1B ) ., During the early phase , only a few parasites can be quantified in the ear pinna , the ear pinna-DLN displaying lower number of parasites ( <100 parasites/DLN; figure 1C ) ., In contrast , in DBA/2 mouse ear pinna , a mild inflammatory process was observed immediately post the inoculation whereas a rapid increase of the amastigote population size was noted in both the ears and ears-DLN ., The second phase was delineated by the persistence of inflammatory process ( Figure 1 ) coupled to the control of parasite load in the ear pinna and ear-DLN ( data not shown ) ., We reasoned that early distinct DLs-dependent immune processes- promoting either rapid or slow remodeling of the dermis as amastigote-protective niches- could account for the distinct features displayed , over time , by the L . amazonensis amastigotes-hosting ear pinna of the C57BL/6 and DBA/2 mice ., Being aware that , whatever the tissues , the DL frequency is very low , we considered biologically sound to start the comparative analysis with GM-CSF-dependent C57BL/6 or DBA/2 cultured DLs , once they were hosting , or not , live L . amazonensis amastigotes ., Briefly , C57BL/6 and DBA/2 bone marrow cell suspensions were exposed or not to live DsRed2 L . amazonensis amastigotes and carefully sorted from otherwise heterogeneous cultures ., The immunolabelling of surface MHC class II allowed us to exclude the low fraction of amastigote-hosting cells that did not express surface MHC class II ., The subsequent step of such an approach was to first monitor , at the transcriptional level with the Affymetrix-based technology any potential distinct reprogramming of live L . amazonensis amastigotes-hosting DLs ., We used a carefully designed in vitro model 20 based on cultures of mouse BMD-DLs in which more than 97% of cells expressed CD11c , CD11a and CD11b ( data not shown ) ., When the presence/absence of surface MHC class II molecules was monitored on whole cell cultures by fluorescence microscopy and FCM , three phenotypically distinct cell subsets were evidenced ( Figures S1A–C ) ., The population of cells that did not express surface and intracellular MHC class II molecules were considered as “Contaminating” Cells ( CC ) ., The two other cell populations partition between, i ) a majority of cells displaying a moderate surface MHC class II amount ( MHC IIlow; bona fide immature DLs ) and, ii ) a minority of cells expressing very high levels of MHC II molecules ( MHC IIhigh; bona fide mature DLs ) ., DsRed2 L . amazonensis/LV79 amastigotes were put in contact with BMD-DLs ( MOI of 5/1 ) and analysed 5 , 24 or 48 hours later ( Figure 2 ) ., Intracellular amastigotes ( 2A3-26+ ) detected by immunofluorescence microscopy analysis were evidenced in all BMD-DL subsets with much higher number of amastigotes in CC ( data not shown ) ., Low percentages of DLs hosting 2A3-26+ parasites were also documented by FCM analyses at 24 hours post amastigote addition ( 23 . 0%+/−12 . 6 and 26 . 0%+/−8 . 1 of 2A3-26+ cells in C57BL/6 and DBA/2 BMD-DLs , respectively , for n\u200a=\u200a9 experiments ) ., Interestingly , while the percentage of DLs housing amastigotes did not change from 5 hours to 24 hours ( Figure 2A ) , the number of intracellular amastigotes did slowly expand whatever the mouse genotype ( Figure 2B ) over the otherwise limited temporal window we did focus on ., L . amazonensis amastigote-hosting DLs were sorted by selecting cells expressing both surface MHC Class II molecules and DsRed2 fluorescence ( see below ) .

Introduction, Methods, Results and Discussion

The inoculation of a low number ( 104 ) of L . amazonensis metacyclic promastigotes into the dermis of C57BL/6 and DBA/2 mouse ear pinna results in distinct outcome as assessed by the parasite load values and ear pinna macroscopic features monitored from days 4 to 22-phase 1 and from days 22 to 80/100-phase 2 ., While in C57BL/6 mice , the amastigote population size was increasing progressively , in DBA/2 mice , it was rapidly controlled ., This latter rapid control did not prevent intracellular amastigotes to persist in the ear pinna and in the ear-draining lymph node/ear-DLN ., The objectives of the present analysis was to compare the dendritic leukocytes-dependant immune processes that could account for the distinct outcome during the phase 1 , namely , when phagocytic dendritic leucocytes of C57BL/6 and DBA/2 mice have been subverted as live amastigotes-hosting cells ., Being aware of the very low frequency of the tissues dendritic leucocytes/DLs , bone marrow-derived C57BL/6 and DBA/2 DLs were first generated and exposed or not to live DsRed2 expressing L . amazonensis amastigotes ., Once sorted from the four bone marrow cultures , the DLs were compared by Affymetrix-based transcriptomic analyses and flow cytometry ., C57BL/6 and DBA/2 DLs cells hosting live L . amazonensis amastigotes do display distinct transcriptional signatures and markers that could contribute to the distinct features observed in C57BL/6 versus DBA/2 ear pinna and in the ear pinna-DLNs during the first phase post L . amazonensis inoculation ., The distinct features captured in vitro from homogenous populations of C57BL/6 and DBA/2 DLs hosting live amastigotes do offer solid resources for further comparing , in vivo , in biologically sound conditions , functions that range from leukocyte mobilization within the ear pinna , the distinct emigration from the ear pinna to the DLN of live amastigotes-hosting DLs , and their unique signalling functions to either naive or primed T lymphocytes .

The rapid and long term establishment of parasites such as L . amazonensis , otherwise known to strictly rely on subversion of macrophage and dendritic leucocyte ( DL ) lineages , is expected to reflect stepwise processes taking place in both the skin dermis where the infective form of the parasite and the skin-draining lymph node ( DLN ) were inoculated ., Relying on mice of two distinct inbred strains—C57BL/6 and DBA/2—that rapidly and durably display distinct phenotypes at the two sites of establishment of L . amazonensis , we were curious to address the following question: could live L . amazonensis-hosting DL display unique signatures that account for the distinct phenotypes ?, Based on flow cytometry , genechip and real-time quantitative PCR analyses , our results did evidence that , once subverted as cells hosting live L . amazonensis , DLs from C57BL/6 or DBA/2 do display distinct profiles that could account for the, i ) distinct parasite load profiles ,, ii ) as well as the distinct macroscopic features of ear pinna observed once the L . amazonensis metacyclic promastigotes completed their four day developmental program along the amastigote morphotype .

genome expression analysis, immune cells, antigen-presenting cells, immunology, microbiology, host-pathogen interaction, parasitology, immune defense, t cells, biology, pathogenesis, immune response, immunity, genomics, genetics and genomics

journal.pntd.0000510

Trypanosoma cruzi IIc: Phylogenetic and Phylogeographic Insights from Sequence and Microsatellite Analysis and Potential Impact on Emergent Chagas Disease

At least 10 million people are thought to carry the infectious agent of Chagas disease , Trypanosoma cruzi , which is considered to be responsible for ∼13 , 000 deaths annually ( www . who . int , 1 ) ., The disease is a vector-borne zoonosis and transmission in its wild transmission cycle is maintained by numerous species of mammal reservoir and over half of approximately 140 known species of haematophagous triatomine bug 2 ., The geographical distribution of silvatic T . cruzi stretches from the Southern States of the USA to Southern Argentina ., Domestic transmission is limited to Central and South America where domiciliated vector species occur ., Human infection occurs primarily through mucosal or broken skin contact with contaminated triatomine faeces egested by the insect during feeding ., Consistent with an ancient association with South America 3 T . cruzi populations are highly diverse , with at least six stable discrete typing units ( DTUs ) reported: TcI , TcIIa , TcIIb , TcIIIc , TcIId , and TcIIe ., Among these , TcI and TcIIb are the most divergent groups in molecular terms - estimates based on nuclear genes date their most recent common ancestor at 3–10 million years ago ( MYA ) 4 ., The phylogenetic status of TcIIc and TcIIa is in full debate 5 , 6 ., Based on mosaic patterns of nucleotide diversity across nine nuclear genes , Westenberger et al . , ( 2005 ) proposed that both are the product of an early hybridisation event ( s ) between lineages TcI and TcIIb 6 ., Others argue that TcIIc and TcIIa represent a single ancestral group in their own right 5 , whereby these lineages share a characteristic mitochondrial genome distinct from both TcI and TcIIb ., These hypotheses are not mutually exclusive and TcIIa and TcIIc are not easily distinguished based on mitochondrial sequences 4 ., However , nuclear gene sequences consistently support their status as genetically separate clades 4 , 6–8 and flow cytometric analysis across a panel of representative strains reveals that TcIIc and TcIIa genomes are divergent in terms of their absolute size 9 ., The current tendency to group TcIIc and TCIIa as a single lineage is an oversimplification that may arise from Miless original Z3 classification 10 ., In fact Miles clearly defines an additional lineage in later publications – Z3/Z1 ASAT , which corresponds to TcIIc 11 , 12 ., Researchers attempting to classify a third major lineage , TcIII - corresponding loosely to TcIIc - almost entirely ignore TcIIa 5 , as well the large divergence between North and South American TcIIa isolates 13 ., By contrast , there is general consensus in the literature regarding the evolutionary origin of the two remaining lineages , TCIId and TCIIe ., These are almost certainly hybrids and nucleotide sequence 4 , microsatellite 5 , and enzyme electrophoretic 14 , 15 data show that the parents are TcIIc and TcIIb ., In line with experimental data 16 , maxicircle kinetoplast DNA inheritance in TcIId and TcIIe appears to have been uniparental 4 , 5 , and both retain a mitochondrial genome similar to that of TcIIc ., TcIIc is infrequently isolated from domestic transmission cycles ., Sporadic reports of this lineage occur from domestic mammals in the Chaco region of Paraguay and Argentina as well as southern Brazil ( Canis familiaris 17–19 ) from humans in Brazil 19 , 20 and from domestic triatomine bugs in Argentina and Peru ( T . infestans 17 Miles M A , unpublished ) ., In total , domestic TcIIc isolates make up only a handful of strains over >30 years of sampling ., By contrast , other lineages - in particular TcI , TcIIb , TcIId and TcIIe - are common in humans , domestic mammals and vectors 21 , TcI in northern South America and TcIIb , IId and IIe in the Southern Cone region ., Although rare to domestic transmission cycles , TcIIc occurs with relatively high frequency in the silvatic environment ., We have shown that this DTU is almost exclusively associated with terrestrial transmission cycles and fossorial mammalian genera , including the Cingulata ( armadillos ) and terrestrial marsupials ( Monodelphis spp . & Philander frenata ) 19 , 22 ., Terrestrial rodents ( Dasyprocta spp . , Proechimys iheringi , Oryzomys spp . and Oxymyctereus sp . 15 , 19 ) and Carnivora ( Conepatus spp . 17 ) have also been implicated ., Among these hosts , the nine-banded armadillo , Dasypus novemcinctus , is probably the most important ., In Paraguay 22 and Bolivia ( Llewellyn et al . , unpublished data ) prevalence of infection in this mammal is consistently 33%–57% across distinct geographic foci ., Although D . novemcinctus does account for most of the TcIIc isolates sampled from mammalian reservoirs in the silvatic environment , it is unclear to what extent D . novemcinctus and TcIIc have shared a common evolutionary relationship ., Trypanosomes rarely co-speciate with their hosts or vectors , instead ‘ecological-host fitting’ is thought to be the major driver behind parasite diversification 23 whereby parasite clades are associated with distinct vector/host cliques characteristic of a particular ecological niche ., Thus far , few vector species have been incriminated in silvatic transmission of TcIIc ., Pantrongylus geniculatus and Triatoma rubrovaria , both principally silvatic vectors and often , although not exclusively , associated with terrestrial ecotopes 24 , as well as Dasypus sp ., armadillos 2 , 12 , 25 , 26 , are both recorded with TcIIc infection 12 , 19 , 27 The occurrence of TcIIc in domestic transmission cycles , albeit infrequently , implies a role as an agent of human disease ., In addition , it is likely that TcIIc is under-reported from both domestic and silvatic transmission cycles because some typing methodologies fail to distinguish between TcIIa and TcIIc ( e . g . 28 ) ., Furthermore , TcIIc is one of the parents of the hybrid lineages TcIId and TcIIe 4 , which are predominant agents of severe Chagas disease in the Gran Chaco and adjacent regions 21 ., TcIIc therefore represents an important focus for study ., As we have recently shown for TcI , an understanding of the dynamics of silvatic T . cruzi infection is a vital step before evaluating the nature of domestic parasite transmission 9 ., For TcIIc , this rationale becomes important as human populations expand into previously undisturbed cycles of natural transmission and secondary vector species re-emerge from the silvatic environment after the eradication of major domestic species 29 , 30 ., With the aim of establishing the diversity of silvatic TcIIc , here we use 49 microsatellite loci , 12 newly identified in this study , in conjunction with sequence from the glucose-6-phosphate isomerase ( GPI ) gene to examine the population genetics of this lineage from foci across South America ., We demonstrate that TcIIc populations are diverse , spatially structured and well established across different climatic regions in South America ., By comparison to a newly available TcI microsatellite dataset , we are able to shed light on the ecological and evolutionary significance of our findings ., A c . 1 kb fragment of the glucose-6-phosphate isomerase ( GPI ) gene was sequenced across a representative subset of 22 TcIIc isolates ., Genbank accession numbers for the corresponding strains are included in Table S2 ., Amplification was achieved according to Gaunt et al . , ( 2003 ) using primers gpi . for ( 5′-CGC ACA CTG GCC CTA TTA TT ) and gpi . rev ( 5′-TTC CAT TGC TTT CCA TGT CA ) 16 in a final reaction volume of 25 ul containing containing 1× Taq polymerase reaction NH4+ buffer ( Bioline , UK ) ) , 2 mM MgCl2 , 200 uM dNTPs; 25 pM of each primer , 1 . 25 units of Taq polymerase , and 35 ng of parasite DNA ., The reaction cycle involved an initial denaturation step for five minutes at 94°C , followed by 28 amplification cycles ( 94°C for 30 seconds , 60°C for 30 seconds , 72°C for 30 seconds ) and a final ten minute elongation step at 72°C ., PCR products were prepared for sequencing with a BigDye® v3 . 1 sequencing kit ( Applied Biosystems , UK ) , according to the manufacturers instructions ., In addition to forward and reverse external primers , one internal primer was also employed , gpi . 1 ( 5′TGT GAA GCT TTG AAG CCT TT ) 16 ., Samples demonstrating two or more heterozygous sequence profiles at individual nucleotide sites were cloned individually using the pGEM T easyVector® system ( Promega , UK ) to derive sequence haplotypes ., Owing to the reported occurrence ( c . 20% e . g 32 ) of artefactual recombinant sequence haplotypes derived from Taq DNA polymerase template switching during PCR amplification , ten different clones were sequenced from each sample ., Minority recombinant sequence artefacts were identified and excluded from the analysis ., Analysis was undertaken of a 980 nucleotide sequence alignment of all experimentally derived haplotypes ., Also included in this alignment were selected fragments available on Genbank from a recent study of T . cruzi GPI sequence diversity ( AY484472–AY484478 ) 7 ., Tree topology was defined using Kimura-2-parameter ( k2p ) distances and reconstructed through Neighbour-Joining ( NJ ) in the PHYLIP v3 . 67 software package 33 ., A thousand bootstrapped datasets were generated in SEQBOOT , analysed using k2p distances , and the resultant NJ trees assessed for congruence in CONSENSE , all in PHYLIP v3 . 67 33 ., The resulting tree ( Figure 1 ) was visualised and prepared for publication using FigTree v1 . 1 . 1 ( http://tree . bio . ed . ac . uk/software/figtree/ ) ., Repetitive motifs were extracted from the draft sequence of the T . cruzi genome available at www . genedb . org for analysis of all 53 TcIIc isolates ., Four Mb of sequence , including at least 13 syntenous sequence fragments ( SSFs ) , were scanned for di- and tri-nucleotide repeats using a pattern matching script ( regular expression ) written in sed ., An extension of the algorithm was included to extract the up and downstream flanking regions of the microsatellite sequence ( ∼200 bp ) ., Primer design was achieved in PRIMER3 34 Over 200 microsatellite loci were identified and screened against a representative subset of five TcIIc isolates ., Forty-nine markers , polymorphic across the test group , were selected for further use , including two employed in previous studies 35 ., Thirty-seven markers correspond to those we have employed in a recent study of TcI intra-lineage diversity 9 ., Twelve are unique to this study ., Primer codes , sequences and binding sites are listed in Supplementary Information ( Table S3 ) ., After optimisation of annealing temperatures , the following reaction cycle was implemented across all loci: a denaturation step of 4 minutes at 95°C , followed by 30 amplification cycles ( 95°C for 20 seconds , 57°C for 20 seconds , 72°C for 20 seconds ) and a final 20 minute elongation step at 72°C ., Reaction conditions , with a final volume of 10 ul , were as follows: 1× ThermoPol Reaction Buffer ( New England Biolabs ( NEB ) , UK ) , 4 mM MgCl2 , 34 uM dNTPs; 0 . 75 pmols of each primer , 1 unit of Taq polymerase ( NEB , UK ) and 1 ng of genomic DNA ., Five fluorescent dyes were employed to label forward primers – 6-FAM and TET ( Proligo , Germany ) as well as NED , PET & VIC ( Applied Biosystems , UK ) ., Microsatellite allele sizes were determined using an automated capillary sequencer ( AB3730 , Applied Biosystems , UK ) in conjunction with a fluorescently tagged size standard and were manually checked for errors ., All isolates were typed “blind” to control for user bias ., Allelic richness estimates were calculated in FSTAT 2 . 9 . 3 . 2 36 and corrected for sample size using Hurlberts rarefaction method 37 in MolKin v3 . 0 38 to obtain an unbiased measure of genetic polymorphism among those populations studied ., Heterozygosity indices ( Table 1 ) were estimated in ARLEQUIN 3 . 0 39 ., They include mean expected ( under Hardy-Weinberg ( HW ) expectations ) and observed heterozygosity over loci , as well as tests for deviation from HW equilibrium at the level of individual loci within populations ., Pair-wise FST values were also estimated in ARLEQUIN v3 . 0 39 , and represent the proportion of variation accounted for by the sub-division between each population pair by comparison to the total level of variation across both populations ., P-values for multiple tests were corrected using a sequential Bonferroni correction 40 to minimise potential Type 1 errors ., A further statistic , FIS was applied as an alternate measure of heterozygosity by assessing the level of identity of alleles within individuals compared to that between individuals where +1 represents all individuals homozygous for different alleles and −1 all individuals heterozygous for the same alleles ., Mean FIS values per SSF per population were calculated in FSTAT 2 . 9 . 3 . 2 ., to examine the genomic distribution of heterozygosity ., Multilocus linkage disequilibrium , estimated by the Index of Association ( IA ) , was calculated in MULTILOCUS 1 . 3b 41 , 42 ( Table 1 ) and tests for evidence of the non-random association of alleles across multiple loci ., Genetic distances between isolates were evaluated in MICROSAT under an infinite alleles model of microsatellite evolution using DAS ( 1-proportion of shared alleles at all loci / n ) 43 ( Figure 2 ) ., To accommodate multi-allelic loci , and asses their influence on the stability of the resulting tree , a script was written in Microsoft Visual Basic to make multiple random diploid re-samplings of each multilocus profile ., Individual-level genetic distances were calculated as the mean across multiple re-sampled datasets ., A single randomly sampled dataset was used for population-level analysis ., A Mantels test for matrix correspondence was executed in GENALEX 6 to compare pair-wise geographical ( km ) and genetic distance ( DAS ) 44 ( Figure 3 ) ., Samples were assigned to populations on an a priori basis according to geography and transmission cycle ., DAS - defined sample clustering was also used to inform population identity , and obvious outliers assigned to the correct genetic group ( Figure 2 ) ., 53 TcIIc isolates were assembled among which 33 are original to this study and were collected in Venezuela and Bolivia between 2005 and 2007 ., Venezuelan isolates were collected from the tropically forested foothills of the Cordillera Oriental to the west of the country around the town of Curbati , Barinas state ( Sample prefix M & PARAMA ) ., Three study sites in Bolivia fall across different ecological zones ., The first , comparable in terms of ecotope but not elevation to the Venezuelan site was in low-lying Beni state ( Sample prefix SJMO & SJM ) ., The second was located in semi-arid Chiquitania dry forest c . 60 km east of Santa Cruz de la Sierra ( Sample prefix CAYMA ) and the last in the arid Chaco region c . 200 km south of Santa Cruz de la Sierra ( Sample prefix MA & SAM ) ., Isolates from Paraguay where collected by M . Yeo ( MY ) between 2001 and 2003 22 ., The northern study site at Campo Lorro lies in the arid Paraguayan Chaco ( Sample prefix MA ) and the southern site in semi-arid savannah in the central department of San Pedro ( Sample prefix SP & ARMA ) ., Four further historical isolates: M5361 , CM17 , CM25 & 85/847 are from North-Eastern Brazil , Eastern Colombia ( CM ) and Alto Beni ( Bolivia ) respectively ., Among numerous mammal species sampled ( >25 - Llewellyn et al . , unpublished;\u200a=\u200a10 - MY 22 ) , most isolates originated from D . novemcinctus , including animals from Venezuela , Brazil , Colombia , Bolivia , and Paraguay ., However , a number of secondary hosts were also present ., In Colombia these included the terrestrial agouti , D . fugilinosa , in Bolivia armadillo genera Euphractus sexcinctus and Chaetophractus vellorosus , and in Paraguay E . sexcinctus and C . vellorosus , as well as the terrestrial marsupial Monodelphis domestica ., A single isolate originates from a Panstrongylus spp ., triatomine nymph found infesting a D . novemcinctus burrow at Curbati , Venezuela ., The tree resulting from sequence analyses is shown in Figure, 1 . Nine GPI sequence haplotypes were resolved among the 25 isolates analysed , and nine variable sites identified - equating to ∼0 . 9% sequence diversity within the TcIIc group ., TcIIc emerged as a moderately well supported sister group to TcI ( 72% bootstrap support ) and clearly distinct to those TcIIa strains included in the analysis ., Among the TcIIc group , some correlation with geography was observed ., The following , weakly supported ( >50% ) , clades were apparent: a ‘northern’ group , corresponding to isolates from Brazil , Venezuela , Colombia and Bolivia; and a southern group , corresponding exclusively to isolates from Paraguay and Bolivia ., This subdivision corresponds to two fixed single nucleotide polymorphisms between the two groups ., One sequence haplotype , ( sjmc19_h1 and m10_hap1 ) fell as an outlier , and could not be assigned to either group ., Removal of these isolates from the analysis improved resolution of the subdivision within the TcIIc group ., Phylogenetic clustering occurred independently of host species ., A final dataset of 4 , 585 alleles ( excluding missing data ) was subjected to analysis ., Most strains presented one or two alleles at each locus ., Multiple ( ≥3 ) alleles were observed at a small proportion of loci ( 0 . 45% ) , only among uncloned strains , and indicate the possible presence of polyclonal infections in reservoir hosts sampled ., Four populations were defined: Venezuela , Colombia and Brazil ( NORTHBraz/Ven/Col ) ; Northern Bolivia ( BOLNorth ) , Southern Bolivia ( BOLSouth ) and Paraguay ( PARANorth/Central ) ., Measures of sample size-corrected genetic diversity ( Allelic richness ( Ar ) , were relatively homogeneous across all populations ( Ar\u200a=\u200a2 . 58–2 . 83 , Table 1 ) , and no support for a specific correlation between genetic diversity and geographic origin was identified ., Diversity indices across all TcIIc populations were equivalent to those observed in lowland silvatic TcI populations ( Ar\u200a=\u200a2 . 23–2 . 34 ) 9 ., Identical TcIIc multilocus genotypes ( MLGs ) were not observed , and clone correction ( removal of identical MLGs ) unnecessary in the calculation of parameters from the current dataset ., Isolate clustering based on pair-wise DAS values ( Figure 2 ) revealed clades broadly defined by geographical origin ., Strong bootstrap support ( 92 . 2% ) was found for a division between isolates from Northern and Southern South America ., SJMC19 ( Table S2 ) , isolated from D . novemcinctus in BOLNorth , and defined by GPI sequence data as an outlier on the basis of one halplotype , represents a possible migrant and fell within the Northern cluster on the basis of microsatellite variation ., As such it was assigned to NORTHBraz/Ven/Col for population level analyses ., Consistent with physical proximity , no bootstrap support was apparent between clades from Bolivia and Paraguay ., As with GPI sequence data , partitioning of isolates by host was not apparent in this dataset ., A tree based on pair-wise distances was also constructed under a step-wise model of microsatellite mutation ( δμ2 45 ) and bootstrapped using the same methodology as that in Figure, 2 . Overall the result was poor by comparison to the DAS derived topology ., The bootstrap value for the major division between northern and southern South America , for example , was c ., 3% ., The extent of spatial structuring among isolates was tested by examining the relationship between genetic ( DAS ) and geographical distance ( km ) ., Strongly significant ( RXY\u200a=\u200a0 . 687 , p<0 . 001 ) isolation by distance was apparent across all TcIIc isolates ., To facilitate a direct comparison between the spatial dynamics of two distinct T . cruzi major genotypes with their principal reservoir species , TcIIc isolates drawn exclusively from D . novemcinctus were compared with a larger dataset of TcI isolates from Didelphis spp ., ( D . marsupialis and D . albiventris ) 9 ( Figure 4 ) ., The following conclusions can be drawn: 1 ) Both D . novemcinctus TcIIc isolates and D . marsupialis TcI isolates show significant spatial structure ( TcIIc - RXY\u200a=\u200a0 . 658 , p<0 . 001; TcI - RXY\u200a=\u200a0 . 429 , p<0 . 001 ) ., Furthermore , the standard error ( SE ) about the regression gradient ( RG ) for each does not encompass zero , confirming this result ., 2 ) TcIIc isolates from D . novemcinctus show greater spatial structure than TcI from D . marsupialis as the RG of the former ( TcIIc - RG\u200a=\u200a6 . 445×10−5+/−SE 2 . 401×10−6 ) is greater than the latter ( TcI -RG\u200a=\u200a2 . 234×10−5+/−SE 1 . 049×10−6 ) and the respective error bars do not overlap ., Importantly TcIIc and TcI isolates from their respective principal host species were sampled across approximately the same geographical range , validating a direct comparison between the two ( Figure 2 , 9 ) ., Heterozygous deficiency with respect to HW expectations was a consistent phenomenon across all population examined ( Table 1 ) ., This effect was most pronounced in NORTHBraz/Ven/Col ., To explore the genomic distribution of homozygosity , mean FIS values were calculated from each SSF ( as defined by the online CL Brener genome – www . tigr . com ) containing ≥2 microsatellite loci ., The results of this analysis are displayed in Figure 3 and suggest that homozygosity is fairly evenly distributed across the SSFs studied and by extension homozygosity is likely to be a genome-wide phenomenon ., Notably , when Brazilian , Colombian and Bolivian isolates were excluded from NORTHBraz/Ven/Col , a marked reduction in FIS was observed ( Figure 3 ) ., Thus , to an extent , high levels of homozygosity within this population may be partially attributable to intra-population subdivision ( Wahlund effect 46 sensu lato as in 47 ) ., Significant pair-wise inter-population subdivision ( FST ) ( p<0 . 004 ) after a sequential Bonferroni correction ( Table S1 ) indicates that all populations studied are fairly discrete in population genetic terms , and values broadly correspond to the geographical distances involved ( e . g . lowest subdivision is observed between populations closest geographically - BOLNorth and BOLSouth ( FST\u200a=\u200a0 . 051 ) ) ., In support of differential levels of spatial structuring between TcIIc and TcI as summarised earlier , gene flow between a silvatic TcI population from BOLNorth and populations from lowland Venezuela and North-Eastern Brazil was higher than that observed from the TcIIc dataset 9 ., However , a possible confounder was the anomalous position of isolate SJMC19 , which clustered alongside isolates from NORTHBraz/Ven/Col ., In this case subdivision between BOLNorth and NORTHBraz/Ven/Col ( FST\u200a=\u200a0 . 284 ) is likely to have been marginally overestimated ., Strongly significant ( p<0 . 001 , Table 1 ) linkage disequilibrium ( measured using the Index of Association ( IA ) ) was detected in all populations except BOLNorth where only marginal significance was observed ( p\u200a=\u200a0 . 032 , Table 1 ) ., Predominantly clonal parasite propagation is thus supported by the non random association of alleles at different loci in most populations ., However , given that the IA is a highly conservative measure , some level of recombination cannot be ruled out in any population , especially BOLNorth ., The widespread spatial distribution and genetic diversity of the TcIIc isolates studied here point to an possible ancient origin for this DTU and certainly a long-term association with terrestrial transmission cycles ., Historically , most TcIIc isolates have originated from the Southern Cone region of South America 19 , 22 ., We can now confirm that TcIIc occurs as far north as Western Venezuela , and by implication throughout the continent ., Levels of genetic diversity among populations studied are comparable to those observed in arboreal silvatic TcI from lowland moist forest ecotopes 9 ., Indeed there is no evidence from the current dataset to suggest that TcIIc is any ‘younger’ than TcI in evolutionary terms , although microsatellites may be a poor estimator of ancient evolutionary events ., Nonetheless , the divergent TcIIc mitochondrial genome ( i . e . kinetoplast maxicircle ) does suggest an ancient origin for this lineage 4 , 5 and lends support to our data ., Also , observed heterozygous deficiency is not superficially consistent with a hybrid origin for TcIIc 6 ., Again , however , microsatellites are an imperfect tool for detecting ancient hybrid signatures ., Informative variation will be lost rapidly via mutation and/or gene conversion ., Genetic diversity was surprisingly homogenous across the populations studied , an observation interesting in the context of the major host species examined ., Molecular dating of the long-nosed armadillos , the Dasypodini ( which includes Dasypus spp . ) suggests an early emergence for this group ( c . 40, MYA ) , if not for the species D . novemcinctus itself , which is likely to have emerged later 48 ., The ancestors of extant Dasypus species were presumably widespread in the tropical-temperate forest environments that predominated throughout South America around this time 49 ., The emergence of the extant Euphractinae ( which include Chaetophractus and Euphractus spp ) is thought to have occurred very recently ( c . 5, MYA ) in response to climatic cooling and the formation of the arid southern Chaco and Pampas ecotopes 48 ., Diversity estimates from our data reject a recent radiation of TcIIc into Paraguay and Southern Bolivia in conjunction with the emergence of Euphractinae hosts ., It seems instead that residual populations of Dasypus spp ., have maintained TcIIc transmission in dryer areas , and indeed these mammals demonstrate a much higher infection rate in Southern Bolivia ( Llewellyn et al . , unpublished data ) and Northern Paraguay 22 than other dry-adapted armadillo genera , despite being less abundant ., This observation could be related to the ease with which the burrows of different armadillo genera are infested with triatomines ., Our field observations suggest that Tolypeutes matacus ( rarely , if ever infected - Llewellyn et al . , unpublished , 22 ) does not dig burrows; E . sexcinctus and Chaetophractus spp ( infrequently infected Llewellyn et al . , unpublished , 22 ) dig very deep burrows; whereas D . novemcinctus burrows are shallower , subject to repeated use by the same individual and provide an easily accessible long-term refuge for triatomines ., Nonetheless , triatomines do transmit TcIIc to other terrestrial genera and secondary hosts must have fairly frequent contact with this DTU as D . novemcinctus and non-D ., novemcinctus isolates are not clearly distinguishable at discrete foci ., TcIIc is thus eclectic in terms of host in terrestrial transmission cycles , as expected under a model of ‘ecological host-fitting’ 23 ., It follows that a stringent co-evolutionary relationship with D . novemcinctus can be ruled out in the context of the current dataset , and , in the context recent data from Brazil , with other known hosts of TcIIc 19 ., Interestingly , a new isolate from a Panstrongylus spp ., nymph in Barinas , Venezuela ( M3-CU ) , recovered from the burrow of D . novemcinctus corroborates earlier reports of TcIIc from this vector genus in North-Eastern Brazil 12 , and provides more support for ‘divergence by niche’ in T . cruzi silvatic populations 22 ., On the basis of microsatellite diversity , and concordant with a related study in Brazil 19 , TcIIc is highly spatially structured across South America ., This observation corresponds with the general epidemiology of silvatic disease transmission , where endemic parasite populations at distinct foci exchange little genetic content in the absence of rapid and long distance host or vector dispersal ., SJMC19 , a strain isolated from D . novemcinctus in Northern Bolivia , is an exception being apparently a northern migrant ., However , the grouping of SJMC19 with isolates from NORTHBraz/Ven/Col could be an artefact of poor sample coverage from Western Brazil and warrants more intensive sampling from this region ., A statistical comparison between TcI and TcIIc isolates from their major reservoirs ( D . marsupialis and D . novemcinctus respectively ) reveals greater spatial structuring among the latter ., This perhaps relates to the larger home range of D . marsupialis as compared to D . novemcinctus 50 , but also to the greater number of secondary hosts involved in TcI transmission 22 , if historical records are broadly representative of the relative abundance of the two lineages among mammalian genera ., GPI sequence data provide a more confused pattern of spatial diversification , where , among the 24 TcIIc strains analysed , the North-South divide is less pronounced ., A single nuclear locus , especially from a relatively conserved sequence class , is clearly insufficient to address a population genetic question ., However , sequence data ( Figure, 1 ) do corroborate the anomalous status of SJMC19 , and two highly divergent haplotypes are evident in this sample , one identical to a Venezuelan haplotype ( itself an outlier ( M10 A1 ) ) , and the other occurring alongside haplotypes from Paraguay , Central and Southern Bolivia , potentially consistent with recombination and worthy of further study ., A common feature between both TcI 9 and TcIIc isolates , and consistent within T . cruzi as a whole 4 , 6 , 35 , with the exception of hybrids TcIId and TcIIe , is an apparent lack of heterozygosity as compared to Hardy-Weinberg expectations ., Heterozygous deficiency is also incongruent with extreme models of long term clonal evolution in diploids , where haplotypes are expected to become increasingly divergent over time in the absence of recombination 47 , 51–53 ., Excess homozygosity in sexual populations , assuming strict neutrality , zero allele drop out and discounting Wahlund effects , is normally indicative of inbreeding 54 ., Heterozygosity in predominantly clonal diploids such as T . cruzi can theoretically be reduced by several processes including gene conversion and occasional recombination ( both out-crossing and selfing events ) , but distinguishing between these processes is challenging ., As in our recent study of TcI microsatellite diversity 9 , we can show that homozygosity in T . cruzi is genomically diffuse ., This suggests that infrequent , localised ( e . g . whole chromosomes or chromosome fragments ) gene conversion events can , therefore , be ruled out in the context of those SSFs we examined ., A recent population genetic study of a related trypanosomatid ( Leishmania braziliensis ) , previously thought to be clonal , partially attributes excess homozygosity to endogamic recombination 55 ., We found no concrete evidence for sexuality within the TcIIc populations studied , but some level of recombination cannot be ruled out , especially in BOLNorth , where only marginal significance could be attributed to the Index of Assocation ( multilocus linkage disequilibrium 42 ) , which is considered a conservative measure of clonality 47 ., Two important issues must be considered when attempting to distinguish between the various non-exclusive sources of homozygosity in a predominantly clonal diploid:, 1 ) It seems illogical to correct for Wahlund effects using population assignment programs that explicitly rely upon Hardy-Weinberg assumptions with the aim of demonstrating endogamic sexuality 55 , 56 – this argument is circular ., 2 ) In order to discount gene conversion by disproving a negative relationship between allele size differences in heterozygotes and the number of heterozygotes across samples , one must assume a purely stepwise model of microsatellite mutation 55 , without significant frequencies

Introduction, Methods and Analyses, Results, Discussion

Trypanosoma cruzi , the etiological agent of Chagas disease , is highly genetically diverse ., Numerous lines of evidence point to the existence of six stable genetic lineages or DTUs: TcI , TcIIa , TcIIb , TcIIc , TcIId , and TcIIe ., Molecular dating suggests that T . cruzi is likely to have been an endemic infection of neotropical mammalian fauna for many millions of years ., Here we have applied a panel of 49 polymorphic microsatellite markers developed from the online T . cruzi genome to document genetic diversity among 53 isolates belonging to TcIIc , a lineage so far recorded almost exclusively in silvatic transmission cycles but increasingly a potential source of human infection ., These data are complemented by parallel analysis of sequence variation in a fragment of the glucose-6-phosphate isomerase gene ., New isolates confirm that TcIIc is associated with terrestrial transmission cycles and armadillo reservoir hosts , and demonstrate that TcIIc is far more widespread than previously thought , with a distribution at least from Western Venezuela to the Argentine Chaco ., We show that TcIIc is truly a discrete T . cruzi lineage , that it could have an ancient origin and that diversity occurs within the terrestrial niche independently of the host species ., We also show that spatial structure among TcIIc isolates from its principal host , the armadillo Dasypus novemcinctus , is greater than that among TcI from Didelphis spp ., opossums and link this observation to differences in ecology of their respective niches ., Homozygosity in TcIIc populations and some linkage indices indicate the possibility of recombination but cannot yet be effectively discriminated from a high genome-wide frequency of gene conversion ., Finally , we suggest that the derived TcIIc population genetic data have a vital role in determining the origin of the epidemiologically important hybrid lineages TcIId and TcIIe .

Trypanosoma cruzi , the etiological agent of Chagas disease , infects over 10 million people in Latin America ., Six major genetic lineages of the parasite have been identified with differential geographic distributions , ecological associations and epidemiological importance ., With the advent of the T . cruzi genome sequence , it is possible to examine the micro-epidemiology of T . cruzi using high resolution genetic markers that assess diversity within these major types ., Here we examine the genetic diversity of TcIIc , a poorly understood T . cruzi genetic lineage found predominantly among wild cycles of parasite transmission infecting terrestrial mammals and triatomine vectors , but also a potentially important emergent human disease agent ., Amongst a number of findings , we show that TcIIc genetic diversity is comparable to other ancient T . cruzi lineages , highly spatially structured , and that a stringent co-evolutionary relationship with its principal reservoir host can be ruled out ., Additionally , TcIIc is one of the two parents of hybrid lineages TcIId and TcIIe , which cause most of the Chagas disease that occurs in the Southern Cone of South America ., The system we have developed will help to clarify the ecological circumstances around the emergence of these epidemiologically important hybrids , and perhaps help predict similar events in the future .

evolutionary biology/microbial evolution and genomics, ecology/evolutionary ecology, molecular biology/molecular evolution, public health and epidemiology/infectious diseases, microbiology/parasitology, ecology/population ecology

journal.pgen.1006861

Splicing stimulates siRNA formation at Drosophila DNA double-strand breaks

DNA is constantly challenged by mutagenic processes of extrinsic and intrinsic origin ., Of these damages , DNA double-strand breaks are particularly problematic lesions because they disrupt the continuity of genetic information ., Their repair can either proceed via end-joining activities or through homology-directed repair 1 ., A detailed mechanistic understanding of these repair processes is not only important for the prevention and treatment of diseases , such as cancer , but also to help researchers direct the outcome of genome editing experiments with precision 2 , 3 ., The information stored in DNA is read-out by the process of transcription into RNA ., DNA damage is therefore not only an impediment for replication , but also a hindrance for RNA biosynthesis ., If DNA damage has occurred in an actively transcribed region , concomitant action of DNA repair factors and the transcription machinery is not possible; access to the DNA must thus be regulated 4 ., Stalling of an RNA polymerase upstream of the damaged site may lead to extensive RNA-DNA hybrids , called R-loops , which can themselves cause genomic instability 5–7 ., Consequently , a domain of specific chromatin states is assembled around a DNA double-strand break and transcriptional silencing can occur 8 ., On the other hand , RNA polymerases stalled by certain types of base damage serve as sensors and thus promote repair of the lesion during transcription-couple repair 9 , 10 ., It is also established that some RNA binding proteins are recruited to sites of DNA damage 5 and non-coding transcription may play an important role in DNA repair 11 , 12 ., Finally , the Prp19 component of the spliceosome can interact with RPA bound to single-stranded DNA and reinforces activation of the protein kinase ATR in a manner that is independent of its function during the splicing reaction13 ., Recently , formation of locus-specific siRNAs has been observed at DNA double-strand breaks 14–17 ., They may promote repair via homologous recombination in mammalian cells and plants 15 , 18 , but the molecular mechanisms through which siRNAs promote homologous recombination are not fully established; in mammalian cells they may involve targeting of Rad51 to the damaged site via protein-protein interactions with Ago2 18 , but it is challenging to exclude indirect effects via perturbed miRNA biogenesis in these experiments ., In Neurospora crassa , their biogenesis is even dependent on , rather than important for , homologous recombination 19–21 ., In Drosophila the generation of DNA damage-induced siRNAs depends on transcription and is limited to only one side of the broken DNA , the region between a transcription start site and the DNA end ., It was thus proposed that the DNA ends serve as transcription initiation sites to generate corresponding antisense transcripts to generate dsRNA 16 ., Consistently , transcription initiation at DNA breaks has now been directly observed in S . pombe 12 ., However , no DNA repair defects could be observed in dcr2 mutant Drosophila melanogaster flies where the siRNA pathway is completely inactivated but the miRNA pathway remains mostly unperturbed 22 ., Although the significance of the siRNAs for DNA repair is thus a matter of debate , their presence demonstrates that transcripts running towards a DNA DSB are subject to some sort of surveillance and , as a consequence , at least partially converted into double-stranded RNA ( the precursor of siRNAs ) ., To shed light on the mechanistic details of this process , we conducted a genome-wide RNA interference screen in cultured Drosophila cells using siRNAs generated from a linearized plasmid ., These siRNAs control expression of a reporter gene ., In addition to DNA double-strand break repair proteins , we discovered that many splicing factors , in particular components from the Prp19 and Prp19-related spliceosome sub-complexes are important for siRNA generation ., Consistently , the presence of upstream introns greatly stimulated siRNA generation at chromosomal DNA DSBs induced by CRISPR-cas9 ., An intron-less gene , on the other hand , only generated few siRNAs upon cleavage ., We propose that the perturbed transcript maturation that ensues when RNA polymerase II encounters a DNA double-strand break is sensed with participation of the spliceosome ., As a consequence , double-stranded RNA is generated and processed into siRNAs ., Intriguingly , the splicing factors identified in our screen for break-induced siRNA generation were also important for siRNA generation from high-copy transgenes ., It is thus conceivable that the transcript maturation surveillance mechanism serves in genome defense beyond DNA double-strand breaks ., We had previously described a reporter system that allows for a dual luciferase-based readout of DNA double-strand break derived siRNA activity 16 ., In short , a linearized plasmid with either a truncated or an inverted coding sequence of Renilla luciferase is co-transfected with a mix of circular expression vectors for Renilla and firefly luciferase to control for transfection efficiency ., The siRNAs generated from the linearized plasmid can then repress full-length Renilla luciferase expression in trans ., When combined with prior experimental RNAi , this assay system has a high signal-to-noise ratio and can easily be scaled up ., Note that the promoters in all reporter constructs contain an intron in the 5’-UTR , which precedes the Renilla or firefly luciferase CDS ., We thus performed a genome-wide RNAi screen in Drosophila S2-cells to identify factors that are required for the generation of DNA double-strand break derived siRNAs ( see S1 Fig and S2 Fig for details ) ., Two independent biological replicates of the entire screen were performed and averaged ., After removing likely false positives , such as retracted gene models or genes that are not expressed in S2-cells , we selected a total of 142 positive and 66 negative candidates from the screen for further validation ( S1 Fig and S1 Table ) ., We re-screened the original dsRNA trigger of our candidates for a third biological replicate and then generated two independent , non-overlapping dsRNAs for each candidate to identify false positives due to off-target RNAi ., Only those candidates that scored positive for at least two out of the three distinct RNAi triggers ( = screened dsRNA and two validation constructs ) were retained ., We also counter-screened the entire set of candidates with a cell line where a GFP reporter is repressed by two perfect matches to miR-277 in its 3’-UTR; expression of this reporter is driven by the same promoter as the Renilla luciferase in the screen ., Since miR-277 is processed by Dcr-1 , then loaded into Ago2 via the Dcr-2/R2D2 complex , we could distinguish between core RNAi pathway components or factors that non-specifically activate transcription of our reporter and those factors that are specifically required for DNA damage-induced siRNA biogenesis ., After this stringent validation process ( summarized in S1 Table ) , we retained a set of 89 genes that promote DSB-derived siRNA generation or function and 36 candidates that are potential repressors of DBS-derived siRNA production ., To obtain an initial overview of the biological processes involved in siRNA generation at the DNA break we performed a gene ontology analysis of the validated candidates ( Fig 1A ) and calculated significances using g:profiler 23 ., As expected for a screen with linearized DNA , we identified a series of DNA replication/repair factors among the positive candidates ( GO-term enrichment of “DNA metabolic process” with a p-value of 9 . 0x10-5 ) ., DNA double-strand breaks are recognized by the Mre11-Rad50-Nbs1 ( MRN ) complex; all components of this complex were among the initial candidates and 2 out of 3 passed our validation experiments ., In addition to DNA damage signaling , the MRN complex initiates 5’ to 3’resection of the break and thus commits the site for homology-directed repair ., The 3’ single-stranded end is subsequently extended further by CtIP/Sae2 , promoting homologous recombination 24 ., Bioinformatic analysis has suggested that the Drosophila CtIP homolog is CG5872 25 and this gene was also required for correct re-localization of a heterochromatic DSB 26 ., We identified CG5872 as a strong candidate in our screen; CG5872 is thus most likely the Drosophila homolog of CtIP/Sae2 and we propose that it should be called dCtIP ( Fig 1B ) ., For further repair , DNA synthesis carried out by the replicative polymerases DNA-polδ and DNA-polε after Rad51-mediated annealing of the exposed 3’ single-stranded regions with a homologous template ., We identified DNA-polδ and all subunits of replication factor C ( RfC ) as stimulators of break-induced siRNA generation ., RfC normally loads the processivity clamp proliferating cell nuclear antigen ( PCNA , called mus209 in Drosophila ) to ensure long-range DNA synthesis ., However , the PCNA-homolog mus209 was initially among the negative candidates but did not pass our validation criteria ( Fig 1C ) ., NHEJ factors , such as the Ku70/Ku80 complex , were not identified in the screen ., Taken together , our screening efforts demonstrate that recognition and processing of the DNA double-strand break for homology-directed repair promotes and thus precedes siRNA generation ., Much more striking than the recovery of the DNA repair factors , however , was the enrichment of splicing factors ( see Fig 1A , positive candidates ) ., For example , the GO-term “RNA splicing” was found enriched among the candidates with a p-value of 2x10-29 ., Among the potential repressors of DSB-derived siRNAs , we found that mRNA 3’-end processing activities were enriched ( e . g . GO-term “mRNA cleavage” p-value 4 . 7x10-14 ) ., Although GO-term analyses must be interpreted with caution , this overview is consistent with the hypothesis that DSB-derived siRNAs are generated with a contribution of the mRNA splicing reaction ., On the other hand , canonical transcript termination via mRNA cleavage appears to remove the trigger for siRNA generation ., The candidates with the GO-term association “splicing” showed a validation success rate comparable to other candidates ( Fig 2A ) ., Since the involvement of splicing is reminiscent of a recently proposed model for transposon recognition in the fungus Cryptococcus neoformans 27 , we tested our candidates for their requirement to repress a genomically integrated , endo-siRNA generating high-copy transgene analogous to a previously published system 28 ., In the current study , we measured a cell line where the high-copy integrated Renilla luciferase responds more strongly to an impaired RNAi pathway than the firefly luciferase integrated at low copy-number; since identical plasmid constructs were used this reporter system allows for direct comparison with the screening data ( Fig 2B ) ., This comparison defined two groups of candidates: The first is required for the generation of DNA break induced as well as high-copy transgene induced siRNAs and comprises , among others , the splicing factors ., The second group is specific for DNA double-strand break induced siRNAs and comprises the homologous recombination factors discussed above ( including dCtIP ) as well as RfC ., Perturbed mRNA splicing may thus be a common trigger for siRNA biogenesis at DNA double-strand breaks as well as high-copy transgenes ., On the other hand , RNAi-mediated depletion of splicing factors could indirectly affect siRNA biogenesis; for example , altered splicing efficiencies could result in lower protein levels of core RNAi factors ., Our analysis with the miR-277 perfect match reporter cell line suggested that upon knockdown of the splicing factors , the core RNAi pathway is unaffected and reporter expression is unchanged ( S1 Table ) ., In addition , we tested several candidates involved in the splicing reaction for de-regulation of RNAi factors via Western blot and saw no consistent protein level changes for core RNAi components ( Fig 2C ) ., We constructed a map of our candidates on the various spliceosome complexes that assemble along the path of a splicing reaction ( Fig 2D ) ., Although we identified factors from all complexes , the recovery was particularly prominent among members of the Prp19- and Prp19-related complexes ( 8 out of 16 vs . 26 out of 138 for all spliceosome components , p<0 , 04 χ2-test , see also S3 Fig ) ., This complex has a pivotal role in enabling the transitions from the pre-catalytic spliceosome into the catalytic phases ., We wanted to confirm the importance of splicing for break-induced siRNA generation by creating DNA breaks at specific positions relative to splice sites ., To this end , we employed the cas9-CRISPR nuclease technology and programmed the enzyme to cleave chromosomal sites before or after an intron , then deep sequenced the small RNAs and mapped them back to the cleaved locus ., To obtain a quantitative measure of siRNA generation at a given site , we calculated the normalized read density per base pair of the affected transcript upstream and downstream of the cleavage site ., If break-derived siRNAs are efficiently generated , then the average reads/bp values are higher between the promoter and the DSB site than in the region following the break 16 ., We first targeted a strongly expressed , intron-less gene ( tctp ) for cleavage at three different positions ., Although cleavage at each site was detectable ( as judged by a T7 endonuclease assay , S4 Fig ) , we observed siRNA generation that was only slightly above background ( average ratios of before vs . after the cut of 1 . 6–2 . 9 , Fig 3A ) ., Analogously , when we targeted the spliced gene CG15098 ( with a similar expression level ) before the first intron , we observed only low levels of siRNA generation ( Fig 3B ) ., A cut close to the first intron-exon junction ( 82 nt downstream ) resulted in rather moderate siRNA induction ., Cleavage further downstream , however , led to a strong generation of siRNAs with an increasing ratio of reads upstream vs . downstream of the break ( up to 58 . 3 ) as the cut was moved downstream along the gene ( Fig 3B , see also S5 Fig and S6 Fig for detailed traces ) ., Thus , splicing of the transcript affected by the DNA break greatly stimulates siRNA generation ., We extended our analysis to the CG18273 gene , which is only moderately expressed in S2-cells ( S7 Fig ) ., Upon cas9-mediated DSB induction , an siRNA response could be induced here as well ., Interestingly , this gene showed moderate , cleavage-independent siRNA coverage in its 3’-portion ., Prior to this zone , the DNA-break induced coverage was about 10-fold lower than the coverages we observed in the cleaved CG15098 gene ., The strength of the DSB-induced siRNA response correlates thus with the host gene expression level , consistent with the notion that the mRNA transcript contributes the sense strand to dsRNA formation ., CG18273 has a short ( 60 nt ) first intron followed by a rather long second exon ( 2375 nt ) ., Cleavage within this second exon resulted in siRNA formation , indicating that a single short intron can suffice to trigger the response ., Furthermore , when we induced DNA cleavage close to the end of the CG18273 gene ( 4686 nt downstream of the transcription start site ) , we observed siRNA coverage all the way to the start of the transcriptional unit ., The DSB-induced small RNA response can thus cover a window of several kbp even in moderately expressed regions ., Since splicing is required to trigger the DNA-damage dependent siRNA response , we tested whether a knockdown of splicing factors identified in our screen simply reduces mRNA splicing and thereby diminishes the trigger for siRNA production ., We thus determined splicing efficiencies at our CG15098 model locus and the tsr gene ( both show strong expression in our S2 cells ) following depletion of candidates recovered in our screen ., After knockdown of the SR protein kinase Doa , the hnRNP protein hrb27C and the spliceosome component l ( 1 ) 10Bb ( the Bud31 homolog from the Prp19-related complex ) , we isolated total RNA and used qRT-PCR ( random hexamer primed ) to quantify the levels of unspliced pre-mRNA and spliced mRNA ( Fig 4 ) ., The values were normalized to an amplicon that was internal to one of the exons and thus reported on the total amount of transcript from each locus ( i . e . spliced and unspliced message ) ., Overall , we found that the unspliced pre-mRNA did not increase relative to a control knockdown ( Renilla luciferase ) ., There was one exception , however: At the short intron in tsr the pre-mRNA became more abundant after RNAi against the spliceosome component l ( 1 ) 10Bb ., Even in this case , though , the amount of spliced message was comparable to the total amount of transcript produced , indicating that the majority of transcripts are correctly spliced ., This was also seen for all other cases where we compared the level of spliced exon-exon junctions to the total amount of message , indicating that the canonically spliced mRNA accounts for essentially all of the transcripts detected at steady-state ., In summary , we conclude that during the time of our knockdown-experiments there is no major change in general splicing efficiency ., This argues that upon our experimental RNAi , the splicing factors and spliceosome components we identified did not yet induce major changes in mature mRNA levels ., Rather than influencing the general cellular protein content , they may thus limit the signaling events that emanate from splicing reactions / spliceosomes perturbed by a nearby DNA break ., We used qRT-PCR to directly test whether a downstream DNA break perturbs progression of the splicing reaction ., We induced cas9-mediated DNA cleavage downstream of the third intron of the CG15098 gene , then isolated total RNA and reverse transcribed both nascent and mature mRNA with random hexamer primers ., We then interrogated nascent RNA with primers covering an exon-intron junction and mature mRNA with primers spanning an exon-exon junction ., The samples were normalized to total CG15098 levels with an amplicon located inside of exon 3 as described above ., Control samples were analyzed analogously and we calculated the cut/uncut ratio of each of the amplicons ( Fig 4C ) ., Indeed , we found significantly more unspliced RNA at the exon3-intron3 junction when the DNA was cut ( student’s t-test p<0 . 01 , 3 biological replicates ) ., A downstream DNA break thus has the potential to stall progression of the splicing reaction at least transiently ., Since siRNA generation at DNA breaks depends on transcription levels in Drosophila , the sense strand of the siRNA precursor is most likely the normal transcript originating at the locus ., Because upstream introns stimulate siRNA generation ( Fig 3 ) , we asked whether the splicing reaction takes place on the transcript molecule that contributes to the siRNA precursor ., To this end , we analyzed the small RNA sequencing data from our cuts in the CG15098 gene in detail ., The siRNA coverage started essentially adjacent to the induced breaks and continued relatively uniformly until the beginning of the CG15098 transcription start site ( see e . g . S6 Fig ) ., The pattern of siRNA coverage was astonishingly reproducible ( Fig 5A ) , but there was no strong correlation between gene structure and local siRNA coverage ( Fig 5B , calculated based on four biological replicates ) ., Intron 2 , but not intron 1 or 3 , showed a somewhat lower coverage than the exons 1–3 ( p = 0 . 016 , two-sided Student’s t-test , n = 4 biological replicates ) ., Exon-exon junction spanning siRNAs were essentially absent , consistent with the notion that the siRNAs are not generated by RdRP-activities acting on mature mRNA ( p<10−4 , S8 Fig ) ., Furthermore , we found that the coverage of the 3’ intron-exon junctions–the site of the second transesterification reaction—was not diminished; rather , there was a trend towards slightly enhanced coverage relative to exonic reads ( 3’-junction 1: p = 0 . 11 , 3’-junction 2: p = 0 . 06 ) ., Clearly , the sense transcript is not fully spliced prior to siRNA generation ., If the splicing reaction is stalled after the first catalytic step , then siRNAs covering the 5’ exon-intron junction should be diminished ., There was no change for intron 1 and 3 of CG15098 ., For intron 2 we observed a reduction of 5’ exon-intron spanning reads when compared with the exonic coverage ( p = 2x10-4 ) , but not when compared with the adjacent intron 2 coverage ( p = 0 , 3 ) ., The reduced coverage for intron 2 as well as its 5’ splice junction remained when the DNA break was located at different positions relative to intron 2 ( S9 Fig ) ., We thus favor the interpretation that it is an inherent property of this intron ( e . g . sequence-dependent ) ., Because upstream splicing stimulates the siRNA response but the reaction is not completed , we conclude that the spliceosome is stalled most likely in a pre-catalytic state when dsRNA formation is triggered ., This is also consistent with our qRT-PCR analysis ( Fig 4C ) where we saw an increase of nascent RNA as detected by an amplicon spanning the exon-intron junction , i . e . the site of the first catalytic step , after DNA cleavage ., The precursor of siRNAs in Drosophila is double-stranded RNA ( dsRNA ) , which must be generated through convergent transcription since flies lack an RNA-dependent RNA polymerase ., It was previously proposed that the DNA end serves as an initiation site for transcription that produces antisense RNA , followed by pairing with the normal transcript to generate dsRNA 16 ., Our screening and validation experiments demonstrate that DNA end processing by the MRN-complex stimulates siRNA generation ., Thus , recognition of the damage is independent of , and can precede , siRNA biogenesis ., This is consistent with the observation that DNA damage signaling occurs normally in the absence of DNA damage induced siRNAs 14 and with the finding that DNA repair is unaffected when these siRNAs can no longer be made 22 ., Our identification of the Drosophila CtIP homolog CG5872 , a nuclease , in the screen further demonstrate that the generation of a 3’-single stranded DNA overhang facilitates the initiation of antisense transcription ., Yet , siRNA coverage started adjacent to the break site , arguing that nucleolytic processing of the 5’-strand is not overly extensive ., Based on our screening efforts we conclude that spliceosome components are required to trigger an siRNA response and cas9/CRISPR mediated cleavage in the genome revealed that an intron upstream of the break stimulates siRNA generation ., The simplest interpretation is that spliceosomes participate in triggering this response independently of the splicing reaction ., Consistently , we did not observe major splicing defects during the course of the knock-down experiments ( Fig 4A and 4B ) , we could demonstrate that a downstream cut does slow down transcript maturation ( Fig 4C ) and the siRNA coverage analysis argued for a pre-catalytic stalling event ( Fig 5 ) ., What is the mechanism of dsRNA generation at the break ?, A straightforward hypothesis is that upon reaching the broken DNA end , the transcribing RNA polymerase II simply turns around and continues transcription of the other DNA template strand , thus forming a hairpin transcript ( “U-turn move” ) ., This phenomenon is well known when DNA templates with protruding 3’-ends are transcribed by bacteriophage T7 RNA polymerase 29 ., Similarly , free RNA polymerases could spontaneously initiate transcription at the newly formed DNA end; this is also readily observed in vitro with DNA templates that bear a 3’ single-stranded extension and was proposed to occur at DNA double-strand breaks in S . pombe 12 ., In both cases , however , one would not predict that the presence of an intron should stimulate the generation of dsRNA; rather , DNA breaks in the intronless gene should have led to a comparable extent of siRNA generation as the ones in the intron-containing gene ., Consistently , we had observed that transfected linear PCR products do not trigger an siRNA response 16 ., Thus , while we cannot exclude that U-turn transcripts are formed , it is unlikely that they are the source of the majority of the siRNA precursors ., It remains possible , however , that association of the spliceosome with the nascent mRNP leads to a remodeling or modification of the RNA polymerase complex , which favors the execution of a U-turn at the DNA end ( see Fig 6 ) ., A formal possibility is that non-canonical enzymes are recruited to serve as RNA-dependent RNA polymerases ( RdRP ) acting on the transcript affected by the DNA break ., For example , it has been demonstrated that RNA polymerase II can use an RNA template to create a corresponding RNA transcript in the case of human hepatitis delta virus or plant viroid replication 30–33 and that bacteriophage T7 RNA polymerase can replicate short RNA templates 34 ., However , this has only been observed for RNAs with a particular secondary structure , while the DNA damage-induced siRNA response appears to be generic ., Since we did not detect any exon-exon junction spanning reads in our siRNA coverage analysis , any RdRP-like activity would be limited to the unspliced , nascent transcript ., Taken together , we do not consider this a likely scenario ., The most parismonious hypothesis is that RNA polymerase stalls at the break , co-transcriptional mRNA maturation is concomitantly delayed and that this induces a signaling event with participation of the spliceosome ( Fig 6 ) ., Such a stalled transcript probably leads to a persistent R-loop with a corresponding displaced , single-stranded DNA region ., This single-stranded DNA , together with a signal from the spliceosome , could serve as an initiation site for antisense transcription ., It is also conceivable that such R-loops may be larger or more persistent if the stalling occurs in the vicinity of an engaged spliceosome without the need for a specific signaling step ., This mechanism may also act when R-loops occur independently of a DNA break , consistent with the observation that the splicing factors we identified were also required for the small RNA response triggered by high-copy transgenes ., Several previous publications have reported a requirement of splicing factors , but not splicing in general , for small RNA-mediated transcriptional silencing in fission yeast 35 , 36 ., Consistent with this , centromeric non-coding transcripts are indeed spliced in S . pombe 36 ., However , messages coding for essential fission yeast silencing factors appear to be particularly sensitive to diminished splicing activity , thus leading to reduced silencing efficiency; a similar phenomenon can affect the expression of the Drosophila melanogaster piRNA factor piwi 37 , 38 ., It remains a matter of debate whether intron-less , cDNA-based rescue constructs can bypass the silencing defect induced by perturbed splicing in S . pombe 36 , 37 ., Other splicing factors , such as smD1 , appear to function independently of their splicing activity during miRNA and siRNA RISC formation 39 , 40 ., We now demonstrate that for DNA double-strand break triggered siRNAs , an intron is required upstream ( with respect to transcription ) of the lesion to trigger siRNA formation ( Fig 3 ) ., This strongly supports the notion that the splicing process acts in cis during siRNA generation rather than in trans via perturbed splicing of silencing factor mRNAs ., Based on our siRNA coverage and qRT-PCR analysis , we propose that the spliceosome is stalled at a pre-catalytic stage ( see the model in Fig 6 ) ., The Prp19 complex promotes the transition into the catalytic splicing phases and members of this complex appeared enriched among the spliceosome components we identified ( Fig 2D and S3 Fig ) ., Prp19 is of central importance for the splicing-mediated identification of transposon transcripts in C . neoformans 27 , but in this case the reaction was stalled after the first catalytic step ., The Drosophila response we describe is conceptually similar to the recently discovered spliceosome-mediated decay in the budding yeast Saccharomyces cerevisiae ., There , nucleases are recruited to intron-less genes as a consequence of non-productive association with the spliceosome 41 ., Splicing controls piRNA biogenesis in fruit flies; this is a class of small RNAs that represses–together with endo-siRNAs–transposable elements in the germline ., Here , spliced transcripts from the so-called master control loci are prevented from entering the piRNA biogenesis pathway 42 ., On the other hand , the Tho/TREX complex , normally deposited on RNA as a consequence of splicing , is essential for piRNA biogenesis and must associate through an alternative route with unspliced piRNA precursors 43 ., Interestingly , we identified the Tho complex component tho2 as a potential inhibitor of both , DNA damage-induced siRNAs as well as high-copy transgene induced siRNAs ( S1 Table ) ., Induction of DNA damage by UV-light revealed that in human cells , splicing is both a sensor for as well as a target of the DNA damage response ., This depends on the formation of R-loops due to stalled polymerases and results in pleiotropic splicing changes ., The phenomenon bears many parallels to our analysis , but it was limited to transcription-blocking lesions and the authors specifically excluded DNA double-strand breaks as triggers 44 ., A consequence of R-loop formation is the generation of a corresponding stretch of displaced , single-stranded DNA ., Potentially , this DNA is covered by replication protein A ( RPA ) , 45 , a situation that can trigger DNA damage signaling via direct interaction with Prp19 and accumulation of ATR-interacting protein ( ATRIP ) in mammalian cells 13 ., This role of Prp19 appears to be independent of its function during the splicing reaction ., Future experiments should therefore address the question whether multiple recruitment platforms rely on the conversion of the Prp19 complex from a regulator of the splicing reaction into a trigger for DNA damage associated signaling events ., Due to the focus on siRNA biogenesis in our screen , rather than their downstream function , we cannot directly conclude on the benefits of the break-derived siRNAs for the organism ., We previously demonstrated that dcr-2 and ago2 play–at best–an accessory role during DNA repair 22 , but this only addresses the importance of the siRNAs and not their precursor molecules ., It is possible that conversion of the stalled transcript into double-stranded RNA or the process of antisense transcription per se are important for DNA repair ., For example , this could limit the extent of R-loop formation behind a stalled RNA polymerase ( both temporally and spatially ) or set the optimal length of RPA-covered single-stranded DNA , as was recently proposed for Schizosaccharomyces pombe 12 ., Control of R-Loop size during transcription has also been described: In the centromeric regions of fission yeast , a specific arrangement of replication origins and repetitive elements leads to frequent collisions of an RNA polymerase and the replication machinery , followed by the generation of siRNAs in an RdRP-dependent process ., In this case , RNA interference prevents excessive R-loop formation by releasing RNA polymerase II and thus fosters genome integrity 46 ., Other publications have described a role for RNAi during DNA repair in other organisms 14 , 15 , 18 , 19 , 47 ., In the case of the damage-induced Neurospora crassa qiRNAs , DNA repair was even proposed to be the trigger for small RNA production 19 ., We note that in the case of our model Gene CG15098 , no repetitive and thus recombination-favoring sequence arrangement was required ., Clearly , more results are needed to delineate common and divergent features between these experimental systems ., Since the splicing-d

Introduction, Results, Discussion, Materials and methods

DNA double-strand breaks trigger the production of locus-derived siRNAs in fruit flies , human cells and plants ., At least in flies , their biogenesis depends on active transcription running towards the break ., Since siRNAs derive from a double-stranded RNA precursor , a major question is how broken DNA ends can generate matching sense and antisense transcripts ., We performed a genome-wide RNAi-screen in cultured Drosophila cells , which revealed that in addition to DNA repair factors , many spliceosome components are required for efficient siRNA generation ., We validated this observation through site-specific DNA cleavage with CRISPR-cas9 followed by deep sequencing of small RNAs ., DNA breaks in intron-less genes or upstream of a gene’s first intron did not efficiently trigger siRNA production ., When DNA double-strand breaks were induced downstream of an intron , however , this led to robust siRNA generation ., Furthermore , a downstream break slowed down splicing of the upstream intron and a detailed analysis of siRNA coverage at the targeted locus revealed that unspliced pre-mRNA contributes the sense strand to the siRNA precursor ., Since splicing factors are stimulating the response but unspliced transcripts are entering the siRNA biogenesis , the spliceosome is apparently stalled in a pre-catalytic state and serves as a signaling hub ., We conclude that convergent transcription at DNA breaks is stimulated by a splicing dependent control process ., The resulting double-stranded RNA is converted into siRNAs that instruct the degradation of cognate mRNAs ., In addition to a potential role in DNA repair , the break-induced transcription may thus be a means to cull improper RNAs from the transcriptome of Drosophila melanogaster ., Since the splicing factors identified in our screen also stimulated siRNA production from high copy transgenes , it is possible that this surveillance mechanism serves in genome defense beyond DNA double-strand breaks .

DNA damage ultimately threatens the integrity of our genome; in addition , it is an impediment for transcription and thus affects cells independently of the mutagenic effects ., DNA double-strand breaks in transcribed regions can induce the production of corresponding small interfering RNAs , a class of regulators that is well known for controlling gene expression ., The reason why they are generated at sites of DNA damage is not understood , putative roles in DNA repair remain controversial ., Thus , the function of DNA damage-induced siRNAs is a major open question ., In an unbiased approach , we screened Drosophila cells genome-wide to detect those factors that are required to produce damage-induced siRNAs ., In addition to DNA repair proteins , we found that many splicing factors and spliceosome components were necessary ., The need for an intron upstream of the break was confirmed by cas9-CRISPR mediated cleavage , followed by small RNA sequencing ., A detailed look at the siRNA coverage revealed nonetheless that unspliced transcripts give rise to siRNAs ., Hence , the spliceosome must be stalled and signal to the transcription machinery ., The new role of damage-induced siRNAs in local RNA surveillance should be considered on par with their putative function during DNA repair .

invertebrates, gene regulation, animals, dna transcription, animal models, dna damage, drosophila melanogaster, model organisms, experimental organism systems, dna, drosophila, research and analysis methods, small interfering rnas, genome complexity, gene expression, rna splicing, insects, arthropoda, biochemistry, rna, double stranded rna, spliceosomes, rna processing, nucleic acids, genetics, biology and life sciences, genomics, non-coding rna, computational biology, introns, organisms

journal.pgen.1002498

Insulin/IGF-1 and Hypoxia Signaling Act in Concert to Regulate Iron Homeostasis in Caenorhabditis elegans

In order to survive in a changing environment , organisms have evolved abilities to sense their surroundings and adaptively adjust their physiology ., For example , the nematode Caenorhabditis elegans is capable of postponing reproduction if conditions are unsuitable for growth and reproduction by forming dauer larvae 1 , 2 , 3 ., This developmentally arrested third larval stage is resistant to starvation and other stressors , allowing the animal to survive until conditions improve ., Should this occur , dauer larvae can re-enter the normal reproductive life cycle ., The decision between reproductive growth and survival with enhanced stress resistance is controlled by a complex sensory/signaling network that includes the insulin/IGF-1 signaling ( IIS ) pathway 2 ., Mutants with reduced IIS exhibit constitutive dauer larva formation , but can also form adults that are resistant to a range of stressors , including reactive oxygen species ( ROS ) , UV irradiation , heat stress and ER stress 4 , 5 , 6 ., IIS controls this response through the DAF-16/FoxO transcription factor , which enters the nucleus under adverse conditions and affects gene regulation 7 , 8 ., DAF-16 promotes increased expression of many genes encoding proteins that protect against stress , including superoxide dismutases , drug metabolizing enzymes and molecular chaperones 9 , 10 ., DAF-16 is also required for the longevity of IIS mutants , for example those with defects in the DAF-2 insulin/IGF-1 receptor 11 ., Both of these roles of DAF-16 , the promotion of stress resistance and longevity , will improve the chances of living through periods of adversity ., Whether the same downstream mechanisms cause increased stress protection and longevity remains unclear 12 ., One factor contributing to growth and stress resistance is cellular iron availability ., Free intracellular iron is toxic to the cell due to its role in catalyzing the Fenton reaction , which generates hydroxyl radicals from hydrogen peroxide:However , while free intracellular iron is harmful to the cell , iron is also an important element for a large number of cellular processes , including electron transport , deoxyribonucleotide synthesis , cellular detoxification , the cell cycle , oxygen transport and many others 13 , 14 ., Lack of iron is thought to affect the health of up to a billion people worldwide 15 ., As well as nutritional iron deficiency , disruption of mechanisms that regulate iron homeostasis can also lead to a number of serious diseases in humans , such as anemias and iron overload disorders 16 , 17 ., The maintenance of appropriate iron levels is therefore important to viability and is tightly regulated by a number of proteins ., These include ferritins , which form 24-subunit spherical nanocages that are each able to safely store up to 4500 atoms of iron ., Heavy chain ferritins ( H-ferritins ) contain a ferroxidase centre , which has the capacity to convert Fe ( II ) to Fe ( III ) when the iron atom enters the complex 18 ., The C . elegans genome contains two H-ferritin genes , ftn-1 and ftn-2 19 ., ftn-1 is predominantly expressed in the intestine , while ftn-2 is expressed in many cell types 19 , 20 ., In vertebrates , regulation of ferritin gene expression in response to iron levels is achieved both transcriptionally 21 , and post-transcriptionally by the actions of iron regulatory proteins ( IRPs ) which bind to iron responsive elements ( IREs ) in the 5′ UTR of ferritin mRNAs 22 ., Expression of C . elegans ferritin genes is also sensitive to iron levels: iron supplementation increases ftn-1 expression , while iron chelation has the opposite effect ., However , ftn-1 and ftn-2 lack IRE sequences in their 5′ UTRs and iron-dependent regulation seems to be achieved solely through transcriptional regulation 23 ., The mechanism by which this occurs remains unknown , but iron-dependent regulation of ftn-1 requires a 63 bp iron-dependent element ( IDE ) in its promoter 20 ., Research on the regulation of ftn-1 in C . elegans has contributed to our understanding of ‘restless leg syndrome’ , a human disease linked to iron deficiency in the brain ., A haplotype of the gene MEIS1 has been associated with inheritance of the syndrome 24 but the genes function was unknown ., The involvement of the C . elegans ortholog unc-62 in regulating iron homeostasis was tested and a repressive role for this gene in ftn-1 regulation was identified ., This regulation may be conserved in humans , since reduced MEIS1 expression seems to cause increased expression of human ferritin as well as of an iron transporter 25 ., Thus , ftn-1 regulation in C . elegans can serve as a model for understanding the mechanisms of iron homeostasis in humans , and of human disease ., In this study , we explore the biology of iron homeostasis in C . elegans by investigating further the regulation of ftn-1 ., We show that ftn-1 is transcriptionally regulated by IIS/DAF-16 , and then perform a genetic screen using RNA mediated interference ( RNAi ) to identify factors influencing expression of a Pftn-1::gfp reporter ., We identify several transcription factors known to act with IIS to regulate lifespan as factors that also regulate ftn-1 expression ., We also reveal a major role for the hypoxia signaling pathway in ftn-1 regulation and iron homeostasis ., To ascertain whether ftn-1 expression might be regulated by insulin/IGF-1 signaling ( IIS ) and daf-16 , we examined published microarray-derived mRNA profiles comparing daf-2 and daf-16; daf-2 mutants 26 , 27 ., These implied that ftn-1 mRNA levels are greatly elevated ( 47-fold increase ) in daf-2 compared to daf-16; daf-2 animals ., This we were able to confirm using qRT-PCR ( Figure 1A ) ., The increase in ftn-1 mRNA levels in daf-2 mutants was fully daf-16 dependent ., Loss of daf-16 also decreased ftn-1 mRNA levels in daf-2 ( + ) animals ., We then created a transgenic C . elegans line bearing a Pftn-1::gfp transcriptional reporter containing 3 . 8 kb of sequence upstream of the ftn-1 start codon ., This was generated by microinjection of transgene DNA , and the resulting extrachromosomal transgene arrays were then chromosomally integrated ., The Pftn-1::gfp transgene showed strong expression throughout the intestine , consistent with previous reports 20 ., Effects of daf-2 and daf-16 upon Pftn-1::gfp expression paralleled those seen in ftn-1 mRNA levels ( Figure 1B , 1C ) ., This confirms that ftn-1 is regulated by IIS , and shows that this regulation occurs principally in the intestine ., We then used the Pftn-1::gfp reporter as the basis of an RNAi screen to investigate the mechanisms by which ftn-1 is regulated ( Figure 1D ) ., The initial aim of this screen was to identify pathways that work coordinately with IIS , and regulatory factors that act downstream of DAF-16 ., Expression of the integrated GFP ( green fluorescent protein ) reporter was intensified by mutation of daf-2 and sensitivity to RNAi was increased by introducing the rrf-3 ( pk1426 ) mutation ., The resulting strain , of genotype rrf-3 ( pk1426 ) ; daf-2 ( m577ts ) ; wuIs177 Pftn-1::gfp , was raised at 15°C until the L4 stage , then transferred to RNAi plates and incubated at 25°C ( non-permissive temperature for daf-2 ( m577 ) ) ., GFP fluorescence levels were measured in a plate-reader two days later ., Given our interest in mechanisms of gene regulation , the RNAi screen was restricted to 812 genes encoding predicted transcription factors or other proteins associated with gene regulation 28 ., RNAi of a number of these genes led to altered Pftn-1::gfp expression ., In an initial screen , RNAi of 30 genes reduced Pftn-1::gfp expression by ≥20% ( Table S1 ) and we investigated these more thoroughly in several genetic backgrounds ., For 10 of these genes , not including daf-16 , RNAi consistently and robustly reduced Pftn-1::gfp expression in multiple trials ( data not shown ) ., We then verified the effect of RNAi on levels of mRNA from the endogenous ftn-1 gene ., This identified four genes where RNAi robustly reduced ftn-1 mRNA levels: hsf-1 , mdl-1 , ada-2 and elt-2 ( Figure 2A , Table S1 ) ., The heat-shock factor hsf-1 was previously shown to mediate effects of IIS on gene expression 29 ., The screen also confirmed that the GATA transcription factor elt-2 plays a role in ftn-1 regulation ., This is consistent with the role of elt-2 as an activator of intestinal gene expression 30; moreover , elt-2 is the only previously described transcriptional activator of ftn-1 expression 20 ., Thus , identification of hsf-1 and elt-2 in this unbiased screen is evidence of the efficacy of the screen ., ada-2 encodes a homolog of the Ada2 subunit of various histone acetyl transferase ( HAT ) complexes that activate gene expression by modifying chromatin via histone acetylation 31 ., It is possible that , ada-2 influences ftn-1 expression via effects on chromatin status ., More notable is the identification of the MAD-like transcription factor mdl-1 as an activator of ftn-1 expression ., mdl-1 plays a role in the protective effects of reduced IIS against a tumorous germline phenotype 32 and is upregulated in IIS mutants 10 , 26 , 32 ., We confirmed that the null mutation mdl-1 ( tm311 ) reduces ftn-1 mRNA levels in daf-2 mutants ( Figure 2B ) ., To explore whether these four factors might be acting downstream of DAF-16 , we tested whether RNAi reduces ftn-1 expression in a daf-16; daf-2 double mutant ., The results imply that only MDL-1 does not require DAF-16 to activate ftn-1 expression ., This suggests that mdl-1 acts downstream of daf-16 , or possibly in parallel to IIS , to regulate ftn-1 expression ( Figure 2C ) ., Given that mdl-1 is a direct transcriptional target of DAF-16 33 , the former seems more likely ., Unexpectedly , RNAi of hsf-1 markedly increased ftn-1 expression in a daf-16; daf-2 background ( Figure 2C ) ., The effects of hsf-1 RNAi ( Figure 2A ) imply that HSF-1 and DAF-16 act together to activate ftn-1 expression , as previously shown for other genes 29 ., That loss of hsf-1 in daf-16; daf-2 mutants increases expression of ftn-1 could imply a repressive role of HSF-1 in the absence of DAF-16 ., Alternatively , this increase might merely reflect a stressed state in the worms , caused by loss of both hsf-1 and daf-16 at 25°C ( see Discussion ) ., Since ftn-1 is known to be responsive to iron levels , we also tested whether DAF-16 , HSF-1 or MDL-1 are required for iron-dependent regulation of ftn-1 ., daf-16 , hsf-1 and mdl-1 mutants were treated with iron ( 25 mM ferric ammonium citrate , FAC ) and ftn-1 transcript levels measured by qRT-PCR ., Iron-induced up-regulation of ftn-1 was unchanged in each case ( Figure S1A ) , i . e . these three factors do not mediate effects of iron on ftn-1 expression ., RNAi of 28 genes further increased expression of the Pftn-1:gfp reporter ( Table S2 ) , already induced by daf-2 ( m577 ) ., Of note was the large increase in expression upon RNAi of unc-62 , a transcription factor with a conserved role in ferritin regulation and , for its human ortholog , a possible role in the iron-related disorder ‘restless leg syndrome’ 25 ., Also among the repressors of Pftn-1::gfp expression identified were hif-1 , encoding the hypoxia-inducible factor , and aha-1 , its binding partner ( HIFβ , also called ARNT ) ., RNAi of either gene strongly increased Pftn-1::gfp expression , in the original daf-2 ( m577 ) ; Pftn-1::gfp strain ( Table S2 ) but also in two separate integrants of the Pftn-1::gfp reporter in a daf-2 ( + ) background ( Figure 3A , 3B and data not shown ) ., We verified this activity of hif-1 by using the loss of function mutation hif-1 ( ia4 ) , which proved to greatly increase ftn-1 mRNA levels ( Figure 3C ) and Pftn-1::gfp expression ( Figure 3D ) ., In a hif-1 ( ia4 ) mutant background , RNAi of aha-1 did not further increase Pftn-1::gfp expression ( Figure 3D ) , indicating that hif-1 and aha-1 act together to repress ftn-1 expression ., The finding that hif-1 RNAi increases Pftn-1::gfp expression in a daf-2 mutant background suggests that hif-1 influences ftn-1 expression independently of IIS ., Consistent with this , hif-1 or aha-1 RNAi increased Pftn-1::gfp expression in the absence of daf-16 ( Figure 3E ) ., Results were similar at both 25°C and 20°C and at L4 and adult stages ( Figure 3E and data not shown ) ., In addition , RNAi of hif-1 increased ftn-1 transcript levels in daf-16 mutants , and also in hsf-1 and mdl-1 mutants ( Figure S1B ) , indicating that none of these factors act downstream of HIF-1 to regulate ftn-1 expression ., If HIF-1 is a repressor of ftn-1 expression , then elevation of HIF-1 levels should decrease expression of Pftn-1::gfp ., Loss of vhl-1 ( von Hippel-Lindau factor ) leads to increased HIF-1 protein levels in C . elegans 34 ., As expected , the deletion mutation vhl-1 ( ok161 ) markedly decreased expression of Pftn-1::gfp ( Figure 4A ) ., Moreover , RNAi of vhl-1 reduced Pftn-1::gfp expression in hif-1 ( + ) but not hif-1 ( ia4 ) animals ( Figure 4B ) , and genetic deletion of vhl-1 led to a reduction in ftn-1 transcript levels that is also completely dependent on hif-1 ( Figure 4C ) ., These results imply that HIF-1 acts downstream of VHL-1 as a repressor of ftn-1 expression ., The prolyl hydroxylase EGL-9 hydroxylates the P621 residue of HIF-1 , which causes VHL-1 to bind to HIF-1 , leading to proteasomal degradation 34 ., This hydroxylation reaction requires iron as a cofactor , suggesting that regulation of ftn-1 expression by iron might involve the HIF-1 pathway ., We therefore tested whether the effects of iron on ftn-1 expression are hif-1 dependent ., As expected given previous findings 23 , both expression of Pftn-1::gfp and ftn-1 mRNA levels were increased upon supplementation with iron ( ferric ammonium citrate , FAC ) and decreased upon treatment with the iron chelator 2′-2 bipyridil ( BP ) ( Figure 4D , 4E and Figure S2A and S2B ) ., This is consistent with the previous observation that BP treatment greatly increases HIF-1 protein levels in C . elegans 34 , since increased HIF levels would be expected to further repress ftn-1 expression ., By contrast , in hif-1 ( ia4 ) mutants , addition of iron did not increase either Pftn-1::gfp expression or ftn-1 mRNA levels ., This implies that hif-1 mediates the induction of ftn-1 expression by iron ., Iron chelation did not decrease ftn-1::gfp and ftn-1 expression in hif-1 ( ia4 ) , but instead increased it ., The cause of this induction remains unexplained ., One possibility is that BP treatment leads to cellular stress and induction of other stress response regulators ( e . g . DAF-16 ) , which can activate ftn-1 expression in the absence of the repressive effects of HIF-1 ( see Discussion ) ., In order to investigate whether HIF-1 represses ftn-1 expression by directly binding to the ftn-1 promoter , we carried out a chromatin immunoprecipitation ( ChIP ) assay using C . elegans expressing Myc-tagged HIF-1 35 and an anti-Myc antibody ., We used three lines: wild type ( N2 ) , ZG429 hif-1::myc and GA654 hif-1::myc vhl-1 ( ok161 ) ., Given that vhl-1 mutants have elevated HIF-1 levels and reduced ftn-1 mRNA levels ( Figure 4C ) , greater levels of HIF-1::Myc binding to the ftn-1 promoter should be detectable in vhl-1 mutants , if the interaction is in fact direct ., We first checked that our ChIP protocol allowed us to measure binding by HIF-1::Myc by testing binding to the promoter of a known HIF-1 target gene , nhr-57 ., We designed one set of primers to amplify the region of the promoter containing two putative hypoxia response elements ( HREs ) and another set of primers targeting an area within the 3′ UTR of this gene ., Quantity of qRT-PCR amplified promoter DNA was then compared to the 3′ UTR quantity as a test of enrichment of the promoter in our ChIP DNA pools ., This amplification from the 3′ UTR ( to which HIF-1 is not expected to bind ) allowed us to control for input quantity ., We saw a large ( 8 . 3-fold ) enrichment of the nhr-57 promoter sequence in the HIF-1::Myc lines and an even greater ( 14 . 9-fold ) enrichment when HIF-1::Myc was stabilized by deletion of vhl-1 ( Figure 4F ) ., Relative amounts of HIF-1::Myc were monitored by Western blotting of the same ChIP samples using the same aliquot of anti-Myc antibody used for ChIP , and we were able to confirm that vhl-1 ( ok161 ) increases HIF-1::Myc protein levels ( Figure 4F ) ., We then measured binding to the ftn-1 promoter through qRT-PCR against the promoter sequence of ftn-1 ., For this , we used a primer pair specific to the IDE sequence ., These results were again normalized against the same nhr-57 3′UTR in order to correct for differences in input quantity ., While weaker than binding to Pnhr-57 , enrichment of the Pftn-1 sequence in HIF-1::Myc and stabilized HIF-1::Myc lines was statistically significantly different to that seen in wild-type controls ( Figure 4F ) ., This is evidence that HIF-1 represses ftn-1 expression through direct binding to its promoter ., The repression of ftn-1 expression by HIF-1 and the requirement for iron in the degradation of HIF-1 by the proteasome suggests a possible mechanism for the iron-dependent regulation of ftn-1 in which changes in iron levels alter the level of HIF-1 protein , which in turn alter ftn-1 expression ., Since the iron-dependent degradation of HIF-1 occurs via the action of VHL-1 , HIF-1 protein levels in C . elegans are not sensitive to iron levels when VHL-1 is absent 36 ., We found that loss of vhl-1 largely abrogated the induction of Pftn-1::gfp expression by iron supplementation , though there was still a significant induction of lesser magnitude ( Figure 5A ) ., Reduction of Pftn-1::gfp expression by iron chelation was not affected by loss of vhl-1 ( Figure 5B ) ., Taken together , this suggests that regulation of ftn-1 by iron may occur partially , but not exclusively , through changes in HIF-1 protein levels regulated by iron-dependent degradation ., The induction of ftn-1 levels by iron requires a 63 bp element ( the iron-dependent element , or IDE ) in the genes promoter 20 ., We wondered whether the hif-1 pathway might mediate the effects of iron on IDE-mediated gene expression ., A reporter strain carrying a ftn-1 promoter lacking the IDE is insensitive to changes in iron levels 20 ., Using these same reporters we found that absence of the IDE abolished hif-1 RNAi-induced induction of expression ( Figure 5C ) ., Another reporter construct with just the IDE sequence fused to a minimal promoter and driving GFP expression was previously shown to be responsive to iron 20 ., We found that loss of hif-1 increased ide::gfp expression , demonstrating that hif-1 does promote gene expression from the IDE ( Figure 5D ) ., Moreover , addition of iron did not induce ide::gfp expression in hif-1 mutants ( Figure 5D ) ., However , in hif-1 mutants treatment with the iron chelator BP still reduced ide::gfp expression ( Figure 5E ) ., This possibly reflects an effect of BP on ftn-1 that is independent of its effects on iron levels , or the existence of a second iron-dependent factor ., These results show that the IDE is subject to regulation by HIF-1 and suggest that HIF-1 mediates the effects of iron on IDE-mediated gene expression ., As previously described , loss of vhl-1 decreases expression of Pftn-1::gfp ( Figure 4A ) ., This is expected given that HIF-1 represses ftn-1 expression and that loss of vhl-1 increases HIF-1 levels 34 ., The prolyl hydroxylase EGL-9 targets HIF-1 for proteasomal degradation , and loss of egl-9 causes a similarly large increase in HIF-1 protein levels as loss of vhl-1 36 ., We therefore expected that loss of egl-9 , like that of vhl-1 , would reduce Pftn-1::gfp expression ., In fact , deletion of egl-9 caused an 11-fold increase in Pftn-1::gfp expression ( Figure 6A ) and a ∼950-fold increase in ftn-1 mRNA levels ( Figure 6B ) ., Animals with a different allele , egl-9 ( n586 ) , also showed increased ftn-1 mRNA levels ( Figure S3A ) ., Visible Pftn-1::gfp expression remained restricted to the intestine in wild type , vhl-1 mutants and egl-9 mutants ., vhl-1-independent effects of EGL-9 on HIF-1 target gene expression have been observed previously 36 ., Our findings suggest that in the case of ftn-1 regulation , egl-9 can act independently of , and antagonistically to , vhl-1 ., As expected , loss of egl-9 induced ftn-1 expression even in the absence of vhl-1 ( Figure S3B and S3C ) ., However , egl-9 RNAi did not increase ftn-1 transcript or Pftn1::gfp expression in the absence of hif-1 ( Figure 6B and Figure S3D ) ., This implies that the inhibition of ftn-1 expression by EGL-9 also requires hif-1 ., Thus , egl-9 and vhl-1 inhibit and activate expression of ftn-1 , respectively , and both activities require hif-1 ., One possibility is that EGL-9 inhibits ftn-1 expression by stimulating HIF-1 activity via an as yet unidentified pathway ., IIS and DAF-16 play a pivotal role in the organismal decision between growth and diapause ., Under growth-promoting conditions , DAF-16 is inactivated through cytoplasmic retention , which facilitates reproductive growth 8 , 37 ., In the absence of sufficient food or given exposure to certain forms of stress , DAF-16 enters the nucleus and transcriptionally specifies a survival program ., This entails delayed reproduction , enhanced stress resistance and increased lifespan ., Modulation of DAF-16 activity is therefore crucial for ensuring an optimal response to the worms environment; with growth and reproduction under conditions that are propitious to growth , and developmental arrest , stress protection and increased longevity under conditions that are not ., Regulation of ftn-1 by DAF-16 suggests the existence of a trade-off between growth and stress resistance involving iron homeostasis ., A role for ferritin in regulating growth via its effects on iron homeostasis has been described previously in mammalian cells 38 ., This study found that Myc , a bHLH transcription factor with a major role in promoting cellular proliferation , can repress H-ferritin expression ., Overexpression of ferritin in cells carrying activated Myc led to a decrease in in vitro clonogenicity , and this effect could be rescued by addition of iron , suggesting that Myc–mediated repression of ferritin expression favors growth by increasing iron availability ., The study identified DNA synthesis as a possible mechanism for iron-dependent control of cellular proliferation by c-Myc , as DNA synthesis is increased by c-Myc in a manner dependent on ferritin repression and the associated increases in iron availability ., This finding is consistent with the requirement for iron in the activity of ribonucleotide reductase , the rate-limiting enzyme in DNA synthesis ., Similar mechanisms may be at play in the regulation of ferritin expression by IIS ., When conditions favor growth , and IIS is increased , reduced ftn-1 expression is expected to increase iron availability , thus fulfilling a key requirement for growth ., While free iron is required for growth , it can also cause harm by catalyzing the Fenton reaction , which increases levels of ROS and molecular damage ., When conditions are not suitable for growth , IIS is reduced , and increased ftn-1 expression is expected to lower levels of free iron and of ROS , thereby protecting against stress ., Consistent with this , induced over-expression of ftn-1 causes resistance to oxidative stress ( S . Valentini and D . Gems , unpublished results ) ., Thus , upregulation of ftn-1 likely contributes to the broader increase in cytoprotection seen when IIS is reduced ., Reduced IIS also increases levels of autophagy in C . elegans 39 , 40 and autophagy releases iron from ferruginous materials , such as mitochondrial metalloproteins 41 ., This predicts that reduced IIS will increase free iron levels , and concomitant elevation of ftn-1 expression could ensure that iron released by autophagy does not cause molecular damage ., Using an RNAi screen we identified new regulators of ftn-1 , including hsf-1 and mdl-1 ., It was previously shown that in daf-2 mutants the heat shock factor HSF-1 acts in concert with DAF-16 to promote expression of small heat shock proteins and other molecular chaperones , which contribute to longevity 29 ., We find that hsf-1 is also involved in the induction of ftn-1 in daf-2 mutants , since loss of hsf-1 reduced ftn-1 expression in daf-2 but not daf-16; daf-2 mutants ., The MAD-like transcription factor mdl-1 is also regulated by IIS ., Microarray and qRT-PCR studies showed it to be up-regulated in daf-2 mutants 10 , 26 , 32 ., mdl-1 also contributes to the resistance of daf-2 mutants to germline tumor formation in the gld-1 tumor model , and to daf-2 mutant longevity 32 ., That MDL-1 activates ftn-1 expression is consistent with the role of mammalian MAD as an inhibitor of Myc , which represses ferritin expression ( see above ) ; however , C . elegans does not possess any clear ortholog of Myc 42 , 43 ., A study of DAF-16 binding sites did not provide evidence that ftn-1 is a direct regulatory target of DAF-16 33 , but suggested that mdl-1 might be ., Given that ftn-1 may be a direct target of MDL-1 44 , 45 , one possibility is that activation of mdl-1 expression by DAF-16 leads to increased ftn-1 expression ., This hypothesis predicts that abrogation of mdl-1 expression should decrease ftn-1 expression more in daf-2 than in daf-16; daf-2 animals , but this is not the case ( Figure 2A , 2C ) ., This could imply that mdl-1 regulates ftn-1 independently of daf-16 , at least in part ., We discovered that loss of hif-1 or its binding partner aha-1 greatly increased ftn-1 expression in daf-2 mutants ., This implicated hypoxia signaling in the regulation of ftn-1 ., The HIF transcription factor is composed of an α and a β subunit , encoded by the genes hif-1 and aha-1 in C . elegans ., HIF regulates the transcriptional response to hypoxia in both worms and vertebrates and , as expected , worms lacking hif-1 are hypersensitive to hypoxia 46 ., Levels of HIFβ protein are relatively stable , whereas HIFα is constantly being degraded by the proteasome under normal , non-hypoxic conditions ., In both worms and higher organisms , this occurs because the HIFα/HIF-1 protein is hydroxylated at conserved proline residues by prolyl hydroxylase ( PHD ) , encoded by the egl-9 gene in worms ., After hydroxylation by PHD/EGL-9 , the von Hippel-Lindau protein VHL-1 binds to HIFα , which targets it for degradation 34 , 47 ., PHDs require oxygen , iron and 2-oxoglutarate for the hydroxylation reaction ., When cells are kept under hypoxic conditions or when an iron chelator is added , the proline residue in HIFα is not hydroxylated and the HIFα protein accumulates 48 ., That loss of hif-1 has such dramatic effects on gene expression under normoxic conditions demonstrates that HIF-1 affects gene regulation even at the very low levels of HIF-1 found when it is being hydroxylated and degraded ., Similarly , it was previously observed that loss of hif-1 can increase C . elegans lifespan under normoxic conditions 49 ., Consistent with this , we find statistically significant levels of binding of the non-stabilized HIF-1::Myc protein to both ftn-1 and nhr-57 promoters ( Figure 4F ) ., Since iron is a required cofactor for hydroxylation of HIF by PHD , levels of iron affect those of HIF ., For example , in C . elegans , depletion of iron using the iron chelator 2-2′ bipyridyl stabilizes HIF-1 34 , and feeding mice a low-iron diet leads to increased HIFα levels 50 ., This increase in HIF-1 levels is not without consequence: chelation of iron has also been shown to increase expression of the C . elegans HIF-1 target gene nhr-57 , indicating that the stabilization of HIF upon loss of iron leads to HIF-1-dependent changes in gene expression 51 ., In vertebrates , HIF activates expression of genes involved in regulating iron homeostasis , including heme oxygenase 52 , the transferrin receptor 53 , 54 , ceruloplasmin 55 , DMT1 56 and possibly ferroportin 57 ., Loss of HIF-2α in mice causes decreased iron levels in the plasma and livers of mice 56 ., It has therefore been suggested that HIF can act as an iron sensor: low iron levels lead to HIF stabilization , which leads to changes in gene expression that increase iron levels 57 ., The results presented here support this hypothesis ., The repression of ferritin expression by hif-1/aha-1 is consistent with a role of HIF in increasing iron availability ., By this view , lower ferritin expression upon HIF activation would reduce iron storage capacity , thereby increasing iron availability ., We therefore investigated whether HIF mediates iron-dependent regulation of ftn-1 , and this proved to be the case: ftn-1 regulation by iron is blocked in hif-1 mutants ., In wild-type animals iron supplementation increases ftn-1 expression while iron depletion decreases it ., By contrast , in hif-1 mutants iron supplementation does not increase ftn-1 expression ., Treatment of hif-1 mutants with the iron chelator 2-2′ bipyridyl ( BP ) caused a large increase , rather than decrease , of ftn-1 expression ., This was unexpected , but we noticed that BP treated worms were somewhat sickly in appearance ., One possibility is that toxicity of BP in hif-1 mutants triggers other stress response mediators ( e . g . DAF-16 ) that activate ftn-1 expression ., This is consistent with our observation that stressful conditions tend to induce expression of this reporter ., Similar to treatment with BP , RNAi of hsf-1 in daf-16 ( mgDf50 ) ; daf-2 ( m577 ) animals raised at 25°C also caused the worms to have a sickly appearance and induced Pftn-1::gfp expression ( Figure 2C ) ., Moreover , we observed that starved animals also show elevated Pftn-1::gfp expression ( data not shown ) ., The requirement for hif-1 in the iron-dependent regulation of ftn-1 suggests that this regulation may occur via iron-dependent degradation of HIF-1 ., However , our data implies that this is not the whole story ., Mutants of vhl-1 have constitutively stabilized HIF-1 and its levels cannot therefore respond to changes in iron ( or oxygen ) levels 34 , 36 ., While the increase in Pftn-1::gfp expression upon treatment with iron was greatly reduced in vhl-1 mutants , Pftn-1::gfp expression was still elevated compared to the control treatment ., This implies that iron-dependent degradation of HIF-1 is not the sole mechanism by which ftn-1 is regulated in response to iron levels ., The control of ftn-1 expression by iron was previously shown to be mediated by the 63 bp iron-dependent element ( IDE ) in the ftn-1 gene promoter 20 ., This implied the presence of an unknown iron-responsive transcriptional activator exerting effects upon the IDE ., Our findings strongly suggest that this factor is HIF ., We found that loss of hif-1 increases ide::gfp expression ., Moreover , in the absence of hif-1 , iron supplementation failed to induce ide::gfp expression ., Furthermore , Romney et al . ( 2008 ) identified three conserved elements ( called DR elements ) , with the consensus sequence: CACGTA ( C/G ) ( C/A/G ) in the IDE to which they attribute the responsiveness of ftn-1 expression to iron levels ., This DR sequence has homology to the E-box motif , which led Romney et al . to suggest that the iron-sensory pathway includes a basic helix-loop-helix ( bHLH ) transcription factor ., Both HIF-1 and AHA-1 belong to this family of proteins ., In fact , the conserved DR sequence described by Romney et al . contains the putative C . elegans hypoxia response element ( HRE ) 58 ( in reverse orientation ) ., Moreover , using ChIP , we found that epitope-tagged HIF-1 bound to the region of the promoter containing the IDE ., Taken together , these results support the view that HIF-1 acts as an iron sensor in C . elegans , suppressing ftn-1 expression by binding to the IDE ,

Introduction, Results, Discussion, Materials and Methods

Iron plays an essential role in many biological processes , but also catalyzes the formation of reactive oxygen species ( ROS ) , which can cause molecular damage ., Iron homeostasis is therefore a critical determinant of fitness ., In Caenorhabditis elegans , insulin/IGF-1 signaling ( IIS ) promotes growth and reproduction but limits stress resistance and lifespan through inactivation of the DAF-16/FoxO transcription factor ( TF ) ., We report that long-lived daf-2 insulin/IGF-1 receptor mutants show a daf-16–dependent increase in expression of ftn-1 , which encodes the iron storage protein H-ferritin ., To better understand the regulation of iron homeostasis , we performed a TF–limited genetic screen for factors influencing ftn-1 gene expression ., The screen identified the heat-shock TF hsf-1 , the MAD bHLH TF mdl-1 , and the putative histone acetyl transferase ada-2 as activators of ftn-1 expression ., It also revealed that the HIFα homolog hif-1 and its binding partner aha-1 ( HIFβ ) are potent repressors of ftn-1 expression ., ftn-1 expression is induced by exposure to iron , and we found that hif-1 was required for this induction ., In addition , we found that the prolyl hydroxylase EGL-9 , which represses HIF-1 via the von Hippel-Lindau tumor suppressor VHL-1 , can also act antagonistically to VHL-1 in regulating ftn-1 ., This suggests a novel mechanism for HIF target gene regulation by these evolutionarily conserved and clinically important hydroxylases ., Our findings imply that the IIS and HIF pathways act together to regulate iron homeostasis in C . elegans ., We suggest that IIS/DAF-16 regulation of ftn-1 modulates a trade-off between growth and stress resistance , as elevated iron availability supports growth but also increases ROS production .

Iron plays a role in many biological processes , including energy generation and DNA replication ., But to maintain health , levels of cellular iron must be just right: too much or too little iron can cause illnesses , such as anemia and hemochromatosis , respectively ., Animals therefore carefully control their iron levels by regulating of iron uptake , transport , and storage within protein capsules called ferritins ., But how do they coordinate this ?, Using the model organism C . elegans , we have discovered a network of genes and pathways that control iron homeostasis ., We find that ferritin is regulated by insulin/IGF-1 signaling , which also controls growth and resistance to oxidative stress in response to harsh environmental conditions ., Ferritin is also regulated by the hypoxia signaling pathway , which responds to oxygen and iron levels as well as to metabolic cues ., We find that the hypoxia pathway acts as an iron sensor , a role it may also play in humans ., Our work defines a network of signaling pathways that can adjust iron availability in response to a range of environmental cues ., Understanding this network in C . elegans can help us to understand the causes of iron dyshomeostasis in humans , which can profoundly affect health .

biochemistry, genetics, biology, metabolism, genetics and genomics

journal.pbio.0060327

BEAF Regulates Cell-Cycle Genes through the Controlled Deposition of H3K9 Methylation Marks into Its Conserved Dual-Core Binding Sites

Chromatin insulators/boundary elements ( BEs ) 1 , 2 are defined as sequences able to insulate a transgene from its chromosomal context and to block promiscuous enhancer–promoter interactions or heterochromatin spreading 1 , 3–5 ., These elements are thought to subdivide the genome into functional chromosome domains , through their ability to cluster DNA loops 1 , 2 and to control the deposition of histone epigenetic marks 6–8 to regulate chromatin accessibility for gene expression 9–13 ., No common signature and/or mechanism of action has been identified among characterized insulators/boundary elements 2 ., Rather , several factors confer insulating activity by targeting different DNA consensus sequences in the known insulators ., In Drosophila , insulating factors include dCTCF 14 , 15 , Zw5 16 , boundary element–associated factor ( BEAF ) 17 , and the well-characterized suppressor of Hairy-wing ( Su ( Hw ) ) 1 , 18 , 19 , which targets hundreds of distinct , largely uncharacterized genomic sites 20–22 ., Whether each of these factors and subfamily of insulators might possess distinct cellular functions is of particular interest ., BEAF blocks both enhancer–promoter communication 17 , 23–25 and repression by heterochromatin , as shown using reporter transgenes 5 , 25 ., This insulating activity of BEAF was also evidenced by a genetic screen in yeast 4 , confirming that , unlike de-silencing activity , BEAF binding sites must bracket a transgene for insulation ., The hundreds of BEAF binding sites have not been characterized in situ , however , and the cellular function of BEAF remains to be elucidated in vivo ., Here we have combined computational and experimental approaches to address the function of BEAF binding sites in vivo ., We have identified ≈1 , 720 BEAF dual-core elements genome-wide that share an unusual organization conserved over 600 bp ., The dual-core signature consists of five to six BEAF binding motifs bracketing 200 bp AT-rich nuclease-resistant spacers ., BEAF dual-cores juxtapose to hundreds of genes highly enriched in gene annotations regulating chromosome organization/segregation and the cell cycle ., Accordingly , BEAF depletion leads to cell-cycle and chromosome segregation defects ., Quantitative RT-PCR analyses further show that dual-cores regulate the expression of key cell-cycle genes including cdk7 and mei-S332 ., These results are also reproduced in embryos expressing truncated beaf mutants , which abolish the proper targeting of BEAF to dual-cores and its insulating activity ., Chromatin immunoprecipitation analyses show that BEAF acts by restricting the deposition of methylated H3K9 marks in dual-cores ., Our data reveal a new role for BEAF in regulating chromosome organization/segregation and the cell cycle through its binding to highly conserved chromatin dual-cores ., The DNA-binding activity of BEAF has been well-characterized in vitro 17 , 20 , 23 , 24 ., Each subunit of the BEAF complex targets one CGATA motif ., Point mutations within this consensus abolish both its binding and insulating activities ., Clusters of three to four CGATA motifs can create high-affinity ( Kd ∼ 10–25 pM ) BEAF in vitro binding sites , which we call single elements ., A computational scan of the Drosophila genome revealed thousands of single elements , yet immunostaining analysis demonstrated that they were not good predictors for BEAF binding in vivo ., For example , Chromosome 4 was found to contain hundreds of single elements , yet immunodetection analysis showed only three major BEAF signals on this chromosome ( Figure 1A ) ., Interestingly , statistical analysis showed that single elements were often organized in a pair-wise configuration ., Genome-wide , 988 single elements form 494 so-called “dual-cores , ” which harbor two separate clusters of three CGATAs , a statistically significant result ( p-value ∼ 1e-9 ) ., Moreover , 1 , 226 additional “dual-core–like” elements have a second cluster of two ( instead of three ) CGATAs ., These elements include all characterized BEAF insulators whose activity involves a second , lower-affinity CGATA cluster ( Kd ∼ 400–600 pM ) where BEAF binding is abolished when the first high-affinity cluster is mutated 20 , 23 ., Detailed analysis by alignment of all 1 , 720 dual-core and dual-core–like elements showed a highly organized distribution of their 12 , 058 CGATAs , which preferentially segregate into two clusters separated by spacers of approximately 200 bp ( Figure 1B ) ., For scs and other characterized BEAF insulators , these spacers were found to be relatively AT-rich 20 , 24 , 26 ., Scanning the 1 , 720 dual-cores for A+T content showed that they all harbor significant AT-rich ( >70% ) sequences in their spacers ( Figure 1C , Figure S1 ) ., The remarkably conserved organization of dual-cores indicates that they likely correspond to a highly specific BEAF-binding signature ., We tested this possibility by assaying BEAF binding to dual-cores by chromatin immunoprecipitation ( ChIP ) and ChIP-on-chip ( Figures 1D and 2 ) ., Based on the signals obtained with anti-BEAF antibodies , dual-cores are expected to be precipitated much more efficiently than single elements ( Figure 1A ) ., Indeed , ChIP analysis confirmed that single elements were not bound by BEAF ( Figure 1D ) ., In contrast , dual-cores from the 7C locus of the X chromosome were efficiently bound by BEAF ( Figure 1D , probes 4 and 5 ) , while nearby control sequences or single elements were not ( probes 1 , 2 , 3 , and 6 ) ., Altogether , 25 out of 25 dual-cores and dual-core–like elements assayed by ChIP were found to be efficiently bound by BEAF in vivo ( Figures 1D and 2; unpublished data ) ., The actin promoter region , which contains six unclustered CGATA motifs , was not bound by BEAF ( Figure 1D; last row ) , indicating that the distribution of CGATAs in dual-cores , rather than the number of CGATAs per se , is important for BEAF binding ., Furthermore , ChIP-on-chip analysis over 350 Kbp of the X chromosome strengthens our conclusions , as all major peaks corresponding to regions where BEAF binds in vivo fit into a dual-core or a dual-core–like element ( hereafter called “dual-cores” , see black rectangles in Figure 2; see our database at http://www . sfu . ca/~eemberly/insulator/ for a complete listing ) ., We note that computer analysis occasionally retrieved minor peaks present in the shoulder of the major BEAF peaks ( enrichment <2; red bars in Figure 2 ) that may be attributed to the cooperative binding of BEAF to additional CGATA motifs present in single elements juxtaposed to dual-cores ( Figure 2 , see black bars for “single” ) ., However , no peaks were present in regions corresponding to dispersed single elements ( Figure 2; see our database ) , confirming that they are not sufficient for BEAF binding ., These results establish that BEAF elements organized into dual-cores indeed define a characteristic in vivo BEAF-binding signature ( Figure 1E ) ., Analysis of the positioning of dual-cores relative to genes showed that they are preferentially associated with gene-dense regions ., 545 dual-cores reside within 500 bp of promoter/transcriptional start sites ( TSSs ) ( p-value = 6 . 7e-119 ) ( Figure 3A ) , and more than 850 are within 2 , 000 bp ., As dual-cores are preferentially distributed in pairs separated by approximately 5–15 kbp ( p-value = 1 . 01e-33; Figure 3B ) , the remaining elements might be found at the 3′ borders of genes ., However , we could not find any specific enrichment for dual-cores in the 3′ UTR of genes ( unpublished data ) , indicating that the clustering of dual-cores can be attributed to the clustering of genes/TSS rather than the bracketing of genes by dual-cores per se ., These features ( see our genome-wide database ) raise the possibility that dual-cores might exert a function distinct from that of Su ( Hw ) binding sites , which rarely juxtapose the TSS of genes 21 , 22 , 27 ., Strikingly , genes containing a dual-core near their promoter were statistically enriched in gene-class ontology ( GO ) groups that include the cell cycle , chromosome organization/segregation , apoptosis , and sexual reproduction ( p-value < 1e-6; Figure 3C ) ., These essential cellular processes require constitutive regulation , whereas genes associated with non-constitutive processes such as sensing and behavior were not enriched for BEAF dual-cores ( Table S1 ) ., Inspection of Table S1 also shows that other cell functions enriched in BEAF dual-cores include GOs that can be linked to phenotypes observed in beaf mutants 25 , 28 , 29 , such as chromosome architecture , germ-cell and imaginal-disc development , and eye morphogenesis defects ., We asked whether BEAF might be involved in regulating the cell-cycle and/or chromosome organization by siRNA-mediated depletion of BEAF from cells ., Reduction of BEAF levels to background occurred from day 3–4 ( Figure 3D ) , when defects in cell growth are first observed ( Figure 3E ) ., In addition , FACS and microscope analyses showed that BEAF depletion led to a significant and reproducible increase ( >3× ) in the proportion of cells with 4N DNA content and with phenotypes typical of chromosome segregation defects ( Figure 3F and 3G ) ., These observations support our conclusion that the selective association of the corresponding GOs with closely linked dual-cores likely reflects a biologically significant localization ., We next asked whether the phenotypes observed upon BEAF-depletion can be attributed to the loss of activity of BEAF dual-cores associated with 160 genes that control cell-cycle chromosome dynamics ., These include mei-S332 and cdk7 , two major chromosome-segregation and cell-cycle regulators 30–32 whose promoter regions are bound by BEAF in vivo ( Dual-cores 38/56 , Figure 1D ) ., Remarkably , further DNA-motif searches showed that the dual-cores associated with cdk7 and mei-S332 , and more generally with all genes belonging to the cell-cycle and/or chromosome dynamic GOs , also contain the TATCGATA consensus sequence recognized by DREF ( p-value ∼ 2 . 4e-6; Figure 4A ) ., DREF activates hundreds of cell-cycle regulatory genes 33 and , importantly , might compete with BEAF for binding to the overlapping consensus 34 ., Hence , DREF-regulated dual-cores may define a distinct regulatory subclass ( Figure 4A , right ) ., To test how BEAF might affect the expression of genes associated with dual-cores that do or do not contain a DREF consensus site , we performed quantitative RT-PCR expression analysis from BEAF-depleted or control cells ( Figure 4 ) ., BEAF depletion did not affect the expression of control genes ( see Figure 4A , left ) , including those located near a single element ( Figure 4B; actin , CG9745 ) where BEAF does not bind ( Figure 1 ) ., The expression of all genes associated with a dual core lacking a DREF element was consistently found to be positively regulated by BEAF by approximately 4-fold to 5-fold ( CG1430 , CG10946 , CG1444 , snf , ras , janus; Figure 4B ) ., These data are in complete agreement with previous work showing that BEAF has a positive effect on gene expression by de-repressing a transgene from surrounding chromatin 17 , 20 , 23 , 24 ., In stark contrast , the expression of all genes associated with a dual-core harboring a DREF consensus , including cdk7 and mei-S332 , specifically increased by approximately 4- to 6-fold upon depletion of BEAF ( Figure 4B; CG32676 , mei-S332 , cdk7 , CG10944 , ser ) ., Accordingly , Western blot analysis showed that Cdk7 and Mei-S332 protein levels increased under these conditions ( Figure S2 ) ., Therefore , two categories of dual-cores may be found ., In those lacking a DREF consensus , BEAF positively regulates gene expression; in those that contain a DREF consensus , BEAF may prevent binding of DREF to its overlapping consensus , thereby controlling the activation of the associated cell-cycle and chromosome organization/segregation GOs ., Quantitative RT-PCR analysis showed that DREF depletion resulted in a more than 10-fold down-regulation of cdk7 ( Figure 5 ) , confirming the role of DREF as a transcriptional activator of this gene ., To further characterize the respective roles of BEAF and DREF in regulating cell-cycle regulatory genes by binding to dual-cores , we eliminated the DREF consensus from the dual-core associated with cdk7 ( dre mutant , Figure 5A ) and transfected this construct or its wild-type version into cells depleted of BEAF or of DREF by siRNA ( Figure 5B ) ., Because the dre mutant does not modify the CGATA BEAF consensus and still harbors the dual-core signature ( 2× 3CGATAs separated by the spacer; Figure S7B ) , this construct may be used to reveal the effect of the BEAF dual-core on the expression of cdk7 independently of DREF ., Importantly , mutating the DREF consensus site led to a down-regulation of cdk7 ( Figure 5B , cdk7-mut , blue bar ) , similar to what is found by depleting DREF ., Strikingly , BEAF depletion further impaired the expression of cdk7 by approximately 5-fold ( Figure 5B , cdk7-mut , red bar ) compared to the expression of the identical construct in control cells ( Figure 5B , cdk7-mut , blue bar ) ., We conclude that , although BEAF regulates DREF-mediated activation , it additionally positively regulates the expression of cdk7 , as found for other genes associated with a dual-core lacking a DREF consensus ., In support of this conclusion , we obtained a similar result for snf , which is transcribed in opposite orientation relative to cdk7 ( Figure 5A ) ., Snf is under the influence of the same dual-core as cdk7 , yet its expression is not regulated by DREF ( Figure 5B ) ., However , BEAF depletion reproducibly impaired snf expression by approximately 6-fold , similar to what we obtained for cdk7 in the absence of DREF ., These results show that BEAF has a positive role on the expression of genes associated with dual-cores , in addition to its role in controlling activation by DREF ., BEAF insulating activity can protect a transgene from repression by chromatin 5 , 25 ., The expression of genes positively regulated by dual-cores might implicate mechanisms similar to those required for insulation , and we asked whether BEAF might control the deposition of epigenetic marks , as shown for other types of insulators 7 , 35 , 36 ., We tested this possibility by measuring the levels of histone H3 methylated on lysine 9 ( H3K9me3 ) , a characteristic mark of heterochromatin , as a function of BEAF depletion ., The deposition of H3K9me3 was strongly increased upon BEAF depletion ( Figure 6A ) ., Double immunostaining analysis showed that this increase was specific , as RNA polymerase II , actin , or unmodified histone H3 levels were unchanged ( Figure 6A and 6B , Figure S3A and S3B ) ., Numerous and broader H3K9me3 foci not restricted to heterochromatin regions appeared in BEAF-depleted cells ( Figure 6B , 3× panels; 37 ) , strengthening the view that H3K9me3 also acts to influence gene expression in euchromatin 8 , 38 , 39 ., ChIP-on-chip analysis confirmed that discrete H3K9me3 peaks are found in many promoter regions , including those associated with a dual-core ( Figure S3C ) ., However , these H3K9me3 peaks appear to be distinct from the broader and larger H3K9me3 peaks found in regions where genes are known to be repressed ( e . g . , eye , Figure S3C ) and where the methylK27 mark is also present ( not shown; B . Schuettengruber unpublished data ) ., We further tested if BEAF affects the deposition of H3K9me3 marks into dual-cores by performing ChIP analysis using anti-H3K9me3 antibodies on BEAF-depleted , DREF-depleted , or control cells ( Figure 6C ) ., BEAF-depletion led to a significant and reproducible increase of approximately 8-fold in H3K9me3 levels for the dual-cores linked to snf-cdk7 , similar to that obtained for mei-S332 and CG1430 , and in stark contrast to the stable levels found for the actin control ( Figure 6C; unpublished data ) ., In contrast , no variation in H3K9me3 levels could be found upon DREF depletion ( Figure 6C ) , showing that this increase is specific to BEAF depletion ., This result also rules out that the changes we observe overall could be due to off-target effects ., Moreover , CDK7 depletion , which severely impaired cell-cycle progression ( unpublished data ) , did not affect the levels of H3K9me3 ( Figure 6C ) , indicating that their increase is not due to an indirect perturbation of the cell cycle upon BEAF depletion ., Finally , H3K9me3 levels did not vary in control regions located a few kbp away from the dual-core , suggesting that BEAF controls the deposition of this mark locally ( unpublished data ) ., These results show that BEAF dual-cores are involved in blocking the deposition of H3K9me3 marks , fully consistent with their ability to positively regulate the expression of dual core-associated genes ., To confirm that the observed increase in H3K9me3 levels is directly linked to the activity of BEAF , we introduced mutations in two of the CGATA motifs of the dual-core associated with cdk7 ( “beaf-mut” , Figure 7A ) and transfected this construct or constructs harboring a wild-type dual-core or a dual-core mutated in the DREF site ( dre mutant , Figure 7A ) into cells ., Quantitative PCR analysis of chromatin immunoprecipitated with anti-H3K9me3 antibodies showed that mutation of the BEAF site led to an increase in H3K9me3 levels of approximately 3 . 8-fold compared to wild-type or dre mutant constructs ( Figure 7B ) , establishing that BEAF directly controls the deposition of H3K9me3 ., This did not affect the levels of H3K9me3 in the endogenous cdk7 dual-core , as measured from the same batch of transfected cells , showing that the observed increase is indeed specific for the mutated dual-core ., We conclude that BEAF serves to restrict the deposition of H3K9me3 marks into dual-cores ., The deposition of epigenetic marks is critical for regulating gene activity at the level of chromatin accessibility 9 , 12 , 13 , which may account for the positive effect of BEAF on gene expression ., We sought to determine whether BEAF-regulated deposition of H3K9me3 marks affects the expression of cell-cycle genes ., BEAF-depleted or control cells were treated with anacardic acid ( AA ) , a histone acetyltransferase ( HAT ) inhibitor that globally affects gene expression by altering the accessibility of chromatin 40 ., AA treatment did not affect the expression of either control genes or dual core-associated genes ( compare grey and black bars in Figure 8 ) ., In contrast , AA severely compromised the activation of snf , cdk7 , or mei-S332 upon BEAF depletion compared to untreated BEAF-depleted cells ( Figure 8; unpublished data ) ., This result strongly supports a model whereby BEAF restricts the deposition of methylated H3K9 marks , thereby protecting the expression of dual core-associated genes from repression by chromatin ( see Discussion ) ., Are these variations in gene expression related to the cooperative binding of BEAF to the two clusters of CGATAs present in dual-cores ?, We sought to answer this question by using transgenic fly lines expressing the C-terminal BEAF self-interaction domain ( BID in Figure 9A ) under the control of a GAL4 activator ., BID lacks the BEAF DNA-binding domain , impairing the insulating activity of BEAF 25 by preventing its cooperative binding to two nearby CGATA clusters ( Figure 9B ) ., Importantly , defects in expression of cdk7 , snf , and/or mei-S332 were highly similar in embryos expressing BID to that observed in BEAF-depleted cells ( compare Figures 9C and 4B ) ., This result supports our conclusion that BEAF binding is required to regulate these genes in vivo ., It also suggests that the cooperative binding of BEAF to conserved dual-cores , which is abolished by BID , may be important for the regulation of gene expression by BEAF ., Accordingly , cell functions enriched in BEAF dual-cores include GOs ( Table S1 ) that correspond to phenotypes observed following expression of beaf mutants , which are lethal to flies 25 , or to GOs found to genetically interact with these mutants 28 ., Results of our in silico analysis reveal ∼1 , 720 BEAF dual-cores in the Drosophila melanogaster genome that share a striking organization ( Figure 1E ) ., Genome-wide ChIP-on-chip analysis detects approximately 1 , 800 significant BEAF binding sites ( C . M . Hart , unpublished observations ) , suggesting that our dual-core database encompasses most in vivo BEAF binding sites ., The few ( <100 ) additional peaks not included in our database but detected by ChIP-on-chip analysis may correspond to elements initially scored as single elements but whose organization is close to that of dual-cores ., These rare exceptions are in part due to the computer stringency of the dual-core signature ., For example , BEAF-1255 can be bound by BEAF in vivo ( Figure S4 ) , yet this element could not be scored as a dual-core because one out of five of its clustered CGATA motifs lies 3 bp outside the defined 100-bp window ( ‘out in Figure S4 ) ., Furthermore , approximately 10% of the minor BEAF sites are found in regions lacking any CGATA motifs , including the scs insulator ( unpublished data ) 16 ., Since this region is not directly bound by BEAF , it is thus possible that some of the minor BEAF peaks are due to indirect interactions between BEAF and other insulator proteins , as previously suggested for the scs′–scs pair of insulators 16 ., Other protein–protein interactions that regulate BEAF binding could also involve the splicing variant of the beaf gene itself , called BEAF-32A 23 , which does not harbor the BEAF DNA-binding domain that recognizes clustered CGATA motifs ., ChIP-on-chip analysis using antibodies that also recognize this isoform showed no additional major peaks ( Figure S5 , compare ‘−32A with ‘+32A ) , indicating that dual-cores constitute the main binding sites for both BEAF isoforms ., Finally , we note that the BEAF-32A isoform is unlikely to play a major role in the activities described here , as its binding is dispensable for the insulating function of BEAF 20 , and its expression is not essential for the development of embryos into adult flies 29 ., Taken together , our results show that the BEAF dual-core signature is a bona fide mark that identifies a cis-regulatory element that regulates the expression of nearby genes ., Results of our experiments using both BEAF depletion in tissue culture cells and BID expression in vivo provide clear evidence for specific functions of the BEAF dual-cores , reflected by a selective association with genes that control cell-cycle and/or chromosome organization/segregation ., The competition between DREF and BEAF for binding to nested consensus sequences is also supported by ChIP analyses showing that DREF targets identical sites 34 clearly enriched nearby genes associated with the cell cycle and chromosome dynamic GOs ( Figure S6; unpublished data ) ., Thus , while DREF levels increase at the G1/S transition to activate mei-S332 and cdk7 within the appropriate window for cell-cycle progression 30–32 , BEAF may further facilitate this activation by restricting the deposition of H3K9me marks ., Indeed , over-expressing BEAF was shown to reduce the phenotypes related to cell-cycle progression in flies that over-express DREF 33 , supporting a role for BEAF in controlling the cell cycle ., Such a model is also supported by our observation that AA treatment strongly represses these genes in BEAF-depleted cells and that mutation of the BEAF-binding site in a dual-core results in a local increase in H3K9m3 levels ., In addition , computer analysis of micro-array expression data for Drosophila embryos during early development shows that the 545 genes associated with dual-cores are positively correlated with beaf expression ( Figure S7A ) , in contrast to genes unlinked to these elements ( p-value ∼ 3e-17 according to the Kolmogorov-Smirnov test ) ., This strict correlation further indicates that BEAF has a global positive role on gene expression genome-wide , and similar analyses did not reveal any significant correlation change between genes whose TSS is closely juxtaposed ( <100 bp ) to dual-cores , including snf or cdk7 ( Figure S7B ) , compared to genes whose TSS is more distant ( 500 bp ) ., Accordingly , the cell-cycle and chromosome dynamics GOs that include cdk7 and mei-S332 are enriched for positively correlated genes ( see our database for a detailed list ) ., Taken together , our results show that BEAF could play an important role in chromosome organization during the cell cycle through a regulated switch involving the BEAF–DREF competition: According to such a mechanism , BEAF would restrict the deposition of H3K9me3 , allowing dual-core–associated genes to remain in a potentially active state , while controlling the time of activation of cell-cycle GOs by DREF ., Accordingly , BEAF depletion leads to down-regulation of genes associated with a dual-core lacking a DREF element ( CG10946 , ras , CG1430 , Janus , CG1444 ) , but to increased expression of CG32676 , mei-S332 , cdk7 , CG10944 , and ser , which are under the control of DREF-associated dual-cores ( Figure 4 ) ., In the latter case , the apparent contradiction between the positive—restriction of H3K9me3 deposition—and negative effects of BEAF can be reconciled by our results showing that BEAF controls the activation of these genes by DREF ., BEAF depletion relieves the competition for binding by DREF , leading to the increased expression of cdk7 or mei-S3332 in spite of an increased deposition of H3K9me3 marks under these conditions ., Mutating the DREF or BEAF binding sites of DREF-associated dual-cores ( Figures 5 and 7 ) allows for distinguishing between these different effects on the expression of linked genes ., It is intriguing that the spacers of dual-cores are well-conserved ., One possibility is that they may be preferentially bound by a nucleosome , as recently shown for CTCF insulators 41 ., Supporting this idea , the known dual core-spacers correspond to nuclease-resistant “cores” , between two nuclease-hypersensitive sites ( BE76 , scs′ ) 20 , 24 , 26 ( Figure S8 ) , where a nucleosome may be present ( C . M . Hart , unpublished observations ) ., Indeed , we found that dual core-spacers fall within predicted nucleosome-positioning sequence ( NPS ) databases 42–44 , as indicated by NPS/dual-core sequence alignments ( Figure S8; not shown ) , possibly accounting for the conserved organization of dual-cores ., Our results further suggest that the cooperative binding of BEAF across these AT-rich spacers may be important for BEAF function ., Indeed , expression of BID , which prevents its cooperative binding across the spacers , mimics the effect of BEAF depletion on the expression of dual-core–associated genes , as also found by mutagenesis of two CGATA motifs from one dual-core cluster ., However , BEAF still efficiently binds in vivo to the few dual-cores that harbor a shorter spacer ( <150 bp; e . g . , see Dual-core 1 , 254 , Figure 1; unpublished data ) , indicating that the conserved dual-core–spacer is dispensable for BEAF binding ., Recent reports have shown that gene expression is differentially regulated through nucleosome positioning in several species 12 , 13 , 42 , 43 ., Positioned nucleosomes may restrict promoter accessibility in yeast , and pausing of RNA polymerase II facing the +1 nucleosome may be regulated through nucleosome positioning in Drosophila 44 ., Similarly , dual-cores are also closely associated with TSSs , and a potential link to nucleosome positioning strengthens the view that BEAF may regulate chromatin accessibility for gene expression through a restriction of the deposition of methylated H3K9 marks into dual-cores ., Our model whereby dual-cores regulate the deposition of specific epigenetic marks is in agreement with the activity of other known insulators 6 , 7 , 9–11 ., Variations in H3K9me3 levels might affect the interplay between the deposition of H3K9me3 and acetylated histone H4 ( H4Ac ) marks 45 ., However , no variation in the deposition of H4Ac could be found in dual-cores compared to control regions after BEAF depletion ( unpublished data ) ., This is not surprising , as BEAF has no de-silencing activity on its own 5 , 25 ., Computer analysis failed to reveal any enrichment of dual-cores near the 3′UTR of genes , and the activity of dual-cores may thus essentially play a role in regulating chromatin accessibility near promoter regions , but not within the 3′ border of genes ., Furthermore , the insulating activity of BEAF was demonstrated in the context of two dual-cores bracketing a transgene 5 , 25 , and most likely also involved higher-level chromatin organization 2 ., Although not enriched near the 3′UTR of genes , dual-cores still bracket/separate groups of genes clustered within 5–15 Kbp , a genomic context that may further require insulating activity to block promiscuous enhancer–promoter interactions and involve DNA looping between distant insulators 2 ., It has recently been shown for a Su ( Hw ) insulator that the regulation of gene expression may further depend on its genomic environment 46 ., Also , other dual-cores are often found in the vicinity of genes exposed to repression by heterochromatin ( see our genome-wide database ) , and the function of BEAF may be particularly important in this context 17 , 20 , 23 , 24 ., We propose that the BEAF dual-cores closely linked to a restricted array of several hundred genes define a family of insulators that provide a link between chromatin organization and the cell cycle ., All genome-wide predictions and analyses are available on our Web site: http://www . sfu . ca/~eemberly/insulator/ ., Additional information , including DNA sequences of single elements , dual-cores or dual-core–like elements , and their position relative to genes or other genomic features ( GOs ) can be directly retrieved from our Web site ., Each single BEAF element that was not a part of a dual-core element was analyzed for the presence of a “dual-core–like” signature ., We define single elements as consisting of three CGATAs within 200 bp , and a dual-core–like element as a single BEAF element ( three CGATAs ) associated with a second nearby ( <800 bp ) cluster of two CGATA sites within a 100-bp window ., 1 , 226 BEAF elements fit into this classification , including all previously identified dual-cores ( BE76 , BE28 , BE51 , Jan/Ser ( BE83 ) ) ., The position of each CGATA site within a dual-core sequence was analyzed relative to the position of the rightmost site of the first BEAF single element ., In Figure S1 , the position of each CGATA motif was measured from the average position ( taken as position 0 on the x-axis ) of all the CGATA locations in the first BEAF single element of the dual-core ., This removes any ambiguity in defining the starting position of the sequence , allowing more precise mapping of dual-cores with respect to gene promoters ., We predicted dual-cores by pairing together the genome-wide set of 7 , 045 single BEAF elements that were separated by a spacer <L bp ., The statistical significance of the number of predicted dual-cores as a function of spacer length L was assessed by comparing it to the expected number for randomly spaced elements ., The p-value was found to reach a flat minima for 600 bp < L < 3 , 000 bp ., For larger L values , the predictions decreased in significance , eventually becoming no more significant than chance ., There are 1 , 720 dual-cores , L = 800 bp with a p-value of 1e-9 , in the sequenced Drosophila melanogaster genome ., The statistical significance of the number of dual-cores within +/− d bp of a promoter was assessed by comparing it to the number expected for randomly placed elements ., Out of 1 , 720 dual-core elements , 545 fall within +/− 500 bp of a promoter ., Beyond this distance , the p-value was found to decrease in statistical significance , yet 850 dual-cores reside within 2 , 000 bp of a promoter ., Additional dual-cores are found

Introduction, Results, Discussion, Materials and Methods

Chromatin insulators/boundary elements share the ability to insulate a transgene from its chromosomal context by blocking promiscuous enhancer–promoter interactions and heterochromatin spreading ., Several insulating factors target different DNA consensus sequences , defining distinct subfamilies of insulators ., Whether each of these families and factors might possess unique cellular functions is of particular interest ., Here , we combined chromatin immunoprecipitations and computational approaches to break down the binding signature of the Drosophila boundary element–associated factor ( BEAF ) subfamily ., We identify a dual-core BEAF binding signature at 1 , 720 sites genome-wide , defined by five to six BEAF binding motifs bracketing 200 bp AT-rich nuclease-resistant spacers ., Dual-cores are tightly linked to hundreds of genes highly enriched in cell-cycle and chromosome organization/segregation annotations ., siRNA depletion of BEAF from cells leads to cell-cycle and chromosome segregation defects ., Quantitative RT-PCR analyses in BEAF-depleted cells show that BEAF controls the expression of dual core–associated genes , including key cell-cycle and chromosome segregation regulators ., beaf mutants that impair its insulating function by preventing proper interactions of BEAF complexes with the dual-cores produce similar effects in embryos ., Chromatin immunoprecipitations show that BEAF regulates transcriptional activity by restricting the deposition of methylated histone H3K9 marks in dual-cores ., Our results reveal a novel role for BEAF chromatin dual-cores in regulating a distinct set of genes involved in chromosome organization/segregation and the cell cycle .

The genome of eukaryotes is packaged in chromatin , which consists of DNA , histones , and accessory proteins ., This leads to a general repression of genes , particularly for those exposed to mostly condensed , heterochromatin regions ., DNA sequences called chromatin insulators/boundary elements are able to insulate a gene from its chromosomal context by blocking promiscuous heterochromatin spreading ., No common feature has been identified among the insulators/boundary elements known so far ., Rather , distinct subfamilies of insulators harbor different DNA consensus sequences targeted by different DNA-binding factors , which confer their insulating activity ., Determining whether distinct subfamilies possess distinct cellular functions is important for understanding genome regulation ., Here , using Drosophila , we have combined computational and experimental approaches to address the function of the boundary element-associated factor ( BEAF ) subfamily of insulators ., We identify hundreds of BEAF dual-cores that are defined by a particular arrangement of DNA sequence motifs bracketing nucleosome binding sequences , and that mark the genomic BEAF binding sites ., BEAF dual-cores are close to hundreds of genes that regulate chromosome organization/segregation and the cell cycle ., Since BEAF acts by restricting the deposition of repressing epigenetic histone marks , which affects the accessibility of chromatin , its depletion affects the expression of cell-cycle genes ., Our data reveal a new role for BEAF in regulating the cell cycle through its binding to highly conserved chromatin dual-cores .

cell biology, computational biology, molecular biology

Chromatin Dual-Cores define new potent nucleosome-associatedcis-regulatory elements that regulate the accessibility of promoters of genes controlling chromosome organization/segregation and the cell cycle.

journal.pcbi.0030150

Chemotaxis Receptor Complexes: From Signaling to Assembly

The chemotaxis network allows bacteria to sense and swim toward attractants ( nutrients such as amino acids and sugars ) and away from repellents ., For this purpose , cells are equipped with ∼10 , 000 chemoreceptors , forming large arrays at one or both cell poles ., The chemotaxis network has remarkable properties , including signal integration by multiple types of chemoreceptors 1 , precise adaptation to persistent stimulation 2 , 3 , and high sensitivity to changes in ligand concentration 1 over several orders of magnitude of background concentrations ., These signaling properties are thought to originate from strongly coupled receptor complexes 4 , 5 ., Specifically , in vivo fluorescence resonance energy transfer ( FRET ) measurements of receptor sensitivity 1 and Hill coefficients 6 indicate coupled complexes of up to 10–20 receptor homodimers 6–10 ., Despite the importance of complex size to signaling , little is known about what controls receptor complex size ( for recent reviews see 11 , 12 ) ., In vivo observation of complex size and dynamics , e . g . , by fluorescence recovery after photobleaching ( FRAP ) , is currently not practical because of limited spatial resolution ., However , the close relation between complex size and the sensitivity and cooperativity of signaling means that receptor activity can be used to probe complex size 8 ., To demonstrate the potential of this approach , we analyze in vitro receptor-activity data 13–15 and present a simple biophysical model for the energetics of complex assembly ., Here we mainly focus on data from Bornhorst and Falke 13 , whose in vitro receptor-activity assay employed a chemotaxis null strain of Escherichia coli overexpressing one of the five receptor types , the high-abundance receptor Tar ., The Tar receptor specifically binds aspartate and its nonmetabolizable analogue methyl-aspartate ( MeAsp ) ., The cytoplasmic membranes were isolated , and incubated with purified CheW , CheA , and CheY proteins ., In vivo , CheW enhances complex formation and mediates binding to the kinase , CheA ., Active CheA autophosphorylates using ATP and transfers the phosphate to the response regulator , CheY ., Phosphorylated CheY diffuses to the flagellar motor and induces clockwise rotation and cell tumbling ., In vitro , CheA kinase activity was measured by assaying the rate of phosphorylation of CheY using radiolabeled ATP ., CheA activity is inhibited by an increase of attractant concentration ., For the assay , receptors were genetically engineered to have either a glutamate ( E ) or a glutamine ( Q ) at each of four specific modification sites in the cytoplasmic domain ., In vivo , these four modification sites are used for adaptation , with the enzyme CheR methylating glutamates to increase the kinase activity , and the enzyme CheB demethylating methylated glutamates to decrease the kinase activity ., In chemotaxis , a Q is functionally similar to a methylated E . For instance , Tar{QQQQ} is highly active at zero attractant concentration , while Tar{EEEE} is generally inactive ., Figure 1 shows experimental in vitro dose-response curves from Bornhorst and Falke 13 , i . e . , CheA activity versus stimulation by different amounts of attractant , for Tar receptors in defined modification states ., Hill coefficients are smaller ( and sensitivities are lower ) than typical for in vivo studies of cells overexpressing Tars 6 , 8 , indicating smaller in vitro clusters ., The in vitro Hill coefficients ( nH ≈ 2–3 ) are in line with expectations from partial crystal structures 16 and cross-linking experiments 17 , 18 indicating that receptors oligomerize into mixed trimers of homodimers as the smallest unit of complexes ., In vivo , larger complexes possibly form with a hexagonal lattice structure 19 , 20 ., Modeling in vitro data using receptor complexes of a single fixed size ( e . g . , trimers of dimers ) does not describe the data well ( inset Figure 1 ) ., Here we examine a model in which the receptor modification state determines the amount of trimers of dimers , yielding a significantly better fit to the data ( solid lines in Figure 1 ) and suggesting that receptor modification may vary complex size , possibly along with other parameters 21 ., In this paper , we analyze in detail the in vitro activity data from Bornhorst and Falke 13 , Shrout et al . 14 , and Lai et al . 15 ., We model homodimers of Tar receptors in membranes as an ensemble of different species , including single dimers , dimers of dimers , trimers of dimers , and the signaling complex formed by the kinase CheA bound to trimers of dimers , in line with recent experiments 22 ., The relative free energies of these species determine their equilibrium distribution , accounting for the different amounts of actively signaling trimers of dimers indicated by the data ., We further propose that the kinetics of receptor-cluster assembly can be measured experimentally by perturbing the receptor free energies , e . g . , through addition of ligand ., The experimental dose-response curves in Figure 1 for Tar receptors in different modification states were obtained from in vitro reaction mixtures which always contained the same total amounts of receptor , adapter protein CheW , kinase CheA , and response regulator CheY 13 ., Addition of MeAsp inhibits the kinase activity , while the number of Qs per receptor increases the kinase activity ., Previously , similar dose-response curves from living cells , obtained by in vivo fluorescence resonance energy transfer ( FRET ) , were successfully modeled using the Monod–Wyman–Changeux ( MWC ) model 23 of strongly coupled two-state receptors 24 , and revealed complex sizes of order N = 10 receptors 6–10 ., Here we employ the same MWC model to estimate the size of receptor complexes in the in vitro assays of Bornhorst and Falke ., In the MWC model , the receptor complex activity is simply the probability for the complex to be on , which is fully determined by the free-energy difference between on and off states of the complex ( Equation 1 ) ., For a homogenous complex of Tar receptors , this free-energy difference is the product of the number of receptors , N , in the complex and the free-energy difference between on and off states of a single Tar receptor ., The free-energy difference of a single receptor has two contributions ., One contribution , Δɛ ( m ) , depends on receptor modification level , m , and ranges from positive for fully demethylated ( m = 0 ) receptors to negative for fully methylated ( m = 8 ) receptors ., The other contribution arises from attractant binding and depends on the ligand dissociation constants, and, of the on and off states , respectively ., If the activity is low in the absence of ligand ( e . g . , for demethylated receptors ) , the inhibition constant ( ligand concentration at half maximal activity ) is Ki ≈, /N and the Hill coefficient is nH ≈ 1 ., In contrast , if the activity is high in the absence of ligand ( e . g . , for highly methylated receptors ) , the inhibition constant is, and the Hill coefficient is nH ≈ N , where N is the number of receptors in the complex ( 8 , see Methods ) ., Inspection of the experimental dose-response curves in Figure 1 shows that the inhibition constant of the low-activity QEEE curve is about Ki ≈ 0 . 01 mM MeAsp and that Hill coefficients of the other curves are nH ≈ 2–3 ., Hence , based on the MWC model and the previously determined value, = 0 . 02 mM for Tar receptors binding MeAsp 8 , the signaling complexes responsible for the data in Figure 1 are likely to be trimers of dimers ., Indeed , the MWC model using N = 3 for trimers of dimers and a different Δ∈ ( m ) for each receptor modification state m ( Equations 1 and 2 ) fits the shapes of the in vitro curves well , while allowing each curve to have a free amplitude αm ( solid curves in Figure 1 ) ., However , in the MWC model , Δ∈ ( m ) is also supposed to determine the relative amplitudes of the curves ., Although amplitudes still depend systematically on the number of Qs ( m ) , the relative amplitudes from the MWC model are substantially different and do not describe the data well ( inset in Figure 1 ) ., Hence , each dose-response curve is well described by the MWC model for trimers of dimers , but the MWC model does not describe the relative amplitudes correctly ., ( Use of a two-state model without cooperativity 21 or use of an alternative MWC model with a methylation-dependent, to fit experimental amplitudes both produce lower than observed Hill coefficients ., ) The discrepancy in amplitudes raises the following question—given that all experiments use the same total amount of receptor , why should the amplitudes systematically differ from the MWC model predictions for different receptor-modification states ?, According to recent in vitro experiments , only receptors in trimers of dimers can signal 22 ., Therefore , the presence of some receptors as ( inactive ) single dimers and dimers of dimers could account naturally for the different amplitudes observed in Figure 1 ., We therefore suggest that in the in vitro assays not all receptors form trimers of dimers , some also partition into single dimers and dimers of dimers , with the fraction in trimers of dimers depending on the receptor-modification state ., In fact , such a partition is required by thermodynamic equilibrium , with entropy favoring single dimers and dimers of dimers over trimers of dimers ., In the following , we formulate an equilibrium model to predict the amounts and activities of trimers of dimers as a function of receptor-modification state ., For this purpose , we include CheA binding to trimers of dimers only , leading to an equilibrium between free trimers of dimers , without signaling capability , and CheA-bound trimers of dimers , the signaling complex ., ( For simplicity , we assume that CheW is present at saturation . ), In our model for Tar receptors in membranes , we consider single dimers , dimers of dimers , trimers of dimers , and CheA-bound trimers of dimers ., These different species can either be active ( on ) or inactive ( off ) as illustrated in Figure 2 , but only active CheA-bound trimers of dimers can signal ., The relative free energies of the various species determine their equilibrium distribution ., To compare the free energies of the different species , we introduce homodimer–homodimer coupling energies , which can be different between active homodimers ( Jon ) and between inactive homodimers ( Joff ) ., We also include a chemical potential , μ , to adjust the receptor density ., The resulting free-energy expressions are given in Equations 3–10 ., To facilitate calculations , we treat the membrane as a lattice where each site can be either empty , or occupied by a single dimers , a dimer of dimers , a trimer of dimers , or a CheA-bound trimer of dimers , yielding the partition function in Equation 11 ., To model the in vitro experiments , in which the same total amount of receptor was used for each assay , we multiply the probability that a given CheA-bound trimer of dimers is active by the fraction of receptors in CheA-bound trimer of dimers ( cf . Equation 14 in Methods ) ., This equilibrium-assembly model ( dashed lines in Figure 1 ) describes the data as well as the ad hoc model with free amplitudes ( solid lines in Figure 1 ) ., Specifically , the equilibrium-assembly model accounts for the systematic dependence of the dose-response curve amplitudes on receptor modification state ., Since for each curve we assume a fixed fraction of CheA-bound trimers of dimers , set by the incubation conditions , the shape of each curve is still determined by the MWC model with N = 3 ( Equation 13 in Methods ) ., While the equilibrium-assembly model requires seven parameters ,, ,, , Jon , Joff , ∈A , μ , and α , plus an offset energy , Δ∈ , for each receptor-modification state , some of these parameters are nearly redundant ., For example , Δ∈ and Jon − Joff play nearly equivalent roles , as do μ and ( Jon + Joff ) /2 , differing only in their effects on the ratio of dimers of dimers and trimers of dimers ., Therefore , our parameter choices represent only one consistent set of values ., In their data , Bornhorst and Falke 13 observed a strong correlation between the activity in the absence of MeAsp and the inhibition constant Ki ., Figure 3A shows this correlated data for all possible modification states except EEEE , for which the measured activity was zero ., The observed functional relation between activity and Ki supports our suggestion that not all receptors form CheA-bound trimer of dimers ., To illustrate , in Figure 3A we have plotted , as a dotted curve , the expected relation between activity and Ki if all receptors did form CheA-bound trimers of dimers ., The curve has a noticeably different shape from the experimental data ., In contrast , the equilibrium-assembly model , with the same parameters as in Figure 1 , is able to capture the observed relation between activity and Ki ( dashed curve ) ., In either case , the one-to-one relation between activity and Ki follows because both quantities depend uniquely on the receptor offset energy Δ∈ ., For ease of comparison , we used the same amplitude parameter α = 10 for both curves in Figure 3A ., This means that the ratio of the two curves gives the fraction of receptors in CheA-bound trimers of dimers in the equilibrium-assembly model , because only those receptors in CheA-bound trimer of dimers contribute to the activity ., The actual fraction of receptors in CheA-bound trimers of dimers ( and in all trimers ) is shown in Figure 3B , both for the equilibrium-assembly model and , by inference , for the in vitro data ., Why does the fraction of receptors in CheA-bound trimers of dimers increase with Ki ?, Within the model , the inhibition constant Ki increases as the offset energy Δ∈ decreases; this behavior follows because decreasing Δ∈ favors the active state of receptors , and therefore more attractant is required to inactivate them ., The same shift of receptors toward higher activity causes the fraction of receptors in CheA-bound trimers of dimers to increase , both because Jon < Joff implies a stronger tendency of active receptors to form trimers of dimers , and simply because increasing the total concentration of active receptors increases their equilibrium partition into trimers of dimers ., Our suggestion that not all receptors form trimers of dimers or CheA-bound trimers of dimers is given further experimental support by Shrout et al . 14 and Lai et al . 15 who used a receptor-activity assay similar to that of Bornhorst and Falke but with E . coli Tar receptors ., Shrout et al . measured the kinase activity for different modification states of cytoplasmic Tar-receptor fragments at zero attractant concentration ., While the measured activities depended strongly on modification state , the same activities normalized by the amount of bound CheA were almost independent of modification state ., We find the same behavior in our equilibrium-assembly model ., Figure 4 shows the calculated activity and activity per CheA ( activity divided by the fraction of receptors in CheA-bound trimers of dimers ) for four different receptor-modification states ( cf . Figure 1 ) ., We observe qualitative agreement with the data in Figure 2A of Shrout et al . 14 , although their receptor fragments tend to be more active than complete receptors 25 ., In the equilibrium-assembly model , if the CheA-bound trimers of dimers were always fully active ( on ) , the normalized activities would be completely independent of the modification state ., However , for receptors with few Qs , the CheA-bound trimers of dimers are not fully active even at zero attractant concentration , resulting in the weak modification-level dependence of the normalized activity seen in Figure 4B ., If an equilibrium exists among single dimers , dimers of dimers , trimers of dimers , and CheA-bound trimers of dimers , one would expect changes in the receptor density to affect the distribution of different sized receptor clusters ., Consistent with this expectation , Lai et al . 15 reported the activity per Tar{QEQE} receptor , in the absence of attractant , as a function of the receptor fraction of total membrane protein ., As shown in Figure 5 , they observed an increase in and saturation of the activity per receptor with increasing receptor fraction ., We interpret their data to mean that at low receptor fractions ( densities ) , it is thermodynamically unfavorable for receptors to come together and form trimers of dimers ( or even dimers of dimers ) , and consequently single dimers , which lack signaling capability , predominate ., This density-dependent activity per receptor is captured by our equilibrium-assembly model , as shown in Figure 5 ( solid lines ) , using the same parameters as in Figure 1 ., The calculated activity is scaled by an overall factor to convert to the activity scale of Lai et al . 15 , and the calculated receptor density ( Equation 15 ) is also rescaled ., Within the equilibrium-assembly model , the kinase activity per receptor increases with receptor density entirely because of the increasing fraction of receptors in CheA-bound trimers of dimers expected from thermodynamics ., The large amount of in vitro data from Bornhorst and Falke 13 can be used to test an additional hypothesis ., Specifically , do the offset energies from each of the four modification sites Δ∈i=1 , 2 , 3 , 4 contribute additively to give the total offset energy Δ∈ ?, The total offset energy Δ∈ for each of the 15 modification states can be obtained from the inhibition constants Ki 13 based on our model that only CheA-bound trimers of dimers can signal ( see Methods ) ., This value can be compared with the additive model , where the Δ∈i are treated as fitting parameters ., Figure 6 shows that the additive model for the total offset energy is indeed a reasonably good approximation ., Interestingly , modification sites 1 to 3 make a similar contribution ( approximately −0 . 5 to −0 . 6 kBT ) while site 4 makes a smaller contribution ( approximately −0 . 3 kBT ) to the offset energy ( see Methods ) ., This may have to do with the fact that , relative to the CheA binding site , modification sites 1 to 3 are nearby on the N-terminal side of the receptor and modification site 4 is on the C-terminal side of the receptor ., The chemotaxis network of E . coli exhibits remarkable sensing and signaling properties that rely on receptor complexes ., Despite recent high resolution electron microscopy 19 , 20 , fluorescence images 26–28 , and in vivo fluorescence recovery after photobleaching ( FRAP ) measurements of protein dynamics ( V . Sourjik , personal correspondence ) , very little is known about what determines receptor-complex size 11 , 12 ., Interestingly , because complex size and signaling sensitivity or cooperativity are closely related 8 , receptor kinase activity can be used to probe complex size ., Starting from in vitro dose-response data of the activity of Tar receptors in native membranes 13–15 , we presented a simple biophysical model for the energetics of complex assembly that can account for these and other data ., An essential feature of the model is that not all receptors form signaling complexes , i . e . , kinase CheA-bound trimers of dimers ., Our model for receptor complexes is based on an MWC model , with constants, and, , in which receptor modification state affects complex size only through the offset energy Δ∈ ( which depends additively on contributions from the four modification sites ) ., At this stage , we cannot rule out alternative models , e . g . , in which modification state affects other parameters as well 21 ., In our model , Tar receptors form an ensemble of different species , including single dimers , dimers of dimers , trimers of dimers , and CheA-bound trimers of dimers , as illustrated in Figure 2 ., The different species can either be active ( on ) or inactive ( off ) , but only active CheA-bound trimers of dimers can phosphorylate CheY ., This is in line with recent in vitro experiments where trimers of dimers were found to signal , but single dimers and dimers of dimers did not signal 22 ., The relative free energies of the various species determine their equilibrium distribution , leading naturally to the observed variation in the signaling activity of receptors in different modification states ( cf . Figures 1 , 3 , 4 , 5 ) ., We find that the fraction of receptors in trimers of dimers and CheA-bound trimers of dimers increases with the number of Qs at the modification sites ( or with Ki , see Figure 3B ) ., Within this picture , the “superactivity” of certain mutant receptors can be attributed to more efficient complex formation rather than enhanced CheA binding or kinase velocity 25 ., Our free-energy model assumes that complex assembly/disassembly is slow compared with changes in signaling ., For instance , if attractant is added together with ATP to initiate the activity measurement , the ensemble of clusters is assumed to stay frozen , i . e . , the ratio of {single dimers}:{dimers of dimers}:{trimers of dimers}:{CheA-bound trimers of dimers} is assumed to be unaffected by the addition of attractant , even though the kinase activity is immediately affected ., This separation of time scales is reflected in Equation 14 , where the fraction of receptors in CheA-bound trimers of dimers ( first factor ) is evaluated at the incubation attractant concentration ( L0 = 0 ) , while the activity ( second factor ) is evaluated in the presence of the added attractant ( L ) ., To model the case where attractant is added during incubation , one only needs to set Lo = L ., In this case , shown by solid curves in Figure 7 , inhibition occurs at lower attractant concentrations , in agreement with the data of Lai et al . 15 for Tar{QEQE} incubated in the presence of MeAsp ( solid symbols ) ., In the model , the inhibition at lower attractant concentrations can be traced to the loss of trimers of dimers in favor of single dimers and dimers of dimers in the new equilibrium produced by incubation with attractant ( see inset Figure 7 ) ., Incubation with attractant is exactly the opposite of adding Qs in terms of receptor free energies , and therefore favors smaller rather than larger complex sizes ., The dose-response curves in Figure 7 for incubation without attractant ( dashed curves ) and with attractant ( solid curves ) are easily distinguishable , which suggests a way to measure the kinetics of complex assembly ., During the period after the addition of attractant , as the clusters re-equilibrate , the dashed curves must evolve toward the solid curves ., The rate of evolution can be quantified by measuring the kinase activity at specific times following the addition of attractant ., In this way , information can be obtained about the kinetics of assembly and disassembly of receptor complexes ., Our equilibrium-assembly model , augmented by kinetic rate constants , provides an appropriate theoretical framework for planning and interpreting kinetic experiments of this type ., There are previously published models for chemoreceptor complex assembly ., These models , however , do not consider the effects of ligand binding , and hence cannot address dose-response data ., Furthermore , Lai et al . 15 assume all receptors form trimers of dimers , hence their model cannot explain the activity versus receptor density data in Figure 5 ., Shrout et al . 14 assume that CheA binding directly depends on the receptor modification state ., While this assumption can explain the increase of activity with modification level , it violates the conventional view of precise adaptation based on the two-state receptor model , where receptors are either on ( active ) or off ( inactive ) ., Precise adaptation occurs because receptor modification responds exclusively to receptor activity so as to exactly balance the effects of ligand binding ., If CheA binding depended directly on receptor modification level , this would increase kinase activity at higher attractant concentrations and , hence , interfere with precise adaptation ., In contrast , in our model , CheA binds to trimers of dimers irrespective of modification level or activity ., The recent model by Asinas and Weis 25 considers the competitive assembly of wild-type and activity-mutant receptors ., The authors come to a similar conclusion to ours , i . e . , that receptor activity determines cluster assembly and , consequently , CheA recruitment and activity ( see also Li and Weis 29 ) ., An approach similar to ours may allow measurement of the kinetics of receptor complexes in living cells ., Complex sizes of 10–20 receptors or more have been inferred from in vivo dose-response curves 6–10 and , in E . coli cells lacking an adaptation system , polar clustering appears to depend on receptor-modification level ( 28 , 30 , 31; V . Sourjik , personal correspondence ) ., This suggests that dose-response curves can be used to measure the real-time evolution of in vivo cluster sizes in response to perturbations of receptor free energy , e . g . , addition of attractant or repellent ., It is not clear why in vivo complexes are significantly larger than the trimers of dimers seen in vitro and why receptors localize predominately at the cell poles ., It is known that receptors are inserted into the membrane by the Sec translocon machinery 32 in large cell-spanning spirals 33 ., Once inserted into the membrane , receptors may localize at the cell poles due to the higher membrane curvature 34 and/or different lipid composition 35–37 at the poles ., A means to probe receptor-assembly kinetics may help reveal what determines complex size in vivo ., Compared with previous modeling of in vivo data 8–10 , the offset energies , Δ∈ , obtained from in vitro data are much larger ., This can be traced to the fact that we explicitly include homodimer–homodimer interactions , which lead to an effective offset energy for each receptor in a trimer of dimer of Δ∈ + Jon − Joff , close to estimated in vivo values ., However , in a large in vivo complex , if each receptor participates in six homodimer–homodimer interactions , as on a hexagonal lattice , the effective offset energy per receptor would be Δ∈ + 3 ( Jon − Joff ) , which is much more negative than the estimated in vivo values ., One possible resolution might be that , in an in vivo cluster , homodimers in different trimers of dimers are coupled together more weakly than homodimers within a trimer of dimers ., However , the coupling between trimers of dimers must still be strong enough to cause clusters of 10–20 receptors to switch on and off together ., An important open question is what mediates the interactions among receptor homodimers in trimers of dimers , or between trimers of dimers ?, One way to address this question may be to measure in vitro or in vivo dose-response curves of mutant receptors specifically engineered to interrupt or strengthen homodimer–homodimer interfaces ., Possible insight can be gained from the observation of large in vitro Tsr clusters 29 , pointing toward a difference between Tsr:Tsr and Tar:Tar interfaces 15 ., We expect that a better understanding of the assembly of E . coli chemoreceptor complexes may provide insights into the oligomerization of other membrane proteins , including bacterial outer membrane proteins such as porins ( e . g . , LamB ) ., For other membrane-bound receptors that form complexes , including ryanodine receptors 38 , 39 and rhodopsin 40 , we hope that analysis of complex size and assembly kinetics based on dose-response curves may also prove feasible ., We mainly model the data of Bornhorst and Falke 13 , who used an in vitro activity assay to study chemotaxis signaling ., Briefly , Tar receptors of Salmonella typhimurium were engineered to be in a particular modification state , e . g . , QQQQ , QEQQ , QEQE , or QEEE , where Q is approximately equivalent to a methylated E . Using a chemotaxis null strain of E . coli , the Tar receptor was overexpressed from a plasmid ., Cytoplasmic membranes were isolated in which Tar receptors constituted approximately 5%–10% of total membrane protein ., Reaction mixtures of the same total amount of Tar and purified CheA , CheW , and CheY were prepared and incubated for 45 min to allow for complex formation in native membranes ., Signaling was initiated by adding radiolabeled ATP ., The activity of CheA was measured by assaying the rate of phosphorylation of CheY and normalized to QEQE ( wild-type ) ., Quantified attractant ( MeAsp ) was added with the ATP ., In the MWC model 6 , 8 , 23 , two-state receptors ( homodimers ) 24 , 41 form complexes with all receptors in a complex either on or off together ., At equilibrium , the probability that an MWC cluster of N Tar receptors will be active is, where N fon ( N foff ) and fon ( foff ) are the free energies of the complex as a whole and an individual receptor to be on ( off ) , respectively ., The individual receptor free-energy difference is given by, Here , L is the ligand ( MeAsp ) concentration , m is the number of Qs per receptor ( m = 0 , … , 8 ) , and, and, are the ligand dissociation constants in the on and off states , assumed to be independent of m ., All energies are expressed in units of the thermal energy kBT ., In our model , Qs or methylated Es favor the on state of a receptor by lowering Δ∈ ( m ) , while attractant binding favors the off state , i . e . ,, <, ., Importantly , the model exhibits two regimes 8 ., In regime I , where Δ∈ > 0 ( e . g . , for Tar{EEEE} ) , receptors have a low activity and an inhibition constant ( ligand concentration at half maximal activity ) , Ki ≈, /N , indicating an N times higher sensitivity than for a single receptor ., In regime II , where Δ∈ < 0 ( e . g . , for Tar{QQQQ} ) , receptors are highly active , and turn off at large attractant concentration Ki ≈, exp ( |Δ∈| ) with high cooperativity , i . e . , a Hill coefficient nH ≈ N . The possible MWC complexes considered here are the single receptor dimer , the dimer of dimers , and the trimer of dimers , corresponding to complex sizes N = 1 , 2 , and 3 , respectively ., We use statistical mechanics to predict the partitioning of receptors into active and inactive single dimers , dimers of dimers , trimers of dimers , and CheA-bound trimers of dimers , as illustrated in Figure 2 ., Since CheA-bound trimers of dimers are the signaling complex , only trimers of dimers can signal , not single dimers and dimers of dimers , in line with recent experiments 22 ., To compare the energies of the different-sized complexes , we generalized the MWC model to include homodimer–homodimer interactions ., The interaction energy between active homodimers ( Jon ) and the interaction energy between inactive homodimers ( Joff ) can be different ., These homodimer–homodimer interactions may originate from interactions of the periplasmic or cytoplasmic domains of the receptors , possibly mediated by the adapter protein CheW ., Specifically , single dimers , dimers of dimers , and trimers of dimers ( as well as CheA-bound trimers of dimers ) have zero , one , and three homodimer–homodimer interactions , respectively ., We also introduce a receptor chemical potential , μ , which determines the receptor density , ρ , in the membrane , and a free energy , ∈A , for the binding of the kinase CheA to trimers of dimers ( assuming for simplicity an equilibrium between bound CheAs and free CheAs at some invariant concentration ) ., The resulting complex free energies for a single dimer ( SD ) , a dimer of dimers ( DD ) , a trimer of dimers ( TD ) , and a CheA-bound trimer of dimers ( A:TD ) are given by, To regularize our calculations , we treat the membrane as a lattice where each lattice site can be either empty , or occupied by a single dimer , a dimer of dimers , a trimer of dimers , or a CheA-bound trimer of dim

Introduction, Results, Discussion, Methods, Supporting Information

Complexes of chemoreceptors in the bacterial cytoplasmic membrane allow for the sensing of ligands with remarkable sensitivity ., Despite the excellent characterization of the chemotaxis signaling network , very little is known about what controls receptor complex size ., Here we use in vitro signaling data to model the distribution of complex sizes ., In particular , we model Tar receptors in membranes as an ensemble of different sized oligomer complexes , i . e . , receptor dimers , dimers of dimers , and trimers of dimers , where the relative free energies , including receptor modification , ligand binding , and interaction with the kinase CheA determine the size distribution ., Our model compares favorably with a variety of signaling data , including dose-response curves of receptor activity and the dependence of activity on receptor density in the membrane ., We propose that the kinetics of complex assembly can be measured in vitro from the temporal response to a perturbation of the complex free energies , e . g . , by addition of ligand .

Chemotaxis allows bacteria to sense and swim toward nutrients and away from toxins ., The remarkable sensing properties of the chemotaxis network , such as high sensitivity to small changes in the chemical environment , are thought to originate from receptor complexes in the membrane , which act as antennas to magnify weak signals ., To adapt to persistent stimulation , receptors are covalently modified ., While the individual protein components of the chemotaxis network are well characterized , making the system well suited for quantitative and computational analysis , direct experimental visualization of receptors and receptor complexes is difficult within the current limits of fluorescence and electron microscopy ., To address questions such as how large are complexes and why do they assemble , we analyze in vitro signaling data using a previously developed model of signaling by receptor complexes ., Based on the data , we propose a statistical physics model for the distribution of complex sizes in the membrane ., Within this model , complex size depends on the receptor free energy with contributions from receptor modification level , ligand binding , receptor–receptor coupling , and binding to accessory proteins ., Our model results compare favorably with a variety of different signaling data , and suggest new experiments to measure the kinetics of assembly of receptor complexes .

biophysics, cell biology, none, computational biology

journal.ppat.1000225

Vesicular Stomatitis Virus-Based Ebola Vaccine Is Well-Tolerated and Protects Immunocompromised Nonhuman Primates

Ebola virus ( EBOV ) has been associated with sporadic episodes of hemorrhagic fever ( HF ) that produce severe disease in infected patients ., Mortality rates in outbreaks have ranged from 50% for Sudan ebolavirus ( SEBOV ) to up to 90% for Zaire ebolavirus ( ZEBOV ) ( reviewed in 1 ) ., A recent outbreak caused by an apparently new species of EBOV in Uganda appears to be less pathogenic than SEBOV or ZEBOV with a preliminary case fatality rate of about 25% 2 ., EBOV is also considered to have potential as a biological weapon and is categorized as a Category A bioterrorism agent by the Centers for Disease Control and Prevention 3–5 ., While there are no vaccines or postexposure treatment modalities available for preventing or managing EBOV infections there are at least four different vaccine systems that have shown promise in completely protecting nonhuman primates against a lethal EBOV challenge 6–12 ., Of these prospective EBOV vaccines two systems , one based on a replication-defective adenovirus and the other based on a replication-competent vesicular stomatitis virus ( VSV ) , were shown to provide complete protection when administered as a single injection vaccine 7–9 ., Most intriguingly , the VSV-based vaccine is the only vaccine which has shown any utility when administered as a postexposure treatment 13 , 14 ., Of these two leading EBOV vaccine candidates that can confer protection as single injection vaccines each has advantages and disadvantages ., Adenovirus vectors are highly immunogenic as documented by clinical trials evaluating gene transfer efficacy and immune responses ., Because they are replication-defective adenovirus vectors are also perceived to be safer for human use than a replication-competent vaccine ., The most significant challenge for the adenovirus-based vaccines is the concern that a significant portion of the global population has pre-existing antibodies against the adenovirus vector which may affect efficacy 15–17 and has performed poorly as a vaccine vector in recent clinical trials 18–19 ., In contrast , pre-existing immunity against VSV in human populations is negligible 20 and efficacy is likely greater with replication-competent vectors ., The main concern with the VSV vaccine vector is that replication-competent vectors may present more significant safety challenges in humans particularly those with altered immune status ., Because EBOV outbreaks in man have occurred exclusively in Central and Western Africa , the populations in this region are among those that may benefit from the development and availability of an EBOV vaccine ., However , populations in this region are among the most medically disadvantaged in the world ., In particular , the prevalence of individuals with a compromised immune system is high and HIV infections rates range up to 10% or more in this area 21 ., While the VSV vaccine vector has been enormously successful in protecting healthy immunocompetent animals against EBOV 7 , 13 , 14 , we are uncertain as to how these vectors would behave in individuals with altered or compromised immune systems ., Therefore , we conducted a study to assess the pathogenicity and protective efficacy of the recombinant VSV-based ZEBOV vaccine vector in rhesus macaques that were infected with simian-human immunodeficiency virus ( SHIV ) which is known to deplete the populations of naive CD4+ T cells , naive CD8+ T cells , and memory CD4+ T cells in these animals 22 , 23 ., In order to take into account the degree or severity of compromised immune function animals were selected with varying degrees of CD4+ T cell loss ., The recombinant VSV expressing the glycoprotein ( GP ) of ZEBOV ( strain Mayinga ) ( VSVΔG/ZEBOVGP ) was generated as described recently using the infectious clone for the VSV , Indiana serotype 24 ., ZEBOV ( strain Kikwit ) was isolated from a patient of the ZEBOV outbreak in Kikwit in 1995 25 ., Nine filovirus-seronegative adult rhesus macaques ( Macaca mulatta ) ( 5–10 kg ) were used for these studies ., The macaques were infected three months prior to the current study with SHIV162p3 ( kindly provided by Dr . Ranajit Pal , Advanced BioScience Laboratories , Inc . , Kensington , MD ) ., These animals all had clinical laboratory evidence of SHIV infection as evidenced by reduced CD4+ T cell counts , decreased ratios of CD4+/CD8+ T cells ( Table 1 ) and the presence of SHIV in plasma of four out of nine animals ( Table 2 ) ., Six of the nine SHIV-infected animals were vaccinated by i . m . injection with ∼1×10∧7 recombinant VSVΔG/ZEBOVGP ., Three animals served as placebo controls and were injected in parallel with saline ., All six VSVΔG/ZEBOVGP-vaccinated animals and two of the three control animals were challenged 31 days after the single dose immunization with 1000 pfu of ZEBOV ( strain Kikwit ) ., The monkeys were challenged with the heterologous Kikwit strain of ZEBOV as our macaque models have been developed and characterized using this strain 1 , 26 ., Animals were closely monitored for evidence of clinical illness ( e . g . , temperature , weight loss , changes in complete blood count , and blood chemistry ) during both the vaccination and ZEBOV challenge portions of the study ., In addition , VSVΔG/ZEBOV and ZEBOV viremia and shedding were analyzed after vaccination and challenge , respectively ., Animals were given physical exams and blood and swabs ( nasal , oral , rectal ) were collected at 2 , 4 , 7 , 10 , 14 , 21 , 28 , and 31 days after vaccination and on days 3 , 6 , 10 , 15 , and 28 after ZEBOV challenge ., The vaccination portion of the study was conducted at BIOQUAL and was approved by NIAID , BIOQUAL , and USAMRIID Laboratory Animal Care and Use Committees ., The ZEBOV challenge was performed in BSL-4 biocontainment at USAMRIID and was approved by the USAMRIID Laboratory Animal Use Committee ., Animal research was conducted in compliance with the Animal Welfare Act and other Federal statues and regulations relating to animals and experiments involving animals and adheres to the principles stated in the Guide for the Care and Use of Laboratory Animals , National Research Council , 1996 ., Both facilities used are fully accredited by the Association for Assessment and Accreditation of Laboratory Animal Care International ., Total white blood cell counts , white blood cell differentials , red blood cell counts , platelet counts , hematocrit values , total hemoglobin , mean cell volume , mean corpuscular volume , and mean corpuscular hemoglobin concentration were determined from blood samples collected in tubes containing EDTA , by using a laser-based hematologic Analyzer ( Coulter Electronics , Hialeah , FL , USA ) ., The white blood cell differentials were performed manually on Wright-stained blood smears ., Serum samples were tested for concentrations of albumin ( ALB ) , amylase ( AMY ) , alanine aminotransferase ( ALT ) , aspartate aminotransferase ( AST ) , alkaline phosphatase ( ALP ) , gamma-glutamyltransferase ( GGT ) , glucose ( GLU ) , cholesterol ( CHOL ) , total protein ( TP ) , total bilirubin ( TBIL ) , blood urea nitrogen ( BUN ) , and creatinine ( CRE ) by using a Piccolo Point-Of-Care Blood Analyzer ( Abaxis , Sunnyvale , CA , USA ) ., 100 ul of whole blood was added to a 12×75 tube and incubate with the antibodies for 15 minutes at room temperature ., The samples was then lysed and fixed in 1% paraformaldehyde and washed three times in PBS ., Samples were analyzed on a Becton Dickinson FACS Calibur ( Becton Dickinson , San Jose , CA ) ., All antibodies were purchased from Becton Dickinson; clones used were CD3 – SP34 , CD4 – L200 , CD8 – RPA-T8 and CD20 – 2H7 ., For measurement of plasma SIV RNA levels , a quantitative TaqMan RNA reverse transcription-PCR ( RT-PCR ) assay ( Applied Biosystems , Foster City , CA ) was used , which targets a conserved region of SIV gag and has an accurate detection limit as low as 200 RNA copies/ml ., Briefly , isolated plasma viral RNA was used to generate cDNA using One-Step RT-PCR Master Mix ( Applied Biosystems ) ., The samples were then amplified as previously described 27 with the following PCR primer/probes: SIV-F 5′ AGTATGGGCAGCAAATGAAT 3′ ( forward primer ) , SIV-R 5′TTC TCTTCTGCGTG AATGC 3′ ( reverse primer ) , SIV-P 6FAM-AGATTTGGATTAGCAGAAAGCCTGTTG GA-TAMRA ( TaqMan probe ) in a 7700 Sequence Detection System ( 40 cycles of 95°C , 15 seconds , and 60°C , 1 minute ) ., The signal was then compared to a standard curve of known concentrations to determine the viral copies present in each sample ., The assay lower limit was 40 copies/ml ., RNA was isolated from blood and swabs using Tripure Reagent ( INVITROGEN , Grand Island , New York ) ., For the detection of VSV we used a Q-RT-PCR assay targeting the matrix gene ( nt position 2497–2556 , AM690337 ) ., ZEBOV RNA was detected using a Q-RT-PCR assay targeting the L gene ( nt position 13874–13933 , AY354458 ) ., The low detection limit for this ZEBOV assay is 0 . 1 pfu/ml of plasma ., Virus titration was performed by plaque assay on Vero E6 cells from all blood and selected organ ( liver , spleen , lung , kidney , adrenal gland , pancreas , axillary lymph node , inguinal lymph node , mesenteric lymph node , ovary or testis , and brain ) and swab samples ., Briefly , increasing 10-fold dilutions of the samples were adsorbed to Vero E6 monolayers in duplicate wells ( 0 . 2 ml per well ) ; thus , the limit for detection was 25 pfu/ml ., IgG antibodies against ZEBOV were detected with an Enzyme-Linked Immunosorbent Assay ( ELISA ) using purified virus particles as an antigen source as previously described 7 , 9 ., Necropsies were performed on each animal and selected tissues were collected for histological analysis ., Histology and immunohistochemistry were performed as previously described for ZEBOV-infected monkeys 26 ., We employed nine SHIV-infected rhesus macaques , of which six animals were vaccinated by i . m . injection with a single dose of VSVΔG/ZEBOVGP ( Subjects #1–6 ) and the remaining three animals ( Controls #1–3 ) received sterile saline ., The animals were monitored closely for clinical symptoms and shedding of recombinant VSVs ., None of the animals vaccinated with VSVΔG/ZEBOVGP or treated with saline showed overt fever or any evidence of clinical illness during the 31 day vaccination period ., Importantly , no evidence of reaction at the vaccine injection site was noted among any of the VSVΔG/ZEBOVGP-vaccinated animals nor was any change noted in activity or behavior during the vaccination phase of the study ( day 0 to day 31 after vaccination ) ., In addition , no changes were detected in hematology or clinical chemistry following vaccination ., A mild VSVΔG/ZEBOVGP viremia ( <103 pfu/ml ) was detected only on day 2 after vaccination by virus isolation ( Figure, 1 ) and RT-PCR ( data not shown ) in four of the six VSVΔG/ZEBOVGP-immunized macaques ( Subjects #1 , 2 , 3 , 4 ) ., Surprisingly , the two animals with the lowest CD4+ counts ( subjects #5 , 6 ) never showed any detectable level of VSV viremia ., VSVΔG/ZEBOVGP was undetectable in all analyzed swab samples ( data not shown ) ., Thus , vaccination led to a transient viremia from virus replication at as yet undetermined sites but no virus shedding of the vaccine virus ., Following successful completion of the safety portion of the study all six of the VSVΔG/ZEBOVGP-vaccinated SHIV-infected monkeys and two of the three placebo control SHIV-infected monkeys were challenged 31 days after the single immunization by i . m . injection with 1000 pfu of ZEBOV ( strain Kikwit ) ., Four of the six VSVΔG/ZEBOVGP-vaccinated SHIV-infected monkeys and both of the placebo control animals started to show clinical signs of disease on day 6 after challenge including fever ( Subject # 1 , 2 and Control #1 ,, 2 ) and lymphopenia and thrombocytopenia ( Subject #2 , 5 , 6 and Control #1 ,, 2 ) ( Table 3 ) ., Disease progressed in two of the VSVΔG/ZEBOVGP-vaccinated SHIV-infected monkeys ( Subject #5 and 6 ) and both of the placebo control animals with the development of additional evidence of clinical illness including increased levels of serum enzymes associated with liver function , depression , anorexia , and the appearance of macular rashes ( Table 3 ) ., All four of these animals succumbed to the ZEBOV challenge with the two VSVΔG/ZEBOVGP-vaccinated monkeys expiring on days 9 ( Subject #6 ) and 13 ( Subject #5 ) and the placebo controls succumbing on days 9 ( Control #1 ) and 10 ( Control #2 ) after ZEBOV challenge ( Figure 2 ) ., Disease did not progress in the two VSVΔG/ZEBOVGP-vaccinated SHIV-infected monkeys that were febrile ( Subjects #1 ,, 2 ) and had changes in hematology values on day 6 ( Subjects #2 ) and both of these animals remained healthy and survived the ZEBOV challenge ( Figure 2 ) ., The remaining VSVΔG/ZEBOVGP-vaccinated macaques ( Subject #3 , 4 ) never showed any evidence of clinical illness and survived ( Figure 2 ) ., Interestingly , the VSVΔG/ZEBOVGP-vaccinated macaques that succumbed were the two animals with the most significant reduction in CD4+ T cells ( 84% , 96% ) ( Table 1 ) , the lowest total CD4+ T cell counts ( 83 , 42 ) ( Table 1 ) , the highest SHIV viremia ( Table 2 ) , and no evidence for VSV viremia ( Figure, 1 ) suggesting that CD4+ T cells may play a role in protection ., Blood samples were analyzed after challenge for evidence of ZEBOV replication by plaque assay and RT-PCR ., By day 6 , both of the placebo control animals developed high ZEBOV titers in plasma as detected by plaque assay ( >104 . 5 log pfu/ml ) ( Table 4 ) ., In comparison , only one of the VSVΔG/ZEBOVGP-vaccinated monkeys ( Subject #6 ) showed a ZEBOV viremia at day 6 by plaque assay ( ∼102 log pfu/ml ) ( Table 4 ) ., ZEBOV was detected in a second VSVΔG/ZEBOVGP-vaccinated monkey ( Subject #5 ) by day 10 ( ∼104 . 2 log pfu/ml ) ., RT-PCR was more sensitive and showed evidence of ZEBOV in plasma of this animal ( Subject #5 ) at day 6 ., In addition , RT-PCR was more sensitive in detecting ZEBOV in swabs which were positive on a number of samples derived from Subject #5 at day 6 and day 10 ( Table 4 ) ., In contrast , no ZEBOV was detected in the plasma by virus isolation or RT-PCR in the four VSVΔG/ZEBOVGP-vaccinated monkeys that survived ZEBOV challenge ., Moreover , no evidence for reactivation of VSVΔG/ZEBOVGP was detected from any blood or swab sample from any animal after ZEBOV challenge ( data not shown ) ., Although we failed to detect ZEBOV viremia in the two surviving animals that were clinically ill ( Subject #1 and, 2 ) at days 3 , 6 , 10 , and 14 after ZEBOV challenge we cannot exclude the possibility that these animals had low levels of circulating ZEBOV at time points not evaluated ., The four surviving VSVΔG/ZEBOVGP-vaccinated macaques ( Subjects #1 , 2 , 3 , 4 ) were euthanized 28 days after the ZEBOV challenge to perform a virological and pathological examination of tissues ., Organ infectivity titration from these four animals showed no evidence of ZEBOV in any of the tissues examined ., In comparison , ZEBOV was recovered from tissues of both VSVΔG/ZEBOVGP-vaccinated animals that succumbed ( Subject #5 , 6 ) and both SHIV-infected control animals ., Organ titers of infectious ZEBOV were consistent with values previously reported for immunocompetent ZEBOV-infected rhesus macaques 27 , 28 ., VSVΔG/ZEBOVGP was not recovered in any of the tissues examined from any animal on this study ., Pathological and immunohistochemical evaluation of tissues from the four VSVΔG/ZEBOVGP-vaccinated animals ( Subjects #1 , 2 , 3 , 4 ) that survived ZEBOV challenge showed no evidence of ZEBOV antigen ., In contrast , ZEBOV antigen was readily detected in typical target organs ( e . g . , liver , spleen , adrenal gland , lymph nodes ) of the two VSVΔG/ZEBOVGP-vaccinated animals that succumbed to ZEBOV challenge ( Subject #5 , 6 ) ( Figure 3 ) and the two placebo controls ., Lesions and distribution of ZEBOV antigen in these macaques was consistent with results reported in other studies 27 , 29 ., While cellular immune responses against ZEBOV GP in macaques vaccinated with VSVΔG/ZEBOVGP vectors have been difficult to detect before challenge in previous studies 7 , humoral immune responses have been more robust and consistent ( 7; TW Geisbert , unpublished observations ) ., Therefore , we measured the antibody responses of the rhesus macaques vaccinated with VSVΔG/ZEBOVGP before vaccination ( day −7 ) , after vaccination ( day 14 and day 31 ) , and after ZEBOV challenge ( day 46 and day 59 after vaccination ) by IgG ELISA ., None of the six VSVΔG/ZEBOVGP-vaccinated macaques developed IgG antibody titers against the ZEBOV GP by the day of ZEBOV challenge ( Figure 4 ) ., Two animals ( Subjects #1 ,, 2 ) developed modest IgG antibody titers against ZEBOV by day 15 after ZEBOV challenge ( day 46 after vaccination ) while a third animal developed a titer by day 28 after ZEBOV challenge ( day 59 after vaccination ) ( Figure 4 ) ., An often raised concern regarding the use of the recombinant VSV vaccine platform in humans is related to the fact that this is a replication-competent vaccine , and thus demonstration of safety is of paramount importance ., Taking into account our previous work it is not surprising that the VSVΔG/ZEBOVGP was tolerated well in our SHIV-infected macaques ., Specifically , we failed to observe evidence of any adverse events in a large cohort of over 90 macaques receiving VSV vectors expressing different GPs from viral HF agents ( 38 cynomolgus macaques and 3 rhesus macaques vaccinated with VSVΔG/ZEBOVGP; 12 cynomolgus macaques and 3 rhesus macaques vaccinated with VSV expressing SEBOV GP; 29 cynomolgus macaques and 3 rhesus macaques vaccinated with VSV expressing the Marburg virus GP; and 6 cynomolgus macaques vaccinated with VSV expressing the Lassa GP ) ( 7 , 30 , 31; TW Geisbert , H Feldmann , and SM Jones unpublished observations ) ., We have also failed to observe any adverse events in a variety of immunocompetent laboratory mice ( different inbred strains ) , outbred guinea pigs ( Hartley strain ) and goats vaccinated with the above mentioned VSV vectors at doses ranging from 2×100–2×105 pfu ( 24 , 32; SM Jones and H Feldmann , unpublished observations ) ., More recently we have also demonstrated that vaccination of severely immunocompromised SCID mice with 2×105 pfu of the VSV-based ZEBOV vaccine ( VSVΔG/ZEBOVGP ) resulted in no clinical symptoms 32 ., While transient VSV viremia in this study was only observed in surviving macaques but not in animals that had succumbed to ZEBOV challenge ( Figure 1 ) , viremia data from previous studies 7 , 30 , 31 do not support any correlation between VSV viremia and survival ., In addition , no evidence for vaccine vector shedding was detected in this study supporting previous results 7 , 30 , 31 with no compelling evidence to suggest that occasional virus shedding ( only detected by RT-PCR; negative on virus isolation ) would lead to vaccine vector transmission ., The VSV glycoprotein exchange vector that we employed in this study has also shown promise as a preventive vaccine and postexposure treatment against Marburg HF 30 , 33 and as a preventive vaccine against Lassa fever in nonhuman primates 31 ., Similar recombinant VSV vectors have been evaluated in animal models as vaccine candidates for a number of viruses that cause disease in humans including HIV-1 , influenza virus , respiratory syncytial virus , measles virus , herpes simplex virus type 2 , hepatitis C virus , and severe acute respiratory syndrome coronavirus 34–40 ., Many of these studies have employed VSV vectors that maintained either the entire VSV glycoprotein ( G ) or the transmembrane and/or cytoplamic domains of this protein to facilitate more efficient incorporation of the foreign antigen ., It is known that VSV G is an important VSV protein associated with pathogenicity 38 , 41 ., It has been shown that truncation of the cytoplasmic tail has greatly reduced vector pathogenicity in mice following intranasal inoculation indicating the importance of this domain for pathogenicity 42 ., In this regard , a VSV vector including portions of the VSV G and expressing HIV genes was found to be insufficiently attenuated for clinical evaluation when assessed for neurovirulence in nonhuman primates 43 ., These investigators subsequently showed that safety and immunogenicity can be improved by genetic manipulation of the VSV genome but it remained unclear whether neurovirulence was associated with the VSV G or other genome manipulations 44 ., Nevertheless , our ZEBOV vaccine is a G-deficient VSV vector 24 and thus lacks G-associated pathogenicity 41 as well as the target for VSV-specific neutralizing antibodies 45 ., Aside from G , the VSV matrix ( M ) protein has been associated with cytopathic effects in vitro including the inhibition of host gene expression , induction of cell rounding and induction of apoptosis 46 , 47 ., It is largely unclear to what extent M alone contributes to pathogenicity , but inoculation studies with the VSV-based vaccines in different animal species ( as described above ) do not suggest a major pathogenic effect of the M protein in vivo 7 , 13 , 32 ., Currently , the mechanism by which any filovirus vaccine confers protection in nonhuman primates is not well understood ., Nearly all studies have detected modest to good humoral immune responses ., For the VSVΔG/ZEBOVGP vaccine a humoral response is detected in macaques by day 14 after vaccination ( 7; TW Geisbert , unpublished observations ) ., However , in the current study and consistent with an impaired immune system , our SHIV-infected macaques did not develop a humoral immune response by the time of ZEBOV challenge ., Three animals developed modest anti-ZEBOV IgG titers 14 to 28 days after ZEBOV challenge ., We are uncertain as to why four of the six VSVΔG/ZEBOVGP-vaccinated macaques survived ZEBOV challenge ., Regardless of any humoral immune response elicited in these animals it is unlikely that antibody alone confers protection ., Specifically , passive antibody studies in nonhuman primates using a variety of anti-ZEBOV immune reagents including polyclonal equine immune globulin 25 , a recombinant human monoclonal antibody 48 , and convalescent monkey blood 49 have uniformly failed to provide protection and more importantly have failed to provide any beneficial effect ., A number of studies have evaluated the cellular immune response in nonhuman primates vaccinated against EBOV and the results have been mixed with some studies showing a modest cellular response and other studies showing weak and/or no cellular immune responses 7 , 9 , 10 ., However , it is likely that the intracellular cytokine assays that have been employed in some of these studies are not sensitive or thorough enough to detect a cellular immune response against ZEBOV ., Indeed , it has been reported that the inability to demonstrate a robust cellular response may illustrate the limitation of the evaluation of cellular immune responses using small numbers of functional measurements ( such as interferon-gamma ) 50 ., One interesting finding in the current study may begin to shed some light on the mechanism of protection elicited by the VSVΔG/ZEBOVGP ., Notably , the two rhesus macaques that grouped together with the most severe loss of CD4+ T cells were the only animals that failed to survive ZEBOV challenge ., This suggests that CD4+ T cells may play a role in mediating protective immunity in EBOV infections ., CD4+ T cells have been shown to be depleted in nonhuman primate following ZEBOV infections 27 , 51 and in vitro ZEBOV infection of human peripheral blood mononuclear cells causes massive bystander death of CD4+ T cells by apoptosis 52 ., While rodents do not appear to faithfully reproduce ZEBOV infection of humans and nonhuman primates 53 studies have suggested that CD4+ T cells are required for protection of rodents against ZEBOV ., Specifically , in a study using liposome-encapsulated ZEBOV antigens , Rao and colleagues showed that treatment of mice with anti-CD4 antibodies before or during vaccination abolished protection , while treatment with anti-CD8 antibodies had no effect , thus indicating a requirement for CD4+ T lymphocytes for successful immunization 54 ., Similarly , depletion of CD8+ T cells did not compromise protection in mice indicating that CD8+ cytotoxic T cells are not a requirement for protection 32 ., In conclusion , our results show that the VSV-based ZEBOV vaccine ( VSVΔG/ZEBOVGP ) did not cause any illness in immunocompromised SHIV-infected rhesus macaques and resulted in sufficient protective efficacy in all but the most severely compromised animals against a lethal ZEBOV challenge ., Protection in the immunocompromised macaques appeared to be dependent on CD4+ T cells rather than the development of EBOV-specific antibodies ., This provides strong support for the safety of the VSV-based vectors and further development of this promising vaccine platform for its use in humans ., While these data are very encouraging , as the number of SHIV-infected macaques in the current study was small , additional safety studies will be needed in order to determine whether vaccines based on attenuated VSV will ultimately prove safe in immunocompromised humans .

Introduction, Methods, Results, Discussion

Ebola virus ( EBOV ) is a significant human pathogen that presents a public health concern as an emerging/re-emerging virus and as a potential biological weapon ., Substantial progress has been made over the last decade in developing candidate preventive vaccines that can protect nonhuman primates against EBOV ., Among these prospects , a vaccine based on recombinant vesicular stomatitis virus ( VSV ) is particularly robust , as it can also confer protection when administered as a postexposure treatment ., A concern that has been raised regarding the replication-competent VSV vectors that express EBOV glycoproteins is how these vectors would be tolerated by individuals with altered or compromised immune systems such as patients infected with HIV ., This is especially important as all EBOV outbreaks to date have occurred in areas of Central and Western Africa with high HIV incidence rates in the population ., In order to address this concern , we evaluated the safety of the recombinant VSV vector expressing the Zaire ebolavirus glycoprotein ( VSVΔG/ZEBOVGP ) in six rhesus macaques infected with simian-human immunodeficiency virus ( SHIV ) ., All six animals showed no evidence of illness associated with the VSVΔG/ZEBOVGP vaccine , suggesting that this vaccine may be safe in immunocompromised populations ., While one goal of the study was to evaluate the safety of the candidate vaccine platform , it was also of interest to determine if altered immune status would affect vaccine efficacy ., The vaccine protected 4 of 6 SHIV-infected macaques from death following ZEBOV challenge ., Evaluation of CD4+ T cells in all animals showed that the animals that succumbed to lethal ZEBOV challenge had the lowest CD4+ counts , suggesting that CD4+ T cells may play a role in mediating protection against ZEBOV .

Ebola virus is among the most lethal microbes known to man , with case fatality rates often exceeding 80% ., Since its discovery in 1976 , outbreaks have been sporadic and geographically restricted , primarily to areas of Central Africa ., However , concern about the natural or unnatural introduction of Ebola outside of the endemic areas has dramatically increased both research interest and public awareness ., A number of candidate vaccines have been developed to combat Ebola virus , and these vaccines have shown varying degrees of success in nonhuman primate models ., Safety is a significant concern for any vaccine and in particular for vaccines that replicate in the host ., Here , we evaluated the safety of our replication-competent vesicular stomatitus virus ( VSV ) -based Ebola vaccine in SHIV-infected rhesus monkeys ., We found that the vaccine caused no evidence of overt illness in any of these immunocompromised animals ., We also demonstrated that this vaccine partially protected the SHIV-infected monkeys against a lethal Ebola challenge and that there appears to be an association with levels of CD4+ lymphocytes and survival ., Our study suggests that the VSV-based Ebola vaccine will be safe in immunocompromised populations and supports further study and development of this promising vaccine platform for its use in humans .

virology/vaccines, virology/animal models of infection, virology

journal.pcbi.1006166

Variability in pulmonary vein electrophysiology and fibrosis determines arrhythmia susceptibility and dynamics

Success rates for catheter ablation of persistent atrial fibrillation ( AF ) patients are currently low; however , there is a subset of patients for whom pulmonary vein isolation ( PVI ) alone is a successful treatment strategy 1 ., PVI ablation may work by preventing triggered beats from entering the left atrial body , or by converting rotors or functional reentry around the left atrial/pulmonary vein ( LA/PV ) junction to anatomical reentry around a larger circuit , potentially converting AF to a simpler tachycardia 2 ., It is difficult to predict whether PVI represents a sufficient treatment strategy for a given patient with persistent AF 1 , and it is unclear what to do for the majority of patients for whom it is not effective ., Patients with AF exhibit distinct properties in effective refractory period ( ERP ) and conduction velocity ( CV ) in the PVs ., For example , paroxysmal AF patients have shorter ERP and longer conduction delays compared to control patients 3 ., AF patients show a number of other differences to control patients: PVs are larger 4; PV fibrosis is increased; and fiber direction may be more disorganised , particularly at the PV ostium 5 ., There are also differences within patient groups; for example , patients for whom persistent AF is likely to terminate after PVI have a larger ERP gradient compared to those who require further ablation 1 , 3 ., Electrical driver location changes as AF progresses; drivers ( rotors or focal sources ) are typically located close to the PVs in early AF , but are also located elsewhere in the atria with longer AF duration 6 ., Atrial fibrosis is a major factor associated with AF and modifies conduction ., However , there is conflicting evidence on the relationship between fibrosis distribution and driver location 7 , 8 ., It is difficult to clinically separate the individual effects of these factors on arrhythmia susceptibility and maintenance ., We hypothesise that the combination of PV properties and atrial body fibrosis determines driver location and , thus , the likely effectiveness of PVI ., In this study , we tested this hypothesis by using computational modelling to gain mechanistic insight into the individual contribution of PV ERP , CV , fiber direction , fibrosis and anatomy on arrhythmia susceptibility and dynamics ., We incorporated data on APD ( action potential duration , as a surrogate for ERP ) and CV for the PVs to determine mechanisms underlying arrhythmia susceptibility , by testing inducibility from PV ectopic beats ., We also predicted driver location , and PVI outcome ., All simulations were performed using the CARPentry simulator ( available at https://carp . medunigraz . at/carputils/ ) ., We used a previously published bi-atrial bilayer model 9 , which consists of resistively coupled endocardial and epicardial surfaces ., This model incorporates detailed atrial structure and includes transmural heterogeneity at a similar computational cost to surface models ., We chose to use a bilayer model rather than a volumetric model incorporating thickness for this study because of the large numbers of parameters investigated , which was feasible with the reduced computational cost of the bilayer model ., As previously described , the bilayer model was constructed from computed tomography scans of a patient with paroxysmal AF , which were segmented and meshed to create a finite element mesh suitable for electrophysiology simulations ., Fiber information was included in the model using a semi-automatic rule based method that matches histological descriptions of atrial fiber orientation 10 ., The left atrium of the bilayer model consists of linearly coupled endocardial and epicardial layers , while the right atrium is an epicardial layer , with endocardial atrial structures including the pectinate muscles and crista terminalis ., The left and right atrium of the model are electrically connected through three pathways: Bachmann’s bundle , the coronary sinus and the fossa ovalis ., Tissue conductivities were tuned to human activation mapping data from Lemery et al . 9 , 11 ., The Courtemanche-Ramirez-Nattel human atrial ionic model was used with changes representing electrical remodelling during persistent AF 12 , together with a doubling of sodium conductance to produce realistic action potential upstroke velocities 9 , and a decrease in IK1 by 20% to match clinical restitution data 13 ., Regional heterogeneity in repolarisation was included by modifying ionic conductances of the cellular model , as described in Bayer et al . 14 , which follows Aslanidi et al . and Seemann et al . 15 , 16 ., Parameters for the baseline PV model were taken from Krueger et al . 17 ., The following PV properties were varied as shown in schematic Fig 1: APD , CV , fiber direction , the inclusion of fibrosis in the PVs and the atrial geometry ., These are described in the following sections ., To investigate the effects of PV length and diameter on arrhythmia inducibility and arrhythmia dynamics , bi-atrial bilayer meshes were constructed from MRI data for twelve patients ., All patients gave written informed consent; this study is in accordance with the Declaration of Helsinki , and approved by the Institutional Ethics Committee at the University of Bordeaux ., Patient-specific models with electrophysiological heterogeneity and fiber direction were constructed using our modelling pipeline , which uses a universal atrial coordinate system to map scalar and vector data from the original bilayer model to a new patient specific mesh ., Late gadolinium enhancement MRI ( average resolution 0 . 625mm x 0 . 625mm x 2 . 5mm ) was performed using a 1 . 5T system ( Avanto , Siemens Medical Solutions , Erlangen , Germany ) ., These LGE-MRI data were manually segmented using the software MUSIC ( Electrophysiology and Heart Modeling Institute , University of Bordeaux , Bordeaux France , and Inria , Sophia Antipolis , France , http://med . inria . fr ) ., The resulting endocardial surfaces were meshed ( using the Medical Imaging Registration Toolkit mcubes algorithm 18 ) and cut to create open surfaces at the mitral valve , the four pulmonary veins , the tricuspid valve , and each of the superior vena cava , the inferior vena cava and the coronary sinus using ParaView software ( Kitware , Clifton Park , NY , USA ) ., The meshes were then remeshed using mmgtools meshing software ( http://www . mmgtools . org/ ) , with parameters chosen to produce meshes with an average edge length of 0 . 34mm to match the resolution of the previously published bilayer model 9 ., Two atrial coordinates were defined for each of the LA and RA , which allow automatic transfer of atrial structures to the model , such as the pectinate muscles and Bachmann’s bundle ., These coordinates were also used to map fiber directions to the bilayer model ., To investigate the effects of PV electrophysiology on arrhythmia inducibility and dynamics , we varied PV APD and CV by modifying the value of the inward rectifier current ( IK1 ) conductance and tissue level conductivity respectively ., IK1 conductance was chosen in this case to investigate macroscopic differences in APD 19 , although several ionic conductances are known to change with AF 20 ., Modifications were either applied homogeneously or following a ostial-distal gradient ., This gradient was implemented by calculating geodesic distances from the rim of mesh nodes at the distal PV boundary to all nodes in the PV and from the rim of nodes at the LA/PV junction to all nodes in the PV ., The ratio of these two distances was then used as a distance parameter from the LA/PV junction to the distal end of the PV ( see Fig 1 ) ., IK1 conductance was multiplied by a value in the range 0 . 5–2 . 5 , resulting in PV APDs in the clinical range of 100–190ms 3 , 21 , 22 ., This rescaling was either a homogeneous change or followed a gradient along the PV length ., Gradients of IK1 conductance varied from the baseline value at the LA/PV junction , to a maximum scaling factor at the distal boundary ., PV APDs are reported at 90% repolarisation for a pacing cycle length of 1000ms ., LA APD is 185ms , measured at a LA pacing cycle length of 200ms ., To cover the clinically observed range of PV CVs , longitudinal and transverse tissue conductivities were divided by 1 , 2 , 3 or 5 , resulting in CVs , measured along the PV axis , in the range: 0 . 28–0 . 67m/s 3 , 21–24 ., To model heterogeneous conduction slowing , conductivities were varied as a function of distance from the LA/PV junction , ranging from baseline at the junction to a maximum rescaling ( minimum conductivity ) at the distal boundary ., The direction of this gradient was also reversed to model conduction slowing at the LA/PV junction 5 ., Motivated by the findings of Hocini et al . 5 , interstitial fibrosis was modelled for the PVs with a density varying along the vein , increasing from the LA/PV junction to the distal boundary ., This was implemented by randomly selecting edges of elements of the mesh with probability scaled by the distance parameter and the angle of the edge compared to the element fiber direction , where edges in the longitudinal fiber direction were four times more likely to be selected than those in the transverse direction , following our previous methodology 25 ., To model microstructural discontinuities , no flux boundary conditions were applied along the connected edge networks , following Costa et al . 26 ., An example of modelled PV interstitial fibrosis is shown in S1A Fig . For a subset of simulations , interstitial fibrosis was incorporated in the biatrial model based on late gadolinium enhancement ( LGE ) -MRI data , using our previously published methodology 25 ., In brief , likelihood of interstitial fibrosis depended on both LGE intensity and the angle of the edge compared to the element fiber direction ( see S1B Fig ) ., LGE intensity distributions were either averaged over a population of patients 27 , or for an individual patient ., The averaged distributions were for patients with paroxysmal AF ( averaged over 34 patients ) , or persistent AF ( averaged over 26 patients ) ., For patient-specific simulations , the model arrhythmia dynamics were compared to AF recordings from a commercially available non-invasive ECGi mapping technology ( CardioInsight Technologies Inc . , Cleveland , OH ) for which phase mapping analysis was performed as previously described 28 ., PV fiber direction shows significant inter-patient variability ., Endocardial and epicardial fiber direction in the four PVs was modified according to fiber arrangements described in the literature 5 , 29 , 30 ., Six arrangements were considered , as follows:, 1 . circular arrangement on both the endocardium and epicardium;, 2 . spiralling arrangement on both the endocardium and epicardium;, 3 . circular arrangement on the endocardium , with longitudinal epicardial fibers;, 4 . fibers progress from longitudinal at the distal vein to circumferential at the ostium , with identical endocardial and epicardial fibers;, 5 . epicardial layer fibers as per case 4 , with circumferential endocardial fibers;, 6 . as per case 4 , but with a chaotic fiber arrangement at the LA/PV junction ., These fiber distributions are shown in S2 Fig . Cases 4–6 were implemented by setting the fiber angle to be a function of the distance along the vein , measured from the LA/PV junction to the distal boundary , varying from circumferential at the junction to longitudinal at the distal end ( representing a change of 90 degrees ) ., The disorder in fiber direction at the LA/PV junction for case 6 was implemented by taking the fibers of case 4 and adding independent standard Gaussian distributions scaled by the distance from the distal boundary , resulting in the largest perturbations at the ostium ., Arrhythmia inducibility was tested by extrastimulus pacing from each of the four PVs individually using a clinically motivated protocol 31 , to simulate the occurrence of PV ectopics ., Simulations were performed for each of the PVs , to determine the effects of ectopic beat location on inducibility ., Sinus rhythm was simulated by stimulating the sinoatrial node region of the model at a cycle length of 700ms throughout the simulation ., Each PV was paced individually with five beats at a cycle length of 160ms , and coupling intervals between the first PV beat and a sinus rhythm beat in the range 200–500 ms . Thirty-two pacing protocols were applied for each model set up: eight coupling intervals ( coupling interval = 200 , 240 , 280 , 320 , 360 , 400 , 440 , 480ms ) , for each of the four PVs ., Inducibility is reported as the proportion of cases resulting in reentry; termed the inducibility ratio ., The effects of PVI were determined for model set-ups that used the original bilayer geometry and in which the arrhythmia lasted for greater than two seconds ., PVI was applied two seconds post AF initiation in each case by setting the tissue conductivity close to zero ( 0 . 001 S/m ) in the regions shown in S3 Fig . For each case , ten seconds of arrhythmia data were analysed , starting from two seconds post AF initiation , to identify re-entrant waves and wavefront break-up using phase ., The phase of the transmembrane voltage was calculated for each node of the mesh using the Hilbert transform , following subtraction of the mean 32 ., Phase singularities ( PSs ) for the transmembrane potential data were identified by calculating the topological charge of each element in the mesh 33 , and PS spatial density maps were calculated using previously published methods 14 ., PS density maps were then partitioned into the LA body , PV regions , and the RA to assess where drivers were located in relation to the PVs ( see S3 Fig ) ., The PV region was defined as the areas enclosed by , and including , the PVI lines; the LA region was then the rest of the LA and left atrial appendage ., The PV PS density ratio was then defined as the total PV PS count divided by the total model PS count over both atria ., A difference in APD between the model LA and PVs was required for AF induction ., Modelling the PVs using LA cellular properties resulted in non-inducibility , whereas , modelling the LA using PV cellular properties resulted in either non-inducibility or macroreentry ., The effects of modifying PV APD homogeneously or following a gradient are shown in Table 1 ., Simulations in which PV APD was longer than LA APD were non-inducible ( PV APD 191ms ) ., As APD was decreased below the baseline value ( 181ms ) , inducibility initially increased and then fluctuated ., Comparing cases with equal distal APD , arrhythmia inducibility was significantly higher for APD following a ostial-distal gradient than for homogeneous APD ( p = 0 . 03 from McNemar’s test ) ., PS location was also affected by PV APD ., PV PS density was low in cases of short APD , an example of which is shown in Fig 2 where reentry is no longer seen around the LA/PV junction in the case of short APD ( 120ms ) ., This change was more noticeable for cases with homogeneous PV APD than for a gradient in APD; PV reentry was observed for the baseline case and a heterogeneous APD case , but not for a homogeneous decrease in APD ., Arrhythmia inducibility decreased with homogeneous CV slowing ( from 0 . 38 i . e . 12/32 at 0 . 67m/s to 0 . 03 i . e . 1/32 at 0 . 28m/s ) ., In the baseline model , reentry occurs close to the LA/PV junction due to conduction block when the paced PV beat encounters a change in fiber direction at the base of the PVs , together with a longer LA APD compared to the PV APD ., In this case , the wavefront encounters a region of refractory tissue due to the longer APD in the LA ., However , when PV CV is slowed homogeneously , the wavefront takes longer to reach the LA tissue , giving the tissue enough time to recover , such that conduction block and reentry no longer occurs ., Modifying conductivity following a gradient means that , unlike the homogeneous case , the time taken for the extrastimulus wavefront to reach the LA tissue is similar to the baseline case , so the LA tissue might still be refractory and conduction block might occur ., In the case that conduction was slowest at the distal vein , the inducibility was similar to the baseline case ( see Table 2 , GA , inducibility is 0 . 38 at baseline and 0 . 34 for the cases with CV slowing ) ., Cases with greatest conduction slowing at the LA/PV junction ( see Table 2 , GB ) exhibit an increase in inducibility ( from 0 . 38 to 0 . 53 ) when CV is decreased because of the discontinuity in conductivity at the junction ., Fig 2 shows that reentry is seen around the LA/PV junction in cases with both baseline and slow CV , indicating that the presence of reentry at the LA/PV junction is independent of PV CV ., PV conduction properties are also affected by PV fiber direction ., Modifications in fiber direction increased inducibility compared to the baseline fiber direction ( baseline case: 0 . 38; modified fiber direction cases 1-6: 0 . 53-0 . 63 ) ., The highest inducibility occurred with circular fibers at the ostium ( cases 1 and 4 , 0 . 63 ) , independent of fiber direction at the distal PV end ., This inducibility was reduced if the epicardial fibers were not circular at the ostium ( case 3 , 0 . 56 ) , or if fibers were spiralling ( case 2 , 0 . 56 ) instead of circular ., Next we investigated the interplay between PV properties and atrial fibrosis ., LA fibrosis properties were varied to represent interstitial fibrosis in paroxysmal and persistent AF patients , incorporating average LGE-MRI distributions 27 into the model ., These control , paroxysmal and persistent AF levels of fibrosis were then combined with PV properties varied as follows: baseline CV and APD ( 0 . 67m/s , 181ms ) , slow CV ( 0 . 51m/s ) , short APD ( 120ms ) , slow CV and short APD ., PS distributions in Fig 2 show that reentry occurred around the LA/PV junction in the case of baseline PV APD for control or paroxysmal levels of fibrosis , but not for shorter PV APD ., Modifying PV CV did not affect whether LA/PV reentry is observed ., Rotors were found to stabilise to regions of high fibrosis density in the persistent AF case ., Models with PV fibrosis had a higher inducibility compared to the baseline case ( 0 . 47 vs . 0 . 38 ) and a higher PV PS density since reentry localised there ., Fig 3 shows an example with moderate PV fibrosis ( A ) in which reentry changed from around the RIPV to the LIPV later in the simulation; adding a higher level of PV fibrosis resulted in a more stable reentry around the right PVs ( B ) ., The relationship between LA fibrosis and PV properties on driver location was investigated on an individual patient basis for four patients ., For patients for whom rotors were located away from the PVs ( Fig 4 LA1 ) , increasing model fibrosis from low to high increased the model agreement with clinical PS density 2 . 3 ± 1 . 0 fold ( comparing the sensitivity of identifying clinical regions of high PS density using model PS density between the two simulations ) ., For other patients , lower levels of fibrosis were more appropriate ( 2 . 1 fold increase in agreement for lower fibrosis , Fig 4 LA2 ) , and PV isolation converted fibrillation to macroreentry in the model ., Arrhythmia inducibility showed a large variation between patient geometries ( 0 . 16–0 . 47 ) ., Increasing PV area increased inducibility to a different degree for each vein: right superior PV ( RSPV ) inducibility was generally high ( > 0 . 75 for all but one geometry ) independent of PV area; left superior PV ( LSPV ) inducibility increased with PV area ( Spearman’s rank correlation coefficient of 0 . 36 indicating positive correlation; line of best fit gradient 0 . 27 , R2 = 0 . 3 ) ; left inferior PV ( LIPV ) and right inferior PV ( RIPV ) inducibility exhibited a threshold effect , in which veins were only inducible above a threshold area ( Fig 5A ) ., There is no clear relationship between PV length and inducibility ., PV PS density ratio increased as PV area increased ( Fig 5B , Spearman’s rank correlation coefficient of 0 . 41 indicating positive correlation ) ., Fig 5C shows that rotor and wavefront trajectories depend on patient geometry , exhibiting varied importance of the PVs compared to other atrial regions ., PVI outcome was assessed for cases with varied PV APD ( both with a homogeneous change or following a gradient ) , with the inclusion of PV fibrosis and with varied PV fiber direction because these factors were found to affect the PV PS density ratio ., PVI outcome was classified into three classes depending on the activity 1 second after PVI was applied in the model: termination , meaning there was no activity; macroreentry , meaning that there was a macroreentry around the LA/PV junctions; AF sustained by LA rotors , meaning there were drivers in the LA body ., These classes accounted for different proportions of the outcomes: termination ( 27 . 3% of cases ) , macroreentry ( 39 . 4% ) , or AF sustained by LA rotors ( 33 . 3% ) ., Calculating the PV PS density ratio before PVI for each of these classes shows that cases in which the arrhythmia either terminated or changed to a macroreentry are characterised by a statistically higher PV PS density ratio pre-PVI than cases sustained by LA rotors post-PVI ( see Fig 6 , t-test comparing termination and LA rotors shows they are significantly different , p<0 . 001; comparing macroreentry and LA rotors p = 0 . 01 ) ., High PV PS density ratio may indicate likelihood of PVI success ., In this computational modelling study , we demonstrated that the PVs can play a large role in arrhythmia maintenance and initiation , beyond being simply sources of ectopic beats ., We separated the effects of PV properties and atrial fibrosis on arrhythmia inducibility , maintenance mechanisms and the outcome of PVI , based on population or individual patient data ., PV properties affect arrhythmia susceptibility from ectopic beats; short PV APD increased arrhythmia susceptibility , while longer PV APD was found to be protective ., Arrhythmia inducibility increased with slower CV at the LA/PV junction , but not for cases with homogeneous CV changes or slower CV at the distal PV ., The effectiveness of PVI is usually attributed to PV ectopy , but our study demonstrates that the PVs affect reentry in other ways and this may , in part , also account for success or failure of PVI ., Both PV properties and fibrosis distribution affect arrhythmia dynamics , which varies from meandering rotors to PV reentry ( in cases with baseline or long APD ) , and then to stable rotors at regions of high fibrosis density ., PS density in the PV region was high for cases with PV fibrosis ., The measurement of fibrosis and PV properties may indicate patient specific susceptibility to AF initiation and maintenance ., PV PS density before PVI was higher in cases in which AF terminated or converted to a macroreentry; thus , high PV PS density may indicate likelihood of AF termination by PVI alone ., PV repolarisation is heterogeneous in the PVs 23 , and exhibits distinct properties in AF patients , with Rostock et al . reporting a greater decrease in PV ERP than LA ERP in patients with AF , termed AF begets AF in the PVs 21 ., Jais et al . found that PV ERP is greater than LA ERP in AF patients , but this gradient is reversed in AF patients 3 ., ERP measured at the distal PV is shorter than at the LA/PV junction during AF 5 , 22 ., Motivated by these clinical and experimental studies , we modelled a decrease in PV APD , which was applied either homogeneously , or as a gradient of decreasing APD along the length of the PV , with the shortest APD at the distal PV rim ., An initial decrease in APD increased inducibility ( Table 1 ) , which agrees with clinical findings of increased inducibility for AF patients ., Applying this change following a gradient , as observed in previous studies , led to an increased inducibility compared to a homogeneous change in APD ., Similar to Calvo et al . 34 we found that rotor location depends on PV APD ( Fig 2 ) ., Thus PV APD affects PVI outcome in two ways; on the one hand , decreasing APD increases inducibility , emphasising the importance of PVI in the case of ectopic beats; on the other hand , PV PS density decreases for cases with short PV APD , and PVI was less likely to terminate AF ., Multiple studies have measured conduction slowing in the PVs 3 , 5 , 21–24 ., We modelled changes in tissue conductivity either homogeneously , or as a function of distance along the PV ., Simply decreasing conductivity and thus decreasing CV , decreased inducibility ( Table 2 ) ., Kumagai et al . reported that conduction delay was longer for the distal to ostial direction 22 ., We found that modifying conductivity following a gradient , with CV decreasing towards the LA/PV junction , resulted in an increase in inducibility in the model ., This agrees with the clinical observations of Pascale et al . 1 ., This suggests that PVI should be performed in cases in which CV decreases towards the LA/PV junction as these cases have high inducibility ., Changes in CV may also be due to other factors , including gap junction remodelling , modified sodium conductance or changes in fiber direction 5 , 29 ., A variety of PV fiber patterns have been described in the literature and there is variability between patients ., Interestingly , all of the PV fiber directions considered in our study showed an increased inducibility compared to the baseline model ., Verheule et al . 29 documented circumferential strands that spiral around the lumen of the veins , motivating the arrangements for cases 1 and 4 in our study; Aslanidi et al . 15 reported that fibers run in a spiralling arrangement ( case 2 ) ; Ho et al . 30 measured mainly circular or spiral bundles , with longitudinal bundles ( cases 3 and 5 ) ; Hocini et al . 5 reported longitudinal fibers at the distal PV , with circumferential and a mixed chaotic fiber direction at the PV ostium ( case 6 ) ., Using current imaging technologies , PV fiber direction cannot be reliably measured in vivo ., In our study , fiber direction at the PV ostium was found to be more important than at the distal PV; the greatest inducibility was for cases with circular fibers at the ostium on both endocardial and epicardial surfaces , independent of fiber direction at the distal PV end ., Similar to modelling studies by both Coleman 35 and Aslanidi 15 , inducibility increased due to conduction block near the PVs ., PVs may be larger in AF patients compared to controls 4 , 36 , and this difference may vary between veins; Lin et al . found dilatation of the superior PVs in patients with focal AF originating from the PVs , but no difference in the dimensions of inferior PVs compared to control or to patients with focal AF from the superior vena cava or crista terminalis 37 ., We found that inducibility increased with PV area for the LSPV , LIPV and RIPV , but not for the RSPV ( see Fig 5 ) ., In addition , PV PS density ratio increased with total PV area , suggesting that PVI alone is more likely to be a successful treatment strategy in the case of larger veins ., However , Den Uijl et al . found no relation between PV dimensions and the outcome of PVI 38 ., Rotors were commonly found in areas of high surface curvature , including the LA/PV junction and left atrial appendage ostia , which agrees with findings of Tzortzis et al . 39 ., However , there were differences in PS density between geometries , with varying importance of the LA/PV junction ( Fig 5 ) , demonstrating the importance of modelling the geometry of an individual patient ., Myocardial tissue within the PVs is significantly fibrotic , which may lead to slow conduction and reentry 5 , 30 , 40 ., More fibrosis is found in the distal PV , with increased connective tissue deposition between myocardial cells 41 ., We modelled interstitial PV fibrosis with increasing density distally , and found that the inclusion of PV fibrosis increased PS density in the PV region of the model due to increased reentry around the LA/PV junction and wave break in the areas of fibrosis ., This , together with the results in Fig 6 , suggests that PVI alone is more likely to be a successful in cases of high PV fibrosis ., There are multiple methodologies for modelling atrial fibrosis 25 , 42 , 43 , and the choice of method may affect this localisation ., Population based distributions of atrial fibrosis were modelled for paroxysmal and persistent patients , together with varied PV properties ., The presence of LA/PV reentry depends on both PV properties and the presence of fibrosis; reentry is seen at the LA/PV junction for cases with baseline PV APD , but not for short PV APD , and stabilised to areas of high fibrosis in persistent AF , for which LA/PV reentry no longer occurred ., This suggests that rotor location depends on both fibrosis and PV properties ., This finding may explain the clinical findings of Lim et al . in which drivers are primarily located in the PV region in early AF , but AF complexity increased with increased AF duration , and drivers are also located at sites away from the PVs 6 ., During early AF , PV properties may be more important , while with increasing AF duration , there is increased atrial fibrosis in the atrial body that affects driver location ., This suggests that in cases with increased atrial fibrosis in the atrial body , ablation in addition to PVI is likely to be required ., Simulations of models with patient-specific atrial fibrosis together with varied PV properties performed in this study offer a proof of concept for using this approach in future studies ., The level of atrial fibrosis and PV properties that gave the best fit of the model PS density to the clinical PS density varied between patients ., Measurement of PV ERP and conduction properties using a lasso catheter before PVI could be used to tune the model properties , together with LGE-MRI or an electro-anatomic voltage map ., It is difficult to predict whether PVI alone is likely to be a successful treatment strategy for a patient with persistent AF 44 ., This will depend on both the susceptibility to AF from ectopic beats , together with electrical driver location , and electrical size ., Our study describes multiple factors that affect the susceptibility to AF from ectopic beats ., Measurement of PV APD , PV CV and PV size will allow prediction of the susceptibility to AF from ectopic beats ., Arrhythmia susceptibility increased in cases with short PV APD , slower CV at the LA/PV junction and larger veins , suggesting the importance of PVI in these cases ., The likelihood that PVI terminates AF was also found to depend on driver location , assessed using PS density ., Our simulation studies suggest that high PV PS density indicates likelihood of PVI success ., Thus either measuring this clinically using non-invasive ECGi recordings , or running patient-specific simulations to estimate this value may suggest whether ablation in addition to PVI should be performed ., In a recent clinical study , Navara et al . observed AF termination during ablation near the PVs , before complete isolation , in cases where rotational and focal activity were identified close to these ablation sites 45 ., These data may support the PV PS density metric suggested in our study ., Our simulations show that PV PS density depends on PV APD , the degree of PV fibrosis and to a lesser extent on PV fiber direction ., To the best of the authors’ knowledge , there are no previous studies on the relationship between fibrosis in the PVs , or PV fiber direction , and the success rate of PVI ., Measuring atrial electrogram properties , including AF cycle length , before and after ablation may indicate changes in local tissue refractoriness 46 ., PV APD can be estimated clinically by pacing to find the PV ERP; and PV fibrosis may be estimated using LGE-MRI , although this is challenging , as the tissue is thin ., PV

Introduction, Materials and methods, Results, Discussion

Success rates for catheter ablation of persistent atrial fibrillation patients are currently low; however , there is a subset of patients for whom electrical isolation of the pulmonary veins alone is a successful treatment strategy ., It is difficult to identify these patients because there are a multitude of factors affecting arrhythmia susceptibility and maintenance , and the individual contributions of these factors are difficult to determine clinically ., We hypothesised that the combination of pulmonary vein ( PV ) electrophysiology and atrial body fibrosis determine driver location and effectiveness of pulmonary vein isolation ( PVI ) ., We used bilayer biatrial computer models based on patient geometries to investigate the effects of PV properties and atrial fibrosis on arrhythmia inducibility , maintenance mechanisms , and the outcome of PVI ., Short PV action potential duration ( APD ) increased arrhythmia susceptibility , while longer PV APD was found to be protective ., Arrhythmia inducibility increased with slower conduction velocity ( CV ) at the LA/PV junction , but not for cases with homogeneous CV changes or slower CV at the distal PV ., Phase singularity ( PS ) density in the PV region for cases with PV fibrosis was increased ., Arrhythmia dynamics depend on both PV properties and fibrosis distribution , varying from meandering rotors to PV reentry ( in cases with baseline or long APD ) , to stable rotors at regions of high fibrosis density ., Measurement of fibrosis and PV properties may indicate patient specific susceptibility to AF initiation and maintenance ., PV PS density before PVI was higher for cases in which AF terminated or converted to a macroreentry; thus , high PV PS density may indicate likelihood of PVI success .

Atrial fibrillation is the most commonly encountered cardiac arrhythmia , affecting a significant portion of the population ., Currently , ablation is the most effective treatment but success rates are less than optimal , being 70% one-year post-treatment ., There is a large effort to find better ablation strategies to permanently cure the condition ., Pulmonary vein isolation by ablation is more or less the standard of care , but many questions remain since pulmonary vein ectopy by itself does not explain all of the clinical successes or failures ., We used computer simulations to investigate how electrophysiological properties of the pulmonary veins can affect rotor formation and maintenance in patients suffering from atrial fibrillation ., We used complex , biophysical representations of cellular electrophysiology in highly detailed geometries constructed from patient scans ., We heterogeneously varied electrophysiological and structural properties to see their effects on rotor initiation and maintenance ., Our study suggests a metric for indicating the likelihood of success of pulmonary vein isolation ., Thus either measuring this clinically , or running patient-specific simulations to estimate this metric may suggest whether ablation in addition to pulmonary vein isolation should be performed ., Our study provides motivation for a retrospective clinical study or experimental study into this metric .

medicine and health sciences, engineering and technology, cardiovascular anatomy, fibrosis, electrophysiology, endocardium, simulation and modeling, developmental biology, epicardium, research and analysis methods, cardiology, arrhythmia, atrial fibrillation, rotors, mechanical engineering, anatomy, physiology, biology and life sciences, heart

journal.ppat.1005949

Methylfolate Trap Promotes Bacterial Thymineless Death by Sulfa Drugs

Sulfonamides , or SULFA drugs , were the first chemical substances systematically used to treat and prevent bacterial infections 1 , 2 , but the use of these drugs gradually declined because of the emergence of resistant organisms 3 ., To increase SULFAs’ potency and prevent further resistance , trimethoprim ( TMP ) , which provides synergy , was later developed 4 ., Combination regimens using TMP and SULFAs have effectively treated acute urinary tract infections , bacterial meningitis , Pneumocystis jiroveci pneumonia , and shigellosis , and are commonly used as prophylaxis against recurrent and drug resistant infections 3 , 5 , 6 ., Unfortunately , TMP has been the only SULFA booster approved for clinical use , and resistance to both TMP and SULFAs has emerged 7 ., In addition , the synergistic effect of TMP remains questionable in many bacteria , including Mycobacterium tuberculosis and Pseudomonas aeruginosa 8 , 9 ., To protect the efficacy of SULFAs and safely expand their clinical use 10 , novel SULFA boosters are required ., A recent strategy for developing antibiotic boosters is “resisting resistance” 11 , in which inhibitors that suppress resistance mechanisms are used to sensitize host bacteria to antibiotics ., Our laboratory recently suggested that targeting antifolate resistance may lead to the development of such adjunctive chemotherapies for SULFAs and TMP 12 ., We found that disruption of 5 , 10-methenyltetrahydrofolate synthase ( MTHFS ) , an enzyme responsible for the conversion of N5-formyltetrahydrofolate ( 5-CHO-H4PteGlun ) to N5 , N10-methenyltetrahydrofolate ( 5 , 10-CH+-H4PteGlun ) in the folate-dependent one-carbon metabolic network ( Fig 1A ) , led to severe defects in cellular folate homeostasis thus weakening the intrinsic antifolate resistance in bacteria 12 ., TMP and SULFAs are bacteriostatic in minimal media ., However , they become more bactericidal in rich media , particularly when cellular levels of glycine , methionine and purines are high ., In such conditions , the multifactorial deficiency caused by SULFAs is reduced to a single deficiency of thymine ( Fig 1A , highlighted in red ) , and cells undergoing such “unbalanced growth” succumb to thymineless death 13–17 ., Exogenous thymine supplementation reduces the bactericidal activity of SULFAs and TMP 14 , 15 ., Classified as folate antagonists , or antifolates , these drugs inhibit bacterial de novo folate biosynthesis ( Fig 1A ) , which is absent in mammalian cells ., While SULFAs target dihydropteroate synthase ( DHPS ) , TMP inhibits dihydrofolate reductase ( DHFR ) ., Both of these enzymes are required for the formation of folate , a vitamin essential for cell growth across all kingdoms of life ., The dominant form of folate in the cell is tetrahydrofolate ( H4PteGlun , with n indicating the number of glutamate moieties ) ., This reduced folate molecule functions as a carrier of one-carbon units in multiple metabolic reactions that are required for the production of purines , thymidine , amino acids , and the recycling of homocysteine ( Hcy ) , a non-protein amino acid harmful to long half-life proteins ( Fig 1A ) 18 ., Antifolate-mediated folate deficiency affects the biosynthesis of nucleic acids and proteins , as well as other important cellular processes including methylation and homeostasis of Hcy 18 ., In humans , defects in Hcy homeostasis , or hyperhomocysteinemia , are often associated with folate and vitamin B12 deficiencies observed in medical conditions such as anemia , neural tube defects , cardiovascular diseases , Alzheimer’s dementia , stroke , cancers , and others 18 ., This interconnected metabolic syndrome has been explained by the “methylfolate trap” hypothesis that assigns its cause to defects in the multi-cycling reaction catalyzed by the B12-dependent methionine synthase ( MetH , EC:2 . 1 . 1 . 13 ) ( Fig 1A , highlighted in yellow ) 19–21 ., This reaction depends on three components:, ( i ) N5-methyltetrahydrofolate ( 5-CH3-H4PteGlun ) , a methyl donor ,, ( ii ) B12 , the intermediate carrier for the methyl group , and, ( iii ) the catalytic activity provided by MetH ., Besides the methylation of Hcy to form methionine , this reaction recycles 5-CH3-H4PteGlun back to free H4PteGlun which can be further converted to other folate forms ( Fig 1A ) 20 , 21 ., This reaction can be compromised by B12 deficiency and/or mutations affecting MetH enzymatic activity ., Consequently , the cellular pool of H4PteGlun is trapped in the methylated form ( 5-CH3-H4PteGlun ) , thus interrupting the normal flow of the one-carbon metabolic network ( Fig 1A ) 21–24 ., 5-CH3-H4PteGlun is generated from N5 , N10-methylenetetrahydrofolate ( 5 , 10-CH2-H4PteGlun ) in an upstream reaction catalyzed by methylenetetrahydrofolate reductase ( MTHFR ) , which is irreversible in vivo 25 and suppressed by S-adenosylmethionine ( SAM ) 26 ., Since SAM is produced from methionine , inhibition of MetH activity leads to reduced SAM levels , thus resulting in derepression of MTHFR , further accelerating the accumulation of 5-CH3-H4PteGlun and Hcy 26 ., Attempts to delete metH in mice were unsuccessful as homozygous knockout embryos all died following implantation 27 ., Although it has been studied in humans , and ex vivo in mammalian cells , the existence or physiological significance of the methylfolate trap in bacteria has never been documented ., Here we report the identification of the methylfolate trap as a novel determinant of SULFA resistance in bacteria ., Upon its formation in response to SULFAs , the methylfolate trap causes impaired homeostasis of folate and related metabolites , including a progressive accumulation of Hcy-thiolactone that is known to be cytotoxic ., More importantly , cells undergoing the methylfolate trap are also unable to deplete glycine and nucleotides , and suffer thymineless death induced by SULFAs ., This metabolic blockage renders pathogenic bacteria , including M . tuberculosis , P . aeruginosa , Escherichia coli and Salmonella typhimurium more susceptible to existing SULFAs both in vitro and in host macrophages ., Furthermore , chemical induction of the methylfolate trap , as shown in our experiments , represents a viable method for boosting the antimicrobial activity of available , clinically approved SULFAs against bacterial pathogens ., A screen of 13 , 500 Himar1-transposon M . smegmatis mutants ( details can be found in Supplemental S1 Text ) identified a collection of strains that displayed normal growth in the absence of antifolates but suffered defects in antifolate resistance ., After 2 rounds of drug susceptibility tests , the disrupted genes were mapped using nested PCRs , followed by sequencing ., Of the 50 chromosomal loci identified as being responsible for the intrinsic antifolate resistance of M . smegmatis ( S1 Table ) , 31 genes ( 62% ) encoded enzymatic activities , 14 of which ( 28% ) were predicted to be involved in folate metabolism or related pathways ., The identification of many genes whose functions are related to folate metabolism indicated that the screen was successful ., Overall , the resistance determinants were evenly distributed throughout the M . smegmatis genome with some relatively discrete regions and gaps ( Fig 1B ) ., Besides many genes encoding homologs of enzymes of the one-carbon metabolic network and related metabolism of amino acids or nucleotides ( fmt , dcd , gabD , cobIJ , metH , glyA , ygfA , and ygfZ ) , genetic mapping revealed Himar1 insertions in genes that encode proteins previously known to provide non-specific antibiotic resistance ( pknG , mshB , cspB , fbpA , and treS ) 28–33 ( S1 Table ) ., In addition , insertions were mapped to chromosomal loci potentially affecting regulatory or signaling processes ( mprA , sigB , sigE , pknG , pafA , pup , pcrB , and pcrA ) , transsulfuration ( cysH and mshB ) , transport ( mmpL and pstC ) , and other cellular activities ( S1 Table ) ., Mutants were further profiled using chemical complementation ., Para-aminobenzoic acid ( pABA ) or a folate derivative ( Fig 1A , blue rectangles , & Fig 1C ) was added exogenously to support growth in the presence of SULFAs or TMP , which inhibit de novo folate synthesis ., These analyses provided useful geno-chemo-phenotypic information to each individual antifolate resistance determinant ( S1 Table ) ., Chemical complementation identified a group of SULFA-sensitive , “white” mutants that lost the yellow pigment typically displayed by M . smegmatis ( Fig 1C , marked with asterisks ) ., The mutants were unable to use exogenous 5-CH3-H4PteGlu1 to antagonize SULFAs ( Fig 1C , panel, ( v ) ) ., Genetic mapping showed that four mutants in this subgroup had Himar1 insertions at three different TA dinucleotides within the same gene , msmeg_4185 ( 2xTA499-500 , 1xTA2881-2882 , and 1xTA3091-3092 , S1 Fig ) , which encodes a homolog of B12-dependent methionine synthase ., Two other mutants had insertions at TA112-113 of msmeg_3873 , which encodes an enzyme ( CobIJ ) that catalyzes two methylation steps , precorrin-2 C20 methyltransferase CobI , EC:2 . 1 . 1 . 130 and precorrin-3B C17-methyltransferase CobJ , EC:2 . 1 . 1 . 131 , of the B12 ( cobalamin ) biosynthetic pathway ., Interestingly , the function of these 5-CH3-H4PteGlun-related genes were reminiscent of factors involved in the methylfolate trap , a metabolic disorder thus far only described in mammalian cells ( Fig 2A ) ., Whereas the metH-encoded enzyme catalyzes the reaction , cobIJ is required for the de novo biosynthesis of B12 , the cofactor required for MetH activity ., Exogenous B12 restored both SULFA resistance and 5-CH3-H4PteGlu1 utilization to cobIJ , but failed to restore the metH strains ( Fig 2B ) , resembling the “pseudo-folate deficiency” phenomenon previously observed in anemia patients ( described in the Discussion ) 19 ., To detect the methylfolate trap at a metabolic level , M . smegmatis strains growing in a liquid medium were challenged with sulfachloropyridazine ( SCP ) for half an hour to starve the cells from newly synthesized folate ., Cultures were immediately harvested and total folate was extracted in subdued light ., Samples added with internal standards were analyzed by LC-MS/MS as previously described 12 ., Both metH and cobIJ exhibited 5-CH3-H4PteGlun accumulation compared to wild type M . smegmatis ( Fig 2C ) ., Exogenous B12 significantly reduced 5-CH3-H4PteGlun accumulation in the cobIJ mutant , though not to the level of wild type ( Fig 2C ) ., This B12-responsive alteration in the cellular folate pool of cobIJ explained its pseudo-folate deficiency-like behavior in susceptibility tests ( Fig 2B ) ., In the cobIJ mutant , the metH gene remained intact but its encoded protein did not have enough B12 , due to the Himar1 insertion into cobIJ disrupting de novo B12 biosynthesis , to activate its methionine synthase activity ., When B12 was exogenously supplemented , the cofactor activated MetH activity , thus bypassing the B12 synthetic defect allowing for the release of the methylfolate trap ., To confirm that MetH and CobIJ contribute to the intrinsic SULFA resistance , and 5-CH3-H4PteGlun metabolism , the encoding genes , msmeg_4185 and msmeg_3873 , respectively , were individually deleted by homologous recombination 34 ., Similar to the transposon mutants , the targeted null mutants , MsΔmetH and MsΔcobIJ , displayed increased SULFA susceptibility and impaired utilization of exogenous 5-CH3-H4PteGlu1 whereas in trans expression of metH and cobIJ , respectively , restored both phenotypes ( Table 1 , Fig 2D ) ., Exogenous B12 restored both SULFA resistance and 5-CH3-H4PteGlu1 utilization to MsΔcobIJ , but failed to do so for MsΔmetH ., Although the mutants were hypersusceptible to all SULFAs tested ( S2 Fig ) , resistance to non-antifolate antibiotics remained unaffected ( S3 Fig ) ., While M . smegmatis encodes a B12-independent methionine synthase ( MetE , EC: 2 . 1 . 1 . 14 ) 35 , deletion of metE did not affect SULFA sensitivity ( S4 Fig and S5 Fig ) ., These observations confirmed that MetH is essential for normal 5-CH3-H4PteGlun metabolism , which is required for the intrinsic SULFA resistance in M . smegmatis ., Mutants lacking metH or cobIJ genes were first constructed from the M . tuberculosis laboratory strain H37Rv ( see S1 Text ) ., Sensitivity tests using the MTT method were performed with two minimal media , 7H9-S or Dubos , in the absence or presence of exogenous B12 ( tested range: 1 μM—0 . 3 mM ) ., In the absence of B12 , SULFA susceptibility of the H37Rv-derived strains were similar ., However , with B12 supplementation , significant differences in SULFA resistance among strains were observed ( Table 1 , Fig 3A ) ., While RvΔmetH displayed high susceptibility to sulfamethoxazole ( SMZ ) , the sensitivity level of RvΔcobIJ was unchanged compared to wild type ( Table 1 , Fig 3A ) ., In trans expression of metH completely restored wild type SULFA resistance to RvΔmetH ( Table 1 , Fig 3A ) ., These results indicated that the methylfolate trap was able to sensitize M . tuberculosis H37Rv to SULFA drugs ., Such trap formation , however , requires the absence of methionine synthase activities ., In agreement with previous studies 36 , 37 , our data suggested that H37Rv is unable to synthesize B12 de novo , and that this organism relies on its uptake system for obtaining B12 from the environment ., In the complete absence of B12 , H37Rv employed the B12-independent methionine synthase MetE to prevent the methylfolate trap ., When B12 was added exogenously , MetE activity was inhibited , making RvΔmetH completely null of methionine synthases ., In such a condition , the methylfolate trap was formed sensitizing RvΔmetH to SULFA drugs ., It is important to note that exogenous supplementation of methionine only partially enhanced SMZ resistance of RvΔmetH ( Table 1 ) , indicating that the lack of methionine due to defective methionine synthases 37 , 38 is not the sole contributor to the enhanced SULFA susceptibility ., To further characterize the methionine-unrelated methylfolate trap-mediated SULFA sensitivity , survival of the M . tuberculosis strains treated with SMZ , B12 , and methionine were assayed by serial dilution and colony forming unit ( c . f . u . ) counting ., With similar inputs , the survival of RvΔmetH was 3 log10 lower than that of wild type M . tuberculosis H37Rv and the RvΔcobIJ mutant ( Fig 3B ) ., This result not only confirmed our observation from the growth inhibition assays ( Table 1 , Fig 3A ) , but further suggested that the methylfolate trap may induce the intrinsic bactericidal activity of SULFA drugs ., To further characterize the methylfolate trap in M . tuberculosis , we used CDC1551 , a clinical strain isolated in a 1994–1996 tuberculosis outbreak in the United States 39 , for constructing several strains related to methylfolate trap formation ( S2 Table ) ., CDC1551 is a natural metH deletion mutant due to a 1 , 196-bp truncation located at the 3’-terminus of its encoding gene ( mt2183 ) ( Fig 3C ) 38 , 40 ., Similar to the M . smegmatis methylfolate trap mutants , colonies of CDC1551 displayed a “white” morphology , differing from the yellow appearance of H37Rv , which resembles wild type M . smegmatis ( S6 Fig ) ., To better understand the molecular mechanisms affecting trap formation , SULFA sensitivity tests were performed with a minimal medium ( Dubos ) and a gradient of increasing B12 concentrations ( Fig 3D ) ., In the absence of exogenous B12 , CDC1551 ( numbered 2 ) displayed higher SULFA sensitivity compared to the CDC1551 strain in trans expressing the intact metH gene from H37Rv ( CDC1551/metH , numbered 1 ) , indicating that , unlike H37Rv , the B12 biosynthesis is functional in CDC1551 ( Fig 3D ) ., The level of internally synthesized B12 was likely enough to partially repress the expression of metE and to activate MetH activity ( see Discussion ) ., When cobIJ was deleted ( CDCΔcobIJ/metH and CDCΔcobIJ , numbered 3 and 4 respectively ) , SULFA resistance increased ( Fig 3D ) , possibly due to the derepression of metE in the complete absence of B12 ( similar to H37Rv in minimal medium ) ., Deletion of bacA ( numbered 5 and 6 ) , encoding the B12 uptake system in M . tuberculosis 37 , did not have any effect in this condition ( far left panel ) ., In the presence of as low as 0 . 25 μM B12 , metE expression appeared to be further suppressed , making CDC1551 highly susceptible to SMZ compared to CDC1551/metH ( Fig 3D , second panel from left ) ., The higher the concentration of exogenous B12 , the less SULFA resistance was displayed by CDCΔcobIJ , most likely due to increased suppression of metE ., This was not seen in the case of CDCΔcobIJ/metH since MetH was further activated in the presence of B12 , thus compensating for metE suppression ., Unlike CDC1551 , CDCΔbacA did not show a severe reduction in SULFA resistance when B12 was added due to its lack of B12 uptake activity ., Similarly , but conversely , CDCΔbacA/metH did not show an increased SULFA resistance compared to CDC1551/metH in response to exogenous B12 ., Most importantly , as seen with the H37Rv background ( Fig 3A ) , exogenous methionine did not enhance the SULFA resistance of CDC1551-derived strains ( Fig 3D ) ., Previous studies suggested that M . tuberculosis is able to uptake and metabolize B12 from its host 41 ., To evaluate if the methylfolate trap can form thus affecting the SULFA sensitivity of M . tuberculosis residing within macrophages , strains were used to infect the macrophage cell line J774 . A1 , grown in a medium containing 10% fetal bovine serum ., The infected macrophages were treated with SMZ , followed by serial plating of the intracellular bacteria and c . f . u . counting ., In both the H37Rv ( Fig 3E ) and the CDC1551 backgrounds ( Fig 3F ) , strains lacking metH exhibited significantly increased sensitivity to SULFA treatment ., In trans expression of H37Rv metH ( rv2124c ) restored SULFA resistance to both RvΔmetH and CDC1551 ( Fig 3E and 3F ) ., As previously suggested 39 , the proliferation of CDC1551 in macrophages in the absence of SMZ was much faster compared to H37Rv ( Fig 3F ) ., However , its survival was more severely reduced compared to H37Rv when the infected macrophages were treated with SMZ ( Fig 3F ) ., This enhanced bactericidal activity of SMZ against CDC1551 was reduced in CDC1551/metH , confirming the correlation of MetH activity and the intrinsic resistance of CDC1551 to SULFAs ., Together , these results demonstrated that, ( i ) the methylfolate trap , when successfully formed , can sensitize M . tuberculosis to SULFAs both in vitro and during infection of host macrophages ,, ( ii ) the methylfolate trap promotes the bactericidal activity of SULFA drugs ,, ( iii ) because of its non-functional B12 biosynthetic pathway , H37Rv relies on its uptake system to obtain exogenous B12 ,, ( iv ) trace amounts of B12 are sufficient to suppress metE expression giving metH a more important role in preventing methylfolate trap formation , and, ( v ) because of its truncated metH gene , CDC1551 is intrinsically more susceptible to methylfolate trap formation , rendering it more sensitive to SULFAs both in vitro and during macrophage infection 42 ( Fig 3D and 3F ) ., Our laboratory is currently investigating how mutations in metH and genes involved in B12 biosynthesis affect SULFA sensitivity among M . tuberculosis clinical isolates ., To assess if the methylfolate trap plays a similar role in SULFA sensitivity in Gram-negative bacteria , we investigated its role in a selected group of significant pathogens with distinct metabolic capacities ., Similar to the M . tuberculosis H37Rv strain , E . coli does not synthesize B12 , instead it imports the vitamin via the transport system BtuBCED 43 , 44 ., Whereas mutations in btuC , btuE , and/or btuD partially reduce uptake , mutations in btuB completely abolish B12 transport 45 ., On a complex medium , an E . coli ΔbtuCED ( b1711 , b1710 , and b1709 , respectively ) triple mutant remained SULFA resistant , whereas ΔmetH ( b4019 ) , ΔbtuB ( b3966 ) , and a ΔbtuBΔCED quadruple mutant all became hypersusceptible ( Fig 4A , Table 1 ) ., In serial dilution-spot tests using 125 μg/ml SMZ , these mutants displayed >104 times increased susceptibility compared to wild type BW25113 ( Fig 4A ) ., Exogenous B12 was unable to restore SMZ resistance in these mutants due to the absence of MetH or B12 transport activity ( Fig 4A ) ., The increased SULFA sensitivity was verified by measuring minimal inhibitory concentrations ( MIC , Table 1 ) , which is defined as the lowest concentration of an antibiotic that inhibits the visible growth of bacteria ., To demonstrate methylfolate trap formation at the metabolic level , E . coli cultures were treated with SMZ and total folate was immediately extracted and analyzed by LC-MS/MS 12 ., As shown in Fig 4B , 5-CH3-H4PteGlun markedly accumulated in ΔmetH and ΔbtuB compared to the parental strain , confirming methylfolate trap formation ., Because of its inability to synthesize B12 de novo , E . coli relies entirely on import to prevent the methylfolate trap ., P . aeruginosa is capable of not only synthesizing de novo but also importing B12 from the environment ., Transposon mutants with insertions in genes encoding metH ( PA1843 ) , cobI ( PA2904 ) , cobJ ( PA2903 ) , cobH ( PA2905 ) and btuB ( PA1271 ) were obtained from the Pseudomonas Transposon Mutant Collection ( Manoil Laboratory , University of Washington Genome Sciences ) 46 ( S2 Table ) ., The mutants were subjected to antifolate susceptibility tests , followed by folate analysis as described above ., All P . aeruginosa mutants became more susceptible to SULFA drugs on a complex medium ( Fig 4C , Table 1 ) ., The P . aeruginosa metH and btuB mutants displayed identical , and the most severe susceptibility to SULFAs ., These strains were at least 105 times more susceptible than wild type as revealed by serial dilution-spotting assays using 125 μg/ml SMZ ( Fig 4C ) ., cob mutants were less susceptible compared to these two strains , suggesting that B12 import is more important than de novo synthesis in the condition tested ( Fig 4C , Table 1 ) ., Indeed , exogenous B12 reinstated growth of the cob mutants but failed to do the same for metH and btuB ( Fig 4C ) ., Chemical analyses also revealed accumulation of the methylfolate trap marker , 5-CH3-H4PteGlun , in both metH and btuB ( Fig 4D ) ., Similar experiments with S . typhimurium strains ( John Roth Laboratory , UC Davis , S2 Table ) confirmed the correlation of the methylfolate trap and increased SULFA susceptibility in bacteria ( Table 1 , S7 Fig , and further studies below ) ., Similar to M . smegmatis and other Gram-negative bacteria , the deletion of metH , but not metE , resulted in the methylfolate trap and reduced SULFA resistance in S . typhimurium on complex media ( S7 Fig ) ., The absence of metH , hence the methylfolate trap , led to increased susceptibility to SULFA drugs classified in all categories ( Fig 5A ) , but not to folate-unrelated antibiotics ( S8 Fig ) ., To investigate if the effect of the methylfolate trap was bactericidal or bacteriostatic , S . typhimurium metH ( + ) and metH ( - ) strains were spotted on filters ( ~104 cells/filter ) , which were placed on the surface of Luria-Bertani ( LB ) agar plates supplemented with or without SMZ ., Following 24 h of incubation at 37°C , cells from the inoculated filters were resuspended , and colony forming units ( c . f . u . ) were measured by serial dilution and plating ., On LB agar free of SULFA , both metH ( + ) and metH ( - ) proliferated to 106 times more cells than the input ( Fig 5B ) ., In the presence of 125 μg/ml SMZ , growth of metH ( + ) was normal whereas only 0–8 . 5% of the metH ( - ) input survived ( Fig 5B ) , indicating an enhanced bactericidal effect of SMZ due to the methylfolate trap ., In liquid LB , addition of 2 . 5 mg/ml SMZ similarly reduced growth of metH ( - ) while still allowing growth of metH ( + ) ( Fig 5C ) , confirming the correlation between SULFA resistance and MetH activity ., To investigate if the increased susceptibility was due to enhanced import , the SULFA uptake of S . typhimurium strains was measured using radioactive SMZ ., However , both metH ( + ) and metH ( - ) displayed identical uptake following the addition of SMZ to the medium ( S9 Fig , panel A ) ., We next examined the effect of the methylfolate trap on the synthesis of macromolecules ( DNA , RNA and protein ) during SULFA treatment ., Cells of metH ( + ) or metH ( - ) bacteria , growing in the presence of SMZ , were labeled using 3H-thymidine , 3H-uracil , or 35S-methionine , respectively ., While DNA and protein synthesis were not affected by the methylfolate trap during SULFA treatment , RNA synthesis was significantly reduced in cells suffering the metabolic blockage ( S9 Fig , panels B-D ) ., To assess changes in the folate pool during SULFA-induced methylfolate trap formation , S . typhimurium cells growing in liquid LB medium were treated with SMZ , followed by sample collection ., Folate was extracted and individual species quantified using LC-MS/MS ., In the presence of MetH , combined levels of both methylated ( 5-CH3-H4PteGlun ) and non-methylated folate species ( R-H4PteGlun R ≠ CH3 ) immediately and continuously declined in response to SMZ ( Fig 5D , top and middle panels , red bars; see also S10 Fig for the dynamics of individual species ) ., In contrast , in metH ( - ) cells , 5-CH3-H4PteGlun gradually accumulated following SMZ treatment ( Fig 5D , top panel , blue bars ) ., Levels of non-methylated folate species in metH ( - ) gradually declined for the first hour , then remained constant for the remainder of the experiment ( Fig 5D , middle panel , blue bars ) ., This result indicated possible cellular feedback , either through an increase in de novo H4PteGlun synthesis or rearrangement in the inter-conversion network of one-carbon metabolism ., To further analyze metabolic alterations in response to such folate homeostatic defects , post-SMZ treatment levels of 41 metabolites were profiled using LC-MS/MS-based metabolomics ., Cells were sampled from growth curves similar to those in Fig 5C from which metabolites were extracted and analyzed by the Metabolomics Lab at the Roy J . Carver Biotechnology Center ( University of Illinois at Urbana-Champaign ) ., Metabolic abnormalities caused by the SMZ-induced methylfolate trap include the accumulation of intermediates within the methionine-homocysteine cycle ( Figs 5E and 6A , orange ) , glycine ( Figs 5E and 6B , red ) and nucleotides ( Figs 5E and 6C , purple ) , as discussed in more detail below ., The MetH reaction connects the one-carbon metabolic network with the methionine cycle through its conversion ( methylation ) of Hcy to methionine ( S9 Fig , panel E ) ., Therefore , impaired MetH would lead to the accumulation of not only 5-CH3-H4PteGlun , but also Hcy , causing hyperhomocysteinemia ., In the cell , Hcy is further converted to Hcy-thiolactone , which is cytotoxic due to its interaction with physiologically important proteins 47 , 48 ., Because it is neutral at physiological pH ( pKa = 6 . 67 ) , Hcy-thiolactone is steadily secreted into exogenous media following its production from Hcy 48 ., Besides harvesting cells for folate and metabolomic analyses ( Fig 5D and 5E ) , culture filtrates from metH ( + ) and metH ( - ) growing in the presence of SMZ were also collected for Hcy-thiolactone analysis ( S1 Text ) 49 ., As shown in Fig 6A , cells of metH ( - ) accumulated S-adenosylhomocysteine ( SAH ) , which led to higher levels of Hcy-thiolactone in the medium compared to metH ( + ) ( S9 Fig , panel F ) ., In the presence of MetH ( red circle ) , production of methionine ( Fig 6A ) and glycine ( Fig 6B ) rapidly dropped while levels of nucleotides ( Fig 6C ) including aminoimidazole carboxamide ribonucleotide ( AICAR ) , a precursor of purine synthesis , slightly increased during the first half an hour to one hour of SMZ treatment ., Thereafter , synthesis of methionine and glycine resumed but nucleotides underwent continuous depletion ., In the absence of MetH ( blue triangle ) , methionine synthesis slightly increased ( Fig 6A ) , most likely due to increased uptake , nucleotides levels also increased ( Fig 6C ) , but glycine levels slightly declined ( Fig 6B ) in the first hour ., After this time period , nucleotides , especially dUMP , remained highly elevated , methionine levels declined and remained constant while glycine levels increased and remained elevated ., Antifolate-responsive depletion of intracellular glycine and purines was recently proposed as an E . coli mechanism to escape thymineless death 15 ., To test if thymine plays a role in the methylfolate trap-promoted bactericidal activity of SULFA , this nucleotide precursor was added to medium and the survival of strains was evaluated by serial dilution and plating method ., Interestingly , thymine abolished the SULFA-induced death of the metH ( - ) strain , and restored its growth ( Fig 6D ) ., These results suggest that the methylfolate trap promotes the intrinsic thymineless death of bacteria by SULFA drugs , by causing an imbalance in nucleotide levels while preventing cellular depletion of glycine ., To investigate if the methylfolate trap renders bacteria more susceptible to SULFAs in a host cell environment , we first monitored the intracellular survival of S . typhimurium strains in J774A . 1 , a macrophage cell line commonly used for antibiotic sensitivity testing 50 ., When the infected macrophages were treated with SMZ at a concentration sub-inhibitory for the S . typhimurium parental strain , mutants undergoing the methylfolate trap displayed significant defects in survival ( Fig 7A ) ., The survival of the S . typhimurium strains in macrophages resembled the patterns of in vitro sensitivity ( S7 Fig ) , suggesting a similar role of the methylfolate trap in promoting SULFA susceptibility of intracellular bacteria ., To assess if SULFA susceptibility of the intracellular bacteria can be promoted through pharmacological induction of the methylfolate trap , we sought to restrict B12 bioavailability using a chemical approach ( Fig 7B ) ., The cellular uptake and conversion of exogenous B12 ( cyanocobalamin ) to biologically active cofactors ( adenosylcobalamin and methylcobalamin ) in mammalian cells requires the enzymatic activity of CblC , also known as MMACHC ( for methylmalonic aciduria ( cobalamin deficiency ) cblC type , with homocystinuria ) 51 ., To investigate if B12 bioavailability , hence SULFA sensitivity , of intracellular S . typhimurium could be controlled through CblC inhibition , expression of cblC in macrophages THP-1 was depleted using RNA interference ., Transfection with cblC-specific siRNA effectively reduced CblC expression , detected by Western Blot using a CblC monoclonal antibody ( Fig 7C , top panel ) ., The reduced cblC expression was found to correlate with increased B12 starvation of the intracellular S . typhimurium bacillus as detected by a B12 molecular probe ( Fig 7C , middle ) 52 ., Within such CblC-depleted macrophages , S . typhimurium became more SMZ susceptible as determined by c . f . u plating assays ( Fig 7C , bottom ) ., We recently developed Coβ-4-ethylphenylcob- ( III ) alamin ( EtPhCbl ) 53 , a cobalamin analog that can function as a vitamin B12 antagonist ( or “antivitamin B12” ) 53 , 54 ., EtPhCbl effectively binds to CblC but resists dissociation from the protein , thereby blocking CblC from its normal functions of decyanation and dealkylation of newly internalized cyanocobalamin and methylcobalamin , respectively 55 , 56 ., Because bacteria do not have CblC homologs , EtPhCbl had no effect when used directly on bacterial cells ( S11 Fig ) ., To test whether EtPhCbl increases methylfolate trap-mediated SULFA susceptibility in bacteria residing within host cells , macrophages were first infected with S . typhimurium ., Thereafter , th

Introduction, Results, Discussion, Materials and Methods

The methylfolate trap , a metabolic blockage associated with anemia , neural tube defects , Alzheimer’s dementia , cardiovascular diseases , and cancer , was discovered in the 1960s , linking the metabolism of folate , vitamin B12 , methionine and homocysteine ., However , the existence or physiological significance of this phenomenon has been unknown in bacteria , which synthesize folate de novo ., Here we identify the methylfolate trap as a novel determinant of the bacterial intrinsic death by sulfonamides , antibiotics that block de novo folate synthesis ., Genetic mutagenesis , chemical complementation , and metabolomic profiling revealed trap-mediated metabolic imbalances , which induced thymineless death , a phenomenon in which rapidly growing cells succumb to thymine starvation ., Restriction of B12 bioavailability , required for preventing trap formation , using an “antivitamin B12” molecule , sensitized intracellular bacteria to sulfonamides ., Since boosting the bactericidal activity of sulfonamides through methylfolate trap induction can be achieved in Gram-negative bacteria and mycobacteria , it represents a novel strategy to render these pathogens more susceptible to existing sulfonamides .

Sulfonamides were the first agents to successfully treat bacterial infections , but their use later declined due to the emergence of resistant organisms ., Restoration of these drugs may be achieved through inactivation of molecular mechanisms responsible for resistance ., A chemo-genomic screen first identified 50 chromosomal loci representing the whole-genome antifolate resistance determinants in Mycobacterium smegmatis ., Interestingly , many determinants resembled components of the methylfolate trap , a metabolic blockage exclusively described in mammalian cells ., Targeted mutagenesis , genetic and chemical complementation , followed by chemical analyses established the methylfolate trap as a novel mechanism of sulfonamide sensitivity , ubiquitously present in mycobacteria and Gram-negative bacterial pathogens ., Furthermore , metabolomic analyses revealed trap-mediated interruptions in folate and related metabolic pathways ., These metabolic imbalances induced thymineless death , which was reversible with exogenous thymine supplementation ., Chemical restriction of vitamin B12 , an important molecule required for prevention of the methylfolate trap , sensitized intracellular bacteria to sulfonamides ., Thus , pharmaceutical promotion of the methylfolate trap represents a novel folate antagonistic strategy to render pathogenic bacteria more susceptible to available , clinically approved sulfonamides .

blood cells, antimicrobials, cell physiology, medicine and health sciences, immune cells, pathology and laboratory medicine, chemical compounds, pathogens, drugs, immunology, microbiology, cell metabolism, organic compounds, bacterial diseases, antibiotics, enterobacteriaceae, amino acids, pharmacology, bacteria, drug metabolism, bacterial pathogens, salmonella typhimurium, infectious diseases, white blood cells, animal cells, proteins, medical microbiology, microbial pathogens, chemistry, actinobacteria, methionine, salmonella, pharmacokinetics, sulfur containing amino acids, biochemistry, organic chemistry, cell biology, mycobacterium tuberculosis, microbial control, biology and life sciences, cellular types, physical sciences, macrophages, organisms

journal.pcbi.1005623

Dissociating error-based and reinforcement-based loss functions during sensorimotor learning

Error feedback and reinforcement feedback can each guide motor adaptation in a visuomotor rotation task 1 ., It has been proposed that error feedback and reinforcement feedback engage different neural mechanisms 2 ., Indeed , depending on the form of feedback used during adaptation , newly acquired motor commands differ in terms of generalizability 1 and retention 3 ., For error-based learning , it is suggested that adaptation occurs by minimizing error to update an internal model 4 ., For reinforcement-based learning , it is proposed that adaptation is model-free and occurs by sampling motor outputs to find a set that maximizes the probability of task success 5 ., Importantly , both these forms of learning can occur in the presence of internally 6 , 7 and externally 8 , 9 generated random variability ( noise ) ., Loss functions are central to several current computational theories of sensorimotor control 10 ., An error-based loss function describes the relationship between potential movements and the associated costs of error 9 ., In other words , an error-based loss function describes how errors of different magnitudes are penalized ., A reinforcement-based loss function describes the relationship between potential movements and the associated probabilities of reward ( or punishment ) 7 ., The idea that the sensorimotor system uses a loss function to select a statistically optimal movement in the presence of noise has been examined in tasks involving error feedback 9 , 11 , reinforcement feedback 1 , and both error and reinforcement feedback together 1 , 3 , 7 , 12 ., In tasks involving error feedback , it has been proposed that the sensorimotor system may use an error-based loss function to select movements 9 ., As an example , here we will describe how an absolute error loss function and a squared error loss function can be used to select aim location during a game of darts ., In this context , error refers to the distance of an individual dart to the bullseye ., Let us assume that , after several throws , when attempting to aim the darts at a particular location that there is some spread , or distribution , of darts on a board ., If the chosen strategy is to minimize absolute error around the bulls-eye , one should adjust their aim until the sum of the absolute distances of the darts is at its lowest possible value ., This strategy corresponds to selecting a single aim location that minimizes the cost ( output ) of an absolute error loss function ( i . e . , ∑|errori|1 ) ., This absolute error loss function linearly weights individual error magnitudes and would result in the median of the dart distribution being directly over the bullseye ., Similarly , if the chosen strategy is to minimize squared error around the bullseye , one should adjust their aim such that the sum of the squared distances of the darts is at its lowest possible value ., This strategy corresponds to selecting a single aim location that minimizes the cost of a squared error loss function ( i . e . , ∑|errori|2 ) ., The squared error loss function places a heavier emphasis on minimizing large errors relative to smaller errors , in a quadratic fashion , and would result in the mean of the dart distribution being directly over the bullseye ., Using tasks that involve noisy error feedback , some researchers have reported that sensorimotor behavior is best represented with an error-based loss function where the exponent on the error term is between 1 ( absolute error ) and 2 ( squared error ) 9 , 13 , while others report that an exponent of 2 best fits behavior 11 , 14 , 15 ., Based on these works , it is possible that the error-based loss function that most aligns with behavior may to some extent be task dependent ., The concept of loss functions also extends to sensorimotor tasks involving reinforcement feedback , where the goal is maximize task success 5 ., The optimal movement that maximizes the probability of success can be found by minimizing the 0-1 loss function 9 , 16 ., Again , we can describe this reinforcement-based loss function using a distribution of darts on a board ., With the 0-1 loss function , every dart that hits the bulls-eye is assigned a value of 0 and each dart that misses the bulls-eye is assigned a value of 1 ., To minimize this loss function , one should adjust their aim such that the greatest number of darts hit the bulls-eye ., This strategy maximizes the probability of success ( by minimizing failure ) and corresponds to placing the mode of the distribution of darts directly over the bullseye ., This loss function can easily be extended to account for graded reinforcement feedback , where either the magnitude 7 , 17 or probability 18 of reinforcement feedback varies according to some function that is externally imposed by the experimenter ., It has recently been shown that the sensorimotor system can maximize task success when using only binary reinforcement feedback ., Shadmehr and colleagues used a visuomotor rotation task , where the true target was displaced from the displayed target by some small amount 1 , 18 ., In line with a reinforcement-based loss function and without any error feedback , they found that participants were able to adapt where they aimed their hand using only reinforcement feedback that signaled whether or not the true target had been hit ., Researchers have also explored how reinforcement feedback and error feedback are used when provided simultaneously 1 , 3 , 7 , 12 , 17 , 19 ., This has been done for a range of tasks and has yielded mixed results ., In studies reported by Trommershäuser and colleagues 7; 20–23 , participants performed rapid reaches with continuous visual feedback of their hand ( visual error feedback ) to a large rewarding target ( positive reinforcement ) with an overlapping punishment region ( negative reinforcement ) ., Participants learned to aim to a location that maximized reward ., Conversely , others have provided evidence that continuous error feedback dominates over , or perhaps suppresses , reinforcement feedback during a visuomotor rotation task 3 , 12 , 24 ., Izawa and Shadmehr ( 2011 ) suggested that with a decrease in error feedback quality , the sensorimotor system might increase its reliance on reinforcement feedback ., However , a feature of these experiments is that they did not separate the predictions for where participants should aim their hand when receiving reinforcement feedback or error feedback ., Such separation would provide a powerful way to investigate how the sensorimotor system weights the relative influence of error feedback and reinforcement feedback when they are provided in combination ., Here , we designed two experimental reaching tasks that separate the predictions of error-based and reinforcement-based loss functions on where to aim the hand ., In doing so , we promoted dissociation in behavior simply by manipulating the form of feedback provided to participants ., Participants reached to visual targets without vision of their arm ., Unbeknownst to participants , visual feedback of their hand ( represented by a cursor ) was laterally shifted from trial to trial by an amount drawn from a skewed probability distribution ., Skewed lateral shift probability distributions allowed us to separate the predictions of error-based and reinforcement-based loss functions on where to aim the hand ., For example , a squared error loss function would predict that we should aim our hand to a location that corresponds to the mean of the skewed lateral shift probability distribution ., Conversely , a reinforcement loss function would predict that we should aim our hand to a location that corresponds to the mode ., Critically , skewed distributions separate several statistics , such as the mean and mode , that align with the optimal predictions of error-based and reinforcement based loss functions ., Thus , by laterally shifting feedback using skewed noise and observing where participants reached , we were able to directly test how the sensorimotor system weights the relative influence of reinforcement feedback and error feedback when deciding where to aim the hand ., In Experiment 1 we tested how reinforcement feedback and error feedback influence where participants aimed their hand ., We predicted that participants receiving only error feedback would minimize some form of error ., We also predicted that participants receiving both error and reinforcement feedback would increasing rely on reinforcement feedback with a decrease in error feedback quality ., Such a strategy predicts a different pattern of compensation for participants who received both forms of feedback when compared to those who only received error feedback ., Surprisingly , we found that both error-only feedback and error plus reinforcement feedback resulted in participants minimizing approximately squared error ., In Experiment 2 we used a modified task to verify that reinforcement feedback alone was capable of influencing where to aim the hand ., Indeed , we found that participants who received only reinforcement feedback maximized the probability of hitting the target ., However , we again found that participants minimized approximately squared error when both error and reinforcement feedback were present ., Taken together , our results describe how the sensorimotor system uses error feedback and reinforcement feedback , in isolation and combination , when deciding where to aim the hand ., Participants performed 2000 reaching movements in a horizontal plane ( Fig 1 ) ., They were instructed to “hit the target” ., A cursor that represented the true hand position disappeared once the hand left the home position ., On each trial , the unseen cursor was then laterally shifted by an amount drawn from a skewed probability distribution ., Participants were randomly assigned to one of three groups ( n = 10 per group ) ., The ErrorSR group and ErrorSL group both received error feedback that was laterally shifted by a right-skewed ( RS; Fig 2A ) or left-skewed ( SL ) probability distribution , respectively ., The third group , Reinforcement + ErrorSR , received error feedback and reinforcement feedback that were both laterally shifted by a SR probability distribution ., Importantly , the skewed lateral shift probability distributions were designed to separate the mean and the mode , corresponding with the optimal solutions of error-based and reinforcement-based loss functions , respectively ., This separation allowed us to investigate how the sensorimotor system weights the relative influence of reinforcement feedback and error feedback when deciding where to aim the hand ., We hypothesized that with a decrease in error feedback quality the sensorimotor system would increase its reliance on reinforcement feedback ., To manipulate error feedback quality , halfway through each reach the laterally shifted cursor was either not presented ( ς∞ ) or briefly presented ( for 100ms ) as a single dot ( ς0mm ) , a medium cloud of dots ( ς15mm ) or a large cloud of dots ( ς30mm ) , before disappearing once again ( Figs 1 and 2 ) ., The medium and large clouds were composed of 25 dots , such that the dots were distributed according to a bivariate normal distribution with a standard deviation of 15mm and 30mm , respectively ., Participants then attempted to hit the target by accounting for the laterally shifted error feedback ( ς0mm , ς15mm , ς30mm , ς∞ ) they had experienced mid-reach ., All participants received additional error feedback ( also a single dot ) at the end of the reach on trials in which single dot ( ς0mm ) error feedback was presented mid-reach 8 ., Participants in the Reinforcement + ErrorSR group were presented with reinforcement feedback only on trials in which the error feedback was presented as a single dot ( ς0mm ) ., The reinforcement feedback was binary ( the target doubled in size , a pleasant sound was played over a loudspeaker , and participants received 2¢ CAD ) and was presented when the laterally shifted cursor hit the target ., On each trial , we estimated how a participant compensated for the lateral shift by recording their hand location relative to the displayed target ( see Fig 1 ) ., This was done for the four different levels of error feedback quality and lateral shift magnitudes ., The average compensatory behavior of each group is shown in Fig 3 ., It can be seen that , with very little visual uncertainty about the magnitude of a lateral shift ( ς0mm ) , all groups had a pattern of compensation that was well matched to the true magnitude of the shift ., As error feedback quality decreased from little uncertainty ( ς0mm ) to some uncertainty ( ς15mm and ς30mm ) to complete uncertainty ( ς∞ ) , participants’ pattern of compensation became increasingly less sensitive to the true magnitude of the lateral shift ., This is consistent with Bayesian inference in that participants were increasing their reliance on their prior with a decrease in error feedback quality ., Interestingly , the average compensation of each group , even for participants who received both reinforcement and error feedback , corresponded quite closely to the predictions made by a Bayesian model whose loss function minimized squared error ( compare Figs 3A–3C to 2E ) ., To quantify the extent to which reinforcement feedback had an influence on behavior , we performed three separate analyses ., First , we compared the average compensatory behavior between groups ., Second , we used a Bayesian model to characterize how error feedback is used to guide behavior , and the extent to which reinforcement feedback influenced compensation ., Finally , we used a simple linear model to characterize how the relationship between compensation and lateral shift is modulated by error signal quality , and the extent to which reinforcement feedback influenced compensation ., All three analyses supported the idea that reinforcement feedback did not influence behavior when it was presented in combination with error feedback ., Below , we describe each group’s compensatory behavior and the results of the Bayesian model ., For brevity , detailed results of the linear model are presented in S2 Data ., In Experiment 1 we found that when both error feedback and reinforcement feedback were presented in combination , participant behavior seemed only driven by error feedback ., There is a possibility that the reinforcement feedback we used was not capable of influencing behavior ., To test this , in Experiment 2 some participants only received reinforcement feedback , without error feedback ., It is important to note that these participants only received reinforcement feedback at the end of each movement ., This represents a difference in experimental design from Experiment 1 , whose participants ( ErrorSR , ErrorSL , Reinforcement + ErrorSR ) often received feedback twice in a single movement—mid-reach and as they passed by the target ., To properly control for this , in Experiment 2 we tested two additional groups that received only error feedback , or error plus reinforcement feedback ., Importantly , however , all participants in Experiment 2 only received feedback once per trial , at the end of movement ., This ensured that the frequency and location of feedback received by the three groups was the same ., As a consequence of not providing error feedback mid-reach and only providing feedback at the target , compensation to the skewed lateral shift probability distribution in Experiment 2 reflects a trial-by-trial updating of where to aim the hand ., This differs from Experiment 1 in which compensation reflects both online ( via mid-reach feedback ) and trial-by-trial ( via target feedback ) updating of where to aim the hand ., In the context of a Bayesian framework , this would indicate in Experiment 2 that the prior representation of lateral shifts is updated after a trial is completed , instead of both during and after a trial is complete as in Experiment 1 . One group of participants only received reinforcement feedback ( Reinforcement ) , a second group received only error feedback ( Error ) , and a third group received both error and reinforcement feedback ( Reinforcement + Error ) ., In total , there were ninety participants ( 30 per group ) ., All participants performed 500 reaching movements in a horizontal plane ., They were instructed to “hit the target” ., On every trial , a cursor that represented the true hand position disappeared once the hand left the home position ., The unseen cursor was laterally shifted by an amount drawn from a skewed-right ( SR; n = 15 per group; Fig 4A ) or skewed-left ( SL , n = 15 per group ) probability distribution ., Binary reinforcement feedback occurred when the laterally shifted cursor hit the target ( the target doubled in size , a pleasant sound was played over a loudspeaker , and participants received 2 ¢ CAD ) ., Error feedback was presented as a single dot at the end of the reach , at the location where the laterally shifted cursor passed by or through the target ., For each reach we recorded each participant’s pattern of compensation , that is , how laterally displaced his or her hand was relative to the displayed target ( Fig 1 ) ., We calculated the compensation location that would maximize the probability of hitting the target ( x a i m m a x ( h i t s ) ; Eqs 7 , 8 and 12 and Fig 4C ) ., This calculation incorporated a measure of movement variability at the target , which was larger in this experiment , relative to Experiment 1 , given that there was no mid-reach error feedback ( see Methods for further details ) ., We also calculated the compensation location that would minimize squared error of cursor positions about the target ( x a i m m i n ( e r r o r 2 ) ; Eqs 7–11 and Fig 4B ) ., Fig 5 shows the pattern of compensation of a participant from each group ., In response to the skewed lateral shift probability distribution , it can be seen that the Reinforcement participant learned to compensate by an amount that was on average close to maximizing the probability of hitting the target ., Conversely , both the Error and the Reinforcement + Error participants had an average compensation that corresponded to minimizing approximately squared error ., Fig 6A shows the average group compensatory behavior in response to the skewed lateral shift probability distribution ., Compensation reached an asymptotic level after approximately 100 reaches ( bin 10 ) ., Thus , for each participant , we averaged their last 400 trials to obtain a stable estimate of their behavior ( Fig 6B ) ., However , the results reported below were robust to whether we averaged the last 100 , 200 , 300 or 400 trials ( Table 1 ) ., To test whether the form of feedback and skew direction influenced behavior , we compared the average pattern of compensation between the three groups ., We found that there was a significant main effect of group F ( 2 , 84 ) = 8 . 928 , p < 0 . 001 , ω ^ G 2 = 0 . 150 ., There was no significant main effect of skew direction F ( 1 , 84 ) = 0 . 164 , p = 0 . 687 , ω ^ G 2 < 0 . 001 nor an interaction between group and skew direction F ( 2 , 84 ) = 0 . 498 , p = 0 . 61 , ω ^ G 2 < 0 . 001 ., Thus , under the influence of the same skewed lateral shift probability distribution , we found that different forms of feedback resulted in significantly different compensatory behavior ., Specifically , we found a statistically reliable difference between the Reinforcement and Error groups ( p < 0 . 001 , one-tailed; θ ^ = 77 . 7 % ) ., The Reinforcement group approached a compensatory position that would maximize their probability of hitting the target x a i m m a x ( h i t s ) ( p = 0 . 081 , two-tailed; θ ^ = 63 . 3 %; CI7 . 1 , 10 . 8mm ) ., Further , the Reinforcement group’s pattern of compensation was significantly different from one that corresponded to the minimization of squared error x a i m m i n ( e r r o r 2 ) ( p < 0 . 001 , one-tailed; θ ^ = 83 . 3 % ) ., These two findings suggest that the reinforcement feedback used in Experiments 1 and 2 is capable of influencing behavior in a way that aligns with a reinforcement-based loss function ., One prediction in the context of reinforcement-based learning is that an individual’s movement variability should influence their pattern of compensation to the lateral shifts ., We tested this for the Reinforcement participants in Experiment 2 . We characterized movement variability as the standard deviation of final hand position during the asymptotic phase of learning ( the last 400 trials ) ., While at the group level , participants appeared to compensate by an amount that approached an optimal strategy ( see above ) , on an individual basis we did not find a statistically reliable relationship between movement variability and compensation ( R2 = 0 . 003 ) ., This finding appears to differ from that reported by Trommershäuser et al . ( 2003a ) ., However , as expanded on in the Discussion , there are many differences between their task and ours , and such findings are not uncommon 16 , 25 ., Nevertheless , the group level data suggests the existence of a reinforcement-based loss function that maximizes the probability of hitting the target ., As expected , the Error group minimized squared error ( Fig 6B ) ., Their pattern of compensation was aligned with one that , on average , minimized squared error ( p = 0 . 795 , two-tailed; θ ^ = 50 . 0 % ) ., It was also significantly different from one that maximized their probability of hitting the target ( p < 0 . 001 , one-tailed; θ ^ = 100 . 0 % ) ., The results of Experiment 2 support the idea that the human sensorimotor system can update where to aim the hand during a reach by using only reinforcement-based feedback ( to maximize the probability of hitting the target ) or only error-based feedback ( to minimize approximately squared error ) ., As in Experiment 1 , we again found that participants in the Reinforcement + Error group minimized squared error , and that reinforcement feedback did not influence behavior ., Participants in the Reinforcement + Error group exhibited a pattern of compensation that was significantly different from the Reinforcement group ( p = 0 . 004 , two-tailed; θ ^ = 76 . 7 % ) , but was indistinguishable from participants in the Error group ( p = 0 . 922 , two-tailed; θ ^ = 52 . 2 % ) ., Taken together , the results from both Experiment 1 and 2 support the idea that the sensorimotor system heavily weights error feedback over reinforcement feedback when updating where to aim the hand during a reaching task ., A key aspect of this study was the use of skewed noise to separate the optimal aim locations predicted by reinforcement-based and error-based loss functions ., This allowed us to probe how the sensorimotor system uses reinforcement feedback and error feedback , in isolation and combination , to update where to aim the hand during a reaching task ., We found that participants minimized approximately squared error when they received only error feedback ., Participants maximized the probability of hitting the target when they received only reinforcement feedback ., When both forms of feedback were presented in combination , participants minimized approximately squared error at the expense of maximizing the probability of hitting the target ., This finding suggests that the sensorimotor system heavily weights error feedback over reinforcement feedback when deciding to aim the hand ., In both Experiments , we found that the sensorimotor system minimized approximately squared error when using error feedback to guide reaching movements ., This agrees well with previous work that examined how humans adapt to a small range of asymmetrical or multimodal noise during proprioceptive 11 , 14 and visual 15 tasks ., In Experiment 1 and 2 , we also used small ranges of asymmetrical noise that respectively spanned 3cm and 2 . 8cm of the workspace ., There is some evidence that as noise exceeds this range , the sensorimotor system is less sensitive to large errors 9 , 26 ., While in the present study our data points to an error-based loss function based on squared error , other studies have focused on other mathematical forms ., Another commonly examined loss function is the inverted-Gaussian 9 , 13 , 16 , which places greater emphasis on penalizing smaller errors and less emphasis on penalizing larger errors ., Sensinger and colleagues ( 2015 ) used a biofeedback task that involved controlling a myoelectric signal corrupted with skewed noise similar to that used by Körding and Wolpert ( 2004b ) ., They then examined several different loss functions and their corresponding best-fit parameters given the data ., The parameters of a loss function define how errors of different sizes are weighted relative to one another ., For the inverted-Gaussian loss function they found its best-fit parameter was much larger ( 9 times ) than that found by Körding and Wolpert ( 2004b ) ., They suggest that the inverted Gaussian loss function may not be generalizable across different motor tasks ., They did , however , estimate a best-fit power loss function exponent of 1 . 69 , a value that was quite close to the figure of 1 . 72 estimated by Körding and Wolpert ( 2004b ) ., In the present study we estimated an average best-fit power loss function exponent closer to 2 . 0 , which was not significantly different from 1 . 72 ., These differences may be due to the range of noise and possibility the shape of the skewed noise ., Nevertheless , and similar to others 11 , 14 , 15 , we were able to explain 80% to 89% of the variability in our data ( see S1 Data ) using a power loss function that minimized approximately squared error ( i . e . , αopt ≈ 2 . 0 ) ., In the current paper , we use a Bayesian framework to interpret and model how the sensorimotor system uses error feedback and reinforcement feedback when deciding where to aim the hand ., This framework combines prior experience and current sensory information , such as sensory cues 27 and sensory uncertainty 8 , in a statistically optimal fashion ., By accounting for both prior and current information , the Bayesian framework has successfully explained a broad range of phenomena , such as reduced movement variability 8 , 28 , perceptual illusions 29 and online feedback control 30 ., An alternative computational framework for error-based learning has been instrumental to our understanding of how the sensorimotor system learns to adapt on a trial-by-trial basis 31–35 ., Of these models , the ones that account for sensorimotor noise 32 , 33 , 35 have been termed , ‘aim point correction’ models 36 ., van Beers ( 2009 ) extended upon this framework with the ‘planned aim point correction’ model ., This model separates central movement planning noise and peripheral movement execution noise ., This model was able to explain reach adaptation patterns in a naturalistic task while demonstrating that the sensorimotor rate of learning was optimal given the properties of planning and execution noise ., It has been successfully applied to explain differences in novice and expert dart throwers 37 , and can account for both learning in task-relevant dimensions and exploratory ( random walk ) behaviour in task-irrelevant dimensions 38 ., Aim point correction models , which can be derived from a Bayesian framework , are attractive because they are computationally tractable and learning is modeled using terms and constructs from sensorimotor control , such as planning noise , motor commands , efference copies , and execution noise 36 ., In their current formulation , however , these models do not incorporate how the sensorimotor system responds to errors of differing magnitudes and different amounts of sensory uncertainty ., We accounted for both of these factors with our Bayesian model , which was essential for testing our hypotheses ., To further study how the sensorimotor system adapts on a trial-by-trial basis in experiments such as the ones used in this paper , a useful future direction would be incorporating the effects of sensory uncertainty and how errors of differing magnitudes are penalized ., While it is most common to study adaptation while providing only error feedback , researchers have also examined adaptation in the context of both reinforcement and error feedback 1 , 3 , 7 , 12 , 20–23 , 39 , 40 ., Using a visuomotor rotation task , Izawa and Shadmehr ( 2011 ) examined how the sensorimotor system uses both a reinforcement and error feedback when deciding where to aim the hand during a reach ., They manipulated the quality of error feedback presented on each trial in the following three ways: first , by displaying the cursor both at mid-reach and also at the target ( mid-reach and target error-feedback condition ) ; second , by displaying the cursor only at the target ( target error-feedback condition ) ; and thirdly , by withholding visual feedback of the cursor completely ( no error-feedback condition ) ., In each of these conditions , participants received reinforcement feedback for hitting the target ( the target expanded and a pleasant sound was played over a loudspeaker ) ., They modeled participants’ aiming behavior using a modified Kalman filter , which increasingly relied on reinforcement feedback as the quality of error feedback decreased ., However , in the Izawa and Shadmehr ( 2011 ) experiment , the predicted aim location for minimizing error and maximizing the probability of hitting the target overlapped , making it difficult to determine the extent to which participants’ adaptation was driven by error feedback versus reinforcement feedback ., Izawa and Shadmehr ( 2011 ) found greater movement variability in trials that provided feedback only at the target when compared to trials that provided feedback both mid-reach and at the target ., Their model attributed a greater proportion of adaptation due to reinforcement feedback on trials in which error feedback was provided at the target , compared to trials in which error feedback was provided both mid-reach and at the target ., While this is certainly a possibility , an alternative explanation for these behavioral differences is that the participants receiving feedback both mid-reach and at the target , unlike those receiving feedback only at the target , were able to compensate for accumulated sensorimotor noise error they sensed mid-reach ., That is , it is difficult to determine whether behavioral differences between conditions were a result of reinforcement feedback , differences in the amount of ( or location of ) feedback , or both ., In the present study we designed experiments aimed at resolving both of these potential issues ., First , we were able to separate the optimal aim location of error-based and reinforcement-based loss functions ., Second , for each group in Experiment 2 we equated the amount of and location of feedback by providing it only at the target ., We found no differences in compensation between participants who received error feedback and participants who received both error and reinforcement feedback ., This finding aligns with the work of Vaswani et al . ( 2015 ) , who similarly found that reinforcement feedback did not influence behaviour when it was combined with error feedback ., This suggests that the sensorimotor system heavily weights error feedback , when available , over competing reinforcement feedback ., Trommershäuser and colleagues ( 2003a ) provided evidence that humans can use reinforcement feedback to adjust where they aim their hand during a reaching task ., In their study , participants reached to a screen displaying a rewarding target area ( positive reinforcement: monetary gain ) and an overlapping penalty area ( negative reinforcement: monetary loss ) ., Thus , participants received both reinforcement feedback for hitting the target , and error feedback that indicated where they touched the screen relative to the target ., The reward and penalty for hitting these respective areas were held constant for a given block of trials ., In their task , internal senso

Introduction, Results, Discussion, Methods

It has been proposed that the sensorimotor system uses a loss ( cost ) function to evaluate potential movements in the presence of random noise ., Here we test this idea in the context of both error-based and reinforcement-based learning ., In a reaching task , we laterally shifted a cursor relative to true hand position using a skewed probability distribution ., This skewed probability distribution had its mean and mode separated , allowing us to dissociate the optimal predictions of an error-based loss function ( corresponding to the mean of the lateral shifts ) and a reinforcement-based loss function ( corresponding to the mode ) ., We then examined how the sensorimotor system uses error feedback and reinforcement feedback , in isolation and combination , when deciding where to aim the hand during a reach ., We found that participants compensated differently to the same skewed lateral shift distribution depending on the form of feedback they received ., When provided with error feedback , participants compensated based on the mean of the skewed noise ., When provided with reinforcement feedback , participants compensated based on the mode ., Participants receiving both error and reinforcement feedback continued to compensate based on the mean while repeatedly missing the target , despite receiving auditory , visual and monetary reinforcement feedback that rewarded hitting the target ., Our work shows that reinforcement-based and error-based learning are separable and can occur independently ., Further , when error and reinforcement feedback are in conflict , the sensorimotor system heavily weights error feedback over reinforcement feedback .

Whether serving a tennis ball on a gusty day or walking over an unpredictable surface , the human nervous system has a remarkable ability to account for uncertainty when performing goal-directed actions ., Here we address how different types of feedback , error and reinforcement , are used to guide such behavior during sensorimotor learning ., Using a task that dissociates the optimal predictions of error-based and reinforcement-based learning , we show that the human sensorimotor system uses two distinct loss functions when deciding where to aim the hand during a reach—one that minimizes error and another that maximizes success ., Interestingly , when both of these forms of feedback are available our nervous system heavily weights error feedback over reinforcement feedback .

learning, control theory, medicine and health sciences, engineering and technology, nervous system, social sciences, limbs (anatomy), neuroscience, learning and memory, control engineering, probability distribution, mathematics, cognitive psychology, systems science, statistical distributions, musculoskeletal system, computer and information sciences, hands, probability theory, arms, psychology, anatomy, biology and life sciences, sensory systems, physical sciences, cognitive science

journal.pgen.1002496

The Empirical Power of Rare Variant Association Methods: Results from Sanger Sequencing in 1,998 Individuals

There is growing evidence that rare variants contribute to the etiology of complex diseases 1 , 2 , 3 , 4 ., A striking difference in the distributions of the odds ratios ( ORs ) for common and rare variants has been illustrated in a wide range of recent publications , favoring higher ORs for some rare variants ( reviewed elsewhere 5 , 6 , 7 ) ., As well , it has been demonstrated that rare coding variants associated with complex traits are sometimes causal through amino acid substitution 3 , 8 , 9 ., For these reasons , rare variants hold promise as a source of heritability which is not explained by common base-pair variants ., Identifying rare variants associated with disease requires large sample sizes since few individuals harbor such polymorphisms ., In addition , for rare variants , the power of single-marker tests , such as those performed by genome-wide association studies ( GWAS ) , is poor ., Development of alternative methods is thus essential ., Over the past two years , a growing body of methods 2 , 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 seeking to overcome this limitation has emerged ., These methods generally employ three main strategies: collapsing markers across a region , weighting and/or prioritizing markers , and distribution-based approaches ., Li and Leal 20 , for example , proposed a method to collapse rare variants across a region ., This and other collapsing methods are based upon the hypothesis that low-frequency variants are rare , but in aggregate , they may be common enough to account for variation in common traits ., Under such models , it is assumed that the probability of being diseased increases with the number of rare minor alleles ., However , this might not always be the case 21 ., Weighting methods assign more importance to alleles based on many possible criteria , such as minor allele frequency ( MAF ) in the control population 17 , or possible alterations in protein function , including measures produced by SIFT and Polyphen2 11 , 22 ., More recently , methods examining changes in distributions associated with rare variants 2 , 23 have been proposed ., Liu and Leal 2 based their novel method on multi-locus genotypic configurations , where each unique pattern of genotypes is tabulated , and the associated risk of disease for each configuration is modeled using a mixture distribution ., Liu and Leal refer to their method as a kernel-based approach ( KBAC ) , since part of the mixture distribution is modeled by nonparametric kernel density estimation ., Neale et al . 23 showed that a test of association can be based on binomial over-dispersion of variance , conditional on the number of rare variants present in a region ., Another innovative and flexible method has been developed by Wu et al 24 ., These authors proposed the sequence kernel association test ( SKAT ) , a supervised , flexible , and computationally efficient regression model ( with the possibility of adjusting for covariates ) , to test the association between rare and common variants and traits or disease status ., SKAT is similar to a classical mixed model , and is based on a score test for non-zero variance associated with the effects of all the rare variants under consideration ., These recently proposed models have often relied upon unverifiable ( and sometimes unnecessary ) hypotheses in order to simulate sequence data ., Certainly , simulation of large sets of sequence data is a complex task and depends on hypotheses concerning the evolution of human genomic regions ., The validity of any particular set of evolutionary hypotheses is unlikely to be consistently true across the 4 genome , as each gene demonstrates a large variance in these parameters 25 , 26 ., The performance of these newly proposed models using real sequence data in a large sample has not been independently evaluated ., We therefore tested the power of commonly-used statistical methods designed to assess the impact of rare variants on continuous and dichotomous traits in 1 , 998 individuals Sanger-sequenced at seven genes ., We employed a variety of possible relationships between genotype and phenotype in order to fully investigate the performance of such models under different realistic scenarios ., We selectively chose some of the recently proposed statistical methods for rare variant association ., These included: collapsing methods ( with and without a variable minor allele frequency MAF threshold for defining rare variants ) , a weighting method ( which assigns weights variants inversely proportional to their MAF ) , a variance-based approach 2 , 11 , 17 , as well as a regression method using the Kernel association test ( SKAT ) 24 ., We used the software provided by 11 to implement the collapsing and weighting methods ., Four models were first investigated: a collapsing method using a threshold of 1% ( T1 ) and 5% ( T5 ) , a weighted approach ( WE ) , and a variable-threshold approach ( VT ) ( see http://genetics . bwh . harvard . edu/rare_variants ) ., ( Note that while the WE method was implemented by 11 , the model was proposed by Madsen and Browning 17 ) ., In addition , we developed an approach for detection of rare-variant association with continuous traits that was inspired by KBAC 2 , that we call “weighted outlier detection” ( WOD ) ., Two different MAF thresholds were applied to this new WOD method at 1% ( WOD1 ) and 5% ( WOD5 ) ( see Text S1 for details ) ., The last method we tested is the regression model ( SKAT ) developed by 24 ., The relative power of each of these methods was then compared assuming different possible relationships between rare variants and continuous traits or disease status ., The first set of simulations , which are designed to act as positive and negative controls for each of the methods tested , assesses potential relationships between rare variants and continuous traits under the relevant hypotheses made in several models ., Scenario 1 is a “null model” , which serves as a negative control ., Scenario 2 acts as a positive control for all collapsing models ., Scenario 3 depicts a mixture of rare and common variants ., Scenario 4 is a positive control for SKAT and WOD , which are designed to perform well under a mixture of protective and deleterious variants ., Scenarios 5 and 6 are positive controls for WE ., Details for these six different scenarios are found in Table 1 ., ( See Text S1 for additional information ) ., Figure 1 shows the average power of each method based on this control set of simulations from all seven genes ( Table 2 and Table 3 ) ., The power is around 5% in the null scenario , as expected , where no associations were assumed between the variants and continuous trait ., On the other hand , Scenario 2 , referred to as a positive control for the collapsing design , demonstrates power of 100% ., It was expected that this latter scenario would lead to very high power , since the simulation assumed that the phenotypes were always altered if the individual carried at least one rare allele , such as would be expected with a highly penetrant allele ., In the remaining scenarios , it is striking that all methods have relatively poor power under most hypotheses , even though our simulation design included large shifts in the mean phenotype in a large number of individuals ., Almost all of these scenarios show power less than 50% in the majority of the methods ., In scenario 3 , the addition of common causal variants to the presence of rare causal variants did not improve the power , except for the SKAT method which demonstrates its advantage when combining common and rare variants ., In Scenario 4 , where bidirectional causal variants are present , only WOD1 and SKAT have power above 50% ., Scenarios 5 and 6 test performance when rarer variants have stronger effects ., While the VT method marginally outperforms the WE method in these scenarios , the WE method improves considerably when compared to the other scenarios where no relationship was assumed between MAF and effect ., These results demonstrate that all methods perform well under their intended hypothesized relationship between rare variants and phenotypes , but their power can vary largely when there is departure from this main hypothesis ., We next assessed to what extent power is influenced by the effect size and proportion of causal rare variants ., In the next set of simulations , we varied these two parameters to explore more systematically how much they influence the strength of the signal between genes and complex traits ., The proportions of causal variants varied from 10 , 15 , 20 , and 30% of all rare variants , where the causal variants were chosen at random from the polymorphisms that had low frequency ( i . e . , MAF≤1% ) ., We assumed seven possible values for the mean effects: 0 . 5 , 0 . 75 , 1 , 1 . 25 , 1 . 5 , 2 , and 2 . 5 standard deviations ., The combination of seven effects , four proportions of causal variants associated with the trait , and seven genes , leads to 196 scenarios ., In these scenarios , we first analyzed the results of each scenario using single-marker tests , and then next applied the seven rare variant methods ( T1 , T5 , VT , WE , WOD1 , WOD5 , and SKAT ) for gene-level analysis ., Next , we applied all seven methods to dichotomous traits , created by selecting from the extreme quarters of the continuous trait distribution ., Results here are restricted to analysis only of the assigned causal variants , and we report the proportion of these causal variants that reach statistical significance , after adjustment for multiple-testing , using a Bonferroni correction ., Figure 2 shows the relationship between the proportion of causal variants assigned and their effect , averaged across all seven genes ., Notably , but as expected , single-marker tests cannot identify more than 20% of the causal variants , even when effects are as large as 1 . 5 standard deviations ., Power is particularly poor when the effect is 0 . 5 or 0 . 75 standard deviations ., Figure 3 shows the relationship between power , effect size , and proportion of causal variants associated with a continuous trait , averaged across all seven genes ., Each dot represents the power of a given method ordered by average effect ( ranging from 0 . 5 to 2 . 5 standard deviations ) within each bin ., Each bin represents the proportion of causal variants ( ranging from 10 to 30% ) ., Each of these 28 scenarios ( 7 different effect times 4 proportions of causal variants ) can also be expressed in terms of proportion of variance explained , as seen in Table 4 ., These values indicate how much variability in the trait each simulated model explains ., It is clear that none of the proposed methods have strong power to detect any gene when rare causal variants have small-to-moderate effects ( less than 1 . 25 standard deviations ) ., For most methods , effects of 1 . 5 standard deviations are needed to have reasonable power to detect an association ., The power for most methods was less than 60% ., Furthermore , our WOD method is not well powered for small-to-moderate effects , but is comparable to other methods when the effects are larger ., Power tends to increase as the proportion of causal variant increases , mainly because there are more causal variants that can possibly influence phenotype ., Note also that WOD does not accommodate covariates but that it remains possible to incorporate covariates into the phenotype by using residuals ., Collapsing methods do not perform well when effects are small or moderate ( <1 . 5 standard deviations ) ., The only situation where the power was greater than 75% is when between 15% and 30% of the rare variants are causal , and effects are moderate-to-large ( Figure 3 ) ., The SKAT method seemed to perform as well as most methods for smaller proportion of causal variants , but underperforms as the proportion of causal variants increases ., We also evaluated the power of the rare variant methods when rare variants are assigned to have either deleterious or protective effects ( Figure 4 ) ., In this set , we permitted half the causal variants to be deleterious and half to be protective ., Again , the assigned absolute effects ranged from 0 . 5 to 2 . 5 standard deviations and the proportion of causal variants ranged from 10–30% ., Figure 4 clearly shows the substantial advantage of SKAT and our distribution-based approach ( WOD ) to detect effects in this context ., In the case of WOD , however , this advantage is limited to effects of more than 1 . 5 standard deviations ., SKAT does perform better than WOD when the mean effect is small , but this advantage tends to disappear for larger effects , e . g . , over 2 . 0 SD ., When individuals carrying causal alleles have phenotypes shifted by less than 1 . 25 standard deviation , all methods , except SKAT performed equally poorly ., In these situations SKAT provides clearly improved power , but absolute power remains relatively low ., These results clearly show the important contribution of methods that can account for mixture of protective and deleterious variants within a gene ., In order to assess the performance of these methods for dichotomous traits , we selected 500 cases and 500 controls from the extreme 25th percentiles of the continuous trait distributions ., This design therefore tests power of rare variant methods for sampling designs targeting more extremes of the distribution ., Figure 5 shows the relationship between power , effects , and proportion of causal variants associated with a dichotomous trait , when causal rare variants only increase risk of disease , averaged across all seven genes ., Note that WOD was not designed for dichotomous traits , so results from this model are not presented in this section ., Again , power increases as the proportion of causal variants increases , and power remains low for smaller effects ., In this particular case-control design , VT appears to have the lowest power compared to all other methods ., The remaining methods , T1 , T5 , WE , and SKAT have power estimates that are in a similar range , but power from T1 , T5 , and WE seemed to outperform SKAT as the proportion of causal variants increases ., Interestingly , power does not seem to be as strongly influenced by the magnitude of the effect , as is it for continuous trait results ., This can be explain by the fact that when the effect is one SD away from the mean , on average , over 90% of the individuals that are carrying a causal allele will have their phenotype shifted and be classified as cases ., In other words , between effects of 1 to 2 . 5 SD , there is not a large difference in the number of shifted individuals that are correctly classified as cases ., The power was low for almost all methods when causal variants could be either deleterious or protective––as was observed for continuous traits ., Figure 6 shows the relationship between power , effects , and proportion of causal variants associated with a dichotomous trait , when causal variants are deleterious , or protective , averaged across all seven genes ., Power increases as the proportion of causal variants increases , and we also observe the “plateau” pattern described in the previous paragraph ., Methods such as T1 , T5 , and WE that are not designed for a mixture of deleterious and protective effects have poor power to detect any association between genes and dichotomous traits ., SKAT clearly outperforms the other methods under these circumstances ., While many large-scale sequencing studies are now underway to identify rare variants associated with complex diseases and traits , our results demonstrate that assessing the association between rare variants and complex disease is a challenging task ., Standard single-marker association methods exhibit low power and the power of the statistical methods tailored for rare variants varies tremendously depending on the true nature of the relationship between the rare genetic variants and the phenotype ., These findings provide guidance in the design , analysis and interpretation of sequencing studies for complex disease ., As it is still unknown how rare variants influence complex disease , we have simulated several phenotypes under models spanning a spectrum of the common hypotheses concerning such associations ., It is likely that the nature of the relationship between rare variants and a phenotype varies from gene-to-gene ., Our findings suggest that no single method gives consistently acceptable power across the range of these relationships , even in a large sample size ., Analysis using different methods clearly imposes an additional multiple testing burden , which cannot be easily addressed ., One , though somewhat cumbersome , way to solve this problem would be by derivation of empirical P-values taking into account the variety of methods tested ., Another , more straightforward , approach would be to undertake replication in an independent sample , using the method which demonstrated best results at the discovery stage ., In this paper , we have also developed a new method conceptually based on Liu and Leals KBAC method 2 to detect the association between rare variants and quantitative traits ., Our extension of 2 is implemented in R and is available from the authors ., We have also developed a simulation framework to compare all major novel statistical methods to identify the contribution of rare variants to continuous phenotypes under identical conditions ., Our new approach performs poorly if all rare variants act in the same direction , but performs well when variants can either increase or decrease phenotype and have large effect ., We note that the presence of randomly assigned rare variants of smaller effect in size , all tests have a distribution of test statistics that follows the null distribution ( see Text S1 ) ., Collapsing methods demonstrate increasing power when the trait varies with an increasing number of rare alleles ., However , examples exist where protective and deleterious rare alleles are present in a gene 21 , and in such situations , collapsing methods do not perform well ., On the other hand , SKAT and WOD performed extremely well compared to other methods in the continuous traits scenarios , and dichotomous traits ( SKAT only ) scenarios , respectively ., SKAT in particular , was the only method that performed well for dichotomous traits when variants could be protective or deleterious ., Methods like WE that assign more weight to rarer alleles are promising , but only if the gene harbors several causal variants whose effects are each inversely proportional to their MAF ., However , we note that the VT method still outperforms WE even when employing this assumption ., Our study also provides empirical data to judge the value of dichotomizing a continuous trait and sequencing only its extremes ., While our design included the extreme quarters of the distribution , thereby eliminating the need to sequence half the study population and consequently reducing sequencing costs substantially , we note that power was similar to that derived from the entire distribution particularly only when the proportion of causal variants was high and the effect sizes moderate ., Nonetheless , sampling of the extremes remains an attractive study design , particularly if the sampled population is large and a more extreme sub-population is selected ., Methods have been proposed to weight the relative importance of rare variants based on various parameters including their estimated deleterious effect on protein function 17 , 27 ., For example , the incorporation of estimated functional information , such as the potential effect of an amino acid change as estimated by Polyphen or SIFT , might improve power ., However , these scores have been criticized for their high level of misclassification 22 ., Moreover , functional prediction is more challenging when the variants are non-coding ., The spectrum and frequencies of rare genetic variants are known to depend on ancestry and age of the population studied 28 ., In this work , we have assumed that our sample consists of a homogeneous population without stratification into population subgroups ., All the methods that we have examined could find false associations if population sub-strata existed and were associated with the phenotype , therefore particular attention must be paid to population structure when designing rare variant studies ., One of the strengths of our study is the use of Sanger sequencing data , rather than simulated genotyping data ., We have been able to avoid the simulation of such data by using fully Sanger-sequenced data on nearly 2 , 000 individuals at seven genes ., Therefore , no genotypic hypotheses were made to generate the sequence data ., Furthermore , the sample size employed is among the largest sequenced datasets in the world at present ., Despite the fact that gene 3 had more missing data and fewer variants , we note that the power results derived from this gene are similar to all other genes ., We note that our simulations assumed no additive effects when an individual carries multiple rare variants ., However , we note that very few individuals carry 2 or more rare variants ( Table 3 ) ., In addition , we assumed that rare variant effects take precedence over common variant effects ., In light of our results , we recommend that single-marker tests should not be used alone when rare variants are present and are assumed to have small-to-moderate effects on the trait of interest ., On the other hand , as power across all novel rare variants methods is generally low , the potential for identifying rare variant associations using gene-based analysis strategies requires improvement ., Ideally , the true underlying nature of the association between the gene and the phenotype should determine the choice of statistical method , however , this relationship is almost always unknown ., Therefore , performing sensitivity analyses , i . e . , assessing different methods that perform differently under various conditions might be a helpful option in order to interpret the results ., Furthermore we suggest that if one method identifies a gene of interest that replication of this result should be performed in an independent sample using the same statistical method ., All methods seemed to perform adequately under their specific model hypotheses , but do not perform as well when these hypotheses are violated ., In the next few years , advances in sequencing technology will enable the production of large quantities of sequence data on large numbers of individuals , allowing for the cost-effective identification of rare variants ., These data will enable researchers to investigate the role that rare variants play in disease etiology , in addition to uncovering functional variants ., Our results may provide guidance in the planning , analysis and interpretation of these large-scale initiatives ., The work described in this manuscript represents a re-use of data and no new human interventions were conducted ., No additional IRB approvals were sought for this specific portion of the work ., The Committee on Ethics in Clinical Research , CHUV , Lausanne University , Lausanne , Switzerland approved the original protocols for sample collection ., The subjects used in this paper are a subset of the CoLaus study , a population-based study of 6 , 188 Lausanne residents aged 35 to 75 years 29 ., Sanger sequence data for the exons and flanking regions of seven genes including PLA2G7 from 1 , 998 individuals were provided by GlaxoSmithKline ( GSK ) ., Methods for performing the sequencing for the PLA2G7 gene and the additional 6 genes have been described 30 ., The identity of the remaining genes was not disclosed for proprietary reasons ., Sanger sequencing has a low error rate and is considered a gold-standard for comparison to high-throughput sequencing studies 31 , 32 ., For simplicity , and since rare variants are not expected to be in high linkage disequilibrium ( LD ) with surrounding variants , we imputed the missing values of each rare variant independently from others based on the computed MAF ., The percentage of missing genotypes per variant in a gene ranged from 3% to 11% , with an average of 5 . 5% individual missing genotype information per variant , across all genes ( Table 2 ) ., All non-polymorphic base-pair markers were removed from the sequence data ., All seven genes contained both rare and common variants: the number of polymorphic variants ranged from 29 to 128 , and the proportion of variants with a MAF≤1% ranged from 81% to 93% ., The majority of these variants were extremely rare , with an average of 55% of all variants across all genes being singletons ., Table 2 and Table 3 describe the allelic frequencies , and rare variant distribution of all seven genes ., We used these known genotypes combined with phenotype simulations to compare several commonly-used and novel statistical methods developed for rare variants and continuous phenotypes ., We developed two simulation sets to illustrate the power of a variety of commonly-held hypotheses about the possible effects of rare variants on complex traits ., In the first set , we tested collapsing and weighting designs and a range of general concepts about the potential role of rare variants , whereas in the second set , we varied the effect and the proportion of causal variants in across a grid of values ., We proposed different phenotype simulation scenarios to explore popular hypotheses regarding the mechanism by which rare variants could influence complex disorders , namely, ( a ) the assumption that risk of disease increases with more rare alleles ( collapsing design ) ,, ( b ) the assumption that the magnitude of the effect depends on MAF ( such as equation ( 1 ) in 17 for the weighting design ) , and, ( c ) performance when a mixture of deleterious and protective causal rare variants influences phenotypes ( Table 1 ) ., Here we describe the motivation behind our choice of scenarios ., Scenario 1 , the null model , contains no causal variants ., Scenario 2 assumes that any rare variant increases the risk of disease , which reflects the hypothesis underlying many of the proposed statistical methods ., Scenario 3 investigates a mixture of common and rare causal variants , Scenario 4 investigates a mixture of deleterious and protective effects , and Scenarios 5 and 6 explore the assumption that variants with lower MAF have larger effect ., In these cases , the effects were derived from equation 1 in 17 ., In our simulation of phenotypes , the following rules were applied in all scenarios ., We assumed that all non-carriers of a causal allele ( deleterious or protective ) variant have a normally distributed trait with mean zero and variance of one , using a standard normal random variable ., When one or more common variant ( s ) is/are assumed to have a deleterious effect , and an individual is carrying at least one of these causal alleles , we randomly drew a phenotypic value from a normal distribution having a mean of −0 . 07 and a standard deviation of 1 . 01 , which allows for an effect typically identified in GWAS studies of continuous traits 33 , 34 , 35 ., When a rare variant is assumed to be deleterious , carriers of at least one rare causal allele had a phenotypic value randomly sampled from a normal distribution with mean at −1 . 64 , and standard deviation of 0 . 2 ., Relative to the phenotype distribution of individuals with no causal variants , these means correspond to the bottom 5% of the distribution ., Similarly , to model protective effects of a rare variant , the assigned effect was normal with mean +1 . 64 and a standard deviation of 0 . 2 ., Such effects for rare variants have been observed in the lipid literature 36 , 37 ., Deleterious variants were randomly sampled from the pool of variants for each simulation ., Rare variants were defined as those having a MAF 1% , and common variants were defined as >1% ., While other thresholds can be used , GWAS have often used a 1% threshold to define rare variants 35 ., We allowed all rare variants to be possibly causal , including singletons ., Table 1 summarizes the parameters investigated ., By varying hypotheses about the sampling of causal variants and their effect , we created these 6 simulation scenarios ., We randomly generated a set of 250 phenotypes per individual , per scenario , per gene ., In each case , we randomly selected causal variants associated with the traits , and then randomly generated a set of phenotypes based on the corresponding parameters for each iteration ., In our second series of simulations , we varied the proportion of causal rare variants and their average effect on the phenotype across a grid of values , i . e . , where proportions ( 10 , 15 , 20 , and 30% ) of causal rare ( MAF≤1% ) variants were combined with values ( 0 . 5 , 0 . 75 , 1 , 1 . 25 , 1 . 5 , 2 , and 2 . 5 standard deviations ) for the mean effects ., We also report in Table 4 the proportion of variance explained by rare variants for each combination of proportion of causal rare variants and their effect ., An individual carrying at least one rare causal allele has their phenotype value chosen randomly from a normal distribution with one of these seven means and with a standard deviation of 0 . 2 ., All 28 combinations between the proportion of causal ( four values ) and effect ( seven values ) were simulated for the seven genes ., Two hundred and fifty sets of phenotypes were generated ., Multiple-testing was taken into account for single-marker test analyses , using a conservative approach with Bonferroni correction for the number of single-nucleotide polymorphisms ( SNPs ) tested ., As for other rare variant methods , permutation was used to control for type-I error in all statistical methods ., Alpha level was set to 0 . 05 ., We also simulated dichotomous phenotypes by assuming selection from the extremes of a quantitative distribution ., In each of the 196 scenarios presented above for continuous traits , we have defined cases as being the 500 individuals with the lowest continuous phenotypes , and the controls as being the 500 individuals with the highest continuous phenotypes ., This study design allows direct comparison of the relative utility of sequencing only the extremes of a distribution , as compared to the entire distribution , which has considerable financial ramifications

Introduction, Results, Discussion, Materials and Methods

The role of rare genetic variation in the etiology of complex disease remains unclear ., However , the development of next-generation sequencing technologies offers the experimental opportunity to address this question ., Several novel statistical methodologies have been recently proposed to assess the contribution of rare variation to complex disease etiology ., Nevertheless , no empirical estimates comparing their relative power are available ., We therefore assessed the parameters that influence their statistical power in 1 , 998 individuals Sanger-sequenced at seven genes by modeling different distributions of effect , proportions of causal variants , and direction of the associations ( deleterious , protective , or both ) in simulated continuous trait and case/control phenotypes ., Our results demonstrate that the power of recently proposed statistical methods depend strongly on the underlying hypotheses concerning the relationship of phenotypes with each of these three factors ., No method demonstrates consistently acceptable power despite this large sample size , and the performance of each method depends upon the underlying assumption of the relationship between rare variants and complex traits ., Sensitivity analyses are therefore recommended to compare the stability of the results arising from different methods , and promising results should be replicated using the same method in an independent sample ., These findings provide guidance in the analysis and interpretation of the role of rare base-pair variation in the etiology of complex traits and diseases .

There is now evidence that rare variants can contribute to the etiology of complex disease ., Next generation sequencing technologies have enabled their detection in large cohorts , and new statistical methods have been proposed to ascertain their association with complex diseases and traits in order to improve power over single-marker analysis ., Each of these new methods assumes a particular nature of the relationship between rare variants and complex disease , yet these hypotheses have been largely unverified ., Therefore we sought to compare the power of commonly used and novel statistical methods for rare variants using Sanger sequencing data from 1 , 998 individuals sequenced at 7 genes by simulating several phenotypes under models spanning a spectrum of the common hypotheses concerning such associations ., While all methods perform reasonably well under their own model-specific hypotheses , no single method gives consistently acceptable power when these hypotheses are violated ., Unlike GWAS , wherein all variants can often be tested using the same method across the entire genome , the analysis and interpretation of sequencing studies will therefore be considerably more challenging .

mathematics, statistics, genetics, biology, genetics and genomics

journal.pcbi.1000351

Towards Prediction of Metabolic Products of Polyketide Synthases: An In Silico Analysis

It is well known that polyketide synthase ( PKS ) gene clusters can generate enormously diverse array of polyketide products by making use of various biosynthetic paradigms like , modular organization of sets of catalytic domains or iterative catalysis of condensation steps using single set of catalytic domains 1 ., In view of the pharmaceutical importance of polyketides , there is tremendous interest in identifying PKS gene clusters capable of producing novel polyketides by genome mining ., However , the relating the sequence of the various catalytic domains present in a PKS biosynthetic cluster to the chemical structure of the final metabolic product is a major challenge ., The availability of the sequences of a large number of experimentally characterized PKS clusters and 3D structural information on homologous protein domains presents a unique opportunity to carry out in silico analysis for addressing structural and mechanistic issues concerning polyketide biosynthesis ., A number of recent theoretical studies have demonstrated the utility of in silico analysis in providing novel insights into the mechanistic details of polyketide biosynthesis as well as in identifying novel natural products by genome mining ., Computational analysis of polyketide synthase ( PKS ) and nonribosomal peptide synthetase ( NRPS ) proteins have provided valuable clues for development of knowledge-based methods for identification of catalytic domains in PKS 2 , 3 and NRPS 4 proteins , prediction of the substrate specificity for AT domains 2 , 3 , 5 and adenylation domains 4 , 6 , 7 ., Such predictions have also been experimentally validated by the recent successful reprogramming of the phthiocerol dimycocerosate ( PDIM ) biosynthetic pathway in Mycobacterium tuberculosis 8 and experimental characterization of a novel exogenous standalone enoyl reductase ( ER ) involved in PDIM biosynthesis 9 ., Bioinformatics analysis of secondary metabolite biosynthetic pathways have also played a crucial role in discovery of novel natural products by genome mining 10–14 ., Very recently it has also been demonstrated that , computational analysis of KS domains from trans-AT PKS clusters can give novel clues about the chemical structures of the final polyketide product 15 ., Similarly , bioinformatics analysis of docking domain sequences ( the original term applied to these regions was “interpolypeptide linker” , but the term docking domain is being increasingly used in recent literature ) have given novel insight into the evolution of specificity in inter polypeptide interactions in modular PKSs 16 ., Pioneering work at Ecopia BioScience using data mining approaches has also led to development of proprietary databases which can aid in genomics driven discovery of cryptic biosynthetic pathways 17 and utility of these databases have been demonstrated by identification of novel secondary metabolites 18 ., Thus , these studies have established that knowledge based computational approaches can play a powerful role in elucidation of novel secondary metabolite biosynthetic pathways ., However , for in silico identification of polyketide products of uncharacterized PKS clusters , the computational method should also take into consideration various different paradigms employed by PKS biosynthetic machinery 19 ., Several excellent reviews 20 , 21 describe the type I , type II and type III biosynthetic paradigms ., Type I modular PKSs harbor distinct sets of catalytic domains , each set termed as a “module” ., Each module is responsible for one condensation step and the number of modules in a modular PKS correlate directly with the number of ketide units in its biosynthetic product ., In contrast , type I iterative PKSs are characterized by a single set of catalytic active sites which are used iteratively for several rounds of successive condensations till the final product is released ., It was initially believed that bacterial PKSs are modular while fungal PKSs function in an iterative manner ., However , discovery of mixed PKS clusters involving programmed iterative modules and several other deviations 22 , 23 from conventional textbook PKS biosynthetic paradigms in various microbes indicate that PKS proteins are not amenable to simple classification based on species of their origin ., Therefore , in silico methods should be capable of predicting from sequence information , whether a given PKS cluster is iterative , the number of iterative chain condensation steps catalyzed by it and crucial amino acids which control the number of iterations ., In contrast to type I iterative PKSs where a single multifunctional enzyme is involved in biosynthesis of the polyketide product , biosynthesis in type I modular PKS clusters often involve multiple ORFs , each containing several modules ., Therefore , predicting the correct order of substrate channeling between various ORFs is crucial for deciphering the final metabolic product of a modular PKS cluster ., Several lines of experimental evidence reveal that inter subunit interactions between C-terminal docking domain region of the upstream ORF and N-terminal docking domain region of the downstream ORF , play a crucial role in channeling of substrates from upstream domains to downstream domains 24–27 ., Moreover , these interactions involving C-terminus and N-terminus amino acid stretches have been reported to increase the maximum velocity ( kcat ) of chain transfer of otherwise disfavored substrates by as much as 100-fold 28 ., Structural studies using NMR suggest that , these terminal docking domain regions of PKS proteins adopt a specific 3-dimensional fold consisting of a four helix bundle structure 29 ., In fact , after the elucidation of this NMR structure , the term ‘docking domain’ is being increasingly used in the recent literature to describe these terminal amino acid stretches , which were earlier called ‘inter polypeptide linkers’ ., Based on this structure , it has been proposed that recognition between upstream and downstream ORFs in a modular cluster is governed by formation of specific contacts in the docking domain ., Several recent experimental studies 30 , 31 have further validated the role of specific inter polypeptide contacts in controlling inter subunit communication in modular PKS clusters ., Very recently NMR studies 32 have also elucidated the role of similar docking domains in governing protein-protein interactions in hybrid megasynthases ., Even though these experimental studies have identified specific residue pairs involved in inter subunit recognition , no systematic analysis of experimentally characterized modular PKS clusters have been characterized to investigate whether correct order of substrate channeling in type I modular PKS clusters can be predicted based on these specific inter polypeptide contacts ., It may be noted that , even though recent study by Thattai et al 16 has attempted to address this question , their algorithm for prediction of PKS multiprotein chain order has been tested on a hypothetical five ORF cluster with only six combinatorial possibilities ., In this work , we have carried out a detailed comparative analysis of the experimentally characterized modular and iterative PKS clusters with known polyketide products to address following major questions relating to in silico prediction of polyketide products ., Is it possible to distinguish between modular and iterative PKS from their sequence alone ?, Can we predict the number of iterations a given iterative PKS protein would catalyze and identify crucial amino acid residues that control the number of iterations ?, Is it possible to predict the correct order of substrate channeling between various ORFs in a modular PKS cluster ?, We have carried out profile Hidden Markov Model ( HMM ) analysis of KS domains to identify signature profiles which can decipher whether a PKS protein is modular or iterative ., Structural modeling of KS domains of iterative PKS proteins and analysis of their active site pockets have given novel insight into the structural features that dictate the number of iterations catalyzed by a PKS protein and crucial amino acids which control them ., Similarly , comparative analysis of crucial inter polypeptide contacts between cognate and non-cognate pairs of ORFs based on the three dimensional structure of the docking domains have given novel clues for prediction of the correct order of substrate channeling ., KS domains are the most conserved among various catalytic PKS domains and are responsible of catalysis of the chain condensation step ., We have analyzed them in detail to identify class specific conserved patterns which distinguish modular and iterative PKS systems ., For KS domains , the total dataset comprised of 217 pure modular KS domains , 82 pure iterative domains , 19 enediyne , 43 trans-type and 34 KS domains from hybrid NRPS-PKS clusters ., Apart from the sequences of 20 experimentally characterized bacterial type I modular clusters included in our earlier analysis 2 , an additional set of 18 modular PKS clusters was used as described in Methods ., Despite sharing a significant degree of homology ranging from 24% to 40% sequence identity , KS domain counterparts from modular and iterative PKSs and other PKS subfamilies , segregate into distinct clusters in a phylogenetic dendrogram ( Figure S1 ) ., We have used profile Hidden Markov Models ( HMMs ) to quantify subtle position specific differences in the probability of occurrence of amino acids in various subfamilies of KS domains ( See methods for description of various subfamilies ) ., The available KS data set was divided into training and test set , and sequences belonging to the training set were used for building profile Hidden Markov Models by the HMMER package 33 ., Benchmarking on the test set indicated that , these HMM profiles were highly sensitive , with a prediction accuracy of 100% for both enediyne and trans-AT sub families , 97% for pure iterative PKSs , 92% for modular KS domains and 88% for hybrid clusters ., Therefore , using HMM profiles it is not only possible to distinguish between modular and iterative PKS with a very high accuracy , these profiles can also be used to classify an uncharacterized sequence of a KS domain into various subfamilies within modular and iterative systems ., This result has interesting implications for genome sequencing efforts towards identification of novel PKS clusters , because from KS sequence alone , one can get clues about PKS family and decide whether to sequence the entire cluster or not ., The polyketide products of various iterative PKS proteins are biosynthesized by different number of iterative condensation steps and undergo varying degrees of reductions ., Phylogenetic analyses of iterative KS domains revealed that the clustering of iterative PKS sequences is highly correlated with the number of iterations they perform and degree of reductions undergone by the metabolite during biosynthesis ( Figure 1 ) ., The biosynthesis of polyketides , lovastatin and bikaverin involve eight condensation steps , but their final structures are different because of the different cyclization patterns ., Our analysis suggests that , the sequence of KS domain encodes information about chemical structure of the polyketide product ., Hence , KS sequences of lovastatin and bikaverin form two different clusters ., Based on similar phylogenetic analysis , earlier reports have proposed that KS domains cluster into groups depending on whether the corresponding type I iterative PKS contains additional reductive domains 34–36 ., We attribute this feature to a complex programming within the KS domains which enables specific molecular recognition of the products ., The observed clustering in Figure 1 could thus be arising from sequence features , that control recognition of specific substrates which have undergone different degrees of chemical and structural modifications due to the presence of reductive domains ., Therefore , we wanted to analyze the structural models of various iterative KS domains for identification of specific amino acids or sequence stretches that can potentially control substrate size and extent of unsaturation ., The various iterative KS domains were modeled using comparative modeling approach ( see Methods for details ) ., The structural templates for various iterative KS domains were identified by BLAST search against PDB or by using threading approach ., The E . coli KAS-II protein ( pdbids 1KAS , 1B3N ) were used as the templates for modeling these iterative KS domains ., Since 1B3N was a ligand bound structure ( Figure 2A ) , the putative active site pockets ( Figure 2B ) of various iterative KS structural models could be identified based on amino acids which were in contact with the bound ligand in 1B3N ., The structural features of the active site pockets of different iterative KS domains were analyzed further to identify the cavity lining residues ( CLRs ) and cavity volumes following protocols described in the methods section ., Active site residue patterns ( Figure 2B ) in these structural models allowed us to correlate the cavity volume and hydrophobicity of the active site pockets to the number of iterations and the degree of unsaturation of the polyketide products they synthesize ., The substrate binding cavity in the 1KAS is highly hydrophobic owing to its completely saturated substrate ., Polyketides , on the other hand , may contain several hydroxyl groups and unsaturated double bonds ., Accordingly , the catalytic pockets in the structural models of polyketide KS domains were found to be less hydrophobic compared to the FAS cavities ., Table 1 compares PKS product characteristics with a variety of cavity features ., We observed a distinct difference in pocket hydrophobicity within polyketides and it correlated negatively with the extent of unsaturation seen in the product ( Figure 3A ) ., For example , the T-toxin PKS model cavity is more hydrophobic than the methylsalicylic acid synthase ( MSAS ) model cavity and this correlates with the fact that T-toxin is a reducing PKS having a greater proportion of saturated carbons in its final product than the partially reducing MSAS polyketide ., Interestingly , cavity volumes correlate positively with the number of iterations ( or corresponding product size ) ., We found that polyketide KS cavity volumes fall into three distinct groups; small , large and intermediate ( Figure 3B and 3C ) ., The smallest cavities ( ∼300Å3 ) belong to the MSAS type PKSs that perform three iterations ., Intermediate sized cavities ( ∼800Å3 ) belong to the napthopyrone ( NAP ) like PKSs that iterate from five to eight times ., The largest cavities , 1780Å3 , were observed for the T-Toxin models that perform 20 iterations ., Figure 2B depicts the residues that line the hydrophobic cavity of the template KAS-II protein ( volume 934 Å3 ) and surround the ligand analogue cerulenin ., A comparison of the modeled structures with the template FAS KS structure revealed that in case of MSAS and NAP , the backbones of the models had not altered significantly during modeling ( Figure S2 ) , and thus , their functional difference could be traced to specific cavity lining residues ( CLRs ) ( Figure 4 ) ., Figure 5A and 5B show the surface topology of the small and intermediate sized cavities ., Figure 5A depicts the modeled MSAS KS domain with two tyrosines protruding into the KS cavity from opposite walls and thus blocking the downward flow of the cavity along the dimer interface ., These two cavity blocking residues correspond to positions 229 and 400 ( 1KAS numbering ) ., Interestingly , the conservation profiles of the CLRs shown in Figure 4 revealed that these two Tyr residues are highly conserved in all PKSs which carry out three iterations ., This further substantiates the important role attributed to these residues based on our structural modeling of the active site pocket ., Remarkably , NAP type KS domains have an Ala at position 400 , that allows the cavity to extend further down thus making their cavities similar to the FAS catalytic cavity , shown for reference in Figure 5C ., Structural analysis thus revealed how substrate binding sites of varying size and hydrophobicity can be generated in type I iterative KS domains by subtle variations of residues on similar backbone folds ., The crystal structure of KS-CLF also highlights how specific residues can regulate chain length in type-II PKSs 37 ., Our results on role of cavity volume in controlling number of iterative condensations or chain length of type I iterative PKS products is also supported by recent experimental studies involving swapping of KS domains in fungal iterative PKSs , where replacement of fumonisin KS domain by KS from lovastatin LDKS resulted in polyketides having short chain length 38 ., Very recent experiments involving generation of altered fatty acid-polyketide hybrid products by rational manipulation of benastatin biosynthetic pathway 39 also suggest that number of chain elongations is dependent on the size of the PKS enzyme cavity ., The in silico analysis of the sequence and structural features of iterative KS domains reported here provides a structural rationale for these experimentally observed variations in substrate specificities and further helps in identification of residues that can be specifically mutated to control the number of iterations in type-I PKSs ., No experimental studies have as yet been reported on altering the number of iterations in type-I PKSs by site directed mutagenesis ., The present in silico analysis gives crucial leads for such experiments ., In modular PKS clusters , the chemical structure of the product is governed by the order in which substrates are channeled between various ORFs ., It has often been observed that the order of PKS ORFs during biosynthesis of a polyketide is not the same as the order of the corresponding ORFs in the genome ., This complexity of module succession has been depicted in Figure S3 using schematic representation of a type I modular PKS cluster ., This biosynthetic cluster has four polyketide synthase ORFs and their order in the genome is Orf1 , Orf2 , Orf3 and Orf4 ., But during the biosynthesis , Orf4 is the first to function and the product of Orf4 is transferred to Orf1 ., Orf2 functions at a later stage and its product is condensed with the rest of the polyketide ., This inconsistency between ordering of ORFs in the genome and the order of substrate channeling is a commonly observed phenomenon , as is evident from the simocyclinone 40 , nanchangmycin 41 , microcystin 42 , pimaricin , rapamycin and nystatin biosynthetic clusters ., The prediction of the correct order of substrate channeling is essential for in silico identification of polyketide products of uncharacterized modular PKS clusters ., Therefore , deciphering the cognate combination of ORFs in a modular PKS cluster from the large number of theoretically possible non-cognate combinations has been the major bottleneck in formulating predictive rules for in silico identification of polyketide products ., Hence , we attempted to investigate whether predictive rules based on specificity of interaction between ORFs can be formulated for deciphering the correct order of substrate channeling in an uncharacterized PKS cluster ., Several experimental studies have suggested that inter protein interactions in modular PKSs are mediated by specific recognition between docking domains or the so called ‘interpolypeptide linker’ regions 24 , 25 , 29 ., The amino acid stretches N-terminus to the first KS domain and C-terminus to the last ACP domain are referred as inter polypeptide linkers or docking domains ., These have been extensively studied and it has been proposed that , the C-terminal ( Cter ) docking domains specifically pair with the N-terminal ( Nter ) docking domains of the succeeding ORF to facilitate cross-talk between the consecutive ORFs ., Structural elucidation 29 of the cognate docking domains from erythromycin PKS ( DEBS ) has revealed that , unlike conventional linker sequences which join protein domains covalently within polypeptides , these docking domain regions are not non-structured , but adopt a relatively compact four helix bundle structure ., It has been proposed that , this four helix bundle structure is the core fold of cross-talk 29 between ORFs of modular PKS clusters ., These structures have been termed inter protein ‘docking domains’ to emphasize that they are responsible for the recognition and subsequent docking between successive protein modules ., The C-terminal docking domain is reported to contain three helices ( hereafter named helix 1 , 2 and 3 ) whereas the N-terminal docking domain contains a single longer helix ( hereafter named helix 4 ) ., This docking domain complex is a symmetrical dimer , consisting of two independent structural units called domain A and domain B . Domain A is an unusual intertwined α-helical bundle comprising helices 1 and 2 ., Domain B is also an α-helical bundle but with an entirely different topology and it comprises helix 3 ( from Cter ) and helix 4 ( from Nter ) ., Thus the actual docking interaction occurs in domain B , via several pairs of charged residues and a conserved set of hydrophobic residues ., However , it has been proposed that , out of these various interacting residues , two pairs of appropriately placed charged residues at critical positions on the docking interface , form a kind of ‘docking code’ for DEBS 29 ( Figure S4 ) ., When DEBS1 docks against DEBS2 , the charges at these positions give rise to favorable interactions ., However , in case of non-cognate combinations between DEBS1 and DEBS3 , the resulting charge interactions are repulsive ., The availability of DEBS docking domain structure provided us the opportunity to test , whether such a code exists in other PKS systems as well ., We have carried out a structure based analysis of docking domain sequences to investigate if rules for identification of cognate ORF combination can be formulated based on key interactions found in DEBS docking domain structure ., It may be noted that , based on bioinformatics analysis of docking domains in type I modular PKS proteins , Broadhurst et al 29 had also proposed that DEBS-like docking domain structures would be present in other type I modular PKS clusters and they govern the cross-talk between ORFs ., Since secondary structure analysis by Broadhurst et al 29 had clearly demonstrated propensity of docking domain sequences for four helix bundle structure similar to DEBS docking domain , inter polypeptide contacts were extracted for both cognate and non-cognate pairs of ORFs in various modular PKSs using the DEBS docking domain structure as a template ., Since recent studies 16 , 29 , 43 suggest that PKS docking domains fall into at least three different phylogenetic classes , our assumption regarding docking domains from various phylogenetic groups adopting similar structural folds requires further justifications ., It is well known that for a given protein family , structure is conserved to a much larger extent than sequence 44 , 45 ., There are many examples of proteins adopting similar three dimensional structural fold even in absence of detectable sequence similarity 44 , 45 ., Recently available structures 46 of mammalian type I FAS proteins also show remarkably high similarity to structures PKS protein domains even if they share only a limited sequence homology ., Therefore , our assumption regarding myxobacterial PKS ‘docking domains’ adopting structural folds similar to docking domains from actinomycetes is not unreasonable ., Hence , we extracted crucial interacting residues for various docking domain pairs based on alignment with DEBS docking domain structure ., Figure 6 shows the alignment of cognate pairs of various PKS docking domain sequences with DEBS docking domain structure ., The interacting residue pairs obtained from this alignment were ranked as favorable , unfavorable or neutral as per a simple scoring scheme ( Table S1 ) ., The interactions between a pair of oppositely charged amino acids or between a pair of hydrophobic amino acids were ranked as favourable , while electrostatic repulsions between a pair of charged amino acids was called unfavourable ., On the other hand , interactions between any other amino acid pairs , specifically the interactions between charged and hydrophobic amino acids was ranked as neutral ., It may be noted that , this simplistic scoring scheme has been defined based on types of amino acid contacts found in interfaces of protein-protein complexes 47 ., A total of 66 cognate pairs of docking domain sequences were checked for the two pairs of positions which give rise to favorable electrostatic interactions in the docking domain structure ., Out of these , 54 pairs of ORFs were found to have at least one residue pair with favorable interaction ., Moreover , there was no cognate pair where both of these interactions were unfavorable ., Thus it can be concluded that cognate pairing of ORFs does generate energetically favorable contacts ., Since a good docking code interaction was observed in more than 80% cases , we investigated if these crucial inter polypeptide contact pairs could be used to predict the correct order of module succession in a given modular PKS ., If all possible combinations of ORFs in a PKS cluster are considered together , there would be only one biosynthetically correct order of ORFs ., This correct combination would in turn have a set of all cognate interfaces and therefore , the highest number of favorable interactions ., The remaining combinations of ORFs would be incorrect and accordingly , they would have varying numbers of non-cognate interfaces , thus resulting in unfavorable interactions ., It may be added here that , the identity of the first and last ORFs can usually be established by the presence of an initiating loading module and the terminal TE domain respectively ., The presence of a very short C-terminal sequence beyond the conserved TE domain can also be used as a criterion for identification of the last module ., Figure 7 shows the example of the Spinosad biosynthetic cluster which has ten modules arranged in five ORFs ., These five ORFs can be combined in six different ways if the first and last ORFs are fixed ., Each of the six combinations would have four interfaces ., All the interfaces were scanned for favorable , unfavorable or neutral interactions at the positions corresponding to the DEBS docking code ., As can be seen in Figure 7 , the correct order of ORFs has the highest number of favorable interactions and no repulsive interaction at any of its interfaces ., In contrast , each of the remaining five combinations has at least two repulsive interactions , and thus can be rejected in comparison with the correct combination ., A total of 39 characterized PKS clusters were analyzed in this manner to test the validity of this assumption ., For a representative set of PKS clusters , Figure 8 shows in tabular format , the number of favorable , unfavorable and neutral contacts in the cognate combination and also the number of non-cognate combinations having a score better , equal or worse compared to the cognate combination ., As can be seen from Figure 8 , in several modular PKS clusters unfavourable interactions are present ., However , the number of unfavourable interactions is much smaller than the favourable or neutral interactions present in the cognate interfaces ., Thus analysis of cognate inter polypeptide contacts in 17 modular PKS clusters suggest that , both the interactions need not be favourable for effective docking domain interactions ., However , non-cognate interfaces have more number of unfavourable interactions ., Hence , there are relatively few non-cognate combinations having a score better than cognate combination ., In ten out of 17 PKS clusters , no non-cognate combination has better score than the cognate combination ., Even though there are non-cognate combinations having scores equal to cognate combination , the cognate combination can still be ranked among top few in these 10 cases ., In case of four other PKS clusters , there are a significant number of non-cognate combinations having score higher then the cognate combination ., However , the cognate combination can still be ranked within top 20% of all possible combinations ., For example , in case of nanchangmycin 480 non-cognate possibilities have better score than cognate , 239 have scores equal to the cognate combination ., Thus the cognate combination is ranked in top 720 combinations ., However , the total number of combinatorial possibilities is 5040 ., Therefore , our computational method ranks the cognate combination in top 14% in case of nanchangmycin PKS cluster ., It is important to note that , despite the large number of combinatorial possibilities , prediction based on docking domain sequences alone is able to reject a sufficiently high number of non-cognate combinations ., Thus , our results on analysis of docking domain sequences indicate that , in more than 80% of the cases the cognate order of substrate channeling can be predicted correctly ., However , we must clarify that , ‘correct prediction’ would mean eliminating significant number of non-cognate combinations and restricting the cognate combination to a relatively smaller number of possibilities ., Such a relaxed definition of ‘correct prediction’ can be justified by the fact that , we are using a simple prediction method involving few crucial contacting residues rather than all the interactions present in the docking domain structure ., Secondly , we are not taking into account role of other catalytic domains in preventing chain elongation in case of non-cognate associations ., Even though very recent theoretical studies 5 , 16 have attempted to predict physical interaction between PKS proteins based on analysis of co-evolution of docking domain sequences , the prediction accuracy for order of substrate channeling has either not been studied in detail 16 or found to be low in cases involving clusters consisting of more than four ORFs 5 ., However , in contrast to these purely sequence based methods , we have used a structure based approach ., Using the conserved core structure of the docking domain as template , we have extracted crucial interacting residues which were suggested earlier by Broadhurst et al 29 to be determinants of specificity of inter subunit interactions ., Exploitation of this crucial information in our study probably helps in improvement of prediction accuracy ., Identification of specific interacting residue pairs also make the predictions easily amenable to experimental testing by site directed mutagenesis approach ., Recent experimental studies 30 , 31 have further established the feasibility of altering specificity of inter subunit interactions based on manipulation of putative interacting residues in the docking domain frame work ., Apart from helping in deciphering the chemical structure of final polyketide product , our computational analysis of “docking code” in cognate and non-cognate interacting pairs in experimentally characterized modular PKS cluster can also provide knowledge base for fruitfully combining non-cognate ORF pairs for generation of novel aglycone structures ., Our analysis of such interacting residues in docking domains of a mycobacterial PKS protein involved in biosynthesis of mycoketide has led to the discovery of a completely novel “Modularly iterative” mechanism of polyketide biosynthesis 48 ., However , we must clarify that , apart from interactions between N-terminal and C-terminal docking domains of PKS proteins , the substrate specificity of various catalytic domains would also have a role in preventing chain elongation in case of non-cognate associations of PKS ORFs ., Similarly , interactions between ACP and downstream KS will also discriminate non-cognate associations ., In this work , we have only addressed the role o

Introduction, Results, Discussion, Methods

Sequence data arising from an increasing number of partial and complete genome projects is revealing the presence of the polyketide synthase ( PKS ) family of genes not only in microbes and fungi but also in plants and other eukaryotes ., PKSs are huge multifunctional megasynthases that use a variety of biosynthetic paradigms to generate enormously diverse arrays of polyketide products that posses several pharmaceutically important properties ., The remarkable conservation of these gene clusters across organisms offers abundant scope for obtaining novel insights into PKS biosynthetic code by computational analysis ., We have carried out a comprehensive in silico analysis of modular and iterative gene clusters to test whether chemical structures of the secondary metabolites can be predicted from PKS protein sequences ., Here , we report the success of our method and demonstrate the feasibility of deciphering the putative metabolic products of uncharacterized PKS clusters found in newly sequenced genomes ., Profile Hidden Markov Model analysis has revealed distinct sequence features that can distinguish modular PKS proteins from their iterative counterparts ., For iterative PKS proteins , structural models of iterative ketosynthase ( KS ) domains have revealed novel correlations between the size of the polyketide products and volume of the active site pocket ., Furthermore , we have identified key residues in the substrate binding pocket that control the number of chain extensions in iterative PKSs ., For modular PKS proteins , we describe for the first time an automated method based on crucial intermolecular contacts that can distinguish the correct biosynthetic order of substrate channeling from a large number of non-cognate combinatorial possibilities ., Taken together , our in silico analysis provides valuable clues for formulating rules for predicting polyketide products of iterative as well as modular PKS clusters ., These results have promising potential for discovery of novel natural products by genome mining and rational design of novel natural products .

Polyketide synthases ( PKSs ) form a large family of multifunctional proteins involved in the biosynthesis of diverse classes of therapeutically important natural products ., These enzymes biosynthesize natural products with enormous diversity in chemical structures by combinatorial use of a limited number of catalytic domains ., Therefore , deciphering the rules for relating the amino acid sequence of these domains to the chemical structure of the polyketide product remains a major challenge ., We have carried out bioinformatics analysis of a large number of PKS clusters with known metabolic products to correlate the chemical structures of these metabolites to the sequence and structural features of the PKS proteins ., The remarkable conservation observed in the PKS sequences across organisms , combined with unique structural features in their active sites and contact surfaces , allowed us to formulate a comprehensive set of predictive rules for deciphering metabolic products of uncharacterized PKS clusters ., Our work thus represents a major milestone in natural product research , demonstrating the feasibility of discovering novel metabolites by in silico genome mining ., These results also have interesting implications for rational design of novel natural products using a biosynthetic engineering approach .

biochemistry/molecular evolution, computational biology/macromolecular structure analysis, biochemistry, genetics and genomics/microbial evolution and genomics, computational biology/sequence motif analysis, computational biology/comparative sequence analysis, biochemistry/chemical biology of the cell, computational biology/protein homology detection, computational biology/protein structure prediction, biochemistry/bioinformatics, biochemistry/biomacromolecule-ligand interactions, computational biology/macromolecular sequence analysis, computational biology/metabolic networks, computational biology, chemical biology/macromolecular chemistry

journal.pntd.0004095

Leptospirosis in Rio Grande do Sul, Brazil: An Ecosystem Approach in the Animal-Human Interface

Even though leptospirosis is now recognized as a disease of epidemic potential with a significant health impact in many parts of the world , it remains a neglected disease ., Its burden is estimated at 500 , 000 severe human cases per year worldwide , but a WHO expert group recently put its annual global incidence at 1 . 03 million people with 58 , 900 deaths 1 , 2 ., Nevertheless , leptospirosis continues to be a silent disease 3 , mainly due to the paucity of data in many countries , including of the Americas 4 , 5 ., The Leptospira bacteria may also affect a wide variety of animal species , both wild and domestic , which serve as reservoirs for human infection 6 ., The diversity of animal carriers represents a significant challenge for prevention and control 7 ., Exposure to water and soil contaminated by the urine of infected animals is the most common route of transmission to people and domestic animals 6 ., Leptospirosis is an excellent example for the “One Health” approach , where the relationship between humans , animals and ecosystems is studied to improve knowledge on a disease and to enhance collaborative intersectoral and multidisciplinary control strategies 8 ., The One Health working definition states that “it is feasible to integrate human , animal , and environmental health efforts to predict and control certain diseases at the human–animal–ecosystem interface; integrated approaches that consider human , animal , and environmental health components can improve prediction and control of certain diseases” 9 ., Yet this approach is rarely used to advance knowledge on leptospirosis transmission , develop evidence-based policies , and create tools to save human lives and reduce the impact on domestic animals ., Leptospirosis is one of the major neglected diseases in Latin America 10 ., It has been reported in a variety of settings , from large urban centers , to remote rural areas 11 , 12 , 13 , 14 , 15 ., Socioeconomic drivers include living in dense urban or peri-urban areas with inadequate waste collection and sanitation , which is often associated with vulnerable populations 16 ., Leptospirosis has been linked to poverty , lack of water and sanitation , and poor housing conditions 12 , 17 ., Environmental drivers have been identified in previous studies ., Heavy rains or floods have been linked to a higher number of cases of leptospirosis 12 , 16 , 18 , 19 , 20 , 21 ., Alkaline and neutral soil are suspected of promoting a longer survival of the bacteria , especially in young , not-yet-structured soils like those of volcanic origin 12 , 22 ., In addition , soil temperature and proximity to water bodies were also reported as potential enablers for bacterial survival 23 ., Leptospirosis is also considered an occupational disease , affecting rice laborers , sewer workers , animal handlers and gold miners 6 , 24 , 25 ., A better understanding of the drivers for leptospirosis would provide crucial information for decision-makers to be able to target risk areas for priority interventions ., Indeed , the current gaps in scientific and technological knowledge hinder the detection of cases and limit surveillance and control programs ., Finally , because the instruments needed for control and elimination–such as broad rapid tests and vaccines–are not available at this time , leptospirosis is not considered to be “tool ready” and as a result , it is not targeted through large-scale global initiatives ., Brazil is the fifth most populous country in the world ( approximately 200 million people ) and has the seventh highest gross domestic product ( USD 2 , 250 , 673 ) ., Even though the economy has been growing steadily , there are still around 20 million people ( 10% of the population ) living in poverty in the country 26 ., This population group is the most vulnerable to neglected diseases and other poverty-related infections ., Previous studies have demonstrated the impact of neglected tropical diseases in Brazil and the need to develop new tools and technologies to fight them 27 , 28 ., There is no accurate surveillance system in place for leptospirosis globally or in the Americas; however , some countries’ surveillance systems include the disease and estimates of its public health burden are available 5 , 29 ., Notification of human leptospirosis is mandatory in Brazil and an annual average of 3 , 888 cases with 9 . 48% fatality are officially reported by the country’s surveillance system 30 ., The Brazilian state of Rio Grande do Sul ranks fifth in the incidence rate ( 4 . 7 cases per 10 , 000 population ) and presents around 15% of the total number of cases in the country 31 ., In a previous study conducted to identify high transmission areas and possible ecological components of leptospirosis transmission in Rio Grande do Sul , the highest incidence rates were found in the coastal sedimentary areas with low altitude and predominantly agricultural land use in the central valley 32 ., The state economy is based on agribusiness , including cattle and rice paddies , with an associated increased risk of leptospirosis in some areas that needs to be evaluated ., The objective of this study is to analyze the distribution of human cases of leptospirosis in the state of Rio Grande do Sul and explore possible drivers using the One Health approach ., This analysis may orient further studies of the disease in the human-animal-ecosystem interface and the results of this study may be used as evidence to inform decision makers in the state ., An ecological study was carried out using aggregated data by municipality to analyze the situation of leptospirosis in all 496 municipalities ( corresponding to the second subnational administrative level ) in the state of Rio Grande do Sul , Brazil , between 2008 and 2012 ., A geo-coded database was created using different sources ., Variables were either downloaded or created from original sources ., The data source used for each variable is described in the S1 Supporting Information ., Human leptospirosis case data , de-identified and aggregated at the municipality level , were obtained from the Ministry of Health of Brazil national surveillance system database ( acronym in Portuguese SINAN ) 33 ., All case data were publicly available by open consultation on the government website ., Seven environmental variables ( ecoregion , type of soil , temperature , precipitation of the wettest month , altitude , slope of the land ( hill incline ) , and drainage ) were gathered from diverse “open-access” data sources and used in the study ., When disaggregated , the ecoregion and type of soil variables turned into six and twelve variables , respectively ., Altitude and hydrology information used in the background were obtained from the USGS-EROS , HYDRO1k Elevation Derivative Database 34 ., Bioclimatic variables were calculated from monthly temperatures and rainfall values 35 ., Geo-processing techniques were applied to assign and measure environmental variables for each municipality ., Data for the socioeconomic variables ( gross domestic product per capita , Gini coefficient , illiteracy rate ) were gathered from the Brazilian Institute of Geography and Statistics ( acronym in Portuguese IBGE ) 36 ., As the economy of Rio Grande do Sul is based on agribusiness , variables related to rice and tobacco production were collected from the IBGE database and data on bovine raising were provided by the Department of Agriculture of the state of Rio Grande do Sul and the Federal University of Rio Grande do Sul 37 ., Leptospirosis cases: According to the Ministry of Health of Brazil , human cases of leptospirosis are those presenting clinical symptoms consistent with the disease and confirmed by laboratory diagnosis either with ELISA–IgM or MAT 38 ., These laboratory confirmation techniques are available at the state level at the Central Public Health Laboratory , which is part of the National Public Health Laboratory Network ., In Brazil , a case could also be confirmed by clinical-epidemiological criteria ( selected symptoms with epidemiological history ) 39 ., All cases in the SINAN database were considered confirmed ., In the original database , the cases were classified according to area of residence ( urban , peri-urban or rural ) 39 ., Cumulative incidence: the number or proportion of a group ( cohort ) of people who experience the onset of a health-related event during a specific time interval 40 ., In this study , it was estimated per 10 , 000 inhabitants ., Criteria for risk stratification: The criteria used were based on a previous risk stratification study conducted in Nicaragua 12 ., In summary , geographical areas were classified into two categories:, i ) Silent Area ( no cases were reported during the study period ) and, ii ) Productive Area ( active transmission was reported during the study period ) that could be an endemic or critical area ( higher quintile ) ., Rio Grande do Sul is the southernmost state in Brazil bordering Argentina and Uruguay ., The state covers an area of 281 , 731 , 445 km² , divided into 496 municipalities ., In 2010 , the population was 10 , 693 , 929 inhabitants , 85 . 1% of which lived in urban areas , among them the Porto Alegre metropolitan area where 15% of the state population resides ( 1 , 472 , 482 inhabitants ) 41 ., In 2010 , the state had the 4th highest gross domestic product per capita in the country and the Gini index ( 0 . 5472 ) was lower than the national level 36 ., There are three major economic regions:, 1 ) the south with greater land concentration , large cattle raising farms , and mechanized plantation of rice , soybean and wheat ., This area also presents higher income inequality;, 2 ) the northeast region , which includes the state capital , with more industries and predominantly small properties; and, 3 ) the northern region , mostly colonized by European immigrants , with higher forest coverage , valleys , and plain areas with small agricultural lands ., The most common agrarian structure in the state ( 90% of properties ) is a small family farm covering an area smaller than 100 hectares 42 ., The hydrology of the state is basically divided into two main areas: the La Plata and Atlantic East Coast watersheds ., The La Plata watershed is located in the northern area bordering with Argentina , and comprises the Uruguay River and mayor tributaries ( Caboa , Pelotas , Ibicui and Mirinay ) ., The Atlantic East Coast watershed is mainly shaped by the Guaiba and Litoranea basins ., The Guaiba basin includes tributaries of the Dos Patos coastal lagoon , the Jacui and Tacuari rivers , which run in the central area of the state and wash its most populated areas; other tributaries include the Sinos and Cai rivers , which also flow into the Dos Patos lagoon ., ( S2 Supporting Information ) ., The Litoranea basin encloses the Camaqua and Piratini rivers also flowing into the lagoon ., The city of Porto Alegre is located in the Atlantic East Coast watershed ., Two types of analyses were carried out to investigate associations between the risk of human leptospirosis and 26 variables selected as possible environmental , socioeconomic or livestock drivers:, 1 ) a spatial analysis and thematic mapping of possible drivers , showing the municipalities in the higher quintiles of rates over the distribution of the variable also divided by quintiles ( range cuts ) and, 2 ) a statistical analysis with univariate and multivariable regressions as described below ., The spatial analysis consisted in computing zonal statistics and surface for the environmental variables , in addition to the geocoding of health and socioeconomic data ., ArcGIS zonal statistics by municipality ( min , mean , max , standard deviation , range ) were calculated for the altitude , slope , temperature , and rain variables ., Geo-processing geometric intersection of environmental features shaped the municipal surface of ecoregions and soils ., Quintile thematic mapping was conducted once the municipal statistics of environmental , health and socioeconomic variables were geo-processed ., For the multivariable regression , the dependent variable was the case count per municipality and the independent variables all 26 possible drivers ., The ecoregion and soil variables were dichotomized as follows: they were coded 0 when they were a minority ( when land coverage or proportion was less than or equal to 50% of the municipality ) and they were coded 1 when they represented a majority ( when land coverage or proportion was above 50 . 01% of the municipality . ) The proportion of farms with up to ten animals per farm ( small farms ) was dichotomized in the same fashion ., In addition , two bovine-related variables were created and used as continuous variable: the proportion of farms per km2 and the proportion of bovines per km2 ., Productive processes such as the cultivation of tobacco and rice were analyzed on a unit of 10 , 000 tons ., Predictors were analyzed with negative binomial regression ( NB ) and robust variance was used to estimate the relative risk ( RR ) and 95% confidence interval ( CI ) of the estimates 43 ., The link function used was the default logit-function and the offset was the natural log-transformed number of population per municipality ., Univariate analyses were first run for each of all 26 variables and 14 were preselected due to P ≤ 0 . 15 ., Variance inflation factors ( VIF ) were estimated to verify the relations among all selected independent variables and check for potential collinearity ., When a high VIF was found ( VIF>4 ) , the variable with a lower P-value was eliminated and the process was reiterated until only variables with a VIF<4 were left ., Confounding effects were investigated by checking changes in the point estimates of the variables that remained in the model ., Parameters with changes in their estimates > 25% were considered confounders and were retained during the variable selection process ., Finally , two-way interaction terms between environmental variables with biological plausibility were investigated ( slope and altitude , altitude and drainage , altitude and precipitation of the wettest month ( mm ) ) ., We used deviance performance as a goodness of fit test for the overall model ., Results of the variables with P value > 0 . 15 are included in the S3 Supporting Information ., Statistical tests were performed for over-dispersion evaluation and model fit comparison using literature-recommended approaches 44 ., For the negative binomial model , the dispersion parameters were tested for difference with chi-squared statistics ., To compare goodness of fit between pairs of the proposed regression models , Vuong statistics were calculated 45 ., The following models were compared: Poisson regression , zero-inflated negative binomial ( ZINB ) and a negative binomial regression ( NB ) 46 ., The differences in AIC and Vuong statistics were computed for all pairs of non-nested models ( i . e . , Poisson vs . ZINB , Poisson vs . NB , NB vs . ZINB ) ., Based on the AIC and Vuong tests , the negative binomial regression model fit the data better in comparison with others ., More details about the procedures carried out in order to compare models can be found in the S4 Supporting Information ., During the study period , 2 , 141 confirmed cases of leptospirosis were reported with an average of 428 cases annually ., The yearly incidence remained similar over the period ( 412–543 cases ) , except for a lower than average number of cases ( 277 ) in 2012 ( Table 1 ) ., A total of 233 municipalities out of 496 reported at least one case ( range 1–208 ) during the study period ., Among the 263 municipalities not reporting cases , most had small populations ( less than 10 , 000 people ) ., On average , there were 4 . 32 cases per municipality ., A high number of urban cases were clustered in the Metropolitan Region of Porto Alegre ( 50 . 52% of all urban cases ) and groups of predominantly rural cases were located in the Center Oriental Region along the Jacui and Tacuari rivers and in the Southeast Region , near the great lagoons ( Fig 1 ) ., Forty-seven municipalities concentrate 79 . 40% of the cases ., Most of these municipalities are located in the Atlantic East Coast watershed , specifically in the Jacui-Tacuari and Cai basins ( 1 , 116 cases ) ., The cumulative incidence for the entire state was 2 cases per 10 , 000 inhabitants ( range: 0–56 . 56 ) ., The spatial distribution of the municipalities’ cumulative incidence by quintiles , as well as of those not reporting cases , are presented in Fig 2 ., The cumulative incidence for rural areas ( 6 . 16 per 10 , 000 people ) was eight times higher than for urban areas ( 0 . 74 per 10 , 000 people ) ., Risk stratification was carried out for the 496 municipalities in Rio Grande do Sul to inform planning of leptospirosis prevention and control activities ., It shows that out of the 496 municipalities in the state , 263 ( 53 . 02% ) can be considered silent areas ., Among the 233 productive areas ( 46 . 98% ) , 58 were found to be endemic and 75 municipalities were considered critical areas ( S5 Supporting Information ) ., These 75 municipalities reported 1 , 766 cases ( 82 . 48% ) out of the total of 2 , 141 cases in the entire state ., The negative binomial regression modeling identified fourteen variables that suggested some relation with the number of cases per municipalities in the univariate analysis ( P≤0 . 15 ) ( Table 2 ) ., The 12 variables that were analyzed as possible drivers ( P>0 . 15 ) are presented in the S3 Supporting Information ., The final model identified four variables as significantly associated with the leptospirosis case count ( P ≤ 0 . 05 ) ( Table 2 ) : the Parana/Paraiba interior forest ecoregion ( RR = 2 . 25; IC95% = 2 . 03–2 . 49; P < 0 . 001 ) ; Neossolo Litolítico soil ( RR = 1 . 93; IC95% = 1 . 26–2 . 96; P = 0 . 006 ) ; to a weaker extent , production of tobacco per 10 , 000 tons ( RR = 1 . 10; IC95% = 1 . 09–1 . 11; P <0 . 001 ) ; and finally , a borderline association with production of rice per 10 , 000 tons ( RR = 1 . 003; IC95% = 1 . 002–1 . 04; P <0 . 001 ) ., The goodness-of-fit model was tested by a deviance chi-squared test and was found not to be significant ( P > 0 . 05 ) ., Leptospirosis is an important public health problem for the state of Rio Grande do Sul , with an average of 428 cases reported annually ., The actual incidence is probably much higher , particularly in vulnerable populations , because leptospirosis is commonly misdiagnosed and unreported ., The study results show that rural populations of the state have an approximately eight times higher risk of contracting leptospirosis than their urban counterparts , even though the number of reported cases was high in both areas ., Rural cases are concentrated mostly in two zones , characterized by higher tobacco production ( central state ) and higher rice production ( south ) ., This result suggests that preventive strategies in these regions require strong collaboration between the Health and the Agriculture sectors ., Preventing cases among rural workers will be the main goal for these areas ., In order to contribute to the One Health discussion and support possible intersectoral collaboration , suggestions for components of a leptospirosis plan and activities in the state of Rio Grande do Sul are presented in the S6 Supporting Information; using both the working definition of One Health and the results of this study as a basic scenario 9 ., The highest concentration of urban cases was found in the metropolitan areas of cities with low altitude , including the state capital , which also suffer from frequent flooding ., At the center of the state , high rate areas were found within the Atlantic East Coast watershed , which receive water from the Jacui , Taquari , Cai , Sinos and others rivers as mayor tributaries , all well known for their periodic flooding 48 ., This suggests that leptospirosis prevention should also be considered in natural disaster plans and that the main goals for these areas should be to reduce the number of severe cases and save lives during an outbreak ., Other possible drivers for leptospirosis identified in the final regression model were the Parana-Paraiba ecoregion , Neossolo Litolítico soil , and the production of tobacco ., The Parana/Paraiba interior forest ecoregion showed an RR of 2 . 25 when compared to other ecoregions ., This ecoregion is part of the Atlantic semi-deciduous forest biome that covers central and northern areas of the state and extends over numerous areas of Brazil and neighboring countries ., It ranges from river plains to middle-level highlands ., The climate is subtropical 49 so humidity may act as an important factor in the leptospirosis bacteria survival ., The Parana-Paraiba ecoregion includes a diversity of soil types that vary from very fertile to impoverished ., Neossolo Litolítico , a very young and not yet structured type of soil , is one of those that correlate geographically with high rates of human leptospirosis ., Its RR is 1 . 93 when compared with municipalities with other predominant soil type ., It has been suggested that its pH 50 and/or its low drainage capacity 51 may favor the bacteria survival ., With each additional 10 . 000 tons of tobacco produced by the municipality , there was an increased RR in the leptospirosis cases of 1 . 10 ., There is spatial overlapping of municipalities with high production of tobacco and higher rates of the disease in the Jacui-Tacuari valley ., In the central area , several conditions concur with higher leptospirosis cases , in particular tobacco ( and rice ) production in a subtropical forest environment , over slope and inclined terrain with shallow weathered soils ., We did not find information in the literature suggesting an association between tobacco plantation and leptospirosis cases ., However , tobacco growth requires a pH of 5–6 . 5 52 , which matches the leptospirosis bacterial soil requirement of 6 . 2 for survival over 7 weeks in the environment 22 , 53 ., PH survival requirements vary among serovars , and as an example L . interrogans serovar hardjo prefers a pH of 6 . 5–6 . 8 54 ., Further studies analyzing which serovars are circulating in humans and in animals in these high risk areas will provide additional knowledge on the cycle of leptospirosis transmission ., The agro-industrial production and export of tobacco are concentrated in three states in the south of Brazil ., Rio Grande do Sul and the neighboring state of Santa Catarina are responsible for 80% of the production ., Brazilian tobacco is grown by approximately 186 , 000 farming families in properties with an average area of 16 hectares 55 ., Every step in the plantation of tobacco in Southern Brazil is carried out manually , mostly by family members from several generations of small farmers descended from Europeans immigrants 55 ., Most of the farms have a small number of bovines , mostly dairy cows , although swine are also very frequent 37 ., In this region , the most common feed for livestock is corn , mostly stocked on the farm where it attracts rodents ., Insects and rodents are responsible for an estimated loss of 15% of all corn storage on small farm barns 47 ., This region also includes large areas of preserved forests , which probably increases contact with wild rodents ., Further studies about the presence of rodents and programs for rodent control would provide important information on their true impact ., In the final model , the association with rice production was weak ( around 1 but statically significant ) , but there was some spatial overlapping of municipalities with high leptospirosis cumulative incidence and rice plantations in the Lagoa dos Patos shoreline ., The state of Rio Grande do Sul produces 63% of all rice in Brazil 56 ., The association between rice paddy workers and leptospirosis has already been described in the literature 6 , 57 ., Working conditions in rice fields have been studied in Peru and results highlight occupational hazards linked to leptospirosis infection , such as long period of exposure to water , lack of use of any personal protection equipment , and presence of skin wounds 57 ., It is very important to raise awareness about the risk of this disease in critical areas and promote personal protection equipment ( PPE ) ., In the state of Rio Grande do Sul , where temperatures exceed 24°Celsius ( 75°Fahrenheit ) for half of the year , educational campaigns for the adequate use of PPE ( such as boots ) among workers are unlikely to be effective , especially during long shifts ., The most important prevention tool for leptospirosis in high risk areas and especially under these conditions is vaccination , however , a human vaccine is not available in the majority of affected countries ., In the central areas of the state , smaller livestock producers ( less than 10 bovines by property ) are predominant 37 , but this variable only presented relevance in the univariate analysis ., Similarly , the proportion of bovine farms per km2 has shown some relation with the number of leptospirosis cases ., The proximity with animals such as dogs increases the risk for leptospirosis 58 ., A previous study found that leptospirosis titers were present in 39% of bovines in the state 59 ., Further studies are required to better understand the epidemiological situation in the animal sector ., This study provides evidence of the importance of the environmental component and the One Health approach for leptospirosis ., As highlighted here , where the ecoregions were suitable for specific plantations ( such as tobacco in Parana-Paraiba ) human occupation of these territories has led to transformations of the natural environment and increased the use of natural resources for economic purposes 60 ., Further research is required , using primary data , to further explore this finding , as well as the potential relationship between the type of ecoregion and economic processes , and their relationship with the animal-human interface ., For other diseases , such as rabies transmitted by vampire bats , outbreaks have been reported rapidly in the Amazon region due to some productive processes such as gold prospection 61 ., Rio Grande do Sul has one of the highest GDP per capita in the country 62 ., For this reason , the study includes the Gini index that reflects possible inequalities related to the distribution of income among the inhabitants of the municipalities ., However , the Gini index presented relevance only in the univariate analysis and no association was found in the final model ., This results of this study provide potentially useful information for government decision-makers in the elaboration of a possible intersectoral plan ., Evidence-based policy-making requires advanced social planning capacity , including the capacity to monitor and evaluate programs in the different ministries and agencies that provide services to the most vulnerable populations ., Risk stratification generates evidence for decision-making and the critical areas identified by this study could be used as pilots for future local intersectoral studies ., In the same way , areas identified as silent require attention from the health authorities to confirm the absence of disease in humans or the need to improve surveillance ., The prediction , detection , prevention , and response to outbreaks of leptospirosis will be improved and better defined through knowledge generated by an integrated approach within the animal-human-ecosystem interface ., This study clearly highlights the importance of a multidisciplinary and intersectoral approach , which was at the root of the creation of initiatives such as the Global Leptospirosis Environmental Action Network ( GLEAN ) ., GLEAN brings together experts from different disciplines and sectors with the objective of orienting knowledge regarding the prediction , prevention , detection , and response to leptospirosis; and transforming relevant research findings into operational tools for affected communities and countries , as well as for organizations 63 ., Although the use of aggregated data by municipality is a possible limitation of this study , the geographical approach helped to gather , unify and shape all factors ( environmental , socioeconomic , and health ) into the same area and detect spatial patterns ., Ecological studies are an inexpensive way to use secondary available data; however they are commonly associated with the ecological fallacy 21 ., Another limitation possibly resides in the large number of municipalities with small populations ( under 10 , 000 people ) ., However , the possible effect of this data characteristic was considered in the statistical analysis through the use of negative binomial regression , by testing the effects using strata with and without small population ., Also , no significant difference was found by analyzing the same approach using only municipalities with at least one positive case ., This study contributes to the basic knowledge on leptospirosis distribution and drivers in the state of Rio Grande do Sul and points to the direction of tool-readiness for the disease ., It also encourages a holistic approach for the disease , taking into account human , animal , and ecosystem interactions and supporting research in the development and validation of a rapid diagnostic test for humans and animals , and eventually production of a vaccine that is still desperately needed .

Introduction, Methods, Results, Discussion

Leptospirosis is an epidemic-prone neglected disease that affects humans and animals , mostly in vulnerable populations ., The One Health approach is a recommended strategy to identify drivers of the disease and plan for its prevention and control ., In that context , the aim of this study was to analyze the distribution of human cases of leptospirosis in the State of Rio Grande do Sul , Brazil , and to explore possible drivers ., Additionally , it sought to provide further evidence to support interventions and to identify hypotheses for new research at the human-animal-ecosystem interface ., The risk for human infection was described in relation to environmental , socioeconomic , and livestock variables ., This ecological study used aggregated data by municipality ( all 496 ) ., Data were extracted from secondary , publicly available sources ., Thematic maps were constructed and univariate analysis performed for all variables ., Negative binomial regression was used for multivariable statistical analysis of leptospirosis cases ., An annual average of 428 human cases of leptospirosis was reported in the state from 2008 to 2012 ., The cumulative incidence in rural populations was eight times higher than in urban populations ., Variables significantly associated with leptospirosis cases in the final model were: Parana/Paraiba ecoregion ( RR: 2 . 25; CI95%: 2 . 03–2 . 49 ) ; Neossolo Litolítico soil ( RR: 1 . 93; CI95%: 1 . 26–2 . 96 ) ; and , to a lesser extent , the production of tobacco ( RR: 1 . 10; CI95%: 1 . 09–1 . 11 ) and rice ( RR: 1 . 003; CI95%: 1 . 002–1 . 04 ) ., Urban cases were concentrated in the capital and rural cases in a specific ecoregion ., The major drivers identified in this study were related to environmental and production processes that are permanent features of the state ., This study contributes to the basic knowledge on leptospirosis distribution and drivers in the state and encourages a comprehensive approach to address the disease in the animal-human-ecosystem interface .

Leptospirosis is a bacterial disease affecting humans and several animal species , which serve as reservoirs for infection ., Contamination happens through exposure to urine of infected animals in water and soil ., A better understanding of the factors that affect the transmission of the disease , incorporating the relationship between humans , animals , and ecosystems within the One Health approach , would allow for tailored prevention and control measures at the local level ., The aims of this study were to analyze the distribution of leptospirosis human cases and the possible factors that influence the transmission of the disease in the state of Rio Grande do Sul , as well as provide evidence to support the development of interventions and guide new studies in the region ., Leptospirosis cases and possible environmental , socioeconomic , and livestock factors were analyzed ., The study used data by municipality ( all 496 ) obtained via an open access database ., Georeferenced maps were constructed and statistical analysis performed for 26 variables ., A multivariable regression analysis was carried out ., An annual average of 428 human cases of leptospirosis was reported in the state from 2008 to 2012 ., The cumulative incidence in rural populations was eight times higher than in urban populations ., Variables significantly associated with leptospirosis cases in the final model were: ecoregion , type of soil and to a lesser extent tobacco and rice production ., Results showed that leptospirosis cases were concentrated in the state capital and in specific ecoregions ., Environmental and agricultural production processes were identified as possible risk factors and these conditions are probably permanent characteristics of the state ., This study contributes to the knowledge on leptospirosis distribution and risk factors in the state , and also highlights the importance of a holistic view and intersectoral approach in preventing the disease .

journal.pcbi.1004216

Learning the Structure of Biomedical Relationships from Unstructured Text

Biomedical research generates text at an incredible rate ., Each year , several hundred thousand new articles enter Medline from over 5 , 500 unique journals 1 , 2 ., The literature’s rapid growth and the rise of interdisciplinary domains like bioinformatics and systems biology are changing how the scientific community interacts with this important resource ., Knowledge bases like OMIM 3 , DrugBank 4 and PharmGKB 5 manually curate and restructure information from the literature to increase its accessibility to researchers and clinicians ., These knowledge bases capture cross-sectional “slices” of the literature , drawing connections among facts reported in different journals , at different times , and in different research domains ., Often , they examine the literature in ways not easily captured by current indexing strategies , such as MeSH terms or key words ., As the literature grows and the information we need to extract increases in complexity , full manual curation of these knowledge bases is rapidly becoming infeasible ., Progress in natural language processing ( NLP ) has encouraged the development of automated and semi-automated methods for enabling more efficient curation of biomedical text 6–9 , especially as biomedical research begins to explore even larger text-based resources , such as electronic medical records ( EMRs ) 10 , 11 ., However , tasks that are simple for human readers , such as recognizing when two different-looking statements mean the same thing , or when one statement is a more general version of another statement , are often extremely challenging for NLP algorithms ., One way around this problem is to infer the meaning of words and phrases by examining their usage patterns in large , unlabeled text corpora , an approach called “distributional semantics” 12–14 ., If two words or phrases are used in similar contexts , they are likely to be semantically related ., Here we introduce a novel algorithm , called Ensemble Biclustering for Classification ( EBC ) , that applies this strategy to uncover relationships between biomedical entities , such as drugs , genes and phenotypes ., We focus on the problem of drug-gene relationship extraction and characterization from unstructured biomedical text , using statistical dependency parsing to extract descriptions of drug-gene relationships from Medline sentences and applying EBC to recognize when two drug-gene pairs share a similar relationship , even when they are described differently in the text ., We show that EBC significantly improves our ability to extract both pharmacogenomic and drug-target relationships , and use it to discover new drug-gene relationships for PharmGKB and DrugBank ., Finally , we combine EBC and hierarchical clustering to map the global “landscape” of drug-gene interactions , revealing much unforeseen complexity in how these relationships are described in text ., We learn , for example , that there are subtle differences in how static knowledge ( past discoveries ) and new experimental discoveries are described , even when they refer to similar phenomena like inhibition , and that seemingly well-defined relationship classes ( such as pharmacogenomic and drug-target relationships ) often exhibit much more detailed chimeric structure than anticipated ., More generally , we demonstrate that extracting biomedical relationships based on corpus-level usage patterns , rather than on the properties of individual sentences , helps bypass the need for large , annotated biomedical training corpora–an important property in a domain where few such corpora are available ., The full set of abstracts from the 2013 edition of Medline contains approximately 184 , 000 sentences in which at least one drug name and at least one gene name are present ., Many of these sentences contain multiple drug and gene names; the total number of unique drug-gene-sentence combinations is approximately 236 , 000 ., As described in the Methods , we use dependency parsing to prune away irrelevant terms and phrases and focus attention on the parts of a drug-gene sentence most relevant to the relationship between a drug and a gene ., The pruned versions of drug-gene sentences are called dependency paths ., Fig 1 illustrates how dependency paths are constructed from raw sentences ., Table 1 provides some common drug-gene dependency paths and associated example sentences ., Details about the meanings of the individual grammatical dependencies , with examples , can be found in 15 ., We can quantitatively estimate the diversity of drug-gene descriptions in Medline by considering the space of all unique drug-gene dependency paths ., The vast majority of dependency paths are rare , indicating high variability in how drug-gene relationships are described ., The total number of unique drug-gene dependency paths in Medline is approximately 197 , 000 , of which 7 , 272 ( 4% ) connect at least two different drug-gene pairs ., The total number of unique drug-gene pairs co-occurring in Medline sentences is 49 , 564 , of which 14 , 052 ( 28 . 4% ) share a dependency path with at least one other drug-gene pair ., Table 2 describes the two datasets used in this paper , which consist of matrices , M , in which the rows are drug-gene pairs and the columns are dependency paths ., A cell of M , Mij , contains “1” if drug-gene pair i is connected by dependency path j somewhere in Medline and “0” otherwise ., Both of the datasets are over 99% sparse ., An important goal , therefore , must be to recognize when different-looking statements are saying the same thing ., Otherwise , we can only recognize that two drug-gene pairs share a relationship if their dependency paths are identical ., The details of how EBC builds connections among different dependency paths can be found in the Methods ., We evaluated EBC’s ability to mine the literature for drug-gene pairs exemplifying two specific types of drug-gene relationships ., The algorithm was given only the full , unlabeled text of Medline and a small number of drug-gene pairs that exemplified each type of relationship ., We refer to the small sets of labeled drug-gene pairs ( sizes 1 , 2 , 3 , 4 , 5 , 10 , 25 , 50 , and 100 ) as “seed sets” ., No text was annotated and no specific sentences were marked as “evidence” for any particular type of relationship ., The two relationship types we examined were:, Pharmacogenomic ( PGx ) relationships ., PharmGKB’s relationships database 5 contains 6283 manually-curated drug-gene associations in which polymorphisms in the gene are known to impact drug response ., Drug-target relationships ., DrugBank 4 maintains a list of known drug-gene relationships in which the protein product of the gene is a known target of the drug ., This list contains 14 , 594 known relationships ., Fig 2 shows EBC’s performance extracting PGx and drug-target drug-gene pairs on the two datasets described in Table 2 , and compares EBC to two alternative classifiers that do not account for the semantic relatedness of different dependency paths ., On both datasets , and on both tasks , EBC outperforms the other classifiers by a significant margin ., On the dense dataset , using seed sets of only 10 labeled drug-gene pairs as input , EBC accurately ( AUC > 0 . 7 ) ranks 89 . 6% of test sets for the PGx task and 96 . 5% of test sets for the drug-target task ., In comparison , using the same seed and test sets , the best-performing non-EBC classifier accurately ranks only 31 . 3% of test sets for the PGx task and 49 . 6% for the drug-target task ., On the sparse dataset , EBC’s increased performance is even more pronounced ., Again using only 10 labeled pairs , EBC accurately ranks 54 . 4% of test sets on the PGx task and 90 . 4% on the drug-target task , compared to 1 . 1% and 6 . 3% for the best-performing non-EBC classifier ., EBC’s raw assessments of the similarity of all drug-gene pairs in both datasets can be found in S1 Data ., The backbone of EBC is a biclustering algorithm called Information-Theoretic Co-Clustering ( ITCC; 16 , see Methods ) ., Fig 3 shows the result of one ITCC run on a small sample dataset consisting of dependency paths that connect different drugs to the gene CYP3A4 ( a liver cytochrome involved in the pharmacokinetic pathways of many drugs ) at least five times in Medline ., This dataset contains 62 drug-gene pairs ( where the gene is always CYP3A4 ) and 14 unique dependency paths ., As with the datasets in Table 2 , these are arranged in a matrix , M , where an element Mij is “1” if drug-gene pair i is connected by path j somewhere in Medline , and “0” otherwise ., We used ITCC to bicluster this matrix into four row clusters and six column clusters ., Besides biclustering the matrix , ITCC produces a “smoothed” version of the matrix where certain elements that were not observed in the original dataset are filled in ., Fig 3 illustrates that the rows fragment into four clusters that reflect distinct ways that drugs can interact with CYP3A4 ., Row cluster 1 contains CYP3A4 inhibitors , a few of which are also substrates ., Row cluster 2 contains CYP3A4 inducers ., Row clusters 3 and 4 contain substrates of CYP3A4 that are not known inhibitors ., EBC combines information from thousands of different biclusterings like this one to assess the relationship similarity of any two drug-gene pairs ( rows ) in the matrix , by looking at how frequently they cluster together ., It is also interesting to examine which columns of the matrix cluster together , as this provides insight into how the method is working ., Fig 3 shows that the dependency paths naturally fragment into clusters reflecting known biomedical properties ., All of the paths referring to inhibition , for example , appear together in column cluster 2 . The sole path referring to induction appears by itself in column cluster 6 ., The other four clusters include paths describing situations where the drug is a substrate of CYP3A4 , or is metabolized by it ., We see a similar pattern emerge when we examine co-clustering frequencies of the columns on a larger dataset: the dense dataset from Table 2 . Table 3 shows some dependency paths from this dataset that frequently cluster together over 2000 separate runs of ITCC ., Paths that frequently cluster together appear to be semantically related ., EBC provides a measure of relationship similarity between every drug-gene pair and every other pair ( the frequency with which each pair of rows in the data matrix cluster together ) ., By combining these assessments with hierarchical clustering , we created the dendrogram shown in Fig 4 , the details of which are described in the figure caption ., Table 4 summarizes the general “themes” of the clusters from Fig 4 and includes the size of each cluster and the density of known PGx and drug-target relationships within that cluster ., The cluster assignments for different slices of the dendrogram are provided in S3 Data ., Cluster 8 , the largest cluster , contains drug-gene pairs whose descriptions mainly refer to inhibition ., This cluster is highly enriched for both PGx and drug-target relationships ., When cluster 8 is subdivided by cutting the dendrogram at a lower height , a subcluster ( 8a ) of antagonists and their protein targets splits off from the main cluster ., EBC has learned that antagonism is a subclass of inhibition ., Cluster 10 , which is a close relative of cluster 8 in the dendrogram , contains drug-gene pairs where the drug is both an inhibitor and a substrate of the protein , such as verapamil/P-glycoprotein ., Cluster 3 , another large cluster , is almost exclusively devoted to metabolism and substrate relationships , and is highly enriched for PGx relationships , though not drug-target relationships ., Cluster 3 contains three subclusters with slightly different properties ., Cluster 3a involves mainly substrate relationships where the concept of “metabolism is not mentioned . These include , for example , transport relationships like aminopterin/hOAT1 . Cluster 3b contains most of the metabolic relationships , many of which involve liver cytochromes like CYP3A4 and CYP2D6 . Cluster 3c includes substrate relationships where the drug is often also described as having an effect on the activity of the protein . Other clusters enriched for drug-target relationships include cluster 12 , where the protein is described as the receptor for the drug , cluster 14a , where the drug is described as an agonist of the protein , and cluster 15 , which refers to protein binding . Notably , cluster 14a ( agonists ) is part of a larger cluster , cluster 14 , that encompasses activation and stimulation relationships . Here , EBC has learned that agonism is a subclass of activation . Interestingly , cluster 14b , the part of cluster 14 that refers to activation more broadly and does not specifically refer to agonism , is not enriched for drug-target relationships . Clusters 1–16 , which comprise 3 of the 4 main high-level groups within the dendrogram , are relatively easy to interpret: in general , each displayed a consistent theme . Clusters 17–25 , however , involve descriptions of experimental methods or results about drug effects on gene expression or protein activity . Here , the dendrogram reveals a distinction between past and present knowledge . Drug-gene pairs that are already well-studied are often reported in a static context–“D is an inhibitor of G” , or “D is a G agonist”–whereas other pairs are reported primarily in an experimental context–“we investigated the effect of D on G expression” , “G was activated by D” , or “exposure to D significantly increased G activity” ., Depending on the relative frequency of different types of descriptions , a drug-gene pair exemplifying an inhibitory relationship might end up in cluster 8 ( mostly static descriptions ) or cluster 21 ( mostly experimental descriptions ) ., Interestingly , drug-gene pairs from cluster 21 appear together in the literature significantly fewer times than drug-gene pairs from cluster 8 ( median 9 times for cluster 21 vs . 16 times for cluster 8; maximum 66 times for cluster 21 vs . 2722 times for cluster 8; p < 0 . 0001 , Mann-Whitney test ) , which seems to corroborate our assertion that the drug-gene pairs from cluster 21 represent more tentative experimental findings as opposed to well-established static knowledge ., Finally , the dendrogram reveals that PGx and drug-target relationships do not constitute distinct classes of relationships , but are chimeras ., PGx relationships are composed of relatively distinct subgroups corresponding to, ( a ) situations where the drug inhibits the gene/protein ( and therefore , mutations in the gene could be expected to impact response to the drug ) , and, ( b ) situations where the protein is involved in the metabolism or transport of the drug ., Drug-target relationships overlap with, ( a ) but not, ( b ) , and include other non-PGx subclasses , such as receptor binding and agonism ., EBC reliably detects new drug-gene pairs reflecting relationships of interest to PharmGKB and DrugBank , so we attempted to discover new examples from our corpus ., We built seed sets containing all known relationships from PharmGKB and DrugBank and incorporated these into EBC to rank the remaining drug-gene pairs according to EBC’s certainty that they represented PGx or drug-target relationships ., There was 13 . 6% overlap between the two seed sets , with 84 drug-gene pairs in both , 206 in PharmGKB only , and 326 in DrugBank only , and 2898 pairs that were unknown to both ., The dendrogram shown in Fig 5 is identical to that in Fig 4 , except that the clusters are replaced by vertical bars , the heights of which correspond to EBCs relative certainty that the pairs in question represent PGx relationships ( shown in orange ) or drug-target relationships ( shown in blue ) ., The raw prediction data can be found in S4 Data ., Known PGx or drug-target pairs are excluded from the bar graphs , but are denoted beneath the bars with orange or blue dots ., As expected , we see high prediction certainty for drug-target and PGx relationships among the inhibitors in cluster 8 , and high certainty for PGx relationships among the metabolic/substrate relationships in cluster 3 . We also observe an interesting area of high enrichment for both types of relationships among clusters 21–23 , where inhibition is mostly reported in an experimental context , but the density of known PGx and drug-target relationships is quite low ., These could represent new experimental findings that will be discussed as static knowledge in a few years ., Table 5 shows the top 20 predictions of new PGx candidate pairs for PharmGKB , and Table 6 shows the top 20 candidate drug-target pairs for DrugBank ., Among the top 20 PGx predictions , five are already known to PharmGKB and have been demonstrated experimentally ( one or more variants of the gene have been shown to impact response to the drug ) , but were coded in the PharmGKB relationships file in such a way that they were not included in the seed set ., One is brand new: polymorphisms in ABCB1 ( P-glycoprotein ) do impact clinical response to fentanyl , but this relationship is currently unknown to PharmGKB ., An additional eight pairs represent likely PGx relationships , such as known inhibitory or metabolic relationships , but no experiments have yet been conducted that might relate polymorphisms in the gene to drug response ., And finally , in five cases , the potential for a PGx association was considered likely enough that it was investigated experimentally , but no significant clinical association between genotype and drug response was found ., Among the top 20 predictions for new drug-target relationships for DrugBank , four are already known but were listed in DrugBank under alternate gene names ., An additional seven are new , proven drug-target relationships ., Of these , five involve drugs that are themselves unknown to DrugBank ( there are no entries for ketanserin , cangrelor , nutlin-3 , or tropisetron in DrugBank ) ., There are also several interesting , yet erroneous findings arising from parser and lexicon errors in which a molecule , such as IL-1 , is mistaken for its receptor , and that receptor is the true target of the drug ., These are explored further in the Discussion ., Although a great deal of research effort has been directed at the problem of relationship extraction in pharmacogenomics 17–19 , and in the biomedical domain in general 20–25 , high-quality biomedical knowledge bases like OMIM , DrugBank and PharmGKB still rely almost entirely on human curators , who comb the literature manually in search of new relationships ., The authors of BioGraph , a new biomedical knowledge base incorporating data from 21 different sources , recently decided to exclude databases that were not manually curated , citing data quality issues 26 ., Why is biomedical relationship extraction so challenging ?, We believe that one key stumbling block lies in how the problem has historically been defined ., Biomedical relationship extraction is usually thought of as a sentence-level problem–does a particular sentence describe a specific type of relationship or not ?, However , as we have seen , sentence-level descriptions are highly erratic ., Faced with a bewildering array of possibilities for how similar relationships can be described , sentence-level relationship extraction algorithms often rely on manually-constructed rules or ontologies that map diverse surface forms onto common semantics 17 , 27–29 ., These systems require a non-trivial amount of human maintenance and must be rebuilt for each new domain ., Machine learning algorithms for sentence-level relationship extraction avoid rules but face another serious problem: the need for annotated training sentences ., Recently , researchers have begun to produce annotated training sets for the biomedical domain 30 , 31 but manual annotation is almost as expensive as manual curation , both in time and human effort ., As a result , little to no annotated training data exist for many classes of biomedically interesting relationships ., These are important problems for NLP , but they only exist because we think of biomedical relationships at the level of individual sentences ., From a biomedical research standpoint , there is no need to do so—we are most interested in the true relationship between a drug and a gene , not in the meaning of any particular sentence ., As a result , we have taken a corpus-level approach where all of the information about a drug-gene pair from all of its available sentence-level descriptions is combined ., Latent connections among different-looking descriptions are then discovered in an unsupervised fashion from structure inherent in the raw text , requiring no human effort and boosting our ability to extract relationships of interest ., We contend that biomedical relationships should be considered properties of biomedical entities like drug-gene pairs , not individual sentences ., A description like “D decreased G levels” does not constitute an inhibitory relationship; it is simply an experimental finding that increases the likelihood of such a relationship ., This allows the same sentence to provide evidence for or against multiple types of relationship , the exact definitions of which are application dependent ., It also allows drug-gene pairs to exhibit multiple relationship types at once ., We see evidence for such an approach when we contrast EBC’s performance at extracting PGx relationships with its performance extracting drug-target relationships ., EBC was uniformly worse at extracting PGx relationships , even though these two sets of experiments used the same data matrices ., We see why in Fig 4: it turns out that what we originally considered to be well-defined relationship classes ( PGx and drug-target relationships ) are actually composites of several finer-grained sub-classes ., A high percentage of PGx relationships reside in cluster 3 , the metabolism/substrate cluster , which inhabits a region of the dendrogram far from the inhibition clusters ., In cases where the seed set consists mostly of metabolic relationships and the test set mostly of inhibition relationships , we would not expect EBC to perform well , even though both groups are still technically PGx relationships ., We initially believed that PGx relationships would be expressed in sentences relating specific polymorphisms to changes in drug efficacy , such as , “The CYP3A4 C3435T polymorphism influences rifampicin exposure in human hepatocytes” ., In reality , however , relatively few such sentences exist ., Most evidence for PGx relationships comes instead from descriptions of other types of relationships , such as inhibition and metabolism ., So we see that although a PGx relationship can be considered a property of a drug-gene pair , it is not generally a property of any particular sentence describing that pair ., EBC is part of a subfield of NLP called distributional semantics , in which patterns in large , unlabeled text corpora are used to create feature representations of words , phrases , or other entities ( in our case , drug-gene pairs ) based on how they are used in context ., The similarity of these representations then serves as a proxy for semantic relatedness 12 ., Distributional semantics algorithms’ theme of discovering semantic relatedness by looking at large-scale usage patterns inspired our corpus-level approach to drug-gene relationship extraction ., For example , in EBC , these representations are the co-clustering frequencies of each drug-gene pair with every other pair , and the contextual features are the dependency paths ., EBC builds on a long history of distributional semantics work in the NLP literature , much of which focuses on assessing the semantic similarity of individual words 12 , 13 , 32 , and some of which has tackled relationship extraction outside the biomedical domain 33–36 ., EBC is most similar in spirit to matrix factorization techniques like Latent Semantic Analysis ( LSA ) 13; ITCC forms a low-rank approximation of the original drug-gene-pair-by-dependency-path matrix , and EBC stacks thousands of slightly different ITCC-based approximations on top of each other to make its similarity assessments ., LSA uses the singular value decomposition ( SVD ) 37 instead of ITCC to accomplish a similar goal , and has been applied in at least one case to corpus-level relationship extraction ( a technique called Latent Relational Analysis , or LRA ) 36 ., We compare EBC to LSA on the PGx relationship extraction task in S2 Text ., There are dozens of other clustering and matrix factorization methods available , and some have already been applied to text mining tasks like relationship extraction ., Several methods cluster textual patterns to discover latent groupings of entity pairs corresponding to distinct relations 38–41 ., Others use the entity pairs flanking different textual patterns to group the patterns themselves into semantically related classes 33 ., Some methods , like EBC , address both problems simultaneously 42–45 ., The issue of textual “entailment”–finding the degree to which one statement implies the existence of another–is also an active area of research in NLP and is closely related to several of the methods described above 46 ., Although these techniques have already shown great promise on related tasks in web and newswire data , to our knowledge none has yet been applied to relationship extraction in the biomedical domain ., In our analysis of drug-gene relationships , we made several choices about, ( a ) how to identify drugs and genes in text ,, ( b ) the type of text to use as our corpus , and, ( c ) what constitutes a “feature” ( a single column in the data matrix ) ., In all cases , we made the simplest choices possible , both to enable others to reproduce our results , and to distinguish EBC’s own limitations from errors/omissions in the preprocessing steps and text itself ., We identify drugs and genes in the text based on simple string matching to single-word drug and gene names from PharmGKB 5 ., Named entity recognition ( NER ) is its own area of NLP , and identifying biomedical entity names in text is itself a nontrivial proposition ., We can see one obvious disadvantage of this approach in cluster 24 of Fig 4 and Table 4 , which includes “gene names” like COPD ( a . k . a . chronic obstructive pulmonary disease ) and NIDDM ( non-insulin-dependent diabetes mellitus ) ., Table 6 also reflects a lexicon error where the term “leukotriene” is listed as a synonym for the leukotriene B4 receptor ., Some such errors might be avoided if we used a more elaborate NER system 47 , 48 , though such systems themselves are not perfect and can introduce new sources of error ., Our stipulation that the entity names be single words also led to errors in cases ( see Table 6 ) where a molecule , such as IL-1 , is mistaken for its receptor , the “IL-1 receptor” , because “IL-1 receptor” is a multi-word phrase not allowed in the lexicon , while “IL-1” is allowed ., We also made no attempt to normalize gene names , so in our results , ABCB1 , MDR-1 , and P-gp are all different ., Again , this was done to avoid introducing normalization errors , and because genes and their corresponding proteins are often described in different contexts ., To construct dependency paths from raw Medline sentences , we used the Stanford Parser 49 , a free and open-source statistical parser ., The Stanford Parser was trained using labeled text from newswire corpora , so it sometimes fares poorly on biomedical text ., For example , the parser often mistakes gene names for adjectives ( “CYP3A4” in the phrase “CYP3A4 polymorphism” is frequently labeled as an adjective ) ., We used the out-of-box implementation of the Stanford Parser and did not perform any manual review or correction of parses to improve its performance ( again in the interest of simplicity ) ., Because EBC operates at the level of drug-gene pairs and not individual sentences , its performance is generally robust to parsing errors as long as the parser makes the same errors consistently ., There are some errors that do lead to incorrect conclusions , however ., For example , we observe some situations where dependency paths bypass important details about relationships , such as a sentence where a drug is described as “transcriptionally up-regulating G expression” and the dependency path only captures the effect on expression , not its directionality ., These are usually generalizations rather than errors , but they do result in some loss of information from the sentence ., Finally , our corpus consisted of all abstracts from the 2013 edition of Medline ., Including information from the full text of the research articles could help discover relationships not mentioned in the abstracts , but many journals do not provide access to the full text , and we did not wish to bias our results in favor of relationships reported in a subset of journals ., Our approach would remain the same regardless of the corpus ., The combination of EBC and dependency path features described here allows us to reliably extract biomedical relationships of interest from Medline sentences , smoothing over differences in how these relationships are described ., This finding opens the door to many interesting possible future applications ., For example , EBC could be used to extract relationships spanning multiple sentences or entire abstracts by using features such as individual dependencies , words , or phrases in place of dependency paths ., As new gold-standard sets of biomedical relationships become available ( such as all drug-gene pairs reflecting inhibitory relationships or specific collections of drug-gene pairs relevant to particular laboratories’ research efforts ) these can seamlessly be incorporated into EBC to extract these relationships at scale ., EBC could also potentially be used for lexicon or ontology expansion in a manner similar to LSA or random indexing 50 , 51 ., At its core , EBC is not relationship extraction-centric ., The algorithm itself is agnostic to the type of data contained in its input matrix ., EBC simply allows us to use latent structure in large , unlabeled datasets to boost our ability to extract new information from those datasets , even when our access to labeled training examples is limited ., Datasets like these occur throughout biomedical research , even beyond NLP ., We look forward to seeing how EBC fares on some other classes of related problems , in NLP and elsewhere ., When applied to drug-gene relationship discovery , the EBC algorithm operates on a data matrix where the rows are drug-gene pairs and the columns are dependency paths that connect them in the literature ., The algorithm has two steps , the first unsupervised and the second supervised ., First , unsupervised biclustering is used to simultaneously discover, ( a ) latent connections among dependency paths ( columns ) that appear different but connect similar drug-gene pairs , and, ( b ) latent similarities among different drug-gene pairs ( rows ) that are connected by similar dependency paths ., Over multiple iterations of, ( a ) and, ( b ) , the algorithm can infer that two drug-gene pairs share a similar relationship , even when they share no dependency paths in common ., To make its similarity assessments , EBC uses an ensemble of biclustering runs where the cluster centers are initialized randomly on each run , providing many different guesses about which dependency paths and drug-gene pairs are related ., In the second step , EBC incorporates a small seed set of drug-gene pairs ( rows ) reflecting some known relationship , and ranks other pairs based on their similarity to the pairs in the seed set ., The specific steps of the EBC algorithm are as follows: Preprocessing ( drug-gene relationship extraction task ) :, Identify all drug-gene pairs co-occurring in sentences within a corpus of text ., ( In our experiments , these were drug-gene pairs co-occurring in Medline sentences .

Introduction, Results, Discussion, Methods

The published biomedical research literature encompasses most of our understanding of how drugs interact with gene products to produce physiological responses ( phenotypes ) ., Unfortunately , this information is distributed throughout the unstructured text of over 23 million articles ., The creation of structured resources that catalog the relationships between drugs and genes would accelerate the translation of basic molecular knowledge into discoveries of genomic biomarkers for drug response and prediction of unexpected drug-drug interactions ., Extracting these relationships from natural language sentences on such a large scale , however , requires text mining algorithms that can recognize when different-looking statements are expressing similar ideas ., Here we describe a novel algorithm , Ensemble Biclustering for Classification ( EBC ) , that learns the structure of biomedical relationships automatically from text , overcoming differences in word choice and sentence structure ., We validate EBCs performance against manually-curated sets of ( 1 ) pharmacogenomic relationships from PharmGKB and ( 2 ) drug-target relationships from DrugBank , and use it to discover new drug-gene relationships for both knowledge bases ., We then apply EBC to map the complete universe of drug-gene relationships based on their descriptions in Medline , revealing unexpected structure that challenges current notions about how these relationships are expressed in text ., For instance , we learn that newer experimental findings are described in consistently different ways than established knowledge , and that seemingly pure classes of relationships can exhibit interesting chimeric structure ., The EBC algorithm is flexible and adaptable to a wide range of problems in biomedical text mining .

Virtually all important biomedical knowledge is described in the published research literature , but Medline currently contains over 23 million articles and is growing at the rate of several hundred thousand new articles each year ., In this environment , we need computational algorithms that can efficiently extract , aggregate , annotate and store information from the raw text ., Because authors describe their results using natural language , descriptions of similar phenomena vary considerably with respect to both word choice and sentence structure ., Any algorithm capable of mining the biomedical literature on a large scale must be able to overcome these differences and recognize when two different-looking statements are saying the same thing ., Here we describe a novel algorithm , Ensemble Biclustering for Classification ( EBC ) , that learns the structure of drug-gene relationships automatically from the unstructured text of biomedical research abstracts ., By applying EBC to the entirety of Medline , we learn from the structure of the text itself approximately 20 key ways that drugs and genes can interact , discover new facts for two biomedical knowledge bases , and reveal rich and unexpected structure in how scientists describe drug-gene relationships .

journal.pcbi.1003680

Large-Scale Genomic Analysis Suggests a Neutral Punctuated Dynamics of Transposable Elements in Bacterial Genomes

Transposable elements ( TE ) are pieces of DNA that encode the enzymatic capability to change location and proliferate within the host genome through a process called transposition ., They are widely distributed in prokaryotes and eukaryotes , and in some cases they constitute substantial fractions of the genome 1 ., Due to their relative autonomy , proliferative ability , and apparent lack of a useful function , they were considered for some time a paradigm of selfish DNA , i . e . a molecular parasite that proliferates at the cost of the genome it “infects” 2 , 3 ., Nowadays , the relationship between TE and host genomes is known to be much more complex ., Particular TE insertions may be beneficial for the host , for instance by inactivating genes whose expression is no longer required 4 , 5 , acting as a vehicle for the exchange of useful genes , or facilitating adaptation to fast environmental changes 1 , 6 , 7 ., Even if TE did not play any beneficial role , hosts often possess regulatory mechanisms that keep TE under control and minimize the risk of possibly deleterious insertions 8 , 9 ., Because of their ability to promote recombination , TE are key contributors to the plasticity of genomes 10 , 11 ., Hence , understanding the dynamics of TE in different organisms is relevant to the comprehension of genome architectures ., Insertion sequences ( IS ) are the simplest form of TE , as they often code for only one gene responsible for their mobility machinery ( the transposase gene ) 9 ., IS first enter host genomes through lateral gene transfer ( LGT ) and they can increase their copy number via transposition ., The broad diversity of effects that IS exert on their hosts has turned the fate of this relationship —long-term coexistence or eventual extinction of the host due to IS proliferation— , into a matter of debate 12 ., Moreover , relatively recent cases of rapid IS expansions in bacterial genomes , which have been attributed to episodes of host restriction and environmental change , raise additional questions on the causes and nature of such IS expansions 11 , 13 , 14 ., As of today , the mechanisms by which environmental perturbations cause IS expansions , the role played by selection in controlling IS copy number , or the significance of decreases in host population sizes in the expansion of IS are mostly unsolved issues ., Even more interestingly , could IS expansions represent transitory punctuations with a relevant role on host evolution ?, 15–17 ., A better understanding of the evolutionary forces that control the IS dynamics is required in order to shed light on all these questions 18 ., The first works aiming at analyzing TE dynamics date back to the decade of 1980 19–22 ., Inspired by the idea that TE are selfish elements , they depicted a scenario where TE spontaneously tend to proliferate and either host regulatory mechanisms or purifying selection keep TE numbers under control 21 , 23 ., Due to the limited data on TE abundance and distribution available at that time , those works either remained mostly theoretical or mainly addressed eukaryotic TE 24 ., In recent years , however , the ever increasing number of sequenced genomes has provided us with an unprecedented amount of data on the abundance and distribution of prokaryotic TE ., This has permitted the evaluation of a series of hypotheses concerning IS dynamics 14 , 25–28 ., In particular , a high homology of IS copies within genomes has been reported 25 and interpreted on the basis of a fast proliferation dynamics following the arrival of an IS element , ultimately leading to the extinction of the host ., This view has been challenged 27 by the large proportion of IS remnants in Wolbachia genomes , implying that IS proliferation does not necessarily lead to extinction ., Statistical approaches directed at identifying the causes behind IS abundance have found that it correlates with genome size but not with LGT rate , host pathogenicity or lifestyle 26 ., Estimations of the fitness cost of IS elements by comparing a simple model with the genomic data available for the IS5 family 28 have found that the fitness cost is small enough to assume that , in practice , IS may be neutral or almost neutral for the host genome ., In this study , we take advantage of the large amount of genomic data currently available and analyze the abundance distributions of 33 IS families in 1811 bacterial chromosomes ., This allows us to test and compare two simple models of IS spreading , namely a neutral model and a model with purifying selection , which are introduced in the next section ., By fitting those models to the genomic data we obtain estimates for the proliferation , loss and LGT rates , as well as the fitness cost associated to an IS copy ., The joint evaluation of such estimates and the original data allows us to disentangle the general forces that control IS dynamics in the long-term and explore the possibility that IS and hosts coexist in an equilibrium state punctuated by transient episodes of IS proliferation ., The models here used are aimed at capturing the main mechanisms that are responsible for the proliferation , spreading and loss of IS within and among genomes ., We first introduce a neutral model that takes into account the following key processes:, ( a ) the IS ability to proliferate ,, ( b ) IS deletion , and, ( c ) IS incorporation through lateral gene transfer ( LGT ) ., As an alternative to this neutral model , we also consider the case of IS entailing a fitness cost ., The processes of proliferation , deletion , and LGT , complemented with a fitness cost that is proportional to the IS copy number , define a model of IS dynamics with selection ., A schematic of the models is shown in Fig . 1 ., The rules of the models and the associated parameters should be understood in an effective manner , and in agreement with the procedure used to detect and classify IS sequences ( see Methods ) ., The duplication rate in our model applies to those insertion events that are not lethal for the host genome ., From the perspective of a neutral scenario any observable insertion is assumed to be neutral or quasi-neutral: genomes hit by a lethal or highly deleterious insertion die shortly afterwards and do not further contribute to the population dynamics ., The duplication parameter r is an effective measure of the duplication rate of an IS family ., In this sense , it includes functional IS copies but also tolerates a fraction of non-functional ( in the sense of non-duplicating ) IS copies that might be detected in the genome and ascribed to that family ., Similarly , the effective deletion parameter d embraces actual deletions , but also excisions that do not reinsert and sequences that , due to mutation accumulation , can no longer be detected ., Finally , the LGT parameter h can only take into account those transfer events that conclude with the insertion of the IS in the genome ., Though preventing lethal insertions of IS elements originated by duplication or LGT is a form of purifying selection , this mechanism acts on each element independently , and is thus included in the neutral model ., Purifying selection that acts to streamline genomes represents a different mode of action which is included in the model incorporating selection , together with any other selective mechanism that penalizes the genome proportionally to its IS content ., The key processes in the neutral model can be summarized into two parameters: the duplication-deletion ratio α , and the LGT-deletion ratio β ., The model with selection includes an extra parameter , the fitness cost-deletion ratio σ ., The advantage of working with relative ratios becomes clear given the difficulty of obtaining reliable estimates of the actual duplication , deletion and LGT rates , which greatly vary depending on the experimental methodology and environmental conditions 8 , 29 , 30 ., Furthermore , the duplication-deletion ratio can be easily interpreted in terms of a proliferation or deletion bias at the level of IS dynamics , as later discussed ., Both models can be solved to obtain the expected abundance distribution of an IS family in the long-term stationary state ( see Methods ) ., The models provide , for each IS family , the probability of finding a genome with a given number of copies ., By comparing that probability with the observed IS abundances it is possible to estimate values for the model parameters and test whether the neutral model or the model with selection are valid to explain the genomic abundances of IS ., Data on the classification and distribution of bacterial IS elements was taken from 31 ( see Methods for further details ) ., Starting from a dataset of 1811 bacterial chromosomes harboring at least one IS element , we selected 1079 of them by choosing randomly only one chromosome per species , in order to minimize redundancy ., For each IS family , its abundance distribution was fitted to both models by means of a maximum likelihood approach ., Most of the 33 IS families show abundance distributions that are well fit by the neutral model ( Fig . 2 shows a representative example ) ., This assertion is supported by the goodness of fit tests , that render non-significant values even if no correction for multiple comparisons is applied ., The only exception is IS21 ( ) , but the fit to this case becomes non-significant once corrected for the 33 comparisons ., The detailed results of the fits are provided in the SI ., It is remarkable that a simple , neutral model is able to explain the data with only two free parameters ., We have checked whether the use of two different LGT rates , one for genomes where the corresponding IS family is absent , and a different one for genomes where the family is present improves the fits to the data ., That is not the case for 31 of the 33 families , once corrected for multiple comparisons , thus suggesting that LGT rates to genomes where a given IS family is either absent or present are similar ., Next , we took the values of the duplication-deletion ratio α estimated in the neutral model and tried to refine the fits by adding fitness cost and selection ., We found that the optimal values of the selection parameter σ were close to zero ., In concordance , selection does not significantly improve the fit for any of the IS families ( detailed results in the SI ) ., This fact remains true even if small changes in α are considered ., As an alternative , we also explored the selection model by adopting a completely different range of values of α , between 102 and 103 , as suggested by 28 ., In that scenario , duplications are overwhelmingly more frequent than deletions , and negative selection is the only factor able to prevent an explosive proliferation of the IS ., As in the previous case , no improvement in the fits with respect to the neutral model is observed ., It is worth mentioning that the estimated selection parameter σ is typically tenfold smaller than the duplication-deletion ratio ., Taken together , our results show that selection needs not be invoked to explain the abundance and distribution of IS ., In the following paragraphs , we face the estimates of the neutral model to the genomic data in order to further explore the possibility that IS behave neutrally ., A global analysis of the estimated parameters for the whole set of IS families reveals that most families behave in a strikingly similar way , with the duplication-deletion ratio close to 0 . 9 ( Fig ., 3, ( a ) ) ., Noticeable exceptions are Tn3 and Tn7 , for which significantly smaller values are found ., In order to evaluate the relevance of LGT in determining the IS abundance , we studied the correlation between the LGT rates of a family ( measured as parameter β ) and the corresponding fraction of genomes that host that family ( Fig ., 3, ( b ) ) ., A strong correlation exists ( Spearmans , ) , confirming the fact that the entry of new IS families into the genome totally relies on LGT ., In contrast , as shown in Fig . 3, ( c ) , LGT rates do not correlate ( Spearmans , ) with the mean number of copies within “infected” genomes ( those genomes with at least one copy for a given family ) ., This is in agreement with the idea that duplication-deletion processes , rather than LGT , is what determines the copy number once the genome has become “infected” 26 ., We also studied whether the host genome size affects IS duplication and LGT rates ., To that end , chromosomes in the database were classified into three subsets according to their sizes ( smaller than 2 . 6 Mbp , between 2 . 6 and 4 . 2 Mbp , and larger than 4 . 2 Mbp ) ., These cut-off points yield equal size subsets with approximately 350 chromosomes each ., The model parameters were recalculated for each data subset and IS family ( Fig . 4 ) ., We found no significant differences in the duplication-deletion ratios among the three size groups ( Friedman test , ) ., By contrast , LGT-deletion ratios show a significant increase in larger genomes ( Friedman test , ) ., In order to complete our analysis , we also fitted the data to the selection model with a strong proliferation bias ( ) and found that the selection coefficients do not vary with the genome size ( Friedman test , ) ., A major issue concerning transposable elements is whether they can coexist with their host for long periods of time or their proliferation ultimately leads to host invasion and death ., Long-term coexistence of IS and hosts becomes possible if proliferative and reductive forces compensate each other , so that the IS copy number remains stable on average ., Stability is meant in a statistical sense , since the process is affected by large fluctuations ., In the framework of the neutral model , this equilibrium condition can be translated into a mathematical relationship among model parameters: , where is the mean copy number of IS in the population of genomes ( see SI ) ., That expression represents a critical balance between duplication and LGT rates on the one side and deletion on the other side that permits a stable , long-lasting coexistence between IS and host ( recall that and ) ., In contrast , situations where the relation above is not fulfiled lead to IS expansions or declines ., Specifically , if , the IS proliferates “explosively” , whereas if , the IS gets quickly extinct ., We explored the empirical relation between the estimated parameters α and for all the IS families in the dataset ., As Fig . 5, ( a ) reveals , there is a trend of the data to be located close to the dashed line that represents the critical balance condition ( coefficient of determination ) ., Empirical data obeying it suggest that IS and hosts have evolved stabilizing mechanisms that prevent both IS extinction and unbound proliferation in most genomes ., Parameters α and were estimated independently in order to ensure that the observed trend is not a product of the fitting algorithm ( see Methods ) ., If parameters are estimated jointly , the agreement between the empirical data and the critical balance condition rises even higher ( ) ., Interestingly , this approach based on the critical balance allows for discrimination between equilibrium and IS states of exponentially fast proliferation or decline ., To check for that , we generated datasets by mimicking situations where the LGT rate remains stable while the duplication rate increases ( IS unbound growth ) or decreases ( IS decline ) ., We found strong deviations from the critical balance , even if the simulated values of α and β were kept inside the previously observed range ( Fig ., 5, ( b ) ) ., The models developed in this work account for the dynamics of IS in an equilibrium state ., The fact that real abundance distributions are well fit by the theoretical curves means that IS are in equilibrium in most genomes ., Conversely , we can take advantage of the theoretical distributions to detect outliers , i . e . genomes that show an abnormally large copy number for a given IS family ( see Methods for further details on the detection procedure ) ., From the perspective of the neutral model , outliers can be interpreted as the result of transient imbalances in duplication , deletion and/or LGT rates , which break down the critical balance ., The search for outliers gave as a result a set of 35 strains ( of a total of 1685 ) , that span over a small number of species ., It is relatively common that the same genome behaves as an outlier with respect to more than one IS family ., For instance , all 12 strains of Yersinia pestis are outliers with respect to IS200 , and three of them also with respect to IS21 ., Genomes belonging to the genus Shigella ( S . boydii , S . dysenteriae , S . flexneri and S . sonnei ) are overcrowded with IS1 , IS3 and IS4a ., Other examples are Xanthomonas oryzae ( outlier for IS1595 , IS5a , IS5b and IS701 ) and Salmonella enterica subsp ., enterica ( outlier for IS200 ) ., A summarized list can be found in Table 1 , while a comprehensive list is provided in the SI ., By fitting the genomic data to a neutral duplication-deletion-LGT model , we were able to observe two general trends: first , the estimated duplication rates are typically one order of magnitude greater than the estimated LGT rates; second , the LGT rate correlates with the number of genomes that host a given IS family , but does not correlate with the IS genomic abundance ., These findings together let us conclude , in agreement with 26 , that transposition and LGT play different roles in the dynamics of IS ., Whereas LGT determines the spreading of IS across genomes , it only plays a minor role once a genome already contains a given IS family ., Inside such infected genomes , the abundance of IS copies is mainly driven by stochastic duplications and deletions ., When looking at the duplication-deletion ratio , we found that it takes a value slightly smaller than one , which can be interpreted in terms of a deletion bias at the level of IS 35 , 36 ., Such a deletion bias makes LGT essential for the long term persistence of IS: in the absence of an external income via LGT , IS copies tend to be deleted faster than they duplicate and , eventually , they disappear ., This mechanism offers a possible explanation to the loss of IS in organisms whose life conditions limit their LGT rates , e . g . in anciently host-restricted endosymbionts 13 ., Some authors have reported a correlation between genome size and IS content 1 , 26 , which motivated us to test whether duplication and LGT rates vary in genomes of different sizes ., In disagreement with the prevailing idea that larger genomes withstand greater IS proliferation rates , we found no significant differences in duplication rates among genomes of different sizes ., On the other hand , the LGT rate becomes greater in larger genomes ( Fig ., 4 ( b ) ) , which opens a new path to explain the above-mentioned correlation ., Actually , an observed correlation between bacterial ecology and genome size 37 suggests that prokaryotic ecological niches might be the proximate cause that determines LGT rate values ., Our results show that purifying selection at the host level needs not be invoked to explain the abundance and distribution of IS , because the genomic data are fully compatible with a neutral scenario ., In fact , the small differences in the distributions derived from neutral and selection models may be insufficient to discriminate between both scenarios ., There are , however , some clues that challenge the prevailing role traditionally ascribed to selection ., First , provided that there is a deletion bias , purifying selection is no longer essential to control IS ., Second , the fact that IS in larger genomes—those with a presumably smaller fraction of essential genes—do not show reduced fitness cost challenges the view that interruption of essential genes by IS insertions generates an efficient selection pressure against IS ., Third , even if there were no deletion bias and duplications greatly overwhelmed deletions , the values we found for the selection-deletion ratio—typically ten-fold smaller than the duplication-deletion ratio—bring along the possibility that IS control takes place in a weak selection scenario ., This same idea had been pointed out in 28 , where the abundance distribution of IS5 under the assumption of a strong proliferation bias was studied ., In a context of weak selection , the composition of the host population experiences random variations that allow for fixation of slightly deleterious genotypes 38 ., Hence , when the host population dynamics is taken into account , opposite predictions are derived from deletion and proliferation biased scenarios ( see Table 2 ) ., In the former case , the IS copy number is controlled by deletions , and selection may be neglected , thus resulting in an effectively neutral dynamics ., In the latter case , explosive IS proliferation would be the expected outcome because weak purifying selection is unable to compensate for IS duplications ( see the SI for analytical calculations ) ., Therefore , finding weak selection rates in a proliferation biased scenario necessarily implies that host genomes are out of equilibrium and in their way to becoming fully invaded by IS 12 , 25 ., At odds with the aforementioned scenario of non-equilibrium proliferative dynamics , our results point towards a stable coexistence of IS and hosts ., Despite the fact that molecular mechanisms of transposition vary 9 , all of the 33 IS families considered show strikingly similar values of the dynamical parameters ., Even more , duplication , deletion , and LGT rates balance according to a critical condition that allows for evolutionary persistence without explosive proliferation ., Such a narrow range of parameter values suggests an implicit role of stabilizing selection acting on IS and promoting those that behave like mild , persistent parasites 39 ., In fact , IS mutants that fall below the critical condition are doomed to disappear; those that excede it proliferate quickly and—even if they entail a minimal fitness cost—eventually kill their local host populations , thus causing their own extinction 40 ., Degenerated IS copies constitute a hallmark of the neutral dynamics based on deletion bias: IS are controlled via deletions , which turn functional IS copies into degenerated ( or vestigial ) ones ., In contrast , if IS are to be controlled via purifying selection , whole genomes rich in IS tend to disappear , without generation of any IS remnants ., On this point , it is worth discussing the case of Wolbachia , a genus of anciently host-restricted endosymbiotic bacteria ., Wolbachia endosymbionts have reduced genomes ( ∼1 Mbp ) and their effective population sizes are thought to be very small ., The strains of Wolbachia that are associated to arthropods ( e . g . Drosophila melanogaster and Culex quinquefasciatus ) are known to coinfect hosts and undergo LGT 41 , 42; while those associated to filarial nematodes ( Brugia malayi and Onchocerca ochengi ) seem to be transmitted in a strictly vertical way , which greatly limits LGT 43 ., In agreement with the idea that LGT is essential for the maintenance of IS , only the arthropod-associated Wolbachia strains host functional IS copies 44 , 45 ., Importantly , the comparative analysis of IS in Wolbachia reveals that more than 70% of IS copies in arthropod-associated strains are nonfunctional 27 , 46 ., Those nonfunctional copies belong to several IS families , which are also represented in nematode-associated Wolbachia with no functional copies ., Large amounts of partial IS copies have also been found in a recent study dealing with thermophilic cyanobacteria of the genus Synechococcus 47 ., These facts suggest that nonfunctional , fragmentary IS copies may be prevalent in bacterial genomes , even if they have experienced strong reductions in size , and that deletions are an important force leading to the loss of IS ., In contrast , group II introns—another kind of TE in prokaryotes—display a smaller fraction of fragmentary copies and their dynamics are possibly driven by selection 48 ., The neutral dynamics that we present here can give rise to punctuated events of IS proliferation ., They occur whenever the LGT , duplication and deletion rates become imbalanced and the critical condition breaks down ., We have identified some of those events by applying an outlier detection algorithm on the abundance distributions ., According to our analysis , the fraction of such outliers is small , hence confirming that non-equilibrium states are the exception rather than the rule ., Some of the outliers that we have detected have already been noticed and interpreted in the literature as IS expansions 14 , supporting the idea that outliers truly represent genomes that have experienced an episode of IS proliferation ., It is not rare that multiple IS families show expansions within the same genome , which suggests that the causes of IS punctuations do not lie at the IS but at the host level ., Indeed , some IS expansions have been associated to episodes where bacteria underwent host restriction 11 , 13 , 14 ., Traditionally , the reduced efficiency of purifying selection in smaller populations has been invoked to explain such expansion events ., There are other mechanisms , though , that may account for IS punctuations in the absence of selection ., Transitory alterations in the deletion and LGT rates may play the same role , as well as stress induced downregulation of host regulatory mechanisms limiting IS transposition 17 , 29 ., In an indirect way , ecological changes—such as host restriction—may imply reductions in the fraction of essential genes 49 , 50 , which would lead to a higher probability of IS insertions being non-lethal , and eventually to increases in the effective duplication rate 26 ., In sum , our results indicate that the persistence of IS in bacterial genomes are the outcome of a neutral process , with little role for purifying selection ., Let us emphasize that the absence of selection here reported should be interpreted as a general trend in the whole set of genomes , averaged over long periods of time ., Sporadic cases of IS insertions affected by selection may occur , but the neutral behavior dominates at large evolutionary scales ., Most genomes contain IS abundances compatible with an equilibrium state , albeit punctual imbalances in the LGT , duplication and deletion rates—but not necessarily in the host population size—may produce transient IS expansions ., In the light of the important role of transposable elements in adaptation and genome evolution 4 , 6 , 17 , 51 , a better understanding of the actual causes behind IS expansions becomes an appealing challenge ., From an “ecological” perspective , most IS families share closely similar values of the relevant dynamical parameters , suggesting that IS and host genomes have coevolved towards a state of stable coexistence ., The apparent equivalence of different IS families brings to mind the concept of a neutral ecosystem 52 ., Hence , it would be interesting to further explore the parallelisms between IS dynamics and neutral ecology , which could provide us with novel insights into the processes that rule the architecture of genomes ., We used the catalog in 31 as a source of information regarding bacterial IS classification and distribution ., IS catalog construction is briefly summarized in the following ., In a preliminary study , transposases and other IS-encoded proteins collected from Pfam ( v2 . 6 ) 53 and ISfinder 54 ( a specialized database focused on prokaryotic IS elements ) were used to generate a manually curated list of protein architectures ( protein domain organization descriptions ) associated to IS-encoded proteins ., Listed architectures represented , by extension , IS-associated genes ., Simultaneously , a table describing the correspondence between gene combinations ( represented by protein architecture strings ) and IS elements classified according the the IS finder classification scheme , was built ., Then , chromosomal and predicted protein sequences , as well as protein translation tables ( gene coordinate files ) for 2074 bacterial chromosomes were downloaded from the NCBI Genome database on October 2012 ., A computational pipeline written in Perl directed the execution of HMMER 3 . 0 and other in-house developed applications to detect , classify and count IS elements in complete genomes ., First , the protein architecture for the complete set of proteins predicted for all bacterial genomes was reconstructed on the basis of HMMER alignments against the Pfam database ., Then , IS-associated genes were identified by comparison with the previously generated list of protein architectures ., Once IS-associated genes had been identified , the system assigned individual genes , or clusters of adjacent genes , to IS elements using the correspondence table also established in the preliminary study ., The system attempted to resolve IS elements located in tandem , as well as to identify complete IS elements that could exist within gene clusters originated by nested insertions ., To do so , clusters of IS-associated genes were segmented into all possible collections of adjacent gene subclusters , which were then classified , when possible , as belonging to a certain IS family ., The segmentation scheme used maximized the total length of successfully classified subclusters ., As result , 69 , 438 IS associated genes , corresponding to 57 , 515 IS elements in 1 , 1811 chromosomes , were identified ., The overall IS detection and classification strategy aimed at reducing the number of wrongly classified genes at the expense of a slight decrease in sensitivity ., With this purpose , the system was based on NCBI published gene predictions and only individual or adjacent gene clusters that could be unequivocally assigned to IS elements belonging to canonical IS families or groups were considered ., Two approaches were followed to evaluate the quality of the annotations generated by the IS detection and classification pipeline ., For the first approach , the set of genes annotated in the NCBI database as encoding for transposases was compared against the set of IS-associated genes detected by the pipeline ., Out of the 65 , 230 genes annotated with the keyword ‘transposase’ at the NCBI database , 85% were correctly identified by the pipeline ., For the second approach , IS family affiliation was compared for the sets of IS-associated genes described both in the genomic component of ISfinder ( ISbrowser 55 ) and in the annotations generated by the pipeline ., At a global level , IS family affiliation agreed for 88% of the 866 shared IS-associated genes ., At the level of individual IS families , the fraction of genes that were affiliated to the same IS family by both systems had average and median values of 79% and 100% , respectively ., We studied the neutral evolution of the number of copies in the genome as a generalized birth and death process ( Fig ., 1, ( a ) ) ., A complete analysis of this kind of processes applied to the study of proteomes has been carried out in 56 ., The neutral model focuses on a particular IS family in a single genome ., Elements belonging to the family are duplicated at a rate r and deleted at a rate ., In addition , new copies can be inserted through lateral transfer at a rate ., We define the state of the genome as the number of copies that it carries , with no upper limit for such copy number ., A genome with copies will turn into a state with copies after duplication or LGT ., Under the assumption that copies behave independently and LGT rate is a constant , the transition rate is equal to ., On the other side , the transition rate due to copy deletion is equal to ., As described in Fig . 1 , the relevant parameters in this case are ( duplication-deletion ratio ) and ( LGT-deletion ratio ) ., F

Introduction, Results, Discussion, Methods

Insertion sequences ( IS ) are the simplest and most abundant form of transposable DNA found in bacterial genomes ., When present in multiple copies , it is thought that they can promote genomic plasticity and genetic exchange , thus being a major force of evolutionary change ., The main processes that determine IS content in genomes are , though , a matter of debate ., In this work , we take advantage of the large amount of genomic data currently available and study the abundance distributions of 33 IS families in 1811 bacterial chromosomes ., This allows us to test simple models of IS dynamics and estimate their key parameters by means of a maximum likelihood approach ., We evaluate the roles played by duplication , lateral gene transfer , deletion and purifying selection ., We find that the observed IS abundances are compatible with a neutral scenario where IS proliferation is controlled by deletions instead of purifying selection ., Even if there may be some cases driven by selection , neutral behavior dominates over large evolutionary scales ., According to this view , IS and hosts tend to coexist in a dynamic equilibrium state for most of the time ., Our approach also allows for a detection of recent IS expansions , and supports the hypothesis that rapid expansions constitute transient events—punctuations—during which the state of coexistence of IS and host becomes perturbated .

Insertion sequences ( IS ) are mobile genetic elements found in most prokaryotic genomes ., They are able to autonomously change position and proliferate in chromosomes ., The nature of the coevolutionary dynamics of IS with the genome that hosts them is a matter of debate: Do IS proliferate to the point of causing the extinction of the host ?, Is it possible that IS and hosts stably coexist ?, Can environmental perturbations cause IS expansions ?, What is the role of selection in controlling IS copy number ?, In this study , we have analysed abundance patterns of IS families to test two different evolutionary hypotheses: in the first one IS evolve neutrally , while in the second case they are affected by selection ., Our results indicate that , most of the time , IS and their hosts coexist stably in a neutral scenario where the proliferation of IS through duplications and lateral gene transfer is balanced by regular deletions ., Occasionally , though , this balance may be disrupted , causing temporary explosions of IS abundance .

mobile genetic elements, genome complexity, computer and information sciences, systems science, mathematics, genome evolution, statistics (mathematics), population modeling, genetic elements, genetics, transposable elements, biology and life sciences, physical sciences, computational biology, evolutionary biology, nonlinear dynamics

journal.pntd.0002939

Proteomic Analysis of Adult Ascaris suum Fluid Compartments and Secretory Products

Ascaris lumbricoides is an extremely prevalent gastrointestinal nematode parasite of humans ., Considered a neglected tropical disease ( NTD ) and widespread in populations of low-to middle-income countries , especially in tropical and subtropical regions , this parasite is estimated to infect as many as 1 . 2 billion people ( many of whom harbor multiple species ) in sub-Saharan Africa , China , South and Central America and East Asia 1–5 ., Adult parasites reside primarily in the duodenum ., Although infections are typically asymptomatic , detectable morbidity occurs in up to 200 million infections , most frequently in chronically infected individuals ., Pathology may result when parasites migrate to the bile ducts , causing cholangitis ., A high worm burden can obstruct the bowel and lead to volvulus and may cause pain , discomfort and megacolon ., Ascaris lumbricoides has also been reported to cause lactose intolerance and to reduce absorption of vitamin A . Severe pathology can occur when larvae migrate through the lungs , causing inflammatory reactions ., This may lead to pneumonitis , depending on the number of larvae penetrating the alveolar walls , and to pulmonary eosinophilia , with symptoms of fever and difficulty in breathing ., Chemotherapy remains the primary method of control for ascariasis , most commonly relying on albendazole or mebendazole 6–9 ., Mass drug administration programs employing these drugs typically target school-age children once or twice a year ., A single oral dose of albendazole , mebendazole or pyrantel pamoate is highly efficacious , but the distribution and incidence of this parasite remain very high ., Achieving a better understanding of how nematodes influence host immune responses to establish a chronic infection may lead to the development of novel control methods ., The success of a parasite in establishing in a host depends on evading or modulating host immune responses , and understanding the molecular basis underlying this strategy is a compelling area of research 10–16 ., These strategies are thought to be orchestrated through molecules released by parasites that shape the host-parasite interaction ., Recently , the protein composition of nematode excretory/secretory products ( ESP ) has been characterized in several species using proteomics approaches based on mass spectrometry ., Species studied include Brugia malayi 14 , 17 , 18 , Heligmosoides polygyrus 19 , 20 , Ancylostoma caninum 21 , Meloidogyne incognita 22 , 23 , Strongyloides ratti 24 and Dirofilaria immitis 25 ., Despite its importance as a highly prevalent NTD , limited knowledge is available about the biology of ESP in A . lumbricoides ., Because A . suum is very closely related to A . lumbricoides and its genome has been published 26 , we characterized the protein composition of perienteric fluid ( PE ) , uterine fluid ( UF ) and total ESP ( the secretome ) from this parasite ., The large size of this species makes it possible to gain insights into the origin of proteins in ESP and the relative contribution of proteins released from the uterus during egg shedding ., Adult A . suum were obtained from JBS Swift and Co . pork processing plant , Marshalltown , Iowa , USA ., They were maintained in Lockes solution ( NaCl 155 mM , KCl 5 mM , CaCl2 2 mM NaHCO3 1 . 5 mM , glucose 5 mM ) at 32°C ., ESP collections were carried out within 24 hr of procurement by maintaining 10 large active females in 500 ml fresh Lockes solution ., PE fluid was collected by snipping the head off adult nematodes with a fine scissors so that the turgor pressure discharged the fluid directly into a 1 . 5 ml screw-capped tube ., Approximately , 300 µl of UF was collected by positioning the ovijector over a similar tube and then gently compressing each end of the parasite toward the opening to expel fluid and eggs ., The thick nature of UF required 1∶1 dilution with sterile phosphate-buffered saline , pH 7 . 6 , for processing after collection ., All solutions were stored in sterile 1 . 5 ml microtubes and shipped overnight on dry ice to the Institute of Parasitology , McGill University , for further analysis ., ESP , PE and UF fluids were collected from 10 nematodes and pooled ., A mixture of protease inhibitors bestatin hydrochloride; 4- ( 2-aminoethyl ) benzenesulfonyl fluoride hydrochloride; N- ( trans-epoxysuccinyl ) -L- leucine 4-guanidinobutylamide; phosphoramidon disodium salt; pepstatin A; Sigma , St . Louis MO ) was added to PE and ESP samples . The ESP sample was sterilized by passage through a 0 . 22 µm filter and concentrated with an Amicon ( MWCO 3000 ) ultrafiltration as described elsewhere 17 ., Only proteins in the ESP sample were precipitated with cold trichloroacetic acid ( TCA; final concentration 20% ) ., TCA pellets were washed with cold acetone ( −30C ) and air-dried ., Pellets were suspended in Tris-HCl ( 20mM , pH 8 . 0 ) and concentration measured ( Quant-iTTM Protein Assay , Invitrogen ) ., UF and PE were centrifuged at 8 , 000×g for 10 min , which pelleted eggs from UF , and the supernatants collected for analysis ., All samples were stored at −80C until further analysis ., Protein pellets were dissolved in Laemmli buffer ( 50 mM Tris-HCl , 2% SDS , 10% glycerol , 1% β-mercaptoethanol , 12 . 5 mM EDTA , 0 . 02 % bromophenol blue , pH 6 . 8 ) ., Aliquots of 40 µl were loaded on a precast gradient gel ( 4–12% , 10×10 cm; Invitrogen ) ., The gel was stained with AgNO3 using standard protocols ., ESP , PE and UF lanes were cut into 1 mm×1 cm slices with a razor blade under a laminar flow hood , which were incubated in 30 mM potassium ferricyanide , 100 mM sodium thiosulfate ( 1∶1 ) for 5 min ., After 3 washes with water , the bands were incubated in ACN for 10 min and prepared for trypsin digestion and mass spectrometry ., The gel slices were destained with 50% methanol , reduced in 10 mM DTT for 1 hr at 56 C and alkylated in 55 mM chloroacetamide for 1 hr at room temperature ., After washing in 50 mM ammonium bicarbonate , gel pieces were shrunk in 100% ACN ., Trypsin ( Promega ) digestion ( 100 ng in 50 mM ammonium bicarbonate ) was conducted for 8 hr at 37 C . Peptides were extracted in 90% ACN/0 . 5 M urea and dried in a speed vac ., Samples were resolubilized in 5% ACN/ 0 . 2% formic acid and separated on a laboratory-made C18 column ( 150 µm×10 cm ) using an Eksigent nanoLC-2D system ., A 56-min gradient from 5–60% ACN ( 0 . 2% FA ) was used to elute peptides from a homemade reversed-phase column ( 150 µm×100 mm ) with flow rate =\u200a600 nl/min ., The column was directly connected to a nanoprobe interfaced with an LTQ-Orbitrap Elite mass spectrometer ( Thermo-Fisher ) ., Each full MS spectrum was followed by 12 MS/MS spectra ( 13 scan events ) from which the 12 most abundant multiply charged ions were selected for MS/MS sequencing ., Tandem MS experiments were performed using collision-induced dissociation in the linear ion trap ., The data were processed using the 2 . 3 Mascot ( Matrix Science ) search engine with tolerance parameters set to 15 ppm and 0 . 5 Da for the precursor and the fragment ions respectively ., The selected variable modifications were carbamidomethyl ( C ) , deamidation ( NQ ) , oxidation ( M ) and phosphorylation ( STY ) ., The database was the Ascaris suum genome draft located at ftp://ftp . wormbase . org/pub/wormbase/species/a_suum/assemblies/v1/Ascaris_suum_geneset_annotated . pep ., Tandem mass spectra were extracted , charge state deconvoluted and deisotoped ., All MS/MS samples were analyzed using Mascot ( Matrix Science , London , UK; version 2 . 3 . 01 ) ., Mascot was set up to search the gs_asc201204 database ( selected for All Entries , 201204 , 18542 entries ) assuming the digestion enzyme , trypsin ., Mascot was searched with a fragment ion mass tolerance of 0 . 50 Da and a parent ion tolerance of 7 . 0 ppm ., The iodoacetamide derivative of cysteine was specified in Mascot as a fixed modification ., Oxidation of methionine was specified in Mascot as a variable modification ., A criterion for protein identification - Scaffold ( Version Scaffold_4 . 2 . 0 , Proteome Software Inc . , Portland , OR ) was used to validate MS/MS based peptide and protein identifications ., Peptide identifications were accepted if they could be established at greater than 95 . 0% probability as specified by the Peptide Prophet and contained at least 2 identified peptides algorithm 27 ., Protein probabilities were assigned by the Protein Prophet Algorithm 28 , which within the Scaffold proteome platform predicted a false discovery rate ( FDR ) of 0 % ., Proteins that contained similar peptides and could not be differentiated based on MS/MS analysis alone were grouped to satisfy the principles of parsimony ., Blast2GO software was used to acquire gene annotations 29 ., Using default parameters , a BLASTP search was performed versus the entire non-redundant NCBI amino acid database using 1×10−3 as the minimum expectation value and a 33 cut-off for high-scoring segment pairs ., Default parameters were used for annotations while setting the value of pre-eValue-Hit-Filter to 1×10−6 , a cut-off value of 55 for annotation and 5 for gene ontology ( GO ) weight ., Annotation Expander ( ANNEX ) , integrated within Blast2GO , was essential to augment annotations 30 ., InterPro database scans were performed within Blast2GO to retrieve functional domains and GO terms , both essential to complement annotation ., Retrieved GO terms and InterPro results were merged to add domains and motifs to annotations 31 ., GO terms were filtered ( in percentage ) in Blast2GO by the number of sequences collected for each ., ESP and UF biological processes ( level, 2 ) GO terms were each filtered at 2% ., ESP , ESP-UF , PE and UF biological processes ( level 4 ) were filtered at 8% , 5% , 35% and 40% , respectively ., UF molecular functions ( level, 2 ) were filtered at 2% ., ESP , ESP-UF , PE and UF molecular functions ( level 4 ) were filtered at 5% , 5% , 12% and 8% , respectively ., Proteins in ESP , PE and UF were scanned for N-terminal signal peptides using SignalP 4 . 1 32 ., We obtained ∼45 µg protein in the ESP sample , ∼60 µg in UF and ∼55 µg in PE , all of which was used for protein content analysis ., We identified 530 distinct proteins in these samples , 175 in ESP , 308 in PE and 274 in UF ., The 20 most abundant proteins in these samples are shown in Tables 1–4 , with the complete lists provided in Tables S1–S3 ., In the ESP sample , 67 proteins were specific to ESP only , 26 were shared only between ESP and PE , and 40 were common only to ESP and UF ( Table S4 ) ., Forty-two proteins ( 8% ) were common among the three compartments; 163 ( 31% ) were appointed solely to PE , 115 ( 22% ) were specific to UF and 77 ( 15% ) were shared between PE and UF ( Figure 1 ) ., Estimates of protein abundance are based on the number of peptides and assigned spectra and peptides from Scaffold 33 ., For comparison , we subtracted proteins found in UF from those detected in ESP ( ESP-UF ) to segregate proteins presumably released through the ES system from those that originate from egg shedding ., In support of this assumption , UF proteins detected in ESP were generally among the most abundant in UF ( Table S3 ) ., Screening the ESP tryptic peptide data against all GenBank proteins identified hits against mammalian keratin and a few bacterial proteins in low abundance in ESP , PE and UF ( data not shown ) , which were automatically excluded from the results ., Polyprotein ABA-1 ( UniProt ID: F1KUF9 ) was among the most abundant proteins in all samples ( Table 1–3 ) ., A protein related to the Onchocerca volvulus OV-17 antigen was abundant in both ESP and UF , suggesting that it may be derived from UF , whereas several homologs of B . malayi antigens were only present in ESP-UF ., The high relative abundance of these antigens suggests that proteins in adult female ESP are derived from both the release of UF during egg shedding and the ES apparatus , a finding supported by the appearance of OV-17 and major sperm protein 2 in the ESP fraction ., Significant overlap in the list of most abundant proteins between UF and PE suggests that the former is derived from the latter compartment and that both can be distinguished from ESP per se ( ESP-UF ) ., The ESP-UF subset was characterized by high abundance of hypothetical proteins compared to PE and UF ., In contrast , glycolytic enzymes were much more abundant in PE and UF than in ESP-UF ., Blast2GO was used to extract GO terms for 134 proteins in ESP , 291 in PE , 264 in UF and 65 in ESP-UF ., The molecular functions “binding” and “catalytic activity” were commonly predicted for proteins in UF , PE , ESP and ESP-UF ( Figure 2 ) ., Higher-level molecular functions GO terms revealed subcategories of binding activity , such as “anion binding” , “nucleotide binding” , “nucleoside binding” and “nucleoside phosphate binding” , which dominated in PE and UF compared to ESP and ESP-UF ( Figure 3 ) ., Although “peptidase activity” and “cation binding” were shared between the three sets of proteins , they were much more common in ESP-UF ., Interestingly , “peptidase inhibitor activity” and “endopeptidase regulatory activity” were only found in ESP and ESP-UF ., Major biological function categories were “metabolic process” , “cellular process” and “single-organism process” in PE and UF ( Figure 4 ) ., ESP and ESP-UF were similar in almost all GO categories ., On a higher level of biological function analysis , most GO terms fell under cellular and metabolic processes ( Figure 5 ) , especially those from PE and UF ., Cellular processes were allocated to “small molecule metabolic process” , “organic substance biosynthetic process” and similar GO terms with cellular processes activity ( Figure 5 ) ., All proteins in ESP , PE and UF were scanned for N-terminal signal peptide sequence using Signal P 4 . 1 32 ., Seventy protein sequences ( 40% ) , 57 ( 19% ) and 39 ( 14% ) had a signal peptide in ESP , PE and UF , respectively ( Tables S1–S3 ) ., We previously analyzed ESP obtained in similar protocols from cultures of the Clade III nematode B . malayi 18 and the Clade V species H . polygyrus , which resides in the same general intestinal niche as A . suum 20 ., We considered two samples to contain the same protein based on the name assigned to it in the Scaffold annotation , as confirmed by BlastP ., The presence of >1 sequence with the same annotation in a sample was not a factor in the analysis ., Because A . suum larval ESP was not investigated , we restricted the analysis to ESP from adult B . malayi ., Comparison of A . suum UF and ESP to B . malayi ESP from female and male ( separately and together ) based on protein composition revealed that female B . malayi ESP was more similar to UF ( UF-ESP: 14% ) than to ESP ( ESP-UF: 7% ) from A . suum ( Figure 6 ) ., Interestingly , protein composition of ESP from male B . malayi equally related to that of UF ( UF-ESP: 18% ) and ESP ( ESP-UF: 18% ) from A . suum ., Overall , B . malayi adult ESP was more similar to A . suum UF ( UF-ESP: 16% ) than ESP ( ESP-UF: 6% ) ., The same approach was used to compare ESP from A . suum , B . malayi and H . polygyrus ( Figure 7 ) ., UF ( UF-ESP ) from A . suum was more similar to ESP from B . malayi ( 19% ) than to ESP from H . polygyrus ( 7% ) ( Figure 7 ) ., In contrast , ESP-UF from A . suum was more similar to ESP from H . polygyrus ( 25% ) than to ESP from B . malayi ( 13% ) ( Figure 7 ) ., Very few tryptic peptides derived from bacteria or of host origin were identified , suggesting that extensive washing eliminated non-nematode proteins from the sample , and that the culture conditions were effectively sterile ., The parasites remained fully motile and appeared healthy during the incubation period , suggesting that proteins released from degenerating nematodes were not major contributors to the dataset ., Experience in our laboratories suggests that A . suum can be maintained without decreases in ATP content or physiological decay for several days under these conditions ., Although it is possible that protein release changes upon removal from the host , similar periods of incubation have been used in all other reports of secretome composition; whether the secretome detected in culture is a faithful reproduction of the in situ secretome is an area of high priority for research ., Whether any of the nematode proteins detected in ESP were excreted from the intestinal tract cannot be determined based on this analysis ., Several types of proteases are abundant in both UF and ESP , but a homolog of a metallo-endoprotease ( MEP ) protease localized in the intestinal tract of hookworms 34 was found in ESP but not UF , suggesting the possibility that proteins released from the parasite intestine may contribute to the secretome ., No cuticle proteins were found in ESP ., While release of proteins from the nematode surface has been reported 35 , the origin of these proteins is not clear ., It seems unlikely that pathways exist for the export of proteins from the hypodermis through the cuticle ., Instead , surface-associated proteins may be released from the ES system and adhere to the cuticle after secretion 18 ., The absence of classical cuticular proteins in ESP mirrors what has been reported in other nematode secretomes and we conclude that turnover of parasite cuticle is at best a minor source of protein release from healthy nematodes under these conditions ., Since the secretome contains proteins derived from UF and anatomical ES pathways , we compared the protein composition of PE , UF and ESP ., The composition of these three samples was highly overlapping ( Figure 1 ) ., Significantly , the ESP fraction included a high proportion of proteins also detected in UF ., As the majority of UF proteins detected in ESP were among the most abundant in the UF proteome , we assume that all UF proteins would appear in the ESP fraction if a large enough sample was analyzed ., It is important to note that non-UF proteins were prominent in the ‘most abundant’ subset of ESP ( Table 4 ) , confirming the hypothesis that anatomical secretory pathways provide an important contribution to the secretome ., Although 28% of proteins in the ESP-UF subset were also identified in the PE fraction , there was no overlap among the 20 most abundant proteins in these two sets ( Table 3–4 ) ., Almost 40% of the proteins detected in UF were present in PE ., Based on GO term analysis , the protein functional compositions of PE and UF were much more similar to each other than either was to ESP-UF ( Figures 2–5 ) ., These results suggest that UF is primarily derived from PE , but that the ESP-UF fraction is derived differently ., Proteins unique to ESP also have distinct GO term profiles compared to PE and UF ., Unique ESP components include homologs of filarial antigens , some proteases , antioxidant enzymes , hypothetical proteins and some proteins involved in glycogenesis ., In contrast , PE unique proteins include other enzymes involved in glycogenesis and antioxidant activities , very few proteases , and proteins involved in binding activity ( not shown ) ., To avoid complications arising from the use of different methods , we only included datasets generated in our laboratory ., Comparisons were based on the primary annotation of proteins ., Based on conservation of proteins , UF from A . suum was more closely related to ESP from B . malayi than to ESP from H . polygyrus ., In contrast , ESP from A . suum was more closely related to ESP from H . polygyrus than to ESP from B . malayi ., Although the number of species is obviously limited , we suggest that secretome composition may be determined by both phylogeny and predilection site in the host ., The figure for B . malayi includes ESP from adult females ( 11% ) and males ( 18% ) ., Interestingly , the protein composition of female B . malayi ESP had little similarity to A . suum ESP-UF ., Male B . malayi ESP was as closely related to UF as to ESP from A . suum in protein composition ., It thus appears that a given protein may be secreted through more than one pathway ., The entirety of the secretome , including proteins derived from UF , must be considered when functional roles in the modulation of host responses are investigated ., Although the three species could be differentiated based on protein composition of ESP , they are difficult to distinguish by GO term classification ., GO term distribution is dependent on the number of proteins in the secretome , the proportion of hypothetical proteins and the level of annotation ., Although the ESP protein composition had somewhat limited similarity among these 3 species based on annotation , they were quite similar in GO terms , suggesting that the general functions associated with proteins in ESP are conserved ., Exosomes , membrane-bound vesicles produced by eukaryotic cells , are commonly found in extracellular compartments ., Considerable evidence points to the involvement of exosomes in cell-cell communication , immune system modulation and tumor progression ., Exosomes contain mRNA , microRNA and proteins 36–38 ., Exosomes have been detected in the secretory system of C . elegans 39 , but work has not been reported in parasitic species ., Exosome-like vesicles produced by the trematodes Echinostoma caproni and Fasciola hepatica are proposed to modulate host-parasite interactions through their uptake by host cells 40 ., Interestingly , the protein composition of F . hepatica ESP shares some similarity with A . suum ESP and UF , particularly with regard to proteins typically associated with exosomes 41 ., Many exosome-associated proteins are present in high abundance in A . suum ESP and other parasitic nematodes see 25 , including proteases , cellular communication proteins , structural proteins , glycolytic enzymes and detoxifying enzymes ., In this regard , the protein compositions of A . suum PE and UF were very similar , but were distinct from ESP ., The number of proteins in PE and UF with signal peptides was similar and low ( 14% and 19% , respectively ) , in contrast to those in ESP ( 40%; Tables S1–S3 ) ., Interestingly , PE and UF were relatively enriched in proteins associated with exosomes compared to ESP ( Table S5 ) ., This could imply that an exosomal secretion is process associated with egg expulsion , or that the protein content of UF is derived , at least in part , by exosome pathways ., The lower abundance of exosome-associated proteins in the ESP-UF fraction may indicate that , although exosomes are involved in protein release from anatomical secretory pathways , classical signal peptide-mediate processes are also important ., We propose that these data support the previous assertion that protein secretion from parasitic nematodes occurs primarily through exosomal pathways 25; further work is needed to verify or refute this hypothesis ., We exploited the unique sensitivity of mass spectrometry combined with the genome of A . suum to identify secreted proteins and proteins in the primary internal fluid compartments of the parasite ., Using bioinformatic tools to mine GO terms , molecular and biological functions were retrieved and compared between the sources of these proteins ., The protein composition profile of ESP differed from those of PE and UF , which were similar to each other ., We suggest that proteins in UF are primarily derived from PE , and that a considerable proportion of secreted proteins originate from sources other than the classical secretory system , at least in female parasites ., Some proteins in nematode ESP appear to be secreted through multiple pathways .

Introduction, Materials and Methods, Results, Discussion

Strategies employed by parasites to establish infections are poorly understood ., The host-parasite interface is maintained through a molecular dialog that , among other roles , protects parasites from host immune responses ., Parasite excretory/secretory products ( ESP ) play major roles in this process ., Understanding the biology of protein secretion by parasites and their associated functional processes will enhance our understanding of the roles of ESP in host-parasite interactions ., ESP was collected after culturing 10 adult female Ascaris suum ., Perienteric fluid ( PE ) and uterine fluid ( UF ) were collected directly from adult females by dissection ., Using SDS-PAGE coupled with LC-MS/MS , we identified 175 , 308 and 274 proteins in ESP , PE and UF , respectively ., Although many proteins were shared among the samples , the protein composition of ESP was distinct from PE and UF , whereas PE and UF were highly similar ., The distribution of gene ontology ( GO ) terms for proteins in ESP , PE and UF supports this claim ., Comparison of ESP composition in A . suum , Brugia malayi and Heligmosoides polygyrus showed that proteins found in UF were also secreted by males and by larval stages of other species , suggesting that multiple routes of secretion may be used for homologous proteins ., ESP composition of nematodes is both phylogeny- and niche-dependent ., Analysis of the protein composition of A . suum ESP and UF leads to the conclusion that the excretory-secretory apparatus and uterus are separate routes for protein release ., Proteins detected in ESP have distinct patterns of biological functions compared to those in UF ., PE is likely to serve as the source of the majority of proteins in UF ., This analysis expands our knowledge of the biology of protein secretion from nematodes and will inform new studies on the function of secreted proteins in the orchestration of host-parasite interactions .

Ascaris lumbricoides , the most prevalent metazoan parasite of humans , is a public health concern in resource-limited countries ., Survival of this parasite in its host is mediated at least in part by parasite materials secreted into the host ., Little is known about the composition of these secretions; defining their contents and functions will illuminate host-parasite interactions that lead to parasite establishment ., Ascaris suum , a parasite of pigs , was used as a model organism because its genome has been sequenced and it is very closely related to A . lumbricoides ., Excretory/secretory products ( ESP ) , uterine fluid ( UF ) and perienteric fluid ( PE ) were collected from adult A . suum ., Proteins were subjected to LC-MS/MS ., ESP proteins ( the ‘secretome’ ) included many also present in UF ., Proteins in ESP but not in UF had considerably different characteristics than those in PE or UF , which were similar to each other ., We conclude that proteins released from the secretory apparatus have distinct patterns of biological function and that UF proteins are likely derived from PE ., Comparing the protein composition of A . suum ESP to ESP from B . malayi and H . polygyrus suggests that the secretome is conserved at the level of both phylogeny and host predilection site .

public and occupational health, biochemistry, infectious diseases, veterinary diseases, medicine and health sciences, veterinary microbiology, biology and life sciences, proteomics, veterinary science

journal.pntd.0003479

Describing the Breakbone Fever: IDODEN, an Ontology for Dengue Fever

Dengue fever is a viral vector-borne disease , limiting the spread of which relies , directly or indirectly , on the control of the vectors that transmit it 1 ., The present lack of vaccines , despite the substantial effort invested for their development , combined with the global increase of dengue cases over the last decades and the fact that a reversion of this trend is not yet apparent 2 make it crucial to identify and mobilize resources in the domain of disease prevention and management , which are either novel , or have not yet been fully established ., Although several modern techniques such as GPS-assisted Geographic Information Systems ( GIS ) 3–7 and decision support systems 8 have found their way into the collection of tools against dengue , the full deployment of Information Technology ( IT ) -related applications is still lagging behind ., Given the spread of the disease across all continents with the exception of Antarctica , it is necessary to improve strategies , to streamline and manage data availability and to enable their access across international boundaries and different technical platforms ., Usage of ontologies was only recently adopted to act as specific IT tools in the field of life sciences ., This methodology has been shown to be exploited for efficient searches of databases , for modeling and , if widely adopted by a relevant research community , to achieve enhanced interoperability of IT resources ., In computer science ontologies represent a formal , explicit specification of a conceptualization and provide a common vocabulary describing classes ( or terms or sets or concepts ) and attributes ( or properties or relationships or relations ) in a given domain ., The complete definitions , listing of synonyms , additional information pertaining to terms ( e . g . cross-references ) and in particular the usage of defined relations linking terms , renders the use of ontologies the tool of choice for driving complicated databases ., Searches that have been directed to use these features of ontologies can perform much more efficiently and , if the same ontologies are in different databases , there is the additional advantage of having the capability of searching across different platforms , thus achieving enhanced interoperability ., Finally , ontologies can be used to model complicated biological knowledge 9–10 ., An ontology , in information sciences , provides definitions of terms in a given domain , as well as , most importantly , the relations that link these terms to each other ., Based on the relationships between terms , the parent-children configuration leads to a tree-like format when an ontology is laid out graphically ., The mandatory integration of relations in ontologies differentiates them from simple controlled vocabularies and renders them powerful ., The first bio-ontology , the Gene Ontology ( GO ) , published 14 years ago , was presented as a tool to be used towards the “unification of biology” 11 , 12 ., In the time that followed , the GO has grown and developed and is now used by researchers from many different fields: indeed , genomics cannot nowadays be imagined without the GO ., Not only has the GO been crucial in the annotation of genes , genomes and related experiments 13–15 , it has also , in turn , provided the impetus for the development of additional IT tools that have greatly enhanced the analysis of the data resulting from high-throughput research ., For example , specific database searches have obtained an additional dimension; it is now possible to search for common attributes ( e . g . cellular localization , functions , and so on ) in repositories that store data from different organisms , obtained by diverse methodologies and by a variety of researchers , and determine functional kinships between gene products that were previously hidden ., This interoperability of databases may indeed represent one of the most efficient usages of ontologies , and of the GO specifically 16 ., In the 14 years since the publication of the GO , tens of novel bio-ontologies were constructed and were deposited in repositories such as the OBO Foundry 17 or the National Center for Biomedical Ontology 18 , describing a wide variety of domains such as anatomy 19 , brucellosis 20 , chemical entities of biochemical importance 21 and others ., Nevertheless , although in general most researchers will agree a priori on the importance of ontologies as tools , it is mostly the field of informatics that has started making extensive use of them while , with the exception of the GO , ontologies are still “underused” in the life sciences domain ., Efforts to surpass methodological difficulties in specifying or understanding and , therefore using , ontologies are underway , especially in health sciences 22 , 23 ., Within the framework of the VectorBase project 24 we initiated the development of a set of ontologies that can be used to describe arthropod vectors and the diseases they transmit 25 ., These ontologies deal with the anatomy of mosquitoes and ticks 26 , mosquito insecticide resistance 27 and malaria 28 ., Here we describe the latest addition to this set of IT tools , namely the ontology for dengue , called IDODEN ., We emphasize the potential usage of the IDODEN , rather than its contents and its architecture , in order to promote a better understanding and appreciation of ontologies by the dengue research community and other life scientists ., Finally , we provide examples of how the ontology can be used in order to model dengue-specific use cases ., We decided to use the same architecture , originally chosen for IDOMAL , for the construction of IDODEN ., The main reason for that was the fact that we had decided to construct vector-borne disease ontologies as extensions to IDO , the Infectious Disease Ontology 33 ., The IDO project is a loose collaborative undertaking that links together ontology developers interested in a variety of infectious diseases ., We have to state here that in spite of our efforts , a few discrepancies between IDO and IDOMAL still exist 34 , and these are carried into IDODEN ., We are hopeful to eliminate these differences in the near future ., Therefore , IDODEN still uses the Basic Formal Ontology ( BFO ) 35 , 36 as a small , upper level ontology , something that helps the exchange of pertinent information with other related ontologies that also use BFO at the upper level ., The BFO does not contain terms that are specific to a given scientific domain , It should be stated here , that we have not yet decided whether to “upgrade” the vector-borne-disease ontologies to BFO 2 . 0 37 awaiting its “formal” adoption by the ontology development community ., Should such a decision be taken in the future , again the fact that our ontologies have a similar blueprint would render this operation easier ., There is one requirement for ontologies to become really useful tools for the community for which they are intended , namely that they be widely accepted and subsequently used/adopted by the community ., Additionally , to promote interoperability wherever terms/concepts are not “thematically” part of the domain described by the ontology in question ( e . g . dengue for IDODEN ) these should be imported directly from ontologies that are themselves used/adopted by a large number of communities ., While the first of these requirements fully depends on the free availability of the ontology as well as , importantly , its objective quality , the second one relies on abiding by rules such as , for example , the ones set by the OBO Foundry 17 ., Among others , the OBO Foundry principles require availability as open source as a development model , shared syntax ( OBO or OWL ) , a clearly specified and clearly delineated content and unambiguous definitions of relations , We decided early on to have our ontologies follow the OBO Foundry rules in order for them to provide the highest degree of “cross-talk” between them and other ontologies ., This prospect was assisted by importing as many terms as possible from other ontologies , rather than describing them de novo ., In other words , these terms have the same ID and the same distinctive features in a given ontology ( here , IDODEN ) as in the one they were imported from ., Thus , definitions , parenthood relations , and synonyms remain the same; this approach is known as MIREOT: the Minimum Information to Reference an External Ontology Term 38 ., As already stated , we also decided to base the architecture of the IDODEN on that of the IDOMAL ., This has some distinct advantages:, i ) should all vector-borne disease ontologies that we construct have the same architecture , it would be easier to use a common matrix for their development; moreover , this would make it simpler , if so decided in the future , to join these ontologies into a major single one covering the domains of more than one disease;, ii ) a second advantage of this is that should these ontologies be used to drive specific databases or other IT tools such as decision support systems , the similar pattern followed by them would enable the database engineers to incorporate the ontologies in question directly into their database and use them for specific purposes ( e . g . searches ) ; and, iii ) thirdly , when our ontologies incorporate IDO immediately below BFO , and at the top of the specifics of “our” diseases , any potential change required will only have to be “designed” once to be adapted for all ontologies describing vector-borne diseases ., Finally , in order for IDODEN to better describe the complexity of vector-borne diseases we decided to have the term “host” describe the human host/patient throughout the ontology while , obviously , the mosquito is always referred to as “vector” ., IDODEN is freely available for browsing and downloading in the OWL format at the NCBO Bioportal ( http://bioportal . bioontology . org/ontologies/3174 ) while we can make available the OBO-format based version on request ., The current version of IDODEN ( version 1 . 0 ) contains 5035 terms , of which 1482 are imported and 3553 were newly created for this ontology ., The imported terms stem from a variety of other ontologies , which are recognized as authoritative by the community ., Not unexpectedly , given the approach chosen ( as mentioned above , the overall format of IDODEN is that of its malaria counterpart ) , almost half of the imported terms have been taken from IDOMAL , while ChEBI 21 , GO 11 , 12 , MIRO 27 and the Environment Ontology ENVO 39 follow in that order ., A list of all donor ontologies can be found in Table, 1 . A large number of the newly-created terms represent the different dengue virus strains , which have been identified by the dengue research community ., Although the inclusion of such instances in an ontology is not necessarily customary it is useful here since IDODEN was constructed with the annotation of entries in databases in mind ., In this context , we have also taken care to keep IDODEN updated since the release of its alpha version , which was made available in November 2012 ., Thus , for example , the current version of the ontology ( version 1 . 0 ) also contains DENV-5 , the latest “addition” to the dengue virus serotype set 40 ., A regular cycle for updating has not been fixed , but every change incorporated in IDODEN , usually depending on community input or literature-dependent changes/additions are immediately moved into the latest version located at the NCBO Bioportal ., IDODEN presently uses 12 relations ( Table, 2 . These have been imported from the Relations Ontology 41 and other collections of relations that are listed by the OBO Foundry 17 ., As is often the case , some relations are not intuitively easy to understand , but become clear when placed in context as in the examples in Table, 2 . Being based on BFO as a top-level ontology , the two uppermost classes of IDODEN are “continuant” and “occurent” ., The latter contains all processes , developmental stages and disease-related temporal regions ., “process” contains all processes , independent of whether these are related to , for example , epidemiology , control , diagnosis and therapy or biological processes of vector , host and pathogen ., The “continuant” class is by far the largest and most heterogeneous class of IDODEN ., We should caution here the “lay” reader of the ontology that in several cases it is not immediately apparent why two terms that describe entities that at first glance are very different from each other , are listed as children of the same parent ., For example , the “Breteau index” 42 is a sibling of “parous rate” , simply because both are children of “measurement datum” ., Needless to say that when the ontology is used by an IT tool , these apparent paradoxes have no negative effect whatsoever on the usage of the ontology ., Given the close kinship between IDODEN and IDOMAL , and in accordance with the latter , the dengue ontology regularly separates specific classes in the hierarchies of knowledge as members of distinct groupings ( see Table 3 ) : dengue fever , i . e . the actual disease , dengue vector , dengue host , and dengue virus , i . e . pathogen ., The populations of the latter , where appropriate , are also handled separately from individuals ., In addition , another related category , “multi-organism” is used to define all processes that relate to more than one of the above three such as , for example , vector-pathogen interactions ., This separation leads to an overall reduction of the number of individual terms included in the ontology without a loss of information ., The separation into these kinds of groupings also proved convenient in terms of a necessary modification: with the recent discovery of the 5th DENV serotype 40 only a few terms had to be added rather than adding a multitude of serotype-specific terms ., An example of the advantage offered by the groupings can be seen in the hierarchy of the term “quality of vector” ., The term “anthropophily” , defined as “The preference for feeding on humans” is common to all vectors of the disease ., No need , therefore , to include additional terms such as “anthropophily of Aedes aegypti” , “anthropophily of Aedes albopictus” , and “anthropophily of Aedes polynesiensis” ., These vectors are described , elsewhere in IDODEN , as the hierarchically last children of the series “object->biotic object-> … ->vector organism->Aedes->Aedes nnnnnn” , whereby “nnnnn” stands here for each individual species that has the “dengue vector” role ., A potential database item could then be annotated with both the terms “anthropophily” and the actual species name , thus reducing the number of terms in the ontology without loosing specificity ., A few examples will illustrate the contents of IDODEN , which , due to the high number of terms included in it , cannot be presented here in detail ., We chose examples , which are “unique” to the dengue ontology , even if some of them , such as the first example presented , are of a more general nature; it deals with the hierarchy of the term “data item” , which belongs to the general term “information content entity”, ( Fig . 1 ) ., It should be noted here that in all figures in which hierarchies are shown , grouped terms appear alphabetically , the way they are listed in the actual ontology ., In IDODEN , like in many application ontologies , several terms are omitted in some hierarchies; the best example of this is with taxonomic ranks ., In cases that data need to be annotated in a database or a decision support system, ( DSS ), with such a missing term , a term higher up in the hierarchy is used instead ., To maximize the usage of ontological terms we nevertheless did include the “information content entity” hierarchy and expanded it to cover certain needs; under “measurement datum” we find a series of indexes , most of which are used in epidemiological and entomological studies ., Examples of the latter are “Breteau index” , i . e . the number of containers that contained larvae for every 100 houses surveyed , and the “house index” , that refers to the number of houses in which Aedes larvae and/or pupae were present ., Of course terms such as “man biting rate” and “mosquito population density” are also found here ., The second example of the contents displays the hierarchy “disposition” , a term that often leads to misunderstandings ., In BFO , the definition of “disposition” is “A realizable entity that essentially causes a specific process or transformation in the object in which it inheres , under specific circumstances and in conjunction with the laws of nature” 35 , 36 ., A general formula for dispositions is: X, ( object ), has the disposition D to, ( transform , initiate a process ), R under conditions C” 43 ., This definition best explains why infectious diseases and , in the particular case of IDODEN , dengue fever are found in this particular class ., This entire hierarchy is shown in Fig ., 2 . Under “disposition” one also finds “insecticide resistance” ., Although these terms were originally described in MIRO , the ontology for insecticide resistance 27 , they have also been imported here in order to make it easier to be accessed by users , given the importance of vector control in the control of dengue fever ., We should note here that “insecticide resistance” is considered to be a phenotype , and phenotypes are ontologically classified as being a “quality” ., Such is the case , for example , in EFO , the Experimental Factor ontology 44 and the Ontology for General Medical Science , OGMS, ( https://code . google . com/p/ogms/ ) ., As a consequence , the Phenotypic Quality Ontology PATO, ( http://bioportal . bioontology . org/ontologies/PATO ? p=summary ), lists “resistance to” directly as “quality” ., On the other hand , IDO 33 , which we are using as a top-level ontology assigns resistance to “disposition” ., We therefore follow IDO’s logic , but once the bio-ontology community solves this question , we will edit IDODEN as/if needed ., A third example of completely practical nature follows ., Diagnosis of dengue fever is usually performed clinically , especially in endemic countries; of course , it has to be confirmed through laboratory tests , particularly in order to differentiate it , in early stages , from other viral infections as well as to determine the virus’ serotype 2 ., To this end several different diagnostic tests are being used to identify the virus , which , in IDODEN , can be found under the term “diagnostic procedure” , a child of the term “process of dengue fever”, ( Fig . 3A ) ., Another method for the detection of the virus is the usage of ready-made test kits , which allows for an early confirmation of the diagnosis ., The term for those kits used in IDODEN is “dengue rapid diagnostic test product“; the kits are listed in the “object aggregate” hierarchy as they are composed of more than one component, ( Fig . 3B ) ., In IDODEN a kit only participates_in the process of virus detection which results_in the diagnosis of dengue ., The next example we detail deals with the class “role” ., This is one of the most populated classes of IDODEN ., The “role” needs some additional description to better understand its significance ., Its definition is “A realizable entity the manifestation of which brings about some result or end that is not essential to a continuant in virtue of the kind of thing that it is but that can be served or participated in by that kind of continuant in some kinds of natural , social or institutional contexts . ”, For example , we naturally tend to think of a mosquito as being a vector and dengue virus as being a pathogen, ( i . e . ontologically , is_a relationships ) ;; this sounds correct to most ears , although this is ontologically incorrect ., An instance of Aedes aegypti, ( i . e . a specific specimen ), can very well exist in nature when it does not carry any dengue virus , thus if it actually does not act as a vector ., A naive view would be to assign a second is_a relation to it , but since ontologies logically only allow for a single is_a relation for each entity , Aedes aegypti simply cannot be, ( thus no is_a ), a vector ., Rather , Aedes aegypti is a child of Aedes , which in turn is a descendant of “biotic object”, ( having gone through a simplified taxonomy ) ., Similar ontologically correct relationships exist for a multitude of terms that are used in a jargon that is difficult to avoid even in scientific publications ., The apparent problem is easily solved by assigning the relation has_role “dengue vector” to Aedes aegypti and the other species that transmit the viruses ., Fig . 4 shows all roles listed in IDODEN ., We stress here that among members of the ontology community there are several discussions as to the ontological “nature” of certain entities ., Without going into long discussions , suffice to say that terms such “enzyme” , “drug” , “parasite” and many more are handled differently by others ., Therefore , all candidate vaccines are is_a children of “chemical compound“; they all bear the role of “candidate vaccine” ., Finally , IDODEN also describes concepts for one of the main research interests of the dengue community , the development of a vaccine against the disease ., Although , so far , no vaccine exists , clinical trials are already underway with different candidates in different phases see 45 , 46 ., Given the significance of this endeavor we have incorporated in IDODEN all relevant and necessary terms related to vaccine development ., In IDODEN the term “dengue vaccine clinical trial” is a child of “process of dengue fever” , the former term having the definition: “A processual entity by which a vaccine against dengue is tested clinically for safety and effectiveness” , while the latter is defined as “A process relating to dengue” ., One hurdle in the development of a dengue vaccine is that the virus has four different serotypes, ( DENV-1–4 ), that have been known for longer , and a fifth, ( DENV-5 ), that is a recent discovery 40 ., Thus , only vaccine candidates against an individual serotype, ( monovalent ), or against the four serotypes known previously, ( tetravalent ), exist ., The way the ontology is structured allows for easy inclusion of new vaccine candidates against the DENV-5 when these are developed ., In IDODEN the children of the parent term “VO:vaccine candidate” have been classified in such a way as to reflect the different vaccine candidates in development ., It has two children , “vaccine candidate using part of organism” and “whole organism vaccine candidate“ . The former is a parent to “DNA vaccine candidate” and “subunit vaccine candidate” ., Both of those terms are parents to children regarding their respective monovalent and tetravalent vaccine candidates ., The term “whole organism vaccine candidate” has the subclass of “live attenuated vaccine candidate” , which is the parent of “live attenuated viral vaccine candidate” ., The latter term has two subclasses , “live attenuated monovalent dengue vaccine candidate” and “live attenuated tetravalent vaccine candidate” ., The term “VO:vaccine clinical trial” which is a “process of dengue fever” , is of importance given the non-availability of vaccines ., The above mentioned term “VO:vaccine candidate” participates in a such a trial , as does “Homo sapiens“ . The clinical trial can result in a “vaccine” which is an agent_in the “IDOMAL:vaccination of population” and “vaccination against dengue”, ( Fig . 5 ) ., The first use of ontologies that comes to mind is that of the annotation of data for their inclusion in specialized databases ., This stems from the huge success of the GO 11 , 12 especially in the annotation of data derived from genome and transcriptome sequencing projects ., This was one of the reasons that led VectorBase to the decision to construct ontologies that describe vector-borne diseases and related knowledge domains 25–28 , 47 ., Clearly , another area in which ontologies can be of great value , concerns overall standardization of data such that IT tools can interact, ( interoperability ) ., Standardization is a conditio sine qua non for a most efficient use of DSSs ., But in addition to these practical uses , ontologies can be excellent tools for modeling complex biological situations 9 , 10 ., A few examples will illustrate this for IDODEN ., In all of them , terms are preceded by the primary ontology that described these entities in case this was not IDODEN ., Since dengue fever is caused by a virus and since it has been shown that Aedes aegypti mosquitoes , similar to Drosophila melanogaster , have the ability to silence gene expression 48 , researchers have turned to RNA interference, ( RNAi ), in an effort to suppress infection 49 , 50 , even creating artificial microRNAs to inhibit viral replication 51 ., In IDODEN the infection , subsequent disease course , its outcome and the RNAi can be modelled as follows, ( Fig . 6 ) ., A “dengue infection” can result in two outcomes:, i ), “HP:death” or, ii ), “OGMS:convalescence” ., The infection itself is preceded by the “initiation of dengue infection” which is part of the “progression of dengue fever” ., The “dengue infection” itself is classified as a process of dengue fever which has the term “BFO:process” as its parent ., In addition what happens during a potential “dengue infection” is that “GO:gene silencing by miRNA” is triggered which results in “OGMS:convalescence” 51 ., This is classified as “GO:posttranscriptional gene silencing” , which is a “GO:regulation of biological process” , a child of “GO:regulation of metabolic process” , which is , in turn a “GO:regulation of biological process” , finally leading up to “GO:biological regulation” ., Higher up this is a “GO:biological process” and the next step is the “BFO:process” ., The “dengue C gene” , the gene responsible for the capsule of the virus 52 , is connected with more than one relation to this mechanism ., It participates in the “GO:gene silencing by miRNA” , while it has the role of a “SO:miRNA target site” and it is also a child of the term “SO:gene” ., In our case “SO:biological region” is the parent term for both “SO:miRNA target site” as well as “SO:gene“ . In addition to all of the above the “dengue C gene” is a part_of the “dengue viral genome” which is itself a part_of the “dengue virus” , which participates in an “acquired immunity to dengue” ., The “dengue virus” is an agent_in the “dengue transmission” that participates in an “occurrence of dengue fever” which is preceded_by a “dengue infection” ., We stress that this represents only a simplified model , in order to demonstrate the possibility of using IDODEN for modeling and therefore several terms/entities have been omitted ., A second example illustrates the description of the course of the disease using IDODEN and other ontologies ( Fig . 7 ) ., Dengue fever has two different forms , non-severe and severe , according to WHO; there are two severe forms , the dengue hemorrhagic fever and the dengue shock syndrome 1 ., Most initial dengue infections result in the non-severe form of the disease 53 ., The percentage of infected people increases when they suffer from a secondary infection from a different viral serotype 54 ., Although there is no consensus as to what exactly causes this increase , it has been suggested that the antibody dependent enhancement ( ADE ) might be responsible 52 , 55 ., The “primary dengue infection” which is preceded by the “initiation of primary dengue infection” results in “asymptomatic dengue” which in the end results in “OGMS:convalescence” ., According to the hypothesis of ADE a “secondary dengue infection” , which is preceded by the “initiation of secondary dengue infection” results in an “occurrence of dengue fever” ., In addition , it results in the “antibody dependent enhancement” which can result in both “dengue hemorrhagic fever” and “dengue shock syndrome” ., These severe forms can result in either “OGMS:convalescence” or in “HP:death” ., In contrast to “asymptomatic dengue” the severe forms happen during an “occurrence of dengue fever” and the “clinical manifestation of dengue” which is part of the “progression of dengue fever” ., This is itself a part of an “occurrence of dengue fever” ., Interestingly , the infection model depicted here can be linked to the model of infection control through miRNAs ., Five concepts are common to both models and one can join the two through those “crossroad” terms ., This exemplifies how models can be expanded using either a comprehensive ontology and/or other ontologies that are built with interoperability in mind ., The last example of modeling based on ontologies deals with the course of an epidemic ., We demonstrate this using the 1927–1928 dengue epidemic in Athens , Greece 56–60 ., Fig . 8 shows how this epidemic , historically unique in several aspects , can be described ontologically ., This was a biphasic epidemic that affected 693 , 000 patients ( possibly 959 , 884 ) , 1553 fatally ( possibly 2700 ) ., The model that we present here also addresses the possibility of an initial DEN-1 infection followed by a DEN-2 infection , a highly possible fact , which has not yet been unequivocally resolved 57 , 58 ., Moreover , due to space constraints , several details are missing ( e . g . the known vector of the infection , synonym Stegomyia fasciata for A . aegypti ) as are all concepts that deal with the control measures taken at the time ., Nevertheless , this model can be expanded almost ad libitum to cover all aspects of this historical event ., Dengue fever is a debilitating disease ., It was given the name “breakbone fever” due to the pain felt by the patients suffering from the disease ., With the number of cases on a constant rise 1 and no therapy being available , emphasis was put in vaccine development ., It was hypothesized that a vaccine could become reality in the next decade 61 , although the newly discovered fifth serotype 40 could delay the development , given the fact that the now existing vaccine candidates only cover the four previously known ones ., With all that in mind , IDODEN was built in such a way that it covers all the important aspects for researchers and medical personnel , in order to assist their efforts ., Furthermore , wherever possible , already existing terms from other ontologies were used in IDODEN ., This is a common practice in ontology development and it was done in an attempt to maximize interoperability between already existing databases that rely on ontologies and to thus facilitate extensive searches on different aspects of the disease ., Many terms in the ontology have been included that cover ontologically entomological and epidemiological surveys that are underway ., With those terms a database containing data of such surveys and using the ontology , will have a powerful search engine to quickly and easily get the relevant information , especially if combined with the recently developed ontology for vector surveillance and management 62 ., The WHO-recommended diagnostic procedures and diagnostic tests are also included in IDODEN , thus enabling potential decision support systems to aid medical personnel in areas were dengue is endemic ., It represents a focused way to access data and knowledge ., The terms can be used to annotate data regarding outcomes of disease course and how effective those diagnostic procedures were at different stages of dengue fever ., It could be even important for the comparison of the results of the diagnostic tests ., We have shown that IDODEN can indeed be used to model complex processes in such a way that they could be understood by machines ( artificial intelligence ) and humans alike , if the data are correctly annotated and the corresponding tools are able to be ontology-driven ., IDODEN has the potential to drive databases , which are “smarter” than those that don’t utilize an ontology ., This way , r

Introduction, Methods, Results and Discussion

Ontologies represent powerful tools in information technology because they enhance interoperability and facilitate , among other things , the construction of optimized search engines ., To address the need to expand the toolbox available for the control and prevention of vector-borne diseases we embarked on the construction of specific ontologies ., We present here IDODEN , an ontology that describes dengue fever , one of the globally most important diseases that are transmitted by mosquitoes ., We constructed IDODEN using open source software , and modeled it on IDOMAL , the malaria ontology developed previously ., IDODEN covers all aspects of dengue fever , such as disease biology , epidemiology and clinical features ., Moreover , it covers all facets of dengue entomology ., IDODEN , which is freely available , can now be used for the annotation of dengue-related data and , in addition to its use for modeling , it can be utilized for the construction of other dedicated IT tools such as decision support systems ., The availability of the dengue ontology will enable databases hosting dengue-associated data and decision-support systems for that disease to perform most efficiently and to link their own data to those stored in other independent repositories , in an architecture- and software-independent manner .

The need for the construction of a dengue ontology arose through the fact that the incidence of dengue fever is on the rise across the world; the number of cases may be three to four times higher than the 100 million estimated by the WHO and a vaccine is still not available in spite of the significant efforts undertaken ., Thus , control of dengue fever still relies mostly on controlling its mosquito vectors ., Large amounts of entomological , epidemiological and clinical data are generated; these need to be efficiently organized in order to further our comprehension of the disease and its control ., IDODEN aims to cover the different aspects and intricacies of dengue fever and syndromes caused by dengue virus ( es ) ., It contains more than 5000 terms describing epidemiological data , vaccine development , clinical features , the disease course , and more ., We show here that it can be a helpful tool for researchers and that , in addition to allowing sophisticated search strategies , it is also useful for tasks such as modeling .

journal.pgen.1003822

The Integrator Complex Subunit 6 (Ints6) Confines the Dorsal Organizer in Vertebrate Embryogenesis

The vertebrate embryonic dorsal organizer , historically referred to as the Spemann organizer , breaks the symmetry of the blastula by defining its dorsal side and ultimately gives rise to axial mesoderm , which forms the notochord , the defining anatomical feature of the chordate lineage ., In fish and frogs , induction of the organizer relies on a maternal Wnt signaling pathway that leads to the accumulation of β-catenin in nuclei on the prospective dorsal side of the embryo 1 , 2 ., A primary function of the organizer is to induce a region in the embryo that is competent to adopt dorsal fates , such as prechordal plate mesoderm and neural ectoderm , in the presence of widespread ventralizing BMP signaling ., Proper partitioning of axial versus non-axial cell fates during gastrulation is essential to ensure proper embryonic patterning ., BMP signaling patterns tissues along the dorsoventral axis ( DV ) , but does not act to partition axial versus non-axial fates ., For example , in zebrafish bmp2b ( swirl ) ligand mutant embryos , loss of BMP signaling causes the expansion of dorsal neurectodermal and non-axial dorsal mesodermal cell fates at the expense of ventral cell fates without expanding the organizer itself 3 , 4 , 5 , 6 ., Thus , in the absence of ventral cell fate specification , other mechanisms ensure that the organizer is confined dorsally ., In zebrafish and Xenopus , several maternal and zygotic genes function to restrict the organizer to dorsal regions ., Three related homeodomain-containing transcriptional repressors , Vox , Vent , and Ved play a key role in repressing dorsal organizer gene expression ventrolaterally in zebrafish 7 , 8 , 9 , 10 , 11 ., These repressors are expressed ventrally and dorsolaterally , and their deficiency causes dorsal organizer gene expression to expand around the ventrolateral margin during late blastula stages at the expense of ventrolateral tissues ., Xenopus Vox and Vent have been shown to directly repress the expression of the organizer genes chordin ( chd ) and goosecoid ( gsc ) 12 , 13 and depletion of Gsc has been reported to lead to a 25-fold increase in vent expression 14 ., Similarly in zebrafish , Vox and Vent have been shown to bind to the gsc promoter and to physically associate with Gsc protein 8 , 9 ., These and other data 15 , 16 illustrate the cross-regulatory interactions between opposing ventralizing and dorsalizing transcriptional repressors that are essential for proper embryonic patterning ., Several additional genes are known to restrict organizer gene expression to dorsal regions and modulate the expression of vox , vent , and ved ., Knockdown of Runx2bt2 , a maternal isoform of Runx2b , delays induction of vox and vent , and eliminates ved expression 17 ., Embryos deficient in maternal Runx2bt2 exhibit an expansion of dorsal organizer gene expression at late blastula stages with a reciprocal loss of ventrolateral tissues 17 ., Expression of vox , vent , and ved is maintained during late blastula and early gastrula stages by zygotic Wnt8a signaling 10 , 18 ., By mid gastrulation , expression of these ventralizing transcriptional repressors is maintained by BMP signaling 10 , 19 ., Thus , a gene regulatory network involving Runx2bt2 , Wnt8a and BMP signaling converges on vox , vent , and ved to maintain the specification of non-axial mesoderm ., The maternally supplied transcription factor pou5f3 ( previously called pou5f1 ) also functions in restricting the organizer to the dorsal midline ., Maternal-zygotic deficiency of pou5f3 ( MZpou5f3 ) leads to severe dorsalization resulting from derepression of organizer genes ventrolaterally in the embryonic margin , and incomplete induction of the BMP pathway 20 ., MZpou5f3 mutants also exhibit aberrant morphogenesis and fail to form endoderm 20 , 21 , 22 ., Pou5f3 likely functions as a transcriptional activator of genes , including vox , that are required to repress dorsal organizer gene expression ventrolaterally 23 , 24 ., Thus , Pou5f3 is another mediator of organizer gene repression operating in parallel to the Wnt8a pathway and partially through the BMP pathway ., One of the earliest organizer genes induced downstream of the maternal Wnt pathway in zebrafish is bozozok ( boz ) , a direct transcriptional repressor of bmp2b , vox , vent , and ved expression 8 , 25 , 26 , 27 , 28 , 29 , 30 ., boz mutant embryos fail to form prechordal plate , notochord , forebrain , and ventral neural structures and display an increase of ventroposterior mesoderm 25 ., boz mutant embryos can be rescued by suppressing Wnt8a signaling , indicating that Boz antagonizes zygotic wnt8a expression in the organizer to block non-axial fate development in the dorsal embryonic midline and allow axial development 26 ., Boz stability is modulated by Lnx2b , a maternally supplied E3 ubiquitin ligase that can directly bind and ubiquitinate Boz 31 , 32 ., Loss of Lnx2b causes expression of boz and other organizer genes to expand into lateral regions of the late blastula , illustrating the importance of proper turnover of Boz ., The transcriptional repressors Vox , Vent , and Ved are essential for partitioning the mesoderm into axial versus non-axial domains in response to positive regulation from Runx2bt2 , Pou5f3 , the Wnt8 , and BMP pathways , and negative inputs from dorsalizing transcriptional repressors such as Boz and Gsc ., It is less clear how these pathways are molecularly integrated to regulate vox , vent , and ved expression and it is likely that additional maternally-provided factors function in this process ., Accordingly , we performed a genetic screen for maternal-effect mutations to identify novel mediators of vertebrate embryonic patterning ., We isolated a novel recessive maternal-effect mutation p18ahub that causes a profound reduction in ventrolateral mesoderm with a reciprocal expansion in axial mesoderm , and frequently multiple independent axial-like domains ., Consistent with radial expansion of the organizer , p18ahub mutant females produce embryos exhibiting ectopic dorsal forerunner cells , a unique population of non-involuting mesendodermal cells at the dorsal margin 33 , 34 , 35 ., We can rescue p18ahub dorsalized mutant embryos either by limiting Nodal signaling or restoring BMP signaling ., We determined through positional cloning that p18ahub is a mutation disrupting the integrator complex subunit 6 ( ints6 ) gene , which encodes a highly conserved component of the Integrator Complex , a large multisubunit complex implicated in 3′ end processing of spliceosomal snRNAs 36 ., Previously , ints6 was named deleted in cancer 1 ( dice1 ) and investigated as a putative tumor suppressor gene in humans 37 , 38 , 39 , 40 ., Using a forward genetic approach , we have revealed a novel role for Ints6 in limiting the extent of dorsal organizer tissues during vertebrate embryogenesis ., We isolated p18ahub , a recessive maternal-effect dorsalizing mutation , in an ENU-induced mutagenesis screen designed to identify novel maternal factors required for early embryonic development and patterning in zebrafish ( similar to that described in 41 , 42 ) ., Mutant females yielded embryos with similar phenotypes whether they were crossed to mutant or wild-type ( WT ) males , indicating a strictly maternal-effect defect with no zygotic contribution ., The first defect evident in embryos from p18ahub mutant mothers ( henceforth referred to as p18ahub mutant embryos ) was a delay in the initiation of epiboly , the morphogenetic process by which the blastoderm cells move over and encompass the yolk cell 43 ., As WT embryos reached the late blastula/50% epiboly stage ( Figure 1A ) , mutant embryos typically had not initiated epiboly movements ( Figure 1B ) ., In early gastrulation stages , WT embryos displayed a single dorsal thickening corresponding to the embryonic axis ( Figure 1C ) , whereas mutant embryos often developed a radial thickening possibly due to hyper convergence of cells around the entire embryonic margin ( Figure 1D ) ., Approximately 50% of mutant embryos ( Figure 1G ) lysed prior to 24 hpf ., Thirty-five percent of mutant embryos surviving to 24 hours post fertilization ( hpf ) exhibited radial symmetry around the animal-vegetal axis and lacked any recognizable structures ( Figure 1E , F ) ., We categorized such embryos as class 6 dorsalized embryos ., Class 5 embryos lacked recognizable structures but were not radially symmetric around the animal-vegetal axis and typically did not survive to 24 hpf ., Class 4 through class 1 embryos ( Figure 1G ) exhibited progressively less severe dorsalization , as described 5 ., To simplify the presentation of the data , we have combined phenotypic classes , C1–C3 , and , C4–C6 , in all figures , except that C5 embryos that did not survive to 24 hpf are included in a lysed category ., To better examine the epiboly and lysis defects of p18ahub embryos , we conducted time-lapse imaging ( supplemental Movie S1 and Movie S2 ) of embryos from mid-blastula to mid-somitogenesis stages ., p18ahub embryos were developmentally delayed and in some mutant embryos displayed prolonged epiboly ( Movie S2 ) ., Some p18ahub embryos failed to undergo epiboly whatsoever and lysed by the equivalent of early gastrula stage in WT , with cells dispersing rapidly and the blastoderm disintegrating ( Movie S1 ) ., In other p18ahub embryos , epiboly progressed to just prior to yolk plug closure ( 100% epiboly ) when the hypoblast rapidly retracted and either the embryo lysed or the tissue dived down into the animal pole of the yolk cell ( Movie S2 ) , accounting for the morphology of embryos like the one shown in Figure 1F ., Based on these studies it is likely that the lysed p18ahub embryos for a given clutch displayed either the early lysis phenotypes or were class 5 or 6 dorsalized ., To investigate if p18ahub embryos exhibit altered DV patterning , we examined the expression of genes of the dorsally-derived neurectoderm , dorsolaterally-derived somitic and paraxial mesoderm , and ventrally-derived pronephros , by in situ hybridization on 3- to 5-somite stage embryos ., The expression of both six3 , which marks forebrain neurectoderm 44 , and pax2 . 1 , a marker of the midbrain-hindbrain boundary 45 , was circumferentially expanded in p18ahub embryos ( Figure 1H , I ) ., krox20 , which is expressed in rhombomeres 3 and 5 in the hindbrain 46 , was often undetectable in severely dorsalized p18ahub embryos due to their severe delay ( 8/23 embryos displayed krox20 expression for the clutch represented in Figure 1I ) ., In p18ahub embryos able to develop longer , krox20 expression was also expanded circumferentially ( Figure 1J , K ) ., The expression of myod , a marker of paraxial and somitic mesoderm 47 , was scattered in clusters of cells distributed circumferentially ( Figure 1I , verified by examination of myod probe alone , not shown ) ., Pronephric pax2 . 1 expression was often undetectable in mutant embryos ( compare Figure 1I p* with H; verified by examination of pax2 . 1 probe alone , not shown ) , indicative of a severe reduction of ventroposterior mesoderm ., To investigate if patterning is also affected during gastrulation in p18ahub embryos , we examined the expression of the fore- and mid-brain marker otx2 48 at mid gastrulation ., We found that otx2 expression was expanded around the DV axis rather than restricted to a dorsoanterior region as in WT ( Figure 1L and M ) ., Importantly , however , otx2 was not expanded posteriorly as in wnt8a mutants 18 , suggesting that the Wnt8a pathway is intact in p18ahub embryos ., We also examined the expression of cyp26a and hoxb1b , markers of anterior neurectoderm and caudal hindbrain , respectively 49 , 50 ., Consistent with dorsalization , p18ahub embryos displayed expanded cyp26a expression around the DV axis ( Figure 1N , O ) ., Note also that the p18ahub embryos were delayed developmentally; the margin of the mutant embryo ( the equivalent time of bud stage for WT ) has not advanced as far as in WT ., Compared to WT ( Figure 1N ) , the p18ahub embryo displays reduced marginal cyp26a expression , which is consistent with the WT cyp26a expression pattern at earlier gastrula stages 50 ., Expression of hoxb1b in the posterior neurectoderm is expanded in p18ahub embryos around the margin , although with reduced intensity compared to WT ( Figure 1P , Q ) , likely also reflecting developmental delay 49 ., It was necessary to age-match embryos for these experiments because we could not obtain sufficient numbers of p18ahub embryos at mid-gastrula stage due to their lysis ., These data indicate that p18ahub embryos display an expansion of dorsal neurectodermal cell fates during gastrulation , but unlike zygotic wnt8a mutants , no significant expansion of anterior at the expense of posterior neurectoderm is observed in p18ahub embryos ., To determine if the dorsalization of p18ahub embryos results from impaired BMP signaling , we examined BMP ligand gene expression , as well as expression of the BMP antagonist chordin ( chd ) in mutant embryos ., bmp2b expression appeared normal in late blastula stage mutant embryos ( data not shown ) ., However , bmp2b and bmp4 expression were significantly reduced in mutant embryos by the early gastrula stage ( Figure 2A , B , E , F ) and severely reduced by mid gastrulation ( Figure 2C , D , and data not shown ) , consistent with dorsalization ., Since BMP gene expression is controlled by an autoregulatory feedback loop 3 , , the loss of bmp2b and bmp4 expression in mutant embryos indicates severely reduced BMP signaling ., chd expression , which is restricted to the dorsal side of the early zebrafish gastrula 53 , is circumferentially expanded in mutant embryos at an early gastrula stage ( Figure 2G , H ) , consistent with reduced BMP signaling and excessive dorsal fate specification in p18ahub embryos ., To determine if p18ahub embryos are defective mechanistically in BMP signal transduction , we injected mutant embryos with bmp2b mRNA , which moderately to severely ventralizes WT embryos 6 ( Figure 2J ) ., We found that forced expression of bmp2b restored ventral fates to WT in two-thirds of mutant embryos or moderately ventralized them ( Figure 2I , K , O , P ) ., Thus , the BMP signal transduction machinery in mutant embryos can function when BMP ligand is provided exogenously ., To further test whether the endogenous BMP signaling machinery is functional in p18ahub embryos , including the endogenous BMP ligands , mutant embryos were injected with translation-blocking morpholinos ( MOs ) targeting the secreted BMP antagonists Chordin , Noggin1 , and Follistatin-like 2b 54 ., Depletion of these BMP antagonists in mutant embryos caused mild to moderate ventralization , similar to that of WT embryos ( Figure 2M , N ) ., Thus , endogenous BMP ligands can signal in p18ahub embryos at a WT or greater level if BMP ligand function is permitted ., Dorsalization can also be caused by a ventral expansion of dorsal organizer gene expression ., To investigate the organizer in p18ahub mutant embryos , we examined expression of the organizer gene goosecoid ( gsc ) 55 ., We found that , although gsc expression was induced normally at the mid blastula stage ( data not shown ) , it was expanded in p18ahub embryos by early gastrulation ( Figure 3A , B ) and remained ectopically expressed through mid gastrula stages ( Figure 3C , D ) compared to WT embryos ., Therefore , the dorsalization of p18ahub embryos involves a prominent expansion of gsc expression by early gastrulation , contrasting dorsalization resulting solely from defective BMP signaling 5 ., Since gsc expression was induced normally in p18ahub mutant embryos , it suggested that the organizer is induced normally by the maternal Wnt signaling pathway 1 , 2 ., To directly test this , we examined β-catenin nuclear localization as a readout of the maternal Wnt signaling pathway ., We immunostained mid-blastula stage embryos ( 3 . 5 hpf ) to visualize β-catenin in nuclei on the presumptive dorsal side of the embryo 1 ., No differences were evident between WT and mutant embryos in β-catenin intensity or its localization in nuclei at the dorsal margin ( Figure 3E , F , arrows ) ., No nuclear localized β-catenin was observed ventrally in mutant embryos ., Consistent with normal induction of the organizer , boz , a direct transcriptional target of the maternal Wnt pathway 56 , was expressed normally in mutant embryos through 6 hpf , the equivalent of early gastrula stage ( Figure 3G–J ) , unlike gsc and chd , which were expanded ( Figure 2H and 3B ) ., Thus , the organizer is induced normally in mutant embryos but the expression of some organizer genes becomes derepressed around the margin between late blastula and early gastrula stages ., Furthermore , the dorsalization of p18ahub embryos does not rely upon ventrolateral expansion of boz ., Zygotic Wnt8a signaling in ventrolateral regions represses the expression of the organizer genes , gsc and chd , thus restricting the size of the organizer 7 , 8 , 9 , 10 , 11 , 18 , 19 , 26 ., Loss of Wnt8a or its mediators Vox , Vent , and Ved causes the expression of some organizer genes to expand and dorsalizes the embryo 7 , 8 , 9 , 10 , 11 , 19 ., Accordingly , we investigated the status of the Wnt8a pathway in mutant embryos ., In WT late blastula and early gastrula stage embryos , wnt8a is expressed at the margin in a large domain extending from ventral to dorsolateral regions ( Figure 4A ) 18 , 57 ., However , in p18ahub late blastula stage embryos , wnt8a expression was restricted ventrally to a smaller domain ( Figure 4B ) ., Furthermore , wnt8a expression was undetectable in mutant embryos age-matched to their WT counterparts at early gastrula stage ( not shown ) , perhaps reflecting a loss in the competence of marginal cells to express wnt8a ., Since wnt8a is required for anteroposterior ( AP ) patterning of neural tissue 18 and a defect is not evident in AP patterning in p18ahub mutants , it suggests that the early , transient expression of wnt8a may be sufficient for AP patterning in p18ahub mutant embryos ., We also examined the expression of vox and ved , two genes encoding transcriptional repressors acting downstream of Wnt8a to restrict organizer gene expression to the dorsal midline 10 , 11 , 19 ., We found that vox expression was reduced in mutant embryos compared to age-matched WT embryos at a late blastula stage ( Figure 4C , D ) ., By early gastrulation , vox expression was greatly reduced or absent in mutant embryos ( Figure 4E , F ) ., We also conducted a time course experiment where embryos were collected from WT and p18ahub females over half hour intervals beginning at a mid-blastula stage ( 3 hpf ) and subsequently examined for wnt8a and vox expression ., In this experiment induction of neither gene was observed in p18ahub embryos by the equivalent of the early to mid gastrula stage ( 7 hpf ) in WT ( not shown ) ., Maternal ved expression was evident in p18ahub embryos ( data not shown ) ; however , by an early gastrula stage ved expression was prominently reduced ( Figure 4G , H ) ., Early gastrula stage embryos also displayed reduced or nearly absent expression of eve1 , a marker of ventroposterior mesoderm that requires BMP and Wnt8a signaling for its expression ( Figure 4I , J ) 58 , 59 , 60 ., Thus , key mediators of the Wnt8a pathway are repressed in p18ahub embryos beginning as early as the late blastula stage , which can account for the loss of ventroposterior mesoderm ., We could not restore Wnt8a signaling in mutant embryos via injection of wnt8a mRNA , because early overexpression of wnt8a mimics the maternal Wnt signal for organizer induction and severely dorsalizes embryos 57 ., To determine if the zygotic Wnt8a signaling pathway is functional in p18ahub embryos , we tested for Wnt8a function in p18ahub embryos rescued by depletion of BMP antagonists ., If Wnt8a signaling is required , then it would indicate that the pathway is functional but likely fails to function in p18ahub embryos due to lack of full induction or maintenance of expression ., To moderately increase BMP signaling in p18ahub embryos , we depleted one BMP antagonist , Chd , via MO injection ., Loss of Chd alone rescued the majority of p18ahub embryos to a mild V1 ventralized phenotype , similar to loss of Chd in WT embryos ( Figure 4K , L , N , O ) ., Loss of wnt8a dorsalized WT embryos 18 ( Figure 4K ) and also dorsalized chd deficient embryos or caused posterior truncations ( Figure 4M , P ) ., Importantly , p18ahub embryos that were enabled to specify ventral tissues by Chd depletion became dorsalized again when Wnt8a was also depleted ( Figure 4K–M , O , Q ) ., These results indicate that the Wnt8a pathway is mechanistically intact in p18ahub embryos and that their ventralization or rescue to a WT phenotype by enhancement of BMP signaling depends on endogenous Wnt8a signaling ., Along with the maternal Wnt pathway , the dorsal organizer also depends on Nodal signaling for its induction ( reviewed in 61 ) ., Thus , we investigated the status of the Nodal pathway in p18ahub embryos ., We examined expression of the Nodal ligand nodal-related 1 ( ndr1 , squint ) in mutant and WT embryos 62 ., ndr1 induction is initiated on the dorsal side of the embryo ( Figure 5A ) and requires Wnt signaling similarly to boz 2 ., At a mid blastula stage , we observed no significant differences in the expression of ndr1 between WT and p18ahub embryos ( Figure 5B ) and ndr1 expression was never observed more animally outside of its normal marginal domain through an early gastrula stage equivalent ( not shown ) ., To further test if Nodal signaling is induced normally in p18ahub embryos , we examined expression of the Nodal feedback antagonist lefty1 ( lft1 , antivin1 ) ., lft1 is initially expressed dorsally but is subsequently expressed around the margin of the late blastula 63 , 64 , 65 ., At an early gastrula stage ( 6 hpf ) , we observed no significant differences in the expression level of lft1 in mutant versus WT embryos ( Figure 5C and D ) ., From these data we conclude that Nodal signaling in p18ahub embryos is induced normally and likely operates normally at an early gastrula stage ., By mid gastrula stages the pan-mesodermal gene no tail ( ntl ) is expressed in two distinct domains , one corresponding to axial mesoderm , the developing notochord , and another corresponding to ventrolateral non-axial mesoderm 66 ., At the equivalent of mid gastrula and early somite stages , ntl expression in presumptive non-axial mesoderm was reduced or absent , while axial mesoderm appeared to be expanded leading to multiple independent axes in some mutant embryos ( 9/9 , 6/10 , and 4/10 in three independent clutches ) ( Figure 5E–H″ ) ., Some mutant embryos displayed a significantly broadened single axial ntl domain with a prominent reduction in marginal ntl expression ( Figure 5F inset ) ., By the equivalent of early somite stages of development , mutant embryos clearly possessed multiple independent presumptive notochords marked by ntl expression ( Figure 5H ) ., floating head ( flh ) , a homeobox gene required for notochord specification , is induced at a late blastula stage independently of ntl but is a direct transcriptional target of ntl by mid gastrula stages 67 , 68 , 69 ., Consistent with excessive axial ntl expression , flh was also circumferentially expanded in p18ahub embryos by mid gastrulation ( Figure 5I , J ) ., Therefore , genes marking axial mesoderm ( gsc and flh ) are ectopically expressed in p18ahub embryos by early gastrula stage , despite normal Wnt-mediated organizer induction and normal induction of the Nodal pathway ., Nodal signaling is required for the expression of the HMG- type transcription factor sox32 ( casanova ( cas ) ) in endodermal precursors by late blastula stages 35 , 70 ., cas is required along with pou5f3 ( formerly spiel-ohne-grenzen , pou5f1 ) to induce sox17 and maintain endodermal precursor cells 21 ., Both cas and pou5f3 are also required for the maintenance of dorsal forerunner cells 21 , 33 , a distinct population of non-involuting cells that also express sox17 at the leading edge of the dorsal margin of the blastoderm as it migrates over the yolk 34 , 71 ., We observed endodermal sox17 expression in p18ahub embryos at mid gastrulation ( Figure 5L ) ., However , endodermal precursor cells were located more animally than in WT ( Figure 5K ) , perhaps due to altered cell movements resulting from loss of BMP signaling and dorsalization 72 , 73 ., Ectopic marginal expression of sox17 was observed in p18ahub mid gastrula stage embryos indicating that ectopic dorsal forerunner cells form in mutant embryos ( Figure 5K , L , arrows ) ., In p18ahub embryos axial mesoderm and organizer gene expression are expanded ventrolaterally , but remain confined within a normal mesodermal domain ., Importantly , we did not observe the animal-ward expansion of Nodal-dependent genes such as ntl or lft1 or excessive specification of mesendodermal precursor cells , which has been reported upon excessive Nodal signaling due to loss of Lefty 74 , 75 , 76 or ectopic expression experiments 77 , 78 ., Thus , Nodal signaling is likely not excessive in p18ahub embryos ., The secreted feedback inhibitor Lefty/Antivin ( Lft1 ) regulates Nodal signaling 63 , 64 , 65 , 79 ., Misexpression of Lft1 in WT embryos severely limits mesendoderm induction with embryos closely resembling ndr1;ndr2 double mutants 62 ., We found that injection of as little as 0 . 7 picograms ( pg ) of lft1 mRNA ( Figure 6A , +0 . 7× middle row ) , a dose that only weakly perturbs WT embryos ( Figure 6A , Minor head defects ) , could restore WT or nearly WT patterning in 33% of mutant embryos ., Injection of 1 pg of lft1 mRNA ( Figure 6A , +1× middle row ) restored WT patterning in a larger fraction of mutant embryos and suppressed mesendoderm formation ( oep-like ) in 20% of mutant embryos ., The same dose of lft1 mRNA injected into WT embryos blocked mesendoderm development in more than 50% of the embryos ( Figure 6A , +1× bottom ) ., Injection of 3 pg of lft1 mRNA into mutant and WT embryos inhibited mesendoderm induction in a similar fraction of embryos ( Figure 6A , +3× ) ., Thus , suppression of Nodal signaling can restore the balance between axial and non-axial fate specification in mutant embryos , similarly to restoring BMP signaling ., We examined lft1 expression to determine if its reduction contributed to excess dorsal mesodermal gene expression at mid gastrulation in p18ahub embryos ., In WT embryos lft1 was expressed around the embryonic margin as well as in the developing axis and dorsal forerunner cells ( not shown ) at mid gastrula stages ( Figure 6B ) ., In p18ahub embryos lft1 was expressed within the presumptive prechordal plate as well as in clusters of cells scattered around the margin at a mid gastrula stage ( Figure 6C ) ., These latter cells may represent remaining marginal cells having non-axial fates ., Hence , an absence of lft1 expression cannot account for the severe dorsalization of p18ahub embryos ., To identify the molecular nature of p18ahub , we mapped the mutation to a chromosomal position by examining linkage to simple sequence length polymorphic ( SSLP ) markers ., We first found linkage of p18ahub to z1660 on chromosome 9 ., Further fine mapping examining over 1100 meioses placed p18ahub within a 1 . 35 Mb interval between the SSLP marker z7120 and an SSLP marker that we generated for BAC CR545476 . 14 ( Zv9 ) ., ( Figure 7A ) ., The interval displays synteny with human chromosome 13 ., It contains just over one dozen genes , none of which were known to function in DV patterning ., We proceeded to sequence the open reading frames of cDNAs of genes within the interval ., We found a T to A transition in an exon of the integrator complex subunit 6 ( ints6 ) gene ( GenBank Accession number KF700696 , OMIM 604331 ) , converting a nearly invariant valine to an aspartate at position 375 of the 854 amino acid predicted protein ( Figure 7B ) ., Human and zebrafish Ints6 orthologs are 66% identical indicating an overall high degree of evolutionary conservation ., The only recognizable domain in Ints6 is an N-terminal von Willebrand factor type A motif ( InterPro IPR002035 ) , a broadly employed motif mediating interactions between diverse proteins ., There is a second gene , ddx26b , on the X chromosome in humans and on chromosome 14 in zebrafish that is highly related to ints6 ., The zebrafish Ints6 and Ddx26b proteins are 61% identical also implying significant conservation of function between these homologs ., We examined the expression of ints6 by in situ hybridization ., ints6 transcripts were present in eggs and embryos through the late blastula stage ( Figure 7C , D , data not shown ) ., ints6 transcripts declined during late blastula stages and were undetectable by early gastrulation ( Figure 7 , data not shown ) ., We observed no alteration in the expression of ints6 in mutant embryos by in situ hybridization ( not shown ) ., These expression data are consistent with the recessive , maternal-effect inheritance of p18ahub and the maternal requirement for ints6 ., To determine if dorsalization of p18ahub embryos was caused by the mutation in ints6 , we injected mutant embryos with mRNA encoding WT Ints6 ., We found that as little as 5 pg of WT ints6 mRNA rescued 50% of mutant embryos completely ( Figure 7E ) and 50 pg rescued nearly 80% of mutant embryos to WT ( Figure 7E , H , I ) ., Thus , ints6 is the defective gene in p18ahub mutant embryos ., We also injected mutant embryos with up to 300 pg of different preparations of p18ahub mutant ints6 mRNA and never detected rescue ( Figure 7F ) ., Thus , p18ahub is likely a strong loss-of-function or null allele ., Overexpression of inst6 in WT embryos produced no phenotype ( data not shown ) ., The related zebrafish ddx26b mRNA also rescued p18ahub embryos ( Figure 7G ) , although it is not provided maternally to the embryo ( data not shown ) ., We did not detect ddx26b expression via in situ hybridization on embryos through 24 hpf , although we were able to amplify cDNA corresponding to ddx26b from ovary RNA ( not shown ) ., We have identified a recessive maternal-effect mutation in integrator complex subunit 6 ( ints6 ) , a highly conserved member of an RNA processing machine for which no specific role in vertebrate development was known ., Importantly , to our knowledge the p18ahub mutation represents the first mutation of the ints6 gene in any organism to be reported ., Interestingly , the loss of ints6 causes a recessive , maternal-effect dorsalization whereby dorsal midline axial fates are expanded , generating multiple dorsal axes at the expense of ventrolateral fates , suggesting an expansion of the dorsal organizer itself or of organizer gene expression mediating dorsal fate specification ., The dramatic radial dorsalization of affected embryos is not caused by an expansion of the maternal Wnt-mediated induction of the organizer or the induction of Nodal signaling ., In ints6 mutant embryos axial mesoderm is expanded along the DV axis at late blastula stages but remains confined near the margin where mesendoderm is normally induced , indicating a DV patterning defect rather than a general expansion of mesoderm ., The Integrator Complex was identified as a complex of 12 subunits that co-purifies with Deleted in split hand/split foot 1 ( DSS1 ) in human HEK293 cells 36 ., The Integrator Complex associates with the C-terminal domain ( CTD ) of the large subunit of RNA polymerase II ( RNAP ) and has been implicated in 3′ end processing of the U1 and U2 snRNAs of the splicesosome 36 ., Phosphorylation of serine 7 in the heptapeptide repeats present in the CTD directs the Integrator Complex-bound RNAP to snRNA genes rather than to the promoters of protein coding genes 36 .,

Introduction, Results, Discussion, Materials and Methods

Dorsoventral patterning of the embryonic axis relies upon the mutual antagonism of competing signaling pathways to establish a balance between ventralizing BMP signaling and dorsal cell fate specification mediated by the organizer ., In zebrafish , the initial embryo-wide domain of BMP signaling is refined into a morphogenetic gradient following activation dorsally of a maternal Wnt pathway ., The accumulation of β-catenin in nuclei on the dorsal side of the embryo then leads to repression of BMP signaling dorsally and the induction of dorsal cell fates mediated by Nodal and FGF signaling ., A separate Wnt pathway operates zygotically via Wnt8a to limit dorsal cell fate specification and maintain the expression of ventralizing genes in ventrolateral domains ., We have isolated a recessive dorsalizing maternal-effect mutation disrupting the gene encoding Integrator Complex Subunit 6 ( Ints6 ) ., Due to widespread de-repression of dorsal organizer genes , embryos from mutant mothers fail to maintain expression of BMP ligands , fail to fully express vox and ved , two mediators of Wnt8a , display delayed cell movements during gastrulation , and severe dorsalization ., Consistent with radial dorsalization , affected embryos display multiple independent axial domains along with ectopic dorsal forerunner cells ., Limiting Nodal signaling or restoring BMP signaling restores wild-type patterning to affected embryos ., Our results are consistent with a novel role for Ints6 in restricting the vertebrate organizer to a dorsal domain in embryonic patterning .

A complex integration of signaling pathways establishes the body plan of the vertebrate embryo ., The dorsal side of the embryo is defined by the organizer , a specialized field of cells that breaks the symmetry of the zebrafish blastula by instructing neighboring cells to adopt dorsal fates based on their proximity ., The isolation of mutant genes in the zebrafish has identified many genes required for embryonic development ., However , our knowledge of the molecular mechanisms integrating different signaling pathways within a gene regulatory network to properly pattern the embryo is still incomplete ., We isolated a recessive maternal effect mutation in the integrator complex subunit 6 ( ints6 ) gene that leads to a circumferential expansion of the organizer and the formation of dorsal tissues at the expense of ventral tissues ., Currently , the only reported role for the Integrator Complex is to mediate processing of snRNAs of the spliceosome ., Our molecular genetic approach indicates that ints6 confines the organizer to dorsal domains , preventing it from extending around the margin into ventral domains ., Thus , we have determined a novel role for a highly conserved component of an RNA processing machine .

journal.pgen.1004064

Natural Selection Reduced Diversity on Human Y Chromosomes

The Y chromosome has often been used as a marker for studying human demographic history 1 , but one implicit assumption in these analyses is that the Y chromosome is not affected by natural selection at linked sites 2 ., However , formal tests of models of selection have been lacking ., In part , this has been due to a paucity of resequencing data for many male human genomes , where autosomal , X , Y and mtDNA for the same individuals could be compared ., Such data eliminate one source of sampling variance that could influence comparisons between genomic regions , and also allow for chromosome-wide estimates of genetic diversity on the Y , which is often ignored in whole-genome analyses 3–5 ., Under simple neutral models with constant and equal male and female population sizes , diversity is expected to be proportional to the relative number of each chromosome in the population: X diversity is expected to be three-quarters autosomal diversity ( because there are three X chromosomes for every four autosomes ) and both the Y and mtDNA diversity are expected to be one-quarter autosomal diversity 6 ., The Y chromosome does not undergo homologous recombination , except in the small pseudoautosomal regions 7 ., In general , diversity is reduced in genomic regions or genomes with little or no recombination 8–11 ., Similarly , previous studies of small segments of the human Y chromosome have found low levels of genetic diversity , but multiple theories exist to explain this reduction 12–16 ., Because the Y chromosome is found only in males , low diversity on the Y could result from neutral processes if , for example , the effective population size of males is reduced relative to that of females ., One factor that can reduce the male population size is high variance in the number of offspring ., Differences in the variance in reproductive success between the sexes , will cause differences in effective population sizes , even when the actual number of males and females is approximately the same 4 , 13 ., Based on comparing patterns of genetic variation on the X chromosome and the autosomes , several recent studies have found evidence of sex-biased demographic processes during human history 3–5 , 17–20 , often suggesting that the effective population size of females was higher than that of males throughout recent human history ( Nf>Nm , if Nf represents the effective number of breeding females and Nm represents the effective number of breeding males ) ., Alternatively , purifying selection acting to remove new deleterious mutations on the Y chromosome , will affect diversity at linked neutral sites through a process called background selection ., Background selection refers to the reduction in genetic diversity at sites that are themselves neutrally evolving , but are linked to other sites where deleterious mutations occur 21–24 ., Background selection may be particularly potent on the Y chromosome , because there is no recombination on the Y chromosome ., As such , deleterious mutations in one area of the chromosome could reduce levels of genetic diversity across the entire chromosome 12 , 14–16 ., However , the strength of selection is also important ., Several weakly deleterious mutations may interact resulting in a Hill-Robertson interference 25 , whereby interference among linked sites weakens their effects on linked neutral sites 26 ., Similarly , positive selection , acting on beneficial mutations is expected to decrease diversity at linked neutral sites ., Given the unique gene content and lack of recombination on the Y chromosome , it is likely to have experienced a complex evolutionary history ., Here , using genome-wide analyses of X , Y , autosomal and mitochondrial DNA , in combination with extensive population genetic simulations , we show that low observed Y chromosome variability is not consistent with a purely neutral model ., Instead , we show that models of purifying selection and background selection affecting linked neutral sites are consistent with observed Y diversity ., Further , the number of sites estimated to be directly under purifying selection greatly exceeds the number of Y-linked coding sites , suggesting the importance of the highly repetitive ampliconic regions 27–29 ., Because the functional significance of the ampliconic regions is poorly understood , our findings should motivate future research in this area ., Analyzing complete genomic sequence data from 16 unrelated males ( Table S1 ) , we observe that normalized diversity on the human Y is extremely low compared to expectations from other genomic regions ( Figure 1; Table 1 ) ., By analyzing resequencing data for the autosomes , X chromosome , Y chromosome , and mitochondria from the same individuals , we reduce sampling variance that might otherwise confound comparisons between regions of the genome ., Here diversity is measured as the average pairwise differences per site , π , in the sample , and is normalized using divergence between humans and outgroup species ( see Materials and Methods ) ., The purpose of this normalization is to account for the possibility that different parts of the genome may have different mutation rates ., The mutation rates could systematically differ across chromosomal types because the different chromosomes spend different amounts of time in the male and female germlines and the male germline has a higher mutation rate than the female germline 30 ., Because the low diversity on the Y chromosome persists after this normalization , it cannot be explained by a correspondingly low mutation rate on the Y chromosome ( Table S2; Figure S1 ) ., Further , the highly repetitive ampliconic regions of the Y were not assembled by Complete Genomics , and so are not analyzed here ( Materials and Methods ) ., Diversity on the Y chromosome is likely not being under-estimated due to the inability to call variants in haploid regions of the genome because diversity on the X measured in females , where the X is diploid , is nearly identical to diversity on the X measured in males , where the X is haploid ( Figure S2 ) ., The pattern of reduced diversity on the Y chromosome is observed in both Africans and Europeans , suggesting that the effect is not population-specific , and holds regardless of whether the neutral sequence analyzed is near or far from genes ( Table 1 ) ., Previous analyses of portions of the Y reported low Y diversity 12–16 , but measuring divergence-normalized π per site at 0 . 0018 for Africans and 0 . 0024 for Europeans , we observe that chromosome-wide Y diversity is an order of magnitude lower than the equilibrium neutral expectation of one-quarter the autosomal level of diversity ( Figure 1 ) ., Conversely , mitochondrial diversity is not reduced compared to expectations under neutrality ( Figure 1 ) ., Additionally , our estimates of diversity on the X chromosome are consistent with previous estimates from Africans 5 , 17 and Europeans 3 , 5 ., These trends held for all populations sampled in the public Complete Genomics data ( Figure S3 ) ., In contrast to diversity in other genomic regions , we observe that diversity is lower on the Y chromosome for the African populations in our sample than for the European populations in our sample ( Table 1 ) ., Previous studies of Y chromosome diversity have also suggested that the difference in diversity on the Y is small between Africans and Europeans 31 , 32 , or that it may , as we observe , be higher in Europeans than some African populations 15 , 33 ., For example , haplotype diversity was found to be higher across Europeans than Africans ( 0 . 852 versus 0 . 841 ) 33 ., Similarly , when the African populations are broken down into Sub-Saharan Africans versus North Africans ( the Complete Genomics samples are Western/Northern Africans ) , European diversity falls in between these two , with European diversity on the Y chromosome actually higher than diversity in North Africans 33 ., Other studies have observed slightly higher diversity in Africans than Europeans , but include a much more diverse group of Africans ., For example , variation on the Y chromosome has been reported previously to be only slightly higher on the Y for African versus Non-African populations , even though the population of Africans is much more diverse ( including Bakola from Cameroon , Dogon from Mali , Bantu from South Africa and Khoisan from Namibia and South Africa ) 32 than the population we analyze ., The uncorrected levels of diversity reported here for the Y chromosome ( Table S2 ) , differ from some previous studies 15 , 31 , 34 , but are not directly comparable to these studies because:, 1 ) they were based on genetic markers that were chosen specifically because they have high mutation rates 15 , 31 , 34; and ,, 2 ) the populations are different than the ones available for this study 34 ., The absolute number of SNPs identified here is not reduced relative to other sequencing platforms 35 ., In fact , overall diversity is similarly observed to be low on the Y using this other technology , but a larger TMRCA is estimated 35 , perhaps because the Y seems to harbor pockets of hidden diversity 36 ., We next consider several possible models that could explain this unexpectedly low amount of diversity found on the Y chromosome relative to other genomic regions ., Such models include differences in the variance in reproductive success between males and females , purifying selection on the Y chromosome , and positive selection on the Y chromosome ., In principle , a greater variance in male reproductive success than female reproductive success ( Nf>Nm ) could result in a lower than expected effective population size of the Y chromosome ., In fact , previous studies have suggested that increased variance in offspring number has reduced the effective population size in human males versus females and might explain the reduced variability on the paternally inherited Y chromosome 4 , 13 ., To test the hypothesis that sex-biased demography explains the decreased Y chromosome diversity , we modeled increasingly skewed sex ratios using coalescent simulations , taking into account the complex demography of the populations analyzed here ( Figure 1; Table S3; Methods ) ., We use the case where Nm\u200a=\u200aNf as the null model ., As expected , decreases in the male effective population sizes ( Nm/Nf<1 ) decrease expected Y diversity ., However , we find that the reduction in the male effective population size required to explain the observed Y chromosome data , predicts levels of normalized autosomal , X and mtDNA diversity that are not consistent with the data in these markers ( Figure 1; Table S3 ) ., This effect can also be illustrated by considering ratios of normalized diversity in each type of marker relative to autosomes ., A skew in the sex ratio large enough to explain the observed reduction in Y/autosome diversity would also cause increases in X/autosome and mtDNA/autosome diversity that are incompatible with observations ( Figure 1; Table S4 ) ., Thus , by analyzing all classes of genomic sequences , we are able to reject extreme sex-biased processes as the sole explanation for patterns of low observed Y variability ., Natural selection has also been suggested to play a large role in reducing diversity on the Y chromosome 12 , 14–16 , and works within the context of the demographic history of the populations ., Purifying selection can reduce genetic variation at linked neutral sites via a process called background selection , which has received extensive theoretical treatment in the literature 21 , 22 , 26 , 37–41 ., Purifying selection has already been documented for the mtDNA 42 ., Due to the lack of homologous recombination throughout most of the Y chromosome , background selection is expected to have a particularly strong effect , severely reducing diversity on the Y chromosome ., Two factors determine the overall effect of background selection on reducing neutral diversity in non-recombining regions:, 1 ) The strength of selection , and, 2 ) the number of sites subject to selection ., At approximately 60 million base pairs , there are orders of magnitude more sites that may be subject to selection on the human Y chromosome than on the mtDNA ., Selection may actually be quite weak on individual mutations that occur on the Y chromosome , but in the absence of recombination , if many sites are possible targets of this weak selection , this can lead to a strong reduction in diversity among Y chromosomes ., Here , we performed forward simulations with purifying selection to assess whether background selection could reduce diversity at neutral sites on the Y chromosome to the levels observed in our data ., We study purifying selection under different assumptions of the variance in male reproductive success ., We chose to use forward simulations , rather than using standard analytical background selection models , which assume the effect of background selection is a simple reduction in effective population size , for several reasons ., First , the standard formulas were derived for equilibrium demographic models , but human populations have a more complex demographic history with unknown effects on the process of background selection ., Second , many mutations have been shown to be weakly deleterious and may persist in the population due to genetic drift 37 , 38 ., The standard theory does not allow for this ., Finally , simulations studies suggest that the standard theory can over-predict the reduction in genetic diversity due to background selection if there are many weakly selected linked mutations 26 ., The forward simulations that we conducted address all of these concerns ., We first evaluated whether purifying selection acting only on new nonsynonymous mutations in the coding regions of the Y chromosome could reduce levels of genetic diversity at linked neutral sites to the levels detected in our observed Y chromosome data ., To do this , we performed forward simulations using realistic demographic models for the populations where only new nonsynonymous mutations were subjected to purifying selection ( see Methods ) ., We find that models of selection acting only on coding sites cannot sufficiently reduce expected diversity at linked neutral sites through background selection on the Y chromosome ., Under the assumption of equal sex ratios , regardless of the mean selection coefficient used , all models result in levels of diversity at linked neutral sites that are significantly higher than the observed values for both Africans ( P<0 . 001 ) and Europeans ( P<0 . 025 , Figure S4 ) ., In principle , models with a larger female effective population size could explain the low diversity observed on the Y chromosome ., However , we have demonstrated that such models cannot match the levels of genetic diversity observed on the X chromosome , mtDNA , and Y chromosome together ., However , sex-biased demography along with purifying selection acting on new nonsynonymous mutations in the coding regions of the Y chromosome could reduce levels of diversity at linked neutral sites ., To evaluate the joint effects of sex-biased demography and purifying selection , we used levels of putatively neutral diversity ( i . e . , diversity far from genes ) on the X chromosome and the autosomes to estimate the degree of sex-biased demography for the populations in our study ( Table 2 ) ., We find that Nm/Nf\u200a=\u200a0 . 335 in the African population which is concordant with estimates from previous studies 4 , 20 , 35 ., Under an assumption of an extremely reduced male effective population size , relative to females ( Nm/Nf\u200a=\u200a0 . 335 ) which matches patterns of diversity on the X chromosome , predicted diversity at linked neutral sites , from models including purifying selection only on nonsynonymous mutations , is still significantly higher than the observations in Africans ( P<0 . 001 , Figure S4 ) ., In Europeans , we estimate that that Nm/Nf\u200a=\u200a1 ( Table 2 ) ., These results hold for a wide range of the mean strength of selection ( Methods; Figure S5 ) ., Given its unique structure , it is possible that purifying selection acts on more than just the nonsynonymous sites on the Y chromosome ., Specifically , in addition to the approximately 100 , 000 single copy coding sites ( predicted from annotated coding genes 43; Methods ) , the Y also contains 5 . 7 Mb of highly repetitive ampliconic regions , composed of long palindrome “arms” , each with nearly-identical sequences 27 , 28 ., Genes in these ampliconic regions are expressed exclusively in the testis 27 , 28 , and so may be under selection related to male fertility ., Further , it has been hypothesized that , in the absence of homologous recombination with the X , intra-chromosome pairing and the resulting gene conversion between palindrome arms may reduce the mutational load on the Y , and so these palindromes themselves , as a means of allowing intra-chromosome recombination , may be subjects of selection 27–29 ., Thus , we developed a novel approximate likelihood approach to estimate the number of sites affected by purifying selection ( L ) required to reduce diversity at linked neutral sites to the low values observed on the Y ( Methods ) ., Simulations show that our method can accurately estimate L ( Methods; Table S5 ) ., Assuming an equal sex ratio , the maximum likelihood estimate of the number of sites subjected to purifying selection on the Y is as much as 30 fold higher than the number of coding sites , for both Africans and Europeans ( Figure 2 ) ., Relaxing the assumption of an equal sex ratio to allow many fewer males relative to females ( to the ratio of the number of males to the number of females that fit neutral diversity on the X and autosomes , Nm/Nf\u200a=\u200a0 . 335 4 , 20 ) , and to an extreme bias in male reproductive success of Nm/Nf\u200a=\u200a0 . 1 , slightly decreases the estimates of the number of sites directly affected by purifying selection ., However , the estimate from the African sample is still significantly greater than the number of coding sites ., Our results strongly support the hypothesis that at least some of the ampliconic regions evolve under the direct effects of purifying selection , where new mutations in these regions are deleterious ., The above estimates assume that the selection coefficients of the deleterious mutations on the Y chromosome are the same as those estimated from nonsynonymous mutations on the autosomes , with appropriate re-scaling to account for the differences in Ne and ploidy on the autosomes and the Y chromosome ( see Methods ) ., However , it is possible that the strength of selection acting on noncoding mutations on the Y chromosome could be different than that acting on nonsynonymous mutations on the autosomes ., It is unclear whether this difference in the strength of selection could bias our estimates of the number of sites directly under selection ., To address this concern , we extended our approach to jointly estimate the number of sites directly affected by purifying selection ( L ) as well as the mean strength of selection ( see Methods ) ., Even when considering a range of different strengths of selection , we find that the estimates of the number of sites to be directly under the effect of purifying selection are largely insensitive to the mean strength of selection , and are still more than the number of X-degenerate coding sites ( Figures S5 and S6 ) ., This suggests that content recruited to the Y chromosome after X–Y recombination was suppressed , including the high-copy-number ampliconic regions , as well as any transcription factor binding sites , may be subject to purifying selection that , due to the lack of homologous recombination , acts to reduce diversity on the human Y chromosome ., We found that a population expansion model matched the average observed levels of autosomal , X and mtDNA polymorphism in the African populations , and a bottleneck model matched the observed levels of polymorphism in the European population ( Figure 1 , Tables S4 , S5 and S7 ) ., Several publications have documented various signatures of background selection throughout the genome 17 , 44–47 ., If background selection had reduced average levels of diversity across the genome ( previous work suggests around a 6% reduction in diversity 24 ) , this would mean that the demographic parameters that fit the data were not truly reflective of population history , but instead reflected both population history and background selection ., Thus , even if background selection is operating on the putatively neutral genomic regions we analyze here , the reduction in diversity on the Y chromosome is still too extreme to be consistent with that level of background selection ., Rather , additional background selection , as we have modeled here , would be required ., Although models of purifying selection are consistent with the low observed diversity , it is also possible that positive natural selection may also be driving low diversity on the human Y via selective sweeps 48 , 49 , when neutral variation is removed due to the fixation of an advantageous mutation ., Although it can be difficult to distinguish between genetic signals of background selection versus positive selection with few nucleotide polymorphisms , as is the case with the Y chromosome , we analyzed the data using two additional measures ., First , we computed the folded site frequency spectrum for Y chromosome SNPs across all unrelated Y chromosomes in the Complete Genomics dataset ( Figure S8 ) ., The abundance of low frequency SNPs is consistent with both positive selection and purifying selection ( Figure S8 ) , and the low overall number of SNPs makes further distinctions between the two models difficult ., Second , we built a neighbor-joining tree for all unrelated Y haplotypes in the Complete Genomics dataset using phylip 50 , then branch lengths were computed using a molecular clock in paml 51 ., There is not an overarching star phylogeny , which would be indicative of a single selective sweep ( Figure S9 ) ., While we cannot rule out such a scenario directly , we note that previous studies also found little or no evidence of selective sweeps 52 or gene-specific positive selection 53 , 54 on the Y chromosome ., However , one might conceive of a complex evolutionary history involving several instances of positive selection along different Y lineages that could result in the observed haplotype topology ., Given recent findings of pockets of Y haplotype diversity , it is possible that recurrent positive selection may contribute to reduced Y diversity 36 ., We observe that diversity across the entire human Y chromosome is extremely low ., We find that neutral models with sex-biased demography may contribute to low Y diversity ., However , models of extreme differences in reproductive success between males and females are insufficient as the sole explanation for patterns of genome-wide diversity ., Alternatively , then , natural selection appears to be acting to reduce diversity on the Y . We show that models of purifying selection affecting Y chromosome diversity are consistent with low observed diversity , if purifying selection acts on more than the few coding regions left on the Y chromosome ., Thus , our results suggest that selection may also act on the highly repetitive ampliconic regions , and support arguments for the functional importance of these regions 29 ., Further strong purifying selection acting on the human Y is consistent with reports of the conservation of both the number and the type of functional coding genes on the Y chromosome in humans 12 and across primates 55 , 56 ., It is also possible that positive selection has been acting to reduce diversity on the Y chromosome , but this explanation would require multiple independent selective sweeps across populations ., Although positive selection is expected to confound evolutionary relationships , if purifying selection is the dominant force on the Y chromosome , the topology of the tree should remain intact , but the coalescent times are expected to be reduced ., This means that the Y chromosome , keeping in mind that it is a single marker without recombination , may actually provide a more useful marker for inferring phylogeographic patterns than other markers ., Indeed , recent resequencing efforts of the Y chromosome identified a single mutation that resolves a previously unresolved trifurcation of lineages , and reports monophyletic groupings of Y chromosomes from distinct populations 35 ., While it a combination of factors influence genome-wide estimates of diversity , and variance in male reproductive success still affects patterns of autosomal , X , Y and mtDNA diversity , selection clearly affects levels of diversity on the Y , and so should be considered when drawing conclusions regarding demography and population history based on patterns of Y-linked markers ., We analyzed unrelated , high quality , publicly available whole genomes generated by Complete Genomics assembly software version 2 . 0 . 0 57 ( Table S1 ) ., Next generation sequence data often suffer from sequence errors , assembly errors and missing information , and non-reference alleles will be less likely to be mapped 58 ., However , the Complete Genomics dataset overcomes many of these errors by using very high coverage ( >30X 57 ) ., Additionally , to be conservative , we only consider sites with data called in all individuals in each population ., We removed putatively functional and difficult to assemble regions including: RefSeq known genes , CpG islands , simple repeats , repetitive elements ( RepeatMasker ) , centromeres , and telomeres , downloaded from the UCSC Genome browser 43 , and filtered using Galaxy 59 ., We also excluded the hypervariable regions on the mtDNA 60 , which might inflate estimates of mitochondrial diversity , and analyzed only the X-degenerate regions of the human Y 27 , because diversity might be reduced in the pseudoautosomal or ampliconic regions ., Divergence was computed from number of nucleotide differences per site between pairwise human and chimpanzee reference sequence alignments for autosomes , X , and mtDNA downloaded from the UCSC genome browser 43 , and for the Y from ref 28 ., The total number of SNPs called on the Y chromosome in the Complete Genomics dataset does not appear to be lower than other chromosome-wide assessments of Y variation ., Of the SNPs across 16 individuals that overlap between the 1000 genomes ( 252 SNPs ) and Complete genomics dataset ( 6236 ) , there are only 12 sites called in the 1000 genomes dataset that are not called in the Complete Genomics dataset; all of these are singletons , and many have missing data across several individuals ( Table S7 ) ., Further , the geographic distribution of Y chromosome sampled for the Complete Genomics dataset does not appear to be wider for the European versus the African populations 61 ., The per generation per site mutation rates estimated from human-chimpanzee alignments , assuming a divergence time of 6 million years and 20 years per generation , are 2 . 11×10−08 for the autosomes , 1 . 65×10−08 for chromosome X , and 3 . 42×10−08 for chromosome Y . For mtDNA we use the mutation rate reported of 1 . 7×10−08 for the mtDNA 62 ., The recombination rates used were 1 cM/Mb and ( 2/3 ) * ( 1 cM/Mb ) , for the autosomes and X , respectively ., Diversity is measured using , π , the average number of nucleotide differences per site between all pair of sequences ., For the inference of the number of sites under selection , we summarize the genetic variation data by S , the number of segregating sites , because the distribution of S , conditional on the underlying genealogy , is known ( Poisson , see below ) ., We do not directly analyze the ampliconic regions , as they were not assembled in the Complete Genomics data ., All estimates of diversity , and human-chimpanzee divergence used for normalization are reported in Table S2 ., Human-orangutan estimates of divergence could not be used because no whole Y chromosome sequence currently exists for orangutan ., Although the Y chromosome sequence was recently published for the rhesus macaque , the sequence has diverged and degraded so much between human and macaque that very little of the noncoding regions are alignable 55 , preventing us from reliably correcting for divergence across all chromosome types using human-macaque divergence ., Population genetics parameters used in coalescent 63 and forward simulations 64 for Europeans and Africans are similar to previously published estimates 65 , 66 ., We use a simple model of drift , which assumes purely random ( Poisson ) variation in offspring numbers for both males and females , and non-overlapping generations ., For Africans , the neutral model is of an expansion from 10 , 000 to 20 , 000 individuals 4 , 000 generations ago ., For Europeans the neutral model is of a bottleneck from 10 , 000 to 1 , 000 individuals 1 , 500 generations ago , followed by an expansion to 10 , 000 individuals 1 , 100 generations ago ( Table S6 ) ., Neutral expectations under equal and skewed sex ratios were modeled using coalescent simulations implemented in ms 63 , assuming the population-specific demographic models described above , and allowing for recombination on the autosomes and X chromosome , but not the Y or mtDNA ., The effective population sizes for each chromosome type ( Nauto , NchrX , NchrY , and NmtDNA ) , for given male and female effective population sizes ( Nm and Nf ) are ( see e . g . , ref 67 ) :For a fixed ratio and males to females ( R\u200a=\u200aNm/Nf ) , and fixed total effective population size ( Nauto ) , we then calculate the male and female effective population sizes as:Using these equations we can use standard neutral coalescent simulations implemented in ms to simulate data for the four chromosome types , while varying R , but keeping Nauto constant ., We keep Nauto constant to mimic the real data , as the demographic parameters were originally estimated from autosomal markers ., Further details about the values used for simulations can be found in Table S8 ., Complete commands for ms simulations are given in Note S1 ., We modeled purifying selection using forward simulations implemented in SFS_CODE 64 ., The exact commands used in the SFS_CODE simulations are given in Note S1 ., Similar to the coalescent simulations , we modeled the African and European populations separately , using the population-specific demographic models described above , the Y chromosome per generation per base pair mutation rate , and sampling 8 chromosomes per simulation to match the sample size of our observed data ., However , unlike ms , which scales parameters by the current population size and moves backward in time , SFS_CODE starts with the ancestral number of chromosomes and simulates a haploid population forward in time ., Thus , when rescaling the effective population size from the autosomal estimates , for SFS_CODE we used the same diploid autosomal ancestral effective population size for both populations ( N\u200a=\u200a10 , 000 ) ., The Y chromosome effective size was then found using the same process described above for the neutral coalescent simulations ., To investigate purifying selection acting only on new nonsynonymous mutations , we simulated 60 , 041 nonsynonymous sites ( 90 , 062 coding sites are estimated from the union of all exons from X-degenerate , non-pseudoautosomal genes on the Y chromosome 43 ) at which new mutations are expected to be subject to purifying selection ., To assess the effect of background selection , each simulation also contained 500 kb of linked neutral sequence from which we calculated diversity ., The effect of background selection is a function of the distribution of selection coefficients for new , deleterious mutations , and can be modeled by varying the mutation rate , the number of sit

Introduction, Results/Discussion, Materials and Methods

The human Y chromosome exhibits surprisingly low levels of genetic diversity ., This could result from neutral processes if the effective population size of males is reduced relative to females due to a higher variance in the number of offspring from males than from females ., Alternatively , selection acting on new mutations , and affecting linked neutral sites , could reduce variability on the Y chromosome ., Here , using genome-wide analyses of X , Y , autosomal and mitochondrial DNA , in combination with extensive population genetic simulations , we show that low observed Y chromosome variability is not consistent with a purely neutral model ., Instead , we show that models of purifying selection are consistent with observed Y diversity ., Further , the number of sites estimated to be under purifying selection greatly exceeds the number of Y-linked coding sites , suggesting the importance of the highly repetitive ampliconic regions ., While we show that purifying selection removing deleterious mutations can explain the low diversity on the Y chromosome , we cannot exclude the possibility that positive selection acting on beneficial mutations could have also reduced diversity in linked neutral regions , and may have contributed to lowering human Y chromosome diversity ., Because the functional significance of the ampliconic regions is poorly understood , our findings should motivate future research in this area .

The human Y chromosome is found only in males , and exhibits surprisingly low levels of genetic diversity ., This low diversity could result from neutral processes , for example , if there are fewer males successfully mating ( and thus fewer Y chromosomes being inherited ) relative to the number of females who successfully mate ., Alternatively , natural selection may act on mutations on the Y chromosome to reduce genetic diversity ., Because there is no recombination across most of the Y chromosome all sites on the Y are effectively linked together ., Thus , selection acting on any one site will affect all sites on the Y indirectly ., Here , studying the X , Y , autosomal and mitochondrial DNA , in combination with population genetic simulations , we show that low observed Y chromosome variability is consistent with models of purifying selection removing deleterious mutations and linked variation , although positive selection may also be acting ., We further infer that the number of sites affected by selection likely includes some proportion of the highly repetitive ampliconic regions on the Y . Because the functional significance of the ampliconic regions is poorly understood , our findings should motivate future research in this area .

population modeling, evolutionary modeling, molecular genetics, population genetics, biology, genomics, evolutionary biology, population biology, computational biology, evolutionary genetics

journal.pgen.1001372

Genome-Wide Association Study Using Extreme Truncate Selection Identifies Novel Genes Affecting Bone Mineral Density and Fracture Risk

Osteoporotic fracture is a leading cause of morbidity and mortality in the community ,, particularly amongst the elderly ., In 2004 ten million Americans were estimated to, have osteoporosis , resulting in 1 . 5 million fractures per annum 1 ., Hip fracture is, associated with a one year mortality rate of 36% in men and 21% in, women 2; and the, burden of disease of osteoporotic fractures overall is similar to that of colorectal, cancer and greater than that of hypertension and breast cancer 3 ., Bone mineral density ( BMD ) is, strongly correlated with bone strength and fracture risk , and its measurement is, widely used as a diagnostic tool in the assessment of fracture risk 4–6 ., BMD is known to, be highly heritable , with heritability assessed in both young and elderly twins , and, in families , to be 60–90% 7–14 ., Although the extent of, covariance between BMD and fracture risk is uncertain , of the 26 genes associated, with BMD at genome-wide significant levels to date , nine have been associated with, fracture risk ( reviewed in 15 ) , supporting the use of BMD as an intermediate phenotype, in the search for genes associated with fracture risk ., There is considerable evidence from genetic studies in humans 12 , 16 , 17 , and in mice 18 , indicating, that the genes that influence BMD at different sites , and in the different genders ,, overlap but are not identical ., Thus far all genome-wide association studies ( GWAS ), of BMD have studied cohorts of a wide age range , and with one exception have, included both men and women; when only women have been studied , both pre- and, postmenopausal women have been included ., Therefore , to identify genes involved in, osteoporosis in the demographic at highest risk of osteoporotic fracture we have, performed a GWAS in postmenopausal women selected on the basis of their hip BMD , and, replicated the GWAS findings in a large cohort of adult women drawn from the general, population ., SNPs at chromosome 2q24 , in and around GALNT3 , achieved near, genome-wide significance in our discovery cohort ( peak P-value rs1863196 , total, hip ( TH ) P\u200a=\u200a2 . 3×10−5; LS, P\u200a=\u200a0 . 037 ) ( Figure 1A ) ., This SNP was not typed or imputed by either the, Rotterdam or the TwinsUK cohorts , but a nearby SNP showed strong association in, both AOGC and the combined replication cohorts ( rs6710518; AOGC discovery , TH, P\u200a=\u200a6 . 9×10−5; combined, replication sets , FN P\u200a=\u200a2 . 7×10−7 ) ., In the combined datasets the finding achieved genome-wide significance at the FN, ( P\u200a=\u200a1 . 7×10−10 ) ., Strong, association was also seen with this SNP at LS, ( P\u200a=\u200a7 . 5×10−5 ) ., Another marker, within GALNT3 , rs4667492 , was also associated with fracture, risk , including vertebral fractures ( OR\u200a=\u200a0 . 89;, 95%CI\u200a=\u200a0 . 80–0 . 99;, P\u200a=\u200a0 . 032 ) and overall low trauma fractures, ( OR\u200a=\u200a0 . 92;, 95%CI\u200a=\u200a0 . 85–0 . 99;, P\u200a=\u200a0 . 024 ) ., We have recently identified a mouse with an, N-ethyl-N-nitrosourea induced, loss-of-function GALNT3 mutation ( Trp589Arg ) , that develops, hyperphosphataemia with extraskeletal calcium deposition , and hence represents a, model for FTC 35 ., To establish further the association of, GALNT3 and BMD , we determined BMD in these, GALNT3 mutant mice ., This revealed that homozygous, ( −/− ) GALNT3 mutant male and female adult mice had, a higher areal BMD than their wild-type ( +/+ ) litter mates , with, heterozygous ( +/− ) mice having intermediate BMD ( Figure 2 ) ., This loss-of-function, GALNT3 mutation is predicted to lead to a reduced, glycosylation of FGF23 , which increases its breakdown and leads to reduced serum, FGF23 concentrations 35 ., A novel genome-wide significant association was also seen at markers on, chromosome 6q22-23 ( Figure, 1B ) ., In the combined dataset , marker rs13204965 achieved genome-wide, significance at this locus at the FN, ( P\u200a=\u200a2 . 2×10−9 ) , with strong, support in both the AOGC discovery set , and the combined replication sets, ( AOGC-discovery , TH P\u200a=\u200a2 . 1×10−4;, combined replication P\u200a=\u200a3 . 5×10−5 ) ., Strong association was also seen with LS BMD ( rs13204965, P\u200a=\u200a0 . 00067 ) ., The peak of association at this locus lies, within a cDNA fragment , AK127472 ., The nearest gene , RSPO3, ( R-spondin-3 ) , is 275 kb telomeric of the strongest associated SNP , but is, within the associated linkage disequilibrium region ( Figure 1B ) ., Association was observed at chromosome 16p13 with SNPs in and around, CLCN7 , which encodes a, Cl−/H+ antiporter expressed primarily in, osteoclasts , and critical to lysosomal acidification , an essential process in, bone resorption ., Peak association at this locus was seen with SNP rs13336428 in, the discovery set ( TH P\u200a=\u200a7 . 0×10−4;, LS P\u200a=\u200a0 . 028 ) ( Figure S3A ) , which was confirmed in the, replication set ( FN P\u200a=\u200a3 . 6×10−5; LS, P\u200a=\u200a0 . 00012 ) , achieving, P\u200a=\u200a1 . 7×10−6 at the FN and, 1 . 2×10−5 at LS in the overall cohort ., Association has, previously been reported between two SNPs in exon 15 of CLCN7, ( rs12926089 , rs12926669 ) and FN BMD ( P\u200a=\u200a0 . 001–0 . 003 ), 36;, no association was seen with either of those SNPs in the current study ( P>0 . 4, at FN and LS ) ., Association was observed with SNPs in IBSP ( integrin-binding, bone sialoprotein ) ( Figure S3B ) , encoded at chromosome 4q22 , a, gene which has previously had suggestive association reported with BMD in two, studies ( rs1054627 , Styrkarrsdottir et al, P\u200a=\u200a4 . 6×10−5, 22;, Koller et al P\u200a=\u200a1 . 5×10−4, 37 ) ., In the, current study , moderate association was observed in the discovery set with the, same SNP as previously reported ( rs1054627 , AOGC discovery TH ,, P\u200a=\u200a6 . 6×10−5 ) , with support in, the replication set and strong association overall ( FN combined replication, P\u200a=\u200a9 . 2×10−5; FN overall, association P\u200a=\u200a7 . 6×10−7 ) ., Nominal, association was observed at LS ( rs1054627 , P\u200a=\u200a0 . 019 ) ., Association with BMD was also seen at chromosome 11p13 , with SNP rs1152620, achieving P\u200a=\u200a4 . 4×10−5 ( TH ) in the, discovery set , P\u200a=\u200a0 . 0051 ( FN ) in the replication set , and, P\u200a=\u200a3 . 6×10−4 overall ( Figure, S3C ) ., This SNP was also nominally associated with LS BMD in the discovery, set ( P\u200a=\u200a0 . 041 ) ., The nearest gene to this locus is, LTBP3 ( latent transforming growth factor beta binding, protein 3 ) , which is located 292 kb q-telomeric of rs1152620 ., At chromosome 6p22 , SNPs in and around SOX4 ( Sex determining, region Y box, 4 ) were moderately associated with BMD in our discovery set ( most, significant association rs9466056 , TH, P\u200a=\u200a5 . 3×10−4; LS, P\u200a=\u200a0 . 0036 ) ( Figure S3D ) , with support at the hip and LS, in the replication set ( FN P\u200a=\u200a0 . 00013 , LS, P\u200a=\u200a0 . 013 ) , achieving association overall with, P\u200a=\u200a2 . 6×10−7 ( FN ) and, P\u200a=\u200a0 . 00081 ( LS ) ., This study demonstrates convincing evidence of association with six genes with BMD, variation , GALNT3 , RSPO3 , CLCN7 , IBSP , LTBP3 and, SOX4 ., Using a moderate sample size , the use of a novel study, design also led to the confirmation of 21 of 26 known BMD-associations ., This study, thus demonstrates the power of extreme-truncate selection designs for association, studies of quantitative traits ., GALNT3 encodes N-acetylgalactosaminyltransferase 3 , an enzyme, involved in 0-glycosylation of serine and threonine residues ., Mutations of, GALNT3 are known to cause familial tumoral calcinosis ( FTC ,, OMIM 2111900 ) 38, and hyperostosis-hyperphosphataemia syndrome ( HOHP , OMIM 610233 ) 39 ., FTC is, characterised by hyperphosphataemia in association with the deposition of calcium, phosphate crystals in extraskeletal tissues; whereas in HOHP , hyperphosphataemia is, associated with recurrent painful long bone swelling and radiographic evidence of, periosteal reaction and cortical hyperostosis ., FGF23 mutations, associated with FTC cause hyperphosphataemia through effects on expression of the, sodium-phosphate co-transporter in the kidney and small intestine , and through, increased activation of vitamin D due to increased renal expression of CYP27B1, ( 25-hydroxyvitamin-D 1 alpha hydroxylase ) 40 ., It is unclear whether FGF23, has direct effects on the skeleton or if its effects are mediated through its, effects on serum phosphate and vitamin D levels ., FGF23 signals via a complex of an, FGF receptor ( FGFR1 ( IIIc ) ) and Klotho 41; mice with a loss-of-function, mutation in Klotho develop osteoporosis amongst other, abnormalities , and modest evidence of association of Klotho with, BMD has been reported in several studies 42 , 43 , 44 , 45 ., We saw no association with, polymorphisms in Klotho and BMD in the current study ( P>0 . 05 for, all SNPs in and surrounding Klotho ) ., To our knowledge , this finding, is the first demonstration in humans that genetic variants in the FGF23 pathway are, associated with any common human disease ., RSPO3 is one of four members of the R-spondin family ( R-spondin-1 to, −4 ) , which are known to activate the Wnt pathway , particularly through effects, on LRP6 , itself previously reported to be BMD-associated 46 , 47 ., LRP6 is inhibited by the, proteins Kremen and DKK1 , which combine to induce endocytosis of LRP6 , reducing its, cell surface levels ., R-spondin family members have been shown to disrupt, DKK1-dependent association of LRP6 and Kremen , thereby releasing LRP6 from this, inhibitory pathway 48 ., R-spondin-4 mutations cause anonychia ( absence or severe, hypoplasia of all fingernails and toenails , OMIM 206800 ) 49 ., No human disease has been, associated with R-spondin-3 , and knockout of R-spondin-3 in mice is embryonically, lethal due to defective placental development 50 ., Mutations of CLCN7 cause a family of osteopetroses of differing age, of presentation and severity , including infantile malignant, CLCN7-related recessive osteopetrosis ( ARO ) , intermediate autosomal, osteopetrosis ( IAO ) , and autosomal dominant osteopetrosis type II ( ADOII ,, Albers-Schoenberg disease ) ., These conditions are characterized by expanded , dense, bones , with markedly reduced bone resorption ., Our data support associations of, polymorphisms at this locus with BMD variation in the population ., IBSP is a major non-collagenous bone matrix protein involved in calcium and, hydroxyapatite binding , and is thought to play a role in cell-matrix interactions, through RGD motifs in its amino acid sequence ., IBSP is expressed in all major bone, cells including osteoblasts , osteocytes and osteoclasts; and its expression is, upregulated in osteoporotic bone 51 ., IBSP knockout mice have low cortical, but high trabecular bone volume , with impaired bone formation , resorption , and, mineralization 52 ., IBSP lies within a cluster of genes, including DMP1 , MEPE , and SPP1 , all of which have, known roles in bone and are strong candidate genes for association with BMD ., MEPE has previously been associated with BMD at genome-wide, significance 17 ., In the current study the strongest association was seen, with an SNP in IBSP , rs1054627 , as was the case with two previous, studies 22 , 37 ., Linkage disequilibrium between this SNP , and the, previously reported BMD-associated SNP rs1471403 in MEPE , is modest, ( r2\u200a=\u200a0 . 16 ) ., Whilst out study supports the, association of common variants in IBSP in particular with BMD ,, further studies will be required to determine if more than one of these genes is, BMD-associated ., Recessive mutations of LTBP3 have been identified as the cause of, dental agenesis in a consanguineous Pakistani family ( OMIM 613097 ) 53 ., Affected family, members had base of skull thickening , and elevated axial but not hip BMD ., LTBP3−/− mice develop axial osteosclerosis with, increased trabecular bone thickness , as well as craniosynostosis 54 ., LTBP3 is, known to bind TGFβ1 , -β2 and -β3 , and may influence chondrocyte, maturation and enchondral ossification by effects on their bioavailability 54 ., Our study also confirms the previously reported association of another TGF pathway, gene , TGFBR3 , encoded at chromosome 1p22 , with BMD 33 ( Figure S3E ) ., In, that study , association was observed in four independent datasets , but overall the, findings did not achieve genome-wide significance at any individual SNP ( most, significant SNP rs17131547 ,, P\u200a=\u200a1 . 5×10−6 ) ., In our discovery set ,, peak association was seen at this locus with SNP rs7550034 ( TH, P\u200a=\u200a1 . 5×10−4 ) , which lies 154 kb, q-telomeric of rs17131547 , but still within TGFBR3 ( rs17131547 was, not typed or imputed in our dataset ) ( Figure S3E ) ., This supports, TGFBR3 as a true BMD-associated gene ., This study also demonstrated that SOX4 polymorphisms are associated, with BMD variation ., Both SOX4 and SOX6 are, cartilage-expressed transcription factors known to play essential roles in, chondrocyte differentiation and cartilage formation , and hence endochondral bone, formation ., SOX6 has previously been reported to be BMD-associated, at genome-wide significant levels 17 ., Whilst, SOX4−/− mice develop severe cardiac abnormalities, and are non-viable , SOX4+/− mice have osteopaenia with, reduced bone formation but normal resorption rates , and diminished cortical and, trabecular bone volume 55 ., Our data suggest that SOX4, polymorphisms contribute to the variation in BMD in humans ., This study has a unique design amongst GWAS of BMD reported to date , using an, extreme-truncate ascertainment scheme , focusing on a specific skeletal site ( TH ) ,, and with recruitment of a narrow age- and gender-group ( post-menopausal women age, 55–85 years ) ., Our goal in employing this scheme was to increase the study, power by reducing heterogeneity due to age- , gender- and skeletal site-specific, effects ., Whilst osteoporotic fracture can occur at a wide range of skeletal sites ,, hip fracture in postmenopausal women is the major cause of morbidity and mortality, due to osteoporosis ., To date , with only one exception , all GWAS of BMD have studied, cohorts unselected for BMD 28 , and no study has restricted its participants to, postmenopausal women ascertained purely on the basis of hip BMD ., Assuming, marker-disease-associated allele linkage disequilibrium of, r2\u200a=\u200a0 . 9 , for, alpha\u200a=\u200a5×10−8 our study has, 80% power to detect variants contributing 0 . 3% of the additive genetic, variance of BMD ., An equivalent-powered cohort study would require ∼16 , 000, unselected cases ., Considering the 26 known genes ( or genomic areas ) associated with BMD , P-values less, than <0 . 05 were seen in our discovery for 21 of the BMD-associated SNPs ., Of the, 26 known BMD genes , 16 would have been included in our replication study on the, basis of the strength of their BMD association in our discovery cohort , but were not, further genotyped as they were known already to be BMD-associated ., Had these 16, genes replicated , 22 genes would have been identified in this single study ,, demonstrating the power of the design of the current study ., A potential criticism of studies of highly selected cohorts , such as the, AOGC-discovery cohort , is that the associations identified may not be relevant in, the general population ., However , the confirmation of our findings in replication, cohorts of women unselected for BMD confirms that our findings are of broad, relevance ., In summary , our study design therefore represents a highly efficient model for future, studies of quantitative traits and is one of the first reported studies using an, extreme truncate design in any disease ., We have identified two new BMD loci at, genome-wide significance ( GALNT3 , RSPO3 ) , with, GALNT3 SNPs also associated with fracture ., Strong evidence was, also demonstrated for four novel loci ( CLCN7 , IBSP , LTBP3 ,, SOX4 ) ., Further support was also provided that TGFBR3, is a true BMD-associated locus ., Our discovery cohort replicated 21 of 26 previously, identified BMD-associated loci ., Our novel findings further advance our understanding, of the aetiopathogenesis of osteoporosis , and highlight new genes and pathways not, previously considered important in BMD variation and fracture risk in the general, population ., Our study also provides strong support that the use of extreme truncate, selection is an efficient and powerful approach for the study of quantitative, traits ., All participants gave written , informed consent , and the study was approved by, the relevant research ethics authorities at each participating centre ., The discovery sample population included 1128 Australian , 74 New Zealand and 753, British women , between 55–85 years of age , five or more years, postmenopausal , with either high BMD ( age- and gender-adjusted BMD z-scores of, +1 . 5 to +4 . 0 , n\u200a=\u200a1055 ) or low BMD ( age- and, gender-adjusted BMD z-scores of −4 . 0 to −1 . 5 ,, n\u200a=\u200a900 ) ( Tables S1 and S2 ) ., BMD, z-scores were determined according to the Geelong Osteoporosis Study normative, range 19 ., Low, BMD cases were excluded if they had secondary causes of osteoporosis , including, corticosteroid usage at doses equivalent to prednisolone ≥7 . 5 mg/day for, ≥6 months , past or current anticonvulsant usage , previous strontium usage ,, premature menopause ( <45 years ) , alcohol excess ( >28 units/week ) , chronic, renal or liver disease , Cushings syndrome , hyperparathyroidism ,, thyrotoxicosis , anorexia nervosa , malabsorption , coeliac disease , rheumatoid, arthritis , ankylosing spondylitis , inflammatory bowel disease , osteomalacia , and, neoplasia ( cancer , other than skin cancer ) ., Screening blood tests ( including, creatinine ( adjusted for weight ) , alkaline phosphatase , gamma-glutamyl, transferase , 25-hydroxyvitamin D and PTH ) were checked in 776 cases , and no, differences were found between the high and low BMD groups ., Therefore no further, screening tests were done of the remaining cases ., Fracture data were analysed comparing individuals who had never reported a, fracture after the age of 50 years , with individuals who had had a low or, non-high trauma ( low trauma fracture = fracture from a, fall from standing height or less ) osteoporotic fracture ( excluding skull , nose ,, digits , hand , foot , ankle , patella ) after the age of 50 years ., Vertebral , hip, and non-vertebral fractures were considered both independently and combined ., All participants were of self-reported white European ancestry ., DNA was obtained from peripheral venous blood from all cases except those, recruited from New Zealand , for whom DNA was obtained from salivary samples, using Oragene kits ( DNA Genotek , Ontario , Canada ) ., We have previously, demonstrated that DNA from these two sources have equivalent genotyping, characteristics 20 ., After quality control checks including assessment of cryptic relatedness ,, ethnicity and genotyping quality , 900 individuals with low TH BMD and 1055, individuals with high TH BMD were available for analysis ., The replication cohort consisted of 8928 samples drawn from nine cohort studies ,, outlined in Tables S3 and S4 ( ‘AOGC replication cohort’ ), which were directly genotyped , These replication cases were adult women ( age, 20–95 years ) , unselected with regard to BMD , and who were not screened for, secondary causes of osteoporosis ., Replication was also performed in silico in, 11 , 970 adult women from the TwinsUK and Rotterdam , and deCODE Genetics GWASs, 21 ,, 22 , 23 , in which, association data were available at LS and FN ., High and low BMD ascertainment was defined according to the TH score , because, this has better measurement precision than FN BMD 24 ., However , neither TwinsUK, nor the Rotterdam Study had TH BMD on the majority of their datasets and, therefore were analysed using the FN measurement for which data were available, on the whole cohort ., All replication findings at the hip are reported therefore, for FN BMD ., TH and FN BMD are closely correlated ( r\u200a=\u200a0 . 882, in the AOGC dataset ) , with FN BMD one of the components of the TH BMD, measurement ., Genotyping of the discovery cohort ( n\u200a=\u200a2036 ) was performed, using Illumina Infinium II HumHap300 ( n\u200a=\u200a140 ) , 370CNVDuo, ( n\u200a=\u200a4 ) , 370CNVQuad ( n\u200a=\u200a1882 ) and, 610Quad ( n\u200a=\u200a10 ) chips at the University of Queensland, Diamantina Institute , Brisbane , Australia ., Genotype clustering was performed, using Illuminas BeadStudio software; all SNPs with quality scores <0 . 15, and all individuals with <98% genotyping success were excluded ., 289499, SNPs were shared across all chip types ., Cluster plots from the 500 most strongly, associated loci , were manually inspected and poorly clustering SNPs excluded, from analysis ., Following imputation using the HapMap Phase 2 data , 2 , 543 , 887, SNPs were tested for association with TH and LS BMD ( Manhattan plot of, association findings , Figure S1 ) ., After data cleaning , minimal, evidence of inflation of test statistics was observed , with a genomic inflation, factor ( λ ) of 1 . 0282 ( qq plot , Figure S2 ) ., A total of 124 SNPs were successfully genotyped in the AOGC replication cohort ., These replication study SNPs were selected from the findings of the discovery, cohort , either based on the strength of association ( P-value ) or following, analysis with GRAIL ( n\u200a=\u200a45 ) 25 , using as seed data, all SNPs previously reported to be associated with BMD at GWAS significant, levels ( results for all replication SNPs presented in Table S5 ) ., GRAIL is a bioinformatic program that assesses the strength of relationships, between genes in regions surrounding input SNPs ( usually derived from genetic, association studies ) and other SNPs or genes associated with the trait of, interest , by assessing their co-occurrence in PubMed abstracts ., Where genes, surrounding input SNPs occur more frequently in abstracts with known associated, genes , these SNPs are more likely themselves also to be associated , and can thus, be prioritized for inclusion in replication studies ., For the replication study , genotyping was performed either by Applied Biosystems, OpenArray ( n\u200a=\u200a113 ) or Taqman technology, ( n\u200a=\u200a11 ) ( Applied Biosystems , Foster City , CA , USA ) ,, according to the manufacturers protocol ., Eleven individuals were removed because of abnormal X-chromosome homozygosity, ( X-chromosome homozygosity either <−0 . 14 , or >+0 . 14 ) ., Outliers, with regard to autosomal heterozygosity ( either <0 . 34225 or >0 . 357 ,, n\u200a=\u200a40 ) and missingness ( >3% ,, n\u200a=\u200a4 ) were removed ., Using an IBS/IBD analysis in PLINK to, detect cryptic relatedness , one individual from 35 pairs of individuals with, pi-hat >0 . 12 ( equivalent to being 3rd degree relatives or closer ), were removed ., SNPs with minor allele frequency <1%, ( n\u200a=\u200a561 ) , and those not in Hardy-Weinberg equilibrium, ( P<10−7 , n\u200a=\u200a170 ) were then, removed , leaving 288 , 768 SNPs in total ., Nine replication SNPs were removed, because of excess missingness ( >10% ) or because they failed tests of, Hardy-Weinberg equilibrium ( P<0 . 001 ) ., To detect and correct for population stratification EIGENSTRAT software was used ., We first excluded the 24 regions of long range LD including the MHC identified, in Price et al . before running the principal components analysis , as suggested, by the authors 26 ., Sixteen individuals were removed as ethnic outliers ,, leaving 1955 individuals in the final discovery dataset ., Imputation analyses were carried out using Markov Chain Haplotyping software, ( MaCH; http://www . sph . umich . edu/csg/abecasis/MACH/ ) using phased data, from CEU individuals from release 22 of the HapMap project as the reference set, of haplotypes ., We only analyzed SNPs surrounding disease-associated SNPs that, were either genotyped or could be imputed with relatively high confidence, ( R2≥0 . 3 ) ., For TH measurements , a case-control association, analysis of imputed SNPs was performed assuming an underlying additive model and, including four EIGENSTRAT eigenvectors as covariates , using the software package, MACH2DAT 27, which accounts for uncertainty in prediction of the imputed data by weighting, genotypes by their posterior probabilities ., For FN and LS BMD analyses ,, Z-transformed residual BMD scores ( in g/cm2 ) were generated for the, entire AOGC cohort after adjusting for the covariates age , age2 , and, weight , and for centre of BMD measurement ., Because the regression coefficient, for BMD on genotype would be biased by selection for extremes , we adopted the, approach detailed in Kung et al ( 2009 ) 28 ., Specifically , the regression, coefficient of genotype on BMD was estimated , and subsequently transformed to, the regression coefficient of BMD on genotype through knowledge of the, population variance of the phenotype and the allele frequencies ., For fracture, data , analysis was by logistic regression ., Only SNPs achieving GWAS significance, were tested for fracture association ., The SNPs used for replication from the, Rotterdam Study were analyzed using MACH2QTL implemented in GRIMP 29 ., Data from, the discovery and replication cohorts were combined using the inverse variance, approach as implemented in the program METAL 30 ., SNPs associated with BMD were also tested for association with fracture in the, AOGC discovery and replication cohorts ( hip , vertebral , nonvertebral , and all, low trauma fractures , age ≥50 years , as defined above ) , by logistic, regression ., Study power was calculated using the ‘Genetic Power Calculator’ 31 ., All animal studies were approved by the MRC Harwell Unit Ethical Review Committee, and are licensed under the Animal ( Scientific Procedures ) Act 1986 , issued by, the UK Government Home Office Department ., Dual-energy X-ray absorptiometry, ( DEXA ) was performed using a Lunar Piximus densitometer ( GE Medical Systems ) and, analysed using the Piximus software ., Data related to this study will be available to research projects approved by a, Data Access Committee including representatives of the University of Queensland, Research Ethics Committee ., For enquiries regarding access please contact the, corresponding author , MAB ( matt . brown@uq . edu . au ) .

Introduction, Results, Discussion, Materials and Methods

Osteoporotic fracture is a major cause of morbidity and mortality worldwide ., Low, bone mineral density ( BMD ) is a major predisposing factor to fracture and is, known to be highly heritable ., Site- , gender- , and age-specific genetic effects, on BMD are thought to be significant , but have largely not been considered in, the design of genome-wide association studies ( GWAS ) of BMD to date ., We report, here a GWAS using a novel study design focusing on women of a specific age, ( postmenopausal women , age 55–85 years ) , with either extreme high or low, hip BMD ( age- and gender-adjusted BMD z-scores of +1 . 5 to +4 . 0 ,, n\u200a=\u200a1055 , or −4 . 0 to −1 . 5 ,, n\u200a=\u200a900 ) , with replication in cohorts of women drawn from, the general population ( n\u200a=\u200a20 , 898 ) ., The study replicates, 21 of 26 known BMD–associated genes ., Additionally , we report suggestive, association of a further six new genetic associations in or around the genes, CLCN7 , GALNT3 , IBSP , LTBP3 , RSPO3 , and, SOX4 , with replication in two independent datasets ., A novel, mouse model with a loss-of-function mutation in GALNT3 is also, reported , which has high bone mass , supporting the involvement of this gene in, BMD determination ., In addition to identifying further genes associated with BMD ,, this study confirms the efficiency of extreme-truncate selection designs for, quantitative trait association studies .

Osteoporotic fracture is a major cause of early mortality and morbidity in the, community ., To identify genes associated with osteoporosis , we have performed a, genome-wide association study ., In order to improve study power and to address, the demographic group of highest risk from osteoporotic fracture , we have used a, unique study design , studying 1 , 955 postmenopausal women with either extreme, high or low hip bone mineral density ., We then confirmed our findings in 20 , 898, women from the general population ., Our study replicated 21 of 26 known, osteoporosis genes , and it identified a further six novel loci ( in or nearby, CLCN7 , GALNT3 , IBSP , LTBP3 , RSPO3 , and, SOX4 ) ., For one of these loci , GALTN3 , we, demonstrate in a mouse model that a loss-of-function genetic mutation in, GALNT3 causes high bone mass ., These findings report novel, mechanisms by which osteoporosis can arise , and they significantly add to our, understanding of the aetiology of the disease .

genetics and genomics/genetics of disease, diabetes and endocrinology/bone and mineral metabolism, rheumatology/bone and mineral metabolism

journal.ppat.1005615

Virus Infections Incite Pain Hypersensitivity by Inducing Indoleamine 2,3 Dioxygenase

Enhanced pain sensitivity is a hallmark of inflammation and is a debilitating feature of many clinical diseases , including chronic Human Immunodeficiency Virus-1 ( HIV-1 ) infections 1 , 2 ., However the underlying causes of chronic pain remain poorly defined 3 ., Pain hypersensitivity in rats with inflamed joints correlated with elevated IDO expression in brain , and pain hypersensitivity induced following treatments to induce chronic inflammation did not manifest in mice lacking intact IDO1 genes 4 ., These findings were interpreted as evidence that sustained inflammation in tissues outside the central nervous system ( CNS ) induced IDO activity in brain that was the underlying cause of pain hypersensitivity ., However , it is not known how local inflammation in peripheral ( non-CNS ) tissues induces IDO expression in brain , nor is it clear if IDO mediates pain hypersensitivity in other inflammatory syndromes ., We tested the hypothesis that virus infections enhance pain sensitivity by stimulating IDO using murine models of acute or chronic virus infection in which IDO enzyme activity is elevated ., Acute influenza A virus ( IAV ) infection stimulates robust increase in lung IDO activity which wanes after virus clearance 5 ., Murine Leukemia retrovirus ( MuLV , LP-BM5 strain ) is a natural mouse pathogen that causes persistent infections and pathologies that resemble aspects of human immunodeficiency virus-1 ( HIV-1 ) infections , including sustained IDO activity in lymphoid tissues 6–8 ., The role of IDO in MuLV pathogenesis is controversial ., A previous study indicated that genetic and pharmacologic IDO ablation led to enhanced interferon type 1 production and increased resistance to MuLV infection 6 ., In contrast , a later report found no differences in viral loads and immune pathologies between IDO-sufficient ( WT ) mice and mice lacking intact IDO1 genes 7 ., As MuLV infection also induces peripheral neuropathy and pain hypersensitivity 9 , 10 we hypothesized that induced host IDO activity mediates pain hypersensitivity during MuLV infection ., We show that IAV and MuLV infections increased pain hypersensitivity via an IDO-dependent mechanism and that a distinctive splenic DC subset expressing the B cell marker CD19 enhanced pain sensitivity in MuLV-infected mice ., IAV respiratory infections in mice stimulate IDO enzyme activity in lungs and lung-draining ( mediastinal ) lymph nodes 5 ., To test if IAV infection incited pain hypersensitivity we assessed mechanical nociception ( pain ) thresholds by applying mechanical stimuli ( von Frey filaments ) of increasing force to hind paws until mice responded as described in Methods ., As soon as one day post infection ( dpi ) paw withdrawal thresholds ( PWT ) were reduced significantly , relative to baseline thresholds in the same mice before infection ( Fig 1A ) ., Increased pain sensitivity persisted during IAV infection and returned to basal levels 2–3 days after IAV clearance at 7-8dpi 5 ., In contrast , no significant change in pain sensitivity manifested during IAV infections in IDO1-deficient ( IDO1-KO ) mice ( Fig 1A ) ., Thus IDO1 genes were necessary to incite pain hypersensitivity during respiratory IAV infections ., MuLV ( LP-BM5 strain ) causes persistent infections and progressive pathologies , including polyneuropathy and pain hypersensitivity 9–11 ., Some features of MuLV infections resemble aspects of clinical HIV-1 infections , including elevated IDO activity in lymphoid tissues 6 , 7 , 12 ., Consistent with previous studies 9 , 10 , MuLV-infection in B6 ( WT ) mice bred locally caused progressive increase in pain sensitivity until 18dpi , and levels remained elevated thereafter ( Fig 1B and S1A Fig ) ., Increased pain sensitivity also manifested in B6 mice purchased from a commercial supplier ( Taconic ) , though pain sensitivity was slightly less severe and took longer to develop , relative to outcomes in B6 mice bred locally ( S1B Fig ) ., Pain sensitivity increased in MuLV-infected IDO1-KO mice relative to naïve mice ( Fig 1B and S1B Fig ) , though responses in IDO1-KO mice were significantly less intense than pain hypersensitivity induced in WT mice ., Profound reduction in MuLV-induced pain hypersensitivity did not correlate with major changes in virus titers or host immunopathogenesis ( splenomegaly , immunosuppression , cytokine induction ) since these parameters were comparable in WT and IDO1-KO mice ( S1C–S1F Fig ) , as reported previously 7 ., Thus transient and sustained increase in pain sensitivity manifested during acute IAV or chronic MuLV infections , respectively , and these responses to virus infection depended on IDO1 gene expression but were not linked to changes in virus infection kinetics or host immunopathogenesis ., To test if pharmacologic IDO inhibition alleviated pain hypersensitivity the IDO inhibitor 1-methyl-D-tryptophan ( D-1MT , 2mg/ml ) was administered continuously in drinking water to B6 mice with established MuLV infections ( 60dpi ) ., Oral D-1MT treatment for 20 days led to significant reduction in pain sensitivity , relative to controls given vehicle only ( Fig 1C ) ., It is unclear why oral D-1MT treatment did not alleviate pain hypersensitivity more robustly ., As spleen IDO activity increased markedly during MuLV infections ( Fig 1E ) D-1MT may reduce but not abolish spleen IDO activity in this model , though potential off-target effects of D-1MT cannot be excluded ., Exposing naïve and MuLV-infected IDO1-KO mice to the natural tryptophan catabolite kynurenine ( Kyn , 200μg/mouse , i/v ) led to rapid increase in pain sensitivity; this effect was more severe in MuLV-infected than in uninfected ( naïve ) IDO1-KO mice ( Fig 2D ) , suggesting that Kyn may synergize with cytokines co-induced by MuLV infection to enhance pain sensitivity ., Thus cells expressing IDO1 cause pain hypersensitivity in MuLV-infected mice and Kyn released by cells expressing IDO may mediate this response ., Consistent with a previous study 6 , spleen IDO activity increased progressively during MuLV infection until IDO activity was >100-fold higher at 120dpi ( Fig 1E ) ., However IDO activity was undetectable in spleens of MuLV-infected IDO1-KO mice ( Fig 1E ) , indicating that IDO1 genes exclusively encoded MuLV-induced IDO enzyme activity , not IDO2 and tryptophan dioxygenase ( TDO ) genes encoding enzymes with similar functions ., In contrast , at early or late stages of MuLV-infection IDO enzyme activity ( Fig 1F ) and IDO1 gene transcription in CNS tissues ( S2B Fig ) were not elevated significantly over basal levels ., To identify cells expressing IDO and MuLV ( GAG ) genes discrete cell populations were purified by flow cytometry from spleens of MuLV-infected B6 mice and gene transcription was assessed using quantitative RT-PCR ., Splenocytes were stained with CD11c and CD19 mAbs since melanoma growth and other inflammatory insults induce selective IDO expression by a discrete subset of dendritic cells ( DCs ) expressing the B cell marker CD19 13–16 ., At early ( 28dpi , Fig 2A ) and later ( 56dpi , Fig 2B ) stages in MuLV infection when immune pathology partially or fully manifests IDO1 transcripts were detected exclusively in sorted spleen cells expressing CD11c and CD19 ( CD19+ DCs ) ., Increased IDO1 transcription was not detected in any other spleen cells , including sorted B cells and conventional ( CD19neg ) DCs ., At early times in MuLV infection ( 28dpi ) , ecotropic helper ( GAGEco ) and pathogenic ( GAGDef ) MuLV retrovirus genes were transcribed at high levels in sorted cells expressing neither CD19 or CD11c ( CD11cnegCD19neg ) and to lesser extents in CD19+ DCs , relative to sorted conventional DCs and B cells ( Fig 2A ) ., At later stages in infection ( 56dpi ) , GAGDef transcription was still relatively high in CD11cnegCD19neg and CD19+ DCs , though GAGEco transcription was relatively high in conventional and CD19+ DCs but not in CD11cnegCD19neg cells ( Fig 2B ) ., However , GAGDef and GAGEco transcription remained relatively low in B cells ( CD19+CD11cneg ) at later stages of MuLV ., Given previous reports of B cell-specific LP-BM5 expression 17 , these findings suggest that LP-BM5 replicates in cells not expressing CD19 or CD11c and in DCs , as well as in B cells ., Alternatively , co-selection of ( or contamination by ) DCs expressing CD19 may explain previous reports of selective MuLV infection in B cells since CD19 and CD11c are commonly used to discriminate between B cells and DCs ., Thus CD19+ DCs were highly susceptible to MuLV infection and were the only spleen cells induced to express IDO1 during MuLV infection ., To test if increased IDO1 transcription by CD19+ DCs led to increased IDO enzyme activity FACS-sorted CD19+ and conventional ( CD19neg ) DCs from MuLV-infected Act-mOVA transgenic mice expressing ovalbumin ( OVA ) in all cells 18 were cultured alone or with splenocytes from ( OVA ) -specific OT1 ( CD8 ) or OT2 ( CD4 ) TCR transgenic mice and Kyn production was assessed after 3 days ., Kyn levels increased significantly in cultures containing sorted CD19+ DCs and OT-2 T cells but Kyn was not detected in cultures containing sorted DCs alone , or sorted DCs cultured with OT-1 T cells ( Fig 2C ) ., Thus interactions with OT2 T cells were essential for IDO activity to manifest in CD19+ DCs from MuLV-infected mice ., Consistent with these findings , anti-CD4 mAb treatments to deplete CD4 cells in vivo reduced IDO activity ( Fig 2D ) and IDO1 transcription ( Fig 2E ) in spleen to basal levels observed in naïve mice ., Collectively , these data show that interactions with CD4 T cells is essential for CD19+ DCs to express functional IDO in MuLV-infected mice and during culture ., CD19+ DCs are a minor DC population in spleens of naïve mice ( Fig 3A , <1% of splenocytes and ~10% of splenic DCs ) ., CD19+ DCs expanded substantially after MuLV infection , accounting for ~10% of splenocytes and ~50% of splenic DCs at 35dpi ( Fig 3B and 3C ) ., Because MuLV infection induces splenomegaly absolute numbers of CD19+ DCs expanded >100-fold relative to numbers in spleens of naïve mice ., Conventional ( CD19neg ) DCs also expanded in this period , while relative proportions of splenic B cells were reduced substantially ( Fig 3A and 3B ) ., Phenotypic analyses revealed striking differences in maturation between CD19+ DCs and conventional DCs in MuLV-infected mice ., At 35dpi , CD19+ DCs expressed uniformly high levels of MHC class II ( MHC II ) and CD80 ( Fig 3D ) characteristic of mature antigen presenting cells ( APCs ) ., In contrast , conventional DCs expressed lower and more variable levels of MHC II and CD80/86 ( Fig 3D ) comparable with levels on immature DCs in naïve mice ., Thus CD19+ DCs expanded and matured as APCs while conventional DCs also expanded but remained immature during MuLV infection ., Higher proportions of splenic DCs stabilized ~28dpi after MuLV infection and in vivo labeling with 5-ethynyl-2-deoxyuridine ( EdU ) at 14-21dpi revealed larger cohorts of dividing ( EdU+ ) splenic CD19+ ( ~30% ) and conventional ( ~16% ) DCs than B cells ( <5% ) from MuLV-infected B6 mice ( Fig 3E and 3F ) ., In vivo treatment with depleting anti-CD4 mAbs reduced EdU incorporation by CD19+ and conventional DCs significantly ( S3 Fig ) ., Thus MuLV infection induced selective CD19+ DC maturation and DC proliferation , as well as selective IDO expression by CD19+ DCs dependent on interactions with CD4 T cells ., We tested if adoptive transfer of splenocytes from B6 mice with fully established MuLV infections ( 56-70dpi ) enhanced pain hypersensitivity in naïve IDO1-KO recipients ., Recipients were sublethally irradiated ( 3 . 5Gy ) to facilitate donor cell chimerism and six days after transfer of splenocytes from MuLV-infected B6 ( WT ) donors pain sensitivity was assessed in IDO1-KO recipients ., Adoptive transfer of splenocytes from MuLV-infected WT donors caused pain hypersensitivity ( Fig 4A ) , which was sustained until experimental endpoints 32 days after transfer ., Splenocyte induced pain hypersensitivity was only slightly less than pain hypersensitivity due to MuLV-infection ( Fig 1B ) ., Though splenocytes from MuLV-infected IDO1-KO donors also induced significant increase in pain sensitivity ( Fig 4A ) these responses were significantly less pronounced than responses to splenocytes from MuLV-infected WT donors; moreover , sublethal irradiation may drive some increase in pain sensitivity ., Thus IDO was the major driver of pain sensitivity following splenocyte transfer ., To complement this approach , we tested if in vivo DC ablation alleviated pain hypersensitivity using transgenic B6 mice expressing human diphtheria toxin receptor ( DTR ) under control of DC-specific CD11c gene promoters ( CD11cDTR mice ) ., MuLV-infected CD11cDTR mice ( 56-70dpi ) were treated with diphtheria toxin ( DT , 10ug/kg , i/p , x2 ) to ablate DCs and pain thresholds were monitored ., DT treatment reduced pain sensitivity rapidly and significantly in MuLV-infected CD11cDTR mice relative to MuLV-infected CD11cDTR mice not exposed to DT and to control naïve WT mice given DT ( Fig 4B ) ., As expected , at experimental endpoints ( 6 days post DT treatment ) the proportions of splenic CD19+ DCs were reduced significantly in DT-treated , MuLV-infected CD11cDTR mice but DT treatment had no effects on CD19+ DCs in MuLV-infected WT ( B6 ) mice ( Fig 4C ) ., DT treatment also reduced spleen IDO enzyme activity ( Fig 4D ) and IDO1 gene transcription ( Fig 4F ) in MuLV-infected CD11cDTR mice significantly , as levels were comparable to basal levels in naïve ( uninfected ) mice 6 days after DT treatment ., Remarkably , spleen weights were also reduced rapidly and significantly following DT treatment ( Fig 4E ) ., In contrast , DT treatment had no significant effects on IDO activity , IDO1 transcription or splenomegaly in MuLV-infected WT ( B6 ) mice ( Fig 4D–4F ) , indicating that the effects of DT treatment were due to ablation of cells expressing DTR ., Thus in vivo depletion of splenic DCs alleviated pain hypersensitivity in MuLV-infected IDO-sufficient mice and this response was not caused by DT treatment per se ., Collectively , these data reveal that splenic DCs expressing IDO mediate sustained pain hypersensitivity in MuLV-infected B6 mice ., IDO1 mediated pain and depression in rodents with chronic limb joint inflammation and elevated IDO1 expression in brain hippocampus correlated with these responses 4 ., Using the spared nerve injury ( SNI ) model , Zhou et al . reported that IDO1 expressed in liver mediated depression but did not enhance mechanical pain sensitivity in this model 19 ., In the current study we show that acute influenza ( IAV ) and chronic retroviral ( MuLV ) infections enhanced mechanical pain sensitivity and that IDO1 ablation alleviated these responses to virus infection ., IAV and MuLV infections stimulate IDO activity in lungs and peripheral lymphoid tissues , respectively ., IAV infections induced rapid increase in IDO activity in lung epithelial cells and in DCs located in lung-associated lymph nodes , and IDO activity at these sites returned to basal levels a few days after virus clearance 5 ., Pain sensitivity correlated with changed IDO activity during and after IAV infection , consistent with a causative link between IAV-induced IDO activity and pain sensitivity ., Similarly , spleen IDO activity correlated with progressive increase in pain sensitivity that peaked before immunopathologies associated with chronic MuLV infections manifested fully ., Pain hypersensitivity peaked faster in mice bred locally than in previous reports 9 , 10 but onset of peak pain hypersensitivity was slower and comparable with previous studies when mice from a commercial supplier were used , suggesting that mouse husbandry factors influence the kinetics of pain hypersensitivity induced by MuLV infection ., This point notwithstanding , IDO1 ablation alleviated pain hypersensitivity almost completely during MuLV infection ., Thus IDO activity encoded by IDO1 genes caused acute pain hypersensitivity during IAV infections and was the major factor driving progressive pain hypersensitivity during persistent MuLV infections ., Furthermore , IDO2 and tryptophan dioxygenase ( TDO ) genes encoding enzymes with identical tryptophan catabolizing activities did not compensate for loss of IDO1 genes to enhance pain sensitivity during IAV or MuLV infections ., Pro-inflammatory cytokines such as IL-6 , TNFα and IL-1β have been reported to enhance pain sensitivity 20 ., IDO may mediate or synergize with these effects since IDO is co-induced with pro-inflammatory cytokines in many settings of inflammation because interferons are potent IDO inducers ., However , IDO1 ablation was sufficient to block induction of pain hypersensitivity during IAV and MuLV infections , which stimulate production pro-inflammatory cytokine responses ., CD19+ DCs were the only cell type induced to express IDO in spleen during MuLV infections and IDO1 expression and enzyme activity were not elevated above basal levels ( in naïve mice ) in CNS tissues from mice with established MuLV infections ., These findings suggested that sustained IDO activity in peripheral lymphoid tissues was sufficient to incite pain hypersensitivity in MuLV-infected mice ., Consistent with this interpretation , adoptive transfer of splenocytes from MuLV-infected IDO1-sufficient mice caused pain hypersensitivity in IDO1-deficient recipients , while selective DC depletion in vivo alleviated pain hypersensitivity in MuLV-infected IDO1-sufficient mice ., While the possibility that IAV and MuLV infections induce IDO activity in CNS tissues to incite pain sensitivity cannot be excluded fully , our findings that splenic DCs from MuLV-infected IDO1-sufficient mice and Kyn enhanced pain sensitivity in IDO1-deficient mice suggest that increased IDO activity in peripheral tissues is sufficient to enhance pain sensitivity ., Cells expressing IDO may activate local sensory neurons in peripheral tissues directly or Kyn produced by IDO-expressing cells may act on peripheral or CNS neurons to enhance pain sensitivity ., Collectively , our findings support the hypothesis that sustained IDO expression by cells in lungs and splenic CD19+ DCs of mice infected with IAV and MuLV , respectively , mediate pain hypersensitivity during infection ., Previously , we reported that splenic CD19+ DCs expressed IDO in response to melanoma growth and inflammatory insults that induce interferon type I production , including B7 and TLR9 ligands , DNA nanoparticles and apoptotic cells 13–16 , 21 , 22 ., Moreover , CD19+ DCs resemble ‘age-associated B cells ( ABCs ) ’ that accumulate in spleens of aged female mice , in Nba2 mice prone to lupus-like syndromes , and in patients with rheumatoid arthritis 23 , 24 ., ABC accumulation in aged female mice was TLR7-dependent , suggesting that endogenous retroviral RNA sensing may promote ABC expansion ., Similar considerations may explain why CD19+ DCs expanded as mature APCs and expressed IDO selectively during MuLV infection ., CD19+ DCs exhibited potent T cell regulatory phenotypes dependent on IDO and interactions between CD4 T cells and DCs were essential to sustain IDO activity in DCs and to promote Foxp3-lineage regulatory CD4 T cell ( Treg ) differentiation and activation 25 , 26 ., Likewise , interactions between CD4 T cells and CD19+ DCs were necessary to sustain IDO activity in CD19+ DCs from MuLV-infected mice ., It is unclear if MuLV antigen-specific interactions between CD19+ DCs and CD4 T cells induce IDO activity early in MuLV infection and if these interactions promote Treg differentiation and activation ., However exogenous antigens were not required to induce splenic CD19+ DCs to express IDO following systemic B7 or TLR9 ligands and DNA nanoparticle treatments 13–15 , suggesting that self antigens or antigen-independent pathways induced CD19+ DCs to express IDO and activate Tregs in vivo ., For example , CD4 T cells may produce IFNγ or stimulate innate immune cells to express IFNαβ to induce IDO during MuLV infection ., Previously , critical roles for B cells and CD4 T cells in MuLV-induced immune pathogenesis were described 27 , 28 ., Our findings suggest that CD19+ DCs , not conventional B cells , play key roles in MuLV pathogenesis ., CD19+ DCs are closely related to B cells and express many B cell markers but are a distinct cell lineage with DC attributes 21 ., During MuLV-infection CD19+ DCs were distinguished from conventional B cells by exhibiting mature APC phenotypes , higher susceptibility to MuLV infection , enhanced proliferation and IDO expression dependent on CD4 T cell interactions ., A previous report described that IDO ablation led to increased plasmacytoid DCs and interferon type I production in response to MuLV infection and to enhanced survival of mice infected with MuLV alone or with MuLV and Toxoplasma gondii 6 ., In contrast , we found no significant effects of genetic or pharmacologic IDO ablation on the course of MuLV infection or immunopathogenesis , consistent with a previous study by O’Connor and Green using IDO1-KO mice , which also revealed that IDO is not critical , or has a redundant role in regulating host immune responses to MuLV infection 7 ., The reason for these disparate outcomes is unclear ., Nevertheless , DC depletion led to rapid reduction in splenomegaly , suggesting that DCs may play pivotal and previously unappreciated roles in immunopathologies associated with chronic MuLV infection ., However IDO ablation also had little impact on the course of acute IAV infections in mice , though primary CD8 responses were more robust and IAV-specific memory CD8 T cell repertoires differed in the absence of IDO 5 ., IDO activity is also elevated in patients with persistent HIV-1 or HTLV1 retrovirus infections , indicating that increased IDO activity is a common response to retroviral infection in mice and humans 12 , 29 ., It is unclear if IDO contributes to virus control or immunopathogenesis in these clinical syndromes , though previous reports have described human pDC subsets that can express IDO and acquire T cell regulatory phenotypes as a consequence 30 , 31 ., Despite the lack of evidence supporting a role for IDO in host-virus immune control and immunopathogenesis , the current study reveals that IDO is a major contributory factor driving pain hypersensitivity during acute IAV and chronic MuLV infections ., Thus sustained , elevated IDO activity may also contribute to chronic pain associated with acute and persistent clinical infections in humans ., If so , inhibition of IDO may help alleviate heightened pain sensitivity induced as a common comorbidity associated with virus infections ., The mechanism by which cells induced to express IDO enhance pain sensitivity during IAV and MuLV infections is unclear ., A previous study by Kim and colleagues concluded that increased brain IDO activity mediated pain hypersensitivity in rodent models of experimentally induced arthritis 4 ., This conclusion was based on findings that microinjecting IDO inhibitor directly into rat brain hippocampus abolished pain hypersensitivity , while microinjecting IL-6 stimulated brain IDO expression in this rodent arthritis model ., Findings from the current study suggest that elevated brain IDO activity may not be necessary to induce pain hypersensitivity during IAV and MuLV infections in mice since lung and lymphoid tissues were the primary sites of elevated IDO activity during IAV and MuLV infections , respectively ., Furthermore , increased IDO expression and activity was not detected in CNS tissues of MuLV-infected mice and adoptive transfer of splenocytes from IDO1-sufficient mice with chronic MuLV infections or injection of Kyn heightened pain sensitivity in IDO1-deficient mice ., Thus elevated IDO activity in non-CNS tissues was necessary and sufficient to induce pain hypersensitivity during IAV and MuLV infections ., It is also unclear how increased IDO activity in lungs or lymphoid tissues leads to elevated sensitivity to mechanical stimulation in hind paws ., Though unlikely that limb extremities are impacted directly by IAV and MuLV infection , inflammatory cells expressing IDO or Kyn produced by distal tissues may enter limb extremities or CNS tissues during viral infections and heighten pain sensitivity via direct affects on local nervous tissues ., How increased IDO activity mediates increased pain sensitivity is not understood ., IDO catabolizes both serotonin and its precursor tryptophan to generate neuroactive quinolinic ( QA ) and kynurenic ( KA ) acids , which mediate diametric neuropathologic and neurodegenerative effects via N-methyl-D-aspartate ( NMDA ) receptors expressed by neuronal tissues to induce pain and behavioral responses 32 ., Unlike QA and KA , which can only cross the blood brain barrier via passive diffusion , Kyn is transported efficiently across the blood brain barrier via large neutral amino acid L-system transporters 33 , and may be converted QA and KA to drive neurological responses that enhance pain sensitivity via NMDA receptors expressed in the CNS ., Alternatively Kyn generated in non-CNS tissues during IAV or MuLV infections may be converted to QA and KA in non-CNS tissues to promote peripheral neuropathies that heighten pain sensitivity ., Kyn is also a weak ligand for aryl hydrocarbon receptors ( AhR ) expressed by multiple cell types in CNS and non-CNS tissues and modulation of AhR signaling during IAV and MuLV infections may also contribute to peripheral neuropathies that drive increased pain sensitivity ., Thus Kyn generated by IDO activity anywhere in the body may potentiate pain sensitivity by interactions of Kyn catabolites with nerve cells in CNS or non-CNS tissues ., Interestingly , muscular exercise reduced stress-induced depression by promoting Kyn uptake and catabolism into KA by muscle tissues , thus decreasing the potential of Kyn and its catabolites to cause neurologic effects 34 ., This finding suggests that muscular exercise may also to alleviate pain hypersensitivity during infections by reducing Kyn availability ., In summary , acute IAV and chronic MuLV virus infections enhanced pain sensitivity by elevating IDO activity to increase Kyn availability ., Links between host inflammatory responses to infection , elevated tryptophan catabolism and increased pain sensitivity suggest that the common co-morbidities of pain and behavioral disorders associated with many progressive inflammatory diseases of clinical significance may arise due to under-appreciated metabolic responses to inflammatory insults such as tumor growth and autoimmunity , as well as infections ., B6 mice were purchased from Taconic ( Hudson , NY ) or bred in a barrier ( SPF ) facility at GRU ., IDO1-KO mice , CD11cDTR , OT1 and OT2 TCR transgenic mice were described previously 15 , 22 ., IAV A/PR/8/34 ( PR8 ) propagated in embryonated chicken eggs was kindly provided by Ralph Tripp ( University of Georgia Athens , GA ) ., Mice were infected with a non-lethal IAV dose ( 30pfu , 30% of LD50 ) as described 5 ., SC1/G6 cells infected with MuLV ( LP-BM5 ) were obtained from the NIH AIDS Reagent Program , Division of AIDS , NIAID , NIH 8 ., SC1 and XC cells were kind gifts from William Green ( Dartmouth ) ., 400ul of SC1/G6 culture supernatant were injected ( i/v ) to infect mice ., Ecotropic ( Eco ) retrovirus titers in supernatants were determined by XC plaque assay 35 ., 1-5x104 pfu Eco retrovirus was injected per mouse 9 ., Adoptive transfer of splenocytes from MuLV-infected mice was accomplished by exposing naïve IDO1-KO recipients to sub-lethal radiation ( 3 . 5Gy ) to create space in hematologic niches ., Mechanical nociception was assessed using von Frey filaments ( North Coast Medical Inc , Gilroy , CA ) to determine paw withdrawal threshold ( PWT ) as described 36 , 37 ., In brief , mice were stimulated on both hind paws using a series of von Frey filaments ranging in force from 0 . 008g to 2g , starting with the 0 . 008g filament ., Positive responses were scored as paw withdrawal occurring two or more times in response to ten successive stimulations ., In the event of negative responses , mice were then stimulated with monofilaments of stepwise increasing force ., The monofilament that first evoked a positive response was designated the threshold ( in grams ) and no further monofilaments were applied ., Cells were analyzed on a LSRII flow cytometer ( Becton-Dickinson ) ., Data were analyzed using FACS DIVA ( BD Bioscience ) or FlowJo ( Tree Star , Ashland , OR ) software ., An Aria flow sorter ( Becton-Dickinson ) was used for sort spleen cells from MuLV-infected mice under BSL2 conditions ., Spleen cells from MuLV-infected mice were stained with PE-conjugated rat anti-mouse CD19 ( clone 1D3 , BD Biosciences ) and APC or PECy7-conjugated hamster anti-mouse CD11c ( clone N418 , eBioScience ) ., Cells were sorted into chilled polypropylene collection tubes ( RPMI , 10% FCS ) for culture or resuspended ( RPMI , 5% FCS ) and cell lysis solution was added ( Omega Bio-Tek , Norcross , GA ) to prepare RNA for analysis ., In vivo Ethynyl deoxyluridine ( EdU ) labeling and staining were performed using Click-iT Plus EdU flow cytometry assay kits ( Life Technologies ) following manufacturer’s instructions with minor modifications ., Briefly , 1mg of EdU was injected into each mouse ( i/p ) and tissues are harvested four hours later ., Spleen cells were surface stained with antibodies then fixed and permeablized followed by incubation with fluorophore conjugated azide ., Cells are then washed and analyzed on a BD FACS LSRII flow cytometer ., 1-methyl-D-tryptophan ( D-1MT , Indoximod ) was kindly provided by NewLink Genetics Inc ., D-1MT was administered in sweetened drinking water ( 2mg/ml ) as described 15 ., IDO enzyme activity was measured by assessing Kyn produced by cell-free tissue homogenates or present in cell cultures using HPLC as described 5 ., RNA was purified using HP total RNA kits ( Omega Bio-Tek , Norcross , GA ) , reverse-transcribed using a random hexamer cDNA RT kit ( Clontech , Mountain View , CA ) , and quantitative RT-PCR was performed using an iQ5 or CFX system with SsoFast EvaGreen supermix ( Bio-Rad , Hercules , CA ) ., Primers for murine β-actin were ( forward ) 5′-TACGGATGTCAACGTCACAC-3′ and ( reverse ) 5-AAGAGCTATGAGCTGCCTGA-3′ ., Validated primers for murine IDO1 were purchased ( realtimeprimers . com ) ., Relative expression of GAGEco and GAGDef were evaluated as described 38 ., Threshold cycle ( Ct ) values were set in the early linear amplification phase; relative expression was calculated as 2Ct ( β-actin ) − Ct ( target gene ) ., Time courses of mechanical nociception ( PWT ) were analyzed by two-way ANOVA with multiple comparisons ., Unpaired Student t tests were used to analyze data generated in all other experiments ., Two-tailed p values <0 . 05 were considered significant ., GraphPad Prism was used to perform all data analyses ., This study was carried out in strict accordance with the recommendations in the Guide for the Care and Use of Laboratory Animals of the National Institutes of Health ., All protocols were reviewed and approved by the Animal Care and Use Committee at the Georgia Regents University ( AUP#2011–0330 ) ., Gene ID: 15930; Ensembl: ENSMUSG00000031551; Vega: OTTMUSG00000020648

Introduction, Results, Discussion, Materials and Methods

Increased pain sensitivity is a comorbidity associated with many clinical diseases , though the underlying causes are poorly understood ., Recently , chronic pain hypersensitivity in rodents treated to induce chronic inflammation in peripheral tissues was linked to enhanced tryptophan catabolism in brain mediated by indoleamine 2 , 3 dioxygenase ( IDO ) ., Here we show that acute influenza A virus ( IAV ) and chronic murine leukemia retrovirus ( MuLV ) infections , which stimulate robust IDO expression in lungs and lymphoid tissues , induced acute or chronic pain hypersensitivity , respectively ., In contrast , virus-induced pain hypersensitivity did not manifest in mice lacking intact IDO1 genes ., Spleen IDO activity increased markedly as MuLV infections progressed , while IDO1 expression was not elevated significantly in brain or spinal cord ( CNS ) tissues ., Moreover , kynurenine ( Kyn ) , a tryptophan catabolite made by cells expressing IDO , incited pain hypersensitivity in uninfected IDO1-deficient mice and Kyn potentiated pain hypersensitivity due to MuLV infection ., MuLV infection stimulated selective IDO expression by a discreet population of spleen cells expressing both B cell ( CD19 ) and dendritic cell ( CD11c ) markers ( CD19+ DCs ) ., CD19+ DCs were more susceptible to MuLV infection than B cells or conventional ( CD19neg ) DCs , proliferated faster than B cells from early stages of MuLV infection and exhibited mature antigen presenting cell ( APC ) phenotypes , unlike conventional ( CD19neg ) DCs ., Moreover , interactions with CD4 T cells were necessary to sustain functional IDO expression by CD19+ DCs in vitro and in vivo ., Splenocytes from MuLV-infected IDO1-sufficient mice induced pain hypersensitivity in uninfected IDO1-deficient recipient mice , while selective in vivo depletion of DCs alleviated pain hypersensitivity in MuLV-infected IDO1-sufficient mice and led to rapid reduction in splenomegaly , a hallmark of MuLV immune pathogenesis ., These findings reveal critical roles for CD19+ DCs expressing IDO in host responses to MuLV infection that enhance pain hypersensitivity and cause immune pathology ., Collectively , our findings support the hypothesis elevated IDO activity in non-CNS due to virus infections causes pain hypersensitivity mediated by Kyn ., Previously unappreciated links between host immune responses to virus infections and pain sensitivity suggest that IDO inhibitors may alleviate heightened pain sensitivity during infections .

Chronic pain is a factor in diseases that afflict many people , yet the underlying causes of pain are poorly understood ., Here we assess the effects of virus infections on pain sensitivity in mice ., Infecting mice with two different viruses , influenza and mouse leukemia virus ( MuLV ) increased pain sensitivity ., Influenza infection caused transient increase in pain sensitivity , which returned to normal levels after infections were cleared ., However persistent MuLV infections caused sustained increase in pain sensitivity ., Virus-induced pain sensitivity was reduced substantially in mice lacking the enzyme indoleamine 2 , 3 dioxygenase ( IDO ) , which degrades the amino acid tryptophan ., Moreover a natural compound produced by cells expressing IDO enhanced pain sensitivity when administered to mice lacking IDO genes ., Thus cells expressing IDO caused increased pain sensitivity in infected mice ., A distinctive cell type expressed IDO selectively and accumulated in spleens of MuLV-infected mice ., Transfer of spleen cells from MuLV-infected mice caused increased pain sensitivity in uninfected mice while eliminating specific cells in MuLV-infected mice abolished enhanced pain sensitivity ., Our findings show that host immune responses to virus infections cause increased pain sensitivity and suggest novel ways to alleviate pain during infections .

blood cells, somatosensory system, medicine and health sciences, immune physiology, immune cells, pathology and laboratory medicine, respiratory infections, nervous system, spleen, pathogens, immunology, microbiology, orthomyxoviruses, neuroscience, pulmonology, viruses, animal models, clinical medicine, model organisms, rna viruses, hypersensitivity, research and analysis methods, sensory physiology, influenza a virus, white blood cells, animal cells, medical microbiology, microbial pathogens, mouse models, antibody-producing cells, pain sensation, cell biology, clinical immunology, influenza viruses, b cells, anatomy, physiology, viral pathogens, central nervous system, biology and life sciences, sensory systems, cellular types, organisms

journal.pgen.1005804

Pedigree- and SNP-Associated Genetics and Recent Environment are the Major Contributors to Anthropometric and Cardiometabolic Trait Variation

Phenotypic variation for a quantitative trait is attributable to the summed effects of genetic and environmental influences together with any covariances and interactions ., The proportion of phenotypic variance contributed by genetic variation is termed the heritability ( h2 ) 1 ., The heritability scales the influence of genetic and environmental factors on phenotypic variation ., This provides us with insights into the genetic and environmental architecture of human complex traits and our potential ability to dissect out loci associated with trait variation and is also useful for the prediction of heritable disease risk 2 , 3 ., As a consequence , such knowledge is of potential value for clinical diagnosis , therapy , prevention and prognosis 4 ., Therefore , obtaining unbiased estimates of variation caused by different factors and the heritability of traits relevant to health and disease processes is important ., A classic approach to gauging the heritability in humans is by comparing the observed phenotypic similarity to the expected genetic resemblance between relatives inferred from family pedigrees 5 ., This method evaluates the pedigree based heritability ( hped2 ) indirectly without requiring information on the inheritance of individual loci and thus , is quite practical and still widely-used in twin , family and other pedigree studies 6 , 7 ., Note that , hped2 is often considered to be an estimate of the true heritability h2 ., Genome-wide association studies ( GWAS ) , on the contrary , identify causal loci through their association with recorded genetic markers and then aggregate the proportion of variance explained by statistically-significant variants 8 , 9 , which is sometimes referred to as the “GWAS heritability” ( hGWAS2 ) ., Each approach has its limitations and drawbacks ., Pedigree studies require genealogical information from known relatives to deduce their expected genetic resemblance and hped2 may be biased due to the factors shared among relatives ( including dominance , epistasis , common environment , genetic-by-environment correlation and genetic-by- environment interaction ) if such effects are present and the available pedigree structure does not allow these to be accounted for in the analysis 10–12 ., Although GWAS have been very successful at discovering novel loci for a range of polygenic disease and complex traits , they have been less successful at capturing the full extent of known trait genetic variance 11 , 12 ., This is probably because of their failure to detect particular types of variants such as common variants with small effects , rare variants , copy number variants and structural variants , as a consequence of inadequate sample size , genotyping platform design and analyses used , together with the stringent statistical tests applied 10 , 13 , 14 ., As a result , there usually is a substantial gap between the estimates of hped2 and hGWAS2 , often termed the “missing heritability” 11 , 15 ., Recently , Yang et al . 16 , 17 have championed an approach , known as GREML 18 , to estimate the amount of trait variance explained by SNPs ., The estimation of the SNP ( or genomic ) heritability ( hg2 ) , which refers to the additive genetic effects captured by genotyped SNPs , utilises a matrix comprising realised genetic relationships inferred from genomic marker data originally gathered for GWAS ( known as genomic relationship matrix or GRM ) 16 , 17 ., The hg2 estimate from this approach , when estimated using unrelated individuals , lies between the hped2 and hGWAS2 estimates , and has been considered as a lower limit for the former and an upper limit for the latter 11 , 12 ., As an example , for height , hGWAS2 , hg2 and hped2 from three different studies are 0 . 10 , 0 . 45 and 0 . 80 respectively 5 , 8 , 17 ., This suggests that a substantial proportion of the genetic contribution to trait variation is SNP-associated and hence contributes to hg2 but not all this variation is detected by current GWAS , probably due to a combination of insufficient sample size and stringent significant thresholds employed ., The difference between hg2 and hped2 may be largely due to trait associated variants not in linkage disequilibrium ( LD ) with genotyped SNPs , such as rare variants , copy number variants ( CNV ) and other structural variants as mentioned above ., Variation associated with such effects is captured by hped2 due to strong LD in relatives 19 ., Recent studies have started dissecting the heritable component of variation and other components shared among relatives by studying more complex populations made-up of both unrelated individuals and extended pedigrees 11 , 12 , 19 ., For instance , Zaitlen et al . 12 have demonstrated that simultaneously including in a GREML analysis a GRM and a modified GRM ( in which entries smaller than a certain threshold in the GRM are set to zero ) can be used to jointly estimate SNP-associated and total heritabilities in the presence of relatives ., We also note that shared environment may be an important contributor to heritability inflation when close relatives are included in analysis ., In this study , we use data from a single homogeneous cohort consisting of approximately 20 , 000 adults with varying degrees of relationships sampled from Scotland ., The individuals have data on over 520 , 000 SNPs distributed across the autosomes ., The dense marker information together with extended genealogical information allows us to partition the phenotypic variance and explore the genetic and environmental effects shared among related individuals ( both biological relatives and couples ) ., We analyse eight anthropometric traits , comprising height , weight , fat , body mass index ( BMI ) , hips , waist , waist-to-hips ratio ( WHR ) and a body shape index ( ABSI ) 20 and eight cardiometabolic traits , comprising levels of creatinine , urea , total cholesterol ( TC ) and high density lipoprotein ( HDL ) in serum , level of glucose in blood , systolic blood pressure ( SBP ) , diastolic blood pressure ( DBP ) and heart rate ( HR ) ., In our work , we implement alternative models to estimate effects that might contribute to the variation in the 16 traits analysed ., Results show that , with these data , we can separate total genetic variation into SNP-associated and pedigree-associated genetic influences ., We also observe that past family environment and shared full-sibling environment generally have a limited impact on trait variation , whereas the effect in couples of living in the current ( shared ) environment is always important in our data ., We conducted variance component analyses to dissect the phenotypic variation for traits recorded in the Generation Scotland: Scottish Family Health Study ( GS:SFHS ) cohort 21 into genetic and environmental factors ., Analyses utilised a mixed-model approach implemented in a restricted maximum likelihood ( REML ) framework using the GCTA software 16 ., The population was divided into two tranches of approximately equal size and genotyped in two stages ., All initial analyses were performed with the first 10 , 000 genotyped individuals , ( named GS10K ) ., GS10K comprised small nuclear families ( largely two parents and two offspring ) together with unrelated individuals , although inevitably there were second degree and more distant relationships included ., The second tranche completed the genotyping of the rest of the population ( another 10 , 000 individuals ) including further relatives in incomplete families ( e . g . missing samples from parents and additional siblings , as well as other relationships ) , resulting particularly in a proportional increase in the number of second and third degree relationships ( Table 1 ) ., To confirm results obtained from GS10K , some of the analyses were repeated in the whole 20 , 000 individual sample ( named GS20K ) ., We first explored the extent to which estimates of hg2 were inflated by the inclusion of relatives ., We subsequently analysed our data allowing trait variation to be potentially influenced by both genetic and environmental effects ., We assumed that the genetic effects comprised additive genetic effects associated with genotyped SNPs ( hg2 ) and additional additive genetic effects associated with pedigree but not with genotyped SNPs ( hkin2 ) , and we assumed that the environmental effects potentially comprised nuclear family effects ( ef2 ) common to both parents and offspring , full-sibling effects ( es2 ) common to just siblings and couple effects ( ec2 ) common to just the members of a couple ( Fig 1 ) ., The total heritability , termed hgkin2 in this manuscript , referred to as hIBS>t*2 in Zaitlen et al . 12 and comparable to hped2 from traditional pedigree studies , was estimated as the sum of hg2 and hkin2 for each model ., To allow estimation of the influence of each effect , we generated five design matrices: GRMg , GRMkin , ERMFamily , ERMSib and ERMCouple respectively , where GRM refers to genomic relationship matrices and ERM refers to environmental relationship matrices ., For brevity , we named different alternative models using abbreviations according to first subscript letter of the effects examined ., We coded ‘G’ for GRMg , ‘K’ for GRMkin , ‘F’ for ERMFamily , ‘S’ for ERMSib and ‘C’ for ERMCouple–e . g . model ‘GKC’ = GRMg + GRMkin + ERMCouple ., All models included a residual matrix ( allowing effects specific to an individual that were not shared with any other member of the population ) ., We identified the most appropriate model for each trait by a stepwise model selection process via removing non-significant components from the full model based on a Wald test of their estimated effect and a likelihood ratio test ( LRT ) , and we estimated the effects of significant factors using the selected models in GS10K ., We repeated the model selection and corresponding variance component analyses in GS20K to identify differences resulting from analysing a more complex population structure , encompassing a larger proportion of close relationships ., More details about traits , matrices and models are given in Material and Methods and S1 Table and S2 Table ., In the main manuscript , we only list results for the final models identified by the model selection procedure and the full model , but a comprehensive list of estimates obtained for the different effects for each trait and each model is available in S3 Table and S4 Table ., Model robustness and the effectiveness of the model selection were tested using simulated data based on GS10K ., We conducted a simulation study using real genotype and pedigree information from GS10K to evaluate the robustness of our models ., To make computation feasible , we mainly focused on data simulated under the simplest and most complex models ( models ‘G’ , ‘K’ , ‘F’ , ‘S’ , ‘C’ , ‘GK’ , ‘GF’ and ‘GKFSC’ ) and those representing the commonest conclusions of model selection in analyses of the real GS10K data ( models ‘GF’ , ‘GFS’ , ‘GKC’ and ‘GKSC’ ) ., S5 Table shows the simulated and observed values for each parameter as well as the model we used for analyses in different scenarios ., In the first scenario , we examined the performance of our models ( models ‘G’ , ‘K’ , ‘F’ , ‘S’ and ‘C’ ) when simulated phenotypes were only contributed by one of the five corresponding effects plus residual variation ., Under these models ( S5 Table ) , the mean of overall estimates per parameter was very close to its simulated value , indicating that our design matrices GRMg , GRMkin , ERMFamily , ERMCouple and ERMSib worked well in simple models and were able to capture their corresponding effects even when the simulated variance associated with an effect was low ( ≤ 3% ) ., In the second scenario , we evaluated the performance of our models ( models ‘GK’ and ‘GF’ ) when the simulated phenotypes were determined by SNP-associated genetic effects and one of the familial effects ( either pedigree-associated genetics or nuclear family environment ) plus residual variation ., Results ( S5 Table ) indicate that , in cohort with familial structure , failure to account for or inaccurate modelling of familial effects ( i . e . when models used were inconsistent with phenotypic contributors ) would result in upward bias for hg2 in the presence of relatives ., However , this upward bias due to the confounding familial factors could be eliminated by either excluding nominally related individuals or using the appropriate models for analysis ., The former method removes the ability to estimate the familial effects as well as reducing the sample size , whereas using the appropriate models , estimates obtained were very close to their parameter settings and gave a good idea of the magnitude and approximate values of SNP and familial effects as well as the total proportion of variance explained by additive genetics ( hgkin2=hg2+hkin2 ) , despite the fact that the means of estimates of hg2 , hkin2 and ef2 were usually significantly different from the original parameter settings ., In the third scenario , we inspected the performance of the full model ‘GKFSC’ and models selected from analyses of real phenotypes in GS10K other than ‘GF’ ( models ‘GFS’ , ‘GKC’ and ‘GKSC’ ) ., Results ( S5 Table ) demonstrate that all models were robust in terms of the mean of overall estimates per parameter being either unbiased or very close to original settings ., Fig 2 summarizes the main results from these simulations , showing the overall performance of our design matrices from simple models to complex models ., The median of estimates for each component was unbiased across simple and complex models , however , the estimates for hkin2 , ef2 and ec2 were quite variable in the full model , probably due to limitations imposed by the data structure ., All of the above verify the robustness of our models ., Although we confirmed that our models were robust ( S5 Table and Fig 2 ) , the potentially high correlation between ERMFamily matrix and combined ERMCouple and GRMkin matrices may make it challenging to jointly estimate hkin2 , ef2 and ec2 accurately in our sample as the standard errors for those parameter estimates obtained from the full model were high ( S4 Table ) ., Thus the most challenging part of our study may be to precisely dissect pedigree-associated genetic effects , shared nuclear family environment and shared couple environment ., Therefore , we performed model selection using simulated data to test our model selection procedure where simulated phenotypes were contributed by moderate SNP-associated genetic effects and low sibling environmental effects plus, a ) moderate nuclear family environmental effects but low pedigree-associated genetic effects and couple environmental effects;, b ) low nuclear family environmental effects but moderate pedigree-associated genetic effects and couple environmental effects; or, c ) moderate nuclear family environmental effects , pedigree-associated genetic effects and couple environmental effects ., All scenarios included residual variation ., S6 Table shows the parameter settings and the summary of model selection procedure performance for these scenarios ., We expected that our model selection procedure was able to identify SNP genetics ( GRMg ) and nuclear family environment ( ERMFamily ) or SNP and pedigree genetics ( GRMkin ) and couple environment ( ERMCouple ) or SNP and pedigree genetics and nuclear family and couple environment accordingly , since they were the major factors in each corresponding scenario ., As results demonstrated , in all situations our model selection procedure generally ( ≥80% ) selected the appropriate model which contains all major components of phenotypic variation ., The remaining times in the first two of these scenarios , pedigree-associated genetic effects or those plus shared couple environment were selected instead of nuclear family environmental effects or vice versa , and in the remaining two replicates in the third of these scenarios we missed pedigree-associated genetic effects ., In addition , our model selection never fully detected all minor contributions to the phenotype in the first two of these scenarios when the minor effects were too small ( e . g . effects contribute to ≤5% of the phenotypic variance ) ., Both issues identified above ( ~20% chance of selecting inappropriate models and failure to identify all minor effects ) are likely to have been due to limitations in the data structure of GS10K , which provides too few of the appropriate relationships for corresponding effects ( pedigree-associated genetics , nuclear family , sibling and couple environment ) to resolve correlations between parameters and detect minor effects ., These limitations have been greatly ameliorated in the GS20K data ., We also conducted variance component analyses using the final selected model for each replicate ( S6 Table ) ., For those replicates that had appropriate models after model selection , the estimates of factors that remained in the models were usually close to , and not significantly different from , their simulated values , indicating that the results from selected models were reliable ., More details about simulation study can be found in S1 Text , S5 Table and S6 Table ., In the first analyses of the real data , we looked for evidence of familial effects ( either pedigree-associated genetics or nuclear family environment ) in our cohort ., As shown by simulation ( S5 Table ) , if there were any familial effects , we should obtain inflated estimates of hg2 when we conducted variance component analyses using model ‘G’ in the presence of relatives , compared to the estimates of hg2 given from the unrelated subpopulation ., GS10K consists of nearly 10 , 000 genotyped individuals with multiple degrees of relationship , which allows us to explore the impact of familial effects on hg2 estimation in this cohort ., Table 1 shows the population structure of genotyped individuals in GS10K ., The degree of relationship between two individuals was identified according to an approximate range of the expected pair-wise relatedness ( r ) , which was from 0 . 5i-0 . 5 to 0 . 5i+0 . 5 for ith degree relatives ( e . g . pairs of individuals with relatedness from 0 . 354 to 0 . 707 were considered as 1st degree relatives ) ., With these criteria , GS10K consisted of more than 3 , 500 pairs of 1st degree relatives , around 450 pairs of 2nd and 500 pairs of 3rd degree relatives , but the majority of pairs of individuals ( over 99 . 9% ) were genetically unrelated ( more distant than 5th degree relatives , r ≤ 0 . 022 ) ., In total , there were around 6 , 600 unrelated individuals ( defined using the criteria described above ) in GS10K ., We estimated hg2 for each trait using model ‘G’ for subpopulations of GS10K made-up of individuals with different degrees of relatedness ( using the upper bound of the expected relatedness of each category as GRM cut-off points in GCTA ) ., Fig 3 shows how hg2 estimates for height , BMI and HDL changed as we progressively included more closely related individuals in the relationship matrix ., Results for the remaining traits are shown in S3 Table ., In general , hg2 estimates were stable as we gradually added more closely related individuals in the analyses until the inclusion of 1st degree relatives that resulted in inflation of the estimates ( Fig 3 and S3 Table ) ., Based on our results , hg2 was overestimated only when 1st degree relatives were included ., For glucose and DBP , the hg2 estimates did not appear inflated after 1st degree relatives were included , suggesting that these traits were not affected by familial effects ( S3 Table ) ., The increase in hg2 estimates resulting from the inclusion of 1st degree relatives provided evidence of familial variation in our cohort ., However , it is not clear whether these familial effects are due to pedigree-associated genetic effects or shared nuclear family environment or both because either of them has the ability to inflate hg2 estimates ( this was also observed in the simulation data: S5 Table: scenario ii ) ., Therefore , we attempted to tease out this familial variance from the total phenotypic variance and dissect the familial variation as well as the remaining trait variation further using the full model ‘GKFSC’ and the stepwise selection procedure to define a final model containing the most important effects contributing to trait variation ., Table 2 shows the results for final models selected from stepwise model selection strategies and for the proportions of total phenotypic variance explained by different effects using final models , as well as for those obtained using the full model ., The mean estimates for hg2 , hkin2 , ef2 , es2 and ec2 across all traits in the full model were 0 . 18 , 0 . 22 , 0 . 03 , 0 . 03 and 0 . 11 , respectively ., However , the majority of estimates for parameters other than hg2 obtained using the full model were not significantly different from zero according to either the Wald test or LRT performed and had large standard errors in general ., These results suggest that the full model ‘GKFSC’ may suffer from the inclusion of correlated factors , as foreseen in the simulation study , probably due to a low number of different types of pairwise relationship in GS10K ., Therefore , we utilised a model selection procedure designed to provide more precise estimates of the parameters retained in a more robust and parsimonious final model , where the least significant effects are removed from the model ., More details about the selection procedure are given in Material and Methods ., We have demonstrated the effectiveness of our model selection procedure by simulation in the previous section and S6 Table ., As shown in Table 2 , SNP-associated genetic effects ( represented by GRMg ) were retained in the final models for all 16 traits , indicating that all traits examined here are heritable ., Regarding variation associated with families , pedigree-associated genetic effects ( represented by GRMkin ) and nuclear family environmental effects ( represented by ERMFamily ) were retained in the final models for 10 and 4 out of 16 traits respectively ., However , in GS10K , the data structure did not allow for both familial effects to be retained together in the final models for any trait ., Additionally , the final models for glucose and DBP included neither GRMkin nor ERMFamily , which is consistent with the previous conclusion derived from S3 Table , suggesting that familial effects may be limited for these traits ., The additional environmental influences of couple environmental effects ( represented by ERMCouple ) were retained in the final models for 12 out of 16 traits and sibling environmental effects ( represented by ERMSib ) only remained for creatinine and TC ., Although the final model varied between traits , the model ‘GKC’ was most often selected ( 9 out of 16 traits ) in the model selection procedure in GS10K ., Therefore , this suggests that the common environment shared by couples , SNP-associated and pedigree-associated genetic effects are important for the control of a large proportion of the human complex traits we examined , while the shared family and full-sibling environment have a more limited impact SNP-associated genetic effects ( GRMg ) in the final models provided estimates of hg2 ranging between 0 . 10 and 0 . 30 with a mean of 0 . 19 for the 15 traits , excepting height for which nearly half of its phenotypic variation ( 0 . 47 ) was SNP-associated ., For the 10 traits that retained pedigree-associated genetic effects ( GRMkin ) in the final models , the estimates of hkin2 ranged from 0 . 13 to 0 . 36 with a mean of 0 . 26 , except for creatinine for which nearly half of its phenotypic variation ( 0 . 45 ) was pedigree-associated ., For the 10 traits that retained both GRMg and GRMkin in the final models , the estimates of hkin2 accounted for 56% of the total heritability ( hgkin2=hg2+hkin2 ) ., Regarding nuclear family environmental effects , the estimates of ef2 for 4 traits that retained ERMFamily in the final models were of 18% for anthropometric and of 10% for cardiometabolic traits ., Creatinine and TC were the only two traits for which the common sibling environment ( ERMSib ) was kept in the final models , and es2 contributed 7% and 12% of their phenotypic variance respectively ., For those 12 traits that demonstrated evidence of couple effects ( i . e . retained ERMCouple in the final models ) , ec2 accounted for 13 . 5% of the phenotypic variance on average ( of 15% for anthropometric traits and of 11% for cardiometabolic traits ) ., Compared to the results from the full model in Table 2 , using the selected final models provided similar but more precise ( i . e . with smaller standard errors ) parameter estimates ., Therefore , whereas the full models gave a general picture of the important components in the architecture of the traits , the final selected models provided a parsimonious model with more precise estimates of the most important effects ., We added an extra 10 , 000 genotyped and phenotyped individuals from the same population , providing 20 , 000 individuals in total , in order to confirm and build upon the results of the model selection in a more complex data set ., The difference in sample sizes and numbers of different relationships between GS10K and GS20K is shown in Table 1 ., The extra 10 , 000 genotyped individuals in GS20K consisted mainly of the relatives of those already genotyped in GS10K , which substantially increased the proportion of 2nd and 3rd degree and sibling relationships in GS20K ., We repeated the model selection procedure and corresponding variance component analyses using selected models in GS20K to identify changes resulting from the increased complexity and sample size of the population ., Results for model selection and variance component analyses using the final selected model as well as the full model are shown in Table, 3 . In general , the parameter estimates obtained from the full model in GS20K were similar to those obtained from the full model in GS10K but the number of non-significant estimates were much lower due to smaller standard errors ., Note that standard errors of estimates are not only reduced using GS20K , but , unlike results from GS10K in Table 2 , are also similar between full and reduced models , suggesting the change is due to improved structure of the data to separate effects as well as increased sample size ., The final models selected from model selection in GS20K were generally similar to those in GS10K , but , owing to the presence of more nuclear family members and siblings in GS20K , we now had better power to detect the past environmental effects ( either nuclear family environment or sibling environment ) , although the estimated effects were usually small ., Moreover , due to an increased number and higher proportion of 2nd and 3rd degree relatives , we had better resolution for familial effects in GS20K ., Pedigree-associated genetics and nuclear family environment were now separable and the data structure in GS20K can provide sufficient evidence for both types of familial effects ., For weight , urea , TC and HR , familial effects switched from nuclear family environment in GS10K to pedigree-associated genetics or pedigree-associated genetics plus nuclear family environment in GS20K ., However , as in GS10K ( Table 2 and S3 Table ) , there was still no evidence of either genetic or environmental familial effects for glucose and DBP in GS20K ., The results from final selected models in GS20K are summarized in Fig, 4 . The heritability estimate is nearly 90% , 60% and 60% for height , creatinine and HDL respectively , and for the remaining anthropometric and cardiometabolic traits , it ranges from 30%-50% and 20–30% for the two types of trait , respectively ( Fig 4B ) ., Although the proportion of genetic variance explained by SNP-associated and pedigree-associated genetic effects varies across traits , each genetic effect explains around 50% of the genetic variance on average ( Fig 4C ) ., In GS20K , the most commonly selected model was ‘GKSC’ ( 10 out of 16 times , Fig 4A and Table 3 ) ., SNP-associated genetic effects , pedigree-associated genetic effects , sibling environment and couple environment appeared in the final models for 16 , 14 , 12 and 16 out of 16 times respectively and the means of estimates for hg2 , hkin2 , es2 and ec2 for traits which retained corresponding matrices ( GRMg , GRMkin , ERMSib and ERMCouple respectively ) in the final models were of 0 . 20 , 0 . 23 , 0 . 05 and 0 . 11 respectively ( Fig 4A and Table 3 ) ., For the nuclear family environment , the mean of estimates for ef2 for 4 traits which retained ERMFamily in final models was of 0 . 04 ( Fig 4A and Table 3 ) ., On average across traits , our environmental matrices and the final selected models retained through our model selection procedure could explain ~16% and ~56% of the total phenotypic variance respectively ( Fig 4B ) ., The major change in GS20K compared to GS10K is the significant evidence of effects of the sibling environment , particularly for cardiometabolic traits , resulting from the higher proportion of sibling relationships in GS20K ( more than 12 times compared to GS10K , Table 1 ) ., However , the sibling effects were only 5% on average and were still relatively low compared to genetic effects and couple environment ., Therefore , despite the change in population structure in GS20K , the major components for anthropometric and cardiometabolic traits were SNP-associated and pedigree-associated genetic effects and couple environment as they were in GS10K ( Table 2 ) ., The aim of this study was to better understand the architecture of human complex traits by dissecting phenotypic variation into SNP-associated additive genetic variation ( hg2 ) , pedigree-associated genetic variation ( hkin2 ) and environmental influences of common environment shared by nuclear family members ( ef2 ) , full-siblings ( es2 ) and couples ( ec2 ) ., We generated five design matrices GRMg , GRMkin , ERMFamily , ERMSib and ERMCouple to describe the five effects and we examined 16 human complex traits using genome-wide genotype data and genealogical information in the Generation Scotland: Scottish Family Health study ( GS:SFHS ) comprising samples from up to 20 , 000 individuals ., The results of these analyses suggest that SNP-associated genetic effects , pedigree-associated genetic effects and current environment shared by couples were the major contributors to phenotypic variation for anthropometric and cardiometabolic traits ., Past environmental influences , such as shared sibling environment or nuclear family environment , made relatively small or undetectable contributions to trait variation ( Table 2 and Table 3 ) ., The relative importance of a couple or spousal effect for most traits was also noted by Liu et al . 22 , in analyses based only on pedigree relationships , although they did not find a significant spousal effect for cholesterol , HDL or glucose for which a significant couple effect was detected in this study ., Considering the low number of non-zero off-diagonal entries in ERMCouple ( 1 , 283 or 1 , 767 pairs in GS10K or GS20K ) , the signal of couple effects was quite strong ., We did observe significant phenotypic correlation between couple pairs for almost all traits in our data ( S7 Table ) ., For some traits this presumably represents current shared environment due to cohabitation , such as living habits and diet ., For traits related to obesity , it is reasonable that current environmental effects are more important than past environmental effects since traits like BMI , fat , HDL and blood pressure are potentially influenced by recent food intake , exercise and medical treatment ., It should be noted that in our sample participants have an average age of ~50 years and individuals currently sharing a common household environment will largely be couples , whereas most individuals involved in sibling and parent-offspring relationships will no longer be cohabiting at the point when the data were recorded ., It has b

Introduction, Results, Discussion, Materials and Methods

Genome-wide association studies have successfully identified thousands of loci for a range of human complex traits and diseases ., The proportion of phenotypic variance explained by significant associations is , however , limited ., Given the same dense SNP panels , mixed model analyses capture a greater proportion of phenotypic variance than single SNP analyses but the total is generally still less than the genetic variance estimated from pedigree studies ., Combining information from pedigree relationships and SNPs , we examined 16 complex anthropometric and cardiometabolic traits in a Scottish family-based cohort comprising up to 20 , 000 individuals genotyped for ~520 , 000 common autosomal SNPs ., The inclusion of related individuals provides the opportunity to also estimate the genetic variance associated with pedigree as well as the effects of common family environment ., Trait variation was partitioned into SNP-associated and pedigree-associated genetic variation , shared nuclear family environment , shared couple ( partner ) environment and shared full-sibling environment ., Results demonstrate that trait heritabilities vary widely but , on average across traits , SNP-associated and pedigree-associated genetic effects each explain around half the genetic variance ., For most traits the recently-shared environment of couples is also significant , accounting for ~11% of the phenotypic variance on average ., On the other hand , the environment shared largely in the past by members of a nuclear family or by full-siblings , has a more limited impact ., Our findings point to appropriate models to use in future studies as pedigree-associated genetic effects and couple environmental effects have seldom been taken into account in genotype-based analyses ., Appropriate description of the trait variation could help understand causes of intra-individual variation and in the detection of contributing loci and environmental factors .

Unravelling overall trait architecture of complex traits and diseases is important for phenotype prediction and disease prevention and correct modelling of the trait will further aid discovery of causative loci ., Here we take advantage of genome-wide data and a large family-based study to examine the role of common genetic variants , pedigree-associated genetic variants , shared family environment , shared couple environment and shared sibling environment on 16 anthropometric and cardiometabolic traits ., By analysing up to ~20 , 000 Scottish individuals , we find that common genetic variants , pedigree-associated genetic variants and recently-shared environment of couples are the most important contributors to variation in these traits , while past family and sibling environment have a limited impact ., Further studies on the pedigree-associated genetic variation and the shared couple environment effect are needed , as little research has been devoted to them so far .

genome-wide association studies, chemical compounds, social sciences, biomarkers, anthropology, organic compounds, carbohydrates, glucose, simulation and modeling, genome analysis, research and analysis methods, anthropometry, chemistry, creatinine, biochemistry, physical anthropology, organic chemistry, phenotypes, heredity, genetics, monosaccharides, biology and life sciences, physical sciences, genomics, computational biology, complex traits, human genetics

journal.pgen.0030039

Function-Altering SNPs in the Human Multidrug Transporter Gene ABCB1 Identified Using a Saccharomyces-Based Assay

Patients vary widely in their drug responses including unpredicted adverse drug reactions that cause a significant loss of lives and a huge toll on health-care costs 1 ., Rational selection and dosage optimization of anticancer agents are particularly important due to their narrow therapeutic index and inherent cytotoxicity ., Membrane transporters affect drug disposition and response by determining whether or not the level of drug is maintained within the therapeutic index ., Of the known human transporters , P-glycoprotein ( P-gp ) is of particular clinical relevance in that this multidrug efflux pump has a broad range of substrates , including structurally and functionally divergent drugs in common clinical use 2–4 ., P-Gp belongs to the ATP-binding cassette ( ABC ) superfamily 5 and is encoded by the human ABCB1 gene ( also known as multidrug resistance 1 gene MDR1 ) ., Multidrug resistance caused by ABCB1 amplification is a major obstacle in cancer chemotherapy ., In fact , the ABCB1 gene was originally identified because of its amplification in tumor cells that had acquired cross-resistance to multiple cytotoxic anticancer agents 2 , 6–9 ., P-Gp is expressed in many tissues , suggestive of a broad physiological role 10 , 11 and functions by pumping cytotoxic drugs and xenotoxins out of cells into the intestinal lumen , bile , and urine , and thus limiting distribution of such compounds to other organs ., Genetic heterogeneity of the ABCB1 gene may be a potent determinant of interindividual variability in resistance to multiple drugs including anticancer agents ., Furthermore , P-gp can act alone or in combination with other genetic variants , particularly polymorphisms in CYP3A4 , a cytochrome P450 monooxygenase that metabolizes a wide range of drugs 12 , 13 ., Naturally occurring null mutations in P-gp have been reported in mice and dogs but not in humans 14 , 15 ., Animals carrying a null ABCB1 variant are viable unless challenged by drugs that are substrates for P-gp ., Likewise , there may be unidentified human ABCB1 variants that cause a total loss of function ., Numerous ABCB1 single nucleotide polymorphisms ( SNPs ) have been identified ., However , the correlation of SNPs with ABCB1 expression and P-gp function in clinical pharmacokinetics has been inconclusive ., A synonymous 3435C>T SNP has been heavily studied , but its function remains under debate 16 ., Moreover , to date there have been no naturally occurring nonsynonymous substitutions with a validated functional consequence 17 ., Robust functional assays of P-gp variants at the cellular and molecular levels are needed to address their impact on clinical pharmacokinetics ., Since human populations are outbred , and each individual is heterozygous for several million polymorphisms , the impact of ABCB1 variants is difficult to separate from the potential contributions of other variations in an individual ., Yeast cells offer an excellent context for functional analysis of foreign eukaryotic transport proteins 18 ., Expressing human proteins and their variants in yeast allows the function of individual variants to be assessed directly ., The human P-gp can be functionally expressed in the yeast Saccharomyces cerevisiae , where it exports at least some of the same compounds that it exports in human cells 19 ., A typical assay for human P-gp function in yeast involves testing its ability to restore growth to cells in the presence of compounds that would otherwise block their growth ., This functional complementation in yeast allows the impact of ABCB1 variants found in human populations to be assessed ., This study tested the functional consequences of ABCB1 genetic variants found in ethnically diverse populations ( Figure S1 ) 20 ., From this dataset ( http://pharmacogenetics . ucsf . edu or http://www . pharmgkb . org ) , we prioritized nonsynonymous SNPs by their predicted impact on P-gp function , selected ten haplotypes carrying high-priority SNP ( s ) , and determined the level of resistance caused by these ABCB1 variants to clinically important drugs ., For those variants that altered function , subsequent experiments tested the mechanism of these effects ., As the first step toward functional analysis of the nonsynonymous variants of human P-gp , we tested the sensitivity of yeast strains harboring mutations in major endogenous multidrug transporter genes , PDR5 , SNQ2 , and YOR1 ., Combinatorial deletions of these three genes confer sensitivity to a variety of toxic compounds including two anticancer agents , daunorubicin and doxorubicin , which are substrates for human P-gp 21 ., The double mutant pdr5 yor1 ( JRY8008 ) displayed increased sensitivity relative to wild-type cells toward doxorubicin , whereas another double mutant pdr5 snq2 ( JRY8004 ) displayed increased sensitivity toward daunorubicin and doxorubicin ., The strain that exhibited the greatest drug sensitivity was the pdr5 snq2 yor1 triple deletion mutant ( JRY8012 ) ( Figure 1A ) ( see Table S1 for the strain list ) ., This result was reminiscent of bacterial multidrug efflux pumps that produce greater drug resistance in combination than alone 22 ., To address the function of human P-gp in yeast , we used a plasmid ( pJR2702 ) that contains a cDNA for the human ABCB1 gene expressed from the promoter for the S . cerevisiae STE6 gene on a multicopy vector 19 ., The yeast STE6 gene encodes an ABC transporter that mediates the export of the a-factor pheromone in MATa cells ., The cloned cDNA carried the G185V SNP of ABCB1 , and therefore site-directed mutagenesis was used to restore it to the most common allele , referred to as the ABCB1 reference allele in the Pharmacogenetics of Membrane Transporters dataset ( pJR2703 ) ( http://pharmacogenetics . ucsf . edu or http://www . pharmgkb . org ) ., Cells expressing the ABCB1 reference cDNA from the multicopy plasmid in the pdr5 snq2 yor1 strain showed highly increased resistance towards daunorubicin and doxorubicin relative to that of the pdr5 snq2 yor1 strain ( Figure 1B ) ., Thus the P-gp reference was functionally expressed in these yeast cells ., The Pharmacogenetics of Membrane Transporters study identified fourteen nonsynonymous SNPs in 247 healthy individuals from an ethnically diverse population ( Figure S1 ) 20 ., These SNPs comprised 25 haplotypes including 15 haplotypes in which the phase relationship of the SNPs was inferred but not directly resolved ., SNPs were prioritized for functional analysis by two criteria: the degree of evolutionary conservation 23 and the biochemical severity of the alteration ., The extent of evolutionary sequence conservation and thus inferred constraint at a particular residue was observed across ten mammalian species ., The severity of missense changes was estimated by the Grantham scale 24 , which formulates the difference in codon substitutions based on chemical dissimilarity of the encoded amino acids ., Grantham values range between 5 and 215 , with higher values indicating more radical chemical changes ., Out of the 14 nonsynonymous SNPs in the dataset 20 , we chose seven SNPs for functional characterization ( Table 1 ) ., We first focused on the five SNPs with highest Grantham values ( >80 ) : M89T , L662R , R669C , A893S , and W1108R ., The M89T polymorphic site was not evolutionarily conserved , but the other four sites were highly conserved ., In addition , the P1051A SNP was chosen because of its conservation despite a low Grantham value , and the S1141T SNP was included due to its relatively high allele frequency ( 11% in African Americans ) and evolutionary conservation ., Although A893S , S1141T , and R669C SNPs are common variants ( minor allele frequency ≥1% in at least one major ethnic group ) , the remaining four chosen variants are observed only once among 494 alleles from different populations ., These rare variants ( minor allele frequency <1% ) were included because rare adverse drug reactions may be due to highly penetrant but rare variants ., The alignment and allele count of ABCB1 haplotypes based on the 14 nonsynonymous SNPs identified in the previous resequencing project are presented in Table S2 ., From the standpoint of functional impact , the R669C SNP was particularly interesting ., First , this Arg-to-Cys substitution had the highest Grantham value ( 180 ) among the fourteen SNPs ., Second , this SNP was observed twice in the African American population exhibiting a 1% allele frequency , whereas the four chosen rare variants occurred only once ., Third , the R669C SNP may be in phase with the W1108R variant ., One of the two R669C SNPs was detected in an individual whose ABCB1 gene also contained the W1108R variant , potentially resulting in haplotype R669C-W1108R ., This observation prompted us to test whether a R669C-W1108R allele had a unique phenotype relative to alleles carrying each individual SNP ., We constructed plasmids expressing P-gp variants by site-directed mutagenesis on the reference plasmid to evaluate the effect of selected SNPs and their combinations on P-gp function ., These plasmids ( pJR2703–pJR2712 ) , along with two control vectors ( YEp352 and pJR2713 ) , were transformed into the pdr5 snq2 yor1 strain ( JRY8012 ) ., These yeast strains carrying plasmids with ABCB1 variants ( JRY8025–JRY8036 ) were examined for their level of resistance to daunorubicin and doxorubicin on solid medium ., Different P-gp variants displayed higher levels of resistance ( A893S-M89T , L662R , and R669C ) or lower levels of resistance ( A893S , S1141T , A893S-R669C , A893S-P1051A , W1108R , and W1108R-R669C ) relative to the P-gp reference ( Figure 2A and 2B ) ., The alleles varied widely in their ability to survive on high concentrations of daunorubicin and doxorubicin ., The replacement of Arg669 by Cys led to one of the most drastic gain-of-function effects on the ability of P-gp to confer drug resistance ., This alleles elevated resistance was compromised when in combination with W1108R ., Cells expressing truncated P-gp ( see Materials and Methods ) were indistinguishable from cells transformed with an empty vector with respect to drug resistance ., To quantify the extent of drug cytotoxicity in liquid medium , median effective concentration ( EC50 ) values were measured for daunorubicin and doxorubicin for each P-gp variant in liquid culture ( Figure 2C ) ., For the majority of the variants , these results were consistent with those observed in the plate assay ., However , the plate assay was more sensitive , allowing variants that were indistinguishable from each other in the liquid assay to be ranked ., There was a discrepancy between the two drug resistance phenotypes with the A893S and A893S-R669 variants: the variants showed a slightly higher level of drug resistance relative to that of the reference in the liquid assay , but a lower survival in the plate assay ., This difference presumably reflects the nature of the two assays: the plate assay measures the level of cell survival on a relatively high fixed concentration of the drug , whereas the liquid assay determines growth rate over multiple drug concentrations ., In the plate assay , all variants for daunorubicin and six variants for doxorubicin exhibited statistically significant differences ( p < 0 . 05 ) ( Figure 2B; Table S3 ) ., In the liquid assay , three variants for daunorubicin ( A893S-R669C , A893S-M89T , and R669C ) and five variants for doxorubicin ( A893S , S1141T , A893S-M89T , L662R , and R669C ) exhibited statistically significant increases in EC50 values ( p < 0 . 05 ) ( Figure 2C; Table S4 ) ., To determine whether the observed differences in drug resistance were due to differences in protein level , we measured the protein level of each P-gp variant by immunoblotting ., The mouse anti-P-gp antibody detected P-gps with an apparent molecular mass of 125 kDa , the expected size of unglycosylated P-gp , in membranes from yeast cells transformed with plasmids carrying reference and variant ABCB1 genes , but not in membranes from control cells transformed with an empty vector ., The amount of P-gp reference and variants differed by no more than 1 . 5-fold ( Figure 3A ) ., The correlation coefficient of the extent of daunorubicin cytotoxicity of each variant relative to the protein level of each variant was 0 . 227 ( Figure 3B ) ., Thus the P-gp variants were present at comparable levels and altered drug cytotoxicity in the variants was not due to the differences in protein levels for P-gp ., In principle , the differing drug resistance of the variants might reflect differences in their subcellular localization if the SNPs affected the P-gp trafficking ., To test this possibility , strains carrying green fluorescent protein ( GFP ) fused in frame to the C terminus of each P-gp variant were evaluated for their subcellular localization patterns ., Fluorescence microscopy indicated that the fusion proteins were localized to both the plasma membrane and the vacuolar membrane in living cells ( Figure 3C ) ., The localization patterns were growth-phase–dependent: GFP fluorescence was observed mostly in the plasma membrane in mid-log phase cells and became more concentrated in vacuoles when the cells were grown into the stationary phase ., The cells carrying each of the GFP-fused P-gp variants fluoresced to similar extents from the same subcellular location under each growth phase ., Thus differences in subcellular localization were unlikely to underlie the differences in drug resistance associated with the variants ., The relative resistance of each P-gp variant to the structurally similar drugs , daunorubicin and doxorubicin , were quite similar ( Figure 2 ) ., Because P-gp can confer cellular resistance to a variety of cytotoxic drugs , we tested whether P-gp variants might exhibit different resistance profiles when tested with additional P-gp substrates , valinomycin and actinomycin D , which are structurally dissimilar from daunorubicin and doxorubicin ., Due to the limited solubility of valinomycin in synthetic ( CSM ) –Ura culture medium , determining the EC50 values was not possible ., However , determining the EC30 proved sufficient to distinguish among the P-gp variants for valinomycin resistance ( Figure 4A ) ., Although some alleles showed similar trends of resistance for valinomycin and daunorubicin/doxorubicin , others ( e . g . , S1141T , W1108R , and W1108R-R669C ) were qualitatively different in their resistances ., Yeast MATa ste6 strains , which lack the a factor pheromone transporter , are reported to be more sensitive to actinomycin D than wild-type strains 25 ., This prompted us to investigate the interesting possibility that MATa cells are intrinsically more resistant to actinomycin D than MATα cells ., Indeed MATα cells were dramatically more sensitive to actinomycin D ( EC50 15 μg/ml ) than MATa cells ( EC50 48 μg/ml ) ., To see if the cytotoxicity profile pattern of P-gp variants is changed with actinomycin D , all variants were tested in a MATa ste6 strain ( JRY8572 ) for their levels of resistance to actinomycin D ( JRY8573–JRY8584 ) ( Figure 4A ) ., We tested the statistical significance of all comparisons between the reference and each variant for each drug ( Table S4 ) ., Five variants ( S1141T , A893S-R669C , A893S-M89T , L662R , and R669C ) exhibited a statistically significant increase in EC50 or EC30 values for two or more drugs ., The A893S and A893S-P1051A variants caused an increase in resistance only for doxorubicin and valinomycin , respectively ., The compromising effect of W1108R on R669C was obvious in resistance for all four drugs ( Figures 2 and 4A ) ., To see if the relative resistance profile of the P-gp variants to one substrate was predictive of the relative resistance profile to other substrates , we determined the correlation coefficient for all combinatorial pairs of the four relative resistance profiles ( Figure 4B ) ., The resistance profiles of three anticancer agents ( daunorubicin , doxorubicin , and actinomycin D ) were highly correlated to each other , whereas the resistance profile of valinomycin exhibited a relatively low degree of correlation with those of the other three drugs ., To understand the correlation between ABCB1 polymorphisms and altered cellular pharmacokinetics , we have developed functional assays of P-gp variants in yeast cells ., The function of nonsynonymous SNPs was quantitatively measured in isolation from all other variations in the human genome in a yeast-based in vivo assay ., The most sensitive measure of drug transport was a colony-counting assay , which provided both qualitative and quantitative measures of drug resistance in yeast expressing reference and variant P-gp ., We observed multiple differences caused by the P-gp variants in the level of resistance to the anticancer agents , daunorubicin , doxorubicin , and actinomycin D , and the potassium ionophore valinomycin ., The functional consequences of five ABCB1 polymorphisms were previously unknown: the M89T , L662R , R669C , and S1141T variants were associated with increased resistance to two or more drugs; and the W1108R variant strongly mitigated the impact of R669C on gain of P-gp function ( Figures 2 and 4A ) ., Due to its high allele frequency ( 11% in African Americans ) , the S1141T SNP in particular deserves further attention to define its clinical significance ., As measured by plating efficiency in an acute exposure test , the difference between the reference and most sensitive ( W1108R ) alleles was approximately 30-fold ., In a chronic exposure involving growth in the presence of the drug , like most quantitative comparisons of the activity of single amino acid substituted P-gp mutants in the published data , the differences among the P-gp variant alleles in EC50 or EC30 values were modest in most cases ., The functional variations can be magnified in clinical practice , especially for anticancer agents due to ABCB1 amplification in cancer patients ., In previous studies , the A893 variant , which is the most common SNP , caused either no significant functional impact 20 , 26 , 27 or increased P-gp function for digoxin efflux 28 ., The data shown here were able to detect an effect of this allele and uncovered unexpected complexity in the response ., In the acute assay , A893S cells were significantly more sensitive to both daunorubicin and doxorubicin than cells with the reference allele ( Figure 2A and 2B ) ., In contrast , in the chronic assay the A893S allele was indistinguishable from the reference allele with respect to daunorubicin and slightly more resistant to doxorubicin ( Figure 2C ) ., Like variants of facilitated drug influx pumps in the solute-carrier superfamily , P-gp variants that increased function were common ., Most random changes in protein sequence are expected to be deleterious or neutral ., The significant enhancement of function common to the alleles tested here may reflect a recent adaptation of human populations to local conditions like toxin exposure , leading to selective pressures on medically relevant phenotypes ., Interestingly , in Europeans CYP genes encoding drug-metabolizing enzymes show strong signals of very recent positive selection 29 ., Despite its distinct chemical structure , the resistance profile of actinomycin D showed a high level of correlation with those of the other anticancer agents , daunorubicin and doxorubicin ( Figure 4B ) ., Valinomycin , which lowers the mitochondrial membrane potential , inducing apoptosis in some cell lines 30 , exhibited a low correlation in resistance relative to other drugs , presumably reflecting differences among P-gp variants in recognition or transport of the drugs ., The resistance profiles of the S1141T , W1108R , and W1108R-R669C variants showed the largest variation across substrates ., Based on this finding , we speculate that the region containing W1108 and S1141 contributes to the substrate discrimination activity of P-gp ., To date , all mutations that alter substrate specificity of P-gp have been located in the transmembrane domains 16 ., In contrast , all seven SNPs for which functional consequences were determined in this study are located either in the extracellular region ( M89T ) or in the cytoplasmic region ( the remaining six variants ) ., We used two widely accepted criteria for predicting the functional effect of uncharacterized SNPs to prioritize for functional characterization ( Table 1 ) ., Our data on functional consequences revealed that these predictions were sound: four functional SNPs ( L662R , R669C , W1108R , and S1141T ) scored highly on both criteria , while the two SNPs ( A893S and P1051A ) that showed no significant functional impact had lower scores on evolutionary conservation and chemical dissimilarity , respectively ., One exception was the M89T variant that altered function despite being poorly conserved among mammals ., Most previous functional studies focused on the impact of individual SNPs rather than that of haplotypes ., However , in at least some cases , drug response correlates with the patients haplotypes rather than individual SNPs 31 , 32 ., We tested SNP interactions to see if a compound allele consisting of two SNPs has a unique phenotype different from those of single-SNP alleles ., Indeed , it is striking that the strong impact of R669C on P-gp function diminished almost completely when combined with W1108R ( Figures 2 and 4A ) ., In contrast , the W1108R variant either alone or with A893S contributed no significant alterations in EC50 or EC30 values ., This result highlighted the importance of testing the impact of all substitutions in a gene together and suggests that compensatory SNPs may exist in nature ., SNPs in the ABCB1 gene have been implicated in altering drug response or susceptibility to diseases such as Parkinsons disease 33 , inflammatory bowel disease 34 , and renal epithelial tumors 35 ., However , in many such cases , the reported effects of ABCB1 polymorphisms are conflicting or inconsistent 26 , 36–38 ., This inconsistency may have several causes ., First , P-gp expression levels may be modified by nongenetic factors , such as diet and comedications , especially when surgical specimens are studied ., Second , previous studies with mammalian cell lines rely on transient expression assays , which swamp the subtle effects of SNPs by variable levels of expression ., Third , only a few coding SNPs have been functionally tested , such as A893S and N21D , which our analysis predicted would have a weak functional impact 26 ., The use of yeast to evaluate the function of nonsynonymous coding SNPs bypasses these issues and allows the function of single coding SNPs and haplotypes to be assessed directly , independent of all other variations in their original human genome ., This “in yeast pharmacogenetics” can function as a robust screening and phenotyping tool to characterize additional SNPs in ABCB1 and presumably other human multidrug transporter genes ., During the course of these studies , we observed that MATα cells were highly sensitive to actinomycin D , whereas MATa cells were resistant ., This was apparently due to expulsion of the drug by the a cell-specific Ste6 transporter ., Perhaps chemical exposures in ecological niches or the consequences of treatment with therapeutics might lead to the extreme mating-type biases observed with some fungal pathogens ., For example , the mating-type–specific niches occupied by Cryptococcus neoformans may reflect the ability to transport toxins out of the cell in certain environments 39 ., S . cerevisiae strains used in this study are listed in Table S1 ., Standard rich medium ( YPD ) , CSM , and synthetic medium lacking nutritional supplement ( s ) ( CSM–Ura , CSM–His , and CSM–Ura–Trp ) were prepared as described 40 ., Yeast cells were grown routinely at 30 °C ., A P-gp-expressing plasmid , pJR2702 ( alias pYKM77; a multicopy-number vector ) , was kindly provided by Jeremy Thorner ( University of California , Berkeley , California , United States ) and used for constructing expression plasmids for ABCB1 bearing different SNPs ., A cDNA for the human ABCB1 coding sequence ( GenBank accession number M14758 . 1 ) was cloned into a multicopy URA3-marked plasmid with the 2 μm origin of replication ( YEp352 ) and expressed from the yeast STE6 promoter ( pJR2702 ) ., Substitutions at the SNP position were carried out in pJR2702 by site-directed mutagenesis with primers designed to generate individual haplotypes ( Table S5 ) , using the QuikChange site-directed mutagenesis kit from Stratagene ( http://www . stratagene . com ) ., We introduced five single SNP alleles and four compound alleles consisting of a two-SNP haplotype into the reference plasmid ( pJR2703 ) , creating plasmids pJR2704 to pJR2712 ( Table S1 ) ., As a negative control , a −1 frameshift mutation at codon 1 , 200 of the ABCB1 sequence ( 1 , 280 amino acids ) was constructed; this cDNA encodes a truncated product of 1 , 228 amino acids expected to be nonfunctional when expressed ( pJR2713 ) ., Presence of the desired substitution in the plasmids was verified by DNA sequencing ., These eleven constructs , along with another control lacking the entire ABCB1 sequence ( pJR1016 ) , were transformed into a MATa yeast strain lacking three different ABC transporter genes ( Δpdr5 Δsnq2 Δyor1 , JRY8012 ) , resulting in strains JRY8025 to JRY8036 ( Table S1 ) ., Daunorubicin and doxorubicin were kindly provided by Robert Schultz in the Developmental Therapeutics Program of the National Cancer Institute , National Institutes of Health ( NIH ) ( Rockville , Maryland , USA ) ., Valinomycin and actinomycin D were from Sigma ( http://www . sigmaaldrich . com ) ., For drug cytotoxicity assays , stock solutions of the drug were prepared at 10 mM in 5% DMSO for daunorubicin and doxorubicin , in 98% ethanol for valinomycin , and in 100% DMSO for actinomycin D . In the spotting assay , cultures from each strain were grown to midexponential phase , titrated to the same concentration ( ~107 cells per 1 ml ) , and serially diluted 5-fold ., Aliquots ( 4 μl ) from the dilution series were spotted onto a CSM–Ura plate containing the indicated concentration of the drug ., Control plates lacking the drug contained the solvent control at the same concentrations as CSM–Ura plates containing the drug ., In the plate assay , cultures from each strain were grown to midexponential phase and titrated to the same concentration ( ~105 cells per 1 ml ) ., Aliquots ( 100 μl ) were spread onto a CSM–Ura plate containing the indicated concentration of the drug ., The same aliquots were further diluted 20-fold ( ~5 , 000 cells per 1 ml ) and spread onto control plates lacking the drug ., After incubation for three days , colony numbers per plate were counted ., Drug resistance was further assayed quantitatively in 96-well microtiter plates ( Corning , http://www . corning . com ) , containing equal volumes ( 200 μl ) of CSM–Ura liquid medium with different concentrations of the drug ., Yeast transformants grown to stationary phase in CSM–Ura were diluted to an OD600 of 0 . 1 ., Equal volumes ( 200 μl ) of these diluted cultures containing increasing concentrations of the drug were added to wells and incubated at 30 °C for 24 h in a Tecan microtiter plate reader ., Cell growth was monitored in the absence of the drug in the presence of the same solvent as a negative control ., For the experiments with liquid medium , the EC50 ( median effective concentration ) value was defined as the drug concentration that reduced growth of the treated cells to 50% of growth of the control cultures as judged by OD600 when the increase in OD600 of the control cultures was 0 . 7 ( midexponential phase ) ., To rule out the possibility that variations in copy number affect the observed differences in drug resistance for the vectors bearing each P-gp variant , all measurements were examined in a series of independent transformants for each of the P-gp variants ., Membrane fractions of yeast cells with the plasmids bearing ABCB1 variants ( JRY8025–JRY8036 ) were prepared as described 41 ., The mouse monoclonal anti-P-gp antibody C219 , kindly provided by Michael Gottesman ( National Cancer Institute , NIH , Bethesda , Maryland , United States ) , was used in immunoblots to quantify the level of P-gp variants in yeast ., A rabbit antibody against the Gas1 protein , kindly provided by Randy Schekman ( University of California , Berkeley , California , United States ) , served as a marker of membrane proteins ., Human P-gp and yeast Gas1 protein were detected simultaneously on the same blot using infrared-labeled secondary antibodies visualized at two different fluorescence channels , 700 and 800 nm ., The blot was developed and quantified by Odyssey Infrared Imaging System ( LI-COR Biosciences , http://www . licor . com ) following the manufacturers protocol ., A codon-optimized GFP gene for yeast , yEGFP1 42 , was amplified by PCR with oligonucleotide primers designed to allow in-frame fusion to the 3′ end of ABCB1 reference and its variants in a yeast expression vector by recombination following transformation into yeast 43 ., The presence of yEGFP in the construct was verified by colony PCR and DNA sequencing ., For fluorescence microscopy , cells were grown in synthetic medium without tryptophan to minimize autofluorescence ., Imaging was done at room temperature using an Olympus IX-71 microscope equipped with 100× NA1 . 4 objectives and Orca-II camera ( http://www . olympusamerica . com ) ., ImageJ ( http://rsb . info . nih . gov/ij ) was used for manipulation of images ., The probability of a statistically significant difference between the mean values of two datasets was determined by one-way ANOVA with Dunnetts post-test using GraphPad Prism version 4 . 03 for Windows , GraphPad Software ( http://www . graphpad . com ) ., The Entrez ( http://www . ncbi . nlm . nih . gov/Entrez ) accession numbers for the genes described in this paper are 5243 for human ABCB1 , 1576 for human CYP3A4 , 854324 for yeast PDR5 , 851574 for yeast SNQ2 , 853198 for yeast YOR1 , 853671 for yeast STE6 , and 855355 for yeast GAS1 ., The RefSeq ( http://www . ncbi . nlm . nih . gov/entrez/query . fcgi ? db=OMIM ) accession number for human ABCB1 cDNA carried in plasmid pJR2702 is M14758 . 1 ., The Online Mendelian Inheritance in Man ( http://www . ncbi . nlm . nih . gov/entrez/query . fcgi ? db=OMIN ) accession numbers are 168600 for Parkinsons disease , 266600 for inflammatory bowel disease , and 144700 for renal epithelial tumors .

Introduction, Results, Discussion, Materials and Methods, Supporting Information

The human ABCB1 ( MDR1 ) -encoded multidrug transporter P-glycoprotein ( P-gp ) plays a major role in disposition and efficacy of a broad range of drugs including anticancer agents ., ABCB1 polymorphisms could therefore determine interindividual variability in resistance to these drugs ., To test this hypothesis we developed a Saccharomyces-based assay for evaluating the functional significance of ABCB1 polymorphisms ., The P-gp reference and nine variants carrying amino-acid–altering single nucleotide polymorphisms ( SNPs ) were tested on medium containing daunorubicin , doxorubicin , valinomycin , or actinomycin D , revealing SNPs that increased ( M89T , L662R , R669C , and S1141T ) or decreased ( W1108R ) drug resistance ., The R669C alleles highly elevated resistance was compromised when in combination with W1108R ., Protein level or subcellular location of each variant did not account for the observed phenotypes ., The relative resistance profile of the variants differed with drug substrates ., This study established a robust new methodology for identification of function-altering polymorphisms in human multidrug transporter genes , identified polymorphisms affecting P-gp function , and provided a step toward genotype-determined dosing of chemotherapeutics .

Patients often show varied drug responses ranging from lack of therapeutic efficacy to life-threatening adverse drug reactions ., Drug therapy would be greatly improved if it were possible to predict individual drug sensitivity and tailor drugs to patients genetic makeup ., Like all other organisms , humans have a set of transporters and enzymes to detoxify and eliminate foreign molecules including drugs ., Understanding the function of genetic variants in these proteins is a key goal toward personalized medicine ., To that end , we examined the functional consequences of naturally occurring genetic variants in P-glycoprotein , the most versatile human multidrug transporter ., A novel method was developed and employed that can identify function-altering variants in human transporters ., This methodology was robust and powerful in that the functional effect of genetic variants can be directly assessed in yeast where all confounding variables in humans are excluded ., Surprisingly , the majority of single amino acid substitutions were found to cause alterations in resistance to three tested anticancer agents ., This study extends the impact of yeast-based medical research to a new niche , pharmacogenomics .

biotechnology, oncology, biochemistry, homo (human), genetics and genomics, saccharomyces

journal.pcbi.1005243

Hierarchical Post-transcriptional Regulation of Colicin E2 Expression in Escherichia coli

Regulation of gene expression occurs at transcriptional and post-transcriptional levels , and has been studied intensively both experimentally and theoretically 1–10 ., Bacterial stress responses , such as the well-studied production and release of the toxin colicin E2 in Escherichia coli , represent one setting in which post-transcriptional control is crucial 11–15 ., Colicins are toxic proteins produced by certain E . coli strains in response to stress as a means to kill bacteria that compete with them for the same resources ., More specificly , colicin E2 is a bacteriocin , which damages the DNA of bacterial cells that absorb it ( a DNAse ) ., Once synthesized , colicin E2 forms a complex with an immunity protein , thus protecting its producer from its otherwise lethal action 14 , 16 , 17 ., The toxin is released only upon cell lysis , which is triggered by the synthesis of a dedicated lysis protein 15 , 18–20 ., As this inevitably entails the death of the producer cell 19 , it is vital for the persistence of the population that only a fraction of its members actually releases the toxin 14 ., The genes for the colicin , immunity protein and lysis protein are organized into the colicin E2 operon , which is depicted in Fig 1 , together with the interaction network that controls colicin E2 expression and release ., Each of the three components is encoded by a single gene—the colicin by cea , the colicin-specific immunity protein by cei , and the lysis factor by cel—and three regulatory regions control their transcription: an SOS promoter upstream of the cea gene 21 , and two transcriptional terminators T1 and T2 , located upstream and downstream of the cel gene , respectively 22 ., The key transcriptional regulator of the SOS operon is the LexA protein ( reviewed in 23 ) , marked in orange in Fig 1 ., LexA dimers repress the SOS promoter region of the ColE2 operon , but also block the transcription of over 30 other SOS genes 24 , 25 , many of which play an important role in DNA repair 26 ., In the event of DNA damage , the LexA dimer undergoes auto-cleavage upon interaction with RecA 27 , and the transcription of SOS genes begins ., The presence of the two transcriptional terminators in the ColE2 operon results in the production of two different mRNAs: A shorter transcript ( short mRNA , marked purple in Fig 1 ) that encompasses only the genes for the toxin colicin E2 and the immunity protein , and a longer transcript ( long mRNA , marked green in Fig 1 ) , which additionally includes the lysis gene 14 , 28 , 29–32 ., Hence , lysis can only be initiated after the translation of long mRNA 18 , and this crucial operation is regulated at the post-transcriptional level , as described below ., Post-transcriptional regulation makes use of many different mechanisms ., Recent studies emphasize the particular importance of non-coding sRNAs 33 for various processes in E . coli , especially because of their ability to introduce delays and set up thresholds for translation 34–37 ., This is done either directly , by sRNAs pairing with their target mRNA ( sRNA-mRNA interaction ) , or indirectly , by sequestering of specific mRNA-binding proteins ( mRNA-protein interaction ) 2 , 38 , 39 ., For the latter form of regulation , recent studies highlighted the importance of the production rates of regulatory components 40 ., In the case of the ColE2 system , the translation of the long mRNA is regulated by the carbon storage regulator protein CsrA 28 , marked red in Fig 1 ., CsrA dimers destabilize target mRNAs by binding to a region that includes the ribosome-binding site ( Shine-Dalgarno sequence ) 41 ., Masking of the ribosome-binding site by CsrA thus not only represses translation of the lysis gene but also promotes degradation of the long mRNA ., However , CsrA is also recognized by two specific sRNAs , CsrB and CsrC 42 , marked blue in Fig 1 ., These sRNAs can therefore sequester CsrA dimers , preventing them from binding to target mRNAs 43–45 ., Thus , translation of the ColE2 lysis gene is indirectly regulated by sequestration of CsrA ., This process , also known as “molecular titration” , exhibits ultrasensitive thresholds and has been extensively studied 46 , 47 ., The basic interaction network that controls the ColE2 regulatory network has been studied in great detail in previous works 48–51 , and many of its functional characteristics , in particular the threshold behavior , were described for a wide range of both bacterial and eukaryotic systems 52 ., However , a detailed theoretical description of the dynamics leading to the release of colicin is still missing , in particular the role of the hierarchically ordered regulation involving CsrB and CsrC ., In this work , we have formulated this post-transcriptional network in a detailed mathematical model , constructed by analogy to studies of simpler sRNA-regulated systems ( for example , 33 , 34 , 36 ) ., We then simplified the model by assuming fast complex equilibration , and combining the sRNAs CsrB and CsrC into a single , effective sRNA ( see S1 Text for details ) ., This reduced the regulation network to three relevant components: free long mRNA , free CsrA and the effective sRNA ( see Fig 2 ) ., We then analyzed this simplified network in detail ., In contrast to previous work 36 , we give a general analytical solution for the three component system , and derive a precise approximation for fast and clear analysis ., This analytic solution exhibits a pronounced threshold in mRNA production due to CsrA-dependent regulation , which was also confirmed using numeric simulations ., We investigated , how this threshold depends on system parameters , and how it affects the actual biological system ., Furthermore , we have analyzed the role of fluctuations in the post-transcriptional regulation network and how fluctuations in long mRNA expression may be dampened by sRNA ., Finally , we extended our model by including the transcriptional regulation , and analyzed how the system behaves during a realistic SOS response ., Previous studies have shown discrete activation peaks in LexA-repressed promoters 26 that can lead to large fluctuations close to the threshold of mRNA expression 9 ., In a stochastic simulation of the complete model , we were able to reproduce this phenomenon ., Comparison with experimental data on lysis time distributions 48 also shows that our model can explain the delayed and broadly distributed release times of colicin complexes ., This underlines the importance of stochasticity for the heterogeneous expression of colicin E2 in E . coli populations ., For our theoretical analysis , we initially developed a detailed mathematical model for the post-transcriptional regulation of colicin E2 release ., To this end , we derived a set of coupled , deterministic rate equations from the interaction network depicted in Fig 1 , with the corresponding rates for transcription , degradation , binding interactions etc . as parameters ., In the following , we briefly review how we reduced the network to its core components , which comprise the theoretical model ., The interaction scheme underlying the complete model is presented in S1 Fig and further explanations can be found in the Supporting Information , where we also detail how our model can account for sequestration by other targets of the global regulator CsrA ., As we wished to study the post-transcriptional regulation of colicin E2 expression , we included in the model only those components that are relevant at that stage ., The model therefore omits the short mRNA and its products ., However , the rate of transcription of the long mRNA is a crucial parameter , which is influenced by the kinetics of activation of the SOS promoter , and thus by the processing of its repressor LexA ., Upon DNA damage , RecA promotes auto-cleavage of LexA dimers , thus removing inhibition of the SOS response ( marked in red in Fig 1 ) ., The LexA-RecA interaction network has recently been modeled stochastically 53 ., Before including this detailed network in our final model , we focused on understanding the post-transcriptional dynamics ., To this end , we initially assumed that activation of the SOS promoter occurs rapidly relative to the rates of production and degradation of the long mRNA 54 , which allowed us to approximate the transcription rate of long mRNA by an effective rate αM ( Materials and Methods ) ., With respect to post-transcriptionally relevant components , we were then left with long mRNA , CsrA , and the two sRNAs CsrB and CsrC , and the mRNA-CsrA- , CsrA-CsrB- , and CsrA-CsrC-complexes ., CsrB and CsrC regulate CsrA by forming complexes with it ., The two sRNAs each have several ( on average: N ) CsrA binding sites , and if every occupation state of the sRNAs were to be modeled as a separate component , a large number of coupled rate equations would need to be added to the model ., However , due to the fast dynamics of the CsrA-CsrB- and CsrA-CsrC-complexes , and their virtually identical biochemical behavior , we were able to reduce the sRNA interaction to a single equation for an effective sRNA , with only one binding site and transcription rate NαS ( see Materials and Methods ) ., As a result , the mechanisms of complex formation , dissociation and degradation are replaced by an effective coupled degradation of complex partners ., Despite the different processes that are integrated to effective ones , the effective sRNA still resembles the dynamical behavior of CsrB/CsrC ., A detailed derivation of the simplified system of rate equations can be found in S1 Text ., The final post-transcriptional model is thus reduced to a set of three coupled , deterministic rate equations that capture the behavior of the free long mRNA ( M ) , free CsrA dimers ( A ) , and an effective free sRNA ( S ) component with a single CsrA binding site:, M ˙ = α M - δ M M - k M M A , ( 1 ) A ˙ = α A - δ A A - k M p M M A - k S p S A S , ( 2 ) S ˙ = N α S - δ S S - A k S S , ( 3 ), where ( 1 − pM ) and ( 1 − pS ) are the probabilities for CsrA to survive the coupled degradation ., A graphical illustration of this differential equation system is depicted in Fig 2 ., Note that in the model the quantities M , A and S represent the abundance of the corresponding free components ., Once a long mRNA , sRNA , or CsrA dimer binds to some other component , it loses its function and is thus removed from the model system ., For the analysis of our model , we had to determine production , degradation and binding rates ., The particular values used are listed in S1 Table ., As far as possible , we chose values that are measured in studies on either the same or comparable systems ( see S1 Text for details ) ., In the other cases , we tried to derive plausible parameters from known factors that influence the particular rate ., A detailed motivation and derivation of these rates is given in chapter 2 of S1 Text ., We analyzed the reduced post-transcriptional model by first calculating its steady state ., In order to obtain a cleaner and simpler result , we derived an approximation ( see Materials and Methods ) for the steady state solution , which agreed very well with the results of numerical simulations ( see S2 Fig ) ., Using these simplified equations , we then investigated the impact of the rates of production of long mRNA ( αM ) and sRNA ( αS ) on the levels of the three components ., The results ( see Fig 3 ) reveal a linear threshold that appears at the same position for all three components ., The threshold divides the parameter space into two regimes , in which either CsrA or long mRNA and sRNA have a non-zero abundance ., This is due to the coupling between the degradation of CsrA and the abundance of both long mRNA and sRNA , such that the presence of CsrA dimers excludes that of long mRNA and sRNA , and vice versa ., This mechanism in turn controls the release of colicin-immunity complexes , since a sufficiency of CsrA dimers ensures reliable repression of the long mRNA and prevents synthesis of the lysis protein ., From the aforementioned analytic solution we calculated the threshold position as a function of the system parameters ( S1 Text ) ., We found that the threshold for non-zero levels of long mRNA lies exactly at the point where the production rate of CsrA αA is equal to the sum of transcription rates for long mRNA αM and sRNA αS ( S1 Text ) ., Thus , we observed no expression of long mRNA in the regime αM + αS < αA , as shown in Figs 3A and 4 ., We find the threshold to be sharp , and attribute this to the very slow degradation of CsrA compared to long mRNA and sRNA 55 , 56 ., Apart from the threshold itself , we find that the levels of free CsrA and free sRNA predicted by our steady state analysis are consistent with experimental in-vivo values determined by previous studies 43 , 57 ., Moreover , our results are also consistent with the total amount of CsrA as well as its ratio to sRNA ( S1 Text ) ., So far , we have demonstrated that our three-component system is capable of producing a threshold behavior ., However , it has been shown previously that a mutually exclusive production of sRNA and a target mRNA is possible with just two components 36 ., The question thus arises why a third component is needed at all ., One possible explanation is that the sRNA makes it easier to trigger lysis , as an increase in sRNA production induces an increase in the abundance of long mRNA ( Fig 3 ) ., After SOS signals , the sRNA controls and accelerates the degradation of CsrA ( see section on expression dynamics below ) , eventually leading to the expression of the lysis protein ., In a next step , we analyzed the stochastic dynamics of the post-transcriptional regulation network ., To this end , we switched to a stochastic description , calculated the Fano factor ( VarM/〈M〉 ) for the abundance of long mRNA ( see Materials and Methods ) , and depicted it as heatmap in Fig 4 ., The Fano factor measures the relative magnitude of fluctuations , and has already been applied to gene regulatory networks in previous studies 58 ., It can also be understood as a quantified comparison with the pure birth process ( Poisson process ) , which has the Fano factor F = 1 ., We found that fluctuations in mRNA were most pronounced close to the threshold position , with the largest fluctuations occurring slightly above the threshold ( Fig 4 ) ., Moreover , Fig 4 also shows that the fluctuations became larger as sRNA production decreases ., Thus , the third component ( sRNA ) in the post-transcriptional regulation network also enables significant dampening of fluctuations in long mRNA ., To understand why the fluctuations are localized to the region near threshold , one must take the characteristics of this parameter regime into account ., Around the threshold , molecule numbers are close to zero , which has a direct affect on the relative size of fluctuations: the lower the abundance , the larger the fluctuations ( stochastic regime ) ., Moreover , the threshold is the only regime in which all three components , CsrA , mRNA and sRNA , can coexist and interact with each other: An increase in the level of CsrA will lead to a decrease in the abundance of long mRNA and sRNA , owing to increased complex formation and subsequent degradation ., Analogously , an increase in long mRNA and sRNA molecule numbers leads to a decrease in CsrA abundance ., Therefore , the abundance of CsrA dimers is anti-correlated with the abundance of both long mRNA and sRNA ., It has been shown for a two-component system , that anti-correlated components can create anomalously large fluctuations 59 if degradation rates are small compared to turnover ( ratio of production rate to abundance ) ., For long mRNA , this is exactly the case close to threshold , where the long mRNA abundance is still very low ., These results show that a third component can reduce intrinsic fluctuations of a hierarchically ordered regulatory network ., To study the dynamical response of the ColE2 system to an SOS signal , we extended the post-transcriptional network by including the LexA-RecA regulatory network 53 ( Fig 1 ) ., LexA not only represses the SOS promoter , it is also an auto-repressor , as well as being a repressor of RecA production ., As outlined in the Introduction , RecA forms filaments after DNA damage , which then induce auto-cleavage of LexA dimers ., Consequently , the levels of RecA , LexA and the colicin mRNAs increase , as repression due to LexA is relaxed ., A stochastic model of this network has been introduced recently 53 ., In that study , promoter activity in the LexA-RecA system was found to occur in ordered bursts that result from fluctuations and the particular structure of the RecA-LexA feedback loop ., In our analysis of the ColE2 post-transcriptional regulation network so far ( see above ) , we have assumed the dynamics of SOS promoter activation to be so fast that we could use an effective transcription rate αM for long mRNA ., To link the LexA regulatory network to the post-transcriptional regulation network , we must drop this assumption and explicitly model the dynamics of LexA dimers , which connect the two networks ., In the biological system , this involves the binding and dissociation of LexA dimers to and from the SOS promoter in the ColE2 operon ., Long mRNA and short mRNA are transcribed only from the derepressed promoter at rates αMl and αMs , respectively ., Thus , the transcription rates of long mRNA and short mRNA are proportional to the number of open SOS promoters in the bacterium ., The majority of transcripts are short mRNAs ., The mathematical implementation of the integrated regulation network is again a system of coupled rate equations , which we describe in S1 Text ., The additional parameters of the LexA-RecA regulation network are to be found in S2 Table ., We simulated the SOS signal by temporarily up-regulating the coupling parameter cp , which quantifies the ability of RecA to induce cleavage of LexA ( Fig 1 ) ., In the uninduced state before and after the SOS signal , the auto-cleavage parameter was set to cp = 0 ., Under SOS stress cp was increased to cp = 6 ., This increase in cp subsequently boosts the long mRNA production , and therefore relates to a transition from a sub-threshold state ( gray area below the white line in Fig 3A ) to a super-threshold state ( green area above the white line in Fig 3A ) ., Due to the stochasticity in the LexA-RecA network and the resulting stochastic promoter dynamics , the overall transcription rate αMl of long mRNA is not constant , but fluctuates about a mean value ., The production rate of sRNA was held constant at αS = 57 . 5 ., Fig 5 shows the dynamics of short and long mRNA levels and the abundance of CsrA dimers and sRNA in response to transient SOS signaling ., When we compared a stochastic realization using Gillespie simulations ( Materials and Methods ) with a numerical solution of the deterministic rate-equation system , we observed significant qualitative and quantitative differences ., First , the stochastic realization exhibited significant fluctuations that manifested themselves in abrupt , short-lived changes in the abundance of short mRNA over the whole time-course ( Fig 5A ) ., Second , the average over 500 stochastic realizations deviated from the deterministically predicted value ., Both phenomena arise from the intrinsic stochasticity of the LexA-RecA-regulatory network , as explained by Shimoni 53 ., Fluctuations may lead to a spontaneous dip in the number of LexA dimers which releases all LexA-regulated genes , including the lexA gene itself , from repression ., This consequently leads to a sudden rise in the abundance of short mRNA ., The open lexA and recA promoters will then generate a burst of newly produced LexA and RecA proteins , which block and regulate the promoters for the next burst ., Focusing on the dynamics of mRNA transcription , we found that , due to initial simulation parameters , only small numbers of the short mRNA are produced in the uninduced state ., After up-regulation of the LexA auto-cleavage parameter cp at t = 200 min , the abundance of short mRNA rises and the aforementioned large bursts appear ., The amount of long mRNA , however , follows a completely different trajectory , conditioned by post-transcriptional regulation ., Before the SOS signal , expression of long mRNA is almost completely repressed by CsrA ( Fig 5B ) ., Even the bursts of SOS promoter activity reflected in fluctuating amounts of the short mRNA have little or no impact on the long mRNA ., This filtering effect is biologically relevant , as it ensures that noisy promoter activity does not erroneously trigger lysis ., After induction of the SOS signal , the deterministic dynamics of the underlying rate equations predicted that , after a delay of about 40 min , the abundance of long mRNA should rapidly rise to a saturation value ( black dashed line in Fig 5B ) ., However , a mean of 500 realizations deviated significantly from this prediction ( Fig 5B ) ., In particular , the average number of long mRNA molecules increased more slowly than predicted by deterministic dynamics ., Hence the abundance saturated at a much lower value ., An appreciable delay between SOS signal induction and expression of long mRNA was still observed , but lasted for only 15 min ., Studying the dynamics of a single stochastic realization , we observed that the number of long mRNA molecules underwent large fluctuations , which were followed by periods of no expression at all ., Moreover , the timing of these bursts varied considerably between different realizations ., This constitutes a significant qualitative difference compared to the average over 500 realizations and to the deterministic dynamics ( Fig 5 ) , both of which exhibit a smooth and continuous temporal behavior ., Fig 5B and 5C indicates the origin of this behavior: The abundance of long mRNA can only grow if the number of free CsrA dimers is low ., The same holds for the abundance of sRNA , which supports the degradation of CsrA and also can only reach non-zero abundances if there is no CsrA left ( Fig 5D ) ., Thus , before any long mRNA can be expressed , the free CsrA concentration must drop to very low values due to degradation or complex formation ., The delay between SOS signal induction and the first burst of long mRNA synthesis therefore depends on the amount of CsrA available ., We went on to study the precise timing of the first burst in long mRNA abundance , since it is crucial for the time-point of release of colicin-immunity complexes ., To this end , we calculated the probability distribution for the first peak from an ensemble of 500 stochastic realizations ., The probability of a peak in long mRNA abundance rose quickly and reached its maximum approximately 60 min after induction of the SOS signal ( Fig 6A ) ., This phenomenon is also seen in experimental systems: time-lapse studies with colicin-producing bacteria revealed that their lysis time is broadly distributed 48 ., The distribution depicted in Fig 6A matches qualitatively with comparable datasets from these experiments ., Moreover , our model is able to numerically predict average lysis times in dependence on different SOS signal strengths ( see S5 Fig ) ., From the probability distribution of the timing of the initial peak in long mRNA abundance we calculated the survival function , i . e . the probability with respect to time that a cell will not release toxin ., Here we assumed that this first burst provides enough long mRNA in the cell to produce the lysis protein , which then induces its lysis with concomitant release of colicin-immunity complexes into the surrounding medium ., The function of lysed cells plotted in Fig 6B shows that the number of cells that release the toxin rises with the duration of the SOS signal ., Incorporation of the LexA-RecA regulatory network allowed us to model the colicin E2 expression dynamics in response to a realistic SOS signal , and the results presented above highlight the importance of CsrA for colicin release ., Gene expression is a process that allows for various forms of regulation at all levels ., In theoretical studies of post-transcriptional regulation of several biological systems , modulation of mRNA production by proteins or sRNA has been shown to create , for instance , temporal thresholds for mRNA translation 9 , 35 , 36 ., Proteins have also been shown to regulate the expression of the toxin colicin E2 28 in the context of an SOS response to environmental stress ., Experimental studies have elucidated the detailed interaction network responsible for the production and release of the colicin 28 ., However , the dynamics of this system , in particular at the post-transcriptional level , have remained elusive ., In close analogy to previous two-component models , we developed a mathematical model for this hierarchically ordered post-transcriptional regulation of colicin E2 release ., Interestingly , the known interaction network for this system necessitated the modeling of three , not two , components: the long mRNA which is necessary for colicin release , its negative regulator CsrA , and sRNA , which in turn negatively regulates CsrA ., Contrary to previous studies 9 , 35 , 36 , 60 , the sRNAs do not regulate the mRNA directly , but control the level of the regulator protein CsrA ., Thus , the sRNA acts as the “regulator’s regulator” ., In our analysis of the model , we used rate constants that were determined from experimental systems ( see chapter 2 of S1 Text for details ) ., Comparing the predicted CsrA levels before the SOS signal ( see Fig 5C ) with in-vivo measurements of E . coli 57 shows that our model results in a pre-SOS free CsrA abundance that agrees with actual bacterial systems ( for other abundances , see S1 Text ) ., Moreover , the model is not just able to predict steady state abundances , but also reproduces the reaction to varying external stress levels as seen in experiments ( see S5 Fig ) ., Investigation of the dynamics revealed that the model exhibits a time delay in the production of free long mRNAs ., This delay is due to the high abundance of CsrA in the non-SOS state of the cell , which causes CsrA to quickly bind to free long mRNA and thus prevents its transcription ., Only during an SOS signal , which indicates external stress for the cell , the level of CsrA gets steadily reduced ., The time this process takes to get CsrA levels so low that fluctuations in long mRNA production result in free long mRNA , causes a delay in colicin release ., As colicin release is coupled to cell lysis , the delay is therefore a mechanism for filtering out transient SOS signals that might erroneously lead to synthesis of the lysis protein ., Moreover , also intrinsic fluctuations , for instance in sRNA production , are filtered out by this mechanism: Even if a large and sudden burst in sRNA were strong enough to drop CsrA abundance close to zero , the CsrA buffer gets restored quickly due to the large production rate of CsrA ., This rate is only effectively lowered during a SOS signal , which increases the production of the CsrA-sequestering long mRNA ., The fact that lysis is regulated by a threshold mechanism of a global regulator protein like CsrA might also be a guarding mechanism for the cell: only prolongued extreme situations will cause the abundance of these regulators to drop to low molecule numbers ., However , delays and similar threshold behavior also emerge in two-component systems , raising the question why a third component is necessary here ., Strikingly , we found that the third component ( sRNA ) in the post-transcriptional interaction network enables the cell to tune the duration of the delay by sequestering CsrA ., In the case of the ColE2 system , this means that cells are able to adjust the ( average ) time between a SOS signal and the onset of cell lysis leading to colicin release ., Furthermore , previous studies of systems with slow , bursting promoter kinetics have also uncovered a major limitation of two-component sRNA-based regulation compared to regulation based on transcription factors: Two-component systems are subject to significantly higher levels of intrinsic noise 9 ., However , Fig 4 ( panels A , C , D ) shows that , in the post-transcriptional regulation of colicin E2 release , fluctuations become smaller at higher values of αS ., The sRNA might therefore allow for significant dampening of these fluctuations ., This idea is supported by the fact that the relatively high degradation rate of sRNA makes it less susceptible to induced fluctuations ., In bacteria , these mechanisms could have several functions: First , a comparison of different sRNA production rates ( S4 Fig ) indicates that the sequestration of CsrA by the sRNA could indeed be crucial for fast release of the colicin , as CsrA degradation rates cannot be arbitrarily increased in bacterial systems ., Second , they can tune the reaction to external stress at the population level ., Experimental studies have shown that , in the absence of stress , 3% of colicin producing cells release the toxin during the stationary phase; but this fraction can be increased up to eventually 100% if an external SOS stress is applied 14 , 48 ., Previous experimental studies also found that colicin systems exhibit heterogenous expression times , which originate from the stochasticity of the SOS signal 49 , 50 ., Recent time-lapse experiments with colicin E2 producing bacteria showed that this lysis time distribution also depends on the strength of the SOS signal 48 ., We reproduced these experiments with stochastic simulations , in which we created different stress levels by different values of the RecA degradation rate parameter cp ., Our predictions for lysis time distributions ( Fig 6A and S5 Fig ) show qualitative agreement with these time-lapse experiments ., Moreover , the ability of the sRNA to tune the average duration of the delay might serve as a mechanism to adjust the cell lysis to different stress levels ., Altering the sRNA level could be an additional mechanism , apart from the stochastic SOS signal , by which bacterial populations can adjust the fraction of cells releasing the toxin depending on the strength and duration of the external stress ., Finally , the co-option of sRNA makes the cells less susceptible to lysis due to adventitious fluctuations in promoter activity ., This is particularly important considering the bursting behavior and large-scale fluctuations seen in the LexA-RecA-regulatory system , which are readily observed in experiments and reproduced by stochastic models 53 ., In order to focus on the interplay between the LexA-RecA system and the hierarchical regulation of long mRNA by CsrA and sRNA , we kept the plasmid number constant ., If we considered random , Poisson-distributed plasmid numbers instead , the effect would be very small , as shown in S4B Fig . This fact demonstrates that the colicin plasmid copy number only has minor influence on the lysis time distribution ( see S1 Text for details ) ., In conclusion , we have provided here the first detailed theoretical description of colicin E2 production and release , and used it to study the dynamical behavior of this system ., Moreover , the general three-component model described here should be applicable to many other systems of toxin production in microorganisms ., In most models of prokaryotic gene expression , it is assumed that promoter kinetics are fast compared to RNA production and degradation rates ., In that case , the promoter state is well approximated by its steady state 54 ., In the analysis of the post-transcriptional regulation network , the promoter status affects the transcription rate of the ( long ) mRNA ., Thus , we replaced it by an effective transcription rate for ( long ) mRNA , which takes into account the probability of a gene being blocked ., In the literature this procedure is referred to as “adiabatic elimination of fast variables

Introduction, Results, Discussion, Materials and Methods

Post-transcriptional regulation of gene expression plays a crucial role in many bacterial pathways ., In particular , the translation of mRNA can be regulated by trans-acting , small , non-coding RNAs ( sRNAs ) or mRNA-binding proteins , each of which has been successfully treated theoretically using two-component models ., An important system that includes a combination of these modes of post-transcriptional regulation is the Colicin E2 system ., DNA damage , by triggering the SOS response , leads to the heterogeneous expression of the Colicin E2 operon including the cea gene encoding the toxin colicin E2 , and the cel gene that codes for the induction of cell lysis and release of colicin ., Although previous studies have uncovered the system’s basic regulatory interactions , its dynamical behavior is still unknown ., Here , we develop a simple , yet comprehensive , mathematical model of the colicin E2 regulatory network , and study its dynamics ., Its post-transcriptional regulation can be reduced to three hierarchically ordered components: the mRNA including the cel gene , the mRNA-binding protein CsrA , and an effective sRNA that regulates CsrA ., We demonstrate that the stationary state of this system exhibits a pronounced threshold in the abundance of free mRNA ., As post-transcriptional regulation is known to be noisy , we performed a detailed stochastic analysis , and found fluctuations to be largest at production rates close to the threshold ., The magnitude of fluctuations can be tuned by the rate of production of the sRNA ., To study the dynamics in response to an SOS signal , we incorporated the LexA-RecA SOS response network into our model ., We found that CsrA regulation filtered out short-lived activation peaks and caused a delay in lysis gene expression for prolonged SOS signals , which is also seen in experiments ., Moreover , we showed that a stochastic SOS signal creates a broad lysis time distribution ., Our model thus theoretically describes Colicin E2 expression dynamics in detail and reveals the importance of the specific regulatory components for the timing of toxin release .

Gene expression is a fundamental biological process , in which living cells use genetic information to synthesize functional products like proteins ., To control this process , cells make use of many different mechanisms ., A well-studied example is the binding of expression intermediates by a cellular component in order to delay the synthesis ., This mechanism is known to regulate the stress-induced release of the toxin colicin E2 by E . coli bacteria ., However , experimental studies have shown that this system is not regulated by just one component , but the interplay of several cellular components , in which the hierarchically ordered main components interact ., Here , we create a mathematical model for the interaction network of colicin E2 release , and study how the component levels evolve ., We show that the system is able to delay the release of the toxin ., Additional components allow to fine-tune the delay and dampen fluctuations in gene expression that would lead to premature toxin release ., A comprehensive analysis of the time evolution reveals a broad distribution of toxin release times , which is also observed in experiments ., This rich dynamical behavior emerges from the interplay of regulatory components , and , due to its generality , may also be transferred to similar regulatory networks , in particular toxin expression systems .

dimers (chemical physics), medicine and health sciences, toxins, pathology and laboratory medicine, engineering and technology, gene regulation, signal processing, messenger rna, signal filtering, toxic agents, toxicology, dna transcription, physiological processes, post-transcriptional gene regulation, tissue repair, gene expression, chemistry, physics, lysis (medicine), biochemistry, rna, nucleic acids, physiology, genetics, biology and life sciences, physical sciences, chemical physics

journal.ppat.1001232

Toxoplasma gondii Lysine Acetyltransferase GCN5-A Functions in the Cellular Response to Alkaline Stress and Expression of Cyst Genes

Stress responses are critical to cell survival , allowing cells to adapt to changing environmental conditions ., In certain pathogenic eukaryotes , such as the protozoan Toxoplasma gondii ( phylum Apicomplexa ) , the stress response takes on added significance as it triggers a developmental change into a latent cyst form ., Parasitic protozoa often rely on stimuli in the environment or host organism in order to progress through the parasite life cycle ., The study of stress-induced developmental changes in Toxoplasma is significant as this process underlies pathogenesis ., This obligate intracellular protist develops from a rapidly growing form ( tachyzoite ) into a latent cyst form ( bradyzoite ) in response to stress 1 ., In human hosts , the cyst forms can re-emerge as destructive tachyzoites if immunity wanes , causing recurring bouts of toxoplasmosis that may endanger immunocompromised individuals 2 ., A major gap in our knowledge that impedes the development of novel therapeutics against Toxoplasma infection is our poor understanding of how tachyzoites reprogram their expressed genome in response to stresses that prompt cyst development ., The identification of proteins that contribute to stress response and bradyzoite formation would be a significant step towards the design of new therapies to treat toxoplasmosis ., How the parasite coordinates the changes in gene expression that accompany stress-induced bradyzoite development is not clear , but epigenetic mechanisms , including histone modifications , have been implicated as contributing to this process 3 , 4 ., Formerly referred to as histone acetyltransferases ( HATs ) , lysine acetyltransferases ( KATs ) of the general control nonderepressible-5 ( GCN5/KAT2 ) family are well-conserved among eukaryotes 5 ., While invertebrates generally possess a single GCN5 , vertebrate species harbor two: GCN5 and the highly similar homologue called PCAF ( p300/CBP-Associating Factor ) 6 ., The GCN5 KAT family has been implicated in cell-cycle progression 7 , chromatin remodeling at specific promoters 8 , transcription elongation 9 , cellular differentiation 10 , and the cellular stress response 11 ., Microarray analyses of knockouts made in yeast suggest that GCN5 is a gene-specific coactivator , regulating 1 . 1% of genes in Schizosaccharomyces pombe and up to 4% in Saccharomyces cerevisiae 12 , 13 ., The GCN5 deletion mutant in S . cerevisiae is viable , but grows poorly on minimal media 14 ., Similarly , GCN5 is not essential for growth under normal conditions in S . pombe , but is required for mounting an appropriate response to KCl and CaCl2-mediated stresses 15 , 16 ., In Arabidopsis plants , GCN5 controls ∼5% of genes and is important for normal growth and development , as well as the response to cold stress 17 ., GCN5 was shown to be instrumental in the control of specific morphogenetic cascades during developmental transitions in Drosophila 18 ., GCN5-null mouse embryos fail to form dorsal mesoderm lineages due to a high incidence of apoptosis and die 10 . 5 days post conception , suggesting a critical role for GCN5 in mammalian development 10 , 19 , 20 ., In contrast , PCAF appears to be dispensable in mice as its loss confers no distinct phenotype 10 ., Collectively , these studies support the idea that GCN5 KATs modulate gene expression during stress , or exposure to other environmental stimuli , to elicit the appropriate response or developmental change ., Toxoplasma is unique among fellow apicomplexan parasites and other invertebrates in possessing two GCN5 HATs , designated TgGCN5-A and –B 21 , 22 ., We sought to delineate the function of TgGCN5-A by creating a genetic knockout using homologous recombination in haploid RH strain tachyzoites ., As in other lower eukaryotes , Toxoplasma lacking TgGCN5-A ( ΔGCN5-A ) showed no discernible phenotype under normal culture conditions 21 , but its response to stress was not addressed ., Here , we analyzed wild-type and ΔGCN5-A parasites under normal and alkaline pH growth conditions using Toxoplasma genome microarrays ., The results illuminate the parasites response to alkaline pH and demonstrate that TgGCN5-A is required for most of these changes in gene expression ., We also show that ΔGCN5-A parasites exhibit greater sensitivity to alkaline pH stress - a novel role for GCN5 KATs ., Moreover , Toxoplasma lacking TgGCN5-A cannot activate bradyzoite-specific genes that are normally up-regulated during alkaline pH-induced cyst development ., These studies establish a novel function for GCN5 KATs in the eukaryotic response to alkaline stress and support the idea that TgGCN5-A is a key contributor to gene expression pertinent to the development of the latent cyst form of Toxoplasma ., We have previously created a null mutation of the TgGCN5-A gene by replacing the majority of the genomic locus with a hypoxanthine-xanthine-guanine phosphoribosyltransferase ( HXGPRT ) minigene in type I RH strain parasites lacking HXGPRT , designated ΔGCN5-A 21 ., The loss of TgGCN5-A had no overt effect on tachyzoites grown in standard culture conditions 21 , mirroring phenotypes reported for other lower eukaryotes such as yeast 14 ., In other species , GCN5 has been shown to be important for stress responses and developmental changes 23 ., In Toxoplasma , stress can lead to expression of bradyzoite-specific genes and the eventual formation of tissue cysts ., We predicted that if TgGCN5-A played a role in stress-induced bradyzoite gene expression , then it may be up-regulated in response to a stress agent ., After 3 days in alkaline culture conditions ( pH 8 . 2 ) , message levels for TgGCN5-A increase >5-fold in wild-type RH Toxoplasma ( Fig . 1 ) ., Actin was monitored as a control to show that alkaline pH stress does not globally increase gene expression ( Fig . 1 ) ., To further assess if TgGCN5-A played a role in the stress response in Toxoplasma tachyzoites , we performed whole genome expression profiling to compare ΔGCN5-A and wild-type parasites grown for 3 days either in alkaline pH ( 8 . 2 ) medium or control medium ., Gene profiling of certain Toxoplasma strains under stress has been reported previously , but the effects of alkaline pH stress on RH strain has not been examined 24 ., Affymetrix ToxoGeneChip microarrays , which contain probe sets for ∼8000 predicted Toxoplasma genes , were used for this study ., The differentially regulated genes were grouped into functional categories based on Gene Ontology ( GO ) annotations in the Toxoplasma genome database at ToxoDB . org ., The entire dataset of Toxoplasma genes affected by alkaline pH stress is available as supplemental data ( Dataset S1 ) ., TgGCN5-A was present in wild-type and absent in ΔGCN5-A , validating the identity of the samples and supporting the fidelity of the microarray analysis ., The fidelity of select microarray results was further confirmed through independent qPCR ( Table S1 ) ., Results show that ∼14% ( 1 , 114 ) of genes are differentially regulated ( p value<0 . 001 ) in intracellular wild-type parasites after 3 days of alkaline pH exposure ., Similar to findings in S . cerevisiae 25 , a broad range of genes have altered expression in Toxoplasma during alkaline pH exposure , including genes involved in invasion , metabolism , protein processing , signaling and gene expression , and membrane transport ., Of the 1 , 114 genes affected , 592 genes were up-regulated ( Fig . 2A ) and 522 genes were down-regulated ( Fig . 2B ) ., Among the genes with largest changes in response to alkaline stress ( an arbitrary cut-off of 2-fold or more ) , 177 were up-regulated ( Table S2 ) and 84 were down-regulated ( Table S3 ) ., A scatter plot of the microarray data reveals that there are virtually no differences ( R2\u200a=\u200a0 . 99 ) in gene expression patterns between wild-type and ΔGCN5-A parasites grown under normal culture conditions ( Fig . 3A ) , which is consistent with the indiscernible phenotype of ΔGCN5-A cultured in normal conditions ., However , relative to wild-type , the ΔGCN5-A parasites differ dramatically in their ability to regulate gene expression when grown in alkaline medium ( R2\u200a=\u200a0 . 43 ) ( Fig . 3B ) ., While 1 , 114 genes are altered in wild-type parasites exposed to alkaline stress , only 502 genes were changed in alkaline-stressed parasites lacking TgGCN5-A ( p<0 . 001 ) ., Since TgGCN5-A is an activator of gene expression we focused on the genes up-regulated during alkaline stress ., Further examination of up-regulated genes reveals that ΔGCN5-A parasites fail to up-regulate 439 of the 592 genes up-regulated by wild-type parasites grown in alkaline pH . In other words , TgGCN5-A is required for increased expression of 74% of the genes activated in response to alkaline pH . TgGCN5-A-dependent genes have diverse roles in signaling and gene expression ( 18% ) , protein processing and translation ( 17% ) , metabolism ( 13% ) , membrane transport ( 8% ) , and adhesion ( 7% ) ( Fig . 4 ) ., TgGCN5-A also controls a substantive number of hypothetical proteins with no known function ( 37% ) ., Hypothetical genes with a change of 2-fold or more are listed in Table S5 ., Our microarray data ( p<0 . 05 ) identifies numerous genes related to Ca2+ signaling that were not up-regulated normally in parasites lacking TgGCN5-A ., These include calcium-dependent protein kinase ( 86 . m00003 ) , calmodulin beta ( 42 . m03474 ) , calmodulin genes ( 50 . m03141 , 59 . m03587 ) , and calcium/calmodulin-dependent 3′ , 5′-cyclic nucleotide phosphodiesterases ( 583 . m05366 , 59 . m03644 ) ( Dataset S1 ) ., Although the eukaryotic response to alkaline exposure is poorly understood , transient increases in intracellular calcium occur , possibly activating calcineurin and leading to a signaling cascade that results in mobilization of transcription factors 25 , 31 ., Previously we have demonstrated that increased acetylation accompanies TgGCN5-A promoter occupancy 3 ., To obtain in vivo confirmation that TgGCN5-A plays a direct role in the co-activation of genes shown to be up-regulated during alkaline culture , we used chromatin immunoprecipitation ( ChIP ) ., RH parasites expressing FLAG-tagged TgGCN5-A ( fGCN5-A ) were employed in ChIP experiments to purify DNA in association with fGCN5-A using anti-FLAG 3 , 32 ., We examined a region ∼1 . 0 kb upstream of the start ATG for phosphatidylinositol 3- and 4-kinase ( PI3-4K , 76 . m01548 ) and protein kinase ( PK , 641 . m01507 ) genes , both of which are up-regulated during pH stress ( p<0 . 05 , Dataset S1 ) ., Two housekeeping genes , actin ( 25 . m00007 ) and glyceraldehyde-3-phosphate dehydrogenase ( GAPDH , 80 . m00003 ) , whose expression was not altered during pH stress , were included as controls ., ChIP data show an enrichment of fGCN5-A at a region upstream of PI3K and PK during alkaline stress ( Fig . 5A ) ., The levels of fGCN5-A remained unchanged at housekeeping genes , demonstrating that the increase of fGCN5-A at pH-responsive genes above is not random ( Fig . 5B ) ., We performed an additional negative control for each ChIP sample using a nonspecific antibody ( anti-TgIF2K-A ) as described previously 33 , none of which produced a signal ( data not shown ) ., Combined with the microarray analysis , these data support the idea that TgGCN5-A is required for proper gene activation in response to alkaline stress ., The impaired response to alkaline pH stress suggests that ΔGCN5-A parasites may also be defective in bradyzoite development ., The knockout was made in the hypervirulent type I RH strain , which does not fully develop into bradyzoites at high frequency ., RH strain will generally not form cyst walls , but will express detectable levels of bradyzoite-specific marker genes in response to stresses that induce cyst formation 34 ., In our hands , we can detect BAG1 and LDH2 mRNAs by day 4 of alkaline pH treatment ., To test if TgGCN5-A plays a role in stress-induced bradyzoite gene expression , we grew wild-type or ΔGCN5-A parasites in pH 7 . 0 ( control ) or pH 8 . 2 ( stress ) media ., At day 4 , intracellular parasites were harvested for quantitative real-time PCR to monitor mRNA levels for bradyzoite-specific genes BAG1 and LDH2 ., While wild-type parasites up-regulate both bradyzoite marker genes in response to pH 8 . 2 , the ΔGCN5-A parasites fail to do so ( Fig . 6A and 6B ) ., Actin was monitored as a control gene that does not significantly change during bradyzoite induction ( Fig . 6C ) ., To ensure the defect was not an indirect effect , we complemented ΔGCN5-A parasites by stably expressing a recombinant copy of TgGCN5-A ., Expression of BAG1 and LDH2 was restored in the complemented ΔGCN5-A parasites exposed to alkaline stress ( Fig . 6A and 6B ) ., The mRNA levels of BAG1 and LDH2 following stress in complemented ΔGCN5-A parasites are higher than wild-type , presumably because the recombinant TgGCN5-A is being expressed above wild-type levels ., Interestingly , this higher level of TgGCN5-A expression in the complemented clone does not affect levels of BAG1 or LDH2 under non-stressed conditions , implying that TgGCN5-A does not activate these bradyzoite genes until a stress signal is perceived by the parasite ., We used ChIP analysis to further demonstrate the involvement of TgGCN5-A in regulating developmentally expressed genes ., ChIP demonstrates that TgGCN5-A is recruited to BAG1 and LDH2 promoter regions during alkaline stress ( Fig . 6D ) ., We conclude that parasites lacking TgGCN5-A are defective in up-regulating bradyzoite-specific genes in response to alkaline stress ., Based on our results , it would be predicted that parasites exposed to alkaline stress would have difficulty recovering from the insult ., To test this hypothesis , intracellular parasites were exposed to pH 8 . 2 for 3 or 5 days , harvested , and then inoculated into fresh host cells and cultured under normal ( pH 7 . 0 ) conditions ., Parasite proliferation was monitored using the PCR-based B1 assay ., Data show that intracellular parasites lacking GCN5-A exposed to pH 8 . 2 for 3 days do not recover as efficiently as wild-type or the GCN5-A complemented clone ( Fig . S1 ) ., The recovery defect is even more pronounced when the intracellular ΔGCN5-A parasites are subjected to pH 8 . 2 for 5 days ( Fig . S1 ) ., The preceding studies were performed on tachyzoite-infected host cells ., We also examined if direct exposure to alkaline stress produced a phenotype in the parasites ., In order to test if alkaline pH impacts ΔGCN5-A parasites directly , we monitored the ability of purified , extracellular ΔGCN5-A tachyzoites to recover from a short term exposure to alkaline pH . Equal numbers of extracellular ΔGCN5-A or wild-type tachyzoites were placed in media of pH 7 . 0 ( control ) or 8 . 2 for 30 min , and then allowed to infect confluent host cell monolayers under normal culture conditions ., At day five , plaques in the infected monolayers were counted ., The ΔGCN5-A mutant displayed increased sensitivity to alkaline pH as evidenced by its impaired ability to produce plaques following this insult ( Fig . 7A ) ., Recombinant TgGCN5-A was able to restore the ability of ΔGCN5-A parasites to recover from alkaline stress ( Fig . 7A ) ., We verified the results with a second , independent type of growth assay that monitors parasite proliferation through quantitative PCR of the parasite-specific B1 gene ( Fig . 7B ) ., Collectively , these studies establish that TgGCN5-A is a key factor that manages the Toxoplasma response to alkaline pH stress in RH strain tachyzoites ., It is curious that the complemented clone , which appears to be over-expressing TgGCN5-A , does not offer greater protection from alkaline stress despite being able to up-regulate BAG1 and LDH2 greater than wild-type ( Fig . 6 ) ., GCN5 HATs function in large multi-subunit complexes , the components of which vary in different cells or under different conditions ., A possible explanation could be that a “minimal” GCN5 complex can up-regulate certain stress response genes , but it is not capable of providing greater protection to the cell because other components are required that are not over produced ., We examined whether ΔGCN5-A parasites were hypersensitive to other stresses , including 30 minute exposure to 0 . 6 M KCl , 5 µM arsenite , 1 µM ionophore , or 42°C heat shock ., Upon being placed back in culture following the exposure to these stresses , the ΔGCN5-A parasites grew similarly to wild-type with exception of those exposed to KCl stress ( Fig . 8A and 8B ) ., Remarkably , TgGCN5-A appears to have a striking specificity for managing the alkaline and possibly KCl stress responses , possibly because each disrupts ion potential ., Such narrow specificity in stress response has been reported for Schizosaccharomyces pombe GCN5 15 ., Toxoplasma gondii possesses two GCN5 KATs , which is unusual as lower eukaryotes tend to have a single GCN5 ., We have sought to delineate the roles of these two GCN5s by making genetic knockouts ., The ΔGCN5-A mutant is viable and does not show growth defects under normal culture conditions; however , attempts to generate a knockout of TgGCN5-B have not been successful ., These results support a model that TgGCN5-B is essential for housekeeping functions while TgGCN5-A is required to overcome certain stress situations ., Such a role for TgGCN5-A is established by the studies described herein ., It remains possible that there is some functional overlap between the two TgGCN5s , but whatever contribution is made by TgGCN5-B is not sufficient to compensate for the loss of TgGCN5-A in terms of responding normally to alkaline pH stress , including the up-regulation of bradyzoite marker genes BAG1 and LDH2 ., A key finding in this study is that TgGCN5-A is required to activate developmental genes in response to pH stress ., These studies were performed in type I RH strain , which is not well suited for a more thorough characterization of bradyzoite development in vitro or in vivo ., Such studies would require the generation of an analogous TgGCN5-A knockout in type II strain Toxoplasma ., Our initial attempts to disrupt the TgGCN5-A knockout in type II strains have not yet succeeded , probably due to technical challenges inherent in working with the slow growing type II strain , but it is possible that TgGCN5-A is essential in type II strain ., It is intriguing that Toxoplasma possesses a duplicate GCN5 HAT that appears to be exquisitely tailored to respond to alkaline , and to a lesser extent , KCl stress ., Adaptation to fluctuations in pH is likely to be relevant to proliferating tachyzoites , as pH stress almost certainly is encountered by Toxoplasma in vivo as it moves in and out of host cells throughout diverse regions of the body ., Additionally , while not addressed for Toxoplasma infection , it has been reported that other infections elevate intracellular pH 35 ., It is difficult to distinguish whether the observed changes in intracellular parasites are due to direct effects of high pH on Toxoplasma parasites themselves or indirect effects produced by the response of host cells ., What is clear is that RH strain parasites lacking TgGCN5-A are defective in regulating changes in the transcriptome that accompany the response to alkaline pH . The data are significant as alkaline pH is considered a “gold standard” method for triggering bradyzoite development in vitro 1 ., In summary , our studies establish that TgGCN5-A plays a major role in the normal response to alkaline pH stress , including the activation of developmentally regulated genes , in Toxoplasma RH strain ., The conclusion is based on multiple lines of data from independent studies , including up-regulation of TgGCN5-A mRNA during pH stress , microarray analysis , TgGCN5-A enrichment at genes up-regulated during alkaline stress , and phenotypic analysis showing that the TgGCN5-A knockout has impaired alkaline stress recovery ., Our microarray analysis also provides novel insight into the molecular basis of the alkaline stress response in intracellular Toxoplasma ., Toxoplasma tachyzoites ( wild-type ( WT ) RH , ΔGCN5-A , and TgGCN5-A complemented lines ) were cultured in primary human foreskin fibroblasts ( HFF ) using Dulbeccos Modified Eagles Medium ( DMEM ) supplemented with 1 . 0% fetal bovine serum ( FBS , Invitrogen ) ., Parasites were grown in a humidified CO2 ( 5% ) incubator at 37°C ., Cultures were confirmed to be free of Mycoplasma contamination by MycoAlert Assay ( Cambrex Bio Science ) ., Parasites were harvested immediately following lysis of host cell monolayers and purified by filtration through a 3 . 0 micron filter 36 ., Bradyzoite growth conditions were identical except the infection medium was replaced with alkaline medium ( DMEM with 20 mM HEPBS , 2 g NaHCO3/L and 1 . 0% FBS , adjusted to pH 8 . 2 using NaOH ) and changed daily ., Tachyzoites were released from HFF monolayers using a 25 gauge syringe needle , and filtered to remove the host cell debris was spun out at 500×g for 5 min ., Extracellular parasites were resuspended at a concentration of 105 parasites/ml in DMEM , alkaline medium ( pH 8 . 2 , adjusted with NaOH as described above ) , or medium containing 0 . 6 M KCl , 5 µM arsenite , 1 µM ionophore ., This suspension was incubated at 37°C ( or 42°C for heat shock experiment ) in 5% CO2 for 30 min , and then 103 parasites were inoculated onto HFF monolayers in 24-well plates ., After 5 day incubation at 37°C , the infected monolayers were fixed with 100% methanol and stained with crystal violet to score the number of plaques 36 or processed for B1 PCR assay 37 ., Confluent HFF monolayers grown in T25-cm2 flasks were infected with 106 parasites using normal parasite culture media ( above ) ., After 2 hr , infection medium was replaced with normal medium ( pH 7 . 0 ) or alkaline medium ( see above ) ., Flasks were maintained in humidified 37°C incubator in 5% CO2 ., Medium for both normal and alkaline cultures was replaced each day for 3 days , at which point the infected monolayers were scraped with a rubber policeman ., Samples were centrifuged ( 1500 rpm , 10 min ) and resuspended in sterile PBS ., Intracellular parasites were released from host cells by syringe passage using a 25 gauge needle and washed in PBS ., Total RNA was isolated from the purified parasites using an RNeasy Mini Kit according to the manufacturers instructions ( Qiagen ) ., To minimize genomic DNA contamination , additional treatment with DNase was performed ., The cDNA and cRNA were synthesized according to the protocols recommended by Affymetrix in their GeneChip Expression Analysis Technical Manual ( Affymetrix , Santa Clara , CA ) ., Briefly , cDNA was synthesized using T7 promoter-dT24 oligonucleotide as primer with the Invitrogen Life Technologies SuperScript Choice system ., Biotinylated cRNA was made using the Affymetrix in vitro transcription ( IVT ) labeling kit ., Fifteen µg of biotinylated cRNA was added to a total hybridization cocktail of 300 µl , and 200 µl was hybridized to an Affymetrix custom T . gondii ToxoGeneChip ( http://roos-compbio2 . bio . upenn . edu/~abahl/Array-Tutorial . html ) after adding control oligonucleotides ., Hybridization was performed at 45°C for 17 h with constant rotation ., The hybridization mixture was then removed and the GeneChips were washed , stained with phycoerythrin-labeled streptavidin , washed , incubated with biotinylated anti-streptavidin , and re-stained with phycoerythrin-labeled streptavidin to amplify the signals; these steps were carried out using the Affymetrix Fluidics Station ., To reduce non-random error , balanced groups of samples were handled in parallel ., Arrays were then scanned using the dedicated scanner , controlled by Affymetrix GeneChip Operating Software ( GCOS ) ., The Affymetrix Microarray Suite version 5 ( MAS5 ) algorithm was used to analyze the hybridization intensity data from GeneChip expression probe arrays and to calculate the set of metrics that describe probe set performance ., The average intensity on each array was normalized by global scaling to a target intensity of 1000 ., For each of the two conditions ( normal and alkaline ) , four independent preparations of WT and ΔGCN5-A RNA were prepared; each of the 16 preparations was hybridized to its own microarray to ensure a strong statistical basis for analysis ., We analyzed only those probe sets ( genes ) that were called “present” by the MAS5 detection call in at least half of the arrays for at least one of the four conditions to eliminate probe sets that are not reliably detected ( those at or near background or that may reflect cross-hybridization ) 38 ., We used a Welchs t-test on log2 transformed MAS5 signals to reveal significant differences between WT and ΔGCN5-A ., The resultant p-values were used to calculate false discovery rates ( FDR ) using the Benjamini and Hochberg method 39 ., Fold-changes were calculated by taking the ratio of the mean of the WT and ΔGCN5-A signal values , using the larger mean as the numerator; by convention we show the result as negative if the mean of the ΔGCN5-A samples was smaller ., Microarray data are available at Gene Expression Omnibus ( http://www . ncbi . nlm . nih . gov/geo/ ) , accession number GSE22100 ., Primers were designed using the Primer Express 2 . 0 software ( Applied Biosystems , CA ) and are listed in Table S6 ., The RT reaction was performed using 1 . 0 µg total RNA isolated from designated tachyzoites , with oligo-dT primers and Omniscript reverse transcriptase according to the manufacturers directions ( Qiagen ) ., 1 . 0 µl of a 1∶10 dilution of the resulting cDNA was amplified in a 25 µl total volume containing SYBR Green PCR Master Mix ( Applied Biosystems , CA ) and 0 . 5 µM of each forward and reverse primer ., All reactions were performed in triplicate using the 7500 Real-time PCR system and analyzed with relative quantification software ( 7500 software v2 . 0 . 1 ) ( Applied Biosystems , CA ) ., The Toxoplasma β-tubulin gene ( Genbank accession number M20025 ) was used to normalize the qRT-PCRs ., β-tubulin was determined to be equivalent between samples using actin and GAPDH as normalizing controls ., ChIP was performed on tachyzoites stably expressing fGCN5-A using polyclonal anti-FLAG antibody ( Sigma F7425 ) immobilized to Dynabeads Protein A ( Invitrogen ) ., Quantitative PCR was performed as described above ., Immunoprecipitated DNA samples were quantified using a standard curve created with serially diluted input DNA ., 0 . 1 ng of total ChIP DNA was added to each reaction and reactions were performed in triplicate ., Primers used are listed in Table S6 , each pair designed to amplify ∼90 bp regions located ∼1 . 0 kb upstream of the ATG start site ., To complement ΔGCN5-A parasites , the ptubXFLAG::HX Toxoplasma expression vector 32 was modified to replace its HX minigene marker with a CAT minigene to confer resistance to chloramphenicol ., Recombinant , tagged full-length TgGCN5-A was cloned into the vector using the NdeI and AvrII sites , referred to as ptubMYCGCN5-AFLAG::CAT ., The TgGCN5-A coding sequence was amplified from Toxoplasma cDNA using Phusion® High-Fidelity DNA Polymerase ( New England Biolabs ) and primers containing NdeI and AvrII restriction enzyme sites ( italicized below ) ., The sense primer contained sequence encoding the MYC epitope tag ( underlined ) and the antisense primer lacked the TgGCN5-A stop codon to allow in-frame fusion with a FLAG tag in the vector 32: sense , 5′-ATACCATCATATGAAAATGGCGTACCCGTACGACGTCCCGGACTACGCGGAGACTGTCGAAGTGCCTGCATTC; antisense , 5′-ATACCATCCTAGGGAAACTCCCGAGAGCCTCGACCTTGGGCC ., 106 ΔGCN5-A parasites were transfected with 20 µg NotI-linearized vector and selected for resistance to 20 µM chloramphenicol before cloning by limiting dilution as previously described 36 ., Multiple clones were selected and verified to express ectopic MYCGCN5-AFLAG protein by IFA using anti-FLAG ( Sigma F7425 ) ., Phenotypes reported were similar for multiple independent clones .

Introduction, Results/Discussion, Materials and Methods

Parasitic protozoa such as the apicomplexan Toxoplasma gondii progress through their life cycle in response to stimuli in the environment or host organism ., Very little is known about how proliferating tachyzoites reprogram their expressed genome in response to stresses that prompt development into latent bradyzoite cysts ., We have previously linked histone acetylation with the expression of stage-specific genes , but the factors involved remain to be determined ., We sought to determine if GCN5 , which operates as a transcriptional co-activator by virtue of its histone acetyltransferase ( HAT ) activity , contributed to stress-induced changes in gene expression in Toxoplasma ., In contrast to other lower eukaryotes , Toxoplasma has duplicated its GCN5 lysine acetyltransferase ( KAT ) ., Disruption of the gene encoding for TgGCN5-A in type I RH strain did not produce a severe phenotype under normal culture conditions , but here we show that the TgGCN5-A null mutant is deficient in responding to alkaline pH , a common stress used to induce bradyzoite differentiation in vitro ., We performed a genome-wide analysis of the Toxoplasma transcriptional response to alkaline pH stress , finding that parasites deleted for TgGCN5-A fail to up-regulate 74% of the stress response genes that are induced 2-fold or more in wild-type ., Using chromatin immunoprecipitation , we verify an enrichment of TgGCN5-A at the upstream regions of genes activated by alkaline pH exposure ., The TgGCN5-A knockout is also incapable of up-regulating key marker genes expressed during development of the latent cyst form , and is impaired in its ability to recover from alkaline stress ., Complementation of the TgGCN5-A knockout restores the expression of these stress-induced genes and reverses the stress recovery defect ., These results establish TgGCN5-A as a major contributor to the alkaline stress response in RH strain Toxoplasma .

Protozoan parasites cause significant disease in humans and livestock , and many of our current therapies have serious side effects or are being rendered useless due to the development of drug resistance ., These parasites typically have complex life cycles involving multiple hosts and some , like Toxoplasma gondii , have the ability to remain in the host for life as a latent tissue cyst ., Toxoplasma is one of the most successful parasites on Earth because the ability to develop into a tissue cyst greatly facilitates transmission through carnivores ., Cyst formation also is responsible for recrudescent infection in immunocompromised patients ., The conversion of Toxoplasma from its replicating cell to the cyst is triggered by stress , but we have little understanding of how the parasite stress response functions ., In this study , we identify the genes involved in Toxoplasmas response to alkaline stress , which is a known trigger of cyst development ., We also establish that a lysine acetyltransferase enzyme called TgGCN5-A is required for type I RH strain Toxoplasma to respond normally to alkaline stress ., Parasites lacking TgGCN5-A are no longer capable of activating genes induced during cyst formation triggered by alkaline pH .

genetics and genomics/epigenetics, molecular biology/chromatin structure, microbiology/parasitology

journal.ppat.1006406

Identification of C/EBPα as a novel target of the HPV8 E6 protein regulating miR-203 in human keratinocytes

Human papillomaviruses ( HPV ) are double-stranded non-enveloped DNA viruses that infect epithelial cells of skin or mucosa in a species-specific manner ., They cause hyperproliferative lesions ranging from benign warts to invasive carcinoma ., More than 180 HPV types are classified into five genera based on sequence homologies in the structural protein L1 1 ., The genus alpha mucosal high-risk ( HR ) HPV types , i . e . HPV16 and 18 , have a well-established causal role in cervical carcinogenesis and they are involved in a significant proportion of other anogenital as well as oropharyngeal cancers 2 ., In contrast , genus beta-HPV types infect cutaneous epithelium and their potential role in skin carcinogenesis particularly in immunosuppressed patients has become a major field of interest ., A link between HPV infection and skin cancer was first demonstrated in patients suffering from Epidermodysplasia verruciformis ( EV ) , a rare inherited skin disease ., EV-patients display a particular susceptibility to productive and persistent infection with cutaneous genus beta-HPV and they have a high risk to develop non-melanoma skin cancer at sun-exposed sites 3 , 4 ., In EV-lesions , genus beta-HPVs strongly replicate and the viral genome is highly expressed 5 , 6 ., EV-cancers preferentially harbor HPV5 or HPV8 7 ., The functions of genus beta-HPV oncoproteins are , however , less well investigated than those of mucosal HPVs ., Viral persistence in lesional skin of EV-patients is linked to the ‘virtual absence’ of Langerhans cells , specialized antigen-presenting cells normally residing in the epidermis 5 ., The HPV8 E7 protein has been identified as a factor critical for viral immune evasion targeting a key pathway responsible for Langerhans cell attraction in the skin 5 ., Studies in transgenic mice unraveled the E6 protein as the major driver of HPV8-induced skin carcinogenesis 8 , 9 ., Beta-HPV E6 proteins can suppress UV-induced apoptosis as well as DNA damage repair 10–12 , stabilize p53 in response to genome-destabilizing events 13 , interfere with Notch-signalling 14–17 and inhibit keratinocyte differentiation 15 , 18 ., Many of these functions depend on their ability to bind the acetyltransferase p300 12 , 13 , 18 ., The in vivo importance of the cutaneous PV E6-p300 interaction for tumorigenesis was highlighted in the cottontail rabbit PV ( CRPV ) model 19 ., However , the mechanisms how the E6 protein contributes to beta-HPV-mediated disturbance of epidermal homeostasis are not yet completely understood ., HPV infects basal keratinocytes of stratifying epithelia ., When basal cells become suprabasal , they lose their proliferative potential and undergo terminal differentiation ., This process is governed by microRNA-203 ( miR-203 ) , which promotes epidermal differentiation and induces cell-cycle exit 20–22 ., miR-203 is up-regulated in response to differentiation-inducing agents including Ca2+ , Vitamin D3 or phorbol ester-induced protein kinase C ( PKC ) activity 21 , 23 ., A major target of miR-203 in suprabasal keratinocytes is the transcription factor p63 , a member of the p53 family 20 , 21 ., The main isoform expressed in basal keratinocytes is the N-truncated isoform ΔNp63α lacking the transactivation domain 24 ., This isoform is targeted by miR-203 in mammary epithelial cells mediating subversion of stem cell properties 25 ., ΔNp63α plays a pivotal role in maintaining keratinocyte ‘stemness’ and proliferative capacity 26 and can suppress keratinocyte differentiation , i . e . expression of involucrin 27 ., Its overexpression has been observed in squamous cell carcinomas of head and neck ( HNSCC ) , lung and skin 28 ., In case of HNSCCs this is associated with a poor prognosis and aggressive course of disease 29 ., Notably , high levels of miR-203 are inhibitory to HPV amplification in differentiating keratinocytes indicating that inhibition of the miR-203 pathway is crucial for the viral life cycle 30 ., In fact , for the mucosal HR-HPV types , it was reported that the E6 , E7 and E5 proteins have the capacity to suppress miR-203 expression 30–32 ., The HPV31 E7 protein was shown to down-regulate miR-203 expression upon phorbol ester induced differentiation 30 ., The HPV16 E6 protein involves a p53-dependent mechanism 31 ., So far , it has been unclear whether or not beta-HPV , which encode an E6 and E7 but no E5 gene ( summarized in 33 ) , can impair miR-203 expression ., In this study we show that the HPV8 E6 protein is a potent suppressor of miR-203 , a key regulator of ΔNp63α expression , proliferation and differentiation in keratinocytes ., We have identified the transcription factor CCAAT/enhancer-binding protein α ( C/EBPα ) , a well-known driver of keratinocyte differentiation 34 , 35 , as a novel target of the HPV8 E6 protein regulating miR-203 expression ., In situ stainings in EV-lesions strongly support our findings ., Since miR-203 and p63 are major regulators of epidermal homeostasis , which may be disturbed during genus beta-HPV infection in vivo , we compared expression patterns of both factors in non-lesional and HPV8-positive lesional skin of EV-patients ., In non-lesional areas p63 was expressed in the nuclei of basal and parabasal keratinocytes ( Fig 1A ) ., As expected from findings in normal human skin 36 , miR-203 displayed a complementary expression pattern and was detected in suprabasal spinous as well as granular layers but not in basal or parabasal keratinocytes ( Fig 1C ) ., Notably , in lesional skin of EV-patients p63-expressing cell layers were dramatically expanded and this corresponded to an almost complete absence of miR-203 ( Fig 1B and 1D ) ., To investigate whether HPV8 oncogenes contribute to the deregulation of both cellular factors , primary human keratinocytes ( NHK ) were engineered to express HPV8 E6 and/or E7 by retroviral gene transfer ( S1A and S1B Fig ) ., In HPV8 E6 expressing NHK , p63 was significantly up-regulated at protein and mRNA levels as shown by Western blot and ΔNp63α-specific qRT-PCR ( Fig 1E and 1F ) ., Correspondingly , in HPV8 E6 expressing keratinocytes miR-203 was significantly down-regulated ( Fig 1G ) ., HPV8 E7 alone had a less strong effect but enhanced the impact of E6 , particularly on miR-203 suppression ., HPV16 E6 can regulate miR-203 via p53 31 ., We therefore included the skin keratinocyte cell line HaCaT harboring mutated TP53 genes in our study 37 ., Interestingly , HaCaT cells expressing HPV8 E6 ( S2A Fig ) , displayed significantly higher p63 protein and ΔNp63α mRNA levels than control cells similar to NHK ( Fig 2A and 2B , S2B Fig ) ., Compared to control cultures , organotypic cultures with HPV8 E6 expressing HaCaT cells formed significantly more layers ( as measured by the number of nuclei per microscopic field ) and significantly more cells were found to express nuclear p63 similar to EV-lesions ( Fig 2C and 2D ) ., To assess the impact of HPV8 E6 on miR-203 expression under differentiation conditions , we also stimulated HaCaT cells with Ca2+ , a potent inducer of keratinocyte differentiation 38 ., MiR-203 was up-regulated by 1 . 2 mM Ca2+ after 72 h as shown by Northern blot and qRT-PCR in control cells ., In contrast , HPV8 E6 expressing HaCaT cells displayed lower constitutive miR-203 expression levels and Ca2+-mediated miR-203 induction was significantly suppressed ( Fig 2E and 2F , S2C Fig ) ., These data indicated that HPV8 E6 exerts its inhibitory effects on miR-203 also in keratinocytes with mutated p53 ., We were then interested whether HPV8 E6 affects the cell cycle and proliferation ., Cell cycle analysis by flow cytometry revealed that HPV8 E6 expression in asynchronously growing HaCaT cells resulted in a significant increase of cells in S-phase and a reduction of cells in G1 ( Fig 3A ) , features critically regulated by p63 in keratinocytes 39 ., Moreover , HPV8 E6 expressing HaCaT cells showed significantly higher BrdU incorporation than control cells indicating a significantly stronger rate of proliferation ( Fig 3B ) ., A similar finding was obtained in scratch assays measuring both proliferation and migration ., Here , the introduced gap was closed significantly faster in HPV8 E6-expressing than in control cells ( Fig 3C ) ., To explore a mechanistic role of p63 or miR-203 , we knocked-down p63 or transfected an hsa-miR-203 mimic ., p63 knock-down efficiency was 70% as quantified by RT-PCR ( S3 Table ) ., p63-specific siRNA strongly reduced p63 protein and ΔNp63α mRNA expression in control cells as well as in HPV8 E6 expressing HaCaT cells ( see Fig 4D and 4E , S3A Fig ) ., The hsa-miR-203 mimic reduced p63 protein and ΔNp63α mRNA levels in the controls and reverted p63 almost to basal levels in HPV8 E6 expressing HaCaT cells ( see Fig 4G and 4H , S3B Fig ) ., Notably , the ability of HPV8 E6-expressing cells to close the gap in the scratch assay was significantly impaired after p63 knock-down ( Fig 3D ) or miR-203 mimic transfection ( Fig 3E ) ., We then studied whether the miR-203 pathway is involved in HPV8 E6-mediated suppression of differentiation ., Organotypic cultures of HPV8 E6 expressing HaCaT cells further revealed a dramatic reduction of the differentiation marker involucrin compared to controls ( Fig 4A and S3C Fig ) ., In monolayer cultures of HPV8 E6 expressing HaCaT cells treated with 1 . 2 mM Ca2+ for 72 h involucrin induction was potently suppressed at protein and mRNA levels ( Fig 4B and 4C ) ., Notably , p63-specific siRNA led to a significant up-regulation of involucrin expression in the control cells and reverted involucrin protein and mRNA expression in the HPV8 E6 positive cells to basal levels ( Fig 4D and 4F , S3D Fig ) ., Also miR-203 mimic transfection led to a significant up-regulation of involucrin expression in E6 expressing HaCaT cells ( Fig 4G and 4I , S3E Fig ) ., These data provided evidence that keratinocyte reprogramming by HPV8 E6 involves the miR-203/p63 pathway ., The impact of HPV8 E6 on mir-203 , p63 and involucrin expression was also investigated in NHK after stimulation with phorbol 12-myristate 13-acetate ( PMA ) , which induces differentiation in cultured normal human keratinocytes 34 , 40 ., Expression of HPV8 E6 significantly counteracted PMA-induced miR-203 and involucrin up-regulation as well as PMA-mediated ΔNp63α suppression in NHK ( Fig 5A–5C ) ., Notably both , PMA and Ca2+ , induce the transcription factor C/EBPα ( Fig 5D and 5E ) and its DNA-binding activity in murine or human keratinocytes 34 , 41 , 42 ., C/EBPα is a potent inducer of keratinocyte differentiation 34 , 35 ., It has been characterized as a tumor suppressor in multiple tissues including skin 43 , and inactivation of C/EBPα in mice strongly supports UV-induced skin carcinogenesis 44 qualifying C/EBPα as an interesting potential target of HPV8 E6 ., Importantly , when we knocked-down C/EBPα in NHK , this did not only reduce C/EBPα mRNA but also potently suppressed endogenous miR-203 expression ( Fig 5F ) indicating that C/EBPα plays a role in miR-203 regulation ., C/EBPα knock-down efficiencies were 52–80% as quantified by RT-PCR ( S3 Table ) ., C/EBPα protein knock-down was confirmed by IHC ( S4A Fig ) ., We further explored a potential role of C/EBPα as a direct transcriptional regulator of miR-203 ., In silico analysis of the miR-203 gene revealed a C/EBPα binding site ( GCGCAAT , nt +57 to +63 ) within the miR-203 hairpin sequence ., EMSAs with nuclear extracts from C/EBPα overexpressing cells showed binding to a well-known C/EBP-binding site 45 as well as to the specific C/EBP-binding site within the miR-203 hairpin sequence ( Fig 5G ) ., Two different antibodies directed against C/EBPα ( clone 14AA and N19 ) resulted in a supershift ., C/EBPα did not bind to oligonucleotides comprising a mutated binding site ( GCTCGAG ) ., These experiments demonstrated for the first time binding of the transcription factor C/EBPα to a specific binding site within the miR-203 gene ., We then generated a miR-203-reporter construct ( -1165 nt to +70 nt ) comprising the miR-203 hairpin sequence and investigated the functional impact of C/EBPα ., PMA strongly stimulated this miR-203-reporter construct in NHK ( Fig 5H ) , while knock-down of C/EBPα significantly reduced basal as well as PMA-stimulated reporter activity ., Co-transfection of a C/EBPα-encoding expression plasmid led to a more than 7-fold induction of reporter activity with wildtype C/EBPα-BS , which was significantly reduced when this site was mutated to the C/EBPα-binding deficient form ( Fig 5I ) ., Similar results were obtained when drastically shortened forms of a miR-203 reporter construct ( +47 nt to +73 nt ) were used comprising either the wildtype or the mutated C/EBPα-BS but largely omitting other regulatory regions within the miR-203 gene , ( Fig 5J ) ., These results demonstrated a direct role of C/EBPα for miR-203 transcription ., Since our data revealed C/EBPα as a novel inducer of miR-203 expression we were interested whether HPV8 E6 might regulate this important transcription factor ., Notably , HPV8 E6 expressing keratinocyes showed strongly reduced C/EBPα mRNA levels ( Fig 6A ) and this was confirmed at the protein level in organotypic cultures ( Fig 6B ) ., In epithelia formed by HPV8 E6 expressing HaCaT cells , C/EBPα expression was almost undetectable ., Moreover , C/EBPα induction was potently inhibited in PMA- or Ca2+-stimulated HPV8 E6 expressing keratinocytes ( S5A and S5B Fig ) ., HPV8 E6-mediated suppression was highly specific for C/EBPα , since C/EBPβ , another transcription factor of the C/EBP family , was not reduced in HPV8 E6 expressing keratinocytes ( S5C Fig ) ., To get a hint on HPV8 E6-mediated regulation of C/EBPα , we investigated a HPV8 E6 mutant lacking amino acids 132–136 ( variously named “Δ8E6” ) deficient in p300 binding 18 , 46 ., We generated this mutant and expressed it by retroviral gene transfer in NHK and HaCaT cells ., When compared to HPV8 wildtype-E6 , the Δ8E6 mutant was unable to suppress miR-203 and involucrin and to induce p63 at mRNA and protein levels in NHK ( Fig 6C–6F ) ., Notably , the Δ8E6 mutant was also unable to suppress C/EBPα mRNA and protein expression ( Fig 6A and 6B ) ., To further explore a role of endogenous p300 for miR-203 expression , we knocked-down p300 in NHK as confirmed by Western blot analysis ( S4B Fig ) ., p300 knock-down efficiencies were between 60 and 81% as quantified by RT-PCR ( S3 Table ) ., This potently suppressed miR-203 ( Fig 6G ) ., Surprisingly , p300 knock-down also significantly reduced the expression of the transcription factor C/EBPα ( Fig 6G ) , while the related transcription factor C/EBPβ , which was determined in the same extracts , was significantly less affected by all p300-specific siRNAs ., These data indicated that p300 is involved in CEBPΑ gene expression ., p300 is a histone acetyltransferase and histone acetylation in the regulatory region of the CEBPΑ gene can be linked to C/EBPα expression levels 47 ., We therefore assessed p300 binding to C/EBPα chromatin in HPV8 E6 expressing HaCaT and pLXSN control cells in a regulatory region previously shown to be susceptible to histone acetylation ( nt -1533 to -1398 ) 47 ., As shown by chromatin immunoprecipitation ( ChIP ) , p300 bound to this regulatory region in pLXSN control cells and this binding was significantly reduced in the HPV8 E6 expressing cells ( Fig 6H ) ., Together these data provided evidence that p300 binds to the regulatory region of C/EBPα , a novel inducer of miR-203 , and that HPV8 E6 interferes with this binding activity ., In order to corroborate our findings in vivo we stained skin sections of EV-patients for C/EBPα and compared the respective expression patterns to miR-203 by in situ hybridization ., In non-lesional skin C/EBPα was restricted to suprabasal , differentiated layers of the epidermis ( Fig 7A ) ., This pattern matched well with miR-203 expression pattern in the same biopsy ( Fig 7D ) ., HPV8-positive EV-lesions showing viral cytopathic effects displayed strongly reduced expression of C/EBPα and miR-203 ( Fig 7B and 7E ) and in highly dysplastic or invasive lesions expression of both factors was almost undetectable ( Fig 7C and 7F ) ., In contrast , the miR-203 target p63 displayed complementary expression patterns ( Fig 1A and 1B , S6 Fig ) corresponding well to the in vitro data ., Our study for the first time documents that miR-203 , a key regulator of epidermal proliferation and differentiation , is potently down-regulated in HPV8-positive EV-lesions ., We have identified C/EBPα , a differentiation-inducing transcription factor as a novel inducer of miR-203 and the HPV8 E6 protein as a potent suppressor of C/EBPα and miR-203 expression ., C/EBPα down-regulation involves p300 , a well-described target of the HPV8 E6 protein ., HPV8 E7 , by contrast , had a less strong effect on miR-203 expression but was able to enhance the impact of E6 ., In situ stainings confirmed congruent suprabasal expression patterns of C/EBPα and miR-203 in non-lesional skin of EV-patients and strong down-regulation of both factors in HPV8-positive EV-lesions in vivo , supporting our in vitro data ., Key findings of this study are summarized in Fig 8 ., MiR-203 is a suppressor of self-renewal in skin 22 ., It represses keratinocyte ‘stemness’ by targeting ΔNp63α in skin keratinocytes 21 , induces cell-cycle exit 22 and promotes epidermal differentiation 20 ., Moreover , miR-203 targets other cellular factors including c-myc 48 , another important regulator of proliferation , suppressor of cytokine signaling 3 ( SOCS3 ) 49 or IL-8 50 and it cooperates with other miRNAs to suppress the B lymphoma Mo-MLV insertion region 1 homolog ( BMI1 ) , which is involved in self-renewal 51 ., This suggests that miR-203 and further targets aside from ΔNp63α might play a broader role in E6-mediated reprogramming of the host cell during cutaneous beta-HPV infection ., In a preliminary gene expression analysis of primary human keratinocytes expressing the HPV8 E6 oncoprotein , we did , however , not observe up-regulation of c-myc but a less than 2-fold decrease ., IL-8 even appears to be suppressed by HPV8 E6 52 pointing to the regulation of IL-8 by pathways other than miR-203 in HPV8 E6-positive keratinocytes ., While miR-203 function has been intensively investigated , little is known about the regulation of miR-203 expression in human keratinocytes ., Our data demonstrate that C/EBPα can bind to a predicted binding site within the miR-203 gene 22 thereby stimulating miR-203 transcription ., Lack of miR-203 promoter activation by C/EBPα using constructs comprising a mutated C/EBPα binding site confirmed a direct effect of C/EBPα on miR-203 transcription ., The finding that C/EBPα , a member of the CCAAT/enhancer-binding protein family , is involved in miR-203 regulation is particularly interesting ., Both , C/EBPα and β , are expressed in a differentiation-dependent manner in the epidermis and play an essential role for interfollicular keratinocyte proliferation arrest , commitment and differentiation 5 , 35 , 42 ., We had previously shown that the HPV8 E7 protein specifically interacts with C/EBPβ via the C-terminal FQELL sequence thereby interfering with its transcriptional activity 5 ., Here , we demonstrate that HPV8 E6 potently suppresses C/EBPα at transcriptional and protein levels as demonstrated in monolayer and organotypic cultures ., These data were strongly supported by the observation that C/EBPα is dramatically down-regulated in EV-lesions in vivo ., Notably , C/EBPβ mRNA levels were rather unaffected by HPV8 E6 ., Thus , the HPV8 E6 and E7 oncoproteins target both C/EBP factors , albeit via different mechanisms ., MiR-203 is also suppressed by mucosal HR-HPV , and this is necessary to maintain genome amplification in keratinocytes under differentiating conditions 30 ., HPV16 E6 and HPV16/31 E7 were shown to down-regulate miR-203 expression via distinct mechanisms 30 , 31 ., HPV16 E6 prevents miR-203 expression by targeting p53 ., How p53 affects miR-203 is currently unclear , since p53 did not directly bind to predicted binding sites within the miR-203 promoter 31 ., Notably , in our study HPV8 E6 efficiently suppressed miR-203 also in the p53-mutated cell line HaCaT 37 ., Mucosal HR-HPV E7 interfered with differentiation-induced miR-203 involving the PMA-inducible MAPK/PKC-pathway 30 ., PMA activates several differentiation-inducing pathways including transcription factors of the AP-1- and C/EBP-families , which are both involved in PMA-induced miR-203 regulation ( 23 and this study ) ., Interestingly , C/EBPα also seems to be a target of the HPV16 E7 protein ., In murine fibroblasts HPV16 E7 was reported to interfere with C/EBPα-mediated proliferation arrest , while ectopic overexpression of HPV16 E7 in these cells had no impact on C/EBPα-induced adipogenic differentiation 53 ., It remains to be determined whether or not mucosal HPV oncoproteins can regulate endogenous C/EBPα in keratinocytes , the natural host cells of HPV , as has been demonstrated for the HPV8 E6 protein in this study ., Our study provides further mechanistic insight into the transcriptional regulation of C/EBPα ., The histone acetyltransferase p300 is a well-known transcriptional co-activator of C/EBPα 54 and a well-described target of cutaneous papillomaviral E6 proteins 18 , 19 , 46 , 55 ., In the CRPV-model , the E6-p300 interaction was found to be crucial for tumorigenesis 19 ., Interestingly , our knock-down experiments provided evidence that endogenous p300 is involved in the regulation of C/EBPα mRNA expression in keratinocytes ., The HPV8 Δaa132-136-E6 deletion-mutant , which is unable to bind to p300 18 , 46 , neither conferred down-regulation of C/EBPα in organotypic cultures nor of its transcriptional target miR-203 ., The question has been raised whether this deletion-mutant of HPV8 E6 could be defective for more than p300 binding 17 ., Of note , in the same series of experiments , we observed that HPV8 E6 also affects expression of the AP-1 transcription factor JunB ., In contrast to C/EBPα , however , JunB was efficiently down-regulated by Δ8E6 , and this suppression did not significantly differ from HPV8 E6-wildtype mediated JunB suppression ( S7A Fig ) ., This demonstrated that the HPV8 Δaa132-136-E6 deletion-mutant retains some functions observed for wildtype HPV8 E6 indicating that it is not generally functionally defective ., Moreover , p300 knock-down did not suppress JunB expression ( S7B Fig ) further underlining the specificity of p300 in C/EBPα regulation ., The regulatory region of the CEBPΑ gene was previously shown to be sensitive to histone acetylation and the acetylation status correlated with C/EBPα expression levels 47 ., Importantly , in HPV8 E6 expressing keratinocytes , which display strongly reduced C/EBPα expression , we found significantly less p300 binding to this C/EBPα regulatory region by ChIP ., This indicated that p300 might act directly on the CEBPΑ gene ., However , since p300 can affect the expression of numerous genes and has broad effects on acetylation of many proteins , it is reasonable to assume that indirect p300 effects could also contribute to the regulation of CEBPΑ gene expression ., In our experiments HPV8 E6 up-regulated the ‘stemness’ factor ΔNp63α 26 as shown by isoform-specific RT-PCR in primary human keratinocytes , induced proliferation or migration and suppressed differentiation ., This involved suppression of miR-203 ., Cell cycle analysis revealed a reduction of the HPV8 E8 expressing cells in G1-phase and a significant increase of cells in the S-phase ., These data are in line with previous observations demonstrating an impact of genus beta-HPV oncoproteins on keratinocyte proliferation and differentiation 15 , 18 , 56 , 57 ., ΔNp63α may in turn increase genus beta-HPV promoter activity as demonstrated for HPV20 58 ., E6 proteins encoded by genus beta-HPVs can also associate with Mastermind-like 1 ( MAML1 ) , a transcriptional co-activator and mediator of canonical Notch signaling ., E6 thereby interferes with Notch signaling and delays Notch-dependent differentiation 14–17 , 59 ., While Notch1 suppresses p63 expression 60 , this appears to be independent of canonical Notch signaling 61 ., In fact , experimental data in a different study indicated that “p63 expression in differentiating” HPV8 E6-positive keratinocytes “may not be strictly NOTCH-dependent” 15 ., However , MAML1 cooperates with p300 in a transcriptional activator complex and potentiates p300 autoacetylation 62 ., Thus , there might be a link to the pathway described in this study ., In fact , our own preliminary data show that knock-down of MAML1 by siRNA contributes to the down-regulation of C/EBPα ( S7C Fig ) indicating a cross-talk between Notch- and C/EBPα-signaling , which will be interesting to study in the future ., In summary , we have identified a novel p300/C/EBPα/miR-203/p63 pathway important for epidermal homeostasis ., Our study provides evidence that this C/EBPα-dependent pathway , which fundamentally reprograms keratinocyte function , is counteracted by HPV8 E6 ., C/EBPα has a substantial role not only for the regulation of keratinocyte proliferation and differentiation 34 , 35 but it is also implicated in their DNA-damage G1 checkpoint response 63 ., Notably , C/EBPα can function as a tumor suppressor 43 , 64 ., The CEBPA gene is mutated in acute myeloid leukemia 65 ., Moreover , C/EBPα expression is down-regulated in various tumor entities including cervical , head and neck as well as skin cancer 43 , 66 , 67 and re-introduction of C/EBPα in carcinoma cells can inhibit their growth 68 ., Strikingly , loss of C/EBPα also confers susceptibility to UV-induced skin carcinogenesis 44 ., Thus , it is tempting to speculate that HPV8 E6 may contribute to the oncogenic functions of HPV8 in the skin of EV-patients and potentially also in immunosuppressed patients where the virus is expressed , via down-regulation of C/EBPα ., The study was approved by the Saarland University at the Saarland Ärztekammer and the Bioethics Committee at the Medical University of Warsaw , Poland and conducted according to the principles expressed in the Declaration of Helsinki ., The data were analyzed anonymously ., pcDNA3 . 1-C/EBPα , pLXSN-8E6 , pLXSN-8E7 , pLXSN 8E6/E7 , pCMV-eGFP were previously described 5 , 69 ., The pLXSN-Δ8E6 binding mutant was generated by site-directed mutagenesis according to 46 ., The hsa-miR-203 promoter ( -1165 nt to +70 nt; nomenclature according to 51 was amplified from human DNA using 5’-ACGCGTTGCTGCCCAACCCCATAC-3’ and 5’-CTCGAGCTCCCCTGGATTGGTCGC-3’ ( restriction enzyme sequences in italics ) and cloned upstream a firefly luciferase into pGL3-Basic vector ( Promega , Madison , USA ) ., Mutagenesis of the C/EBPα BS was performed using Q5 site-directed mutagenesis kit ( New England BioLabs ) according to the manufacturer`s protocol using 5’-GTTCTGTAGCTCGAGTGTGAAATGTTTAG-3’ and 5’-TGTTGAACTGTTAAGAACC-3’ oligonucleotides ., The short hsa-miR-203 promoter ( +47 nt to +73 nt ) was generated by direct cloning of annealed oligonucleotides into pGL3Basic vector ( wildtype: 5’-CGCGTCAGTTCTGTAGCGCAATTGTGAAATGTC-3’ and 5’-TCGAGACATTTCACAATTGCGCTACAGAACTGA-3’; mutated: 5’-CGCGTCAGTTCTGTAGCTCGAGTGTGAAATGTC-3’ and 5’-TCGAGACATTTCACACTCGAGCTACAGAACTGA-3’ ) ., Restriction sites are indicated in italics , the C/EBPα binding site is underlined , mutations are in bold ., All constructs were verified by DNA sequencing ., NHK were isolated from foreskin tissue ( Saarland University Medial Center ) and cultured in supplemented KBM-Gold medium ( Lonza , Basel , Switzerland ) ., NHK and the spontaneously immortalized keratinocyte cell line HaCaT ( a gift from Dr . P . Boukamp , German Cancer Research Center , Heidelberg , Germany ) 37 were retrovirally engineered to express HPV8E6 , E7 or E6/E7 using the pLXSN vector system ( BD Biosciences , Heidelberg , Germany ) as described 5 ., HaCaT and the HPV-negative cervical carcinoma cell line C33A ( HTB-31; American Type Culture Collection , Manassas , VA ) were cultured in supplemented DMEM 70 ., For differentiation-inducing experiments 6x105 or 1x105 cells were seeded onto 10 cm dishes or 6-well plates , respectively , treated with 1 . 2 mM CaCl2 ( Sigma Aldrich , Steinheim , Germany ) and harvested after 72 h ., To generate organotypic cultures , 3x105 foreskin fibroblasts , passage 3–5 , were seeded in 4 mg/ml rat-tail collagen ( as described in 71 ) on a collagen-fleece ( MedSkin Solutions , Billerbeck , Germany ) in 24-well plates and cultured in DMEM medium ., Next day , 6x105 HaCaT-pLXSN or 8E6 expressing HaCaT were seeded onto the collagen-fibroblasts in DMEM containing 25% Hams F-12 medium with supplements as described in 69 ., 100 U/ml penicillin/streptomycin was used as antibiotic ., 24 h later , cultures were transferred on a metal grid in 6-well plates to culture them at the liquid-air interface ., Two weeks later the cultures were fixed in 4% formaldehyde and embedded in paraffin ., To verify C/EBPα knock-down at the protein level , HaCaT cells were transfected with 10 nM siRNA or control siRNA 16 h before seeding on collagen/fibroblasts and cultured for 6 d at the liquid-air interface ., 2 . 5x105 cells were seeded into 6-well plates and transfected the next day with 10 nM siRNA directed against p63 , p300 , C/EBPα , 20 nM hsa-miR-203 mimic or control siRNA ( S1 Table ) and 5 μl Lipofectamine RNAiMax reagent ( Invitrogen , Darmstadt , Germany ) ., The cells were harvested for RNA or protein after 48 h ., For luciferase assays 1x105 cells were seeded into 12-well plates and transfected with 200 ng pGL3Basic-miR-203 promoter constructs , 100 ng pEGFP-C1 , 100 ng pcDNA3 . 1-C/EBPα and 2 μl Lipofectamine 2000 ( Invitrogen ) or 2 . 4 μl Transfast ( Promega ) for HaCaT or NHK , respectively as described in 72 ., The total amount of DNA was adjusted to 0 . 8 μg with the respective empty vectors ., 48 h post transfection cells were checked for transfection efficiencies by FACS analysis and lysed with luciferase extraction buffer containing 0 . 1 M potassium phosphate buffer ( pH 7 . 5 ) and 0 . 5% IGEPAL-630 ., Luciferase activity was determined with a Victor II luminometer ( Perkin-Elmer LAS ) and normalized to protein concentrations and transfection efficiencies in the respective extracts ., In some experiments cells were treated with 50 ng/ml PMA ( Sigma-Aldrich ) 24 h after transfection for additional 24 h ., In some experiments , cells were transfected with 10 nM C/EBPα siRNA or control siRNA 24 h prior to transfection with the promoter construct ., cDNA was generated from 1 μg RNA with SuperscriptII ( Invitrogen ) ., qRT-PCR was performed with the LightCycler 480II ( Roche , Mannheim , Germany ) using the Universal Probe System ( Roche ) ., Expression levels were normalized to ribosomal protein L13A ( RPL13A ) , which is a well-proven housekeeping gene in keratinocytes 73 , or beta-actin as described 74 ., Intron-spanning oligonucleotides were designed with the Universal Probe Library ( UPL ) software ( Roche ) ( S2 Table ) ., miRNA was transcribed using TaqMan MicroRNA RT Kit ( Life technologies ) with oligonucleotides for hsa-miR-203 and hsa-RNU6B in each reaction ., miRNA-cDNA detection was based on TaqMan MicroRNA Assays ( Life Technologies ) ., The amount was calculated by the 2–ΔΔCt method normalizing to RNU6B ., The BrdU Assay was performed according to the manufacturer`s protocol ( Roche ) ., 1 . 5x104 cells/96-well plate were seeded in triplicates in supplemented KBM-Gold medium ., After 20 h , cells were serum-starved ., 24 h later the medium was replaced by supplemented KBM-Gold with 10 μM BrdU ., The incorporation of BrdU was detected after 17 h by anti-BrdU-peroxidase labeled antibody ., Enzymatic reaction with TMB ( tetramethyl-benzidine ) was measured after stopping the reaction with 2 N H2SO4

Introduction, Results, Discussion, Materials and methods

Patients suffering from Epidermodysplasia verruciformis ( EV ) , a rare inherited skin disease , display a particular susceptibility to persistent infection with cutaneous genus beta-human papillomavirus ( beta-HPV ) , such as HPV type 8 ., They have a high risk to develop non-melanoma skin cancer at sun-exposed sites ., In various models evidence is emerging that cutaneous HPV E6 proteins disturb epidermal homeostasis and support carcinogenesis , however , the underlying mechanisms are not fully understood as yet ., In this study we demonstrate that microRNA-203 ( miR-203 ) , a key regulator of epidermal proliferation and differentiation , is strongly down-regulated in HPV8-positive EV-lesions ., We provide evidence that CCAAT/enhancer-binding protein α ( C/EBPα ) , a differentiation-regulating transcription factor and suppressor of UV-induced skin carcinogenesis , directly binds the miR-203 gene within its hairpin region and thereby induces miR-203 transcription ., Our data further demonstrate that the HPV8 E6 protein significantly suppresses this novel C/EBPα/mir-203-pathway ., As a consequence , the miR-203 target ΔNp63α , a proliferation-inducing transcription factor , is up-regulated , while the differentiation factor involucrin is suppressed ., HPV8 E6 specifically down-regulates C/EBPα but not C/EBPβ expression at the transcriptional level ., As shown in knock-down experiments , C/EBPα is regulated by the acetyltransferase p300 , a well-described target of cutaneous E6 proteins ., Notably , p300 bound significantly less to the C/EBPα regulatory region in HPV8 E6 expressing keratinocytes than in control cells as demonstrated by chromatin immunoprecipitation ., In situ analysis confirmed congruent suprabasal expression patterns of C/EBPα and miR-203 in non-lesional skin of EV-patients ., In HPV8-positive EV-lesions both factors are potently down-regulated in vivo further supporting our in vitro data ., In conclusion our study has unraveled a novel p300/C/EBPα/mir-203-dependent mechanism , by which the cutaneous HPV8 E6 protein may expand p63-positive cells in the epidermis of EV-patients and disturbs fundamental keratinocyte functions ., This may drive HPV-mediated pathogenesis and may potentially also pave the way for skin carcinogenesis in EV-patients .

Cutaneous genus beta-HPV types infect skin keratinocytes ., Their potential role in skin carcinogenesis , particularly in immunosuppressed patients , has become a major field of interest ., Patients suffering from the rare genetic disorder Epidermodysplasia verruciformis ( EV ) are highly susceptible to persistent genus beta-HPV infection and have an increased risk to develop non-melanoma skin cancer at sun-exposed sites ., Thus , EV serves as a valuable model disease for studying genus beta-HPV biology ., Here , we demonstrate that in human HPV8-infected EV skin lesions , the ‘stemness-repressing’ microRNA-203 is strongly down-regulated ., In contrast , cells expressing the miR-203-regulated ‘stemness-maintaining’ factor p63 , are highly amplified ., Notably , we identified the transcription factor C/EBPα , a well-known suppressor of UV-induced skin carcinogenesis , as a p300-dependent target of the HPV8-encoded E6 oncoprotein and as a critical inducer of miR-203 gene expression ., Our data provide evidence for a novel p300/C/EBPα/miR-203-dependent pathway , which links HPV8 infection to the expansion of p63-positive cells in the epidermis of EV-patients ., This may contribute to the beta-HPV-induced disturbance of epidermal homeostasis and pave the way for skin carcinogenesis .

transfection, keratinocytes, medicine and health sciences, gene regulation, regulatory proteins, dna-binding proteins, epithelial cells, oncology, protein expression, transcription factors, molecular biology techniques, immunologic techniques, research and analysis methods, small interfering rnas, animal cells, proteins, gene expression, biological tissue, molecular biology, molecular biology assays and analysis techniques, gene expression and vector techniques, biochemistry, rna, carcinogenesis, immunohistochemistry techniques, cell biology, anatomy, nucleic acids, genetics, epithelium, biology and life sciences, cellular types, non-coding rna, histochemistry and cytochemistry techniques