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journal.pcbi.1003703 | 2,014 | Inferring Clonal Composition from Multiple Sections of a Breast Cancer | Many clones exist within each cancer , and selective pressure imposed by environmental factors , most notably treatments directed at tumor eradication , favors the emergence of clones that grow increasingly resistant to successive rounds of therapy ., Incorporating this intra-tumor heterogeneity into strategies for planning , monitoring , and revising cancer treatment could improve outcomes for oncologists and their patients ., Therefore , methods for estimating the number , size and mutational content of clones within a patients tumor are being explored ., New approaches are being developed to assess the clonal content of a given tumor ., Methods based on the interrogation of individual cells have relied on the use of fluorescent markers 1 , 2 or single cell sequencing 3–6 ., Whereas fluorescence-based approaches are inevitably limited by the relatively small number of features they can accommodate , single cell sequencing brings the highest possible resolution to characterizing an individual patients tumor ., Nonetheless , single cell sequencing also faces obstacles to its widespread implementation ., Evaluating sufficiently large numbers of single cells to obtain statistical power can be prohibitive , for technical or financial reasons ., Additionally , it is often difficult to ascertain the identity of the cells being sequenced , and details regarding the spatial positioning of cells relative to each other and to other cells in the tumor are lost when the single cells are obtained ., These disadvantages pose significant challenges to the widespread adoption of single cell sequencing as a means for assessing tumor heterogeneity ., Complementing single cell approaches are efforts to deconvolve clonal subpopulations based on the frequencies of mutated alleles within one or more bulk tumor specimens ., Shah et al . 7 , who sequenced a breast cancer at the time of diagnosis and nine years later , after metastasis , pointed out that allele frequencies of the mutations shared between the two samples could be used to segregate primary mutations into those that occur in a dominant versus subdominant clone ., This insight is the basis for a variety of approaches that apply clustering algorithms to mutation allele frequencies , including kernel density estimation 8 and Dirichlet process modeling applied either to the allele frequencies 9 or to a combination of allele frequency , loss-of-heterozygosity status and copy number 10–13 ., Clearly , statistical power to infer variants and , ultimately , clonal composition , is increased if multiple samples are available for analysis ., Accordingly , various studies have examined the progression of cancer within one or more patients over time ., Sets of variants that exhibit similar allele frequencies within a single sample are suggestive of a clonal population ., Hence , clustering methods to identify groups of mutations associated with a single clone have been applied ., For example , kernel density estimation has been applied to allele frequencies from tumor-relapse pairs from eight acute myeloid leukemia ( AML ) patients 14 and from seven secondary AML patients 15 ., An orthogonal approach taken by Newberger et al . 16 employs triplet samples of neoplasia , matched normal and carcinoma from six patients to infer lineages of various genetic events ., They characterize each locus in terms of a binary vector representing the presence of the mutation across the various samples and then group the loci into classes on the basis of these vectors ., After filtering low frequency classes , the classes are used to manually construct a phylogenetic tree ., The focus of the study is to identify the shared characteristics of the evolutionary process across six patients with breast cancer ., In the current study , we adopt an alternative approach to identify clonal structure ., Rather than measuring allele frequencies in multiple samples from the same patient over time , we physically subdivide a single breast cancer specimen and measure allele frequencies within each subsection ( Figure 1 ) ., We are aware of two previous studies that have adopted such an approach ., Yachida et al . 17 analyzed seven metastatic pancreatic cancers , sequencing from multiple samples per patient ., Clones are initially defined relative to sample types ( peritoneal , liver and lung metastases ) ., Subsequently , the tumors from two patients are resected and a clonal phylogeny is inferred manually ., More recently , Gerlinger et al . 18 carried out exome sequencing followed by targeted deep sequencing on samples from four patients with renal carcinoma ., Each primary tumor was divided into 9 regions , and a phylogeny was manually constructed by assuming that higher alternate allele frequencies correspond to earlier mutations ., In neither of these studies was an algorithm proposed to automatically infer from such data both the clonal genotypes and the relative frequencies of the clones within each subsection ., The method proposed here bears some similarity to the recently proposed Tree Approach to Clonality ( TrAp ) method 19 ., The TrAp algorithm aims to identify the number , relative frequencies and genotypes of clones within a tumor using a formalism somewhat similar to ours , based on matrix decomposition ., However , rather than analyzing data from multiple sections , the authors use as input a single set of variant allele frequencies and then constrain the resulting optimization problem by introducing a series of four assumptions about cancer evolution ., It is not clear whether the method can easily generalize to analysis of data from multiple sections or multiple time points ., Here we describe a generative binomial model that incorporates information from multiple sections from a single tumor at a single time point to infer the frequencies and genotypes for a specified number of clones ., An implementation of our algorithm is available through Bioconductor as an R package called Clomial ( http://www . bioconductor . org/packages/release/bioc/html/Clomial . html ) ., We use Clomial version 1 . 1 . 7 to apply this approach to a breast cancer specimen and demonstrate that the results from our model predict relationships that are phylogenetically and spatially plausible ., We assume that a tumor is comprised of multiple populations of cells ( “clones” ) , each with a unique genotype , and that these populations are heterogeneously distributed within the tumor itself ., We collect , from several physical subsections of the tumor , shotgun sequencing reads ., We also collect sequencing data from a non-tumor subsection from the same patient ., Using the called genotypes from the normal subsection , and restricting ourselves to positions that are homozygous in the normal subsection , each read from a tumor subsection exhibits either a normal allele or a variant allele at each location ., We exclude positions that exhibit homozygous normal alleles in all of the tumor subsections ., Our goal is to infer , from the remaining mutated positions , the genotype of each clonal population and their relative frequencies within each physical subsection of the tumor ., Formally , the problem can be stated as follows ., Note that we use bold face letters for random variables , and that and respectively denote the row and the column of matrix ., We are given two primary input matrices and , where is the number of mutated loci , is the number of subsections ( of which one is normal and are tumor ) , is the total number of reads ( i . e . , the coverage ) at locus in subsection , and is the number of cancerous reads ( those supporting the mutation ) at locus in subsection ., We assume , without loss of generality , that the first of the subsections corresponds to normal tissue , and that the remaining subsections are from the tumor ., In addition , we consider , the number of distinct clones in the tumor , as a hyperparameter , and train a model based on a given value of ., We assume that the first clone corresponds to the normal cell population and the tumor is composed of tumor clones ., Later , we will discuss whether can be estimated from the data ., Our task is to infer two matrices: a clone frequency matrix in which is the proportion of cells of clone in subsection , and a genotype matrix in which if clone has the variant allele at locus , and otherwise ., The first column of contains all zeroes because it represents the “normal clone . ”, By definition , each column of sums to ., Also , by construction , the first column of corresponds to the normal subsection and hence consists almost entirely of zeroes , although small non-zero counts may be possible due to contamination from tumor or due to sequencing error ., If the first column of consisted entirely of zeroes , then we would expect the first column of to be of the form , but in order to allow for the possibility that the allegedly normal subsection can have slight tumor contamination , we infer the first column of ( as well as the other columns ) ., We propose to solve this problem using a generative model whose parameters are learned via expectation-maximization ( EM ) 20 ., Accordingly , we define a matrix of hidden variables representing the unknown genotypes of the clones; for instance , if , then the clone has a tumor allele at the locus ., We assume that each follows an independent Bernoulli distribution with parameter , i . e . , ( 1 ) We also assume that if a mutation is present in a particular clone , then at that locus the clone is heterozygous with copy number equal to 1 ., Therefore , for subsection , if clone has a mutation at locus ( ) , then its contribution to the observed count of cancer alleles is by , half of its proportion in the subsection ., Conversely , if a clone does not have a mutation at ( ) , then it does not contribute to the count of variant alleles ., By summing up the contributions of all clones , we obtain the total probability that an observed read corresponds to a variant allele rather than a normal allele ., Therefore , the probability that a read contains the variant allele at locus in subsection is given by ( 2 ) where is the row of , and is the column of ., Finally , we introduce a matrix of random variables representing the observed data , where is the number of reads exhibiting the variant allele at locus in subsection ., This matrix encodes our primary assumption about the distribution of the data: for each and , we observe an independent sample of that has a binomial distribution with two parameters and , i . e . , ( 3 ) The first parameter of this distribution is the ( known ) total number of reads at locus in subsection ., The second parameter , , is the probability of observing a variant allele; it will be inferred by EM ., Given the joint distribution over observed variables and latent variables , governed by parameters , our goal is to maximize the likelihood function ., We do so using EM , exploiting three assumptions: ( 1 ) that each subsection contains non-zero normal contamination , i . e . , for all , ( 2 ) independence of the subsections from each other , and ( 3 ) independence of mutations from each other ., The first assumption is based on the widely accepted difficulty associated with obtaining perfectly pure samples of tumor cells 21 , 22 ., The two independence assumptions essentially state that each locus and each sample is informative ., These assumptions are unavoidable: in the presence of very high dependence , only very limited information about the underlying clonal composition of the tumor would be provided by the loci and samples ., Furthermore , it is worth noting that these independence assumptions are made conditional on the parameters in the model: that is , the elements of are independent conditional on and ., In other words , if we knew the true underlying parameters for the model ( that is , the true genotypes for the clones , and the true proportion of each clone present in each sample ) , then the actual number of “tumor” reads that we would observe for each locus-sample pair would be independent ., While the formulation of our inference problem shows some similarity to well-studied matrix factorization problems 23–25 , such techniques cannot be directly applied here ., Unlike most matrix factorization techniques , which assume a normal distribution , our observations are binomially distributed ., Moreover , the elements of the latent matrix are binary , and each column of must sum to 1 ., These constraints required us to develop a customized inference algorithm ., To frame the EM optimization , we consider the following complete-data log likelihood function of the model: ( 4 ) which can be computed as follows ( for details see Note S4 in Text S1 ) : ( 5 ) where ., Our goal is to find the parameters which maximize the likelihood ., Because our model involves the hidden variable , we cannot directly maximize the given in Equation 5 with respect to ., Instead , we use the EM algorithm to fit the model to the data 26 ., EM is an iterative algorithm with two steps—E ( for expectation ) and M ( for maximization ) —in each iteration ., In the E step , we use the current estimates of the parameters , , to compute the conditional expectation of ., In the M step , we find the new parameters that maximize the conditional expectation ., To validate our implementation of the EM optimization procedure and to understand our models behavior , we produced simulated deep sequencing data and measured the extent to which the model successfully recovers the true clonal structure of the data ., For each simulation , we began by randomly generating four matrices ., First , we generated a simulated matrix of total read counts with respect to a fixed number ( ) of loci and a fixed number ( ) of subsections with a mean coverage of 1000 reads per locus ., The matrix was generated by independently sampling each column ( corresponding to a single subsection ) from a multinomial distribution , where the parameters and correspond to the total number of trials , and the probability of success for each of the loci , respectively ., Second , for any clone number , we generated a corresponding Boolean matrix , in which the entry at row and column indicates whether locus exhibits the variant allele in clone ., Entries in were generated independently from a Bernoulli distribution with a probability of success , with the exception of the first ( “normal” ) column of , which contains all zeroes ., Third , we generated a clone frequency matrix as follows: each element of is independently drawn from a Uniform distribution , and then each column of was divided by the column sum , so that the columns summed to 1 ., We then set so that the first column of corresponds to the normal subsection ., Finally , for each locus and subsection , we generated the observed number of variant alleles by sampling from a binomial distribution with parameters ( representing the total number of reads ) and ( representing the probability that a given read corresponds to the variant allele ) ., This last step complies with our primary assumption about the distribution of the data ( Equation 3 ) ., We ran the EM algorithm using the simulated data and and then evaluated the extent to which the estimated clone frequency matrix and mutation probability matrix differed from the corresponding true matrices and ., Specifically , we computed the genotype error , defined asand the clone frequency error , Note that , because we did not know which columns of correspond to which columns of , we compared to every permutation of the columns of and selected the permutation that resulted in the smallest genotype error ., The selected permutation was then also used in the calculation of the clone frequency error ., Our simulation results ( Figure, 2 ) exhibit two primary trends ., The overall error rate , as measured by either genotype or clone frequency error , decreases systematically as the number of subsections increases , and increases as the number of clones increases ., Overall , both error rates are low , especially for ., The observed trends are expected: for a fixed number of clones , the availability of more subsections leads to more accurate estimation of the true parameter values; and for a fixed number of subsections , the presence of more clones leads to a greater number of parameters that must be inferred , leading to greater error in estimation ., To assess the affect of sequencing error on the performance of Clomial , we added noise to the simulated data and repeated the above experiments ., Specifically , we modeled noise by Bernoulli random variables with probability of success interpreted as the probability that a non-tumor allele is read as a tumor allele or vica versa ., Running the EM algorithm on the noisy data revealed that Clomial is robust with respect to noise for all reasonable levels of sequencing error ( Figure S6 ) in Text S1 ., We obtained breast cancer tissue from a 44 year old premenopausal female with infiltrative ductal carcinoma ( IDC ) with ductal carcinoma in situ ( DCIS ) , stage pT1c pN1 , Grade II/III , estrogen receptor ( ER ) positive , progesterone receptor ( PR ) positive and Her2 negative ., Axillary lymph node dissection revealed that one out of 13 nodes was positive for metastatic disease ., A total of 6 tissue sections were obtained , including 2 sections from adjacent normal breast tissue , 3 from the primary breast cancer , and 1 from the positive lymph node ., The tumor content , including both IDC and DCIS , ranged from 40% to 55% in the primary tumor and axillary lymph node tissue sections based on pathological examination ., For subsequent analysis , each tissue section was subdivided into subsections ( Figure 3 ) ., To identify mutations and quantify allele frequencies , we performed two rounds of DNA sequencing ., Initially , DNA was extracted from each individual subsection and subjected to exome capture followed by Illumina sequencing ., Variants were detected independently in each subsection using the SeattleSeq Annotation Server ., We focused on single nucleotide variants and short indels that exhibited a coverage of reads in at least one of the subsections , ranking them using DeepSNV 31 and Fishers exact test ( Methods ) ., This analysis produced an initial set of 281 variants ( Dataset S1 ) ., To better quantify the allele frequencies at these loci , we designed primer pairs surrounding each locus and used these primers to perform a second round of targeted DNA sequencing ., This experiment successfully sequenced 244 of the 281 loci , with a mean and median coverage of 1615 and 1118 , respectively , reads per locus ., Each of these loci was individually validated by visual inspection using the Integrative Genomics viewer ( IGV ) ., Manual inspection showed that many of the initially identified mutations were flanked by homopolymer repeats , suggesting that the alternate alleles were read calling errors , rather than true mutations 32 ., For all downstream analysis we focused on a set of 17 confirmed somatic variants ., For clarity of presentation , we refer to each somatic variant by the chromosome where it resides , appending a letter if more than one somatic variant occurred within a chromosome ( Table S1 in Text S1 ) ., The targeted sequencing thus produced two 17-by-12 matrices containing , respectively , the total coverage and the tumor allele count at each locus ( Table S1 in Text S1 ) ., Visual inspection of the allele frequency profiles shows , not surprisingly , a markedly different pattern of allele frequencies among the subsections from primary and metastatic sites ( Figure 3 ) ., In addition , several of the samples ( e . g . , P1-4 and P3-1 ) exhibit consistently lower frequencies across all loci , presumably indicating a higher prevalence of normal cells within these samples ., We applied our EM optimization procedure to the two counts matrices , varying the number of assumed clones from C\u200a=\u200a3 up to C\u200a=\u200a6 ., For each value of C , we ran EM 100 , 000 times from different random initializations , and we selected the solution with the highest likelihood ( Figure 4 ) ., The resulting three-clone solution identifies two mutations , chr4a and chr9b , that occur in both the primary and metastatic samples and segregate the remaining mutations into nine that occurred in the primary tumor and six that occurred in the metastatic lymph node ., The four- and five-clone solutions further subdivide the primary tumor mutations , and the six-clone solution separates the two metastatic mutations into distinct clones ., To better understand the inferred clonal landscape , we investigated the relationship between clone frequencies and the anatomy of the three primary and one metastatic tumor sections ., We hypothesized that clone frequencies should vary smoothly between adjacent subsections , reflecting the physical spread of successful clonal populations ., This hypothesis is supported by the data ( Figure 5 and Figure S1 in Text S1 ) ., The trends are most striking in sections P1 and P2 , for which we obtained four separate subsections ., In each case , the primary clone frequencies vary in a monotonic fashion as we traverse the sample ., Given that the EM inference procedure was provided with no information about which subsection was derived from which section , nor the relative orientation of the subsections to one another , the smoothly varying frequencies among adjacent subsections provides evidence that the method has successfully identified true clonal variation ., Cancer progression is an evolutionary process in which clones accrue mutations over time , forming new clones ., Accordingly , it should be possible to organize the clonal progression of a tumor into a phylogenetic tree with the founder clone at the root ., We therefore investigated whether the clones inferred by our EM procedure obey some simple phylogenetic constraints , with two complementary goals ., First , because our EM procedure makes no use of phylogenetic constraints , this analysis can provide further evidence for the validity of our inferred solutions ., Second , the phylogenetic analysis has the potential to provide significant insights into the clonal and mutational history of this specific cancer ., We started with the C\u200a=\u200a3 solution to our EM algorithm , manually constructing a phylogenetic tree in which each node is a clonal population , and edges are marked with the mutations that occurred in the evolution from the parent clone to the offspring ( Figure 6A ) ., This particular tree shows two founder mutations , chr4a and chr9b , occurring prior to metastasis , six mutations occurring along the metastatic lineage , and nine along the primary lineage ., This is the only phylogenetic tree that is consistent with the inferred clonal genotypes ., In contrast , for the solutions inferred from the EM algorithm assuming C\u200a=\u200a4 through 6 , we found that it is not possible to construct a tree without requiring that the same mutation occur independently along multiple branches ., We therefore considered all possible “nearby” trees ( where “nearby” means that , among the distinct rows of the genotype matrix , the two trees differ by only one bit ) that produce a valid phylogenetic tree with no repeated mutations ., For example , for the C\u200a=\u200a4 solution , we evaluated the likelihood of six nearby trees , yielding log-likelihoods of −28482 , −21282 , −7500 , −6692 , −5659 , and −4333 ( Table S2 in Text S1 ) ., The highest of these likelihoods is −4333 , compared to −4244 for the solution initially inferred by EM ., The selected solution requires changing only one bit in the genotype matrix from “0” to “1” ( indicated by asterisks in Figure 4 ) ., The resulting phylogenetic tree ( Figure 6B ) closely resembles the C\u200a=\u200a3 tree , except that one mutation initially assigned to the metastatic clone C3 is instead assigned to clone C2 in the C\u200a=\u200a4 tree ., Also , the nine mutations associated with the primary section in the C\u200a=\u200a3 tree are further subdivided into three that occur shortly after metastasis and six that lead to clone C1 ., Reassuringly , the C\u200a=\u200a5 and C\u200a=\u200a6 solutions , constructed in a similar fashion ( Figure 6C–D ) , are largely consistent with this story , each introducing a subdivision among the existing sets of mutations to produce a larger set of clones ., Among these trees , the only inconsistencies concern ( 1 ) three mutations ( chr5 , chr9a and chr20b ) that occur later according to the C\u200a=\u200a4 solution than according to the C\u200a=\u200a5 or C\u200a=\u200a6 solutions and ( 2 ) two mutations ( chr1 and chr4b ) that are assigned their own branch , directly off the normal clone , in the C\u200a=\u200a5 and C\u200a=\u200a6 solutions ., In practice , the chance that a randomly generated genotype matrix would produce a valid phylogenetic tree is vanishingly small ( Note S3 in Text S1 ) ., Therefore , the fact that each of our inferred solutions very nearly produce a valid phylogenetic tree provides evidence for the validity of these solutions ., We also investigated the extent to which the observed mutation frequencies obey the phylogenetic tree ., In principle , a mutation that occurs earlier in the evolution of the cancer should have a higher frequency than mutations that occur later along the same lineage because a child clone necessarily contains all of the mutations belonging to its parent clone ., This investigation is hampered , however , by copy number variation ., In practice , we cannot directly compare the allele frequencies of two distal sites because the observed allele frequencies are actually the product of mutation frequency and copy number ., Empirically , we observe variation in copy number along the genome and differences in copy number variation from one subsection to the next ( Figure S2 in Text S1 ) ., A consistent duplication of a large portion of chromosome 8 is known to occur commonly in breast cancer 33 ., We were lucky , however , that two of our mutated loci occur quite close to one another on chromosome 9 ( chr9a and chr9b , separated by only 3 . 3 Mbp ) ., Given the observed data , the likelihood that a change in copy number occurring between these two loci is small , thereby allowing us to safely compare the corresponding mutation frequencies ., Across all nine primary tumor subsections , we observe that the frequency of the parent mutation ( chr9b ) is higher than that of the child mutation ( chr9a ) ., Hence , these mutation frequencies are consistent with the inferred phylogeny ., To assess the stability of our inference , we performed leave-one-out analysis and compared the inferred phylogenies as follows ., We held out each of the 12 tumor subsections one at a time and trained the model using the data from only 11 subsections for the case of C\u200a=\u200a4 ., When samples p1-1 or p1-3 were excluded , the inferred genotypes were exactly the same as the genotype obtained from the full data ., Excluding any of the other of 10 subsections resulted in a genotype which was different only in one bit; namely , the mutation chr4a was predicted to be present in all clones ., However , this difference did not affect the inferred phylogeny because the change of this bit was in fact required to build a valid phylogenetic tree ( Figure 4 ) ., In other words , by excluding any of the 12 tumor subsections , the inferred genotype always led to the same valid phylogenetic tree , which suggests that our algorithm is stable ., Once a tumor has been resected , clinicians pay a great deal of attention to characterizing its anatomy ., Features such as necrosis , extension beyond normal anatomical boundaries , and microvascular invasion convey important prognostic information ., In addition , the cancer cells within any given tumor are frequently heterogeneous with respect to features such as differentiation state , the fraction of cells undergoing mitosis ( as determined by Ki67 staining ) , or ( for breast cancer ) the fraction of cells expressing HER-2 or estrogen receptor ., The method described here provides a framework for linking a tumors molecular anatomy to its structural anatomy as well as its phylogenetic evolution ., Several lines of evidence support the validity of the clonal genotypes and relative frequencies inferred by our model ., One prediction from our phylogenetic reconstruction is that somatic variants at the trunk will be present at higher frequencies throughout all tumor subsections than variants appearing at the branches ., While copy number variation across the somatic genome complicates these comparisons , one of two closely juxtaposed somatic variants ( chr9b ) is positioned at the trunk of our phylogenetic tree , while its neighbor ( chr9a ) arises in one of the branches ., Consistent with this representation , the variant allele frequencies for chr9b are consistently higher than for chr9a in all ten tumor subsections examined ., Interestingly , phylogenies can be built from the inferred genotypes even given the relatively low purity of the tumor sections: contamination with normal tissue was in 9 out of 12 subsections in our data ( Figure 4 , ) ., In particular , although we estimate that the metastatic subsections contained tumor cells in M1-1 and in M1-2 , the corresponding branch of the phylogenies is stable and consistent ., Similar to phylogenetic analysis , reassembly of the tumor subsections indicates that our assignment of mutations to clones produces spatial representations that are anatomically reasonable ., With further refinements , our method should enable reconstructions that layer a tumors phylogeny on top of its spatial organization ., While our results underscore the potential power of this new method , our study also has several limitations ., Our assessments were confined to heterozygous somatic variants , and did not take into account the many chromosomal structural changes that were present in the tumor we examined ., A comparison of exome copy numbers between primary tumor and lymph node indicates that the vast majority of these chromosomal changes preceded the divergence shown in our phylogenetic tree ( Figure S2 in Text S1 ) ., In theory , one could imagine generalizing our generative model to take copy number variations into account by replacing the 2 in the denominator of Equation 2 with a hidden random variable for each locus , but without some form of aggressive regularization , this formulation would lead to a prohibitively complex and overfit model ., Additionally , a key characteristic of our method is the requirement to specify the number of clones prior to the EM inference procedure ., It is important to recognize that this choice should depend upon properties of the data set itself , rather than fundamental properties of the cancer ., After all , each cell division results in multiple mutations , such that every cancer cell constitutes a distinct clone ., Consequently , a picture of the full clonal history of a cancer would consist of a phylogenetic tree with one leaf for each cancer cell ., In practice , such a tree would be of limited utility and , more importantly , could not be accurately estimated from any reasonably sized data set ., Perhaps the most useful definition of a tumor clone is a population of cells that exhibit distinct spatial or functional properties ., Our approach allows the user to specify the number of clones and , hence , the resolution at which the clonal history is viewed ., Because Clomial does not impose any assumption on the distribution of mutation frequencies , the number of inferred clones may not exceed the number of samples; otherwise , the resulting optimization problem will be under-constrained ., In the particular cancer studied here , the three-clone solution appears to provide an inaccurate view of the clonal history ., The placement of the chr17c mutation along the path leading to metastatic clone C2 is surprising , given that this particular locus has such low counts for both met | Introduction, Results, Discussion, Materials and Methods | Cancers arise from successive rounds of mutation and selection , generating clonal populations that vary in size , mutational content and drug responsiveness ., Ascertaining the clonal composition of a tumor is therefore important both for prognosis and therapy ., Mutation counts and frequencies resulting from next-generation sequencing ( NGS ) potentially reflect a tumors clonal composition; however , deconvolving NGS data to infer a tumors clonal structure presents a major challenge ., We propose a generative model for NGS data derived from multiple subsections of a single tumor , and we describe an expectation-maximization procedure for estimating the clonal genotypes and relative frequencies using this model ., We demonstrate , via simulation , the validity of the approach , and then use our algorithm to assess the clonal composition of a primary breast cancer and associated metastatic lymph node ., After dividing the tumor into subsections , we perform exome sequencing for each subsection to assess mutational content , followed by deep sequencing to precisely count normal and variant alleles within each subsection ., By quantifying the frequencies of 17 somatic variants , we demonstrate that our algorithm predicts clonal relationships that are both phylogenetically and spatially plausible ., Applying this method to larger numbers of tumors should cast light on the clonal evolution of cancers in space and time . | Cancers arise from a series of mutations that occur over time ., As a result , as a tumor grows each cell inherits a distinctive genotype , defined by the set of all somatic mutations that distinguish the tumor cell from normal cells ., Acertaining these genotype patterns , and identifying which ones are associated with the growth of the cancer and its ability to metastasize , can potentially give clinicians insights into how to treat the cancer ., In this work , we describe a method for inferring the predominant genotypes within a single tumor ., The method requires that a tumor be sectioned and that each section be subjected to a high-throughput sequencing procedure ., The resulting mutations and their associated frequencies within each tumor section are then used as input to a probabilistic model that infers the underlying genotypes and their relative frequencies within the tumor ., We use simulated data to demonstrate the validity of the approach , and then we apply our algorithm to data from a primary breast cancer and associated metastatic lymph node ., We demonstrate that our algorithm predicts genotypes that are consistent with an evolutionary model and with the physical topology of the tumor itself ., Applying this method to larger numbers of tumors should cast light on the evolution of cancers in space and time . | oncology, medicine and health sciences, cancer genetics, basic cancer research, genetics, biology and life sciences, genomics, genomic medicine | null |
journal.pcbi.1003412 | 2,014 | Encoding of Natural Sounds at Multiple Spectral and Temporal Resolutions in the Human Auditory Cortex | Understanding how natural sounds and scenes are processed in the human auditory cortex remains a major challenge in auditory neuroscience ., Current models of auditory cortical processing describe the sound-evoked neural response patterns at the level of preferential regional activations for certain behavioral tasks ( e . g . localization vs recognition 1 , 2 ) , sound categories ( e . g . voices , speech 3 ) and ( complex ) acoustic features 4 , 5 ., However , the computational and representational mechanisms underlying these responses remain largely unknown ., The overall aim of the present study is to derive a computational model of how natural sounds are encoded in the human brain by combining high-resolution fMRI ( 3 and 7 Tesla ) with computational modelling ., Most natural sounds are characterized by modulations of acoustic energy in both the spectral and temporal dimensions ( Figure 1A ) ., These modulations occur at multiple scales 6 and are crucial for behaviorally relevant auditory processing such as speech intelligibility 7–10 ., Psychophysical investigations indicate that humans are able to detect and discriminate modulations that occur in one dimension alone ( temporal: 11; spectral: 12 ) as well as combined spectro-temporal modulations 9 ., Similarly , neurophysiological studies in animals and humans have revealed neuronal tuning for temporal modulations 13–15 and spectral modulations 16 alone , and the combination of the two 17–21 ., This evidence suggests that spectral and temporal modulations are critical stimulus dimensions for the processing of sounds in the auditory cortex ., Just as the cochlea generates multiple “views” of the sound pressure wave at different frequencies , an explicit encoding of spectral and temporal modulations would allow the cortex generating multiple “views” of the sound spectrogram with different degrees of spectral and temporal resolution 22 ( Figure 1B ) ., Multiple simultaneous representations of the same incoming sounds may be crucially relevant for enabling flexible behavior , as different goal-oriented sound processing ( e . g . sound localization or identification ) may benefit from different types of representations ., Furthermore , the representations of sounds at multiple resolutions may provide the computational basis for binding acoustic elements in sound mixtures and solve complex auditory scenes 23 ., Despite extensive investigations in a variety of experimental settings , the specific computational mechanisms used by the human auditory cortex to represent energy modulations in the spectrogram of natural sounds are still a matter of speculation ., Here , we use an fMRI “encoding” approach 24 to compare competing computational models of sound representations and select the best model as the one that can predict most accurately fMRI response patterns to natural sounds ., We focus on three well-defined aspects of the representation of spectral and temporal modulations: ( 1 ) dependency , ( 2 ) frequency specificity , and ( 3 ) spatial organization ., Dependency refers to the relation between spectral and temporal processing ., The spectrogram of natural sounds is characterized by concurrent spectral and temporal modulations and these sound qualities might be represented jointly or independently of each other ., An independent representation implies separate processing mechanisms for spectral and temporal modulations , such that the response to one dimension is invariant to a change in the other dimension ., By contrast , a joint representation relies on combined selectivity for the conjunction of spectral and temporal modulations ., The joint representation can be modeled as an array of spectro-temporal filters that are selective for combinations of spectral and temporal modulations ( Figure S1A ) , whereas the independent representation can be seen as a bank of filters that are selective for either temporal or spectral modulations ( Figure S1B ) ., In other words , the two models differ with respect to the dimensions employed by the auditory cortex to encode natural sounds ( combined spectro-temporal modulations , and spectral and temporal modulations alone , respectively ) ., Testing for the interdependency of spectral and temporal modulation processing has relevant implications , as the superiority of such a model would indicate that results obtained using sounds that only vary along one dimension ( e . g . amplitude modulated tones or stationary ripples ) cannot be generalized to mechanisms of representation and processing of natural sounds ., The analysis of the spectro-temporal modulation content of the sound spectrogram can be global ( 2D Fourier transform ) or localized ( e . g wavelet transform ) ., A global representation indicates integration along the frequency axis , while in a local analysis spectral and temporal modulations are encoded in a frequency-specific fashion ., Frequency specific responses are ubiquitous in the auditory cortex; yet it is not clear how this dimension is exploited for the representation of natural sounds ., Understanding the nature of the modulation analysis performed by the human auditory cortex can provide insights about the functional role of this representational mechanism ., Finally , the third aspect that we consider is the existence and layout of a large-scale spatial organization of spectro-temporal modulation tuning ., Topographic maps of stimulus dimensions are a well-established organizational principle of the auditory cortex 25 ., In humans , the primary 26 as well as the non-primary 27 auditory cortex contain multiple topographic representations of sound frequency ( tonotopic maps ) ., Beyond tonotopy , however , the spatial organization of other sound features remains elusive 25 ., Our methodological approach provides the possibility to obtain maps of multiple sound features and feature-combinations from the same set of fMRI responses and within the ecologically and behaviorally-relevant context of natural sounds processing ., Here , we exploit this possibility to study the regional specificity and the spatial organization of spectro-temporal modulation tuning ., Such knowledge can reveal the representational and computational basis underlying the functional specialization of auditory cortical subdivisions ., Our results show that the human brain forms multiple representations of incoming natural sounds at distinct spectral and temporal resolutions ., The encoding of spectral and temporal modulations is joint and frequency-specific and is governed by a trade-off between spectral and temporal resolution ., Regional variations of voxels modulation preference put forward the hypothesis that the functional specialization of auditory cortical fields can be partially accounted for by their modulation tuning ., We applied an “encoding” approach ( see 24 and Figure S2 ) and compared several computational models of auditory processing ., A first model we tested describes auditory cortical neurons as a bank of frequency-localized filters with joint selectivity for spectral and temporal modulations ( see 22 and Materials and Methods ) ., Considering that one voxel reflects the mass activity of a great number of neurons , we modelled each voxels receptive field as a combination of modulation selective filters , each tuned to a different spectral modulation , temporal modulation and frequency ( Figure 2 , panel A ) ., Using a subset of fMRI data ( training ) , we estimated a modulation transfer function ( MTF , Figure 2 , panel A1 ) for each voxel ( see Figure 3 for two MTF examples ) ., We then assessed the ability of this MTF-based model to accurately predict the fMRI responses in new , independent data sets ( testing ) ., In the 3T experiment , training and testing data involved a single set of natural sounds , whereas two completely distinct sound sets were used for the 7T training and testing datasets ., We quantified models prediction accuracy by performing a sound identification analysis 24 ., Namely , we used the fMRI activity patterns predicted by the estimated models to identify which sound had been heard among all sounds in the test set ., Each testing sound was assigned with a score ranging between 0 and 1 and indicating the rank of the correlation between sounds predicted and measured activity patterns ( 0 indicates that the predicted activity pattern for a given stimulus was least similar to the measured one among all test stimuli; 1 indicates correct identification ) ., The overall models accuracy was obtained as the average score across all test sounds ( see Materials and Methods ) ., For both the 3T and 7T datasets , the accuracy of the joint frequency-specific MTF-based model was significantly higher than chance ( 0 . 5 ) both at group level ( 3T: mean SE\u200a=\u200a0 . 66 0 . 02 , p\u200a=\u200a0 . 003; 7T: mean SE\u200a=\u200a0 . 78 0 . 03 , p\u200a=\u200a0 . 002; two-tailed paired t-test; Figure 4 ) and for each individual subject ( p\u200a=\u200a0 . 01 for subject S4 , p\u200a=\u200a0 . 005 for all other subjects , permutation test; Figure 5 ) ., Remarkably , for the 7T dataset the joint frequency-specific MTF-based model was able to generalize to stimuli not used for parameter estimation ., FMRI activity from voxels in primary and non-primary auditory regions reflects the tonotopic organization of neural responses ., Therefore , as a control analysis we compared the prediction accuracy of the MTF-based model against the prediction accuracy of a tonotopy model , which incorporates the hypothesis that voxels simply reflect information about the frequency content of the stimuli ( see Materials and Methods and Figure 2 , panel C ) ., The tonotopy model performed above chance both at group level ( 3T: mean SE\u200a=\u200a0 . 62 0 . 02 , p\u200a=\u200a0 . 002; 7T: mean SE\u200a=\u200a0 . 69 0 . 03 , p\u200a=\u200a0 . 004; two-tailed paired t-test; Figure 4 ) and for each individual subject ( p\u200a=\u200a0 . 015 for subject S4 , p\u200a=\u200a0 . 005 for all other subjects , permutation test; Figure 5 ) ., However , the tonotopy model performed significantly worse than the joint frequency-specific MTF-based model ( 3T: p\u200a=\u200a0 . 009; 7T: p\u200a=\u200a0 . 007; two-tailed paired t-test ) ., The significant improvement of the MTF-based over the tonotopy model indicates that a model accounting for the joint , frequency-specific modulation content of the spectrogram is a better representation of fMRI responses to natural sounds ., To assess the relevance of frequency-localization in the encoding of joint spectro-temporal modulations , we trained a model that represents frequency and joint modulation content independently of each other ( see Materials and Methods and Figure 2 , panel A2 ) ., The joint frequency non-specific MTF-based model performed above chance both at group level ( 3T: mean SE\u200a=\u200a0 . 63 0 . 02 , p\u200a=\u200a0 . 004; 7T: mean SE\u200a=\u200a0 . 71 0 . 02 , p\u200a=\u200a0 . 0003; two-tailed paired t-test; Figure 4 ) and for each individual subject ( p\u200a=\u200a0 . 02 for subject S4 , p\u200a=\u200a0 . 01 for subject S6 , p\u200a=\u200a0 . 005 for all other subjects , permutation test ) ., However , the frequency non-specific model performed significantly worse than the frequency-specific MTF-based model ( 3T: p\u200a=\u200a0 . 002; 7T: p\u200a=\u200a0 . 021; two-tailed paired t-test ) ., In order to quantify the contribution of joint selectivity to identification performance , we trained an independent frequency-specific MTF-based encoding model ., We modelled each voxels receptive field as a combination of purely temporal and purely spectral modulation selective filters , operating in a frequency-specific fashion ( see Materials and Methods and Figure 2 , panels B and B1 ) ., The independent model performed above chance both at group level ( 3T: mean SE\u200a=\u200a0 . 63 0 . 01 , p\u200a=\u200a0 . 001; 7T: mean SE\u200a=\u200a0 . 72 0 . 02 , p\u200a=\u200a0 . 0007; two-tailed paired t-test; Figure 4 ) and for each individual subject ( p\u200a=\u200a0 . 015 for subject S4 , p\u200a=\u200a0 . 01 for subject S7 , p\u200a=\u200a0 . 005 for all other subjects , permutation test ) ., However , the independent model performed significantly worse than the joint MTF-based model ( 3T: p\u200a=\u200a0 . 012; 7T: p\u200a=\u200a0 . 011; two-tailed paired t-test ) ., As an additional control , we tested a model that simulates independent selectivity for spectral modulations , temporal modulations and frequency ( see Materials and Methods and Figure 2 , panel B2 ) ., The independent frequency non-specific model performed above chance both at group level ( 3T: mean SE\u200a=\u200a0 . 63 0 . 02 , p\u200a=\u200a0 . 002; 7T: mean SE\u200a=\u200a0 . 71 0 . 02 , p\u200a=\u200a0 . 0008; two-tailed paired t-test; Figure 4 ) and for each individual subject ( p\u200a=\u200a0 . 01 for subject S1 , S4 and S9 , p\u200a=\u200a0 . 005 for all other subjects , permutation test ) ., However , the independent frequency non-specific model performed significantly worse than the joint frequency-specific MTF-based model ( 3T: p\u200a=\u200a0 . 011; 7T: p\u200a=\u200a0 . 016; two-tailed paired t-test ) ., To investigate the cortical topography of voxels tuning properties , we computed maps of voxels characteristic spectral modulation ( CSM ) , temporal modulation ( CTM ) and frequency ( CF ) ., For each feature , the estimated MTF was marginalized across irrelevant dimensions ( i . e . spectral and temporal modulations for CF ) and the point of maximum of the marginal sum was assigned as the voxels preferred feature value ( see example in Figure 3 ) ., We obtained maps of CSM , CTM and CF by color-coding the voxels preferred values and projecting them onto an inflated representation of the subjects cortex ( see Materials and Methods ) ., Maps of CF confirmed the presence of multiple tonotopic gradients in primary auditory regions ( Heschls gyrus - HG ) and surrounding superior temporal cortex 27 ( Figure S3 and S4 ) ., The spatial distribution of voxels CSM and CTM appeared to be more complex and variable across subjects ( Figure 6 for the group and Figure S5 and S6 for all individual subjects ) ., However , the group data and the majority of the individual subjects suggested distinct regional sensitivities to modulation frequencies ( see schematic summary in Figure 7 ) ., In both hemispheres , clusters with a preference for fine spectral modulations ( high CSM , purple colors ) were primarily and consistently localized along the HG and anterior superior temporal gyrus ( STG ) ( see circles on group maps - Figure 6 ) , while clusters with a preference for coarse spectral modulations ( low CSM , orange color ) were mostly located posterior-laterally to HG , on the planum temporal ( PT ) and on STG ( see squares on group maps – Figure 6 ) ., Bilaterally , a preference for slow temporal modulations ( low CTM , orange color ) was found along HG and STG , whereas clusters with a preference for fast temporal modulations ( high CTM , purple ) were observed on the PT , posteriorly to HG and in a region medially adjacent to HG ., Supporting the spatial dissociation between spectral and temporal modulation at map level , we found a significant negative correlation between voxels characteristic spectral and temporal modulation ( 3T: mean SE\u200a=\u200a−0 . 19 0 . 01 , p\u200a=\u200a0 . 02; 7T: mean SE\u200a=\u200a−0 . 11 0 . 01 , p\u200a=\u200a0 . 01; group level random effects two-tailed t test , see Materials and Methods ) ., Our results show that the representation of natural sounds in the human auditory cortex relies on a frequency-specific analysis of combined spectro-temporal modulations ., By showing superior performance of the joint MTF-based model over the independent model , we have demonstrated that the hypothesis of independent tuning for spectral 16 and temporal modulations 30 is insufficient to account for the representation of natural sounds in the human auditory cortex ., Furthermore , the frequency-specificity that we revealed indicates that the organization of the auditory cortex according to frequency extends beyond the representation of the spectral content of incoming sounds ., We show that , at least for spectro-temporal modulations , the integration along the whole range of frequencies occurs at a later stage than the extraction of the feature itself ., The encoding mechanism that our results support is consistent with a recent study showing that a frequency-specific representation of combined spectro-temporal modulations allows the accurate reconstruction of speech in the human posterior superior temporal gyrus 31 ., The present study generalizes these observations to sounds from natural categories other than speech ., Furthermore , our results are in line with psychophysics studies showing that tuning for combined spectro-temporal modulations provides a better account of human behavior during the performance of auditory tasks 32 , 33 ., Previous neuroimaging studies had examined the processing of spectral and temporal modulations by measuring the tuning to synthetic stimuli with varying spectral modulation frequency , temporal modulation frequency or the combination of the two ., This approach suffers from two main limitations ., First , natural sounds are complex stimuli with characteristic statistical regularities 6 , 34–36 and it has been suggested that the auditory system is adapted to such regularities in order to efficiently encode sounds in natural settings 37 ., Even the most complex synthetic stimuli lack both the statistical structure and the behavioral relevance of natural sounds; therefore there is not guarantee that they engage the auditory cortex in processing that is actually used during the analysis of natural sounds ., Second , tuning per se only allows indirect inference on cortical encoding mechanisms: proofing a general computational strategy requires building a model that is able to predict brain responses to a broad range of natural stimuli 38 ., The approach that we followed in the present study allowed overcoming these limitations , therefore providing direct evidence for a specific encoding mechanism ., However , two important caveats should be mentioned ., First , by estimating a linear mapping between modulation acoustic space and fMRI responses , we only modeled the linear response properties of voxels ., One might argue that because of the linear approximation , the use of natural sounds provides no advantage over synthetic stimuli ( e . g . dynamic ripples ) ., However , it has been shown that tuning properties of both auditory 39–41 and visual 42 , 43 neurons differ significantly under natural and synthetic stimulus condition and that linear models obtained from natural stimuli predict neurons responses significantly better ., This shows that natural and synthetic stimuli activate neurons in a different manner and that , despite being an incomplete description , linear models estimated from responses to natural stimuli may be more accurate ., We suggest that this is true also for models of voxels receptive fields ., Second , it might be possible that some auditory cortical locations are selective to higher-level sound attributes ( i . e . sound categories ) that co-occur with specific spectro-temporal modulations ., As a consequence of this co-occurrence , these locations would then be assigned with a preferred temporal and spectral modulation frequency , only in virtue of their category selectivity ., To examine the role of category selectivity on our results , we performed additional analyses on the 7T dataset and tested a model that included categorical predictors together with the original MTF-based model ( Text S1 ) ., The results showed that predictions of new sounds do not improve with the inclusion of categorical information ( mean SE\u200a=\u200a0 . 76 0 . 03 ) and that estimated CTM and CSM maps do not change ( Figure S8 ) ., This analysis suggests that category tuning may result from preference to specific lower level features or combination of features ., However , it would be important to further investigate this issue and compare responses and voxels receptive fields obtained with both natural and synthetic sounds ( see 27 for a similar comparison for frequency responses ) ., Such an investigation is experimentally challenging , as it would require as many stimuli ( dynamic ripples ) as model parameters used in the present study ., However , it could be crucial for understanding the relation between acoustic and perceptual levels of sound representation in the auditory cortex ., On the basis of positron emission tomography responses to tone sequences that differed either in the temporal or spectral dimension , Zatorre and Belin 44 reported a left-hemispheric preference for rapid temporal processing and complementary preference in the right hemisphere for fine-grained spectral analysis ., While the analyses we conducted cannot exclude that hemispheric differences exist at regional level , our maps - obtained at a much higher spatial resolution and with natural sounds - suggest a more complex spatial pattern of spectral and temporal modulation preference within each hemisphere ., The most evident characteristic is that – in both the hemispheres - regions located posterior-laterally to HG ( see squares in Figure 6 and the schematic summary in Figure 7 ) preferably encode coarse spectral information with high temporal precision while regions located along HG or antero-ventrally ( see circles in Figure 6 and the schematic summary in Figure 7 ) preferably encode fine-grained spectral information with low temporal precision ., Both the two previous human neuroimaging studies that investigated tuning for combined spectro-temporal modulations with dynamic ripples ( 20 , 21 ) reported a role of anterior auditory regions in the analysis of fine spectral details , which is consistent with our observations , whereas results are less coherent for temporal modulation maps ., Again , a direct comparison between maps obtained with dynamic ripples and natural sounds would be required to address this issue ., Our results of spatial topographies for CTM and CTF support the view that the auditory cortex forms multiple ( parallel ) representations of the incoming sounds at different spectro-temporal resolutions ( 45 , 46 ) ., We suggest that this may be relevant for enabling flexible behavior , as different goal-oriented sound processing may benefit from different types of auditory representations ., Importantly , this suggestion can be tested empirically in future experiments and studies where ( natural ) sounds are presented in the context of multiple behavioral tasks ., A spectral-temporal resolution “trade-off” analogous to the one reported here has previously been described for neurons in the inferior colliculus of the cat 47 , 48 and is in agreement with the low-pass behavior of the MTF of the human auditory cortex 21 and the psychophysically derived detection thresholds for spectro-temporal modulations 9 ., Furthermore , modulation spectra of natural sounds exhibit a similar trade-off , i . e . natural sounds rarely present both high spectral and high temporal modulation frequencies 6 , 10 ., A match between stimulus statistics and neuronal response properties is generally interpreted as an evidence for the theory of efficient coding 19 , 36 , 37 , 48 , 49 ., Thus , our data provide further support to the idea that the auditory system has adapted in order to efficiently encode the statistical regularities of natural sounds ., Besides providing insights into the representation of natural sounds in the human auditory cortex , our results pave the way to future research aiming at testing increasingly complex encoding models of auditory processing ., The combination of fMRI and “encoding” techniques has proven to be a successful tool to investigate the representation of natural images in the human visual cortex 24 , 50 , 51 , as well as to predict the brain activity associated with the meaning of words 52 ., In the auditory domain , the application of such powerful method has lagged behind ., We have recently demonstrated that “encoding” makes it possible to detect the spectral tuning of voxels in the human auditory cortex from fMRI responses to natural sounds 27–29 ., In the present study , we show that models embedding more complex representations than frequency selectivity can be learned from fMRI activity ., The challenge for future studies is to explore more sophisticated voxels receptive field models ., Here we only considered voxels tuning along three stimulus dimensions ( frequency , spectral modulations and temporal modulations ) ., However , natural sounds vary in a higher dimensional acoustic space and interactions with parameters not considered here might occur ., Interestingly , we consistently observed higher prediction accuracy for the 7T compared to the 3T dataset ( Figure 4 ) , despite the fact that at 7T the model was trained and tested on independent sound ensembles ( while different presentations of the same sounds were used for the 3T data set ) ., We interpret this difference as a result of the interplay between two important factors , namely the number of stimuli and the functional contrast to noise ratio ( CNR ) ., The larger amount of different sounds employed in the 7T experiment has probably increased the variance along the dimensions represented by the model; this , together with the higher CNR and the higher spatial specificity achieved at 7T , has likely led to a more accurate model estimation , which in turn has resulted in higher prediction accuracy ., These observations provide important guidelines for the design of future experiments in this framework ., It should be mentioned that in our study , accuracy based on percent correct was significantly above chance ( 12 . 5% , 12 . 5% , 16 . 7% , 20 . 8% , 25% for subjects S6–S10 for the best performing model at 7T; chance\u200a=\u200a4 . 2% ) , but still quite small compared to the outstanding results reported in similar encoding studies in the visual domain ( e . g . 24 ) ., However , the distribution of ranks was skewed towards 1 ( correct identification ) , indicating that for most sounds the correlation between predicted and measured response was ranked very high ( e . g . second or third ) ., The lower percent correct performance for sound identification can be ascribed to a variety of reasons ., It might be due to the lower functional CNR , as BOLD responses observed in the auditory cortex are substantially lower than those in the visual cortex , probably because of the effects of the scanner noise 53 ., Furthermore , our clustered fMRI acquisition with a silent gap between scans limits the number of sounds used for training/testing the model ( compared e . g . to the number of images in 24 ) ., Finally , the model of receptive field based on spectro-temporal modulations might be too simple for allowing distinguishing two acoustically similar sounds ( e . g . two speech sounds ) ., Although the proposed combination of high field fMRI with the encoding approach is valuable for testing well-defined hypotheses on sound processing in the human brain , there are intrinsic limitations ., A voxel - even at the high spatial resolution achievable with 7T fMRI - samples a large number of neurons and the relation between the measured BOLD signal and the neural activation is only partly understood ., Results based on BOLD fMRI ( and thus fMRI encoding ) reflect a complex mixture of neuronal ( spiking and synaptic activity , excitation , inhibition ) as well as neurovascular phenomena ., In particular , neural inhibition may be associated with both positive and negative BOLD , depending on the specific neural network configuration 54 ., Understanding the neuronal dynamics underlying our fMRI observations would thus require combining electrophysiological ( at single-cell and neuronal population level ) and fMRI investigations in animal models 55 and/or humans 40 ., In summary , our study represents a first demonstration of how fMRI data and “encoding” techniques can be successfully combined to test competing computational models of auditory processing and to concurrently estimate response properties of cortical locations along multiple dimensions within an ecologically valid framework ., Also , by using a biologically inspired computational model , we pave the way for linking electrophysiology in animals and non-invasive research in humans ., The Ethical Committee of the Faculty of Psychology and Neuroscience at Maastricht University and the Institutional Review Board for human subject research at the University of Minnesota granted approval for the study at 3T and 7T respectively ., Subjects , stimuli , experimental design , MRI parameters , and data preprocessing have been reported in previous publications from our group 27–29 ( see Text S1 ) ., In the following , the most relevant details of the experimental design will be briefly described ., We used 60 ( 168 ) recordings of natural sounds for the 3T ( 7T ) experiment ., Stimuli included human vocal sounds ( both speech and non-speech , e . g . , baby cry , laughter , coughing ) , animal cries ( e . g . , dog , cat , horse ) , musical instruments ( e . g . , piano , flute , drums ) , scenes from nature ( e . g . , rain , wind , thunder ) , and tool sounds ( e . g . , keys , scissors , vacuum cleaner ) ., Sounds were sampled at 16 kHz and their duration was cut at 1000 ms . Sound onset and offset were ramped with a 10 ms linear slope , and their energy ( RMS ) levels were equalized ., The 3T and 7T experiments consisted of 3 and 8 runs , respectively; in the 3T ( 7T ) experiment , each run lasted approximately 25 ( 10 ) minutes ., In the 7T experiment , data were subdivided into six train runs and two test runs ., In the train runs , 144 of the 168 stimuli were presented with 3 repetitions overall ( i . e . each sound was presented in 3 of the 6 train runs ) ., The remaining 24 sounds were presented in the test runs and repeated 3 times per run ., Sounds were presented in the silent gap between acquisitions with a randomly assigned inter-stimulus interval of 2 , 3 , or 4 TRs - plus an additional random jitter ., Zero trials ( trials where no sound was presented; 10% of the trials in the 3T experiment; 6% ( 5% ) of the trials in train ( test ) runs in the 7T experiment ) , and catch trials ( trials in which the sound which was just heard was presented; 6% of the trials in the 3T experiment; 6% ( 3% ) of the trials in train ( test ) runs in the 7T experiment ) were included ., Subjects responded with a button press when a sound was repeated ., Catch trials were excluded from the analysis ., The stimulus representation in the modulation space was obtained as the output of a biologically inspired model of auditory processing 22 , that explicitly encodes the modulation content of a sound spectrogram ., The auditory model consists of two main components: an early stage that accounts for the transformations that acoustic signals undergo in the early auditory system , from the cochlea to the midbrain; and a cortical stage that simulates the processing of the acoustic input at the level of the ( primary ) auditory cortex ., The spectral analysis performed by the cochlea is mimicked by a bank of 128 overlapping bandpass filters with constant-Q ( Q10 dB\u200a=\u200a3 ) , equally spaced along a logarithmic frequency axis over a range of 5 . 3 oct ( f\u200a=\u200a180–7040 Hz ) ., The output of each filter enters a hair cell stage , where it undergoes high-pass filtering , optional non-linear compression and low-pass filtering ., A midbrain stage models the enhancement of frequency selectivity as a first-order derivative with respect to the frequency axis , followed by a half-wave rectification ., Finally , a short-term temporal integration ( time constant τ\u200a=\u200a8 ms ) accounts for the loss of phase locking observed in the midbrain ., The | Introduction, Results, Discussion, Materials and Methods | Functional neuroimaging research provides detailed observations of the response patterns that natural sounds ( e . g . human voices and speech , animal cries , environmental sounds ) evoke in the human brain ., The computational and representational mechanisms underlying these observations , however , remain largely unknown ., Here we combine high spatial resolution ( 3 and 7 Tesla ) functional magnetic resonance imaging ( fMRI ) with computational modeling to reveal how natural sounds are represented in the human brain ., We compare competing models of sound representations and select the model that most accurately predicts fMRI response patterns to natural sounds ., Our results show that the cortical encoding of natural sounds entails the formation of multiple representations of sound spectrograms with different degrees of spectral and temporal resolution ., The cortex derives these multi-resolution representations through frequency-specific neural processing channels and through the combined analysis of the spectral and temporal modulations in the spectrogram ., Furthermore , our findings suggest that a spectral-temporal resolution trade-off may govern the modulation tuning of neuronal populations throughout the auditory cortex ., Specifically , our fMRI results suggest that neuronal populations in posterior/dorsal auditory regions preferably encode coarse spectral information with high temporal precision ., Vice-versa , neuronal populations in anterior/ventral auditory regions preferably encode fine-grained spectral information with low temporal precision ., We propose that such a multi-resolution analysis may be crucially relevant for flexible and behaviorally-relevant sound processing and may constitute one of the computational underpinnings of functional specialization in auditory cortex . | How does the human brain analyze natural sounds ?, Previous functional neuroimaging research could only describe the response patterns that sounds evoke in the human brain at the level of preferential regional activations ., A comprehensive account of the neural basis of human hearing , however , requires deriving computational models that are able to provide quantitative predictions of brain responses to natural sounds ., Here , we make a significant step in this direction by combining functional magnetic resonance imaging ( fMRI ) with computational modeling ., We compare competing computational models of sound representations and select the model that most accurately predicts the measured fMRI response patterns ., The computational models describe the processing of three relevant properties of natural sounds: frequency , temporal modulations and spectral modulations ., We find that a model that represents spectral and temporal modulations jointly and in a frequency-dependent fashion provides the best account of fMRI responses and that the functional specialization of auditory cortical fields can be partially accounted for by their modulation tuning ., Our results provide insights on how natural sounds are encoded in human auditory cortex and our methodological approach constitutes an advance in the way this question can be addressed in future studies . | null | null |
journal.pcbi.1003060 | 2,013 | Coalescent Tree Imbalance and a Simple Test for Selective Sweeps Based on Microsatellite Variation | Consider the coalescent tree for a sample of size ., It is a binary tree without left-right orientation , with ordered internal nodes and branch lengths representing a measure of time ., All leaves are aligned on the bottom line , representing the present ., We use the term tree topology when talking about the branching pattern and tree shape when talking about topology and branch lengths ., We remark that topology and shape can be conceptually distinguished , but in practice estimating topology relies on polymorphism patterns ., Since these depend on branch lengths , i . e . on shape , topology can usually not be estimated independently ., We call the size of a tree the number of leaves and the length of a tree the combined length of all branches ., The height is the time interval between present and root , indicated by in Figure 1 ., Let the label of the root be ., The leaves can be grouped into two disjoint sets , and , the ‘left-‘ and ‘right-descendants’ of the root ., Let be the smaller of the two sets and ., Hence , ., Let be the ‘right’ child of , i . e . the root of the subtree with leaf set ., The descendants of can again be grouped into two disjoint subsets , and , the left- and right-descendants of ., Again , without loss of generality , let and denote ., Hence , ., Proceed in this way to define subsets , , and so on ., For any tree there are such pairs where , with depending on the topology of the tree ., The set constitutes a – not necessarily unique – top-down sequence of maximal subtrees ., Consider a coalescent tree of size under the neutral model with constant population size , where is assumed to be large ., Root imbalance is measured by the random variable ., The distribution of is ‘almost’-uniform 18 , 19 on ., More precisely , ( 1 ) where ., , ., denotes here the Kronecker symbol ., The expectation isThe variance isand the standard deviationprovided is sufficiently large ., The compound random variables , , have support which depends on , ., More precisely , the distribution of , given , , is almost-uniform on with ( 2 ) where ( ) is a random variable which is bounded below by and above by ., The moments are somewhat more complicated ., For instance , Continuing this way , evaluating sums iteratively and using the above approximation , one derives ( 3 ) Similary , one can obtain the second moments and combine these to ( 4 ) Define now the normalized random variables ., Since is a constant , we have for andTo calculate the moments of , , we replace by ., Simulations suggest that this is acceptable , as long as is not too small ., Figure 2 shows this fact for ., Here we focus on for , where is small and is large ( , , say ) ., Since , we obtain ( 5 ) Similarly , ( 6 ) andIt is very convenient to work with the normalized random variables instead of ., Their support is bounded by and for all and they are well approximated by independent continuous uniforms on the unit interval ., This considerably facilitates the handling of sums and products of ., For instance , the joint distribution of is then approximated by the continuous uniform product with distribution function ( 7 ) expectationand varianceThe coefficient of variation , , isAs is well known , the normalized sum of continuous uniforms converges in distribution to a normal random variable rather quickly ., In fact , we have for the standardized sum ( 8 ) In practice , already yields a distribution which is reasonably close to a normal ( see Suppl . Figure S1 ) ., A positively selected allele sweeping through a population leads to a drastic reduction of tree height due to its short fixation time ( see Figure 1C ) ., The fixation time depends on the selection coefficient and population size ., In units of , , where 23 ., This is much smaller than the neutral average fixation time ., The reduced fixation time leads to a severe reduction of genetic variability ., Furthermore , external branches of the tree are elongated relative to internal branches , yielding a star-like phylogeny of an approximate length of ., Replacing the neutral tree length in eq ( 9 ) by this figure , we obtain the following estimate for the correlation half-life ( 10 ) For the parameters used in Figure 3 , we have bp , which agrees well with the simulation result ., In contrast to tree height and length , tree topology at the selected site does not necessarily differ from a neutral tree; only when moving away from the sweep site , and with recombination , topology may drastically change ., In fact , given a shallow tree , recombination leads with high probability to an increase of tree height and to unbalanced trees 15 ., Thus , recombination events next to the selected site tend to increase tree height ( see sketch in Figure 1B and C ) and to create a bias in favour of unbalanced trees , i . e . trees with small ( Figure 4A ) ., The expected proximal distance from the selected site of such a recombination event can be estimated as ( 11 ) where , is the per site recombination rate , and is the length of a star-like phylogeny; the factor accounts for the fact that it is more likely to recombine with an ancestral chromosome ( thereby increasing tree height ) as long as these are more abundant than the derived chromosomes carrying the selected allele ., Roughly , this is the case during the first half of the fixation time ., Assuming instead of the star phylogeny a random tree topology of average length at the selected site , one obtains the larger ( call it distal ) estimate ( 12 ) where ., Unbalanced trees tend to have strongly elongated root branches and harbor an over-abundance of high frequency derived SNP alleles 6 , 16 ., With microsatellites it is usually not possible to determine the ancestral and derived states of an allele , because they mutate at a high rate and possibly undergo back-mutation ., However , under the symmetric single step mutation model , the expected distance between a pair of alleles ( in terms of motif copy numbers ) behaves as the distance in a one-dimensional symmetric random walk and therefore increases at a rate proportional to the square root of the scaled mutation rate ( see Methods ) ., Thus , alleles which are separated by long root branches tend to form two distinct allele clusters ., Tree topology is ususally not directly observable and has to be estimated from data ., We focus on estimating , , from microsatellite data ., Given a sample of microsatellite alleles with tandem repeat counts , , we use UPGMA 24 to construct a hierarchical cluster diagram ., If subtree topology within a particular cluster node should not be uniquely re-solvable , for instance if alleles are identical , we randomly assign the alleles of the subtree under consideration to two clusters with equal probability ., This gives preference to clusters of balanced size in case of insufficient resolution ., We then use the inferred tree topology to estimate of the true tree ., This procedure is conservative for the test statistics described below , since it gives preference to large values when the true value is small ( Figure 4 , column A ) ., For a cluster pair , , define the distance as ( 13 ) We find that UPGMA clustering gives good estimates of when clusters are clearly separated from each other , i . e . when ., Let be the indicator variable for this event ., Then , we have for the median ( Figure 4 , column B ) ., Without requiring the estimate is more biased ., In part , this is due to the conservative UPGMA strategy mentioned above ., However , estimation of is very accurate when root branches are strongly elongated , i . e . under conditions of selective sweeps or certain bottlenecks ( Figure 4 , bottom ) ., We now turn to an application of the above results and explain how a new class of microsatellite based tests of the neutral evolution hypothesis can be defined ., Consider a sample of alleles at a microsatellite marker and record their motif repeat numbers ., Applying UPGMA clustering to the alleles , we obtain estimates , as described above ., These are transformed to ., Then , we determine the following test statistics ( 14 ) ( 15 ) ( 16 ) Thus , the test variable in eq ( 14 ) is the estimate of given in eq ( 8 ) ., Similarly , and are the estimates of the product and of ., We now test the null hypothesis for a critical value ., For a given level we obtain the critical value for from the standard normal distribution and for from the uniform product distribution in eq ( 7 ) ( Table 1 ) ., For we use the critical value of the normalized version of eq ( 1 ) ., Generally , these critical values are conservative , since tends to over-estimate , when small ( Figure 4 ) ., In particular , statistic is very conservative due to the additional condition on the distance ., The true critical values for level would be larger than those shown in Table 1 ., Emergence of drug resistance in malaria parasites is among the best documented examples for recent selective sweeps ., We re-analyzed microsatellite markers surrounding a well studied drug resistance locus of malaria parasites 29 ( Figure 7 ) ., The signature of recent positive selection is consistently detected by all tests on two markers somewhat downstream of the drug resistance locus pfmdr1 ( marker l– and l– in the notation of 29; Table 5 ) ., Highest significance is reported by test ( -value close to ) ., reports a -value of and reports -values slightly above ., In addition , reports locus l– ( located upstream of pfmdr1 ) to be significant at ., This locus is also detected by ( ) ., Other four loci are reported only by ( l– ( ) , l– ( ) , l– ( ) , l– ( ) and l– ( ) ) ., Discrepancies in the test results are due to their different sensitivities to various parameters ., The simple and compound tests have different power profiles with power peaks at different positions from the selected site ( Figure 6 ) ., Plasmodium in South-East Asia is most likely expanding and sub-structured; however , there is only limited knowledge about the details ., As shown above , is quite sensitive to biased sampling from different sub-populations ., Some of the significant results of may be inflated due to sub-structure ., There is also some disagreement between tests and regarding significance , although both test imbalance at tree nodes , and ., In fact , the cases reported by the two tests may still differ in their details ., Comparing the three components , and with respect to their maximum and minimum , we find that the cases reported as significant by have a and a up to ., In contrast , for , the maximum is close to while the minimum tends to be less than ( Figure S4 ) ., Thus , test is more restrictive in the sense that all components , and have to be small to yield a significant result ., is more permissive and accepts that one of the three components may be large ., All tests agree on significance of two markers close to a site which was previously shown to have experienced a selective sweep ., They also agree all on strongly increased -values in the immediate vicinity of the selected site ( l– , l– ) ., Together , these results confirm the accuracy and practical utility of our tests ., The binary coalescent has a number of well-studied combinatoric and analytic properties 1 , 30 , 31 ., Here we only concentrate on tree topology and use a classic result of Tajima 19 to define a simple measure , , of tree balance ., It is the minimum of the left and right subtree sizes under internal node ., Its normalized version is approximately uniform on the unit interval and the summation over internal nodes , , is close to normal ., Another summary statistic of tree balance is Colless index 32 ., It also depends on the sizes of left- and right subtrees of the internal nodes , but its distribution is more complicated ., has received attention in the biological literature before 33 and , more recently , in theoretical studies , for instance by Blum&Janson 34 ., A problem with Colless index is that it is difficult to estimate if the true tree structure is unknown ., But , limiting attention to the tree structure close to the root , we show that the balance measure can be estimated , for instance , from microsatellite allele data by a clustering method ., We found that a version of UPGMA clustering gives most reliable results ., Coalescent trees for linked loci are not independent ., However , correlation dissipates with recombinational distance ., In fact , under neutral conditions only about ten recombination events are sufficient to reduce correlation in tree topology by 50% ., Thus , estimating tree imbalance at multiple microsatellites can be performed independently for each marker , if they are sufficienty distant from each other ., Conversely , with a very small number of recombination events , is not drastically altered on average 15 ., Thus , when working with SNPs , one may afford to consider haplotype blocks containing a few more recombination events than segragting sites and still be able to reconstruct a reliable gene genealogy ., This possibility will be explored in more detail elsewhere ., Microsatellites have been used before as markers for selective sweeps ., Schlötterer et al . 35 have proposed the lnRH statistic to detect traces of selection and Wiehe et al . 28 have shown that a multi-locus vesion of lnRH for linked markers can yield high power while keeping false positive rates low ., However , a severe practical problem with the lnRH statistic is that it requires data from two populations , and for each of them two additional and independent sets of neutral markers for standardization ., There are a few methods to detect deviations from the standard neutral model based on single microsatellite locus data from one population ., For instance , the test by Cornuet and Luikart 36 , which compares observed and expected heterozygosity , is designed to detect population bottlenecks ., A test by Schlötterer et al . 37 uses the number of alleles at a microsatellite locus and determines whether an excess of the number of alleles is due to positive selection ( SKD test ) ., However , as the authors pointed out , the test depends critically on a reliable locus-specific estimate of the scaled mutation rate ., We have compared SKD and the test proposed here with respect to power and false positive rates ., While the SKD-test is generally more powerful , especially at larger distances from the selected site ( Table 4 and Suppl . Tables S1 , S5 ) , it has higher false positive rates than the tests proposed here , in particular when compared to ( Suppl . Table S6 ) , and for non-standard mutation models ( Suppl . Tables S13 , S14 ) ., Note also that under population sub-structure SKD yields up to times more false positives than our tests ( Suppl . Tables S9 to S12 ) ., It should be emphasized that it is the topology of the underlying genealogical tree , not the genetic variation , which constitutes the basis for the test statistics proposed here ., The two steps , estimating topology , and performing the test are two distinct tasks ., The quality of the tests hinges on the quality of the re-constructed genealogy ., With a perfectly re-constructed genealogy the false positive rates are completely independent from any evolutionary mechanisms which do not affect the average topology , such as historic changes of population size ., However , simulations show that power would still remain under 100% in this case ., The robustness of topology based tests with respect to demographic changes has been shown before by Li 16 for a similar test which uses SNP data to reconstruct ., But Lis test can only be performed if an additional non-topological criterion is satisfied and thus can only test a subset of trees with ., The tests and defined here rely only on topological properties of the genelaogy and we argue that multi-allelic markers , such as microsatellites , help estimating the true genealogy and improving test results ., Although our analyses and simulations are based on the binary Kingman 1 coalescent , we expect that the new test statistics should be robust also under more general coalescent models , for instance when multiple mergers during the selective sweep phase are allowed 38 ., Despite a shift to high throughput sequencing technologies in the last decade , microsatellite typing continues to be a cost-efficient and fast alternative to survey population variability in many experimental studies ., This is in particular true for projects directed towards parasite typing , e . g . of Plasmodium , and projects with non-standard model organisms , e . g . social insects 39 , 40 , but also for many biomedical studies ., We simulated population samples under neutral and hitchhiking models with modified versions of the procedures described by Kim and Stephan 41 and Li and Stephan 42 and of ms 43 , termed msmicro ., In the modified versions we incorporated evolution of microsatellite loci under the symmetric , single step and multi-step mutation models ., Microsatellite mutations are modeled as changes to the number of motif repeats , where only numbers but not particular sequence motifs are recorded ., Output data comprise coalescent trees in Newick format and the state of microsatellite alleles for each of sequences ., With msmicro also multiple linked microsatellites can be modeled ., Coalescent simulations were run under different evolutionary conditions: neutral with constant population size ( ) , neutral with bottleneck ( bottleneck severity , time since bottleneck ) , population size expansion ( growth rate ) , neutral two-island model with migration , and hard selective sweeps ( selection and , time since fixation of sweep allele ) ., Realizations of the ‘true’ random variables , were extracted from the simulation results ., Estimation of was performed by UPGMA hierarchical clustering ., If a cluster node could not be uniquely resolved then we gave preference to a bi-partite partition in which the left and right subtrees were of equal or similar size ., This was accomplished by randomly assigning alleles to two clusters with equal probability ., To estimate we also explored a simple clustering method which works in the following way: we first sorted alleles by size; then we divided the sorted list into two halfs ., The separator was placed between those two alleles which had maximal distance ( in terms of microsatellite repeat units ) from each other ., If this was not unique , the separator was placed between those two alleles that resulted in two sets of most similar size ., While this clustering method is very effective in estimating , it is less accurate than UPGMA clustering for , ., The single step symmetric mutation model behaves as a one-dimensional symmetric random walk of step size one ., The theory of random walks ( e . g . 44 ) tells that the average distance between the origin of the walk and the current position scales with the square root of the number of steps ., More precisely , The variance is linear in ., Here , steps are represented by mutational events occuring at rate ., Thus , and , where is Eulers constant ., The empirical distance between two clusters and can be calculated as | Introduction, Results, Discussion, Methods | Selective sweeps are at the core of adaptive evolution ., We study how the shape of coalescent trees is affected by recent selective sweeps ., To do so we define a coarse-grained measure of tree topology ., This measure has appealing analytical properties , its distribution is derived from a uniform , and it is easy to estimate from experimental data ., We show how it can be cast into a test for recent selective sweeps using microsatellite markers and present an application to an experimental data set from Plasmodium falciparum . | It is one of the major interests in population genetics to contrast the properties and consequences of neutral and non-neutral modes of evolution ., As is well-known , positive Darwinian selection and genetic hitchhiking drastically change the profile of genetic diversity compared to neutral expectations ., The present-day observable genetic diversity in a sample of DNA sequences depends on events in their evolutionary history , and in particular on the shape of the underlying genealogical tree ., In this paper we study how the shape of coalescent trees is affected by the presence of positively selected mutations ., We define a measure of tree topology and study its properties under scenarios of neutrality and positive selection ., We show that this measure can reliably be estimated from experimental data , and define an easy-to-compute statistical test of the neutral evolution hypothesis ., We apply this test to data from a population of the malaria parasite Plasmodium falciparum and confirm the signature of recent positive selection in the vicinity of a drug resistance locus . | evolutionary modeling, genetics, population genetics, biology, computational biology | null |
journal.pgen.1008088 | 2,019 | Mutations in PIK3C2A cause syndromic short stature, skeletal abnormalities, and cataracts associated with ciliary dysfunction | Identifying the genetic basis of diseases with Mendelian inheritance provides insight into gene function , susceptibility to disease , and can guide the development of new therapeutics ., To date , ~50% of the genes underlying Mendelian phenotypes have yet to be discovered 1 ., The disease genes that have been identified thus far have led to a better understanding of the pathophysiological pathways and to the development of medicinal products approved for the clinical treatment of such rare disorders 2 ., Furthermore , technological advances in DNA sequencing have facilitated the identification of novel genetic mutations that result in rare Mendelian disorders 3 , 4 ., We have applied these next-generation sequencing technologies to discover mutations in PIK3C2A that cause a newly identified genetic syndrome consisting of dysmorphic features , short stature , cataracts and skeletal abnormalities ., PIK3C2A is a class II member of the phosphoinositide 3-kinase ( PI3K ) family of lipid kinases that catalyzes the phosphorylation of phosphatidylinositol ( PI ) 5 ., PI3Ks are part of a larger regulatory network of kinases and phosphatases that act upon the hydroxyl groups on the inositol ring of PI to add or remove a phosphate group 6 ., The combinatorial nature of phosphorylation at the -3 , -4 , and -5 position of the inositol ring gives rise to seven different PI species , termed polyphosphoinositides ., Among these polyphosphoinositides , class II PI3Ks are generally thought to catalyze the phosphorylation of PI and/or PI ( 4 ) P to generate PI ( 3 ) P and PI ( 3 , 4 ) P2 , respectively 7 ., PI ( 3 ) P , PI ( 3 , 4 ) P2 , and the other polyphosphoinositides each account for less than ~1% of the total phospholipid content of a cell 8 ., However , despite their relatively low abundance , they play central roles in a broad array of signaling pathways and are central to the pathophysiology underlying cancer , metabolic disease , and host-pathogen interactions 6 ., The functions of class II PI3Ks are poorly understood relative to many other kinases and phosphatases that regulate PI metabolism , in part because there was no causal link between any class II PI3K and a monogenic human disease ., In contrast , a number of disorders of PI metabolism have previously been described that have provided invaluable insight into the physiological functions of specific PI metabolizing enzymes 9 ., These include Charcot-Marie-Tooth type 4J ( FIG4 ) 10 , 11 , Centronuclear X-linked myopathy ( MTM1 ) 12 , and primary immunodeficiency ( PIK3CD ) 13 , 14 , among others ., As just one example , detailed studies of FIG4 subsequent to its identification as a cause of Charcot-Marie-Tooth type 4J have revealed both genetic and physiological interactions with VAC14 and PIKFYVE , which together generate PI ( 3 , 5 ) P2 and are required for melanosome homeostasis , oligodendrocyte differentiation , and remyelination 15–18 ., Collectively , the array of PI metabolism disorders is striking for its phenotypic diversity , affecting a wide range of organ systems including those described above as well as others that lead to neuromuscular , skeletal , renal , eye , growth , and immune disorders ., The diversity of phenotypic manifestations resulting from PI metabolism defects highlights the lack of functional redundancy between genes that regulate nominally the same enzymatic transformation of PIs ., PIK3C2A has previously been attributed a wide-range of biological functions including glucose transport , angiogenesis , Akt activation , endosomal trafficking , phagosome maturation , mitotic spindle organization , exocytosis , and autophagy 19–28 ., In addition , PIK3C2A is critical for the formation and function of primary cilia 23 , 26 ., However , as mentioned above , there is as yet no link between PIK3C2A or any class II PI3K and a Mendelian disorder ., Here , we describe the evidence that homozygous loss-of-function mutations in PIK3C2A cause a novel syndromic disorder involving neurological , visual , skeletal , growth , and occasionally hearing impairments ., Five individuals between the ages of 8 and 21 from three unrelated consanguineous families were found by diagnostic analyses to have a similar constellation of clinical features including dysmorphic facial features , short stature , skeletal and neurological abnormalities , and cataracts ( Fig 1 , Table 1 , S1 Table ) ., The dysmorphic facial features included coarse facies , low hairline , epicanthal folds , flat and broad nasal bridges , and retrognathia ( S1 Table ) ., Skeletal findings included scoliosis , delayed bone age , diminished ossification of femoral heads , cervical lordosis , shortened fifth digits with mild metaphyseal dysplasia and clinodactyly , as well as dental findings such as broad maxilla incisors , narrow mandible teeth , and enamel defects ( Fig 1B and 1C , S1 Table , S1 Fig ) ., Most of the affected individuals exhibited neurological involvement including developmental delay and stroke ., This was first seen in individual I-II-2 when she recently started having seizures , with an EEG demonstrating sharp waves in the central areas of the right hemisphere and short sporadic generalized epileptic seizures ., Her brain MRI showed a previous stroke in the right corpus striatum ( Fig 1E ) ., Hematological studies were normal for hypercoagulability and platelet function ( S2 Table ) ., In addition , brain MRI of patient II-II-3 showed multiple small frontal and periventricular lacunar infarcts ( S1E Fig ) ., Unclear episodes of syncope also led to neurological investigations including EEG in individual III-II-2 , without any signs of epilepsy ., Her brain MRI showed symmetrical structures and normal cerebrospinal fluid spaces but pronounced lesions of the white matter ( S1E Fig ) ., Other recurrent features included hearing loss , secondary glaucoma , and nephrocalcinosis ., In addition to the shared syndromic features described above in all three families , both affected daughters in Family I were diagnosed with congenital adrenal hyperplasia ( CAH ) , due to 17-alpha-hydroxylase deficiency , and were found to have a homozygous familial mutation: NM_000102 . 3:c . 286C>T; p ., ( Arg96Trp ) in the CYP17A1 gene ( OMIM #202110 ) 29 , 30 ., The affected individuals in Families II and III do not carry mutations in CYP17A1 or have CAH , suggesting the presence of two independent and unrelated conditions in Family I ., The co-occurrence of multiple monogenic disorders is not uncommon among this highly consanguineous population 31 ., To identify the genetic basis of this disorder , enzymatic assays related to the mucopolysaccharidosis subtypes MPS I , MPS IVA , MPS IVB , and MPSVI were tested in Families I and II and found to be normal ., Enzymatic assays for mucolipidosis II/III were also normal and no pathogenic mutations were found in galactosamine-6-sulfate sulfatase ( GALNS ) in Family I . Additionally , since some of the features of patient II-II-3 were reminiscent of Noonan syndrome , Hennekam syndrome , and Aarskog-Scott syndrome , individual genes involved in these disorders were analyzed in Family II , but no pathogenic mutation was identified ., In patient III-II-2 , Williams-Beuren syndrome was excluded in childhood ., Additionally , direct molecular testing at presentation in adulthood excluded Leri-Weill syndrome , Alstrom disease , and mutations in FGFR3 ., Given the negative results of targeted genetic testing , WES and CNV analysis was performed for the affected individuals from all three families ., Five homozygous candidate variants were identified in Family I , including the CYP17A1 ( p . Arg96Trp ) mutation that is the cause of the CAH 29 , 30 , but is not known to cause the other phenotypes ., The remaining four variants affected the genes ATF4 , DNAH14 , PLEKHA7 , and PIK3C2A ( S3 Table ) ., In Family II , homozygous missense variants were identified in KIAA1549L , METAP1 , and PEX2 , in addition to a homozygous deletion in PIK3C2A that encompassed exons 1–24 out of 32 total exons ( S3 Table ) ., The deletion was limited to PIK3C2A and did not affect the neighboring genes ., Sequence analysis of Family III showed a homozygous missense variant in PTH2R , nonsense variant in DPRX , and splice site variant in PIK3C2A ( S3 Table ) ., Sequencing analyses revealed that all affected family members in the Families I , II , and III were homozygous for predicted loss-of-function variants in PIK3C2A , and none of the unaffected family members were homozygous for the PIK3C2A variants ( Fig 1A and 1G ) ., The initial link between these three families with rare mutations in PIK3C2A was made possible through the sharing of information via the GeneMatcher website 3 ., The PIK3C2A deletion in Family II was confirmed by multiplex amplicon quantification ( S2A Fig ) ., The single nucleotide PIK3C2A variants in Families I and III were confirmed by Sanger sequencing ( S2B and S2C Fig ) ., In Family I , the nonsense mutation in PIK3C2A ( p . Tyr195* ) truncates 1 , 492 amino acids from a protein that is 1 , 686 amino acids ., This is predicted to eliminate nearly all functional domains including the catalytic kinase domain , and is expected to trigger nonsense-mediated mRNA decay 25 ., Accordingly , levels of PIK3C2A mRNA are significantly decreased in both heterozygous and homozygous individuals carrying the p . Tyr195* variant ( Fig 2A ) ., The deletion in Family II eliminates the first 24 exons of the 32-exon PIK3C2A gene and is thus predicted to cause a loss of protein expression ., This is consistent with a lack of PIK3C2A mRNA expression ( Fig 2B ) ., The variant in PIK3C2A in Family III affects an essential splice site ( c . 1640+1G>T ) that leads to decreased mRNA levels ( Fig 2C ) ., Deep sequencing of the RT-PCR products revealed 4 alternative transcripts in patient-derived lymphocytes ( p . Asn483_Arg547delinsLys , Ala521Thrfs*4 , Ala521_Glu568del , and Arg547SerinsTyrIleIle* ) of which the transcript encoding p ., Asn483_Arg547delinsLys that skips both exons 5 and 6 was also observed in patient’s fibroblasts ( S3 Fig ) ., Although this transcript remains in-frame , no PIK3C2A protein was detected by Western blotting ( Fig 2D and 2F ) ., This is consistent with Families I and II , for which Western blotting also failed to detect any full-length PIK3C2A in fibroblasts from the affected homozygous children ( Fig 2E and 2F ) ., Thus , all three PIK3C2A variants likely encode loss-of-function alleles ., Importantly , among the 141 , 456 WES and whole genome sequences from control individuals in the Genome Aggregation Database ( gnomAD v2 . 1 ) 32 , none are homozygous for loss-of-function mutations in PIK3C2A , which is consistent with total PIK3C2A deficiency causing severe early onset disease ., To test whether the observed loss-of-function mutations in PIK3C2A cause cellular phenotypes consistent with loss of PIK3C2A function , we examined PI metabolism , cilia formation and function , and cellular proliferation rates ., PIK3C2A deficiency in the patient-derived fibroblasts decreased the levels of PI ( 3 , 4 ) P2 throughout the cell ( Fig 3A ) as well as decreased the levels of PI ( 3 ) P at the ciliary base ( Figs 3B and S4A ) ., The reduction in PI ( 3 ) P at the ciliary base was associated with a reduction in ciliary length ( Fig 4A ) , although the percentage of ciliated cells was not altered ( Fig 4B ) ., Additional cilia defects include a reduction in the levels of RAB11 at the ciliary base ( Figs 4C and S4B ) , which functions within a GTPase cascade culminating in the activation of RAB8 , which together with ARL13B selectively traffics ciliary proteins to the cilium 33 ., Additionally , there was increased accumulation of IFT88 along the length of the cilium ( Figs 4D and S4C ) , which is a component of the intraflagellar transport sub-complex IFT-B , and is essential for the trafficking of ciliary protein cargoes along the axonemal microtubules 34 , 35 ., Together , these findings are suggestive of defective trafficking of ciliary components ., Finally , the proliferative capacity of PIK3C2A deficient cells was reduced relative to control cells ( Fig 5 ) ., As PIK3C2A is a member of the class II PI3K family , we tested whether the expression of the other family members PIK3C2B and PIK3C2G were altered by PIK3C2A deficiency ., The expression of PIK3C2G was not detected by qRT-PCR in either patient-derived or control primary fibroblasts ., This is consistent with the relatively restricted expression pattern of this gene in the GTEx portal 36 , with expression largely limited to stomach , skin , liver , esophagus , mammary tissue , and kidney , but absent in fibroblast cells and most other tissues ., In contrast , PIK3C2B expression was detected , with both mRNA and protein levels significantly increased in PIK3C2A deficient cells ( Fig 6A–6D ) ., Downregulation of PIK3C2A using an inducible shRNA in HeLa cells also resulted in elevated levels of PIK3C2B ( Fig 6E ) ., Together , these data are consistent with increased levels of PIK3C2B serving to partially compensate for PIK3C2A deficiency ., Here we describe the identification of three independent families with homozygous loss-of-function mutations in PIK3C2A resulting in a novel syndrome consisting of short stature , cataracts , secondary glaucoma , and skeletal abnormalities among other features ., Patient-derived fibroblasts had decreased levels of PI ( 3 , 4 ) P2 and PI ( 3 ) P , shortening of the cilia and impaired ciliary protein localization , and reduced proliferation capacity ., Thus , based on the loss-of-function mutations in PIK3C2A , the phenotypic overlap between the three independent families , and the patient-derived cellular data consistent with previous studies of PIK3C2A function , we conclude that loss-of-function mutations in PIK3C2A cause this novel syndrome ., The identification of PIK3C2A loss-of-function mutations in humans represents the first mutations identified in any class II PI-3-kinase in a disorder with a Mendelian inheritance , and thus sheds light into the biological role of this poorly understood class of PI3Ks 7 , 37 ., This is significant not only for understanding the role of PIK3C2A in rare monogenic disorders , but also the potential contribution of common variants in PIK3C2A in more genetically complex disorders ., There are now numerous examples where severe mutations in a gene cause a rare Mendelian disorder , whereas more common variants in the same gene , with a less deleterious effect on protein function , are associated with polygenic human traits and disorders 38–40 ., For example , severe mutations in PPARG cause monogenic lipodystrophy , whereas less severe variants are associated with complex polygenic forms of lipodystrophy 41 , 42 ., In the case of PIK3C2A deficiency , the identification of various neurological features including developmental delay , selective mutism , and the brain abnormalities detected by MRI ( S1 Table ) may provide biological insight into the mechanisms underlying the association between common variants in PIK3C2A and schizophrenia 43–45 ., Other monogenic disorders of phosphoinositide metabolism include Lowe’s syndrome and Joubert syndrome , which can be caused by mutations in the inositol polyphosphate 5-phosphatases OCRL and INPP5E , respectively 46 ., All three of these disorders of PI metabolism affect some of the same organ systems , namely the brain , eye , and kidney ., However , the phenotype associated with mutations in INPP5E is quite distinct , and includes cerebellar vermis hypo-dysplasia , coloboma , hypotonia , ataxia , and neonatal breathing dysregulation 47 ., In contrast , the phenotypes associated with Lowe’s syndrome share many of the same features with PIK3C2A deficiency including congenital cataracts , secondary glaucoma , kidney defects , skeletal abnormalities , developmental delay , and short stature 9 , 48 ., The enzyme defective in Lowe’s syndrome , OCRL , is functionally similar to PIK3C2A as well , as it is also required for membrane trafficking and ciliogenesis 49 ., The similarities between Lowe’s syndrome and PIK3C2A deficiency suggest that similar defects in phosphatidylinositol metabolism may underlie both disorders ., In addition to Lowe’s syndrome , there is partial overlap between PIK3C2A deficiency and yet other Mendelian disorders of PI metabolism such as the early-onset cataracts in patients with INPP5K deficiency 50 , 51 , demonstrating the importance of PI metabolism in lens development ., The viability of humans with PIK3C2A deficiency is in stark contrast to mouse Pik3c2a knockout models that result in growth retardation by e8 . 5 and embryonic lethality between e10 . 5–11 . 5 due to vascular defects 20 ., One potential explanation for this discrepancy is functional differences between human PIK3C2A and the mouse ortholog ., However , the involvement of both human and mouse PIK3C2A in cilia formation , PI metabolism , and cellular proliferation suggests a high degree of functional conservation at the cellular level 26 , 28 ., An alternate possibility is that the species viability differences associated with PIK3C2A deficiency result from altered compensation from other PI metabolizing enzymes ., For instance , there are species-specific differences between humans and mice in the transcription and splicing of the OCRL homolog INPP5B that may uniquely contribute to PI metabolism in each species 52 ., Alternately , PIK3C2B levels were significantly increased in human PIK3C2A deficient cells , including both patient-derived cells and HeLa cells surviving PIK3C2A deletion , suggesting that this may partially compensate for the lack of PIK3C2A in humans , although it remains to be determined whether a similar compensatory pathway exists in mice ., It is intriguing that both PIK3C2A and OCRL have important roles in primary cilia formation 26 , 53 , 54 ., Primary cilia are evolutionary conserved microtubule-derived cellular organelles that protrude from the surface of most mammalian cell types ., Primary cilia formation is initiated by a cascade of processes involving the targeted trafficking and docking of Golgi-derived vesicles near the mother centriole ., They play a pivotal role in a number of processes , such as left-right patterning during embryonic development , cell growth , and differentiation ., Abnormal phosphatidylinositol metabolism results in ciliary dysfunction 55 , including loss of PIK3C2A that impairs ciliogenesis in mouse embryonic fibroblasts , likely due to defective trafficking of ciliary components 26 ., The importance of primary cilia in embryonic development and tissue homeostasis has become evident over the two past decades , as a number of proteins which localize to the cilium harbor defects causing syndromic diseases , collectively known as ciliopathies 56 , 57 ., Hallmark features of ciliopathies share many features with PIK3C2A deficiency and include skeletal abnormalities , progressive vision and hearing loss , mild to severe intellectual disabilities , polydactyly , and kidney phenotypes ., Many of these disorders , including Bardet-Biedl Syndrome , Meckel Syndrome , and Joubert Syndrome are also associated with decreased cilium length 58 , as seen in PIK3C2A deficient cells ., Ciliary length is a function of both axoneme elongation and cilium disassembly , and is molecularly regulated by intraflagellar protein transport , including the velocity of transport and cargo loading , as well as soluble tubulin levels and microtubule modifications 59 , 60 ., As defects in intraflagellar protein transport were likely indicated by abnormal IFT88 localization along the length of the cilium in PIK3C2A deficient cells , this may represent a potential mechanism underlying the shortened cilium ., Further work and the identification of additional patients with mutations in PIK3C2A will continue to improve our understanding of the genotype-phenotype correlation associated with PIK3C2A deficiency ., However , the identification of the first patients with PIK3C2A deficiency establishes a role for PIK3C2A in neurological and skeletal development , as well as vision , and growth and implicates loss-of-function PIK3C2A mutations as a potentially new cause of a cilia-associated disease ., The study was approved by the Helsinki Ethics Committees of Rambam Health Care Campus ( #0038-14-RMB ) , the University Hospital Institutional Review Board for Case Western Reserve University ( #NHR-15-39 ) , the Ethics Committee of the Friedrich-Alexander University Erlangen-Nürnberg ( #164_15 B ) , and was in accordance with the regulations of the University Medical Center Groningen’s ethical committee ., Written informed consent was obtained from all participants ., Whole exome sequencing ( WES ) of two patients from Family I was performed using DNA ( 1μg ) extracted from whole blood and fragmented and enriched using the Truseq DNA PCR Free kit ( Illumina ) ., Samples were sequenced on a HiSeq2500 ( Illumina ) with 2x100bp read length and analyzed as described 61 ., Raw fastq files were mapped to the reference human genome GRCh37 using BWA 62 ( v . 0 . 7 . 12 ) ., Duplicate reads were removed by Picard ( v . 1 . 119 ) and local realignment and base quality score recalibration was performed following the GATK pipeline 63 ( v . 3 . 3 ) ., The average read depth was 98x ( I-II-1 ) and 117x ( I-II-2 ) ., HaplotypeCaller was used to call SNPs and indels and variants were further annotated with Annovar 64 ., Databases used in Annovar were RefSeq 65 , Exome Aggregation Consortium ( ExAC ) 32 ( v . exac03 ) , ClinVar 66 ( v . clinvar_20150330 ) and LJB database 67 ( v . ljb26_all ) ., Exome variants in Family I were filtered out if they were not homozygous in both affected individuals , had a population allele frequency greater than 0 . 1% in either the ExAC database 32 or the Greater Middle East Variome Project 68 , and were not predicted to be deleterious by either SIFT 69 or Polyphen2 70 ., Whole exome sequencing was performed on the two affected individuals of Family II and both their parents essentially as previously described 71 ., Target regions were enriched using the Agilent SureSelectXT Human All Exon 50Mb Kit ., Whole-exome sequencing was performed on the Illumina HiSeq platform ( BGI Europe ) followed by data processing with BWA 62 ( read alignment ) and GATK 63 ( variant calling ) software packages ., Variants were annotated using an in-house developed pipeline ., Prioritization of variants was done by an in-house designed ‘variant interface’ and manual curation ., The DNAs of Family III were enriched using the SureSelect Human All Exon Kit v6 ( Agilent ) and sequenced on an Illumina HiSeq 2500 ( Illumina ) ., Alignment , variant calling , and annotation were performed as described 72 ., The average read depth was 95x ( III-II-2 ) , 119x ( III-I-1 ) and 113x ( III-I-2 ) ., Variants were selected that were covered by at least 10% of the average coverage of each exome and for which at least 5 novel alleles were detected from 2 or more callers ., All modes of inheritance were analyzed 72 ., Variants were prioritized based on a population frequency of 10−3 or below ( based on the ExAC database 32 and an in-house variant database ) , on the evolutionary conservation , and on the mutation severity prediction ., All candidate variants in Families I , II , and III were confirmed by Sanger sequencing ( primers listed in S4 Table ) ., Microarray analysis for CNV detection in Family I was performed using a HumanOmni5-Quad chip ( Illumina ) ., SNP array raw data was mapped to the reference human genome GRCh37 and analyzed using GenomeStudio ( v . 2011/1 ) ., Signal intensity files with Log R ratio and B-allele frequency were further analyzed with PennCNV 73 ( v . 2014/5/7 ) ., In Family III the diagnostic chromosomal microarray analysis was performed with an Affymetrix CytoScan HD-Array and analyzed using Affymetrix Chromosome analysis Suite-Software , compared with the Database of Genomic Variants and 820 in house controls ., All findings refer to UCSC Genome Browser on Human , February 2009 Assembly ( hg19 ) , Human Genome built 37 ., CNV analysis on the WES data of Families II and III were performed using CoNIFER 74 ., Variants were annotated using an in-house developed pipeline ., Prioritization of variants was done by an in-house designed ‘variant interface’ and manual curation as described before 75 ., Subsequent segregation analysis of the pathogenic CNV in Family II was performed with MAQ by using a targeted primer set with primers in exons 3 , 10 , 20 and 24 which are located within the deletion and exons 28 , 32 , 34 which are located outside of the deletion ( Multiplex Amplicon Quantification ( MAQ ) ; Multiplicom ) ., Human dermal fibroblasts were obtained from sterile skin punches cultured in DMEM ( Dulbeccos Modified Eagles Medium ) supplemented with 10–20% Fetal Calf Serum , 1% Sodium Pyruvate and 1% Penicillin and streptomycin ( P/S ) in 5% CO2 at 37°C ., Control fibroblasts were obtained from healthy age-matched volunteers ., Fibroblasts from passages 4–8 were used for the experiments ., To measure cell proliferation , cells were detached using trypsin and counted with an Automated Cell Counter ( ThermoFisher ) ., Cells ( n = 2500 ) were plated in triplicate in 96-well plates ., Viability was measured at day 2 , 4 , 6 and 8 ., Each measurement was normalized to day 0 ( measured the day after plating ) and expressed as a fold increase ., Viability was assessed by using CellTiter-Glo Luminescent Cell Viability Assay ( Promega ) ., Three independent experiments were performed ., HeLa cells were infected with lentiviral particles containing pLKO-TET-PI3KC2A-shRNA or pLKO-TET-scramble-shRNA in six-well plates ( n = 50 , 000 cells ) ., After two days , the medium containing lentiviral particles was replaced with DMEM 10% FBS , 1 . 5μg/ml puromycin ., After 7 days of selection , cells were detached and 100 , 000 cells were plated in six-well plates in triplicate in the presence of doxycycline ( 0 . 5 , 1 and 2 μg/ml ) ., Medium containing doxycycline was replaced every 48 hours ., After 10 days of doxycycline treatment , cells were lysed and analysed by Western blot ., Total RNA was purified from primary fibroblasts using the PureLink RNA purification kit ( ThermoFisher ) or RNAPure peqGOLD ( Peqlab ) ., RNA was reverse transcribed into complementary DNA with random hexamer using a high-Capacity cDNA Reverse Transcription Kit ( ThermoFisher ) ., RT-PCR from lymphocytes to detect exon-skipping in family III was performed using primers flanking exon 6 ., The resulting product was sequenced on an Illumina HiSeq2500 ( Illumina ) to detect splicing variants with high sensitivity ., Gene expression was quantified by SYBR Green real-time PCR using the CFX Connect Real-Time System ( BioRad ) ., Primers used are detailed in S4 Table ., Expression levels were calculated using the ΔΔCT method relative to GADPH ., Protein was extracted from cultured primary fibroblast cells as described 76 , 77 ., Extracts were quantified using the DC protein assay ( BioRad ) or the BCA method ., Equal amounts of protein were separated by SDS-PAGE and electrotransferred onto polyvinylidene difluoride membranes ( Millipore ) ., Membranes were blocked with TBST/5% fat-free dried milk and stained with antibodies as detailed in S5 Table ., Secondary antibodies were goat anti-rabbit ( 1:5 , 000 , ThermoFisher #31460 ) goat anti-mouse ( 1:5 , 000 , ThermoFisher #31430 ) , goat anti-rabbit ( 1:2 , 000 , Dako #P0448 ) , and goat anti-mouse ( 1:2 , 000 , Dako #P0447 ) ., Primary fibroblasts were grown on glass coverslips to approximately 80% - 90% confluency in DMEM + 10% FCS + 1% P/S , at which time the medium was replaced with DMEM without FCS for 48 hours to induce ciliogenesis ., Cells were fixed in either methanol for 10 minutes at -20°C or 4% paraformaldehyde for 10 minutes at room temperature ( RT ) ., Fixed cells were washed in PBS , and incubated with 10% normal goat serum , 1% bovine serum albumin in PBS for 1 hour at RT ., If cells were fixed with paraformaldehyde , blocking solutions contained 0 . 5% Triton X-100 ., Cells were incubated with primary antibody overnight at 4°C , washed in PBS , and incubated with secondary antibody including 4 , 6-diamidino-2-Phenylindole ( DAPI ) to stain nuclei for 1 hours at RT ., Coverslips were mounted on glass slides with fluoromount ( Science Services ) and imaged on a confocal laser scanning system with a 63x objectives ( LSM 710 , Carl Zeiss MicroImaging ) ., Primary antibodies are detailed in S5 Table ., To induce ciliogenesis , cells were grown in DMEM with 0–0 . 2% FCS for 48 hours ., Cells were washed in PBS , then fixed and permeabilized in ice-cold methanol for 5 minutes , followed by extensive washing with PBS ., After blocking in 5% Bovine Serum Albumin , cells were incubated with primary antibodies for 1 . 5 hours at RT and extensively washed in PBS-T ., Primary antibodies used for Centrin and ARL13B are detailed in S5 Table ., To wash off the primary antibody , cells were extensively washed in PBS-T ., Subsequently , cells were incubated with secondary antibodies , Alexa Flour 488 ( 1:800 , Invitrogen ) and Alexa Fluor 568 ( 1:800 , Invitrogen ) , for 45 min followed by washing with PBS-T ., Finally , cells were shortly rinsed in ddH2O and samples were mounted using Vectashield with DAPI ., Images were taken using an Axio Imager Z2 microscope with an Apotome ( Zeiss ) at 63x magnification ., Cilia were measured manually using Fiji software taking the whole length of the cilium based on ARL13B staining ., At least 300 cilia were measured per sample ., Cilia lengths were pooled for 3 control cell lines and compare to 2 patient-derived samples ( II-II-2 and II-II-3 ) ., Statistical significance was calculated using a Student t-test ., PI ( 3 ) P at the ciliary base was detected in randomly chosen cells using the same exposure for each acquisition ., A specific anti-PI ( 3 ) P antibody ( Echelo Z-P003 ) was used to quantify the PI ( 3 ) P by measuring the green fluorescent intensity around the ciliary base in a region with a diameter of 8 μm and a depth of 10 μm as previously illustrated and described 26 . | Introduction, Results, Discussion, Materials and methods | PIK3C2A is a class II member of the phosphoinositide 3-kinase ( PI3K ) family that catalyzes the phosphorylation of phosphatidylinositol ( PI ) into PI ( 3 ) P and the phosphorylation of PI ( 4 ) P into PI ( 3 , 4 ) P2 ., At the cellular level , PIK3C2A is critical for the formation of cilia and for receptor mediated endocytosis , among other biological functions ., We identified homozygous loss-of-function mutations in PIK3C2A in children from three independent consanguineous families with short stature , coarse facial features , cataracts with secondary glaucoma , multiple skeletal abnormalities , neurological manifestations , among other findings ., Cellular studies of patient-derived fibroblasts found that they lacked PIK3C2A protein , had impaired cilia formation and function , and demonstrated reduced proliferative capacity ., Collectively , the genetic and molecular data implicate mutations in PIK3C2A in a new Mendelian disorder of PI metabolism , thereby shedding light on the critical role of a class II PI3K in growth , vision , skeletal formation and neurological development ., In particular , the considerable phenotypic overlap , yet distinct features , between this syndrome and Lowe’s syndrome , which is caused by mutations in the PI-5-phosphatase OCRL , highlight the key role of PI metabolizing enzymes in specific developmental processes and demonstrate the unique non-redundant functions of each enzyme ., This discovery expands what is known about disorders of PI metabolism and helps unravel the role of PIK3C2A and class II PI3Ks in health and disease . | Identifying the genetic basis of rare disorders can provide insight into gene function , susceptibility to disease , guide the development of new therapeutics , improve opportunities for genetic counseling , and help clinicians evaluate and potentially treat complicated clinical presentations ., However , it is estimated that the genetic basis of approximately one-half of all rare genetic disorders remains unknown ., We describe one such rare disorder based on genetic and clinical evaluations of individuals from 3 unrelated consanguineous families with a similar constellation of features including short stature , coarse facial features , cataracts with secondary glaucoma , multiple skeletal abnormalities , neurological manifestations including stroke , among other findings ., We discovered that these features were due to deficiency of the PIK3C2A enzyme ., PIK3C2A is a class II member of the phosphoinositide 3-kinase ( PI3K ) family that catalyzes the phosphorylation of the lipids phosphatidylinositol ( PI ) into PI ( 3 ) P and the phosphorylation of PI ( 4 ) P into PI ( 3 , 4 ) P2 that are essential for a variety of cellular processes including cilia formation and vesicle trafficking ., This syndrome is the first monogenic disorder caused by mutations in a class II PI3K family member and thus sheds new light on their role in human development . | medicine and health sciences, diagnostic radiology, lens disorders, enzymology, fibroblasts, magnetic resonance imaging, connective tissue cells, enzyme metabolism, cellular structures and organelles, enzyme chemistry, research and analysis methods, imaging techniques, animal cells, connective tissue, biological tissue, metabolic disorders, clinical genetics, biochemistry, radiology and imaging, diagnostic medicine, cell biology, anatomy, cilia, cataracts, gene identification and analysis, ophthalmology, genetics, mutation detection, biology and life sciences, cellular types | null |
journal.ppat.1004419 | 2,014 | Leishmania donovani Infection Causes Distinct Epigenetic DNA Methylation Changes in Host Macrophages | Leishmania parasites have a complex life cycle usually alternating between an insect vector and a vertebrate host , or between vertebrate hosts ., The parasite is spread to humans through sandflies of the genus Phlebotomus or Lutzomyia during a blood meal 1 ., Within the mammalian host , Leishmania infect macrophages , cells that play a critical role in regulation of immune system and in host defense 2 ., Pivotal to cellular immune responses , macrophages function as antigen processing and presenting cells and produce a variety of cytokines that have pleiotropic effects within the host ., Leishmania have evolved to evade the defense mechanism of these cells through inhibition of macrophage activation that enables pathogen replication and survival 3–6 ., For example , essential macrophage activation signaling molecules and pathways such as PKC , JAK/STAT , MAPK , NF-kB as well as the transcription factor AP-1 are deactivated following infection with Leishmania 7 ., In addition , molecules such as SHP-1 are activated during Leishmania infection causing SHP-1 mediated JAK2 inactivation in macrophages 7 ., Thus Leishmania evolved several strategies to inhibit macrophage activation , the ability to present antigens on their surface as well as to interfere the communication of macrophages with cells from the adaptive immune system 7 ., Molecular mechanisms of cell programming often involve epigenetic changes by chromatin remodeling , histone modifications , and/or DNA methylation leading to regulation of cellular gene expression for normal development and establishing and maintaining cellular differentiation 8 ., DNA methylation , the addition of a methyl group to the 5′ cytosine primarily in the context of CpG dinucleotides , is arguably the most commonly studied epigenetic mark ., While shaping the cellular DNA methylation patterns is in large parts a developmental- and tissue-specific dynamic process 9 , recent work suggest that it can be affected also by a broad variety of environmental factors 10 ., CpG dinucleotides are not randomly distributed across the genome; rather , they are enriched in relatively infrequent distinct stretches of DNA termed “CpG islands” 11 , over half of which are located in known promoter regions of genes 12 ., These regions can be further classified into high , intermediate , intermediate shore , and low categories , based on their CpG density 12 ., Generally , high levels of DNA methylation in promoter regions are associated with decreased gene expression and vice versa , but this relationship is not always straightforward 13 ., Changes in DNA methylation patterns that occur mainly in proximate promoter regions , but also in gene body regions , can result in aberrant gene transcription of associated genes 13–15 ., The field of microbe-induced epigenetic changes in host cells is just starting to be explore 16–18 ., Recently , microbe-induced epigenetic changes in host cells emerged as a mechanism whereby intracellular pathogens such as viruses and bacteria manipulate host processes to favour their intracellular survival 16 , 17 , 19 ., Alterations in macrophage DNA methylation in response to intracellular protozoan pathogens remains largely unknown and permanent inhibition of innate immune response could be explained by changes to the host cell epigenome ., In this study we set out to test the intriguing hypothesis that L . donovani induces epigenetic changes in DNA methylation of the human macrophage genome ., Using unbiased DNA methylation array technology a set of CpG sites was identified with changes in methylation that correlated with live L . donovani infection ., These loci occurred in regions with distinct CpG densities and affected signaling pathways associated with host defense ., Collectively , this work suggests that L . donovani causes specific effects on the epigenome of the macrophage host , which might enable better survival ., To evaluate epigenetic changes in host cells caused by infection with a protozoan parasite , DNA methylation of genomic DNA from human macrophages infected with L . donovani was studied ., DNA methylation of CpG sites in the genome of host cells was quantified using the Illumina Infinium HumanMethylation450 BeadChip array ., This technology allows for the quantitative measurement of DNA methylation at over 480 , 000 CpG dinucleotides , broadly representing promoter and coding regions of almost all RefSeq genes 20 ., To differentiate among changes induced specifically by Leishmania infection versus those triggered by phagocytosis , macrophages treated with heat killed L . donovani promastigotes , as well as uninfected macrophages were used as controls ., Three biological replicates were performed for each experimental condition ., The infection rates of the three independent experiments were very similar ( experiment 1: 81% , experiment 2: 79% and experiment 3: 83% ) ., Overall , the correlations between replicates for the same treatment were slightly higher than those between the treatments ( r\u200a=\u200a0 . 998 and r\u200a=\u200a0 . 997 respectively ) ., Using unsupervised clustering , we found that individual samples from specific treatments did not necessarily cluster next to each other ( Figure S1 ) ., To monitor whether heat-killed Leishmania were successfully phagocytosed by the THP1 cells , CFDA pre-stained Leishmania ( either live or heat-killed ) were used to infect THP1 cells and then processed for confocal fluorescence microscopy ., Both , live- as well as heat-killed Leishmania were phagocytosed by THP1 cells ( Figure 1 ) ., Collectively , these data suggest that L . donovani infection of macrophages did not result in wholesale changes to the host DNA methylome ., To more carefully investigate whether infection with L . donovani caused DNA methylation changes at specific genes in host epigenome , we performed linear modeling with the R limma package using all possible pairwise comparisons among the three groups of samples ( live infected , heat killed treated , and uninfected ) 21 ., Probes with a p-value of 0 . 05 or less after Benjamini Hochberg correction for multiple testing were considered significantly differently methylated between the groups ., Changes in DNA methylation were expressed as Δ Beta values , defined as the difference between mean DNA methylation of a sample group and mean DNA methylation of control samples ( heat killed treated or uninfected ) at a particular probe ., A detailed description of the analysis is provided in the methods section ., Importantly , as evidenced by Volcano plots that display −log10 P-Values versus Δ Beta values , we found a large number of statistically significant changes in CpG methylation ( coloured in red in Figure 2 ) when comparing live promastigote infected versus uninfected macrophages ( Figure 2A ) , and live promastigote infected versus heat killed promastigote treated macrophages ( Figure 2B ) ., In contrast , no statistically significant different methylated CpG sites were identified when comparing heat killed treated versus uninfected macrophages , demonstrating that phagocytosis does not alter methylation of macrophage CpG sites ( Figure 2C ) ., These data strongly suggested that infection with L . donovani indeed resulted in specific changes in the macrophage host DNA methylome ., We next quantified and compared the number of statistically significant differentially methylated CpGs between the live infected versus uninfected macrophages and live infected versus heat killed treated macrophages respectively ., Given that no significant changes in DNA methylation status of CpG sites were observed in heat killed treated versus uninfected macrophages ( Figure 2C ) , this group was not analyzed further ., Using the criteria outlined above , we determined 733 and 624 CpG sites with altered methylation between live infected versus uninfected macrophages and live infected versus heat killed treated macrophages , respectively ( Table S1 , Table S2 , and Figure 3 ) ., To derive a high confidence set of CpGs whose methylation was specific for Leishmania infection as opposed to being triggered by phagocytosis , we focused on the subset of 443 CpG sites that were significantly different in both live promastigote infected versus heat killed treated macrophage and live promastigote infected versus uninfected macrophage data sets ( Table S3 ) ., The 443 CpGs from our overlapping high confidence set all had changes in the same direction when comparing their original conditions , although their absolute magnitude differed for some CpGs between the two ( Table S3 ) ., 315 of the 443 CpG sites were associated with a gene ID ( Table S3 ) ., Overall , in the high confidence group there was a slightly larger fraction of CpGs that had decreased methylation compared to increased methylation ( 51 . 47% versus 48 . 53% ) ( Table S3 ) ., Next , we filtered significant loci for absolute change in DNA methylation , as we reasoned that larger differences might be more likely to exert biological effects ., For live infected versus heat killed with a 10% delta beta cutoff , 37 sites decreased and 135 increased in DNA methylation , and with a 20% cutoff , 3 sites showed a decrease and 23 an increase ( Table 1 ) ., For live infected versus uninfected with a 10% delta beta cutoff , 38 sites decreased and 147 increased , and with a 20% cutoff three showed a decrease and 31 an increase ( Table 1 ) ., The largest differences in absolute magnitude of CpG methylation ( i . e . statistically significant when compared between live infected and control macrophages ) are listed in Table 2 ( 25 CpGs that gained methylation and 25 CpGs that lost methylation ) ., We next tested whether CpGs whose methylation pattern changed specifically in response to Leishmania infection shared common genomic characteristics ., Of the 215 CpG sites that gained methylation in the live infected versus control cells ( Table S3 ) , the majority , 79 . 5% ( 171 ) localize to low CpG density , 16 . 7% ( 36 ) to intermediate CpG density , 3 . 3% ( 7 ) to high CpG density and 0 . 5% ( 1 ) to intermediate CpG density shore ( Figure 4A ) ., The enrichment for low density CpG loci was highly statistically significant as determine by hypergeometric test ( p-value 4 . 20e-39 ) ., In contrast , in the group of CpG sites that lost methylation , 56 . 6% ( 129 ) localize to intermediate CpG density , 21 . 9% ( 50 ) to high CpG density , 17 . 1% ( 39 ) to low CpG density and 4 . 4% ( 10 ) to low CpG density shore ( Figure 4B ) ., Using a hypergeometric test , we found that the enrichment for intermediate density loci was highly statistically significant ( p-value 7 . 15e-128 ) ., Next , we tested for functional enrichment among the 315 CpG sites belonging to an annotated gene in our high confidence set of CpGs ( Table S3 ) ., Using the web-accessible Database for Annotation Visualization and Integrated Discovery ( DAVID ) v6 . 7 22 , 23 , we identified a number of participating genes of the chemokine signaling pathway , the calcium signaling pathway , the Notch signaling pathway , as well as genes involved in natural killer cell mediated cytotoxicity and others ( Table S4 ) ., All enriched pathways are listed in Table 3 ., To validate results of the DNA methylation array , pyrosequencing was performed for the regions containing cg18527651 and cg21211645 in IRAK2 and LARS2 , respectively ., Cg18527651 and cg21211645 are the top two CpG sites , annotated with a gene name , showing increased methylation when comparing live infected versus heat killed treated cells ( Table 2 ) ., They are also among the highest differentially methylated CpG sites in live infected versus uninfected ( Table 2 ) ., The annotated genes , IRAK2 and LARS2 play essential roles in immune response of Leishmania infected host cells ( see discussion ) and are thus very interesting candidates to validate ., Both , cg18527651 and cg21211645 , reside in the 3′UTR of their corresponding gene ., The results were consistent with the array , showing a significant increase in DNA methylation in the same three biological replicates of infected cells when compared to either heat killed treated or uninfected cells at both sites of interest ( p<0 . 01; Figure 5 ) ., For the IRAK2 assay , which assessed the methylation at 4 additional CpG sites , one adjacent CpG site showed a similar pattern between the conditions ( Figure 5 ) ., Furthermore , there was a major difference in DNA methylation values for this amplicon as two of the CpG sites were highly methylated ., This difference is likely attributed to a DNase I hypersensitivity site; the 3 CpG sites with decreased methylation values , including cg18527651 reside within it , whereas the two highly methylated CpG sites are located adjacent to it , according to the UCSC genome browser ., The hypersensitivity data were taken from ENCODE tracks from UCSC Feb . 2009 ( GRCh37/hg19 ) ., Since DNase I hypersensitivity sites are generally characterized by open , accessible chromatin , it makes sense that the 3 CpG sites that reside within it are less methylated ., The LARS2 pyrosequencing assay revealed similar DNA methylation differences between the three experimental conditions at all 3 additional CpG sites assessed in this amplicon , indicating a broad dynamic epigenetic change in this region in host cells upon infection with L . donovani ( Figure 5 ) ., To determine whether the changes in CpG methylation observed in macrophages following infection with L . donovani resulted in altered gene expression , five genes were selected for further analysis ( CDC42EP3 , LARS2 , HDAC4 , IRAK2 , ADPRHL1; listed in Table 2 ., This group of genes belongs to the high confidence set of CpGs ( Table 2 ) and consists of some that gained and some that lost methylation at their CpG sites ., Within the group of genes , the differentially methylated CpG sites are representatives of different localization with respect to the annotated gene , i . e . located in the 5′UTR , the first exon body , the intron body or the exon 3′UTR ( see also Figure 6A and Table 4 ) ., Gene expression patterns of all five selected genes were studied by quantifying their mRNA levels in live infected and heat killed treated macrophages using quantitative real time PCR ., Since a delayed effect on gene expression might be expected following the alteration of the DNA methylation pattern , mRNA levels were measured at 72 hr and 96 hr post-infection ., Figure 6B shows a bar diagram representing the fold difference of mRNA level of each selected gene in comparison to the Δ Beta value of their annotated differentially methylated CpG site ., The fold difference of mRNA levels of LARS2 , IRAK2 ( 72 hr time point ) , HDAC4 , and ADPRHL1 were inversely correlated to the Δ Beta value of their differentially methylated CpG site , regardless of the localization of the CpG site ., In contrast , the fold difference of mRNA level of CDC42EP3 and IRAK2 ( 96 hr time point ) was directly correlated to the methylation pattern of its CpG site: loss of methylation of the CpG site annotated to CDC42EP3 and located at its 5′UTR , results in lower gene transcription , whereas gain of methylation of cg18527651 , located at the exon 3′UTR of IRAK2 results in elevated mRNA levels after 96 hr infection ., Differential gene expression of CDC42EP3 ( p\u200a=\u200a0 . 0014 ) , HDAC4 ( p\u200a=\u200a0 . 0010 ) , ADPRHL1 ( p\u200a=\u200a1 . 5E-05 ) , and LARS2 ( p\u200a=\u200a0 . 0027 ) was found to be statistically significant for the 72 hr values , whereas IRAK2 ( p\u200a=\u200a0 . 238 ) was not ., For the 96 hr samples , none of the selected genes had a statistically significant change in their gene expression comparing live- versus heat-killed infected THP1 cells ., Epigenetic changes such as DNA methylation and histone modifications play a major role in eukaryotic gene regulation ., In this study we demonstrated extensive epigenetic changes in DNA methylation in the host macrophage genome in response to L . donovani infection ., This was supported by identification of statistically significant differently methylated CpGs between live infected versus heat killed treated macrophages and uninfected macrophages respectively and the absence of differentially methylated CpGs when comparing heat killed to uninfected macrophages ., In the high confidence group , there was a slight overrepresentation of CpG loci gaining methylation upon infection ., Furthermore , a large fraction of CpGs with altered methylation had substantial overall magnitude of changes of more than 10% ( see Table 1 ) ., Given that many of the DNA methylation differences currently identified as being associated with disease or environmental exposures are characterized by small absolute changes , often in the range of 5% , this filtering was applied to identify DNA methylation changes that might have a higher likelihood of functional consequences 24 , suggesting functional consequences ., Consistent with this , targeted mRNA profiling of loci with altered DNA methylation revealed coinciding changes in gene expression ., Interestingly , the genomic features of CpGs that gained DNA methylation upon L . donovani infection were strikingly different from those that lost DNA methylation , with the former being enriched for low density loci and the latter being enriched for intermediate density loci respectively ., Many of the differentially methylated CpG sites characterized in this study are annotated to genes whose functions have been previously reported to be modified during a Leishmania infection ., These include genes coding for proteins involved in signaling pathways such as the JAK/STAT signaling 7 , calcium signaling 25 , MAPK signaling 26 , Notch signaling 27 , and mTOR signaling 28 , as well as cell adhesion involving integrin beta 1 29 , and changes in host oxidative phosphorylation 30 ., We thus propose that L . donovani infection induces epigenetic changes in host DNA methylation to enable L . donovani survival differentiation and replication within the infected macrophage ., Similarly , it was recently reported that Toxoplasma gondii induces chromatin remodeling leading to unresponsiveness of its host cells to IFN-γ 18 ., In addition , intracellular bacteria and viruses 16 , 19 , 31 , 32 may trigger epigenetic changes in their host cells , an elegant mechanism to alter gene transcription favoring the pathogens infection , replication and survival ., As an integral component of the epigenome , DNA methylation is at the interface between the static genome and changing environments , acting in part through potentially persistent regulation of gene expression ., In order to study a possible role of DNA methylation in the modulation of host cell response upon infection with L . donovani , we determined host gene expression of five selected genes ( CDC42EP3 , LARS2 , HDAC4 , ADPRHL1 , IRAK2 ) annotated to CpG sites that show a variable methylation pattern between live promastigote infected- and control macrophage DNA samples ., We selected five CpG sites with annotated probe binding sites distributed from the 5′UTR , first exon body , intron body and exon 3′UTR ( Table 4 ) ., In accordance to the differentially CpG methylation pattern in the two condition compared , CDC42EP3 , HDAC4 , ADPRHL1 and LARS2 showed a statistic significant difference in RNA expression level between live infected- and control samples after 72 hr incubation ., The gene expression data after 96 hr infection showed a similar ratio as the 72 hr results , but were not statistically significant ., Different gene regulation of the selected genes might thus be a transient event during infection ., All genes with statistic significant changes in gene expression in the two conditions tested , except CDC42EP3 , showed an inverse correlation with DNA methylation ( Table 4 ) ., It is widely accepted that methylation of CpG sites located in promoter regions specifically ( 5′UTR , including first exon body ) down regulates gene expression while demethylation reverses silencing of genes 14 ., This is consistent with our finding that ADPRHL1 CpG sites were demethylated and corresponding mRNA levels increased , in macrophages infected with live-promastigotes compared to cells exposed to heat killed Leishmania ., Interestingly , cg14339867 , the CpG site annotated to ADPRHL1 , showed the highest score for demethylation in our comparison ( 32% , 31% in live infected versus uninfected , live infected versus heat killed treated respectively; see Table 2 ) and accordingly , the highest ratio of differential RNA-expression among the genes tested in this study ( 4 . 91 fold , see Table 4 ) ., ADPRHL1 is predicted to be an ADP-ribosylhydrolase like protein that reverses the reaction of ADP-ribosyltransferases , which transfer ADP-ribose from NAD+ to a target protein ., Both ADP-ribosylation and de-ADP-ribosylation are posttranslational modifications regulating protein function 33 ., Three of the five selected genes ( LARS2 , IRAK2 and HDAC4 ) are annotated to CpG sites in non-promoter regions ( Table 4 ) ., Functional interpretation of methylation changes in non-promoter locations of CpG sites such as the gene-body and 3′UTR are more complex and , in contrast to promoter proximate sites , do not follow a linear relationship between methylation and gene expression 14 ., However for all three , LARS2 , IRAK2 ( 72 hr value ) and HDAC4 , an inverse correlation was observed between CpG methylation and mRNA expression ( Figure 6B , Table 4 ) ., The leucyl-tRNA synthetase ( encoded by LARS ) senses intracellular leucine concentration and , in its activated stage , is involved in mTORC1 activation ., mTORC1 is a serine/threonine kinase that indirectly regulates gene expression by controlling the translational repressor 4E-BP ., The results of the current study demonstrate an increased methylation of the LARS2-related CpG site cg21211645 and down regulation of LARS transcription in live infected macrophages compared to control cells suggesting decrease in mTORC1 activity in live infected macrophage cells ., Interestingly , we and others have recently demonstrated that upon infection the Leishmania surface zinc metalloprotease GP63 cleaves mTORC1 resulting in inactivation of the mTOR complex1 and activation of the translational repressor 4E-BP1 facilitating Leishmania proliferation 28 ., Consistent with these results , pharmacological activation of 4E-BPs with rapamycin , results in a dramatic increase in parasite replication whereas infectivity is reduced in 4E-BP1 double knock out mice 28 ., LARS gene expression was also shown to be downregulated in L . major infected macrophages 3 suggesting that this mechanism may be conserved among different Leishmania species ., Our analysis revealed also CpG site cg11824764 , annotated to NM_001163034 ( Table S3 ) , with differentially DNA methylation pattern in live infected versus control cells ., NM_001163034 is involved in the mTOR pathway ( Table S4 ) ., DNA methylation and gene transcription was also inversely correlated for IRAK2 encoding the interleukin-1 receptor-associated kinase 2 that binds to the interleukin-1 receptor and is involved in the upregulation of NF-kappaB leading to gene expression of microbicidal molecules ., IRAK2 mRNA level was down regulated 1 . 5-fold in live infected macrophages compared to heat killed infected macrophages ( see Table 4 ) suggesting a decrease in NF-kappaB levels and activity contributing to an immune silencing mechanism in live infected macrophages ., We previously reported down regulation of IRAK2 gene expression in L . major infected macrophages 3 ., Interestingly , it was demonstrated that Leishmania cells escape NF-kappaB induced immune response by preventing the degradation of IkappaB , an inhibitor for NF-kappaB 34 ., In addition , elevated levels of ceramide in host cells after Leishmania infection was shown to result in the inhibition of NF-kappaB transactivation 35 ., Taken together , Leishmania cells seem to have developed several independent pathways to inactivate NF-kappaB dependent gene regulation to facilitate onset and progression of successful parasite infection ., We also identified the transcription of HDAC4 to be up-regulated ( 1 . 45-fold ) in live infected macrophage samples compared to heat killed infected cells ., This up-regulation in Leishmania infected macrophages is consistent with DNA microarray studies we previously reported 3 ., HDAC4 encodes a histone deacetylase that is involved in controlling chromatin structure , DNA accessibility and gene expression 36 ., CDC42EP3 mRNA levels were down regulated in host cells upon infection with L . donovani ( Figure 6B ) ., CDC42EP3 ( also called CEP3 37 ) is an effector protein of CDC42 , a protein involved in the formation of a protective shell of F-actin around promastigote infected phagosomes 38 ., It was suggested that F-actin at higher concentration prevents the phagosomal maturation ( a condition favorable to promastigotes until they have differentiated into amastigotes ) while in lower concentration might guide lysosomes to phagosomes to enable phagosome-lysosome fusion 39 ., In contrast to promastigotes , amastigotes require a phagolysosome environment for survival and successful replication 40 ., Thus , downregulation of CDC42EP3 transcription after a 72 hr and 96 hr infection may be an additional mechanism that Leishmania uses to direct host phagosomes to form phagolysosomes to ensure amastigote survival ., Taken together , these data demonstrate significant and likely physiologically relevant epigenetic changes in host cells upon infection with a protozoan pathogen ., We propose a new host cell response mechanism upon infection with the parasite L . donovani ., In this mechanism , invading Leishmania parasites trigger methylation changes of specific CpG sites in the host cell genome resulting in an altered gene expression pattern to facilitate Leishmania parasite replication and survival ., Alternatively the epigenetic changes may be a result of the macrophage innate immune response to L . donovani infection ., As macrophages are terminally differentiated the epigenetic changes may also be permanent leading to macrophage downregulation of innate immunity ., The mechanism of how L . donovani may induce epigenetic changes in host cells remains to be determined ., The parasite may transfer a factor such as methyltransferase inhibitor or alternative methyltransferases into the macrophage via Leishmania exosome secretion or may trigger macrophage factors regulating the methylation machinery ., THP-1 cells ( American Type Culture Collection , Rockville , MD , USA ) were cultured in 25 cm2 tissue culture flasks containing RPMI-1640 Medium ( 1x ) +2 . 05 mM L-Glutamine ( Thermo Fisher Scientific Inc . , Waltham , MA , USA ) supplemented with 10% heat inactivated Fetal Bovine Serum ( Thermo Fisher Scientific Inc . , Waltham , MA , USA ) , at 37°C in a humidified atmosphere containing 5% carbon dioxide ., L . donovani ( strain 1S from Sudan , WHO designation MHOM/SD/00/1S-2D ) promastigotes were cultured in M199 medium ( Thermo Fisher Scientific Inc . , Waltham , MA , USA ) supplemented with 10% heat inactivated Fetal Bovine Serum ( Thermo Fisher Scientific Inc . , Waltham , MA , USA ) , 40 mM HEPES ( Mediatech Inc . , Manassas , VA , USA ) , 10 mM hemin ( Sigma-Aldrich , St . Luis , USA ) , 10 U/ml penicillin ( Thermo Fisher Scientific Inc . , Waltham , MA , USA ) , and 10 U/ml streptomycin ( Thermo Fisher Scientific Inc . , Waltham , MA , USA ) ., Viable THP-1 cells , determined with a trypan blue exclusion test , were counted using a hemocytometer ., 3×105/ml THP-1 cells were seeded in a tissue culture treated 6-well dish , differentiated for 24 hr using 100 ng/ml PMA ( Sigma ) , washed with complete RPMI medium and allow to rest for 48 hr at 37°C ., Differentiated THP-1 cells were infected ( MOI 20 ) with stationary phase live or heat killed ( 65°C for 45 min ) L . donovani promastigotes , incubated at 37°C for 24 hrs washed with complete RPMI medium to remove unbound parasites and incubated for an additional 24 hrs ( or additional 48 hrs , 72 hrs respectively for the real time PCR experiments ) , at 37°C before harvesting ., To assay for successful phagocytosis of heat-killed Leishmania by THP1 cells , stationary phase live Leishmania were incubated in 30 µM Vybrant CFDA SE Cell Tracer ( Invitrogen ) for 45 min at 26°C , washed once with PBS , resuspended in M199 medium and incubated for additional 30 min at 26°C ., Leishmania were pelleted and resuspended in fresh M199 medium ., For the heat-killed samples , Leishmania were incubated at 65°C for 45 min ., Pre-stained Leishmania were then used for infection as explained above ., A 24 hr infection time was chosen since heat-killed Leishmania get degraded by macrophages at later time points ., Confocal images of fluorescently labeled samples were acquired with a Zeiss LSM 780 confocal microscope ., Total DNA was isolated from three independent infections for each condition ( life infected , heat killed treated , uninfected ) using All Prep DNA/RNA/Protein Mini Kit ( Qiagen Toronto , ON , Canada ) following the manufacturers instruction ., Total RNA was extracted using All Prep DNA/RNA/Protein Mini Kit ( Qiagen Toronto , ON , Canada ) following the manufacturers instructions ., Total DNA was isolated from 48 hr treated ( live or heat killed promastigotes ) or untreated macrophages as described above and unmethylated cytosines were bisulfite-converted to uracil by using the EZ-DNA Methylation Kit ( Zymo Research ) ., After whole genome amplification , DNA was enzymatically fragmented , purified and hybridized to the Illumina Infinium HumanMethylation450 BeadChip arrays ( Illumina , San Diego , CA ) according to manufacturer protocol ., The array contained site-specific probes designed for the methylated- and unmethylated locus of each CpG site covering 99% of the genes from the THP-1 genome ., Upon binding of host DNA to their site-specific probes , labeled ddNTPs were incorporated through single-base extension and stained with a fluorescent reagent ., Array output was interpreted using the GenomeStudio software from Illumina , after which signal A , signal B , and probe intensities for a total of 485577 CpG sites were exported into R for further processing 41 ., First of all , 65 SNP sites residing at rs sites used for subject identification were removed ., Then to control for data quality , probes at which one or more samples had undesirable detection p-values ( p-value>0 . 01 ) or missing measurements were removed , leaving 483329 CpG sites for analysis ., Array normalization was performed using color-correction , background subtraction and quantile normalization functions in the Lumi package with default settings 42 ., Peak based normalization was then applied to increase data accuracy and reproducibility ., Statistical analysis of the three different experimental conditions was done using Limma to identify significant changes in the methylation pattern ., For this study , M-value was used for all statistical analysis due to its approximate homoscedasticity ., M-values have shown to be statistically robust , and it yields better detection and true positive rates for CpG sites that become more or less methylated 43 ., Beta value was used for assessing Δ Beta changes and data visualization ., To test for differential methylation , we employed the bayesian adjusted t-statistics from the R limma package 44 ., First a design matrix was constructed involving the categorical variable that specifies the three different treatments ., Then using the design matrix , a linear model was fitted onto the data , after which pair-wise comparisons between the three groups were achieved by constructing a contrast matrix as per specifications in the limma user guide:, a ) live promastigote infected versus uninfected macrophages;, b ) live promastigote infected versus heat killed promastigote treated macrophage;, c ) heat killed promastigote treated versus uninfected macrophages ., Multiple tes | Introduction, Results, Discussion, Materials and Methods | Infection of macrophages by the intracellular protozoan Leishmania leads to down-regulation of a number of macrophage innate host defense mechanisms , thereby allowing parasite survival and replication ., The underlying molecular mechanisms involved remain largely unknown ., In this study , we assessed epigenetic changes in macrophage DNA methylation in response to infection with L . donovani as a possible mechanism for Leishmania driven deactivation of host defense ., We quantified and detected genome-wide changes of cytosine methylation status in the macrophage genome resulting from L . donovani infection ., A high confidence set of 443 CpG sites was identified with changes in methylation that correlated with live L . donovani infection ., These epigenetic changes affected genes that play a critical role in host defense such as the JAK/STAT signaling pathway and the MAPK signaling pathway ., These results provide strong support for a new paradigm in host-pathogen responses , where upon infection the pathogen induces epigenetic changes in the host cell genome resulting in downregulation of innate immunity thereby enabling pathogen survival and replication ., We therefore propose a model whereby Leishmania induced epigenetic changes result in permanent down regulation of host defense mechanisms to protect intracellular replication and survival of parasitic cells . | The L . donovani parasite causes visceral leishmaniasis , a tropical , neglected disease with an estimated number of 500 , 000 cases worldwide ., Current drug treatments have toxic side effects , lead to drug resistance , and an effective vaccine is not available ., The parasite has a complex life cycle residing within different host environments including the gut of a sand fly and immune cells of the mammalian host ., Alteration of host cell gene expression including signaling pathways has been shown to be a major strategy to evade host cell immune response and thus enables the Leishmania parasite to survive , replicate and persist in its host cells ., Recently it was demonstrated that intracellular pathogens such as viruses and bacteria are able to manipulate epigenetic processes , thereby perhaps facilitating their intracellular survival ., Using an unbiased genome-wide DNA methylation approach , we demonstrate here that an intracellular parasite can alter host cell DNA methylation patterns resulting in altered gene expression possibly to establish disease ., Thus DNA methylation changes in host cells upon infection might be a common strategy among intracellular pathogens for their uncontrolled replication and dissemination . | biology and life sciences, medicine and health sciences | null |
journal.pgen.1004439 | 2,014 | Recombination Accelerates Adaptation on a Large-Scale Empirical Fitness Landscape in HIV-1 | Recombination , here broadly defined as the shuffling of genetic material , is widespread in nature and occurs among a wide range of taxa , including most eukaryotes but also bacteria and viruses ., It has long been believed that sex and recombination facilitate adaptation by increasing the genetic variance upon which natural selection can act 1 ., However , recombination can also reduce variation if there is a preponderance of co-adapted allelic associations ., This cost , referred to as recombination load , arises because recombination tends to unravel combinations of genes that are favored by selection , thus impeding adaptation 2–4 ., Several hypotheses have been developed to account for the conditions under which recombination accelerates adaptation 4–8 ., Epistasis-based hypotheses state that if beneficial mutations increase fitness less in combination than expected based on their individual effects ( negative epistasis ) , recombination can accelerate adaptation by increasing genetic variance and thus enhancing the efficacy of selection 6 , 7 , 9 ., In contrast , recombination is predicted to decelerate adaptation with positive as well as sign epistasis ( i . e . , when the direction of selection on an allele depends on the allelic status at other loci ) 10–12 ., According to another class of hypotheses , random genetic drift resulting from finite population size is believed to provide an advantage to recombination 4 , 13–15 ., This occurs because in finite populations , beneficial mutations are likely to occur on different backgrounds and compete with each other , thus reducing selection efficacy ., Recombination alleviates this competition by bringing beneficial mutations together on the same background and consequently speeds up adaptation ( the Fisher-Muller effect ) 14–16 ., Prior experimental studies have demonstrated that sex and recombination can facilitate adaptation 17–21 ., Yet , our understanding of the costs and benefits of recombination during adaptation on empirical complex fitness landscapes is still limited ., The structure of the underlying fitness landscape is a decisive factor for the effect of recombination on adaptation since it determines how natural selection creates non-random combinations of alleles ., Although realistic fitness landscapes are believed to exhibit a complex structure characterized by intricate patterns of fitness interactions among genes , not much is known about the structure of large-scale fitness landscapes ., This lack of knowledge hampered efforts to obtain insights into the effect of recombination on adaptation in realistic situations ., To our knowledge , only one study explores the effect of recombination on an empirical fitness landscape 22 , but this fitness landscape comprises only six loci , and thus , the results may not be generalizable to larger landscapes ., Recently , Hinkley et al . 23 have estimated a fitness landscape in HIV-1 for 1859 mutations , based on an in-vitro assay for viral replicative capacity ., This empirical fitness landscape , by far the largest available empirical database characterizing epistatic interactions , allows us for the first time to scrutinize the impact of recombination on the evolutionary dynamics of an adapting population on a realistic fitness landscape ., Through simulations on this fitness landscape , we examined the effect of recombination on adaptation under different conditions ., We found benefits of recombination are sufficiently high to accelerate adaptation under a wide range of parameters ., Our findings highlight the important evolutionary role of recombination in adaptation , and in particular , in HIV evolution ., Recombination was found to produce a substantial increase in the rate of adaptation ( Figure 1A ) ., This effect was robust with respect to the initial composition of the population , as it is observed not only when starting from the reference sequence but also when initializing the population with a random sequence ( Figure S1 and Section S1 in Text S1 ) ., The acceleration of adaptation by recombination can be attributed to increased genetic variance in fitness , which in turn enhances the efficacy of natural selection , as proposed by the fundamental theorem of natural selection 14 ., Indeed , we see a markedly stronger increase in genetic variance in fitness over time in the recombining , compared to the non-recombining population ( Figure 1B ) ., These results on population fitness are also in agreement with patterns of genetic diversity within the evolving populations: the recombining populations accumulate within-population diversity faster than the non-recombining populations ( Figure S2A and Section S2 in Text S1 ) , and they diverge faster from the initial sequence ( Figure S2B and Section S2 in Text S1 ) ., We also observed that with increasing recombination rate the fittest genotypes that emerged by the end of each simulation became more divergent from each other across replicate runs ( Figure S3A and Section S3 in Text S1 ) ., However , it appears that this diversifying effect of recombination is primarily a consequence of the fact that recombination increases the rate at which the populations adapt and traverse the fitness landscape: when sequences are compared when the population reaches a certain threshold mean fitness , recombination has little effect on divergence ( Figure S3B and Section S3 in Text S1 ) ., In other words , recombination accelerates adaptation but does not increase the number of evolutionary trajectories available to the populations in the course of adaptation ., We also considered how different drug treatments affect the impact of recombination on adaptation ., To this end , we measured the effect of recombination using fitness landscapes obtained for 16 environments with different drug treatments ., Our results indicate that the effect of recombination is markedly stronger in the presence of antiviral drugs compared to the drug-free environment ( Figure S4 and Section S4 in Text S1 ) , which appears to be due to the higher selection pressure in environments with drug treatments ( results not shown ) ., We next explored how the population size and mutation rate affect the extent to which recombination accelerates adaptation ., Figure 2A indicates that , for a given population size , adaptation is already accelerated with moderate recombination rates and this effect increases monotonically with increasing recombination rates ., In contrast , the effect of recombination depends non-monotonically on mutation rate ( Figure 2B ) : whereas modest mutation rates enhance the accelerating effect of recombination on adaptation , at very high mutation rates this effect is reduced ( see Discussion ) ., Finally , our results indicate that with increasing population size , the accelerating effect of recombination becomes stronger ( Figure 2A and S5 and Section S5 in Text S1 ) ., This is because with increasing population size , more beneficial mutations co-segregate in the population on different backgrounds and as a result the Fisher-Muller effect is enhanced ., We expect that in very large populations this acceleration would become weaker again ( because then all combinations of beneficial alleles would be present in the population even without recombination ) , but in the range of population sizes that was computationally feasible this was not observed ., We also found the effect of recombination for populations with the same population mutation rate ( population size×mutation rate ) to be dependent on the population size: the effect is maximized for intermediate values of the population mutation rate and this maximum occurs at higher values for larger populations ( Figure S6 and Section S6 in Text S1 ) ., This is presumably because for larger populations a higher mutation rate is required for multiple beneficial mutations to occur on the same background and thereby mitigate the Fisher-Muller effect ., Our fitness model incorporates both main and pairwise epistatic effects , both of which are known to influence the effect of recombination on the rate of adaptation ., To assess the relative contribution of these effects , we simulated adaptation on fitness landscapes where , starting from the original MHL fitness matrix , we decreased the epistatic and main effects by varying amounts ., Figure 3 indicates that both main effects and epistatic effects contribute to the acceleration of adaptation and that in combination these two effects appear to operate additively ., Similar results were obtained for smaller recombination rates ( Figure S7 ) ., We also found that both main effects and epistatic interactions increase the rate of adaptation and the effect of recombination becomes stronger with increasing adaptation rate ( results not shown ) ., These findings suggest that both main and epistatic effects can enhance selection ., This seems to result in stronger interferences between arisen beneficial mutations and therefore a higher advantage of recombination ( see Discussion ) ., We next determined the predominant form of epistasis in the adapting populations ., To infer epistasis , one can use the fitness values of a set of sequences that are chosen irrespective of the composition of the evolving population and that therefore may not represent the sequences formed during adaptation ( ‘physiological epistasis’ ) ., Alternatively , only fitness values of sequences that are present in the adapting population can be utilized to estimate epistasis ., This form of epistasis , referred to as population epistasis , provides a real time estimate of the epistasis that is responsible for generating the standing linkage disequilibrium in the population , and is therefore more accurate than physiological epistasis ( see Discussion ) ., We calculated population epistasis by regressing log fitness against Hamming distance , i . e . , the number of sites where the corresponding sites are different between two sequences ., The regression was done for sequences that are present in the population at the end of simulation according to , where Hamming distance is measured relative to the reference sequence ., The parameter , determining the curvature , is used as a measure of epistasis ., Our results indicate that in the majority of simulations , population epistasis is significant ( ANOVA test for comparison of a quadratic and a linear model , p≪0 . 001 for the simulations in Figure 4 ) and predominantly negative , indicating diminishing returns with each additional beneficial mutation in increasing fitness ., Population epistasis in the recombining population becomes less negative on average than in the non-recombining population ( Wilcoxon test for the significance of epistasis in Figure 4 , p≪0 . 001 and for the significance of the difference between recombining and non-recombining simulations Figure 4B , p≪0 . 001 ) ., We obtained similar results for populations at other time points ( results not shown ) ., Thus far , we considered the effect of recombination on adaptation by comparing evolving populations characterized by different recombination rates ( including the absence of recombination ) ., To examine whether recombination is selectively favored within an adapting population , we performed additional simulations in which we competed a resident non-recombining with an invading recombining genotype during adaptation ., Figure S8A shows that the frequency of the recombining genotype gradually increases over time if the recombination rate is high enough ., In accordance with our previous findings , the results of the invasion analysis demonstrate that the benefit of recombination is most pronounced for intermediate mutation rates ( Figure S8B and S8C and Section S7 in Text S1 ) ., Our results can be interpreted as support for the proposed accelerating role of recombination in the adaptive process through the Fisher-Muller effect ., In our simulations , this effect seems to be sufficiently strong to outweigh potential costs of recombination ., The Fisher-Muller effect is based on strong selective interference between beneficial mutations in an asexual ( non-recombining ) population ., Previous mathematical models have provided important insights into how the strength of selection , mutation rate and population size affect selective interference and the Fisher-Muller effect , but these models have ignored epistatic interactions between mutations 24–30 ., Our results demonstrate that both epistatic interactions and main fitness effects contribute to the accelerating effect of recombination ., It is clear that this effect in the absence of epistatic interactions is due to the Fisher-Muller effect ., Adding epistatic interactions to this model enhances the effect of recombination but the interpretation of why this occurs is challenging ., On the one hand , this may be because the epistatic interactions increase the overall strength of selection and thereby produce stronger clonal interference , but on the other hand we cannot exclude explanations based purely on epistasis ( see below ) ., One important determinant of the Fisher-Muller effect is the mutation rate ., With a finite number of loci , an increasing mutation rate leads to a higher number of co-segregating beneficial mutations and this augments the Fisher-Muller effect ., However , at very high mutation rates , it becomes increasingly likely that several beneficial mutations arise on the same genetic background so that recombination becomes less important ( Figure 2B ) ., A similar effect is expected for population size ( with finite sites for beneficial mutations ) but for the range of population sizes we examined here the effect of population size was monotonic ., In addition to the Fisher-Muller effect , recombination can also accelerate adaptation in the absence of random genetic drift when there is epistasis 9 , 31 ., Several studies have attempted to determine the prevailing form of epistasis in nature but have yielded inconsistent results: sometimes strong positive epistasis 32–36 , negative epistasis 37 , 38 or pervasive sign epistasis 39–42 was reported ., The HIV-I fitness landscape is characterized by extensive epistatic interactions 23 ., We demonstrated that population epistasis during adaptation is predominately negative ., This result is in apparent contrast with the predominant positive epistasis in HIV-I sequences reported in Bonhoeffer et al . 33 ., We think this discrepancy arises mainly because Bonhoeffer et al . estimated physiological epistasis , which is based only on the structure of fitness landscape itself and may be very different from population epistasis that we estimated here 43 , 44 ., The difference between the two estimates is that in determining population epistasis only the mutations that pass the sieve of natural selection are taken into account , whereas in measuring physiological epistasis all mutations are used indiscriminately ., In addition to these different measures of epistasis , the two studies also differ in the way fitness was estimated ., First , Bonhoeffer et al . 33 obtained fitness values from a much smaller data set than was used to estimate the fitness landscape in our study ( 9466 vs . 70 , 081 sequences ) ., Second , in their study the main effects of a mutation and the epistatic effects for a given pair of mutations were calculated by averaging over the fitness effects of other mutations in the genetic background ., By contrast , we obtained fitness using a predictive fitness model 23 that explicitly accounts for mutational effects in different genetic backgrounds during estimation of the fitness landscape , and therefore provides a more accurate estimate ., It is tempting to interpret the significant negative population epistasis that we observed as a support for the mechanism proposed by the mutational deterministic hypothesis , i . e . acceleration of adaptation through reduction of negative linkage disequilibria generated by negative epistasis ., However , we would like to caution that it is very difficult to explain how population epistasis arises in our model and how it impacts the effect of recombination ., For example , population epistasis on a complex fitness landscape can also be generated by variation in main fitness effects of mutations ., The underlying causes of negative epistasis in our model and the extent to which it contributes to the accelerating effect of recombination ( in isolation from the Fisher-Muller effect ) is difficult to determine on a complex fitness landscape because deterministic models are not feasible ., Therefore , we cannot exclude the possibility that stochastic benefits of recombination due to the Fisher-Muller effect may be sufficiently strong to override any direct effect of epistasis in our simulations , as reported by other studies 11 , 45 , 46 ., Our study is based on an estimated fitness landscape and therefore the limitations of this approach should be taken into account while interpreting the results ., First , in the fitness landscape that we used only main and pairwise epistatic effects were estimated but higher order fitness interactions ( >2 ) were neglected ., To accurately estimate higher orders epistasis , a much larger number of sequences with measured fitness values would be required ., It is not clear what the strength of these higher-order interactions is and how they affect the impact of recombination ., Second , although the accuracy of our fitness model ( predicting 54 . 8% of the variance; see Methods ) is acceptable as the only available fitness landscape , the predicted fitness landscape is yet to become more realistic by using a greater number of empirical fitness data ., This is important as the estimated fitness values become increasingly unreliable for the regions in the sequence space far from the reference sequence due to the lack of data ., To account for this problem , we focused on the population dynamics in the region of the fitness landscapes that is close to the reference sequence ., Finally , the empirical fitness data used to predict the structure of the fitness landscape was obtained from an in vitro assay , and therefore might not completely correspond to in vivo fitness values ., It should also be noted that this study mainly examines the effect of recombination at the population level and does not address the evolution of recombination rate ., One interesting extension of this work would be to incorporate variation in recombination rates in the model and study the spread of a recombination modifier gene in a non-recombining population ., One problem with using the modifier approach in a realistic way with the current fitness model is that probably the best candidate for a recombination modifier gene in the HIV-1 genome is the reverse transcriptase gene see 47 , which is itself part of the fitness landscape and therefore changes during adaptation because of direct selection ., Our study relates to the debate over the advantage of recombination in retroviral , and in particular HIV , evolution ., Recombination is believed by some to be beneficial because it generates genetic diversity to facilitate the development of multidrug resistance 48–50 or escape from host immune reaction 51 ., Nonetheless , some studies have suggested that recombination has not evolved to facilitate adaptation but is a mere by-product of other mechanisms such as genomic organization reviewed in 52 , see also 45 ., Unlike some prior studies 53–55 , our model does not include any specific feature of HIV biology , such as viral dynamics during infection or specificities of recombination in HIV ., Nonetheless , we believe that our findings are generic enough to highlight the potential role of recombination in accelerating HIV evolution ., This study utilizes data derived from a high-throughput fitness assay to address one of the long-standing questions in evolutionary biology ., The advent of systems biology approaches made it possible to obtain a comprehensive picture of a large-scale fitness landscape ., This serves as a framework for us to demonstrate that recombination has a substantial accelerating effect on adaptation on a realistic complex fitness landscape ., Our model is based on a recently estimated fitness landscape of HIV-1 23 ., Briefly , to obtain this fitness landscape , the in vitro replicative capacity of 70 , 081 samples from HIV-1 subtype B infected individuals were measured and the corresponding amino acid sequences of the protease and partial sequences of the reverse transcriptase were obtained for all of these samples ., This enabled estimation of the fitness effects of 1 , 857 single mutations and 257 , 536 pairs of mutations in these samples by fitting a fitness model to the data ., This fitness model , as detailed in Hinkley et al . 23 , invokes a generalized kernel ridge regression ( GKRR ) method to estimate the fitness effect of individual amino acid variants and the epistatic effects between variants ., Based on these results , we used the following fitness model to obtain fitness values for a given sequence: Here , the amino acid sequence x is a binary vector indicating the presence or absence of amino acid variants ., M is a triangular matrix where an entry Mii on the diagonal determines the main effects of the amino acid variant i and the off-diagonal entries Mij ( with ) determine pairwise epistatic effects between variant i and j ., Higher order epistatic interactions were not considered ., Note that the original model in Hinkley et al . 23 also includes an intercept term that gives the log fitness of a reference sequence ( NL4-3 ) and that is added to Equation ( 1 ) ., However , since natural selection only depends on relative fitness in our model , this term was not considered in our simulation setting ., We used two different types of matrices M determining fitness ., The first matrix type , MRL , describing the ‘reference fitness landscape’ , was obtained by Hinkley et al . 23 by estimating both main and epistatic effects simultaneously ., This estimation was done in 16 different environments: one drug-free environment and 15 environments each characterized by the presence of a different antiretroviral drug ., On average , this matrix predicts 54 . 8% of the variance in fitness across different environments ., Unless stated otherwise , we use the fitness landscape in the drug-free environment as the reference fitness landscape in our simulations ., To obtain the second matrix type , MHL , describing a ‘hierarchical fitness landscape’ , two fitting steps were performed 56 ., In the first step , MHL was estimated by assuming that there are only main effects ( all off-diagonal elements set to zero ) , and in the second step , epistatic effects were estimated by fitting the residuals under the assumption that main effects are absent ., This fitness landscape was obtained only for the drug free environment ., Since the main and epistatic effects are estimated separately for these fitness landscapes , this approach allowed us to generate fitness landscapes where we could scale the magnitudes of main and epistatic effects and thus evaluate their relative contribution with respect to the effects of recombination ., Hierarchical fitness landscapes with different magnitudes of epistatic effects were shown to provide accurate predictions of the reference fitness landscape 56 ., For details about the estimation procedures , we refer to Hinkley et al . 23 and Kouyos et al . 56 ., We consider a discrete-time model based on the classic the Wright-Fisher model to simulate adaptive evolution under mutation , recombination and natural selection on the HIV-1 fitness landscape ., The population consists of a constant number of amino acid sequences , each of which contains the protease , as well as a partial reverse transcriptase , gene ., Initially , this population is monomorphic , consisting only of copies of a reference sequence ( NL4-3 ) ., In each generation , the new population is formed from the previous one through three steps ., First , reproduction and selection are implemented through random sampling of sequences , weighted according to the relative fitness value of each sequence ., Second , to implement mutation events , sequences are randomly chosen from the population with replacement ( denotes the per genome mutation rate ) , thus allowing for several mutations per sequence ., For <1 , this number is treated as a random number with mean ., For each of these sequences , the allele at a randomly selected site for which there exists more than one possible variant is substituted with one of the other possible allelic variant ., Amino acid variants at a given site that are not present in the data set used to estimate fitness are neglected ., In the final step , selected sequences undergo homologous recombination ., We denote the recombination rate by ., Here , pairs of sequences are chosen randomly ( without replacement ) and for each of these pairs , a single crossover site at which the two parental sequences exchange genetic material is determined at random ., Note that recombination may result in daughter sequences that are identical to the parental sequences if identical pieces are exchanged ., The simulations were run for 100 generations ., This period is long enough for the population to adapt but the adapting population still remains in the proximity of the reference sequence , where due to the availability of empirical fitness data , the estimation of the fitness by our model is reliable ., To examine the effect of recombination on adaptation , we computed the ratio of the logarithm ( base 10 ) of the population mean fitness of a population evolving with recombination to that of a population evolving without recombination ., In this case , finding a proper definition of error bars is not straightforward since the data in question are the logarithms of ratios ., However , this logarithm can be written as a difference ( ) , so that we can use the standard deviation of the difference between two log normal distributions , , as error bars ., This is justified because we found the log fitness values of sequences at generation 100 across 100 simulations to be normally distributed ( for instance , non-significant Shapiro-Wilk and Anderson-Darling test to reject normal distribution for results in Figure 1 , with p>0 . 05 ) . | Introduction, Results, Discussion, Methods | Recombination has the potential to facilitate adaptation ., In spite of the substantial body of theory on the impact of recombination on the evolutionary dynamics of adapting populations , empirical evidence to test these theories is still scarce ., We examined the effect of recombination on adaptation on a large-scale empirical fitness landscape in HIV-1 based on in vitro fitness measurements ., Our results indicate that recombination substantially increases the rate of adaptation under a wide range of parameter values for population size , mutation rate and recombination rate ., The accelerating effect of recombination is stronger for intermediate mutation rates but increases in a monotonic way with the recombination rates and population sizes that we examined ., We also found that both fitness effects of individual mutations and epistatic fitness interactions cause recombination to accelerate adaptation ., The estimated epistasis in the adapting populations is significantly negative ., Our results highlight the importance of recombination in the evolution of HIV-I . | One of the most challenging issues in evolutionary biology concerns the question of why most organisms exchange genetic material with each other , e . g . during sexual reproduction ., Gene shuffling can create genetic diversity that facilitates adaptation to new environments , but theory shows that this effect is highly dependent on how different genes interact in determining the fitness of an organism ., Using a large data set of fitness values based on HIV-1 , we provide evidence that shuffling of genetic material indeed raises the level of genetic diversity , and as a result accelerates adaptation ., Our results also propose genetic shuffling as a mechanism utilized by HIV to accelerate the evolution of multi-drug-resistant strains . | mutation, natural selection, genetics, biology and life sciences, evolutionary adaptation, population genetics, evolutionary biology, evolutionary processes, genetic drift, evolutionary genetics | null |
journal.ppat.1003401 | 2,013 | Increased Long Chain acyl-Coa Synthetase Activity and Fatty Acid Import Is Linked to Membrane Synthesis for Development of Picornavirus Replication Organelles | ( + ) RNA viruses of eukaryotes are a very successful group of pathogens infecting organisms from unicellular algae to humans ., In spite of adaptation to diverse hosts the basic processes of genome expression and replication are highly conserved among these viruses ., One such feature shared among all ( + ) RNA viruses is the association of RNA replication machinery with cellular membranes ., It has been proposed that assembly of replication complexes on membranes may facilitate infection in several ways: increase local concentration of viral proteins; provide structural scaffold for assembly of replication machinery; hide viral dsRNA replication intermediates from the cellular innate immunity mechanisms ( reviewed in 1 , 2 ) ., Poliovirus ( PV ) is a prototype species of the Picornaviridae family ., Its genome RNA of about 7500 nucleotides is directly translated into one polyprotein which is cleaved co- and post-translationally into a dozen of structural and replication proteins ., Proteins encoded in the P2-P3 region of the viral genome as well as the intermediate products of the polyprotein processing are responsible for RNA replication ., Other members of the Picornaviridae family share the same basic genome organization and expression strategy with minor modifications 3 ., PV infection induces rapid development of new membranous agglomerates harboring viral replication complexes ., The current models of the development of picornavirus replication structures suggest hijacking of either elements of the cellular secretory pathway or autophagy machinery 4 , 5 , 6 ., However even closely related viruses vary greatly in their sensitivity to the inhibitors of the secretory pathway , and effects of manipulation of autophagy may vary even for the same virus 7 , 8 , 9 , suggesting that these cellular processes are not obligatory for the development of replication complexes ., At the same time previously accumulated data show that diverse picornaviruses similarly induce strong stimulation of phospholipid biosynthesis , especially phosphatidylcholine ( PC ) , upon infection with 10 , 11 , 12 , 13 ., PC constitutes about 50% of the total phospholipid content in eukaryotic membranes 14 ., Phospholipids found in cellular membranes include fatty acids ( FAs ) with C16 and longer carbon atoms chains 15 ., In mammalian cells fatty acid synthase can de novo synthetize palmitic acid ( C16:0 ) , which can subsequently be processed into other FA species 16 , 17 ., However , most of the cells import the majority of long chain FAs from extracellular media ., The mechanism of FA transport through plasma membrane is not yet completely understood , however it is believed that acyl-CoA synthetase activity plays a key role in this process ., According to the current model of vectorial acylation , long chain FAs as hydrophobic molecules can freely diffuse through lipid bilayers , and inside the cell they are converted into hydrophilic acyl-CoAs that can no longer escape ., Indeed most proteins that have been shown to facilitate FA uptake possess acyl-CoA synthetase activity and its inactivation prevented transport of FAs into cells 18 , 19 , 20 , 21 ., Thus lipid biosynthesis is intrinsically dependent on acyl-CoA synthetases which activate FAs derived from either external or internal cellular sources ., There are 26 different acyl-CoA synthetase genes in human genome 22 ., Five members of the long chain acyl-CoA synthetase ( Acsl ) family; six proteins of the very long chain acyl-CoA synthetase family also known as fatty acid transport proteins ( Acsvl or FATP ) ; and two bubblegum acyl-CoA synthetases ( ACSBG ) can activate long chain fatty acids ., Their differential tissue expression and sub-cellular localization , existence of multiple splice isoforms , and enzymatic preference towards certain classes of FA provide foundation for complex pattern of uptake and channeling of FA into different metabolic pathways 23 ., In this study we show that PV infection results in fast up-regulation of long chain FA uptake due to activation of cellular long chain acyl-CoA synthetase activity , and we identify long chain acyl-CoA synthetase 3 ( Acsl3 ) as a novel host factor required for polio replication ., We found that in mock-infected cells the newly-imported FAs are mostly channeled to lipid droplets , while in infected cells they are immediately utilized for highly up-regulated PC synthesis ., The infected cells demonstrate preference for import of different FAs than mock-infected cells , resulting in significant changes in the spectrum of PC molecules ., The enrichment of phosphatidylcholine species with short palmitoyl ( C16:0 ) moieties likely generates more fluid membranes with intrinsic capacity to assemble into convoluted tubular matrix of the membranous replication organelles ., We find that stimulation of FA import requires PV protein 2A , but is independent of its protease activity , thus revealing a new important function this protein plays in alteration of the cell metabolism ., The activation of FA import is observed upon infection of diverse picornaviruses in different cell types ., Our work explains previous data on stimulation of membrane synthesis and morphology of replication structures in picornavirus-infected cells , and provides a new model of the development of the membranous scaffold of the replication organelles apparently shared by diverse picornaviruses ., The increase of phospholipid synthesis in PV-infected cells 10 , 12 , 13 should be sustained by sufficient supply of corresponding precursors including long chain FAs ., To monitor FA import we pulse-labeled PV-infected HeLa cells with a fluorescent fatty acid Bodipy 500/510 C4 , 9 ( bodipy-FA ) which is believed to mimic FA with 18 carbon atoms backbone ., This and similar molecules are extensively used in lipid metabolism research and it was previously shown to be rapidly utilized by cellular lipid synthesis machinery and incorporated into phospholipids , triglycerides and other natural lipids 24 , 25 , 26 , 27 , 28 ., The cells were infected at a multiplicity of 50 PFU/cell to ensure simultaneous development of infection , and bodipy-FA was added for 30 min at 4 hours post infection ( h p . i . ) , in the middle of the infectious cycle ., The infected cells showed strongly increased import of bodipy-FA ( Fig . 1A and B ) ., In mock-infected cells the label was distributed into multiple round structures in the cytoplasm and was also found in intracellular ER-like staining ( Fig . 1C , mock ) ., The round bright dots were identified as lipid droplets since they co-localized with a well-established lipid droplet marker ADRP 29 ( Fig . 1D ) ., Note that some ADRP-positive structures did not accumulate bodipy-FA during 30 min labeling period ( Fig . 1D , arrow ) , consistently with the previous results that individual lipid droplets accumulate newly-synthesized lipids at different rates 30 ., In infected cells , bright bodipy-FA fluorescence surrounded the nuclei and often occupied the total cytoplasmic area reflecting robust development of the poliovirus replication complexes ( note pycnotic nuclei in infected cells , characteristic of polio-induced cytopathic effect ) ( Fig . 1E ) ., For the experiment shown on Fig . 1 the cells were incubated in serum-free media during the labeling period , so bodipy-FA was the only fatty acid available exogenously ., We also monitored FA transport when cells were incubated in normal growth media supplemented with fetal bovine serum which provides ample supply of natural FA and other lipids ., As expected , the level of fluorescent signal was lower in the presence of serum , due to competition with the fatty acids from serum , but the overall picture of strong stimulation of fatty acid import upon infection was the same ( not shown ) ., Cells on Figure 1C and E are imaged directly after formaldehyde fixation without further detergent permeabilization which we found to deteriorate the fine structure of the distribution of the bodipy-FA label , especially in weakly labeled mock-infected cells ., Staining of cells for a viral antigen 2B , a marker of membranous replication complexes , revealed extensive co-localization of bodipy-FA fluorescence with polio replication structures , especially in the cells where viral protein staining could still be visualized as discrete domains in the confocal plain ( Fig . 1F , arrowheads , also co-localization panel ) ., With the further development of infection staining for both viral proteins and bodipy-FA tend to occupied all available perinuclear space reflecting massive development of membranous replication complexes ., Staining for other membrane-targeted poliovirus replication proteins 2C and 3A revealed similar pattern of distribution of a viral antigen and bodipy-FA label ( not shown ) ., Thus in PV-infected cells the import of FAs from media is strongly increased , their intracellular targeting is different from mock-infected cells , and they are used for building of viral membranous replication complexes ., To investigate the metabolic targeting of the imported FAs we pulse-labeled cells with bodipy-FA for 30 min at 4 h p ., i ., , extracted the lipids and resolved them by thin layer chromatography ( TLC ) using solvent systems optimized for separation of either neutral or polar lipids ., The chromatograms were first photographed in a fluorescence imager to reveal the newly synthesized lipids , and then developed with conventional stains to visualize the total lipid material ., We did not recover noticeable amount of free bodipy-FA ., Virtually all the fluorescence was found in newly synthesized complex lipids , thus validating the use of bodipy-FA in our system ( Figure S1 , compare bodipy-FA marker lane 8 on the fluorescent polar lipids panel with the fluorescent lipids extracted from the cells ( lanes 1–4 on the same panel ) ) ., There were no dramatic differences due incubation of cells in the presence or absence of serum , but as expected the fluorescent signal recovered from the serum-free samples was higher , about 1 . 5× for mock-infected cells and ∼2× for virus-infected cells as quantitated from the fluorescence of the total lipid spots loaded at the start position before TLC resolution ( not shown ) ., In the mock-infected cells incubated without serum significant amount of fluorescent label appeared in a spot on neutral lipids plate likely representing triglycerides with abnormal mobility due to the presence of bodipy-FA ( Fig . 2A , lane 4 ) , correlating with the microscopy observation of fluorescence accumulation in lipid droplets ., Note that free bodipy-FA in the neutral lipid separation system moved much slower than the long chain free FA markers , while in the polar lipid separation system its mobility was close to C18 long chain free FAs ( Figure S1 , compare lanes 8 ( free bodipy-FA , white horizontal arrows ) and 5 ( stearic acid C18:0 ) , 6 ( palmitic acid C16:0 ) , 7 ( linoleic acid C18:2 ) on neutral and polar lipid plates ) ., We cannot exclude that this spot represents some other type of neutral lipid , but in any case synthesis of this compound is active in mock-infected cells and shut down in infected cells ., In the lipids isolated from infected cells almost no fluorescence was resolved in the non-polar system , ( Fig . 2A compare lanes 3 and 4 ) , indicating that synthesis of neutral lipids is shut down ., In fact in the TLC system optimized for neutral lipids most of the labeled lipids isolated from infected cells remained as a bright spot at the loading position ( Fig . 2A , lanes 1 and 3 ) ., In contrast , on the TLC plate resolved using the polar solvent system we observed a very strong signal for newly synthesized PC in infected cells ( Fig . 2B , compare lanes 1 , 3 and 2 , 4 ) ., The staining of the TLC plates for total lipid content did not reveal significant differences between infected and mock-infected cells ( Fig . 2 ) ., It should be noted that the samples were analyzed after 4 h p ., i ., , meaning that the period of up-regulated synthesis of new lipids was relatively short , and they apparently did not significantly change the overall lipid content of the cells ., Thus , PV infection does not only increase the level of FA import but modifies their metabolic channeling by down-regulating synthesis of neutral lipids , and redirecting the newly imported FAs for the highly up-regulated production of PC ., Import of FAs is inextricably connected to activity of acyl-CoA synthetases 23 ., Transport of saturated or unsaturated long-chain fatty acids containing 18 or fewer carbons across biological membranes is rapid and not thought to be rate-limiting 31 , 32 ., Thus the increased uptake of FFA probe suggests that long chain acyl-CoA synthetase activity must be up-regulated upon infection ., To measure this activity we prepared lysates from HeLa cells infected at the multiplicity of 50 PFU/cell and incubated without serum for different times post-infection ., The infected cells demonstrated elevated level of acyl-CoA synthetase activity as early as 2 h p . i which steadily increased at later times ( Fig . 3A ) ., Cellular acyl-CoA synthetases have different preferences for the backbone length and degree of saturation of FA , although the substrate specificity is generally not very strict and one enzyme can activate multiple FA species 23 , 33 ., To assess the substrate specificity of acyl-CoA synthetases activated upon infection we performed FA import competition assay by labeling the cells at 4 h p ., i ., with bodipy-FA in the presence of 125× molar excess of competitor FAs ., If the competitor FA is a preferred substrate over the bodipy-FA probe , it should result in the corresponding reduction of fluorescence ., The control samples showed that infected cells incorporated more than 250% of bodipy-FA relative to mock-infected cells , in agreement with microscopy and TLC data ( Fig . 3 B–E ) ., In mock-infected cells incubated with serum no FA tested showed significant influence on the incorporation of bodipy-FA , likely because of the substantial amount of FAs already present in serum ( Fig . 3B , C ) ., In mock-infected cells incubated without serum the strongest competition was shown by palmitic acid ( C16:0 ) ( ∼37% of mock control ) while myristic acid ( C14:0 ) demonstrated a weaker effect ( ∼22% of mock control ) ( Fig . 3D ) ., The addition of unsaturated FAs to mock-infected cells incubated without serum actually significantly enhanced incorporation of bodipy-FA , up to more than 100% in case of linolenic ( C18:3 ) acid ( Fig . 3E ) , indicating that they were stimulating FA uptake by the cells starved without exogenous FAs for 4 hours ., The virus-infected cells showed a completely different pattern ., Unsaturated FAs: oleic ( C18:1 ) , linoleic ( C18:2 ) and linolenic ( C18:3 ) , mildly reduced bodipy-FA incorporation in the presence and in the absence of serum ( Fig . 3C , E ) ., Palmitic acid ( C16:0 ) inhibited import of the fluorescent label in the presence of serum from ∼270% to ∼220% , and in the absence of serum from ∼250% to ∼190% ( Fig . 3A and C ) ., The strongest competitor for the bodipy-FA incorporation in infected cells was myristic acid ( C14:0 ) inhibiting bodipy-FA incorporation in the presence of serum from ∼270% to ∼150% and from ∼250% to ∼130% in the absence of serum ( Fig . 3B , D ) ., These data show that acyl-CoA synthetase activity in infected cells is strongly stimulated from the early time post infection and that its substrate preference is changed ., The competition experiments suggest that the pool of acyl-CoAs available for new phospholipid synthesis should be different in infected and mock-infected cells ., To investigate the changes in the spectrum of PC molecules , TLC-MALDI was used to couple the power of solvent resolution of phospholipids by TLC to the mass identification capacity of matrix assisted laser desorption-ionization time-of-flight mass spectrometry ( MALDI-TOF-MS ) ( Fig . 4A ) ., PC was first identified by characteristic TLC migration , and reflectron positive mode MALDI-TOF-MS was used to scan the TLC lane ., The mass to charge ratio ( m/z ) was used to secondarily identify the major PC molecules and acyl variants ( Fig . 4B ) ., We observed a substantial drop in the diversity of the PC molecules containing long C18 chains , running in the high Rf chromatography zone , and correspondingly a rapid increase in the PCs with shorter acyl chains from the low Rf zone upon infection ( Fig . 4C ) ., The analysis of the individual PC classes demonstrates a fast shift in the composition of PCs during infection ., At 2 h p ., i ., there is already a significant increase in PCs with C18/C18 acyl chains , as well as C16/C18 ones , accompanied by a noticeable drop in the abundance of the C14/C16 and C16/C16 PCs , compared to mock-infected cells ., This general trend continues later in infection with an especially strong increase in the C16/C18 PC species at 4 h p ., i ., ( Fig . 4D ) ., Changes in lipid abundance at 6, h . p . i . do not follow the general trends observed at 2 and 4, h . p . i . likely due to the significant degree of cell lysis observed at this late stage of infection at high MOI ., It should be noted that while the competition assay showed a strong preference for import of C14 myristic acid to infected cells , it only reflects the changes in the prevalent cellular acyl-CoA synthetase activity induced by polio infection , and cannot be directly interpreted as that myristic acid is the predominant imported FA in natural conditions ., The actual composition of intracellular acyl-CoA pool will be shaped by the availability of the corresponding FA substrates ., The resolution of TLC-MALDI is not sufficient to separate PC molecules with saturated and unsaturated FA chains with the same number of carbon atoms ., Thus , PV infection does not only up-regulates the overall synthesis of PC but specifically changes the molecular composition of this structural phospholipid indicating that membranes of PV replication complexes are significantly different from the pre-existing cellular membranes ., We investigated effect of different inhibitors of cellular metabolism and viral replication on the activation of FA import ., The possibility that a PV protein ( s ) may have acyl-CoA synthetase activity is unlikely since all known acyl-CoA synthetases have signature of two conserved motifs 22 lacking in the PV polyprotein ., PV replication proceeds in the cytoplasm and induces rapid shut-off of cellular mRNA translation , inhibition of nuclear transcription and disruption of nucleo-cytoplasmic barrier 3 ., Indeed replication of poliovirus t ., I Mahoney is not affected by actinomycin D ( AMD ) , an inhibitor of nuclear transcription 34 , 35 , our observations ( not shown ) ., We assessed the effect of inhibition of cellular transcription on increase of fatty acids import upon polio infection ., The cells were pre-incubated with AMD for 30 min before the infection , and the inhibitor was present in the media further on during the whole time of infection and bodipy-FA labeling ., Consistent with the sufficiency of pre-existing cellular factors for poliovirus replication , we observed that actinomycin D had no effect on the activation of bodipy-FA import in infected cells ( Figure S2 , panels A and B ) ., We also investigated if continuous synthesis of PV RNA and proteins are required to sustain the elevated FA uptake by infected cells ., The infection was allowed to proceed normally for 3 . 5 h without the inhibitors , and then guanidine-HCl , a strong specific inhibitor of PV RNA replication 36 , or cycloheximide , a general inhibitor of translation , were added ., After 30 min incubation with the inhibitors , the medium was replaced with the labeling media that contained bodipy-FA and the corresponding inhibitors , and the cells were incubated for another 30 min ., Thus the labeling was performed when synthesis of the viral macromolecules was already inhibited for 30 min ., The experiment shows that inhibition of polio RNA and protein synthesis did not prevent enhanced import of fatty acids ( Figure S2 , C , D and E ) ., Since infection in the control sample effectively proceeded an hour more than in the inhibitor-treated samples ( 30 min pre-incubation +30 min labeling in the presence of inhibitors ) , control infected cells show higher bodipy-FA accumulation , consistently with the correlation between the amount of viral proteins and the level of FA import stimulation ., Thus , increase of FA import in infected cells does not depend on new expression of cellular genes and likely relies on activation of pre-existing cellular factors by viral proteins ., The human genome contains genes for 13 long and very long chain acyl-CoA synthetases that may facilitate FA uptake by the cells 22 ., The data on expression profiles of these proteins as well as on their contribution to cellular metabolism are still very fragmentary and controversial 37 ., First , we monitored by western blot several long-chain acyl-CoA synthetases for which the reliable antibodies were available ., We observed specific proteolytic cleavage of FATP3 and to a lesser extent Acsl3 proteins in infected cells , suggesting that their activity is being actively regulated ( Figure 5A , arrows ) ., Western blots of Acsl5 and FATP4 did not reveal obvious modifications of these enzymes in infected cells ( Figure 5A ) ., To see if association of acyl-CoA synthetases with cellular components is changed upon infection we treated the cells with digitonin ., The membrane-targeted viral proteins 2C and 2BC were virtually totally recovered from the permeabilized cells ., At the same soluble proteins 3D and 3CD were mostly lost upon cell permeabilization confirming optimal permeabilization conditions ( Fig . 5A ) ., Some amount of 3D and 3CD is expected to remain associated with the membrane-bound viral replication complexes ., We observed significant loss of FATP3 protein from permeabilized cells at 4 and 6 h p . i . indicating that its association with cellular components is changing ( Figure 5A , arrowhead ) ., FATP3 was previously shown to be an-ER-localized protein with its N-terminus inserted into the ER lumen 21 , and would be expected to remain in cells after digitonin treatment , as we see in the mock-infected cells ., Thus its loss from the infected samples demonstrates that association of this protein with cellular structures is changing upon infection ., Interestingly , FATP3 is one of the two long chain acyl-CoA synthetases undergoing proteolytic processing upon infection ., To implement an unbiased approach to identify acyl-CoA synthetases that support replication of poliovirus we performed screen with siRNA pools targeting all 13 long chain acyl-CoA synthetases ., Only siRNA against FATP5 showed significant toxicity in HeLa cells likely due to some non-specific effect ( Figure S3 ) since this protein is believed to be expressed only in liver 38 ., Depletion of other acyl-CoA synthetases was well tolerated by the cells , the apparent slight toxic effect rather reflects somewhat slower growth of cells treated with certain siRNA pools ( Figure S3 ) ., Our initial screen identified three siRNA pools that induced significant , ∼80% reduction of replication: anti-acyl-CoA synthetase Bubblegum 2 ( AcsBG2 ) , FATP3 and Acsl3 ( Figure S3 ) ., Western blot analysis of the targeted proteins revealed that only effect of Acsl3 siRNA was specific ., AcsBG2 was not expressed in our HeLa cells as expected , since this protein was previously shown to be specific for brain stem and testis 39 , and treatment of cells with either pooled or individual siRNAs against FATP3 did not result in significant reduction of the amount of the protein ( not shown ) ., All siRNAs from the anti-Acsl3 pool resulted in reduction of the targeted protein and decreased replication of PV , siRNA #2 was the most potent ., The specificity of the ACSL3 knock-down effect was confirmed by rescue of polio replication by expression of the ACSL3 with mutated siRNA #2 targeting sequence ( Figure S4 ) ., The strongest reduction of polio replication with the least cellular toxicity was observed after treatment of cells with the all four anti-Acsl3 siRNAs pool ( Figure 5B ) ., We also monitored PV infection in the cells expressing recombinant protein GFP-Acsl3-HA ., Accumulation of viral proteins was significantly delayed in such cells , compared to cells expressing just EGFP , or transfected with an empty pUC plasmid ( Fig . 5C ) , suggesting that fusion protein GFP-Acsl3-HA works like a dominant-negative mutant in the context of polio infection ., Note that transfection efficiency of HeLa cells is about 60–80% and protein accumulation is measured in the total cell population , therefore the actual reduction of polio replication in transfected cells only should be even stronger ., Since knock-down of Acsl3 expression inhibits polio replication , it is impossible to directly examine the role of Acsl3 in activation of FA import upon infection ., Thus we expressed poliovirus non-structural P2P3 polyprotein fragment ( Fig . 6A ) in cells treated with control or ACSL3-targeting siRNAs with the help of vaccinia virus expressing T7 RNA polymerase 40 ., This system is independent of polio replication and is discussed in details in the section below ., Expression of poliovirus proteins was induced by infection of cells with vaccinia-T7 virus ∼72 hours post siRNA transfection ., At 4 hours post vaccinia infection bodipy-FA probe was added to the media for 30 min ., The cells treated with control siRNA which were positive for a polio antigen showed strong activation of FA import ( Fig . 5D , arrows ) , while import of bodipy-FA in the cells with ACSL3 knock-down was significantly lower ( Fig . 5E , arrowheads ) ., The statistical analysis confirmed that bodipy-FA fluorescence normalized to polio protein 2B signal strongly declined in ACSL3 knockdown cells ( Fig . 5D ) ., Our data show specific limited proteolysis of Acsl3 and FATP3 in infected cells , accompanied by the loss of membrane association of FATP3 , which likely leads to modulation of activity of these enzymes , and demonstrate that functional Acsl3 is required for polio replication and is directly involved in import of FA upon expression of polio proteins ., To identify a PV protein ( s ) responsible for activation of FA import we expressed fragments of the viral polyprotein with the help of the vaccinia virus expressing T7 RNA polymerase 40 ., The cells are transfected with a plasmid coding for a viral protein under control of T7 RNA polymerase promoter , and the next day they are infected with a vaccinia virus expressing T7 RNA polymerase gene ., Thus the gene of interest is only expressed when T7 RNA polymerase accumulates in vaccinia-infected cells ., This system provides rapid expression of high amount of recombinant proteins independent of nuclear transcription and RNA processing machinery , thus allowing synthesis of poliovirus proteins uncoupled from replication of viral RNA , on the timescale similar to the normal polio infection ., It was successfully used previously to assess effects of individual PV proteins on cellular membrane architecture 41 ., The P1 region of poliovirus genome codes for the structural proteins which are dispensable for replication , so we focused on the non-structural proteins encoded in the P2P3 genomic region ( Figure 6A ) ., The cells transfected with the plasmids coding for fragments of PV cDNA were infected with vaccinia virus , and bodipy-FA probe was added to the media for 30 min at 4 hours post vaccinia infection ., All cells displayed significant vaccinia-induced CPE at that time ( Fig . 6 B and C ) ., Three of the polio proteins: 2B , 2C , 3A have membrane localization domains , and they have long been implicated in membrane rearrangements in infected cells 5 , 6 , 42 ., However individual expression of 2B , 2C , 2BC , 3A , as well as 3CD and 3D did not result in increased FA import ( not shown ) ., Expression of the whole P2P3 polyprotein ( 2A-3D ) ( Fig . 6A ) induced strong increase in bodipy-FA import ( Fig . 6B 1 and 5 , arrows; and Fig . S5 ) ., Expression of the 2B-3D or 2C-3D polyprotein fragments ( Fig . 6A ) never stimulated accumulation of bodipy-FA to the level comparable to the 2A-3D expressing cells ( Fig . 6B 2 , 6 and 3 , 7; and Figure S5 ) , showing that 2A is the protein responsible for triggering activation of FA import ., Compared to the control cells infected with vaccinia-T7 virus after transfection with an empty vector , cells expressing 2B-3D fragment showed small , but reproducible increase in the baseline level of bodipy-FA accumulation ( Figure S5 ) ., In the context of the P2P3 2A is expressed together with all the other non-structural poliovirus proteins and thus the activation of fatty acid import may depend on coordinated action of 2A and other viral factors ., To investigate if expression of 2A alone can induce activation of FA import we generated a construct that expresses the 2A protein with an HA tag between amino-acids 144–145 since suitable anti-2A antibodies were not available ., This position was previously identified to tolerate insertions in the context of the polio genome 43 ., The full length polio RNA with 2A-HA had the same infectivity as the wt RNA , although it displayed somewhat smaller plaque phenotype ( not shown ) , showing that 2A-HA is fully functional in the viral life cycle ., When we expressed the 2A-HA protein individually it did not induce activation of FA import on its own ( not shown ) ., To investigate the requirement for 2A protease activity we engineered a point mutation in the 2A sequence substituting the catalytic amino acid C109 to A 44 ., The lack of the protease activity of the 2A C109A mutant was confirmed by the absence of processing of eIF-4G , a well-established cellular target of 2A ( Fig . 6D ) ., Expression of the P2P3 piece of the PV polyprotein with the inactive 2A induced activation of the FA import like the wt construct ( Fig . 6C ) , showing that complex role of 2A in modification of metabolism of infected cells is not restricted to the proteolitic processing of cellular proteins ., Thus poliovirus protein 2A is necessary for activation of FA import , independent of its protease activity , but expression of 2A alone is not sufficient and requires contribution from other viral non-structural proteins from the P2P3 region ., Viruses in the animal host encounter diverse cellular environments , even when their tropism is limited to a few specific tissues ., At the same time the core essential processes of replication machinery are expected to operate similarly in every cell type permissive for viral infection ., To see if the activation of long chain FA import is a universal attribute of picornavirus infection , we assessed FA import in different types of cells upon infection with different picornaviruses ., PV replication induced strong activation of bodipy-FA import in Vero ( green African monkey kidney ) , 293HEK ( human embryonic kidney ) and SH-SY5Y ( human neuroblastoma ) cells similarly to what we observed previously in HeLa cells ( Fig . 7A–D ) ., The Figure 7B shows that only Vero cells actively expressing polio proteins demonstrate high FA import phenotype ., To see if different viruses induce activation of FA import we compared PV-infected HeLa cells with the cells infected with Coxsackie virus B3 ( CVB3 ) , another enterovirus related to polio; as well as with a significantly more distantly related encephalomyocarditis virus ( EMCV ) ., These viruses efficiently replicate in HeLa cells with similar duration of their infection cycles ( not shown ) ., The cells infected with all these viruses showed strong activation of the bodipy-FA import which was distributed into similar membranous structures ( Fig . 7 E–F ) ., These data show that activation of long chain FA import is a universal mechanism of altering host cell membrane metabolism activated by diverse picornavir | Introduction, Results, Discussion, Materials and Methods | All positive strand ( +RNA ) viruses of eukaryotes replicate their genomes in association with membranes ., The mechanisms of membrane remodeling in infected cells represent attractive targets for designing future therapeutics , but our understanding of this process is very limited ., Elements of autophagy and/or the secretory pathway were proposed to be hijacked for building of picornavirus replication organelles ., However , even closely related viruses differ significantly in their requirements for components of these pathways ., We demonstrate here that infection with diverse picornaviruses rapidly activates import of long chain fatty acids ., While in non-infected cells the imported fatty acids are channeled to lipid droplets , in infected cells the synthesis of neutral lipids is shut down and the fatty acids are utilized in highly up-regulated phosphatidylcholine synthesis ., Thus the replication organelles are likely built from de novo synthesized membrane material , rather than from the remodeled pre-existing membranes ., We show that activation of fatty acid import is linked to the up-regulation of cellular long chain acyl-CoA synthetase activity and identify the long chain acyl-CoA syntheatse3 ( Acsl3 ) as a novel host factor required for polio replication ., Poliovirus protein 2A is required to trigger the activation of import of fatty acids independent of its protease activity ., Shift in fatty acid import preferences by infected cells results in synthesis of phosphatidylcholines different from those in uninfected cells , arguing that the viral replication organelles possess unique properties compared to the pre-existing membranes ., Our data show how poliovirus can change the overall cellular membrane homeostasis by targeting one critical process ., They explain earlier observations of increased phospholipid synthesis in infected cells and suggest a simple model of the structural development of the membranous scaffold of replication complexes of picorna-like viruses , that may be relevant for other ( + ) RNA viruses as well . | Eukaryotic cells feature astonishing complexity of regulatory networks , yet control over this fine-tuned machinery is easily overrun by viruses with expression of just a handful of proteins ., One of the striking examples of such hostile take-over is the rewiring of normal cellular membrane metabolism by ( + ) RNA viruses towards development of new membranous organelles harboring viral replication machinery ., ( + ) RNA viruses of eukaryotes infect organisms from unicellular algae to humans ., Many of them induce diseases resulting in significant economic losses , public health burden , human suffering and sometimes fatal consequences ., We show how picornaviruses reorganize cellular lipid metabolism by targeting long chain acyl-CoA synthetase activity ., This induces increased import of fatty acids in infected cells and up-regulation of phospholipid synthesis , resulting in formation of replication organelles different from the pre-existing cellular membranes ., This mechanism is utilized by diverse viruses and may represent an attractive target for anti-viral interventions . | molecular cell biology, virology, membranes and sorting, viral replication complex, biology, microbiology, viral replication | null |
journal.ppat.1000491 | 2,009 | The Structure of a Biologically Active Influenza Virus Ribonucleoprotein Complex | The influenza A viruses belong to the family Orthomyxoviridae and are genetically and antigenically heterogeneous ., They are responsible for annual epidemics of respiratory disease and represent an important public-health problem 1 ., All viral subtypes can be found in their natural reservoir , that comprises several wild avian aquatic and terrestrial species ., From this reservoir , influenza viruses can occasionally infect mammalian species , including man , by either gene reassortment with already established mammalian viruses or by direct adaptation 2 , and thus produce a pandemic ., Since 1997 , transmissions of avian H5N1 influenza viruses to humans have originated hundreds of highly pathogenic infections and generated fears for a new pandemic of unprecedented impact 2 , 3 ., The recent transmission of swine H1N1 influenza viruses to humans could represent the first time that a new pandemic can be followed on line ( http://www . who . int/csr/disease/swineflu/en/index . html ) ., The genome of the influenza A viruses comprise eight single-stranded RNA molecules of negative polarity with partially complementary ends that form a closed structure ., The native ribonucleoprotein ( RNP ) particles are formed by the association of these single-stranded RNAs to multiple monomers of nucleoprotein ( NP ) and a single copy of the polymerase , a heterotrimer composed by the PB1 , PB2 and PA subunits 4 , 5 ., Such RNPs are independent molecular machines responsible for transcription and replication of each virus gene ., When analysed structurally by electron microscopy , virion RNPs appear as flexible , supercoiled structures 6 , 7 ., The helical organization of the RNPs is determined by the structure of the NP , as complexes of NP and unrelated RNA also adopt helical structures 8 , and purified NP can form RNP-like helical particles in the absence of RNA 9 ., The polymerase complex binds the vRNA promoter , that is formed by the partially complementary 5′- and 3′-terminal sequences 10–12 , and determines the superhelical arrangement of natural virus RNPs 13 ., Although the RNPs are the essential elements for virus replication and gene expression , their structural analysis has been hampered by their heterogeneity and flexibility ., However , in vivo replication of recombinant model-RNPs indicated that helical- , elliptic- or circular-shaped structures could be generated with RNA templates of diminishing lengths 14 ., The clone 23 model-RNP , which represents the smallest efficient replicon , was circular in shape and showed sufficient structural rigidity to be analysed by electron microscopy and image processing after negative staining 15 ., Here we report the purification of recombinant clone 23 RNPs to near homogeneity and their structural analysis by cryo-electron microscopy ( cryo-EM ) ., It is important to stress that the RNPs analysed were the final products of in vivo replication , as no RNP accumulation was observed when NP or polymerase negative mutants were used for in vivo reconstitution ., The final structure shows a resolution of 12 Å for the NP and 18 Å for the polymerase complex and represents the first structure of a functional influenza virus RNP and indeed of the RNP from any negative-stranded RNA virus ., Previously , we used recombinant RNPs purified by successive glycerol gradient centrifugation steps to analyse their structure by electron microscopy of negative-stained samples 15 ., To improve the purity and yield of the RNP preparations , we used a PB2 subunit containing a His-tag at the C-terminus , a modification that did not alter the in vivo replication activity of the RNPs , as described previously 16 ., The purification protocol involved an optimised Ni-NTA-agarose affinity step , a gel-filtration chromatography and a final concentration on a Ni-NTA-agarose resin ., Such procedure allowed the routine preparation of essentially homogeneous and biologically active RNPs with a concentration appropriate for structural analysis ( Fig . 1A–E ) ., Most of the cellular contaminants could be removed in the first Ni-NTA column , while active RNPs were concentrated ( Fig . 1A , B ) ., The remaining contaminants were eliminated by gel filtration ( Fig . 1E ) , a step in which the signals for the polymerase and NP co-migrated with the in vitro transcriptional activity ( Fig . 1C , D ) ., The purified RNPs ( Fig . 1E , frame ) were concentrated by binding to and elution from Ni-NTA-agarose ( data not shown ) and used for cryo-EM ., To generate an initial model for reconstruction , a purified RNP sample was stained with uranyl-acetate and imaged at 20° tilt in a FEI Tecnai G2 field emission gun microscope ., A total of 2035 particle images were employed to generate a three-dimensional reconstruction using the SPIDER algorithms 17 ., To generate a three-dimensional reconstruction of frozen-hydrated RNPs , samples of purified RNPs ( Fig . 1E , frame ) were fast-frozen on holey-grids and imaged in the same microscope ., A total of 9571 individual particle images were selected from the micrographs after CTF correction and used for refinement ( see Fig . S1 for a gallery of single particle images ) ., Two independent refinement processes were carried out , with and without imposing 9-fold symmetry ., The three-dimensional reconstruction obtained by imposing 9-fold symmetry lacked information about the polymerase complex but could achieve better resolution for the NP ring ., On the contrary , refinement without imposing symmetry allowed reconstruction of the complete RNP particle but the resolution obtained was significantly lower ( Fig . S2 ) ., The final structure is shown in Fig . 2 and Video S1 , and represents a composite map formed by the 7 NP monomers not contacting the polymerase , that are derived from the structure refined with symmetry , while the polymerase complex , as well as the 2 adjacent NP monomers are derived from the volume refined without symmetry ., Therefore , the resolutions for either section of the model are different: 12 Å for the NP ring and 18 Å for the polymerase complex ( Fig . S3 ) ., Each NP monomer consists of two domains , an upper head domain and a centred body , which contains a small mass protruding at the bottom ., When represented at the calculated threshold no massive contacts among the NP monomers were apparent , suggesting that the interaction sites are flexible or random coil chains ., The polymerase complex is in contact with two of the NP monomers , which lack apparent interaction with each other ( Fig . 2 ) ., The structure of the polymerase complex resembles that previously obtained by negative-staining 16 , 18 , but has higher resolution ., A comparison between both structures allowed the localisation of specific subunit domains , as defined earlier by binding of monoclonal antibodies or tagging ( Fig . 3A ) and suggest that the main NP-polymerase interactions are mediated by the PB1 and PB2 subunits ., These interactions are quite different in intensity , the former being stronger than the latter ( Fig . 2 , Fig . S2 and Video S1 ) ., Docking the recently reported atomic structure of the PA ( C ) -PB1 ( N ) dimer 19 , 20 was consistent with its predicted localisation ( Fig . 3B ) 16 and would suggests that the PB1 and PA subunits account for the upper , bulkier section of the complex while PB2 would be localised at the bottom region ., We also carried out a docking of the atomic structure of the NP in the cryo-EM reconstruction ., The handedness of the cryo-EM map was determined on the basis of the correlation coefficient of the NP atomic structure docked into the symmetrised volume ., The fitting assays were carried out with both handednesses , using either volumetric or laplacian criteria ., The maximum correlation coefficients were 0 . 854 and 0 . 341 for volumetric and laplacian tests , respectively ., These values were 2 to 30% better for the selected as compared to the alternative handedness ., In addition , another important consideration indicates that the selected handedness is correct ., In the atomic structure of the influenza NP ( pdb accession number 2IQH ) there are some portions of the molecule that are not defined ., The connections between the loop 402–428 ( which is involved in NP-NP interaction; see below ) and the body of the molecule could not be determined ( sequence A428-S438 ) ., The distance between these two amino acids in the selected fitting was around 25 Å , compatible with a 10 amino acids distance , whereas in the fitting performed in the structure with the opposite handedness , these two amino acids were 41 Å apart ., The result of the docking is shown in Fig . 4A and confirms the quality of the structural model obtained ., A good fit is observed between the two domains described in the atomic structure and the volume of the NP monomer ., However , additional masses are observed at the top and at the bottom of each NP monomer ., It could be argued that such additional masses arise as a consequence of using an initial negative-stain model that was derived by conical-tilting ., However , we used the same image data set to carry out a control refinement , using as initial model a 9-mer-ring structure generated with the atomic model of the NP filtered to a resolution of 30 Å , and the final model obtained was indistinguishable from that shown in Fig . 2 ( data not shown ) ., Furthermore , the angular coverage of the images ( Fig . S4 ) was sufficient to exclude the missing cone as an explanation for this extra volume ., Thus , we feel that the additional masses detected in the cryo-EM model of the NP monomers are bona fide ., We propose that the extra mass at the top of the NP monomer corresponds to the protein sequences not solved in the crystal structure 21 while that at the bottom may contain genomic RNA ., In fact , when decreasing threshold values were used to represent the RNP volume , the additional mass at the bottom of the NP was persistent , suggesting a high mass density ( data not shown ) ., To test the potential RNA-dependence of the RNP structure , these were purified by affinity chromatography on Ni-NTA-agarose , extensively treated with T1 and pancreatic RNAses and analysed by gel filtration ., The results are shown in Fig . 5A and clearly indicate that the interaction between the polymerase complex and the NP ring is highly RNA-dependent , as both substructures could be separated after RNAse treatment ., On the other hand , the size of the template RNA before and after digestion with RNAse was analysed and a resistant band of around 18 nt was apparent ( Fig . 5B ) ., Since an average content of 24 nt per NP monomer has been determined 14 , this result would suggest that most of the template RNA is uniformly distributed along the RNP structure and protected by association to the NP ., Docking of the atomic structure of the NP monomers into the cryo-EM structure also allowed us to predict their interaction interfaces ., It was earlier proposed that interaction of the loop 420 ( positions 402–428 ) with a neighbouring NP monomer would account for NP polymerisation 21 , but this was suggested on the basis of the formation of a crystallographic trimer and no functional data was reported ., The interaction among NP monomers is conserved in the NP docking presented here , with the only need to alter the angle between NP monomers from about 120° in the crystal to 40° in the RNP volume ( Fig . 4A ) ., This interaction interface would be more realistic , as no NP trimeric structure has been described in natural virus RNPs , and would imply a small change in the arrangement of the connections between the loop and the body of the NP ( positions 428–438 and 396–402 ) ., These connections are in any case highly flexible and were not resolved in the atomic structure of the trimer 21 ., Although such a flexible connection is not detectable in the cryo-EM map at the threshold shown in Fig . 4A ( σ\u200a=\u200a2 . 5 ) , it is clearly visible when the volume is represented at σ\u200a=\u200a1 . 5 ( Fig . 4B , blue arrow ) ., It is not clear whether the contacts between the NP monomers observed in the atomic structure of the trimer would be strictly conserved in the functional RNP nonameric structure ., Hence we mutated several of the positions in the loop , affecting either conserved or non-conserved amino acids ( Fig . S5 ) , and tested the biological activity of the RNP ., The replication of a viral RNP does not lead to a naked progeny RNA but rather a progeny RNP structure and it is generally accepted that encapsidation of the newly synthesised RNA by the polymerase complex and NP monomers is coupled to RNA replication ., Hence , if the mutations were to affect the NP-NP interaction , a deficiency in RNP replication would be expected ., Thus we reconstituted in vivo mini-RNPs by transfection of plasmids encoding the polymerase subunits ( of which PB2 as a His-tagged protein ) , a clone 23 template RNA and either wt or mutant NP , and purified them by Ni-NTA-agarose chromatography ., The accumulation of progeny RNPs was determined by Western-blot using anti-NP antibodies and represents the in vivo replication phenotype ., Mutants R416A and F412A were strongly affected in replication , whereas mutants S413T , F420A , K422A and S423A behaved as wt ( Fig . 6A , B and Fig . S5 ) ., These results confirm the relevance of the interaction between R416 in the loop and E339 in the connecting NP 21 and suggest that residue F420 in the loop does not play an important role in the interaction ., On the other hand , the phenotype of mutant F412A indicates that it is important for viral RNA replication ., To further analyse the phenotype of the NP mutants generated , the amount of purified mutant RNPs recovered by replication in vivo was determined by measuring their in vitro transcription activity ., The results of a typical experiment are presented in Fig . 7A and average of two independent experiments is shown in Fig . 7B ., These results are consistent with the deficiency in the replication activity observed for mutants R416A and F412A ., The replication-defective phenotype observed for these mutants could be the consequence of a defect in their homopolymerisation capacity ., To analyse this possibility mutant or wt NP were expressed by transfection in COS1 cells and total extracts were analysed by gel-filtration after extensive RNAse treatment ., Under these conditions , wt NP formed large complexes compatible with NP polymers ., On the contrary , mutant R416A , that was shown as negative in NP-NP association 21 , behaved as monomer ( Fig . 8 ) ., The phenotype of the other mutants correlated with their replicative activity in vivo ., Thus , mutant F420A behaved as wt while mutant F412A showed an intermediate phenotype ., In this report we have presented the three-dimensional structure of an active influenza virus RNP , as determined by cryo-EM ., In fact , this represents the first structure of a biologically active RNP from any negative-stranded RNA virus ., Two technical developments have allowed this breakthrough:, ( i ) the generation of recombinant RNPs that are efficient replicons and have sufficient structural rigidity 14 and, ( ii ) the optimisation in the purification protocols of RNPs amplified in vivo ., As compared to full-length virion RNPs , the structure reported here would represent a minimal RNP in which the helical section has been deleted and only the promoter region bound to the polymerase complex and the terminal loop remains ., The structure obtained for the polymerase complex present in the RNP is compatible with those reported earlier by negative-staining 16 , 18 and represents the most accurate model for a complex polymerase of a negative-stranded RNA virus thus far reported ., The correlation with the sites previously mapped 16 and the docking of the atomic structure of specific domains permitted the rough localisation of the polymerase subunits ( Fig . 3 ) ., Unfortunately , the other polymerase domains whose atomic structure is known 22–24 are not large and conspicuous enough to allow unambiguous docking in the cryo-EM structure ., The interaction among NP monomers was analysed by docking of the atomic structure into the NP ring ., The model obtained is compatible with the interaction mode proposed earlier 21 and further indicated that additional side-by-side interactions are now possible due to the tighter packing of the monomers ( see Fig . 4A , black arrow ) ., The relevance of the 420–428 loop in the NP-NP interaction was verified functionally: The contacts of amino acid R416 and F412 are essential for replication , while amino acid K422 does not appear to be important , in spite of being conserved among type A and B viruses ( Fig . S5 ) ., Previous biochemical studies had shown that residue R416 is involved in NP-NP interaction 25 and that both F412 and R416 were important for RNA binding 26 ., In view of the results presented here it is possible that the RNA-binding defect detected might be secondary to the homopolymerisation failure ., In addition , the residue at position 412 appears to be important for the template activity of the RNP , since mutation F412A specially affected the in vitro transcription of mutant RNPs ( compare Figs . 5 and 6 ) ., Contrary to the N-RNA complexes in the Mononegavirales 27 , 28 , that contain 9 nucleotides associated to each N molecule , we have estimated an average of 24 nucleotides per NP monomer in influenza RNPs 14 ., The structure of the RNP presented here is compatible with the RNA-binding site being located at the groove between the head and body domains in the NP , as previously suggested 21 , 29 ., Indeed , a connecting mass is apparent in the appropriate position ( see Fig . 3A , black arrow ) that could represent the template RNA in addition to protein contacts ., However , most of the RNA sequence present in the RNP is resistant to extensive RNAse treatment and the main protected fragment is around 18 nt long ( Fig . 5 ) ., This would suggest that the template RNA is distributed uniformly along the RNP structure , i . e . variations of the average value of 24 nt per NP monomer are small ., Furthermore , the size of the protected fragment ( 18 nt ) is similar to the average assignment of RNA per NP , suggesting that the template RNA associates to several regions of the NP and could contribute to the extra mass observed at the bottom of each NP monomer ., In addition , the N-terminal region of NP , which has been implicated in binding to RNA by biochemical assays 30 and is not represented in the atomic structure of the protein 21 , 29 , could also contribute to this extra mass ., In summary , we have reported the first structure of a biologically active influenza RNP ., This three-dimensional structure reveals the NP-NP interaction domain and will serve as a framework to generate a quasi-atomic model of the molecular machine responsible for viral RNA synthesis ., The origin of plasmids pGPB1 , pGPB2His , pGPA , pGNP ( polyA ) and pT7ΔNSRT clone 23 , containing sequences derived from the A/Victoria/3/75 influenza virus strain , has been described 14 , 16 , 31 ., The vaccinia recombinant virus expressing T7 RNA polymerase ( vTF7-3 ) 32 was provided by B . Moss ., The origin of antibodies specific for PB1 , PB2 and PA has been described 14 , 33 , 34 ., Antibodies specific for NP were generated by immunisation of rabbits with purified His-NP ., The NP mutants were generated by site-directed mutagenesis on pGNP ( polyA ) plasmid using the Stratagene Quickchange kit and specific oligonucleotides ( sequences available upon request ) and their genotype was verified by sequencing ., Recombinant RNPs containing the ΔNS clone 23 genomic RNA ( 248 nt ) were generated and amplified in vivo by transfection of plasmids pGPB1 , pGPB2His , pGPA , pGNP ( polyA ) and pT7ΔNSRT clone 23 into vaccinia vTF7-3-infected COS1 cells as described previously 16 ., For RNP purification , the clarified cell extracts were incubated overnight at 4°C with Ni-NTA-agarose resin in a buffer containing 50 mM Tris-HCl-100 mM KCl-5 mM MgCl2-0 . 5% Igepal-20 mM imidazol-1 u/µl RNAsin-EDTA-free protease inhibitors cocktail , pH 8 ., The resin was washed with 80 volumes of 50 mM Tris-HCl-100 mM KCl-5 mM MgCl2-0 . 5% Igepal-20 mM imidazol , pH 8 and 20 volumes of the same buffer containing 50 mM imidazol ., Finally , the RNPs were eluted with 50 mM Tris-HCl-100 mM KCl-5 mM MgCl2-0 . 5% Igepal-150 mM imidazol , pH 8 ., The eluted RNPs were filtered on a Sephacryl S300 column equilibrated with 50 mM Tris-HCl-100 mM KCl-5 mM MgCl2-0 . 5% Igepal-20 mM imidazol , pH 8 and the peak RNP fractions were further bound to Ni-NTA-agarose in 50 mM Tris-HCl-100 mM KCl-5 mM MgCl2-0 . 5% Igepal-20 mM imidazol-1 u/µl RNAsin-EDTA-free protease inhibitors cocktail , pH 8 , washed once with 50 mM Tris-HCl-100 mM KCl-5 mM MgCl2-0 . 5% Igepal-20 mM imidazol , pH 8 and eluted with 50 mM Tris-HCl-100 mM KCl-5 mM MgCl2-0 . 3% CHAPS-150 mM imidazol , pH 8 ., Western-blotting was performed as described 14 ., Protein silver-staining was carried out as indicated before 35 ., To determine the transcription activity of purified RNPs , samples were incubated in a buffer containing 50 mM Tris-HCl-2 mM MgCl2-100 mM KCl-1 mM DTT-10 µg/ml actinomycin D-1 u/µl RNAsin-1 mM ATP-1 mM CTP-1 mM UTP-10 µM α-P32-GTP ( 20 µCi/µmol ) -100 µM ApG for 60 min at 30°C ., The RNA synthesised was TCA precipitated , filtered through a nylon filter in a dot-blot apparatus and quantified in a phosphorimager ., To test the in vivo RNP replication , cultures of COS1 cells were infected with vaccinia vTF7-3 and transfected with plasmids pGPB1 , pGPB2His , pGPA , pGNP ( polyA ) ( or mutants thereof ) and pT7ΔNSRT clone 23 ., Total cell extracts were used for purification by affinity chromatography on Ni-NTA-agarose as indicated above and the accumulation of progeny RNPs was determined by Western-blot with anti-NP-specific antibodies and by measuring their in vitro transcription activity ., To determine the NP aggregation state , cultures of COS1 cells were infected with vaccinia vTF7-3 and transfected with plasmid pGNP ( polyA ) ( or mutants thereof ) ., Total cell extracts were prepared , treated with 50 µg/ml of RNAse A for 2 hours at room temperature and analysed by filtration over a Sephacryl S300 column calibrated with ferritin ( 440 kDa ) and BSA ( 67 kDa ) ., For electron microscopy of negatively stained samples 4 µl aliquots of purified RNPs were applied to glow-discharged carbon grids for 1 min and then stained for 1 min with 2% uranyl acetate ., Low-dose images were taken on a 200 kV FEI Tecnai G2 Field emission gun electron cryomicroscope operated at a nominal magnification of 50 k at 20° tilt ., A total of 2035 individual RNP images were extracted and processed to generate an initial model using the SPIDER software 17 ., For cryoelectron microscopy , 5 µl aliquots of purified RNPs were applied to glow-discharged Quantifoil holey grids for 2 min , blotted and frozen rapidly in liquid ethane at −180°C ., Images were taken with the same conditions as in the negative stain experiments but without tilting ., The selected micrographs were scanned on a Zeiss scanner ( Photoscan TD , Z/I Imaging Corporation ) with a final pixel size corresponding to 2 . 8 Å ., Contrast transfer function ( CTF ) of micrographs was estimated using ctffind software 36 and corrected using Bsoft 37 ., A total of 9571 images were subjected to two independent refinements with and without imposing 9-fold symmetry using SPIDER software 17 ., After reaching the convergence of these refinements , the reconstructions yielded resolutions of 18 and 12 Å for non-symmetrized and symmetrized structures , respectively ( FSC 0 . 3 criterion ) ., The final tilt range assigned in the refinement for the whole set of individual images was checked ( Fig . S5 ) and showed an angular distribution where the effect of missing cone in the reconstruction could be considered as negligible ., The absolute handedness of the volumes was determined using the atomic structure of NP protein 21 , and turned out to be the opposite to that previously published 15 , 16 ., Docking experiments were carried out using SITUS software 38 ., Finally , and to verify the positions of the extra mass and the quality of the three-dimensional reconstruction , an additional refinement was carried out using as initial model the structure of the 9 NP-mer ring resulting from the docking experiments , filtered at 35 Å ., This refinement yielded a reconstruction similar to the final structure presented here , showing that the additional masses detected in the cryo-EM structure protruding from the NP monomers are bona fide ., Volume handling was carried out using XMIPP software 39 and general visualization was performed using Chimera 40 and Amira ( http://amira . zib . de ) ., The cryo-EM map has been deposited in the Electron Microscopy Data Bank ( accession code EMD-1603 ) and the fitted atomic structure in the Protein Data Dank ( accession code 2wfs ) . | Introduction, Results, Discussion, Materials and Methods | The influenza viruses contain a segmented , single-stranded RNA genome of negative polarity ., Each RNA segment is encapsidated by the nucleoprotein and the polymerase complex into ribonucleoprotein particles ( RNPs ) , which are responsible for virus transcription and replication ., Despite their importance , information about the structure of these RNPs is scarce ., We have determined the three-dimensional structure of a biologically active recombinant RNP by cryo-electron microscopy ., The structure shows a nonameric nucleoprotein ring ( at 12 Å resolution ) with two monomers connected to the polymerase complex ( at 18 Å resolution ) ., Docking the atomic structures of the nucleoprotein and polymerase domains , as well as mutational analyses , has allowed us to define the interactions between the functional elements of the RNP and to propose the location of the viral RNA ., Our results provide the first model for a functional negative-stranded RNA virus ribonucleoprotein complex ., The structure reported here will serve as a framework to generate a quasi-atomic model of the molecular machine responsible for viral RNA synthesis and to test new models for virus RNA replication and transcription . | The influenza viruses cause annual epidemics of respiratory disease and occasional pandemics that constitute a major public-health issue ., The recent spillover of avian H5N1 and H1N1 swine influenza viruses to humans poses a serious threat of a new pandemic ., These viruses contain a segmented RNA genome , which forms independent ribonucleoprotein particles including the polymerase complex and multiple copies of the nucleoprotein ., Each of these ribonucleoprotein particles are replicated and express the encoding virus genes independently in the virus-infected cells ., To better understand how these processes take place we have determined the three-dimensional structure of a model ribonucleoprotein particle that only contains 248 nucleotides of virus RNA but is biologically active in vitro and in vivo ., The structure shows a circular appearance and includes 9 nucleoprotein monomers , two of which are associated to the polymerase complex ., Docking of the available atomic structures of the nucleoprotein and domains of the polymerase complex has permitted us to propose a quasi-atomic model for this ribonucleoprotein particle and some of the predictions of the model have been confirmed experimentally by site-directed mutagenesis and phenotype analysis in vitro and in vivo . | molecular biology/rna-protein interactions, virology/viral replication and gene regulation, infectious diseases/respiratory infections | null |
journal.pntd.0006635 | 2,018 | Human plague associated with Tibetan sheep originates in marmots | Plague is an acute infectious disease caused by Yersinia pestis that killed millions of people in Europe in the 14th century and tens of thousands in China in the 19th century 1 ., Plague is mainly a disease of wild rodents , and their parasitic fleas are considered the transmitting vectors ., So far , four subspecies of Y . pestis have been recognized on the basis of their biochemical properties: Y . pestis antiqua , mediaevalis , orientalis , and pestoides ( microtus ) 2 , 3 ., To date , at least 12 plague foci covering >1 . 4 million km2 have been identified in China 4; the largest focus is the Marmota himalayana focus on the Qinghai-Tibet plateau in northwestern China ., The overwhelming majority of Y . pestis pathogens on the Qinghai-Tibet plateau are biovar antiqua , with the exception of biovar microtus ( qinghaiensis ) in the Microtus fuscus focus , which is located in Chengduo county in Qinghai Province and in Shiqu county in Sichuan Province 4 ., The Qinghai-Tibet plateau is the highest risk area for human plague in China and M . himalayana is the primary host in this area ., The pathogen Y . pestis ( biovar antiqua ) in the Qinghai-Tibet plateau M . himalayana natural plague focus frequently causes pneumonic and septicemic plague with high mortality ., Other rodents ( Allactaga sibirica , Mus musculus , Cricetulus migratorius , Microtus oeconomus , and Ochotona daurica ) , some wild animals ( foxes , lynxes , and badgers ) , and domestic animals ( sheep , cats , and dogs ) have been found to be infected by Y . pestis 5 ., Human plague originating from Ovis aries ( Tibetan sheep ) was first reported in 1956 in Qinghai Province 5 , though no bacterial evidence was obtained at that time ., Tibetan sheep account for ~1/3 of the total number of sheep in China 6 ., And the distribution areas of Tibetan sheep plague broadly overlap with the habitat of marmots in the Qinghai-Tibet plateau M . himalayana plague focus 6 , 7 ., In August 1975 , a patient suffered from plague after butchering a dead Tibetan sheep in Yushu Prefecture , Qinghai Province ., The meat of the sheep was eaten by 10 people; two individuals suffered intestinal plague that then developed into pneumonic plague , and one died 5 ., Three Y . pestis strains were isolated from the dead individual , Tibetan sheep , and Capra aegagrus hircus ( Tibetan goat ) ., This incident was the first time that human plague associated with Tibetan sheep or Tibetan goats was confirmed with bacteriological evidence in China 5 ., In this study , we report human plague cases associated with Tibetan sheep on the Qinghai-Tibet plateau since the 1950s ., Meanwhile , to further determine the ecological function of Tibetan sheep in Y . pestis endemic epidemics , we performed a genome-wide single nucleotide polymorphism ( SNP ) analysis of Tibetan sheep-related plague events , including pathogens isolated from humans , Tibetan sheep , and marmots ., The genome-wide SNP analysis confirmed that the human plague strains were transmitted from Tibetan sheep , while the Tibetan sheep plague strains originated from marmots ., This study was approved by the Ethics Committee of the Qinghai Institute for Endemic Disease Control and Prevention ( FLW2013-001 ) and the Institute for Communicable Disease Control and Prevention ( ACUC2013-002 ) ., All animal plague surveillance procedures were performed in accordance with the National Regulations for the Administration of Affairs Concerning Experimental Animals approved by the State Council ., All procedures were in accordance with the ethical standards of the National Research Committee ., Y . pestis was isolated and identified by Gram staining , the reverse indirect hemagglutination assay , and the bacteriophage lysis test ., All Y . pestis strains isolated from Tibetan sheep ( 15 ) or humans ( 7 ) associated with Tibetan sheep on the Qinghai-Tibet plateau were included ( S1 Fig and S2 Table ) ., The 18 outbreaks of human infection were designated from A to R ( Fig 1 and S1 Table ) ., The Tibetan sheep involved in human plague outbreaks based on epidemiological investigations were designated using the same alphabetic code ( see S1 Table ) ., In addition , 14 Y . pestis strains isolated from M . himalayana were selected; whenever possible , they were from the same region as the Tibetan sheep and in the same year in order to match the isolates from Tibetan sheep plague and human plague ., Furthermore , two Y . pestis strains isolated from patients infected by M . himalayana in Nangqian County ( 2004 ) were also included 7 ., All the strains were collected from the Qinghai Institute for Endemic Disease Control and Prevention , Xining , China ., In addition , we plotted the geographical distribution of human plague , Tibetan sheep plague , and the isolates involved on a satellite map sourced from the Institute of Geographical Sciences and Natural Resources Research , Chinese Academy of Sciences , and we have received permission to publish it under a CC BY license from the institute ., A total of 38 Y . pestis strains isolated from Tibetan sheep or humans or M . himalayana were included in this study ., Genomic DNA from each bacterium was extracted using the following method in a Biosafety Level 3 Laboratory of the Qinghai Institute for Endemic Disease Control and Prevention ., Y . pestis strains were cultivated in Luria–Bertani broth at 28°C for 48 h , and the collected strains were suspended in 0 . 5 ml of TE buffer ( 10 . 0 mM Tris-HCl pH 8 , 1 . 0 mM EDTA ) and incubated at 28°C for 20 min , Then 80 μl of 10% SDS was added to the preparation ( 10 μg in 1 ml PBS ) , and maintained at 65°C for 10 min ., Next , 20 μl RNase ( 10 mg/ml ) was added , and the solution incubated at 37°C for 2 h ., Following the addition of 10 μl of proteinase K , the preparation was incubated at 37°C for 2 h ., The DNA was extracted twice with equal volumes of phenol and once with an equal volume of chloroform ., The DNA was precipitated by adding two volumes of absolute ethanol ., The precipitated DNA was washed with 70% ethanol and re-suspended in TE buffer ( pH 8 . 0 ) ., The 38 isolates were sequenced using the Illumina HiSeq 2000 platform ( Illumina , San Diego , CA ) ., Two paired-end libraries were constructed with average insertion lengths of 500 bp and 3 , 000 bp ., The raw data were filtered by FastQC , and then the clean data were assembled into contigs using SPAdes v3 . 9 . 1 ., Gene prediction was performed using Glimmer with the default parameters ., The whole-genome raw SNPs were detected through pairwise comparisons of Y . pestis genomes to the reference genome of the Angola strain ( NC_010159 ) 8 using Bowtie 2 software 9 and MUMmer 10 with the default parameters ., Twenty-one completed genomes or draft genomes obtained from the NCBI database were also included in the analysis 1 , 11–17 ( S2 Table ) ., Then the SNPs were combined , and those of low quality ( read depth <5 ) and those located within 5 bp on the same chromosome were removed to avoid the effect of recombination ., A phylogenetic tree of Y . pestis was established based on these SNPs with the Bayesian evolutionary method in BEAST software 18 using the 38 Y . pestis genomes from our study and the 21 genome sequences of Y . pestis from GenBank and rooted with Y . pseudotuberculosis ( IP32953 ) 1 , 13 ., The sequencing data of the Y . pestis strains are available in GenBank under accession numbers SRP131404 , and the genome sequences of 38 Y . pestis strains sequenced in our study have been deposited in GenBank with accession Nos SRR6512812 to SRR6512849 ., According to the epidemic history of plague on the Qinghai-Tibet plateau and the annual national plague surveillance data in China , a total of 18 human outbreaks ( events , designated A to R ) associated with Tibetan sheep have occurred since 1956 ( S1 Table and S1 Fig ) ., Among these events , a total of 78 human cases associated with Tibetan sheep ( cases of original infection and successive secondary generation ) and 47 deaths were reported , of which 70 cases and 42 deaths occurred in Qinghai ., In addition , 8 human cases ( 5 deaths ) associated with Tibetan sheep occurred in Tibet ., All index infectious cases had an exposure history of butchering or skinning diseased or dead Tibetan sheep ., Massive deaths or larger numbers of infection cases mainly occurred in four events before 1975; for example in 1956 , the index case ( Tianjun county ) suffered pneumonic plague and died after skinning a dead Tibetan sheep , and this individual infected a total of 13 individuals of whom 11 died ., Eating meat from infected sheep that is not fully cooked is another cause of human plague infection , such as those in 1961 ( Dulan county ) , 1963 ( Yushu county ) , and 1965 ( Zhaduo county ) , that caused 26 cases of infection due to eating the meat; only the index individual in each outbreak slaughtered or skinned a diseased or dead Tibetan sheep ., Considering the months in which Tibetan sheep plague , M . himalayana plague , and human plague events have occurred on the plateau since 1956 , 14 of the 27 Tibetan sheep plague events occurred during October and November ., In contrast , the peak occurrence of M . himalayana plague was during June and July and usually ended in October ( National Plague Surveillance data and reference 5 ) ., The plague in Tibetan sheep clearly lagged that in M . himalayana ( Wilcoxon signed rank test , P <0 . 05 ) ., In addition , 9 of the 18 human plague events in which the index case ( s ) originated from Tibetan sheep occurred during October and November , while the peak months of human plague originating from M . himalayana were during August and September 5 ( Fig 1 ) ., From 1997 to 2016 , no human plague cases were caused by Tibetan sheep due to active prevention and intervention measures in Qinghai , even though Y . pestis was still isolated from local Tibetan sheep and Tibetan goats on the Qinghai-Tibet plateau ., The genomic sequences of the 38 isolates of Y . pestis were assembled de novo , producing 52 contigs and 70 scaffolds on average ., The number of genes per strain ranged from 2 , 623 to 2 , 990 ., The phylogenetic tree of Y . pestis was established using all isolates in our study as well as 21 complete genomes or draft genome sequences from the NCBI GenBank database ( S2 Table ) ., We identified 1663 high-quality SNPs compared with the reference genomes and 216 within our isolates , with 39–63 SNPs per genome ., Among these SNPs , 149 were located in 143 genes , including 41 synonymous SNPs and 108 nonsynonymous SNPs , with 1–2 in each gene , whereas the remaining 67 SNPs were located in intergenic regions ., The 108 nonsynonymous sites were distributed among 106 genes ., The phylogenetic relationships we constructed ( Fig 2A ) were very similar to the genomic maximum parsimony tree reported previously 1 ., The nomenclature of the lineages in the phylogenetic tree are according to the literature 1 , 19 ., The pathogens associated with Tibetan sheep plague were clustered into the 1 . IN2 lineage in the phylogenetic tree ., These strains were comparatively closer to Y . pestis Z176003 , which was isolated from M . himalayana in Naqu County , Tibet , in 1976 11 ., In addition , strains H21 and H22 ( human plague isolates originating from M . himalayana in Bangqian Village , Nangqian County in 2004 ) were clustered in 2 . ANT1 ., Y . pestis isolated from Tibetan sheep or local M . himalayana all fermented glycerin and reduced nitrate to nitrite , i . e . , they belonged to biovar antique , the same as human plague in this focus ., Combining the epidemiological information ( S1 Table and S2 Table ) and the population structure based on the genome-wide SNP analysis , we divided the 36 Y . pestis in the 1 . IN2 lineage ( including those originating from Tibetan sheep ( 15 ) and humans ( 7 ) associated with Tibetan sheep , as well as 14 Y . pestis strains isolated from M . himalayana ) into four clusters ( I–IV ) , corresponding to eight clades ( 1–8 ) ( Fig 2B ) ., Generally , the clade-based classification agreed well with the geographical area , i . e . , the strains isolated from the same area were found in the same clade ( Fig 2B ) ., In fact , where no geographic barrier existed between adjacent areas , the pathogens isolated from adjacent areas also grouped together; for example , Juela Village in Nangqian County and Xialaxiu Village in Yushu are adjacent , and the strains isolated from the two villages grouped into Clade-1; Shanglaxiu , Batang , and Guoqing Villages are neighbors , and the lineages were grouped in Clade-4 ., This shows that the genomic phylogenetic analysis of the Tibetan sheep-related strains have territory-specific characteristics ., In addition , in Clade-1 and Clade-4 , the strains isolated in different years were grouped together ., For example , the human plague cases and those corresponding to Tibetan sheep plague occurring in 1979 ( in Xialaxiu Village , Yushu County ) and in 1997 ( in Juela Village , Nangqian County ) were grouped together into Clade-1 ., In 1975 , in Yushu County , the first human plague associated with Tibetan sheep was confirmed by bacteriological evidence ., However , in 2005 in Yushu County , a larger-scale Tibetan sheep plague occurred , in which a total of 13 Tibetan sheep and 1 Tibetan goat in the same flock died ., The isolates from these two events were grouped into Clade-4 ., This indicated that the same strains of Y . pestis successively caused Tibetan sheep or human plague outbreaks in these areas ., Of course , some isolates could not be grouped together by event although the strains were isolated in the same area , such as lineages M8 and S20 ., In fact , finding any clear epidemiological connection between these two isolates and the rest was difficult ., One possible explanation is the genomic diversity of the strains in these foci ., In Clade-1 , the Y . pestis isolated from patients ( H19 ) and Tibetan sheep ( S30 and S24 ) in Juela Village , Nangqian County in 1997 , as well as isolates from M . himalayana ( M39 ) , were grouped together ., According to the epidemiological information , the diseased herdsman ( H19 ) suffered pneumonic plague after processing a dead Tibetan sheep ( S24 ) , and isolate S30 was obtained from a sheep in the same breeding herd as the dead sheep ( S24 ) ., In addition , one strain ( M39 ) from a dead M . himalayana in a sheep grazing area had been isolated four months earlier ., In fact , a raging animal plague epidemic had occurred one year previously ( in 1996 ) in Juela Village , and a total of three strains ( M18 , M32 , and M38 ) were collected in the area ( National Plague Surveillance data ) ., The above isolates were grouped together in Clade-1 ., Two strains ( S31 and S23 ) isolated from Tibetan sheep in Xialaxiu Village , Yushu County ( adjacent to Nangqian County ) also grouped into Clade-1 ., Furthermore , the strain ( H5 ) from the human plague in 1979 , the corresponding Tibetan sheep strains ( S6 and S7 ) , and some strains isolated from M . himalayana also clustered into Clade-1 ., As noted above , in 2005 , three Y . pestis isolates ( S11 , G12 , and S13 ) were identified in two Tibetan sheep and one Tibetan goat from an outbreak of Tibetan sheep plague in Guoqing Village , Yushu ., The Y . pestis isolated from the dead M . himalayana found in the same village and in the same year ( 2005 ) were clustered into the same clade ( Clade-4 ) ., In fact , it was in Shanglaxiu Village , Yushu County , that the first human plague case associated with Tibetan sheep was confirmed in 1975 ., In addition , three Y . pestis strains isolated from dead patients and Tibetan sheep and Tibetan goats in the same herd were also clustered in Clade-4 ., Similar clustering of Tibetan sheep and M . himalayana was also found in Zongwulong Village , Delingha County , in 1996 ( Clade-5 ) ., The above findings , together with the epidemiological connections , support the conclusion that human plague came from Tibetan sheep and Tibetan sheep plague originated from marmots ., The Qinghai M . himalayana natural plague focus was first identified in 1954 as a result of the isolation of Y . pestis from a dead marmot in Qinghai Province 20 ., M . himalayana is the primary plague host in this area 5 ., According to plague surveillance data in Qinhai Province , a total of 468 human plague cases with 240 deaths were reported , of which 162 cases originated from M . himalayana ( 34 . 62% ) , 39 from Tibetan sheep ( 8 . 33% ) , 16 from carnivorous animals ( 3 . 42% ) , and 216 from successive infection of pneumonic plague by person-to-person transmission ( 46 . 15% ) 5 ., Tibetan sheep plague was sporadic on the Qinghai-Tibet plateau , and was restricted to areas that had M . himalayana plague epidemics ., One previous investigation in Yushu Prefecture in 2005 found that the infection rate of Y . pestis in Tibetan sheep was 6 . 08% ( 64/1051 ) with serum titers in the range of 1:20 to 1:1280 7 ., Tibetan sheep-related human plague infection is always associated with slaughtering or skinning diseased or dead Tibetan sheep ., Eating incompletely cooked meat from infected sheep or goats is another cause of human infection 5 ., In previously research , the incidence of Tibetan sheep-related human plague outbreaks occurring in Qinghai Province between 1975–2009 were counted , and a total of 10 Tibetan sheep-related human plague outbreaks occurred during this period , resulting in 25 cases and 10 deaths , including bubonic plague ( 9 ) , primary pneumonic plague ( 6 ) , secondary pneumonic plague ( 6 ) , septicemic plague ( 3 ) , and intestinal plague ( 1 ) 2 , 3 ., The even-toed ungulates ( Artiodactyla ) , including camels and goats 21–24 , donkeys , and cows 25 , can be naturally infected by Y . pestis ., Previous studies have shown that the sheep is a plague reservoir with high susceptibility and moderate sensitivity 26 , 27 ., And , under natural circumstances , only individual Tibetan sheep in a flock are infected , and they do not become infected directly by sheep-to-sheep contact , even when the same flock contains a mixture of sick and healthy sheep 26 ., These findings indicate that the ecological function of the Tibetan sheep in associated human plague should be considered as an intermediate or accidental host ., Another piece of supporting evidence is the fact that the occurrence of Tibetan sheep plague during the year lags behind the occurrence of M . himalayana plague ., October and November were the high incidence months for the Tibetan sheep plague and human plague originated from Tibetan sheep ., On the Qinghai-Tibet plateau , marmots begin hibernation from October to early November ., One possible reason is that the fleas living in the caves escape after the marmots enter hibernation in October and attack other animals , such as Tibetan sheep ., A minor peak for the human plague associated with Tibetan sheep occurs in June to July and presumably is caused by the massive death of marmots ., Such an ecological change could also result in more fleas escaping from dead hosts and colonizing Tibetan sheep or human beings ., Several possible scenarios may explain how Tibetan sheep become infected by marmots ., First , they could be infected by contact with the bodies of dead marmots ., Our field observations showed that Tibetan sheep have a habit of licking the bodies of dead rodents such as marmots , which may be a means of ingesting micronutrients in the plateau environment ., Previously , a study successfully induced plague infection by feeding or smearing Y . pestis in the mouths of Tibetan sheep 27 ., Another possible cause is that Tibetan sheep could be infected by fleas such as Callopsylla dolabris or Oropsylla silantiewi ., These are the main parasitic fleas in M . himalayana ., Even though they have comparatively specific host selection , they have been found to attack human beings or other animals after the death of their preferred host 6 ., Previous research has shown that C . dolabris and O . silantiewi bite and can suck the blood of Tibetan sheep in the laboratory , and the sheep can become infected and die after being challenged for 10 days 27 ., The above evidence shows that fleas play an important role in Y . pestis transmission from marmots to Tibetan sheep ., Through genomic analysis , we confirmed that human plague came from Tibetan sheep , and Tibetan sheep plague originated from marmots ., To the best of our knowledge , natural infection of sheep with Y . pestis is rare elsewhere in the world ., The Tibetan sheep plague epizootic has some novel features , such as a complex transmission route , an extended epizootic period , and the possibility of transmission across long distances ., Therefore , the hazards of Tibetan sheep plague should not be underestimated . | Introduction, Materials and methods, Results, Discussion | The Qinghai-Tibet plateau is a natural plague focus and is the largest such focus in China ., In this area , while Marmota himalayana is the primary host , a total of 18 human plague outbreaks associated with Tibetan sheep ( 78 cases with 47 deaths ) have been reported on the Qinghai-Tibet plateau since 1956 ., All of the index infectious cases had an exposure history of slaughtering or skinning diseased or dead Tibetan sheep ., In this study , we sequenced and compared 38 strains of Yersinia pestis isolated from different hosts , including humans , Tibetan sheep , and M . himalayana ., Phylogenetic relationships were reconstructed based on genome-wide single-nucleotide polymorphisms identified from our isolates and reference strains ., The phylogenetic relationships illustrated in our study , together with the finding that the Tibetan sheep plague clearly lagged behind the M . himalayana plague , and a previous study that identified the Tibetan sheep as a plague reservoir with high susceptibility and moderate sensitivity , indicated that the human plague was transmitted from Tibetan sheep , while the Tibetan sheep plague originated from marmots ., Tibetan sheep may encounter this infection by contact with dead rodents or through being bitten by fleas originating from M . himalayana during local epizootics . | Plague is mainly a disease of wild rodents , and their parasitic fleas are considered the transmitting vectors ., However , human plague originating from Ovis aries ( Tibetan sheep ) is found in the Qinghai-Tibet plateau in China , where Marmota ., himalayana is the primary plague host ., Tibetan sheep-related human plague infection is always associated with slaughtering or skinning diseased or dead Tibetan sheep ., The plague in Tibetan sheep clearly lags that in M . himalayana ., In this study , we performed a genome-wide single nucleotide polymorphism analysis of Tibetan sheep-related plague events , including pathogens isolated from humans , Tibetan sheep , and marmots ., Through genomic analysis , together with the epidemiological connections , we confirmed that human plague came from Tibetan sheep , and the Tibetan sheep plague originated from marmots ., Tibetan sheep account for about 1/3 of the total number of sheep in China ., Tibetan sheep and goats are important domestic livestock on the Qinghai-Tibet plateau ., Therefore , the hazards of Tibetan sheep plague should not be underestimated . | invertebrates, livestock, medicine and health sciences, plagues, pathology and laboratory medicine, ruminants, pathogens, microbiology, vertebrates, genomic library construction, animals, mammals, ethnicities, bacterial diseases, yersinia, fleas, dna construction, molecular biology techniques, bacteria, bacterial pathogens, tibetan people, research and analysis methods, infectious diseases, sheep, yersinia pestis, medical microbiology, microbial pathogens, molecular biology, goats, insects, agriculture, arthropoda, people and places, eukaryota, dna library construction, biology and life sciences, population groupings, amniotes, organisms | null |
journal.pntd.0005294 | 2,017 | European Aedes albopictus and Culex pipiens Are Competent Vectors for Japanese Encephalitis Virus | Japanese encephalitis is one of the major viral encephalitides in Asia , with an estimated 68 , 000 human cases per year 1 ., Up to 30% of the symptomatic cases are fatal , and long-term neurologic sequelae can occur in 30 to 50% of survivors 2 ., Japanese encephalitis virus ( JEV ) is the causative agent of Japanese encephalitis , and is transmitted through the bite of an infected mosquito ., JEV is a member of the Flavivirus genus in the Flaviviridae family and has a positive-sense RNA genome ., The viral polyprotein is processed into 10 proteins: three structural proteins and seven nonstructural proteins ., JEV strains can be differentiated into five genotypes ( 1 to 5 ) based on phylogenetic studies of the viral envelope protein sequences ., Until recently , most of the strains of JEV at the origin of major epidemics in the South , East and Southeast Asia regions belonged to genotype 3 3 , 4 ., Recently a shift in prevalence from JEV genotype 3 to 1 has been observed in several Asian countries 5–7 ., JEV genotype 5 was first isolated in Malaysia in 1952 , and is genetically and serologically distinct from other genotypes 8–10 ., No other JEV genotype 5 strain had been identified until its recent isolation from Culex spp ., mosquito pools in China in 2009 11 and in South Korea in 2010 and 2012 12 , 13 ., Most of the vectors for JEV belong to the Culicinae subfamily in the Culicidae family ., In most Asian countries , the main vector is Culex tritaeniorhynchus 7 , 14–18 , while Cx ., annulirostris was identified as the main vector for JEV transmission in Australia 19 , 20 ., Several secondary vectors are known to efficiently transmit JEV: Cx ., annulirostris , Cx ., annulus , Cx ., fuscocephala , Cx ., gelidus , Cx ., sitiens or Cx ., vishnui ., The fact that JEV can be detected in field-caught mosquitoes belonging to numerous species , such as Cx ., pipiens 12 , 17 , 21 , Aedes albopictus 7 , 22 , or Anopheles species 7 , 23 , poses the question if those mosquito species could also act as secondary vectors for JEV ., The JEV enzootic cycle involves mosquitoes and amplifying vertebrate hosts , such as water birds and domestic swine 24 ., Humans are considered as dead-end hosts , while they can be infected by JEV , they do not develop high levels of blood viremia , and thus cannot infect mosquitoes 25 ., A fragment of JEV genome was detected in a pool of Cx ., pipiens and in birds caught in 2000 and 2010 in Northern Italy 21 , 26 raising the threat of a putative emergence of the virus in Europe 27 ., Recent studies have shown that Ae ., detritus from England and Ae ., japonicus japonicus from Germany were competent to transmit JEV 28 , 29 ., These observations emphasize on the need to study the vector competence of European mosquito populations for JEV ., Ae ., albopictus is currently expanding its range , predominantly in temperate areas in North America and Europe , and this invasion raises a public health threat for pathogens transmitted by this vector , such as Zika and dengue viruses ., Cx ., pipiens is the most widely distributed species of mosquito in the world , and is typically found in temperate regions ., Cx ., pipiens complex mosquitoes play important roles in the transmission of several medically relevant pathogens such as West Nile virus ( WNV ) , Saint Louis encephalitis virus , and filarial worms 30–32 ., In the present study , we evaluated the competence of Ae ., albopictus and Cx ., pipiens populations collected in the South of France for two representative strains of JEV , belonging to distinct genotypes ., We found that both viruses could infect and disseminate to high efficiency in either vector and could be readily transmitted ., We additionally evaluated the influence of mosquito salivary factors on viral pathogenesis and showed that they had no impact on the development of Japanese encephalitis in a mouse model for the disease ., Overall , these findings highlight the need for investigation of the other factors that could contribute to JEV emergence in Europe ., The protocols and subsequent experiments were ethically approved by the Ethic Committee for Control of Experiments on Animals ( CETEA ) at the Institut Pasteur and declared to the French Ministère de l’Enseignement Supérieur et de la Recherche ( n° 000762 . 1 ) in accordance with European regulations ., Experiments were conducted following the guidelines of the Office Laboratory of Animal Care at the Institut Pasteur ., Euthanasia was performed by CO2 asphyxiation , followed by cervical dislocation ., Anesthesia was performed by intraperitoneal injection of a mixture of Xylazine ( Rompun , 5 to 10 mg/kg ) and Kétamine ( Imalgène , 80 to 100 mg/kg ) ., Cx ., pipiens form pipiens and Ae ., albopictus mosquito colonies were established in the laboratory using mosquitoes collected in Montpellier and Nice , in 2010 and 2011 , respectively ., Eggs of each mosquito colony were hatched in tap water ., Larvae were reared in plastic trays containing tap water supplemented with brewer’s yeast tablets and cat food ., Adults were maintained at 27°C , 80% relative humidity with a 12 h:12 h light: dark cycle and were given continuous access to 10% sucrose solution ., Mosquito Ae ., albopictus C6/36 cells were maintained at 28°C in Leibovitz medium ( L15 ) supplemented with 10% heat-inactivated fetal bovine serum ( FBS ) ., Baby hamster kidney-derived BHK-21 ( purchased from ATCC ) , chicken fibroblast-derived DF-1 ( obtained from Nadia Naffakh ) , and human kidney-derived HEK293T cells ( purchased from ATCC ) were maintained at 37°C in DMEM supplemented with 10% FBS ., Mouse hybridomas producing the monoclonal antibody 4G2 anti-Flavivirus E were purchased from ATCC and a highly purified antibody preparation was produced by RD Biotech ., The anti-mosquito saliva antibody was produced in house in rabbits exposed to mosquito bites ., Horseradish peroxidase ( HRP ) -conjugated goat anti-mouse and anti-rabbit IgG antibodies were obtained from Bio-Rad Laboratories ., Alexa Fluor 488-conjugated goat anti-mouse IgG antibody was obtained from Jackson ImmunoResearch ., A molecular cDNA clone of JEV genotype 3 strain RP-9 was kindly provided by Yi-Lin Ling and was modified as described previously 33 ., A molecular cDNA clone of JEV genotype 5 strain XZ0934 was described previously 33 ., To produce infectious virus , the molecular clones were transfected into HEK293T cells using Lipofectamine 2000 ( ThermoFischer Scientific ) ., At 3 days post-transfection , viral supernatants were collected and used to infect DF-1 cells in order to grow final virus stocks for experiments ., For infections , C6/36 cells were seeded in 24-well tissue culture plates in L15 , supplemented with 2% FBS ., Aliquots of virus were diluted in 200 μl of medium and added to the cells ., Plates were incubated for 1 h at 28°C ., Unadsorbed virus was removed by two washes with Dulbeccos phosphate-buffered saline ( DPBS ) and then 1 ml of L15 supplemented with 2% FBS was added to the cells , followed by incubation at 28°C until collection ., BHK-21 cells were seeded in 24-well plates ., Tenfold dilutions of virus samples were prepared in duplicate in DMEM and 200 μl of each dilution was added to the cells ., The plates were incubated for 1 h at 37°C ., Unadsorbed virus was removed , after which 1 ml of DMEM supplemented with antibiotics and antifungals , 1 . 6% carboxymethyl cellulose ( CMC ) , 10 mM HEPES buffer , 72 mM sodium bicarbonate , and 2% FBS was added to each well , followed by incubation at 37°C for 32 h ., The CMC overlay was aspirated , and the cells were washed with PBS and fixed with 4% paraformaldehyde for 15 min , followed by permeabilization with 0 . 1% Triton-X100 for 5 min ., After fixation , the cells were washed with PBS and incubated for 1 h at room temperature with anti-E antibody ( 4G2 ) , followed by incubation with HRP-conjugated anti-mouse IgG antibody ., The assays were developed with the Vector VIP peroxidase substrate kit ( Vector Laboratories ) according to the manufacturer’s instructions ., The viral titers were expressed as focus forming units ( FFU ) /ml ., Seven day-old female mosquitoes were deprived of sucrose 24 h prior to the infectious blood meal ., They were then allowed to feed for 2 h on blood-soaked cotton pledgets in the dark at 28°C ., The infectious blood meal was comprised of washed rabbit erythrocytes ( obtained from animals housed at the Institut Pasteur animal facility ) , viral suspension , and ATP ( as a phagostimulant ) at a final concentration of 5 μM ., The virus titer in the blood meal was adjusted to 8 x 106 FFU/ml ., Blood-fed females were sorted and transferred into cardboard containers covered with mosquito nets ., After exposure , engorged mosquitoes were maintained at 26°C , 80% relative humidity , with a 10 h: 10 h light: dark cycle with simulation of dawn and sunset during 2 h ., Mosquitoes were dissected at various time points after oral exposure ., For titrations , the mosquitoes or individual organs were collected in a tube containing 0 . 5 mm glass beads and 300 μl of DMEM supplemented with 2% FBS ., The organs were ground for 30 sec at maximum speed , using a Minilystissue homogeneizer ( Bertin ) and stored at -80°C until analysis ., Experiments were reproduced twice with 5 to 10 mosquitoes collected at each time point for dissection ., JEV exposed mosquitoes were anesthetized at 4°C , legs and wings were removed and the bodies were attached to a glass slide using double-sided tape ., The proboscis was manually inserted into a 10 μl low binding pipette tip filled with 10 μl DMEM containing 2% FBS ., The tip contents were collected 30 min later in a tube ., Two μl were transferred to a tube containing 2 μl SDS sample buffer and analyzed by dot-blot to verify the presence of saliva ., Four μl were analyzed by FFA to determine virus titer ., Ten to 20 mosquitoes were analyzed for each time point ( days 11 , 12 and 13 post-virus exposure ) and experiments were reproduced twice ., Five days after emerging , mosquito females were blood-fed on mice previously anesthetized by intraperitoneal injection of a mixture of Xylazine ( 5 to 10 mg/kg ) and Ketamine ( 80 to 100 mg/kg ) ., Three weeks later , 100 salivary glands ( SG ) were dissected and placed in 100 μl 1X PBS ., SG extracts were prepared by sonicating the SG ( five times at 4 min each with a pulse ratio of 2 sec on / 2 sec off ) and centrifuging the crude extract at 10 , 000 g for 15 min at 4°C ., The supernatant was transferred to clean tubes and stored at −80°C ., The inocula used in our experiments contained the equivalent to a pair of SG ., Protein lysates were prepared by cell lysis in radio-immunoprecipitation assay ( RIPA ) buffer ( Bio Basic ) containing protease inhibitors ( Roche ) ., Equal amounts of proteins were loaded on a NuPAGE Novex 4–12% Bis-Tris protein gel ( ThermoFisher Scientific ) and transferred to a polyvinylidene difluoride membrane ( Bio-Rad ) using the Trans-Blot Turbo Transfer System ( Bio-Rad ) ., After blocking the membrane for 1 h at room temperature in PBS-Tween ( PBS-T ) plus 5% milk , the blot was incubated overnight at 4°C with appropriate dilutions of the primary antibodies ., The membrane was then washed in PBS-T and then incubated for 1 h at room temperature in the presence of HRP-conjugated secondary antibodies ., After washes in PBS-T , the membrane was developed using Pierce ECL Western Blotting Substrate ( ThermoFisher Scientific ) and exposed to film ., The saliva collected from each mosquito was blotted onto a nitrocellulose membrane ., Two μl of DMEM containing 2% FBS and 1 μg of mosquito salivary gland extract were deposited on the membrane as negative and positive controls , respectively ., The membranes were blocked for 1 h in PBS-T plus 5% milk and incubated overnight at 4°C with an anti-mosquito saliva antibody ., The blots were then processed as indicated above for Western blotting ., After dissection , midguts ( MG ) and salivary glands ( SG ) were placed on slides and the PBS removed ., MG were fixed in acetone for 15 min ., SG were fixed in 4% paraformaldehyde for 15 min ., Both slides were dried and stored at 4°C until use ., The MG and SG were then rehydrated in PBS for 15 min ., The MG and the SG were incubated in Triton X100 ( 0 . 2% ) for 2 h and 15 min , respectively ., They were then washed with PBS and incubated for 30 min with PBS + 0 . 1% Tween 20 containing 1% BSA ., The slides were drained and incubated overnight at 4°C with anti-flavivirus protein E 4G2 antibody diluted in PBS , then washed with PBS ., The slides were next incubated for 1 h with a fluorophore conjugated antibody , and washed with PBS ., After washing , a drop of ProLong Gold Antifade reagent with DAPI ( ThermoFisher Scientific ) was placed on each slide and a cover slide was added ., All preparations were examined using a fluorescence microscope ( Axioplan 2 Imaging , Zeiss ) ., Three-week-old female BALB/c mice were housed under pathogen-free conditions at the Institut Pasteur animal facility ., Groups of mice were anesthetized as described above , and were next intradermally inoculated with 50 FFU of JEV genotype 5 in absence or in presence of salivary gland extract or with JEV-infected saliva diluted in 100 μl of DPBS supplemented with 0 . 2% endotoxin-free serum albumin ., An unpaired t test was used to compare quantitative data , and a Log-rank ( Mantel-Cox ) test was used to compare survival data ., GraphPad Prism was used for all statistical analysis ., Rabbit and mice were housed in Institut Pasteur animal facilities ., To assess the vector competence of European mosquitoes for JEV , we decided to use two molecular clones of viruses ( RP-9 and XZ0934 ) , which are representative of two currently circulating genotypes ., The well-characterized genotype 3 strain , JEV RP-9 , was isolated from Cx ., tritaeniorhynchus mosquitoes in Taiwan in 1985 34 , 35 , while the genotype 5 strain , JEV-XZ0934 , was recently isolated from Cx ., tritaeniorhynchus mosquitoes in China in 2009 11 ., For simplification , JEV-RP-9 and JEV-XZ0934 will be hereafter referred to as JEV g3 and JEV g5 , respectively ., Both viruses were produced by transfection of cDNA into mammalian cells , as previously described 33 , followed by amplification of viral stocks in chicken fibroblasts DF-1 cells ., Those viruses displayed comparable growth after infection of Ae ., albopictus derived C6/36 cells ( Fig 1 , 33 ) ., To evaluate the vector competence of European mosquito species for JEV , we exposed mosquitoes to either JEV g3 or JEV g5 by feeding on blood meals containing approximately 8 x 106 FFU of virus per ml ., We note that the viremia in infected pigs or in birds can reach up to 107 PFU/ml , but is on average 104 PFU/ml 36–41 ., While we offered blood meals that contained relatively high levels of virus , it is generally accepted that a greater quantity of virus is needed to infect mosquitoes orally with artificial mixtures than with viremic hosts 42 ., For each experiment , 3 blood-fed mosquitoes were harvested immediately post-virus exposure , and the ingested virus titers were evaluated by FFA ., The amount of ingested infectious virus was comprised between 400 and 9 , 000 FFU per mosquito , with an average titer of 4 , 000 FFU ., Previous studies on vector competence of various species of mosquitoes for JEV have shown the peak for JEV infection and transmission occurs between 5 and 23 days after peroral infection 23 , 29 , 43–48 ., Our preliminary studies showed that , under our experimental conditions , the majority of Cx ., pipiens and Ae ., albopictus were infected from 10 to 15 days post-virus exposure ., We chose to focus collection times around the peak of viral transmission and harvested samples at 7 , 11 , 12 and 13 days post-virus exposure ., We note that the survival rate of exposed mosquitoes dropped considerably after 2 weeks of infection , and consequently did not analyze the levels of mosquito infection beyond this point ., First , we determined the infection rates in Ae ., albopictus ( Fig 2A ) and Cx ., pipiens ( Fig 2B ) mosquitoes by titrating the midguts harvested from mosquitoes ., Next , we measured the levels of JEV infection in the heads of infected mosquitoes , and calculated infected dissemination rates ( Fig 2C and 2D ) ., We did not observe any statistically significant differences in infection rates amongst genotypes for each mosquito species or by time after the infectious blood meal ., We did note that dissemination of JEV was faster in Ae ., albopictus mosquitoes , when compared with Cx ., pipiens mosquitoes ., Notably , at 7 days post-virus exposure , we found that 57 to 90% of Ae ., albopictus mosquitoes were systemically infected , whereas only 26 to 36% of Cx ., pipiens were ( Fig 2C and 2D ) ., Last , we determined transmission rates through titration of saliva collected from blood-fed mosquitoes ( Fig 2E and 2F ) ., While this is a method widely used to determined transmission rates , we observed that salivation assays are highly dependent on salivation efficiency , and that the levels of virus in saliva can sometimes be below the detection limit of our titration assay ., Keeping in mind that this determination of the virus transmission rates has limitations , we observed that both mosquito species transmitted JEV at rates ranging from 20 to 63% for Ae ., albopictus , and from 12 to 41% for Cx ., pipiens ( Fig 2E and 2F ) ., Next , we analyzed the levels of JEV g3 and g5 accumulation in the different mosquito organs that had been harvested ( Fig 3 ) ., We noted that JEV levels in the midguts slowly decreased between 7 and 13 days post-virus exposure , while viral levels in heads and salivary glands increased over time , which is consistent with patterns of viral dissemination in mosquitoes ., We noted that at 7 days post-virus exposure , the rates of salivary glands infection ranged from 40 to 80% for Ae ., albopictus , and from 5 to 9% for Cx ., pipiens ., Viral infection of salivary glands has been shown to correlate well with infection of saliva 43 , and thus we hypothesize that Ae ., albopictus mosquitoes were likely to transmit JEV at earlier times than Cx pipiens ., Interestingly , in Ae ., albopictus mosquitoes midguts , we observed a significant difference in the titers of JEV g5 when compared to JEV g3 titers ( Fig 3A ) ., Notably , it appeared that JEV g5 accumulated to higher levels than JEV g3 at 7 days post-virus exposure , and to lesser levels at later infection times ( 11 to 13 days post-virus exposure ) ., Additionally , we analyzed the distribution of JEV envelope protein in the organs of infected mosquitoes ( Fig 4 ) ., First , we performed immuno-localization within organs harvested from Ae ., albopictus mosquitoes at 14 days post-virus exposure , which corresponds to a peak in viral transmission ( Fig 4A ) ., While envelope protein staining within the midgut was relatively weak , there was a strong staining of numerous cells within both lobes of salivary glands ., Samples collected at 11 days post-virus exposure were also analyzed by western blotting and showed good detection of the envelope protein in midguts and salivary glands ( Fig 4B ) ., To evaluate the levels of virus secreted in mosquito saliva , we collected mosquitoes at 11 , 12 and 13 days post-feeding on an infectious blood meal , and performed forced salivation ., Since not all of the mosquitoes salivate when subjected to this assay , we also performed a survey of successful salivation ., A fraction of the collected saliva was dotted on a membrane , and was next incubated with an antibody specific for mosquito saliva ( Fig 5A ) ., We noted that both mosquito species efficiently salivated under our experimental conditions , and that the levels of actual salivation were above 40% for either mosquitoes ( Fig 5A ) ., The collected saliva was then subjected to a standard infectivity assay to determine the levels of JEV transmitted in JEV-positive saliva at each time point ( Fig 5B and 5C ) ., We noted that for both mosquito species , higher levels of virus were secreted in saliva at later times post-virus exposure , which mirrored the increase in viral load in salivary glands ( Fig 3C and 3F ) ., The levels of infectious virus in saliva ranged between 2 and 200 FFU for JEV g3 ( 45 and 55 FFU in average for Ae . albopictus and Cx . pipiens , respectively ) , and between 2 and 196 FFU for JEV g5 ( 38 and 35 FFU in average for Ae . albopictus and Cx . pipiens , respectively ) ., Next we assessed whether the virus transmitted by European mosquitoes was capable of developing a productive infection in mammalian hosts ., To evaluate this , we used a previously characterized murine model for Japanese encephalitis , based on JEV g5 infection of 3-week-old BALB/c mice 33 ., Three-week-old BALB/c mice were injected via intradermal route with JEV , as this mode of injection most resembles a mosquito bite ., First , JEV-positive saliva samples collected from Ae ., albopictus and Cx ., pipiens mosquitoes ( Fig 5 ) were used as an inocula ( Fig 6A ) ., Saliva containing various loads of virus was used , with a titer comprised between 7 and 98 FFU ., A control group of mice were similarly injected with JEV grown from C6/36 cells , using a single dose of 50 FFU ( Fig 6A ) ., As expected , the animals rapidly exhibited limb paralysis and encephalitis ., We did not observe any significant differences in survival rates amongst the different inocula ( Fig 6A ) ., The survival rate was between 33 and 40% , with a mean survival time of 11 to 12 . 5 days ., The detection of JEV-specific antibodies showed that all surviving mice had been exposed to the virus ( S1 Fig ) ., Since it was shown that mosquitoes can inject salivary components that influence the outcome of viral infection 49–51 , we next evaluated the impact of European mosquito salivary glands on JEV pathogenesis in a murine model ., As described above , we used the intradermal route to inject 50 FFU of JEV g5 to 3-week-old BALB/c mice ( Fig 6B ) ., For two groups of mice , the inoculum was mixed with salivary glands extracts obtained from Ae ., albopictus or Cx ., pipiens mosquitoes ., In accordance with what was previously observed after injection of saliva collected from infected mosquitoes , we did not observe any significant difference in the development of JEV pathogenesis in presence of mosquito salivary glands ( Fig 6B ) ., Overall these experiments show that European mosquitoes are fully competent at transmitting infectious JEV , but that saliva does not facilitate the development of viral pathogenesis in a susceptible murine model ., In recent years , the increase in locally acquired exotic arbovirus diseases in Europe can be linked to the presence of appropriate combinations of vectors and vertebrate hosts , which could ultimately lead to the establishment of these diseases in Europe 52–54 ., Since 2010 , sporadic cases of locally acquired chikungunya and dengue fevers have been noted in Europe 55 , 56 ., The driving forces behind these events are viraemic travelers and the increasing presence of competent vector species , such as Ae ., aegypti and Ae ., albopictus , in temperate regions ., Likewise , the circulation of WNV and Usutu virus–two Flaviviruses—was reported in 10 European countries 57 ., Two studies in Italy reported the infection of local Cx ., pipiens populations with both WNV and Usutu virus 58 , 59 and there is an increase in WNV disease incidence in Europe 60 ., While JEV RNA was recently detected in mosquitoes and birds in Northern Italy 21 , 26 , to date , human infections with JEV were only reported in travelers returning from endemic countries 61–63 ., Our study shows for the first time that European strains of Cx ., pipiens and Ae ., albopictus are both competent vectors to transmit two genotypes ( 3 and 5 ) of JEV ., High levels of infection , dissemination and transmission rates were observed in both vectors for either genotypes after oral exposure of mosquitoes to a blood meal containing virus at 8 x 106 FFU/ml ., In the present study , we did not evaluate vector competence for viral strains belonging to the genotype 1 ., Strains belonging to this genotype have displaced the prevalent genotype 3 in several countries in recent years 5–7 ., Genotype 1 and 3 strains are genetically close when compared to the more distant genotype 5 strains 8–10 ., Since we observed equivalent vector competence of European mosquitoes for genotypes 3 and 5 , we hypothesize that those mosquitoes will also be competent at transmitting genotype 1 viruses ., Several mosquitoes from the Culex genus are established vectors for JEV ., Cx ., tritaeniorhynchus is the main vector in the enzootic cycle of JEV in tropical and subtropical regions of Asia ., Interestingly , the vector competence of a Cx ., pipiens molestus population from Taiwan was found to be similar to that of Cx ., tritaeniorhynchus , when tested in laboratory conditions 46 , which is in line with our observations ., Other investigators reported that Cx ., pipiens populations from other world regions ( Cx . pipiens molestus from Uzbekistan , Cx . pipiens pallens from Korea , Cx . pipiens from the United States of America ) were less susceptible to JEV and were not always capable of transmitting the virus 50 , 64 ., Of note , there has been some reports of isolation of JEV from field-caught mosquitoes along the years 12 , 17 , 21 , 65 , and all strains of JEV isolated from Cx ., pipiens mosquitoes in Korea in 2012 belonged to the genotype 5 12 ., As strains belonging to the genotype 5 were only rarely isolated , one can wonder if the transmission cycles that are involved in the maintenance of those viruses involve mosquito and amplifying host species different from the established Cx ., tritaeniorhynchus / swine model ., Since the currently available vaccines do not confer full protection against JEV genotype 5 strains 66 , 67 , the risks of JEV g5 transmission to human populations must be carefully examined ., Similarly to Cx ., pipiens , field-collected Ae ., albopictus mosquitoes were occasionally found positive for JEV 22 , 68 ., Various transmission rates were observed in laboratory settings , from less than 17% for Australian populations 69 to 45% for Taiwanese populations 22 ., In our experiments , European Ae ., albopictus was also able to transmit different strains of JEV to high efficiency , which supports the hypothesis that European mosquito populations belonging to these two species have a better vector competence for JEV than populations isolated in other parts of the world ., Our results also showed that the extrinsic incubation period ( i . e . the time between ingestion of the virus and the ability of the mosquito to become infectious ) for JEV is shorter in Ae ., albopictus than in Cx ., pipiens ., We have not performed a formal analysis of the relative life span of our mosquito populations after ingestion of an infectious blood meal ., If we assume that both mosquito species have similar life spans , then this would imply that Ae ., albopictus mosquitoes can transmit JEV for a longer period than the French population of Cx ., pipiens and therefore might be a more efficient vector ., Specifically , host biting preferences may have consequences on the emergence of the disease in Europe and its transmission dynamics ., Arbovirus circulation is defined by many aspects including the population dynamics of the mosquito vector , the extrinsic incubation period , and the population densities of the vertebrate amplifying hosts , all of which are influenced by environmental factors ., In the case of JEV , the classic Cx ., tritaeniorhynchus–pig transmission cycle was observed in Japan , a region of high pig farming density , but other species and scenarios could be invoked in regions where pig farming is less abundant , or where Cx ., tritaeniorhynchus is not found 70 ., An example of this is the 1995 outbreak of Japanese encephalitis in Australia that involved the presence of domestic pigs and high populations of Cx ., annulirostris 19 , 20 ., Ae ., albopictus is considered to be an opportunistic feeder: it primarily feeds on mammalian hosts ( humans , wild and domestic animals ) but can also acquire blood from avian sources 71 , 72 ., Analysis of feeding patterns in temperate regions showed that populations of Ae ., albopictus in the United States of America mainly fed on mammals and rarely on birds 73 ., Cx ., pipiens mosquitoes feed mostly on birds ( 83% ) but also on mammals 74 ., Interestingly , it was shown that 20% of Cx ., pipiens emerging from diapause in temperate habitats fed on mammals 73 ., Considering the natural cycle of JEV implying birds as reservoir and pigs as amplifying hosts , specificity in host preferences may have consequences on the possible emergence of the disease in Europe and its transmission dynamics ., Favorable conditions for JEV emergence may be gathered in several places in Europe where pig breeding sites , bird sanctuaries and Ae ., albopictus and/or Cx ., pipiens mosquitoes coexist ( Marquenterre parc , Baie de Somme , France; Camargue , Rhone delta , France; Danube delta , Roumania ) ., The last part of our study was to investigate the role of mosquito saliva in the transmission of JEV to mice ., When insects take a blood meal , they trigger defensive responses from the vertebrate , such as hemostasis and various immune responses ., The saliva proteins injected by the mosquito can counteract these defenses , through their angiogenic , anti-hemostatic , anti-inflammatory and immunomodulatory properties 75 ., This complex interaction may significantly affect the evolution of the disease; notably co-injection of virus and saliva was shown to potentiate infection of the vertebrate host by arboviruses belonging to various families 49–51 , 76 , 77 ., While we did not observe any enhancement of Japanese encephalitis disease in mice , in presence of salivary gland extract or of saliva collected from either European vectors , further studies are needed to evaluate the impact of saliva on viral burden in different organs ., Additionally , we cannot exclude the possibility that JEV pathogenesis might be enhanced by salivary factors of other mosquito species , or that saliva from the two species tested in the present study might enhance pathogenicity in other mammalian species ., In this study , we have clearly demonstrated that European populations of Ae ., albopictus and Cx ., pipiens were efficient vectors for JEV transmission ., Conditions for a putative emergence of JEV in Europe are linked to the possibility for an enzootic cycle to take place in temperate areas ., In order to complete the infection cycle , JEV must be transmitted to a susceptible vertebrate host , capable of producing sufficient viral titers for subsequent acquisition by the insect vector ., It is therefore important to further investigate whether any European swine or water birds populations can be infected with JEV and produce sufficiently high viremias to infect mosquitoes that feed on them ., Such knowledge is critical to assess the potential for JEV to establish local transmission cycles similar to the closely related WNV in Northern Italy . | Introduction, Methods, Results, Discussion, Conclusion | Japanese encephalitis virus ( JEV ) is the causative agent of Japanese encephalitis , the leading cause of viral encephalitis in Asia ., JEV transmission cycle involves mosquitoes and vertebrate hosts ., The detection of JEV RNA in a pool of Culex pipiens caught in 2010 in Italy raised the concern of a putative emergence of the virus in Europe ., We aimed to study the vector competence of European mosquito populations , such as Cx ., pipiens and Aedes albopictus for JEV genotypes 3 and 5 ., After oral feeding on an infectious blood meal , mosquitoes were dissected at various times post-virus exposure ., We found that the peak for JEV infection and transmission was between 11 and 13 days post-virus exposure ., We observed a faster dissemination of both JEV genotypes in Ae ., albopictus mosquitoes , when compared with Cx ., pipiens mosquitoes ., We also dissected salivary glands and collected saliva from infected mosquitoes and showed that Ae ., albopictus mosquitoes transmitted JEV earlier than Cx ., pipiens ., The virus collected from Ae ., albopictus and Cx ., pipiens saliva was competent at causing pathogenesis in a mouse model for JEV infection ., Using this model , we found that mosquito saliva or salivary glands did not enhance the severity of the disease ., In this study , we demonstrated that European populations of Ae ., albopictus and Cx ., pipiens were efficient vectors for JEV transmission ., Susceptible vertebrate species that develop high viremia are an obligatory part of the JEV transmission cycle ., This study highlights the need to investigate the susceptibility of potential JEV reservoir hosts in Europe , notably amongst swine populations and local water birds . | Japanese encephalitis virus ( JEV ) is the leading cause of viral encephalitis in Asia ., JEV is maintained in a cycle involving mosquitoes and vertebrate hosts , mainly pigs and wading birds ., Humans can be infected when bitten by an infected mosquito ., Culex tritaeniorhynchus is the main vector of the disease in tropical and subtropical areas ., The recent detection of JEV in birds and mosquitoes collected in Northern Italy has led us to evaluate the putative emergence of this arboviral disease in Europe ., For this purpose , we have tested the competence of European populations of Cx ., pipiens and Aedes albopictus to transmit this virus in a laboratory setting ., We showed that these local mosquitoes could be infected and were capable of transmitting a pathogenic virus to mice ., It is thus urgent to evaluate the risks of JEV emergence in European regions displaying a favorable environment for mosquito vectors , susceptible pigs and wading birds . | invertebrates, medicine and health sciences, body fluids, animal models of disease, geographical locations, microbiology, saliva, animals, animal models, physiological processes, model organisms, salivation, experimental organism systems, insect vectors, digestive system, research and analysis methods, animal models of infection, animal studies, epidemiology, exocrine glands, mouse models, disease vectors, insects, hematology, arthropoda, people and places, mosquitoes, blood, anatomy, physiology, salivary glands, biology and life sciences, europe, organisms | null |
journal.pntd.0001210 | 2,011 | The Geographic Distribution of Loa loa in Africa: Results of Large-Scale Implementation of the Rapid Assessment Procedure for Loiasis (RAPLOA) | Loiasis is a neglected tropical disease caused by infection with the filarial parasite Loa loa ., It is an African disease restricted to the equatorial rain forest regions of Central and West Africa 1 , 2 , 3 , 4 ., The limits of its geographical distribution are Benin to the west , Uganda to the east , latitude 10° to the north , and Zambia to the south 5 ., The disease is transmitted by Chrysops vectors with the major species being C . silacea and C . dimidiata 6 ., The clinical manifestations of loiasis include sub-conjunctival migration of the adult L . loa worm , oedema ( Calabar swelling ) and pruritus 7 ., Loiasis has recently emerged as a disease of public health importance , not because of its own clinical manifestations but because of its negative impact on the control of onchocerciasis and lymphatic filariasis in areas of co-endemicity ., During the 1990s several patients who harboured a high intensity of L . loa infection developed severe adverse neurological reactions after treatment with ivermectin for onchocerciasis in Cameroon 8 , 9 ., Based on the data for Cameroon , a relationship between the risk of severe adverse reactions and the intensity of L . loa infection was established and it was estimated that individuals harboring more than 30000 L . loa microfilaria per millilitre of blood ( mf/ml ) are exposed to a significant risk of serious neurological reactions following ivermectin treatment 8 , 9 , 10 ., The prevalence of high L . loa microfilarial loads in endemic communities is directly related to the prevalence of microfilaraemia , and it has been suggested that a microfilarial prevalence of 20% in individuals above the age of 15 years be regarded as the threshold above which there is an unacceptable risk of severe adverse reactions ( SAEs ) with ivermectin treatment 11 ., When the first cases of SAE after ivermectin treatment were reported 9 , adequate knowledge was lacking on the geographic distribution of loiasis ., Boussinesq and Gardon undertook therefore in 1997 a literature review of available data on the prevalence of L . loa microfilaraemia in west and central African regions 12 and identified several zones where loiasis was highly endemic and overlapped with onchocerciasis , e . g . in parts of Cameroon , Gabon and the Democratic Republic of Congo ( DRC ) ., However , the available data were limited and there were many areas that were potentially loiasis endemic but for which no local data on L . loa infection existed ., The published data also had limitations as they were collected over different periods by different researchers using non-standardized diagnostic procedures ., Hence there was an urgent need for more detailed , standardized information on the distribution of loiasis in Africa as a basis for operational planning of community directed treatment with ivermectin ( CDTi ) of onchocerciasis and lymphatic filariasis ., Between 2000 and 2004 , environmental risk models were developed and applied for the prediction of loiasis endemicity based on environmental factors ( land cover , forest cover and soil type in the initial model 13 , and Normalised Difference Vegetation Index or NDVI and elevation in later models 14 , 15 ) that were favorable for the development of Chrysops ., These models have helped to clarify the approximate distribution of loiasis endemicity in Africa , but their predictions were not always sufficiently precise 16 ., Hence there was a need for local epidemiological surveys in areas that were potentially loiasis endemic and where ivermectin treatment was planned ., The standard parasitological method for the diagnosis of loiasis is the thick blood film ., However , this method is not very suitable for large-scale surveys because of its invasiveness and operational constraints ., Immunological and molecular methods 17 , 18 have been proposed for the diagnosis of loiasis , but have not been sufficiently developed and tested to make them suitable for large-scale epidemiological surveys ., There was therefore an urgent need for a non-invasive , simple and rapid method to identify communities in which individuals are at risk of developing SAEs ., A study carried out in Cameroon and Nigeria in 2001 , sponsored by the UNICEF/UNDP/World Bank/WHO Special Program for Research and Training in Tropical Diseases ( TDR ) , led to the development of the Rapid Assessment Procedure for Loiasis ( RAPLOA ) 19 , 20 ., This method is based on a key clinical manifestation of loiasis , the subcutaneous migration of the adult L . loa worm under the conjunctiva of the eye , which is a well-known and highly noticeable experience in loiasis endemic areas ., The study demonstrated a close correlation between the prevalence of a history of eye worm and the prevalence L . loa microfilaraemia at the community level ., Using a threshold of 40% , the prevalence of eye worm history was a good predictor of high-risk communities , i . e . communities where the prevalence of microfilaraemia >20% or where the prevalence of very high intensities of infection ( more than 30 , 000 mf/ml ) >2% , with a sensitivity of 100% and specificity ranging from 75 to 90% 20 ., The RAPLOA method was subsequently validated successfully in a study in the Republic of Congo and the Democratic Republic of Congo ( 21 , 22 ) ., The Mectizan Expert Committee and the Technical Consultative Committee of the African Programme for Onchocerciasis Control ( APOC ) 23 jointly issued in 2004 guidelines for the treatment of onchocerciasis with ivermectin in areas co-endemic for onchocerciasis and loiasis , and recommended that RAPLOA be undertaken to assess the prevalence of L . loa before commencing ivermectin distribution in areas suspected , or known , to be endemic for loiasis 24 ., APOC subsequently adopted RAPLOA for large-scale loiasis mapping in all potentially endemic areas in APOC countries 25 ., This article presents the results of the large-scale implementation of RAPLOA in the 11 APOC countries that were potentially endemic for loiasis ( Angola , Cameroon , Central African Republic , Chad , Democratic Republic of Congo , Ethiopia , Equatorial Guinea , Gabon , Republic of Congo , Nigeria and Sudan ) and presents a comprehensive map of loiasis as a basis for decision making on ivermectin treatment for the control and elimination of onchocerciasis and lymphatic filariasis in Africa ., RAPLOA is based on a simple , non-invasive diagnostic method using a short questionnaire , which was developed and validated by the World Health Organization ., The RAPLOA survey protocol was reviewed by Technical Consultative Committee of APOC and approved for loiasis mapping in Africa ., The surveys in each country were approved by , and undertaken under the authority of , the Ministries of Health of the 11 African countries ., Informed consent was obtained from each respondent through a consent procedure as described in the protocol ., Each adult above the age of 15 years in a selected household was individually briefed on the objectives of the survey and informed that he/she was free to participate or refuse ., Informed consent was orally as many respondents were illiterate ., For those who refused to participate , no further questions were asked and no information was recorded ., For those who consented , their name , age and years of residence in the community were recorded before proceeding with the RAPLOA interview ., The surveys were conducted using the RAPLOA methodology as described in the Guidelines for Rapid Assessment of L . loa 26 ., This methodology consists of three steps: At the beginning of the RAPLOA survey in each village , the community questionnaire was administered to key informants ( village heads , headmasters , schoolteachers , health workers , patent medicine dealers , traditional healers , and women and group leaders ) to determine the local names for the eye worm , the population size and the number of households in the community ., After administration of the community questionnaire , the geographic coordinates ( latitude , longitude , altitude ) of the community were collected using a geographical positioning system ( GPS ) unit in a central point or in front of the house of the village chief ., Households to be included in the survey were then selected randomly ., The direction to start was determined by spinning a bottle on the ground and selecting the direction in which the neck of the bottle pointed when it came to a standstill ., All adults in the first household , fulfilling the criteria for inclusion - aged 15 years and above , resident in the community for at least 5 years -were interviewed , followed by all adults in the next household , and so on until the required number of 80 individuals per community has been reached ., Some villages , notably in Equatorial Guinea , were too small to reach the required sample size and in such villages all adults were interviewed ., However , when the total number of adults in the village was less than 20 , the village was excluded from the analysis ., The individual questionnaire was designed to elicit responses on experience of eye worm ., Three key questions were asked chronologically to collect data on the experience of eye worm ., The first question in each interview was “Have you ever experienced or noticed worms moving along the white of the lower part of your eye ? ” ., After recording the response , the interviewer then showed a photograph of the eye worm to each respondent , guided him/her to recognize the worm on the photograph and then asked the second question: “Have you ever had the condition in this picture ? ” ., After recording the answer , the interviewer proceeded to ask the third question: “The last time you had this condition , how many days did the worm last before disappearing ? ” ., A respondent was classified as having a history of eye worm when the answers to the first two questions were positive and the duration in the third question was less or equal to 7 days ., For each village the percentage of respondents with a history of eye worm was computed to give the prevalence of history of eye worm ., In each country , villages for the survey were selected in areas that were potentially endemic for loiasis ., The surveys were conducted in two phases Phase 1: 2002–2006: During this period , RAPLOA surveys were conducted in areas that were earmarked for ivermectin treatment for onchocerciasis control by APOC and that were located in areas that were potentially endemic for loiasis ., Only areas that were meso or hyper endemic for onchocerciasis were targeted ., Phase 2: 2008–2010: with the increasing expansion of NTDs programmes that included the distribution of ivermectin for the elimination of lymphatic filariasis , there was an urgent need by country programmes and partners to have a better knowledge of the distribution of loiasis throughout the African region , including in areas that were not targeted for onchocerciasis control ., After it was mandated by its board , the Joint Action Forum , APOC undertook to complete the RAPLOA surveys in the areas outside the onchocerciasis endemic areas not yet covered by RAPLOA surveys ., In every target area , villages were selected with a random spatial sampling procedure to ensure good geographical coverage of the area ., The distance between sample villages was around 10 km during phase 1 , but when the results of phase 1 showed that the distribution of loiasis was much less localised than initially thought and that there was strong spatial correlation in eye worm prevalence over distances up to 100–200 km , the distance between sample villages was gradually increased to about 25 km during the last round of surveys of phase 2 ., Villages were selected using the Healthmapper software and data base ( http://www . who . int/health_mapping/tools/healthmapper ) or a 1∶200 , 000 scale local paper map of the area ., Data entry was mostly performed by the survey teams at country level using Microsoft Excel@ but sometimes at APOC headquarters using SPSS data entry builder@ ., Only aggregate village level data were entered: total population , number interviewed , number and percentage with eye worm history and location information , i . e . GPS readings of latitude and longitude , name of village , names of all administrative levels ., When RAPLOA results were received at APOC headquarters , systematic data checking was undertaken including the validation of geographical coordinates ( latitude , longitude ) of all surveyed villages using geographical information system software ( Atlas*GIS@ , ArcGIS@ ) ., Where available , the geographic coordinates of survey villages were compared to the coordinates found in the GADM database of the Global Administrative Areas ( http://www . gadm . org ) ., All RAPLOA data were then integrated into a master database in Microsoft Access@ at APOC headquarters ., The survey data were first analyzed using SPSS version 15 ( www . spss . com ) to generate summary tables and bar charts on the survey activities by country and year ., The geographical information system software ArcGIS version 10 ( ESRI Inc . , Redlands , USA ) was used for spatial analysis of the RAPLOA data ., The prevalence of history of eye worm for each village was submitted to a logit transformation ., The transformed prevalence data were then analyzed through a geostatistical method called kriging using the Geostatistical Analyst Extension of ArcGIS v10 ., The kriging analysis involved variography to determine the spatial correlation pattern in the survey data and a process of weighted spatial smoothing to predict the distribution of the logit prevalence throughout the surveyed area ., Kriging gives a predicted prevalence at any location , but with poor precision at large distances from the sampled locations ., We therefore defined the “surveyed area” pragmatically as the area where the local prediction standard error was smaller than , or equal to , the average standard error obtained in the cross validation analysis of the difference between predicted and observed logit prevalences for the surveyed villages ., This definition ensured that the “surveyed area” covers all surveyed villages but does not extend beyond a distance of 40 to 100 km from the nearest surveyed village ., For each location in the surveyed area , the predicted probability that the true prevalence exceeds 40% was estimated by calculating Z\u200a=\u200a{ ( logit ( 0 . 4 ) −M ) /S , where M is the local predicted logit prevalence and S the prediction standard error , and using the normal distribution to determine the corresponding probability ., The predicted logit prevalences were back transformed to the original scale to produce prevalence and probability contour maps for the surveyed area ., The contour map was also used to estimate the proportion of each country surface that was mapped by RAPLOA , and to divide the mapped surface into 4 loiasis endemicity classes with prevalence of eye worm 0–4%; 5–19%; 20–39% and >\u200a=\u200a40% ., The rural population in each class was tentatively estimated as the total rural population for the country multiplied by the proportion of country surface falling in that class , assuming a uniform distribution of the rural population in the country ., These estimates will be refined when a detailed population density map for the rural population of Africa becomes available ., The boundaries and the surface ( in square kilometers ) of the 11 African countries were obtained from the GADM database of Global Administrative Areas ( http://www . gadm . org ) ., The total rural population for each of the 11 countries was extracted from the database of the United Nations department of Economic and Social Affairs ( http://esa . un . org/unpd/wpp2008/tab-sorting_population . htm ) ., RAPLOA surveys were undertaken in a total of 4798 villages in the 11 APOC countries that were known or suspected to be endemic for loiasis ( see table 1 ) ., In 10 countries , the RAPLOA surveys confirmed the presence of loiasis and in each of these countries there were high risk villages where 69% to 100% of those interviewed reported a history of eye worm ., In Equatorial Guinea and Gabon , eye worm was reported from all surveyed villages ., Only in Ethiopia did none of the respondents report a history of eye worm ., The surveys were done in two major phases between 2002 and 2010 ( figure 1 ) ., The first phase from 2002 to 2006 was triggered by the occurrence of SAEs after ivermectin treatment in Cameroon and DRC , and the urgent need of CDTi projects in these two countries to understand the local endemicity of loiasis and the corresponding risk of SAEs ., The need for such information was especially great in DRC where a large number of CDTi projects were to be launched around that time ., A major survey effort was therefore undertaken in 2005 during which as many as 1 , 771 RAPLOA surveys were done in DRC alone ., The second major survey effort was in 2010 after APOC undertook to complete the RAPLOA mapping in Africa , including in areas not targeted for onchocerciasis control but that were of importance for lymphatic filariasis elimination with ivermectin treatment ., This second effort filled several remaining gaps in the survey coverage of the total area in Africa where loiasis is potentially endemic ., The locations of the survey villages and the boundaries of the “surveyed area” are shown in figure 2 ., The geographic distribution of survey villages is not uniform and in Cameroon and DRC there are some areas with a heavy concentration of surveyed villages ., This reflects the intensified efforts of 2003 to 2005 in response to urgent survey needs of specific CDTi projects in those areas ., In subsequent years , and particularly in 2010 , a grid-based sampling method was introduced to select RAPLOA survey villages at more regular distances to ensure better spacing of the sample ., Altogether , the RAPLOA survey villages cover a vast area of some 2500 km×3000 km centred on the heartland of loiasis in central equatorial Africa ., The spatial analysis of the RAPLOA data showed a strong spatial correlation pattern ., This is illustrated in the variogram in figure 3 which shows the semi-variance , a measure of the variation in prevalence data in relation to the distance between survey villages ., At short distances , the semi-variance is small , indicating that villages that are located closely together tend to have similar prevalences of history of eye worm ., With increasing distance , the semi-variance increases and consequently the spatial correlation declines ., This spatial correlation pattern has been modeled as shown by the solid line in figure 3 ( spherical model with range 5 , nugget 0 . 477 and sill 2 . 6345 ) ., This model was subsequently used in a kriging analysis of the RAPLOA data to produce , through a process of spatial smoothing , a map of the prevalence of eye worm history throughout the surveyed area ., Figure 4 shows the results of the kriging analysis ., This map provides the best estimate of the geographic distribution of loiasis based on the RAPLOA data ., The main geographic pattern is clear ., There are two zones of highly endemic loiasis: a western zone that comprises the totality of the continental part of the Equatorial Guinea and Gabon , Cameroon south of 6°N , and parts of the Republic of Congo , the Central African Republic and Chad ., This western zone also comprises the Mayombe forest in the west flank of the Bas-Congo province in the DRC and the Cabinda and west of Bengo provinces in Angola ., The second hyper-endemic zone is mainly made up of the North-Eastern part of the Democratic Republic of Congo ., It has its epicenter in the Province Orientale with extensions towards the Equateur province in the west , Maniema province in the south and Sudan in the north-east ., There are also vast areas where there is no loiasis or where its endemicity is very low , e . g . in most of DRC , north Cameroon and large sections of Angola , Nigeria , Chad and Sudan ., In between there are some intermediate zones where the estimated prevalence of eye worm history ranges between 20 and 40% ., The estimates given in figure 4 involve statistical uncertainty which is important to take into account , especially around the policy threshold value of a prevalence of 40% eye worm history ., Figure 5 therefore provides a map of the predicted probability that the local prevalence of eye worm history exceeds 40% ., In most of the surveyed area , there appears to be little uncertainty and the probability that the prevalence exceeds the threshold is whether very high ( >0 . 9 ) or very low ( <0 . 1 ) ., Hence , these results strengthen the above conclusion with respect to areas with very high and very low endemicity ., However , in some intermediate areas , the results are less clear-cut ., In such areas it will be important to inspect the available data in greater detail to assess the operational implications of the RAPLOA findings for local ivermectin treatment programs ., As an example of this process , figure 6 provides a detailed map of the border area of Chad , Cameroon and the Central African Republic ( CAR ) ., In south Chad the RAPLOA data revealed the existence of a previously unknown focus of hyperendemic loiasis ., Across the border in CAR the spatial analysis showed a vast area of hyperendemic loiasis where the prevalence of eye worm was very high for all surveyed villages ., In the centre of the map , between these two hyperendemic areas , there is a zone for which the RAPLOA prevalence data are between 20% and 40% and which the kriging analysis has classified as intermediate and below the risk threshold of 40% ., Nevertheless , being so close to two highly endemic zones , it might be prudent in such a borderline area to take the same precautionary measures as in the surrounding highly endemic areas when implementing ivermectin treatment ., Such a strategy might also be operationally more convenient if the intermediate and high endemicity groups of villages fall under the same implementation unit of the health system ., Table 2 shows the result of an attempt to estimate the population at risk in the different countries using the RAPLOA map ., Five countries , Cameroon , Congo , DRC , Equatorial Guinea and Gabon , have been nearly completely mapped for loiasis ., The other six countries were only partly covered by RAPLOA surveys ., Some regions were purposely excluded because they were known to be loiasis free , e . g . the desert regions of Chad and Sudan ., The North East of CAR and the bordering area in Sudan could not be surveyed because of security reasons while the mapping of Angola is not yet complete ., However , the vast majority of potentially loiasis endemic areas in Africa have been mapped ., Table 2 shows a breakdown of the mapped area by loiasis endemicity level ., Equatorial Guinea and Gabon are the most endemic countries where nearly the whole area falls into the high risk category with more than 40% RAPLOA prevalence ., In DRC , only 18% of the area falls into this category but because of its much larger population , this translates into an estimated population of 7 . 4 million people living in high risk areas ., In terms of population at high risk , Cameroon comes second with 4 million people ., DRC and Cameroon together account for 80% of the estimated 14 . 4 million people living in high risk areas ., The RAPLOA surveys represent a major effort of the African Programme for Onchocerciasis Control in response to a serious operational challenge for onchocerciasis control and lymphatic filariasis elimination ., Within two periods of a few years , thousands of rapid assessment surveys were done in order rapidly to generate the local data on loiasis endemicity levels that were needed for planning of ivermectin treatment in potential loiasis areas ., The surveys were undertaken by the Ministries of Health in the affected countries , with technical and financial support from APOC ., The technical support , provided by a group of African experts , has also contributed to strengthening national capacity for epidemiological evaluation and surveillance ., Initially , the RAPLOA surveys targeted areas where CDTi projects were planned and where information on loiasis endemicity was urgently needed ., These CDTi projects needed to understand the local risk of adverse reactions to guide decision making on appropriate measures for monitoring and management of possible SAEs in accordance with the requirements of the Mectizan Donation Program ., As the RAPLOA survey data accumulated , the beginning of a loiasis map began to emerge ., The final round of surveys in 2010 filled most of the remaining gaps in survey coverage and a comprehensive evidence-based map of loiasis is now available that covers most of the potentially loiasis endemic area in the world ., The only large areas that remain to be mapped are a border area between CAR and Sudan , which has a low population density but is likely to be highly endemic for loiasis , and much of Angola , where loiasis may not be widespread ., Beyond the APOC countries , loiasis is rare; one focus of low endemicity is known in south Benin , and a few sporadic cases reported from Zambia 12 ., The resulting map of the prevalence of eye worm history is unique and provides the first global map of loiasis based on actual survey data ., The map shows a clear geographic distribution of loiasis with two zones of hyper-endemicity , large areas that are free of loiasis or of low endemicity , and several borderline or intermediate zones including one zone in north-west DRC that bridges the two hyper-endemic zones ., The implications for ivermectin treatment are evident: in the hyper-endemic zones there is a high risk of SAEs and special precautionary measures are required in accordance with the MDP guidelines 16 , 24 ., For the loiasis-free and low endemic areas no special measures are required and ivermectin treatment can be implemented without risk ., The intermediate zones will generally require more detailed assessment of the available data , as demonstrated above by the example for South Chad , in order to support local decision-making on ivermectin treatment ., APOC will therefore make the necessary detailed maps available to endemic countries and their partners in onchocerciasis control and lymphatic filariasis elimination , and publish these maps on its website ( www . who . int/apoc ) ., The data for Chad also provide a good example of important new information that has become available through the RAPLOA surveys ., It is always been assumed on the basis of few data that loiasis was rare and of very low endemicity in Chad 12 , 27 , 28 , 29 and the discovery of a hyperendemic focus in the southern part of this country was a surprise ., Similarly , RAPLOA has clarified the distribution of loiasis in the Central African Republic and large parts of Congo and DRC for which hardly any information was available previously ., In addition to providing important new information on the distribution of loiasis , the RAPLOA surveys have confirmed the continued existence of known loiasis foci in several countries ( 12 ., In Cameroon the previously documented L . loa foci in the south region 30 , the centre region 31 , Adamaoua region 32 , Littoral region 30 , 33 , and the south-west region 1 have all been confirmed ., New foci have been revealed in the North West and Adamaoua regions situated in savannah areas that were not known to be endemic for loiasis ., In Nigeria , the RAPLOA surveys indicate that the level of endemicity of loiasis is relatively low ., The most affected areas are south of latitude 6°N , between the Niger delta and the border with Cameroon , which is in conformity with previous knowledge 34 , 35 , 36 ., For the Central African Republic , hardly any data were available 37 but the RAPLOA surveys have shown that loiasis is highly endemic in the south west and south east of the country ., In the Democratic Republic of Congo , the well known highly endemic focus of Mayombe in the most western part of Bas-Congo province and of Ueles in the North-eastern part of province Orientale ( 38 , 39 have also been confirmed ., The main endemicity pattern as shown on the RAPLOA map is broadly similar to the pattern on the map produced by Thomson et al 13 using an environmental risk model which also shows high endemicity in the West and the East , and a zone of lower endemicity in between ., However , there are also major discrepancies between the two maps ., The environmental risk map predicts the highest endemicity in Congo and south-west DRC , but the RAPLOA survey showed that endemicity levels in both areas were very low ., Conversely , the map of Thompson et al suggests that the Central African Republic is largely loiasis free while the RAPLOA surveys showed a very high endemicity level throughout nearly half the country ., Hence the environmental risk models , though useful for showing general trends , are not reliable enough for use in operational decision making for ivermectin treatment ., Diggle et al 15 subsequently developed a spatial statistical model that incorporated the environmental risk variables NDVI and elevation , for the analysis of epidemiological survey data on the prevalence of L . loa microfilaraemia ., The application of this model to prevalence data for Cameroon showed a significant improvement over the Thompson model ., However , a comparison with the RAPLOA map showed that model predictions at more than 100 kilometres from the nearest survey village were sometimes also very inaccurate ., One possible explanation is that NDVI and elevation have only limited predictive value on their own , as suggested by the low correlation between these environmental variables and the prevalence of MF 14 , 15 ., Using the results of a calibration analysis of the relationship between the prevalence of RAPLOA and the prevalence of MF , the RAPLOA data are now being incorporated into the spatial statistical model in order to enhance its predictive value ., On the basis of the RAPLOA results , it is tentatively estimated that some 14 . 4 million people live in high risk areas where the estimated prevalence of eye worm history is greater than 40% , and 15 . 2 million in intermediate areas with estimated eye worm prevalences between 20 and 40% ., The number of people at high risk varies considerably between countries ., Nearly the whole country of Gabon is classified as high risk , and represents a large proportion of the total high risk area in Africa , but because of the low population density in Gabon it represents less than 2% of the total high risk population ., DRC with 7 . 4 million and Cameroon with 4 million represent together 80% of the estimated total population at high risk ., Not all highly endemic loiasis areas overlap with the geographic distribution of onchocerciasis or lymphatic filariasis but in most of the surveyed countries there is considerable overlap and thus a significant risk of SAEs with ivermectin treatment ., The map of the prevalence of eye worm therefore provides critical information for ivermectin treatment programs among millions of people in Africa ., This information comes particularly timely for lymphatic filariasis elimination for which loiasis has been a major barrier in Central Africa 40 but which can now go ahead in the many areas where loiasis endemicity is low or nil . | Introduction, Methods, Results, Discussion | Loiasis is a major obstacle to ivermectin treatment for onchocerciasis control and lymphatic filariasis elimination in central Africa ., In communities with a high level of loiasis endemicity , there is a significant risk of severe adverse reactions to ivermectin treatment ., Information on the geographic distribution of loiasis in Africa is urgently needed but available information is limited ., The African Programme for Onchocerciasis Control ( APOC ) undertook large scale mapping of loiasis in 11 potentially endemic countries using a rapid assessment procedure for loiasis ( RAPLOA ) that uses a simple questionnaire on the history of eye worm ., RAPLOA surveys were done in a spatial sample of 4798 villages covering an area of 2500×3000 km centred on the heartland of loiasis in Africa ., The surveys showed high risk levels of loiasis in 10 countries where an estimated 14 . 4 million people live in high risk areas ., There was a strong spatial correlation among RAPLOA data , and kriging was used to produce spatially smoothed contour maps of the interpolated prevalence of eye worm and the predictive probability that the prevalence exceeds 40% ., The contour map of eye worm prevalence provides the first global map of loiasis based on actual survey data ., It shows a clear distribution with two zones of hyper endemicity , large areas that are free of loiasis and several borderline or intermediate zones ., The surveys detected several previously unknown hyperendemic foci , clarified the distribution of loiasis in the Central African Republic and large parts of the Republic of Congo and the Democratic Republic of Congo for which hardly any information was available , and confirmed known loiasis foci ., The new maps of the prevalence of eye worm and the probability that the prevalence exceeds the risk threshold of 40% provide critical information for ivermectin treatment programs among millions of people in Africa . | Loiasis is a neglected tropical disease caused by infection with the filarial parasite Loa loa , transmitted by Chrysops vectors ., Loiasis has recently emerged as a disease of public health importance when neurologic serious adverse events ( SAEs ) were reported in individuals with high L . loa microfilaraemia after ivermectin treatment ., This had a negative impact on the control of onchocerciasis and lymphatic filariasis in areas of co-endemicity with loiasis ., Microfilarial prevalence of 20% has been suggested as the threshold above which there is an unacceptable risk of SAEs with ivermectin treatment . The African Programme for Onchocerciasis Control ( APOC ) undertook large scale mapping of loiasis in 11 potentially endemic countries using a rapid assessment procedure for loiasis ( RAPLOA ) that uses a simple questionnaire on the history of eye worm ., A geostatistical analysis method called kriging applied to the results in 4798 sampled villages generated a contour map of eye worm prevalence , providing the first global map of loiasis based on actual survey data ., This map showed high risk levels of loiasis in 10 countries where an estimated 14 . 4 million people live in high risk areas . | medicine, disease mapping, public health and epidemiology, survey methods, epidemiology, spatial epidemiology, epidemiological methods, disease informatics | null |
journal.pcbi.1004112 | 2,015 | On the Firing Rate Dependency of the Phase Response Curve of Rat Purkinje Neurons In Vitro | The intrinsic electrical activity of Purkinje cells ( PCs ) exhibits a large repertoire of dynamical behaviors , including spontaneous firing of simple action potentials ( APs ) , bistability of the firing rate , and hysteresis 1–4 ., In addition , the extended range of PCs firing rates during behavior suggests that the rate of APs , its sudden transitions , its coherence across PCs , and the AP timing synchronization may contribute to information representation , processing , and downstream relaying ., Thus , investigating how distinct firing regimes affect spontaneous and evoked response properties is imperative for dissecting cerebellar computation ., Recently , key results from the mathematical theory of coupled oscillators sparked a lot of interest: a simple input-output characterization of the units composing a network , known as their phase response ( or phase resetting ) curve ( PRC ) , is sufficient to classify and predict individual and collective properties ., In the context of tonically firing neurons , the PRC quantifies the impact of an infinitesimal depolarizing current pulse on the time of occurrence of subsequent APs 5–10 ., As the cell oscillates regularly , the pulse advances or delays the time of the next AP , depending on the oscillation phase φ corresponding to the time of pulse delivery ., The resulting change of the time of the next AP can also be quantified in terms of the cell’s firing period and thus expressed as a phase shift Δφ ., By capturing the relationship between the evoked phase shift Δφ and the phase φ at which the input pulse occurred , the PRC predicts how , upon receiving weak synaptic inputs , neurons transiently delay or accelerate AP firing , contribute to network-wide AP synchrony , integrate external inputs or detect their temporal coincidences ., So far , not only has the PRC been considered in theoretical and computational studies , but it has also been computed in experimental works ( see 11 for a review ) , where different methods have been devised for its estimation 11–13 ., Recently , Phoka et al . 2010 14 proposed a correction to a traditional estimation method and tested it in PCs of juvenile mice ., Unexpectedly , they reported that the PC’s intrinsic firing rate has a profound effect on the response properties: the PRC of PCs firing at low rates displays a flat profile , suggesting that neurons behave like phase-independent inputs integrators; on the other hand , the PRC of PCs firing at high firing rates has a prominent peak , indicating a phase preference similar to coincidence detectors ., While it was not the first time that PRCs were shown to undergo changes over a range of AP frequencies 15 , the wide physiological range of PCs spontaneous firing rates and their ease of experimental access in in vitro preparations , made the report on the rate dependence of the PRC relevant ., Furthermore , intrinsic membrane properties might promote synchrony in a way that is relevant to information processing 16 , particularly in the cerebellum 17 , 18 ., Inspired by these perspectives , here we focused on revisiting , improving , and extending the earlier experimental characterization of Purkinje cells’ PRCs ., We aimed at its systematic exploration , both at the single-cell and at the population levels , which may be directly relevant for modeling studies ., In particular , in the light of the known bistable behavior of PCs , and their ability to abruptly toggle between distinct AP firing rates , we found it urgent to clarify whether the changes in PRC occur abruptly or smoothly , for increasing AP frequencies ., In addition , we tested the effect of the current pulse amplitude , verifying that the PRC prominent phase-dependency of PCs firing at high rates is an intrinsic property and not an artifact of the stimulation protocol ., The key contribution of this work is twofold:, ( i ) we developed a novel ad hoc closed-loop electrophysiological protocol to regulate PCs slow scale adaptation and achieve highly significant PRC estimates at fixed firing rates in a relatively short experimental time ., By such an approach ,, ( ii ) we confirmed and extended the observations of 14 , considerably improving the earlier observation statistics , and demonstrating conclusively and unambiguously that no abrupt switch in PRC occurs ., Instead , PCs smoothly shift from integrators to coincidence detectors , as their AP frequency increases ., Finally , verifying that these observations are not affected by the particular PRC estimation method , we tested our conclusions employing both the corrected direct method , as in 14 , and an indirect method 11–13 ., All procedures were performed according to institutional and national ethical guidelines ( license no . LA1100469 from the Belgian Federal Public Service Health , Food Chain Safety and Environment ) ., Cerebellar acute slices ( sagittal , 250 μm thick ) were prepared from 15- to 25-days-old Wistar rats , employing 4% isoflurane anesthesia and rapid decapitation , as described in 19 ., Briefly , after isolating the cerebellar vermis , the tissue was glued with cyanoacrylate glue to a flat metal platform and surrounded by agar blocks to improve stability during slicing 3 , 20; the tissue was then cut in 250 μm slices using a vibratome ( VT1000S , Leica Microsystems , Wetzlar , Germany ) in ice-cold artificial cerebrospinal fluid ( ACSF ) , containing ( in mM ) : 125 NaCl , 2 . 5 KCl , 1 . 25 NaH2PO4 , 26 NaHCO3 , 25 glucose , 2 CaCl2 , and 1 MgCl2 , balanced with 95% O2 and 5% CO2 ., The slices were incubated for 30 – 45 min at 32°C and then stored at room temperature , until they were transferred to the recording chamber of a fixed-stage upright microscope ( DMLFS , Leica Microsystems , Wetzlar , Germany ) ., The microscope was equipped with differential interference contrast ( DIC ) video-microscopy and mounted a 63x water immersion objective ., Purkinje cells ( PCs ) were visually targeted for somatic patch-clamp recordings , upon visual identification by size and location within the cerebellar microcircuitry , under DIC ., Some PCs were filled with Lucifer yellow and imaged by epifluorescence microscopy , confirming that the entire dendrite was always in the plane of the slice ., Whole-cell patch-clamp recordings were performed at 33 ± 1°C , employing an EPC10 amplifier ( HEKA , Lambrecht/Pfalz , Germany ) or an Axon Multiclamp 700B amplifier ( Molecular Devices , USA ) , both used in current-clamp mode ., Patch electrodes were pulled from thick-walled borosilicate glass capillaries ( 1BF150 , World Precision Instruments , Hitchin , UK ) with a horizontal puller ( P97 , Sutter , Novato , USA ) to a resistance of 3 – 6 MΩ ., Electrodes were filled with an intracellular solution containing ( in mM ) : 130 methanesulfonic acid , 10 HEPES , 7 KCl , 0 . 05 EGTA , 2 Na2ATP , 2 MgATP , 0 . 5 Na2GTP , and pH adjusted to 7 . 3 with KOH ., All recordings were obtained employing ACSF as the extracellular solution , balanced with 95% O2 and 5% CO2 , and routinely supplemented with 10 μM SR95331 ( a selective antagonist of GABAA receptors ) to abolish incoming spontaneous synaptic potentials ., Amplified analog signals were low-pass filtered at 10 kHz , sampled at a rate of 30 kHz and digitized at 16 bits with a DAQ board ( PCI-6229 , National Instruments , USA ) ., The same board was used to generate the amplifier control commands waveforms , synthesized at the same rate and resolution of the data acquisition ., Stimulation and response data were generated and collected by using the public domain software LCG 21 , and analyzed by custom scripts written in MATLAB ( The Mathworks , Natick , MA ) ., The amplifier built-in on-line capacitance compensation circuitry was always applied , while the on-line bridge balancing circuitry was employed alternatively to the off-line ( software ) active electrode compensation ( AEC ) 22 , whose implementation is built-in in LCG 21 ., Liquid junction potentials were left uncorrected and all the chemicals and drugs were obtained from Sigma-Aldrich ( Diegem , Belgium ) ., Analysis scripts , LCG configuration files and LCG command line strings to precisely replicate our experimental protocol and modeling are available from ModelDB 23 at http://senselab . med . yale . edu/modeldb ( accession number 155735 ) ., PRCs were experimentally estimated using direct and indirect methods 11 , 13 ., Applying direct methods 24 in tonically firing cells , such as the PCs , required the repeated injection of very brief square pulses of current ( i . e . , Ipulse = 50 − 150 pA , Tpulse = 0 . 5 − 1 ms , at least 1400 repetitions ) , each timed at a different phases φ of the cell firing cycle ( e . g . , Fig . 1 , panels E and A ) ., The phase-shift Δφ of the next AP induced by each pulse ( Fig . 1 , panels E , B and C ) , was first quantified and then normalized by the total injected charge Q = Ipulse ⋅ Tpulse , 8 , 25 allowing comparison across stimulation conditions ., Briefly , upon ( online or offline , see below ) digital detection of the timing tk of individual AP peaks , the mean ⟨ISI⟩ of the distribution of inter-spike intervals ISIk = ( tk+1 − tk ) was computed , and taken as an estimate of the ( regular ) firing period ., The occurrence of each pulse was expressed as the corresponding phase φ = τ/⟨ISI⟩ , by relating its absolute time of occurrence tpulse to the AP immediately before ( i . e . , say tj ) , τ = tpulse − tj ., Note that due to jitter in the next AP , the value of φ may slightly exceed its upper theoretical limit φ = 1 ( i . e . , φ ∈ 0; 1 + ε ) ., Because with no pulse the next AP would have occurred at tj + ⟨ISI⟩ , the actual phase-shift induced by the external perturbation was determined as Δφ = ( ⟨ISI⟩ − ISIperturbed ) /⟨ISI⟩ , with ISIperturbed = tj+1 − tj and where tj+1 is the ( perturbed ) time of the AP immediately following the pulse ., By this convention , positive ( negative ) values of Δφ represent phase advances ( delays ) ., Finally , normalizing Δφ to the charge Q of each pulse , the traditional direct estimate of the PRC can be expressed as:, Z ( φ ) = ⟨ I S I ⟩ - I S I p e r t u r b e d ⟨ I S I ⟩ · Q . ( 1 ), However , although φ ∈ 0; 1 + ε , Z ( φ ) cannot be sampled homogeneously by definition ., In fact , since t ( j+1 ) cannot precede tpulse , an upper bound always limits Z ( φ ) ( i . e . , ISIperturbed ≥ φ ⋅ ⟨ISI⟩ , thus Z ( φ ) ≤ ( 1 − φ ) /Q ) ., We therefore considered an unbiased and more accurate direct method , employing the correction proposed in 14 ., This method uses information from higher-orders PRCs 11 , including the contributions from the two APs preceding the pulse: tpulse was also related to the time of the second preceding AP ( i . e . , tj−1 ) , τ2 = tpulse − tj−1 and expressed as τ2 = ( τ + ISI ( perturbed−1 ) ) /⟨ISI⟩ , with ISI ( perturbed − 1 ) = tj − t ( j−1 ) ., Note that due to jitter in the APs before and after the pulse , φ2 may slightly exceed its theoretical limits ( i . e . , φ2 ∈ 1 − ε; 2 + ε ) and thus sample part of the domain of Z ( φ ) ., The phase-shift of the AP preceding the perturbation can be expressed as Δφ2 = ( ⟨ISI⟩ − ISIperturbed−1 ) /⟨ISI⟩ and thus the higher order PRC can be written as, Z 2 ( φ 2 ) = ⟨ I S I ⟩ - I S I p e r t u r b e d - 1 ⟨ I S I ⟩ - Q . ( 2 ), It can be proven that Z2 ( φ2 ) ≤ ( 2 − φ2 ) /Q and that Z2 ( φ2 ) ≥ ( 1 − φ2 ) /Q: thanks to the AP jitter , Z2 ( φ2 ) can restore an unbiased estimate of the domain of Z ( φ ) , precisely above its upper bound , where Z ( φ ) could not be properly determined ., The unbiased direct estimate of the PRC was then obtained by joining the data sets Z ( φ ) ∪ Z2 ( φ2 ) ; with φ , φ2 ∈ 0; 1 and indicated for simplicity as Z ( φ ) in the following ., Concerning the indirect methods for the PRC estimate , we employed the Weighted Spike-Triggered Average ( WSTA ) , reviewed in 13 ., Despite its potential bias due to the non-stationary firing regimes , we used it here solely as a control method and for confirming the firing rate dependency of the PRC in PCs ., In tonically firing cells , such as the PCs , WSTA required repeatedly recording the times {t}k of APs elicited by weak-amplitude fluctuating currents I ( t ) , generated as exponentially filtered white-noise 26 ξ ( t ) , lasting for ∼ 30 s:, τ I · I ˙ ( t ) = - I ( t ) + s 2 · τ I · ξ ( t ) , ( 3 ), where the steady-state variance s2 and autocorrelation time-constant τI of the injected current were chosen as 25 – 75 pA and 4 ms , respectively ( τI ≪ ⟨ISI⟩ ) ., Stimuli were applied at least twice for each firing rate , employing each time distinct realizations of I ( t ) and additional offsets to induce distinct discharge frequencies ., For each inter-spike interval ISIk = ( tk+1 − tk ) , the corresponding portion of I ( t ) was isolated and rescaled to the same duration , Ik ( φ ) = I ( φ ) ; φ = t/ISIk , t ∈ tk; tk+1 ., The PRC was approximated by the sum of the portions Ik ( φ ) , weighted by αk = ⟨ISI⟩/ISIk − 1 and after normalization by the area of the autocorrelation function of I ( t ) 27:, Z ( φ ) ≃ ∑ k α k · I k ( φ ) 2 · s 2 · τ I . ( 4 ) Instead of binning and constructing a histogram of the sampled PRC data-points , a standard nonparametric smoothing technique based on Gaussian kernel convolution 28 was employed as in 14 , aimed at increasing the signal-to-noise ratio of each PRC estimate:, Z ˜ ( φ ) = ∫ 0 1 K ( φ - x ) · Z ( x ) d x ∫ 0 1 K ( x ) d x K ( x ) = 1 ( 2 π h 2 e - x 2 2 h 2 ( 5 ) In discrete coordinates , the convolution integral became a sum over each of the N data points available , with the kernel K ( x ) centered over each available phase ., The optimal kernel bandwidth h was directly inferred from the data 28 as h = h φ ⋅ h Z , where, h φ = 4 3 N 1 5 m e d i a n | φ - φ m e d i a n | 0 . 6745 h φ = 4 3 N 1 5 m e d i a n | Z ( φ ) - Z ( φ ) m e d i a n | 0 . 6745 ( 6 ) Throughout the text and in the figures , the smoothed PRC estimates have been indicated for simplicity as Z ( φ ) ., Due to the intrinsic variability of each cell’s inter-spike intervals , repeating the pulse injections over and over in time conveniently allowed us to sample uniformly the range of φ , while stimulating the cell at a frequency much lower than its firing rate ( i . e . , 2–6 pulse/sec ) ., As a consequence of the need to study the firing rate dependency , large parts of the recording were often discarded when the cell was not firing at a fixed rate , further increasing the time needed to obtain a PRC estimate ( often greater than 30 min for a single firing rate ) ., Even though PCs in vitro fire spontaneously with a range of AP rates , we aimed at studying systematically the rate-dependency of the PRC in the same neuron ., In a first series of experiments , a constant holding current was therefore applied ( i . e . , on the top of Ipulse or of I ( t ) ) , adapting its value manually from −0 . 2 to 1 nA , to alter the firing rate of PCs by depolarizing or hyperpolarizing their membranes ., Depolarizing or hyperpolarizing a cell instantaneously alters its firing rate , although several minutes are typically required for the cell to reach a ( new ) steady firing rate ., This caused long waiting intervals before estimating the PRC at a given firing rate ., In addition , occasional slow drifts of the mean inter-spike intervals occurred , over a window of several seconds , thus altering or biasing the shape of the PRC ., To address these limitations , in an additional set of experiments , we made use of a spike rate controller 29 , using a closed-loop paradigm similar to the one employed in 30 and inspired by the response-clamp paradigm 31 was adopted: the frequency-clamp ., In short , an iterative estimate F ˜ n of the cell’s instantaneous firing rate was updated online after each AP , detected in real-time ( i . e . , as a positive crossing of a voltage threshold ) , using the following formula:, F ˜ k = I S I k - 1 · 1 - e - I S I k / τ ) + F ˜ k - 1 · e - I S I k / τ , ( 7 ), where τ = 1s acts as the time scale over which the instantaneous firing rate is estimated , weighing each new AP and the previous firing history 21 , 31 ., The running value of F ˜ k was compared to a target frequency Ftarget and employed to define an error signal e k = F t a r g e t − F ˜ k ., This was fed into a Proportional-Integral-Derivative controller ( PID ) , realized via software in LCG , and employed to automatically update in closed-loop the value of the constant holding current, I k h o l d i n g = g P · e k + g I · ∑ i = 0 k e i + g D · ( e k - e k - 1 ) , ( 8 ), where gP , gI , gD are the proportional , integral , and derivative gains , respectively ( i . e . , gP = 0 . 001 pA/Hz , gI = 0 . 1 pA/Hz , gD = 0 pA/Hz ) ., The value of Ftarget was also used as the initial value for the estimator F ˜ 0 , in order to reduce undesired transients ., While the output of the PID controller was updated every time an AP was detected , it was held constant during the time interval starting from the AP preceding the perturbation pulse to the second spike following it , in contrast to what was done in 30 ., In other words , the PID controller was temporarily disconnected , while holding its most recent output , before delivering the external current perturbation required for estimating the PRC , in order to minimize any artifact ., A detailed description of the experimental setup is given elsewhere 21 ., Our frequency-clamp allowed us to rapidly and precisely explore several firing-rates , simply varying Ftarget ., In addition , as in closed-loop the timing of the external current pulse required for the PRC estimation could be precisely chosen in reaction to an AP and after a certain time delay , we optimally synthesized the values of these delays in order to sample the range 0; 1 of φ with maximal efficiency ( i . e . , more uniformly than pseudo-random number generation ) ., We employed a Sobol sequence , first used in 32 and described in 33: briefly , after online detection of an AP , the external pulse was delivered in a reactive-clamp fashion 34 after a delay Ti , generated as the i-th element of a Sobol sequence ( Grey code variant 35 , after discarding the initial points ) ., This was repeated at least 1400 times at a rate of one perturbation every 6 APs , independently of the cell’s firing rate ., In an initial set of experiments we compared the PRCs estimated in the same cell or across cells , for fixed firing rate , with and without the PID controller and found no differences ., Furthermore the PRCs estimated are in good agreement with those obtained for PCs using a similar method 14 , which provides an additional control to our methods ., In order to study the influence of the PID controller in our PRC estimates we used the Khaliq and Raman 36 model ( see S1 Fig , Discussion and Modeling methods ) ., In order to obtain a concise description of a PRC and to compare those obtained at different AP firing rates , for the same cell and across cells , we adopted the peak-to-baseline ratio r as in 14:, r = | m l - m e | | m l | + | m e | , ( 9 ), where ml and me are the values of the late and of the early local extrema ( i . e . , largest peaks in absolute values ) for each of the two halves of Z ( φ ) ( i . e . , in φ ∈ 0; 0 . 5 and in φ ∈ 0 . 5;1 ) ., Note that when ml and me have opposite signs ( e . g . , as in type II PRCs , 37 ) , r = 1 . This also allowed us to concisely quantify the dependency of the PRC shape on the firing rate F , by fitting to r ( F ) the parameters of a sigmoidal function, r ( F ) ∼ ( 1 + e - ( F - a ) / b ) - 1 . ( 10 ) All simulations were performed using the NEURON simulation environment 38 ., The simulation code is available on ModelDB or by request from the corresponding author ., Single-compartment model ., In a first set of simulations , we used the single-compartment , conductance-based PC model described in 36 and available on ModelDB at the URL https://senselab . med . yale . edu/modeldb , accession number 48332 ., To test the hypothesis that voltage fluctuations endogenously generated by the random opening and closing of ion channels might influence the shape of the PRC , we incorporated channel noise into the model using the method described in 39 ., Briefly , the fluctuations induced by channel noise can be accounted for by extending the dynamics of the ionic conductances present in the model according to the equation, g ( t ) = g ¯ p o ( t ) + ∑ i = 1 N - 1 η i ( t ) , ( 11 ), where g ¯ is the maximal conductance , po ( t ) is the fraction of open channels ( for a deterministic model , g ( t ) = g ¯p o ( t ) ) , N is the number of states of the equivalent kinetic scheme and each ηi ( t ) is the solution to a stochastic differential equation of the form, τ i η ˙ i ( t ) = - η i ( t ) + σ i 2 τ i ξ i ( t ) , ( 12 ), where ξi ( t ) is a delta-correlated Gaussian process with zero mean and unitary variance ., For the general case of arbitrary kinetic schemes , the N − 1 time constants τi and standard deviations σi are obtained numerically from the N × N transition matrix of the system that contains the transition rates between all possible states in the kinetic scheme ., In the case of the Khaliq-Raman model , this approach was employed only for the resurgent sodium current , which is described by a kinetic scheme that cannot be mapped into the composition of multiple two-state subunits ., For all other ionic conductances , the coefficients τi and σi were analytically calculated using the procedure detailed in 39 ., The dimensions of the single compartment were adjusted in order to produce the desired coefficient of variation of the unperturbed spiking pattern ., We chose two values of CV , low ( around 5% ) and high ( around 10% ) , corresponding to values of both length and diameter of 160 and 80 μm , respectively ., In the deterministic model , the length and diameter were set to 80 μm and an additional noisy current , modeled as delta-correlated Gaussian white noise , was injected to obtain comparable values of CV ., For the computation of the PRC , we evolved the model until it reached a steady state and then applied pulses of current ( 0 . 5 ms duration and 0 . 5 nA amplitude ) at random times with a mean period between perturbations of 2 . 5 Hz ., A constant current offset was injected to vary the baseline firing frequency of the model ., In some simulations , this offset current was computed by a PID controller , to replicate and validate in silico the closed-loop technique employed in the experiments ., Multi-compartment model ., To elucidate whether the presence of an extensive dendritic tree might influence the firing rate dependency of the PRC , we used the De Schutter-Bower model 40 , 41 ., PRCs were computed without and with ongoing synaptic activity ., In both cases , the algorithm employed to compute the PRC differed from that used in the single compartment model and resembled the one adopted in 42 , 43: briefly , after evolving the model until it reached a steady state , we identified two spike times t0 and t1 such that the ISI t1 − t0 was of the appropriate duration ., Then , we simulated the model again until t0 − 5 ms and saved the full state of the model at this point in time ., Finally , starting from t0 − 5 ms , we evolved the model until t1 + 10 ms for N = 50 trials: current perturbations ( 0 . 5 ms duration and 0 . 2 nA amplitude ) were applied at times given by, t p i = t 0 + i · t 1 - t 0 N for i = 1 … N , ( 13 ), where t p i is the perturbation time in the i-th trial ., In the case of the model without synaptic inputs we used the PM10 model 40 , set the temperature of the simulation to 28°C and injected a somatic current of varying amplitude to span a wide range of firing rates ., In the case of the model with synaptic inputs , we used the PM9 model with synapses distributed on the dendritic tree as described in 41 , set the temperature to 37°C and fixed the presynaptic excitatory and inhibitory firing rates to 35 and 2 Hz , respectively ., Using our closed-loop system and considering a wide range of firing rates ( i . e . , 20 − 150 Hz ) , we could for the first time systematically and extensively investigate how the PRC depends on the firing rate in the very same PCs ., Fig . 2A displays 12 PRCs , estimated under stable recording conditions , while repeatedly altering the PC firing rate in a shuffled order ., While at low firing rates the profile of the PRC appears relatively flat and independent off the input phase φ , at higher rates the profile changes: a late-phase peak ( i . e . , in the range 0 . 5; 1 ) becomes sharper and shifts to the left , while the average amplitudes in 0; 0 . 5 decrease ., This was quantified for this PC , and four other individual cells , by defining the peak-to-baseline ratio ( Fig . 2B; see Methods ) ., This is a measure of the absolute difference between absolute peak values in the ranges 0; 0 . 5 and 0 . 5; 1 , and it is maximally 1 when these two peaks have different signs ., For all individual neurons , the peak-to-baseline ratio increased smoothly in the range of physiological firing rates under consideration , and it could be best fit by a sigmoidal function ., According to the definition of phase φ , which is normalized by the average inter-spike interval , the existence of a preferred , rate-independent time-to-spike would correspond to a linear rate-dependence in the phase domain ., Since we observed a rate-dependent shift in the PRC late-peak ( Figs . 2A and 3A ) we asked whether this reflects a time-to-spike preference ., To test this possibility we applied the following change of variables φ = 1 + tAP/⟨ISI⟩ , where the tAP is the relative time to the AP following the stimulus , and plotted the PRC as a function of time , for distinct firing rates ( Fig . 3D ) ., The location of the maxima of these plots ( Figs . insets in 2B and 3B ) , displayed a marked dependence on the firing rate in the range 0; 100 Hz while , at very high firing rates , i . e . above 100 Hz , it became rate-independent and equal to 2 ms . This suggests that the rate-dependence of the PRC late-peak ( Figs . 2A and 3A ) does not result from a rate-independent time-to-spike preference ., Our observations are not changed when using the truncated Gaussian method 45 as illustrated in S2 Fig . In an initial set of experiments , we wondered whether the rather flat profile of the PRC observed at low firing rates was an intrinsic property of the cells ., Very weak input stimuli may be in fact effectively ignored by the cell and result in a phase-independent PRC profile ., The benefits of systematically acquiring PRCs , across distinct conditions along stable recording sessions , were again exploited: we injected in the same PCs an external input with different amplitudes , over distinct firing rates ., An example of such experiments is reported in panels A-B of Fig . 4 , where the impact of the pulse amplitude is apparent: the signal-to-noise ratio increases for stronger pulses , while the PRC is indeed phase-independent ., Fig . 4C-D further visualize graphically the 68% confidence intervals of the PRC estimates , revealing an almost two fold reduction when using doubled pulse amplitudes ., Across the entire data set collected , several amplitudes of the external stimuli and various firing rates were explored over 42 PCs ., When expressed in terms of a population summary , the PRCs confirmed our previous observation ( Fig . 3A ) ., When pooling the PRCs obtained across cells , in 15 Hz-wide bins , according to the PC firing rate at which each curve was measured , a moderate amount of variability was observed ( Fig . 3B ) ., The overall quantification in terms of the peak-to-baseline ratio , already discussed in Fig . 2B , is comparable to that measured for single cells , revealing and confirming the same marked smooth dependency on the firing rate ( Fig . 3C ) ., As in Fig . 2B , the inset of Fig . 3C displays the location in time of the peak of the PRC against the firing rate , and the average PRCs were also plotted as a function of time to the next AP ( Fig . 3D ) ., While slightly noisier than the data acquired within the same PCs , this evidence prompts us to exclude that the late-peak in the PRC is an artifact of the phase normalization ., We further employed an alternative method for PRC estimation , based on the weighted spike triggered average ( WSTA ) ( see Materials and Methods and 13 for a review ) ., We computed 95 PRCs in 16 PCs , with firing rates below 100 Hz ., The average PRC profiles , obtained from pooled data as in Fig . 3B across several firing rates , are shown in Fig . 5A-B were the PRCs are plotted both as a function of the phase of the time to the next AP ., As for Figs ., 2 and 3 , performing a quantification based on the peak-to-baseline ratio revealed a qualitatively similar frequency dependence of the PRC profile on the PC firing rate ., Quantitatively , however , the dependency on the firing rate observed with WSTA methods did not match that obtained by the direct estimation method of the PRCs ., To some extent , we attribute this inaccuracy to the WSTA method: while for direct methods the stationarity of the firing rate could be precisely monitored and controlled in closed-loop , indirect methods do not allow the same precision , as they require the injection of a noisy current waveform that elicits a train of APs with some variability ., It should be noted that a method has been developed recently that allows a more accurate estimation of PRCs while using fewer spikes 44 , 46 ., We did not employ this method since we were mostly concerned with validating the results obtained with the direct method ., Nonetheless , the presence of a late peak in the PRCs at high firing rates confirms that our observations do not depend on the PRC estimation method in use ., When referred to the time of the next AP , the location of the PRC peak averaged to a value of −1 . 7 ± 0 . 3 ms ( Fig . 5C , inset ) , with the exception of some ( n = 4 ) cases where the PC fired at low rates and the PRC did not exhibit a peak at late phases ., Active conductances are thought to modulate the shape of the PRC , and therefore computational modeling 7 , 47 , 48 could be a powerful tool to dissect the ionic bases of the PRC ., The conductance-based models developed in 36 , 49 to recapitulate several experimental observations in Purkinje cells were used in 14 to attempt to reproduce the PRC shape and in particular its dependency on the firing rate ., Such attempts have proved largely unsuccessful and up to today a model able to reproduce the rate dependency observed experimentally remains elusive ., Having observed a rate dependency in the CV of PCs in our experimental data ( S1 Fig , panel C ) we asked whether spiking variability could account for the flat PRC profile observed at low firing rates ., To test this hypothesis , we employed the Khaliq-Raman model 36 and computed PRCs at low and high firing rates ., We compared the results obtained when spiking variability was introduced via additive noise fluctuations ( i . e . , by injecting a noisy current into the model neuron ) with those obtained in the presence of endogenously generated channel noise ( see Methods ) ., The results are shown in Fig . 6: solid lines denote the stochastic model , whereas dashed lines represent the deterministic model with external noisy current ., In both conditions , the model is more sensitive to perturbations when firing at low rates ( left panels ( A and C ) ) , which translates both to a larger amplitude of the PRC and to a higher CV , for the same number of channels ( and therefore magnitude of the internally generated fluctuations ) or variance of the noisy current ., This result holds true both in the case of low and high variability , as exemplified by the top and bottom panels , respectively ., Importantly , in all four conditions tested ( low and high firing rate , low and high CV ) , the PRCs of the stochastic and deterministic models are strikingly similar and , for a given firing rate , do not depend on the amount of endoge | Introduction, Materials and Methods, Results, Discussion | Synchronous spiking during cerebellar tasks has been observed across Purkinje cells: however , little is known about the intrinsic cellular mechanisms responsible for its initiation , cessation and stability ., The Phase Response Curve ( PRC ) , a simple input-output characterization of single cells , can provide insights into individual and collective properties of neurons and networks , by quantifying the impact of an infinitesimal depolarizing current pulse on the time of occurrence of subsequent action potentials , while a neuron is firing tonically ., Recently , the PRC theory applied to cerebellar Purkinje cells revealed that these behave as phase-independent integrators at low firing rates , and switch to a phase-dependent mode at high rates ., Given the implications for computation and information processing in the cerebellum and the possible role of synchrony in the communication with its post-synaptic targets , we further explored the firing rate dependency of the PRC in Purkinje cells ., We isolated key factors for the experimental estimation of the PRC and developed a closed-loop approach to reliably compute the PRC across diverse firing rates in the same cell ., Our results show unambiguously that the PRC of individual Purkinje cells is firing rate dependent and that it smoothly transitions from phase independent integrator to a phase dependent mode ., Using computational models we show that neither channel noise nor a realistic cell morphology are responsible for the rate dependent shift in the phase response curve . | The phase response curve ( PRC ) quantifies the effect of an infinitesimal perturbation on the phase of an oscillator , be it mechanical , electronic or biological ., In the particular case of neurons , PRCs can be employed to infer several network properties that are influenced by intrinsic membrane mechanisms ., It has been shown that the PRC of tonically firing Purkinje Cells is flat at low firing rates , which has profound implications for information processing in the cerebellum ., Here , we propose a novel method to estimate the PRC of single Purkinje cells at various firing rates and use it to unveil the smooth transition between flat and phasic PRC ., Furthermore , we address potential explanations for the observed transition using computational modeling . | null | null |
journal.pntd.0001615 | 2,012 | Modeling the Control of Trypanosomiasis Using Trypanocides or Insecticide-Treated Livestock | Across sub-Saharan Africa , a variety of Trypanosoma spp transmitted by tsetse flies ( Glossina spp ) cause human and animal trypanosomiases ., There are >10 , 000 cases/year of Human African Trypanosomiasis ( HAT ) 1 with an estimated burden of ∼1 . 3 million Disability Adjusted Life Years ( DALYs ) 2 and economic losses in excess of $1 billion due to human and animal trypanosomiasis 3 ., While interventions can be directed against the vector or the parasite , emphasis has usually been on the use of drugs to treat the disease both in humans and in livestock ., While the importance of treating cases , especially human ones , cannot be overstated , several advances in our understanding of tsetse biology and ecology , and improvements in the cost-effectiveness of tsetse control 4 , 5 , have revived interest in that approach to disease management ., First , the use of satellite navigation as an aid to nocturnal aerial spraying , spraying much larger areas than previously , and protecting the sprayed areas with odor-baited targets , has provided impressive results , such as the eradication of G . m ., centralis from Botswana 6 ., Second , the demonstration of the importance of odor for host location in some species of tsetse provided a means of attracting them to insecticide-treated targets and , by killing the flies , provided control of cattle and human trypanosomiasis 7–10 ., Third , the particularly low reproductive rate in tsetse made it possible to use as few as four such targets per square kilometer to eliminate isolated populations of G . pallidipes Austen and two sub-species of G . morsitans 9 , 11 ., The method is cheaper than aerial spraying and more environmentally friendly than insecticidal ground spraying , game destruction or habitat clearance 11 ., Issues of cost , logistics , government commitment , and theft of materials have meant , however , that the approach has not been used in large-scale control programs except in Zimbabwe and in the Western Province of Zambia 11 , 12 ., Part of the reason for this limited use stems from the fact that , simultaneously with the development of insecticide-treated target technology , it was realized that tsetse control could be achieved equally effectively by applying insecticide to the very livestock - generally cattle - off which the tsetse were feeding ., This approach has been used very successfully in areas where tsetse feed predominantly on cattle 13 , 14 , though it would be less effective in areas where – as in large parts of Zimbabwe and Tanzania – the predominant food source for the tsetse are wild mammals ., Whereas insecticide-treated cattle ( ITC ) can be used in operations aimed at eliminating tsetse populations , animal trypanosomiasis can also be reduced to low levels even where tsetse populations persist 15 ., It is , of course , relief from cattle disease – rather than issues of tsetse fly control versus eradication – which most interests stockholders in tsetse areas and which can be used to interest the stockholder in becoming actively involved in tsetse and trypanosomiasis control 13 ., Recent advances in our understanding of the feeding behavior of tsetse on cattle have led to even cheaper methods of tsetse control where the insecticide is applied to the body regions and/or individual animals on which most tsetse feed 16 , 17 ., This restricted application of pyrethroids is comparable in its cost and simplicity to the widespread use of trypanocides by farmers to prevent or cure trypanosomiasis in their livestock 16 ., There are several possible reasons why these advances in affordable , low-technology tsetse control have not , as yet , played a significant role in efforts against HAT ., First , there is an imperative to find and treat infected humans and livestock and this approach is thus the foundation of all efforts against the disease ., Second , the odor-baited devices used so effectively in efforts against animal trypanosomiasis 10 are less effective against the important vectors of HAT 18 , 19 ., This poor efficacy is probably related , in part , to the distinctions between the host relationships of the various tsetse species ., The important vectors of animal trypanosomiasis , i . e . , the Morsitans-group tsetse , feed almost exclusively on mammals ( e . g . warthog , kudu , buffalo and cattle ) which they locate largely by odor , whereas the Palpalis-group species , which are the main vectors of HAT , are less responsive to odors and include reptiles and birds in their diet ., For instance , between 50 and 90% of meals taken by Glossina fuscipes fuscipes are from monitor lizard 20 which themselves do not support all the trypanosome species infective to mammals 21 ., In this paper , we investigate the theoretical effects of two different approaches to trypanosomiasis control , both of which have already been shown to be of interest to small-scale stockholders in resource-limited settings 22 ., First we consider the effect of treating animals with trypanocides , which prevent the disease without having any insecticidal effect ., Second , we consider the use of the ITC method , which has no direct trypanocidal effect but which increases mortality in the vectors ., We limit our study to the situation typical of eastern and southern Africa , where Trypanosoma vivax , T . congolense and T . brucei rhodesiense occur in livestock and wildlife - and where the last-named parasite also causes “Rhodesian” sleeping sickness in humans 23 , 24 ., We generalize the Rogers 25 two-host model for trypanosomiasis to one where a single species of tsetse can feed off any finite number ( n ) of vertebrate hosts ., The formal proof that Rogers model can be generalized in this way is given in the Supporting Information ( Text S1 ) ., The overall basic reproductive rate ( R0 ) of a trypanosome species is given by: ( 1 ) where D\u200a=\u200a1 for T . vivax and T . congolense andfor T . brucei , and where the following definitions apply: R0\u200a=\u200aoverall basic reproductive rate; formally , in a completely susceptible population , the number of trypanosome-infected tsetse arising from each infected fly; c\u200a=\u200aP ( infected blood meal gives mature infection in fly ) ; u\u200a=\u200aDaily mortality rate of the flies; T\u200a=\u200aIncubation period in tsetse ( all time units are days ) ; ai\u200a=\u200api/d , where pi\u200a=\u200aProportion of tsetse bloodmeals from species i , d\u200a=\u200aDuration of feeding cycle in flies; bi\u200a=\u200aP ( infected fly bite produces infection in species i ) ; mi\u200a=\u200aV/Ni , where V\u200a=\u200aNumber of tsetse , Ni\u200a=\u200aNumber of animals of species i , 1/ri\u200a=\u200aDuration of infection in species i ., The parameter D differs between T . brucei and the other species of trypanosomiasis because it is assumed that tsetse can only be infected with T . brucei when they take their first bloodmeal ., It is assumed that the probability of infection for the other species is independent of a flys feeding history: to distinguish this situation Rogers also replaced c with c′ for T . brucei 23 ., The default values for the parameters of his two-host model for Rhodesian sleeping sickness 23 are copied here for convenience , in Tables 1 and 2 ., We extend the model to consider cases where , in addition to humans and domestic stock ( cattle ) , the following vertebrate species are present: ( 1 ) wild mammals; ( 2 ) monitor lizards; ( 3 ) wild mammals and monitor lizards ., The interventions to be considered involve the treatment of cattle with: ( 1 ) prophylactic trypanocides that kill trypanosomes but have no effect on tsetse mortality; ( 2 ) ITC , i . e . , topical application to hosts of insecticides that kill tsetse but have no direct effect on trypanosome mortality ., The use of ITC can reduce R0 in two ways ., First , in common with all insecticidal techniques , it reduces the average life expectancy of tsetse , so decreasing the abundance of the flies and the proportion of the population that is old enough to harbor mature , transmissible infections ., Second , and in contrast with other insecticidal techniques such as traps or insecticide-treated targets , ITC kills specifically those tsetse that become infected from the reservoir of disease in cattle ., Since the Rogers model assumes that the abundance and age structure of the tsetse population is constant , it is particularly suitable for highlighting the second type of effect , and so for comparing ITC and trypanocides as means of reducing the probability that a fly will become infected ., In the present paper we first use the Rogers model to address this matter under circumstances in which various levels of the use of trypanocides or insecticide treatment are applied to cattle that represent different proportions of the overall cattle population , and with host populations composed of various species ., We then identify the extra benefit that ITC produces via reductions in the abundance and mean age of the tsetse population , and predict the relative merits of using ITC and trypanocides , as assessed via the model ., As a preliminary check we inserted the published default parameter values ( see Tables 1 and 2 , 25 ) into Equation ( 1 ) for the scenario where only ( untreated ) cattle and humans provided the source of tsetse bloodmeals , and obtained the published values for R0: 388 . 2 for T . vivax , 64 . 4 for T . congolense , and 2 . 65 for T . brucei ., The last value is made up the sum of two components , 2 . 54 from the cattle and 0 . 11 from humans , implying that T . brucei would not survive in the absence of the cattle reservoir 25 ., To control , and eventually eliminate , T . brucei the goal therefore must be to reduce the combined R0 , for human and non-human hosts , to a value less than unity ., We now turn to the use of the insecticide-treated cattle ( ITC ) method of control – where the vectors , rather than the trypanosome , are targeted ., In the previous sections we have assumed a fixed daily rate for adult tsetse mortality ( Table 1 ) ., When considering the use of ITC , however , we need to decompose this factor into the mortality occurring at the time of feeding and that occurring between feeds ., The former has generally been considered the dominant component 28 , 29 even where the host is not treated with insecticide ., If the probability of surviving a feed is qf and the probability of surviving a non-feeding day is qn then a fly survives a complete feeding cycle of d days with probability qf qnd ., With qf\u200a=\u200a0 . 96 , qn\u200a=\u200a0 . 98 , and with the assumed four-day feeding interval 25 , the probability of surviving from one feeding cycle would then be approximately 0 . 96×0 . 984\u200a=\u200a0 . 885 and the daily mortality rate is calculated as −ln ( 0 . 885 ) /4≈0 . 03 , as originally assumed 25 ., Where some hosts are treated with insecticide we assume that flies always die if they feed off a treated animal; the probability of a given fly surviving a feed is thus the product of the probabilities that it feeds off an un-treated host and survives that meal ., We assume further that flies feed off all cattle at random , particularly with respect to the animals treatment status ., If the proportion of cattle treated is pi then the probability of a fly surviving a feeding cycle is now ( 1−pi ) qf qnd ., For example , with the above values for qf , qn and d , and if 10% of the cattle are treated , the survival probability will be 0 . 9×0 . 885\u200a=\u200a0 . 797 and the daily mortality is now approximately 0 . 057 ., As a first approximation we ignore any extra mortality arising from a fly feeding off a human , rather than cattle or wildlife ., Figures 1 , 2 , 3 , and 4 provide estimates of the control of trypanosomiasis , by way either of the use of trypanocidal drugs or ITC , in the situation where there is sufficient birth , of uninfected flies , to ensure that the tsetse population stays at a constant level 25 ., This should be a reasonable assumption in the case where trypanocidal treatment is used to control trypanosomiasis and there is no imposed mortality on the tsetse population ., When ITC is used , the population could only be kept constant if the increase in mortality is balanced by an increase in birth and/or immigration ., If birth is the predominant source of replacements then Figures 3 and 4 reflect the control situation ., If , however , the population is kept constant due to immigration then the replacement flies will be predominantly older flies , with above-average probability of being infected with trypanosomes , so that Figures 3 and 4 over-estimate the efficacy of ITC ., However , where ITC is used , either against closed populations of tsetse or on a sufficiently large scale that immigration is limited at sites far from the boundary , the expectation is that the fly population will decrease ., Inspection of Equation ( 1 ) shows that , other things being equal , R0 changes linearly with the tsetse population so that , where the use of ITC produces a decline in population levels the effect on R0 will be larger than indicated in Figure 3 ., We follow Smith & McKenzie 31 in estimating that , if mortality was increased from some value u to u′ , the initial vector population ( V ) would decrease to Vu/u′ ., Taking this factor into account changes the threshold value for the required percentage of cattle among non-human hosts ., Thus , under the assumption of a constant tsetse population , it was impossible to force R0<1 for T . vivax ( Figures 3A , 4A , 5 ) ., However , if tsetse populations are reduced as a consequence of ITC , R0<1 for T . vivax as long as cattle make up >90% of the non-human hosts ( Figure 5 ) ., The proportions of cattle among non-human hosts , required to force R0<1 , declines from roughly 70% to 55% for T . congolense and 40% to 30% for T . brucei ( Figure 5 ) ., For purposes of comparing our results with previous work we have , initially , adhered closely to the design , and the parameterization , of the Rogers model – which provides a useful tool for investigating the dynamics of trypanosomiasis ., It is recognized , however , that some fundamental details of the model can be improved ., For example , the model makes no distinction between male and female tsetse , which are known to differ with respect to longevity , mobility , infectivity and responses to baits 32 , and does not allow that mortality changes as a function of age 33 ., Moreover , advances in our knowledge over the past 23 years allow the selection of parameter values that better reflect the field situation ., Thus , the feeding interval is certainly shorter than four days and where tsetse make more than one visit to a host per feeding cycle 30 , 34 this will impact on both the probability that they transmit a trypanosome , and the probability that they are killed when they alight on an animal that has been treated with insecticide ., Most seriously , the model assumes that the abundance and age structure of the tsetse population is constant ., This can be a reasonable assumption where no tsetse control efforts are in place , or when trypanosomiasis control consists simply of treating livestock with trypanocides that have no insecticidal effect ., If cattle provide a substantial proportion of tsetse bloodmeals and if a significant proportion of these cattle are treated with insecticide , however , it may be expected that both the size of the population in the area under treatment , and its mean age , will tend to decline ., On the other hand the model also ignores the problem of invasion from adjacent infected areas and this further complicates the estimation of the effect of ITC ., Finally , we have not modified Rogers implicit assumption that tsetse feed at random off the individuals of a given host species ., This is known not to be the case and this consideration will complicate the modeling 17 ., Nonetheless , in the limit , where some individuals provide no bloodmeals at all for tsetse , they effectively do not exist from the modeling point of view ., One could thus simplify the problem by considering the “effective” number of individuals in a herd – being the numbers that do provide bloodmeals ., In the same way , baboons and impala – which provide almost no bloodmeals for tsetse – do not need to be considered when modeling the dynamics of trypanosomiasis ., It would not be easy to incorporate all of these details into the present model and still maintain the simplicity that allowed the model to be generalized to apply to the variety of situations considered here ., The more general model can , however , be investigated using simulation models; the results of such an exercise will be reported in a separate paper ., Despite the above limitations , the theoretical development presented here suggests that the use of ITC should provide a potent tool for controlling , or even eliminating , trypanosomiasis in situations where cattle provide the majority of bloodmeals for tsetse ., The dynamics of transmission ensure that the requisite proportion favoring the use of ITC depends on the species of trypanosome involved; for T . vivax there is little hope of eliminating the disease unless at least 90% of the tsetse bloodmeals are from cattle – and then only if insecticide treatment is such that all tsetse feeding off cattle are killed , and if the situation is such that the increased tsetse mortality results in a decline in the fly numbers ., For T . brucei the situation is very much more favorable; even if 70% of bloodmeals are being taken from wildlife , treatment with insecticide of the cattle providing the remaining meals from non-humans allows R0 to be reduced to unity ., The situation for T . congolense is intermediate between these extremes ., By contrast , the use of trypanocides will never allow T . vivax and T . congolense to be eliminated , even where tsetse feed only on cattle – unless all animals are kept permanently on a perfect trypanocide ., T . brucei could be controlled – but only in the absence of wildlife hosts ., The classical Rhodesian sleeping sickness foci are often associated with protected areas 35 , the vectors are Morsitans-group tsetse and the hosts are wild mammals such as warthog , buffalo and bushbuck ., Tackling these foci is very difficult: block treatment of wild hosts with trypanocides is impossible and hence vector control is the only option ., Moreover , the flies are highly mobile 36 and widely dispersed across a range of habitats and hence , to be effective , tsetse control must be applied across the entire protected area ., This approach is illustrated by the use of aerial spraying and insecticide-treated targets to eliminate tsetse from the Okavango Delta ( area≈15 , 000 km2 ) of Botswana 6 ., Few countries have the resources for such large-scale interventions and hence sleeping sickness persists in parts of east and southern Africa ., By contrast , tackling Rhodesian sleeping sickness transmitted by G . fuscipes might be more tractable for several reasons ., First , the underlying R0 of T . b ., rhodesiense is likely to be low ., Studies of the diet of G . f ., fuscipes in Uganda and Kenya have shown that monitor lizards ( Varanus nilotica ) provide between ∼50% and >90% of bloodmeals 20 , 37–39 and it seems likely that poikilothermic hosts such as monitor lizards will not be competent hosts for mammalian trypanosomes ., The only published study 21 confirms this for T . congolense and the results for T . brucei are equivocal but not compelling: no human-infective trypanosome has been recovered from a lizard , only one wild lizard ( N\u200a=\u200a46 ) has been found with T . brucei s . l . , and experimental infections of captive lizards – which were not subject to the range of temperatures found in nature – produced , at most , a low and transient parasitaemia ., Our results suggest that if lizards are indeed refractory to mammalian trypanosomes and form >80% of the diet of tsetse , then the R0 for T . b ., rhodesiense is less than 1 ., Hence we might expect that Rhodesian sleeping sickness will be associated with areas where lizards are not abundant such as away from the shores of Lake Victoria and/or in densely settled areas where wild hosts are absent ., Consistent with this hypothesis , the current foci of Rhodesian sleeping sickness in Uganda are , paradoxically , not near the shores or islands of Lakes Victoria and Kyogu , where tsetse are abundant , but rather at sites further inland 40 , 41 ., In areas where lizards are not important hosts , then livestock , particularly cattle , are important 20 , 38 ., In the case of Uganda , the densities of cattle frequently exceed 50 head/km2 42 and the degraded environment leads to relatively low densities of tsetse 38 ., Increasing the host∶vector ratio reduces R0: for densities of 10 host/km2 and 5000 tsetse/km2 our model ( with other parameters as in Tables 1 and 2 ) suggests R0\u200a=\u200a13 for T . brucei; with 50 hosts and 5000 tsetse/km2 the value is 3 , and with 50 hosts and 500 tsetse/km2 it is 0 . 3 ., Second , G . f ., fuscipes are restricted to riverine habitats and are less mobile than Morsitans species such as G . pallidipes 36 and hence vector control can be applied on a smaller scale , focused on riverine and lacustrine habitats ., Third , the abundance of cattle in settled areas , their importance as a host for tsetse and their need for water – and hence daily presence in the riverine and wetland habitats where G . f ., fuscipes is concentrated – means that insecticide-treated cattle should be particularly effective baits ., Hence , SE Uganda , the place where Rhodesian sleeping sickness is most serious , accounting for over half ( 2848/5086 ) of all cases across Africa 35 , is probably the easiest to tackle ., Present evidence for the superior efficacy of ITC assumes greater importance due to indications over the last decade that the economy of this technique can be improved substantially , with no material loss of performance ., The application of insecticide can be restricted to the legs and belly of cattle where most tsetse feed , thereby reducing the material costs of treatment by ∼90% 16 ., In addition , since most tsetse feed on the larger and older animals within a herd 17 , 43 , only these animals need be treated , with further savings in cost ., As a consequence , the annual material cost of ITC is reduced to <US$2 per beast per year 44 – comparable to the cost of a single dose of diminazene aceturate to cure trypanosomiasis ., The restricted application of pyrethroids to older cattle allows young stock to be exposed to ticks and hence develop a natural immunity to tick-borne diseases 45 and reduces impact on dung fauna 46 , 47 which play an important role in maintaining soil fertility and , ultimately , productive pasturage ., Against these favorable indications for the usefulness of ITC there is the problem that the technique can be used only in districts where cattle occur , although modeling suggests that ITC can be effective even when cattle are distributed patchily , i . e . , absent from bands of habitat up to several kilometers wide 48 ., Nonetheless , for the densely-settled rural areas of central and southern Uganda where Rhodesian sleeping sickness is most acute , our findings suggest that relatively modest levels of treatment ( ∼20% even if tsetse numbers are not reduced by the intervention ) could lead to the elimination of HAT ., Hence there is the exciting prospect that an important public health benefit might arise through the private actions of livestock keepers using cheap , simple and environmentally-benign methods to control vector-borne diseases in their livestock 22 . | Introduction, Methods, Results, Discussion | In Uganda , Rhodesian sleeping sickness , caused by Trypanosoma brucei rhodesiense , and animal trypanosomiasis caused by T . vivax and T . congolense , are being controlled by treating cattle with trypanocides and/or insecticides ., We used a mathematical model to identify treatment coverages required to break transmission when host populations consisted of various proportions of wild and domestic mammals , and reptiles ., An Ro model for trypanosomiasis was generalized to allow tsetse to feed off multiple host species ., Assuming populations of cattle and humans only , pre-intervention Ro values for T . vivax , T . congolense , and T . brucei were 388 , 64 and 3 , respectively ., Treating cattle with trypanocides reduced R0 for T . brucei to <1 if >65% of cattle were treated , vs 100% coverage necessary for T . vivax and T . congolense ., The presence of wild mammalian hosts increased the coverage required and made control of T . vivax and T . congolense impossible ., When tsetse fed only on cattle or humans , R0 for T . brucei was <1 if 20% of cattle were treated with insecticide , compared to 55% for T . congolense ., If wild mammalian hosts were also present , control of the two species was impossible if proportions of non-human bloodmeals from cattle were <40% or <70% , respectively ., R0 was <1 for T . vivax only when insecticide treatment led to reductions in the tsetse population ., Under such circumstances R0<1 for T . brucei and T . congolense if cattle make up 30% and 55% , respectively of the non-human tsetse bloodmeals , as long as all cattle are treated with insecticide ., In settled areas of Uganda with few wild hosts , control of Rhodesian sleeping sickness is likely to be much more effectively controlled by treating cattle with insecticide than with trypanocides . | In Uganda , cattle are an important reservoir for Trypanosoma brucei rhodesiense , the causative agent of Rhodesian sleeping sickness ( human African trypanosomiasis ) , transmitted by tsetse flies Glossina fuscipes fuscipes , which feed on cattle , humans , and wild vertebrates , particularly monitor lizards ., Trypanosomiasis can be controlled by treating livestock with trypanocides or insecticide – killing parasites or vectors , respectively ., Mathematical modeling of trypanosomiasis was used to compare the impact of drug- and insecticide-based interventions on R0 with varying densities of cattle , humans and wild hosts ., Intervention impact changes with the number of cattle treated and the proportion of bloodmeals tsetse take from cattle ., R0 was always reduced more by treating cattle with insecticide rather than trypanocides ., In the absence of wild hosts , the model suggests that control of sleeping sickness ( R0<1 ) could be achieved by treating ∼65% of cattle with trypanocides or ∼20% with insecticide ., Required coverage increases as wild mammals provide increasing proportion of tsetse bloodmeals: if 60% of non-human bloodmeals are from wild hosts then all cattle have to be treated with insecticide ., Conversely , it is reduced if lizards , which do not harbor trypanosomes , are important hosts and/or if insecticides are used at a scale where tsetse numbers decline . | veterinary diseases, mathematics, theoretical biology, pest control, applied mathematics, animal management, biology, population biology, veterinary science, agriculture | null |
journal.ppat.1004128 | 2,014 | Hepatitis C Virus Cell-Cell Transmission and Resistance to Direct-Acting Antiviral Agents | There is accumulating evidence that viruses use different routes for transmission and spread in infected tissue 1 , 2 ., A well-characterized example is hepatitis C virus ( HCV ) that is transmitted between hepatocytes via classical cell entry using cell-free diffusion but also uses direct cell-cell transfer to infect neighboring cells 3 , 4 ( Figure 1A ) ., While cell-free entry is most appropriate for the initiation of HCV infection , cell-cell transmission is thought to play an important role in viral persistence and in the maintenance of infection 5 ., A key feature of cell-cell transmission is its resistance to neutralizing antibodies present in HCV-infected individuals 4 ., Several host factors involved in cell-free viral entry have also been shown to contribute to cell-cell transmission ., These include scavenger receptor BI ( SR-BI ) , CD81 , tight junction proteins claudin-1 ( CLDN1 ) and occludin ( OCLN ) as well as host cell kinase epidermal growth factor receptor ( EGFR ) and its signal transducer HRas 6–12 ., HCV envelope glycoproteins are also essential for this process 11 ., However , whereas the majority of monoclonal antibodies ( mAbs ) targeting the viral envelope fails to inhibit cell-cell transmission , several host-targeting entry inhibitors ( HTEIs ) have been shown to inhibit HCV cell-cell transmission 6–12 ., Antiviral resistance remains a major challenge for the treatment of chronic viral infections including HCV , hepatitis B virus ( HBV ) , human immunodeficiency virus ( HIV ) and influenza infection ., Antiviral resistance to nucleos ( t ) ide analogs is a major cause of treatment failure in chronic HBV-infected patients 13 ., Although the combination of antiretroviral drugs has markedly improved the effective control of the progression of HIV disease , the emergence of multidrug-resistant viruses still threatens their long-term efficacy 14 ., The recent introduction of a first-generation protease inhibitor to pegylated interferon-alfa/ribavirin ( PEG-IFN-α/RBV ) therapy has improved the outcome for HCV genotype 1-infected patients 15 , 16 , but a main limitation of these direct-acting antivirals ( DAAs ) is their low genetic barrier for resistance 17 , 18 ., Next generation viral protease inhibitors , NS5A and polymerase inhibitors are currently being evaluated in combination with PEG-IFN-α or other DAAs in IFN-free regimens , with or without RBV 19–26 with sofosbuvir and simeprevir having received FDA approval ., Although newly developed DAAs are very effective in the majority of previously untreated patients , antiviral resistance as well as differences in virological outcomes for different genotypes and subtypes have been reported 24 , 27 , 28 ., Furthermore , a significant number of patients with advanced liver disease and who are null or partial responders to previous therapy still do not achieve a sustained virological response 18 , 22 , 26 , 29 ., The functional role of cell-cell transmission and spread in the emergence of antiviral resistance is unknown ., The aim of this study was to assess the role of cell-cell transmission in antiviral resistance using HCV genotype 2 infection as a model , and to explore cell-cell transmission as a target to prevent and treat DAA-resistance ., Culture of Huh7 . 5 . 1 30 , Huh7 . 5-GFP 11 and CD81− Huh7 . 5 cells 31 has been described ., CLDN1- ( OM-7D3-B3 ) 32 , SR-BI- ( NK-8H5-E3 ) 8 and CD81- ( QV-6A8-F2C4 ) 9 specific mAbs and respective control mAbs have been described ., Erlotinib was obtained from LC Laboratories ., Anti-HCV neutralizing antibodies ( AP33 from Genentech and purified human anti-HCV IgG from our laboratory ) have been described 33 , 34 ., Mouse/human IgG was from BD Bioscience and NS5A-specific mouse mAb was from Virostat ., The E2-specific human antibody ( CBH-23 ) was a kind gift from Dr . Steven Foung ( Stanford University , USA ) 35 ., Inhibitors of HCV protease ( telaprevir , boceprevir and simeprevir ) and HCV NS5A ( daclatasvir ) were synthesized by Acme Bioscience , Inc ., Primers used to generate NS3 mutations: 5′-GTT GGG CTC TTC CGA TCA GCT GTG TGC TCT C-3′ ( A156S , sense ) , 5′-GAG AGC ACA CAG CTG ATC GGA AGA GCC CAA C-3′ ( A156S , antisense ) , 5′- CGG GGA AGT CCA AAT CAT GTC CAC AGT CTC TCA-3′ ( L36M , sense ) , 5′-TGA GAG ACT GTG GAC ATG ATT TGG ACT TCC CCG-3′ ( L36M , antisense ) , 5′-CGT CGT TGG GCT CTT CAA AGC AGC TGT GTG CTC T -3′ ( R155K , sense ) and 5′- AGA GCA CAC AGC TGC TTT GAA GAG CCC AAC GAC G-3′ ( R155K , antisense ) ., Primers used in nested PCR for direct sequencing of NS3 mutations: NS3 outer forward , 5′-ATC GTC TGG GGA GCG GAG AC-3′; NS3 outer reverse , 5′-AAT TTG CCA TAT GTG GAG TAC GT-3′; NS3 inner forward , 5′-ACG GCT GCA TGT GGG GAC AT-3′; NS3 inner reverse , 5′-GTG CTC TTT CCA CTG GT-3′ ., Primers used for amplifying and sequencing E1E2 mutations: 5′-TTT GCC GAC CTC ATG GGG TAC AT-3′; reverse , 5′-TCC GCT AAG AAG AGC AGG AAT AAG AGT A-3′ ., Primers used in nested PCR for amplifying NS5A cDNA fragments: NS5A outer forward , 5′-CTA CGT GAC GGA GTC GGA TG-3′; NS5A outer reverse , 5′-AAC TTT TCC TCT TCG GGG CT; NS5A inner forward , 5′-CAG CGT GTG ACC CAA CTA CT-3′; NS5A inner reverse , 5′-TCG GGG CTA CAG GGA GTT AT-3′ ., Primers used for sequencing NS5A region: TAA CTG AGG ACT GCC CCA TCC CAT , TTA AGC CCA ACG CAG AAC GA , CGC AGA CGT ATT GAG GTC CAT GCT AA ., Drug-resistant individual or combined mutations were introduced in the NS3 region of the Luc-Jc1 ( genotype 2a/2a ) and/or Jc1 plasmid 36-38 using the QuikChange II XL site-directed mutagenesis kit ( Stratagene ) as previously described 39 ., A one-step polymerase chain reaction ( PCR ) mutagenesis was performed using the primers as described in “Primers” ., Mutations NS3-A156S , NS3-R155K and NS3-L36M/R155K were confirmed by DNA sequence analysis ( GATC Biotech ) for the desired mutation and for exclusion of unexpected residue changes in the NS3 encoding sequences ., HCVcc J4/JFH1 ( genotype 1b/2a ) and HCVcc J4/JFH1 NS5A-Y93H ( Y2065H ) have been described 40 ., HCVcc ( TCID50 103/mL to 104/mL ) were produced as described 6 ., Viral infection was analyzed by assessing the intracellular luciferase activity 6 , 32 or intracellular HCV RNA levels as described 6 , 32 , 41 ., Huh7 . 5 . 1 cells transfected with HCVcc Luc-Jc1 or Luc-Jc1 containing the NS3 A156S mutation were cultured with fresh Huh7 . 5 . 1 cells ( 1∶1 ) in 24-well plates ., Medium was replenished every 3–4 days until the end of the experiment ., Cells were harvested at different time points and HCV infection was assayed in cell lysates by monitoring luciferase activity and the percentage of infected cells was assessed by NS5A immunostaining with flow cytometry over 14 days ., To investigate the role of cell-cell transmission for viral spread and dissemination , neutralizing antibodies ( nAbs , 25 µg/mL AP33 or 25 µg/mL anti-HCV IgG ) potently inhibiting cell-free entry 6 , 11 were added to block cell-free transmission until the end of the experiment ( Figure 1B ) ., Cell-cell transmission was assessed as illustrated in Figure 1C and described previously 6 , 11 ., Producer Huh7 . 5 . 1 cells were electroporated with HCV Jc1 or J4/JFH1 RNA with DAA-resistant mutations and co-cultured with naive target Huh7 . 5-GFP cells in the presence of 1 or 10 µg/mL CLDN1-specific mAb , 10 µM erlotinib , 10 µg/mL SR-BI-specific mAb or DMSO solvent/rat IgG control ., A well-described pool of anti-HCV nAbs ( anti-HCV IgG , 25 µg/mL ) 34 was added to block cell-free transmission ., After 24 h of co-culture , cells were fixed with 1% paraformaldehyde , stained with an NS5A-specific mouse antibody ( 0 . 1 µg/mL ) or an E2-specific human antibody ( CBH-23 , 1 µg/mL ) and analyzed by flow cytometry 6 , 11 ., Dead cells and doublets were excluded by scatter gating and cell doublets were discriminated based on FSC-A and FSC-H parameters as described 42 ., Cell-cell transmission was defined as percentage HCV infection of Huh7 . 5-GFP target cells in the presence of anti-HCV neutralizing Abs ., Huh7 . 5 . 1 cells were electroporated with Jc1 RNA and seeded in 12-well plates ( 105 cells/well ) ., Cells were treated with control medium , CLDN1- or SR-BI-specific mAb ( 10 µg/mL ) , simeprevir ( 500 nM ) , daclatasvir ( 5 nM ) , the combination of CLDN1- or SR-BI-specific mAb and simeprevir or the combination of daclatasvir and simeprevir ., 1% DMSO medium was used during the whole cultivation process to transition the cells into non-dividing stage as described recently 43 ., Media were replenished every 3–4 days and HCV RNA was monitored by RT-PCR 44 ., Absent HCV RNA quantification by RT-PCR was confirmed using the Abbott RealTime HCV assay ( Abbott ) ( LOD 48 IU/mL with 250 µL liquid sample ) ., 5 µL of purified extracellular viral RNA , isolated and purified using QIAamp Viral RNA Mini Kit ( Qiagen ) , was reverse-transcribed into cDNA ( Thermo Scientific ) ., HCV E1/E2 , NS3 and NS5A cDNA fragments were amplified by nested PCR using the primers as described in “Primers” ., The PCR products were then separated on a 1% agarose gel and purified using Wizard SV Gel and PCR Clean-Up System ( Promega ) ., The presence of predominant mutations was analyzed by DNA direct sequence analysis ( GATC Biotech ) using Chromas Pro Version 1 . 7 . 3 software ( Technelysium Pty Ltd ) ., To further identify DAA-resistant mutations in the HCV NS3 gene , the purified second round PCR products were ligated into a pGEM-T vector ( Promega ) and then used to transform JM109 competent cells for clonal selection on LB/ampicillin/IPTG/X-Gal plates according to the manufacturers protocol ., Plasmid DNA from selected clones was amplified and purified using a Qiagen Miniprep Kit ( Qiagen ) for DNA sequencing ( GATC Biotech ) ., Cytotoxic effects on cells were assessed at the end of the long-term HCV infection assay by analyzing the ability of the cells to metabolize 3- ( 4 , 5-dimethylthiazol-2-yl ) -2 , 5-diphenyltetrazolium bromide ( MTT ) as previously described 6 ., Unless otherwise stated , results are expressed as means±standard deviation ( SD ) from at least 3 independent experiments performed in triplicate ., Statistical analyses were performed using Students t-test , with a p-value of <0 . 05 being considered statistically significant ., The spread of DAA-resistant viruses has an important impact for the development of antiviral resistance , leading to viral breakthrough and treatment failure ., However , the role of viral cell-cell transmission and spread for resistance is unknown ., To address this question , we first generated DAA-resistant viruses by introducing well-characterized DAA-resistance mutations in NS3 ( NS3-A156S , NS3-R155K and NS3-L36M/R155K ) or NS5 region ( NS5A-Y93H ) 45 ., Introduction of mutations increased the IC50 of telaprevir , boceprevir and daclastavir up to 10-fold ( Figure S1 and Table S1 ) , demonstrating that these DAA-resistant viruses are indeed able to escape inhibition by DAAs ., We then investigated the spread of DAA-resistant viruses using a recently developed state-of-the-art model for viral spread 6 , 8 which is displayed in Figure 1B and described in Materials and Methods ., As shown in Figure 2A–B , both wild-type and DAA-resistant ( A156S ) viruses efficiently spread during the first 14 days after infection , despite the presence of anti-HCV nAb ( AP33 ) efficiently blocking viral entry through cell-free transmission , with an increase of more than 2 log10 in their viral load ., Sequence analysis demonstrated that DAA-resistant virus ( A156S ) was indeed the predominant variant at day 14 in the experiments displayed in Figure 1B , D and F ( data not shown ) ., Thus , given an inhibition of viral entry through cell-free transmission of more than 95% in the presence of nAb ( AP33 ) 6 ( Figure 2G ) , we conclude that cell-cell transmission represents the main transmission route for DAA-resistant viruses ., We quantified the percentage of HCV positive cells at the end of the viral spread assay ( Figure 3 ) ., The majority of the cells became HCV positive ( 96%/WT , 92%/A156S ) after 14 days of viral spread ( Figure 3A–B ) ., Although cell-free HCV in the supernatant was efficiently neutralized by nAb ( anti-HCV IgG ) ( Figure 3C ) , the spread of wild-type and DAA-resistant HCV was still efficient in the presence of nAb ( 86%/WT , 82%/A156S ) ( Figure 3A–B ) ., Given the minor effect of nAbs efficiently inhibiting cell-free transmission , these data confirm that cell-cell transmission is the major route of viral dissemination for both wild-type and DAA-resistant HCV ., We next investigated whether HTEIs inhibit total spread of DAA-resistant viruses ., As shown in Figure 2C-F and Figure 3A–B , in contrast to telaprevir , which did not inhibit viral spread of the DAA-resistant virus , HTEIs such as CLDN1-specific mAb and erlotinib markedly inhibited viral spread of wild-type and DAA-resistant viruses ., Collectively , these data demonstrate that blocking the spread of DAA-resistant viruses by HTEIs is useful to prevent viral breakthrough caused by DAA resistance ( Figures 2C–F , 3A–B , S2 , S3 and Table 1 ) ., To confirm that HTEIs inhibit viral spread by inhibition of cell-cell transmission of DAA-resistant HCV , we applied a well-established cell-cell transmission assay ( Figure 1C ) ., In this assay HCV producer cells are co-cultured with HCV target cells for 24 h in the presence of broadly nAbs ( anti-HCV IgG ) 6 , 8 to inhibit cell-free viral entry ., Since anti-HCV IgG inhibited up to 95% of HCV cell-free entry ( Figure 2G and 3C ) , viral transmission thus occurs predominantly via cell-cell transfer in this assay ., As shown in Figure 4A and 4C ( left panels ) , HCVcc Jc1 ( 2a/2a ) NS3-A156S and Jc1 ( 2a/2a ) NS3-L36M/R155K are indeed efficiently transmitted through cell-cell transmission , and the extent of their spread through this route was similar to the wild-type strain ( data not shown ) 6 , 8 , demonstrating that DAA-resistant HCVcc are transmitted through cell-cell transfer and thus escape circulating neutralizing antibodies ., CLDN1-specific mAb and erlotinib markedly inhibited cell-cell transmission of protease inhibitor-resistant viruses ( Figure 4 ) ., J4/JFH1 NS5A ( 1b/2a ) is hundreds of times less infectious than Jc1 46 , resulting in less efficient viral cell-cell transmission than Jc1 NS3-A156S and NS3-L36K/R155K ( Figure S4 ) ., Although cell-cell transmission for this strain was very low , the HTEIs appeared also to have a potential inhibitory effect on cell-cell transmission of NS5A inhibitor-resistant viruses ( Figure S4 ) ., These results demonstrate that HTEIs prevent cell-cell transmission of DAA-resistant viruses in cell culture models ., Interestingly , in Figure 2E–F , the HTEIs ( CLDN1-specific mAb and erlotinib ) not only inhibited viral spread , but also were capable of decreasing viral load 7 days after treatment with HTEIs , suggesting that blocking HCV cell-cell transmission impairs maintenance of HCV infection ., To further test this hypothesis , we monitored HCV infection in CD81 knock-out hepatoma cells ( CD81−Huh7 . 5 ) that are resistant to cell-free entry and only display minimal levels of CD81-independent cell-cell transmission 11 , 31 ., Briefly , we transfected CD81−Huh7 . 5 cells with HCV RNA ( Luc-Jc1 ) and monitored HCV infection in the viral spread assay over 2 weeks ., Consistent with the results shown in Figure 2E–F , HCV load in CD81−Huh7 . 5 cells gradually decreased , while it increased over 30 times in control CD81-expressing Huh7 . 5 cells ( Figure 2H ) ., Collectively these results indicate that cell-cell viral spread is essential for maintenance of persistent HCV infection in cell culture models ., To assess whether blocking cell-cell transmission of DAA-resistant variants by HTEIs impairs the emergence of viral resistance in cell culture models we established long-term HCV infection experiments using HCV-Jc1 transfected Huh7 . 5 . 1 cells incubated in the presence of DMSO 43 , 47 ., The incubation of cells in the presence of DMSO has been shown to allow studying virus-host interactions during long-term infection and has been suggested to be one of the most physiological HCV cell culture models based on liver-derived cell lines 43 , 47 ., We chose a well-characterized protease inhibitor , simeprevir , which has recently received FDA approval to treat chronic hepatitis C , for further studies ., Approximately 60% of the cells stained HCV-positive before initiation of treatment ( Figure S5 ) ., It has been shown that simeprevir efficiently inhibits HCV replication in HCV cell culture with IC50 being below 13 nM 48 ., We used a dose of 500 nM ( >IC90 ) , which reduced viral load more than 10-fold within 3 days in HCV cell culture confirming that the dose is highly potent and relevant for inhibition of genotype 2 replication ( Figure 5A ) ., As shown in Figure 5A , simeprevir treatment resulted in a rapid decline of the viral load initially , reducing the viral load of HCV-infected cells by up to 1 . 5 log10 within 5–6 days after introducing the DAA ., However , viral rebound was observed after 2–3 weeks , with a viral load increasing to the same level as untreated cells in line with previous reports 48 ., In contrast , treatment using an HTEI such as CLDN1-specific mAb ( OM-7D3-B3 ) , which has been shown to inhibit HCV entry in a pan-genotypic activity without displaying any cytotoxic effect on hepatic cells 32 , led to a slow but steady decrease of the viral load ( Figure 5A ) ., No viral rebound was observed during CLDN1-specific mAb treatment , demonstrating that CLDN1 as a target has a high genetic barrier to HCV resistance ., Finally , combination of CLDN1-specific mAb and simeprevir resulted in a more rapid , efficient and sustained reduction in viral load than simeprevir monoexposure ( Figure 5A ) ., Most interestingly , during combination treatment , HCV RNA became undetectable by qualitative RT-PCR and using the clinically licensed Abbott RealTime HCV assay ( Abbott ) with a limit of detection of 48 IU/mL ( Figure 5A ) ., Viral load remained undetectable after withdrawal of the combination treatment , indicating that viral eradication was sustained and indeed due to the combination of entry and protease inhibitor ( Figure 5A ) ., According to our previous study and reports from others , anti-CLDN1 mAb and simeprevir exhibit no toxicity to hepatoma cells in vitro at the concentrations used in this study 32 , 48 ., Nevertheless , we performed additional experiments to exclude that toxic effects were responsible for decline in viral load and loss of virus ., As shown in Table 2 , MTT-based cell viability assays at the end of the long-term experiments showed no differences between treated and untreated cells ., These data confirm that the clearance of viral infection is indeed due to HTEI treatment and not related to adverse effects of the compounds during long-term treatment ., To further explore the development of viral resistance , we performed sequence analyses of viral variants at different time points ( the start of treatment , 10 and 23 days after treatment ) ., Whereas DAA-monotherapy resulted in the emergence of well-described NS3 resistance mutations 23 days after treatment ( Figure 6 and 7 ) , wild-type NS3 HCV remained the predominant strain in CLDN1-specific mAb alone as well as in combination of CLDN1-specific mAb and simeprevir treated cells ., Although sequence analyses revealed some rarely occurring variants associated with low DAA resistance ( e . g . NS3 I170T ) in the presence of combination of CLDN1-specific mAb and simeprevir , these variants were cleared at the end of the treatment as indicated by undetectable viral RNA ( Figure 5A ) ., These results demonstrate that the HTEI functionally prevents antiviral resistance to DAA by impairing the spread of resistant viruses in cell culture models ., To assess whether prevention of resistance is universal to HTEIs or compound-dependent , we performed side-by-side experiments using a well- characterized SR-BI-specific mAb NK-8H5-E3 ., This antibody has been shown to block efficiently cell-free viral entry and viral dissemination in cell culture models 8 , Although the combination of this SR-BI-specific mAb and simeprevir transiently decreased viral load and delayed viral rebound , it did not result in viral clearance as observed in CLDN1-specific mAb/simeprevir combination therapy ( Figure 5B ) ., Sequence analysis in cells treated with anti-SR-BI mAb and simeprevir revealed emergence of variants conferring resistance to HCV protease inhibitors ( NS3 Y56H ) 49 ( data now shown ) and to SR-BI inhibitors ( N415D 39 ) ( Figure S6 ) ., Using direct sequencing we did not detect mutation G451R 50 , indicating that G451R is not emerging at high frequency ( Figure S6 ) ., These data indicate that distinct HTEIs have different genetic barriers for antiviral resistance and that the CLDN1-specific mAb OM-7D3-B3 used in this study appears to have a higher genetic barrier than the SR-BI-specific mAb NK-8H5-E3 ., This SR-BI-specific antibody was less efficient in inhibiting HCV cell-cell transmission as compared to the CLDN1-specific mAb ( Figures S7 and S8 ) , further confirming that an efficient inhibition of HCV cell-cell transmission appears to be required to prevent emergence of DAA-resistant virus in cell culture models ., Finally , we also performed a long-term cell culture infection assay investigating a combination of two DAAs on HCV infection ., We tested a highly potent NS5A inhibitor , daclatasvir , which has been shown to have potent pan-genotypic activity against HCV 51 , first alone and then in combination with simeprevir in the long-term HCV infection assay ., In cell culture , a concentration of 0 . 1 nM has been shown to alter the subcellular localization and biochemical fractionation of its target NS5A 52 ., The concentration of daclatasvir ( 5 nM ) used in the experiment resulted in a more than 10-fold decrease of viral load indicating that the dose is below the IC90 in this experimental setting ( Figure 5C ) ., However , during long-term treatment the viral load rebounded to the level of the untreated cells at day 31 with emergence of the DAA-resistant NS5A mutation , Y93H ( Table 3 ) ., Furthermore , in contrast to the combination of an HTEI and a protease inhibitor simeprevir , the combination of daclatasvir and simeprevir ( at concentrations > IC90 ) failed to eradicate HCV genotype 2 infection in Huh7 . 5 . 1 cells and HCV load rebounded from day 45 on with emergence of DAA-resistant mutations in both NS3 and NS5A regions ( Figure 5C and Table 3 ) ., Taken together , these data indicate that blocking virus cell-cell transmission by an HTEI prevents emergence of drug resistance to DAAs ., The amino acid sequence of HCV polyprotein recombinant Hepatitis C virus J6/JFH1 has been previously deposited in NCBI under access number AEB71614 . 1 ., The access numbers of human CD81 , CLDN1 , SR-BI , OCLN , EGFR , HRas and IFN-α are NP_004347 . 1 , claudin-1 NP_066924 , NP_005496 . 4 , AAB00195 . 1 , AAB19486 . 2 and CAG47067 . 1 and AAA52724 . 1 , respectively ., The nucleotide sequence of complete genome of recombinant hepatitis C virus J6/JFH1 has been previously deposited in GenBank under access number JF343793 . 1 | Introduction, Materials and Methods, Results, Discussion | Hepatitis C virus ( HCV ) is transmitted between hepatocytes via classical cell entry but also uses direct cell-cell transfer to infect neighboring hepatocytes ., Viral cell-cell transmission has been shown to play an important role in viral persistence allowing evasion from neutralizing antibodies ., In contrast , the role of HCV cell-cell transmission for antiviral resistance is unknown ., Aiming to address this question we investigated the phenotype of HCV strains exhibiting resistance to direct-acting antivirals ( DAAs ) in state-of-the-art model systems for cell-cell transmission and spread ., Using HCV genotype 2 as a model virus , we show that cell-cell transmission is the main route of viral spread of DAA-resistant HCV ., Cell-cell transmission of DAA-resistant viruses results in viral persistence and thus hampers viral eradication ., We also show that blocking cell-cell transmission using host-targeting entry inhibitors ( HTEIs ) was highly effective in inhibiting viral dissemination of resistant genotype 2 viruses ., Combining HTEIs with DAAs prevented antiviral resistance and led to rapid elimination of the virus in cell culture model ., In conclusion , our work provides evidence that cell-cell transmission plays an important role in dissemination and maintenance of resistant variants in cell culture models ., Blocking virus cell-cell transmission prevents emergence of drug resistance in persistent viral infection including resistance to HCV DAAs . | In spite of the rapid development of antiviral agents , antiviral resistance remains a challenge for the treatment of viral infections including hepatitis B and C virus ( HBV , HCV ) , human immunodeficiency virus ( HIV ) and influenza ., Virus spreads from infected cells to surrounding uninfected host cells to develop infection through cell-free and cell-cell transmission routes ., Understanding the spread of resistant virus is important for the development of novel antiviral strategies to prevent and treat antiviral resistance ., Here , we characterize the spread of resistant viruses and its impact for emergence and prevention of resistance using HCV as a model system ., Our results show that cell-cell transmission is the main transmission route for antiviral resistant HCV strains and is crucial for the maintenance of infection ., Monoclonal antibodies or small molecules targeting HCV entry factors are effective in inhibiting the spread of resistant HCV in cell culture models and thus should be evaluated clinically for prevention and treatment of HCV resistance ., Combination of inhibitors targeting viral entry and clinically used direct-acting antivirals ( DAAs ) prevents antiviral resistance and leads to viral eradication in cell culture models ., Collectively , the investigation provides a new strategy for prevention of viral resistance to antiviral agents . | hepatitis c, viruses, infectious diseases, medicine and health sciences, infectious hepatitis, hepatitis, rna viruses, gastroenterology and hepatology, viral classification, virology, biology and life sciences, microbiology, viral diseases, liver diseases, organisms | null |
journal.pgen.1007385 | 2,018 | Ancestry-specific recent effective population size in the Americas | Effective population size is a key factor in evolutionary genetic processes , such as drift and selection , which have important implications for medical genetics 1 , 2 ., With the development of agriculture , human populations have grown super-exponentially during the past few thousand years 3 , 4 ., More recently improved sanitation , modern medicine , and industrialized food production have further accelerated population growth ., During the last few generations , growth rates have slowed or become negative in many human populations due to the availability of effective birth control methods , higher levels of education for women , urbanization , and other factors 5 ., In addition to these global trends , populations in the Americas have experienced bottlenecks due to migrations , introduced diseases , and other effects of colonization ., Effective population size is a genetics-based measure of population size 6 ., Here we use inbreeding effective population size defined in terms of coalescence probability ( the probability that a given pair of haplotypes are descended from a single haplotype in the previous generation ) ., Consider a population of diploid individuals ., Randomly select a neutrally evolving locus and a pair of haplotypes from the population ., Let qg be the probability that the two haplotypes have a common ancestor g generations ago at that locus , conditional on not having a common ancestor in the last g − 1 generations ., In a randomly mating population with Ng breeding individuals g generations ago ( 2Ng haplotypes ) , that probability would be 1/ ( 2Ng ) because there are 2Ng possible ancestors for the second haplotype , each equally likely and only one of which is the ancestor of the first haplotype ., Thus , solving qg = 1/ ( 2Ng ) for Ng we obtain the effective population size g generations ago as Ng = 1/ ( 2qg ) ., Consequently , to estimate the effective size of a population , we first estimate the conditional coalescence probability qg , and then use the relationship Ng = 1/ ( 2qg ) ., The effective size of a population is generally smaller than the census size of a population due to demographic factors such as overlapping generations 7 ., In general , populations are not closed , but experience migration ., Thus the effective size g generations ago reflects the conceptual population of ancestors that contributed to the current generation , rather than an actual population residing in a certain location g generations ago ., Furthermore , the definition of effective size assumes random mating , but actual populations are structured by geography and by cultural factors , with preference for mating within sub-population ., Thus the effective size depends on the sampling scheme , which may over-represent some of the sub-populations ., Admixed individuals have ancestry that is recently derived from more than one continental population ., In the Americas , many individuals have admixed ancestry derived from indigenous peoples of the Americas , European settlers , and enslaved Africans forcibly brought to the Americas , as well as more recent immigration ., Because the migration events that brought these continental groups together are recent ( less than 20 generations before present in the Americas ) , the chromosomal segments of single-continent ancestry tend to be long , and it is possible to infer the ancestry at most points in the genome from genotype data 8–11 ., Analyses can then be performed using only the parts of the genome that are inferred as derived from a particular ancestry ., This enables inference about the ancestral populations ., An example of ancestry-specific analysis of admixed data is ancestry-specific principal components analysis , which can be used to investigate the degree of genetic similarity between the ancestors of individuals in an admixed sample and present-day individuals in a geographical region 12 , 13 ., When considering the effective size of an admixed population , the overall effective size of the population is generally the most relevant quantity subsequent to admixture ., In contrast , preceding admixture the overall effective population size of the combined contributing ancestral populations may not be the quantity of interest ., Instead , one may wish to know the historical effective sizes of those individual ancestral populations ., Consider two haplotypes of a particular ancestry that are sampled from the admixed population at a fixed locus ., Looking back in time , if those two haplotypes have not coalesced at this locus by the time of the onset of admixture , the probability that they coalesce in the previous generation depends ( as noted above ) on the effective size of the population in which the haplotypes were located at that time , which is the particular source ancestral population ., For example , in a sample of admixed individuals from a Latin American location , the ancestral populations may be from Europe ( primarily Spain ) , Africa ( primarily West Africa ) , and America ., The ancestral American populations are primarily those that were resident pre-admixture in the region around the sampling location , but can include a broader region if the sampling location is home to significant numbers of migrants from elsewhere in Latin America ., When we calculate ancestry-specific effective population size for pre-admixture times , we are estimating the effective sizes of the source populations that participated in the admixture , which could be subsets of larger populations ( e . g . Spanish colonizers within the larger Spanish population ) ., Although we can also calculate ancestry-specific effective sizes post-admixture , these may not be particularly meaningful because they do not correspond to any actual population of individuals ., Identity-by-descent ( IBD ) sharing in population samples can be used to estimate recent effective population size 14 , 15 ., Segments of IBD can be detected in genotype data ., We consider segments with genetic length greater than 2 centiMorgans ( cM ) ., These segments are due to inheritance from recent common ancestors within the past several hundred generations ., The numbers and lengths of IBD segments contain information about coalescence probabilities ., Shorter segments of IBD represent coalescent events that occurred further back in time ( up to several hundred generations ago ) , while longer segments represent coalescent events that occurred in the past few generations ., If the number of IBD segments is high , a larger number of coalescence events have occurred , indicating that the coalescence probability is high and thus the effective size is low ., Similarly , if the number of IBD segments is low , the effective size is high ., These relationships can be quantified mathematically for estimation of effective size , including estimation of changes in effective population size over time ., A previous study estimated ancestry-specific historical effective population size using the site frequency spectrum of alleles in ancestry segments from admixed individuals 16 ., The site frequency spectrum interrogates a much more distant time period than the IBD-based method that we use here ., Site frequency spectrum methods also require sequence data , whereas our IBD-based method can use single nucleotide polymorphism ( SNP ) array data , increasing the range of existing data to which it can be applied ., We demonstrate the effectiveness of our ancestry-specific effective population size estimation methodology with simulated data , and then use our methodology to estimate ancestry-specific recent effective population size in populations from the Hispanic Community Health Study/Study of Latinos ( HCHS/SOL ) , and in African-American and European-American populations in two US cities from the Healthy Aging and Body Composition ( Health ABC ) study ., We first summarize the key points of the IBDNe method for estimating the effective population size of homogeneous populations ( further details may be found in 14 ) , and then describe how this method can be applied to ancestry-specific effective size in admixed populations ., Consider a population with an effective size of Ng diploid individuals g generations before the present ( generation 0 is the current generation , generation 1 is the most recent previous generation , and so on ) ., If qg is the probability that a pair of haplotypes randomly sampled from the current generation coalesce ( have most recent common ancestor ) at generation g given that they have not coalesced by generation g − 1 , then Ng = 1/ ( 2qg ) by definition ( see Introduction ) ., Suppose we have a sample of individuals from the current generation , and we identify long segments of identity by descent between pairs of haplotypes drawn from different individuals , finding all those segments of identity by descent that exceed some length threshold C . In most settings , we use C = 2 cM , since we have high power to detect such segments using existing methods such as Refined IBD 17 ., Then , if the past trajectory of effective size N = {Ng: g ≥ 1} was known , we could calculate Eg , the expected total length of detected IBD genome-wide that is attributable to most recent common ancestry at generation g ., Also , if the effective size trajectory N was known , we could calculate the probability that an IBD segment of length l is due to most recent common ancestry at generation g , and hence obtain Og , the expected total length of detected IBD genome-wide that is attributable to most recent ancestry at generation g , conditional on the observed IBD segments ., Both Eg and Og are functions of N . Finding values of Ng that give equal values of Eg and Og provides a methods of moments estimate of N . We iterate estimating N and re-calculating Eg and Og until convergence of our estimate of N . During the iterative estimation process we also impose a smoothness requirement on Ng as a function of g to aid in the estimation ., Now consider an admixed population , in which local ancestry has been determined ., The ancestry-specific effective size Ng ( a ) for ancestry a considers only those haplotypes that are descended from ancestry a ., If qg ( a ) is the probability that a pair of such haplotypes randomly sampled from the current generation coalesce at generation g given that they have not coalesced in generations 1 to g − 1 , then Ng ( a ) =1/ ( 2qg ( a ) ) ., For generations prior to the admixture event , this ancestry-specific effective size Ng ( a ) represents the total effective size of the ancestral population that contributed to ancestry a in the admixed population ., For example , if considering European ancestry , and the European ancestors came from some population in Spain , the pre-admixture European-specific effective population size will be the effective size of that population in Spain ., Our interest in the ancestry-specific effective population size is mainly for the pre-admixture effective population sizes , but we also obtain estimates of post-admixture ancestry-specific effective population sizes ., If the post-admixture population is randomly mating , and has proportion p ( a ) of its ancestry being of ancestry a , then it is straightforward to show that Ng ( a ) =p ( a ) Ng where Ng is the overall effective size of the admixed population ., If there is assortative mating or ongoing migration , this relationship will not hold ., We now discuss how to estimate N ( a ) ={Ng ( a ) :g≥1} using the IBDNe framework ., The IBDNe framework needs the following information: the IBD lengths , in order to obtain Og; and the number of pairs of sampled haplotypes , which are needed to obtain Eg ., With a homogenous population , we obtain the IBD lengths directly from the detected IBD segments , and we calculate the number of pairs of sampled haplotypes from the number of sampled individuals ., With ancestry-specific analysis , there are differences because we are only interested in IBD between haplotypes of the given ancestry , but ancestry is not constant along the genome ., This affects both the way in which the IBD lengths are handled , and the way in which the number of pairs of sampled haplotypes is calculated ., Some IBD segments in an admixed population will have a very recent common ancestor ( from the generations post-admixture ) , and since this ancestor is admixed , the IBD segment may include more than one ancestry ., Only those parts of the segment that are derived from the ancestry of interest will contribute to the total length of detected IBD for this ancestry , but we still need to know the length of the whole IBD segment in order to calculate the probability that the most recent common ancestor lived in generation g ( see Methods ) ., Further , the number of pairs of sampled haplotypes of the given ancestry now varies from one genomic position to another , because the ancestry of each individual’s DNA varies along the genome , but the expected number of pairs of sampled haplotypes of the given ancestry can be calculated from the genome-wide ancestry proportions ( see Methods ) ., Apart from these differences , estimation of ancestry-specific effective population size is the same as for estimation of overall effective population size , and the existing IBDNe software may be used ., An example analysis pipeline is provided at http://faculty . washington . edu/sguy/asibdne/ ., We simulated an admixed population with ancestry from three continental groups in order to test the accuracy of our methods ., The simulation includes a low rate of genotype error ( 0 . 1% ) and the simulated data have a marker density that is similar to a 1M-feature SNP array ., A full description of the data simulation can be found in Methods ( in the “Simulated data” section ) ., Briefly , we used a coalescent-based simulator to simulate the Africa-Europe-Asia demographic history estimated by the 1000 Genomes Project 18 , and added population bottlenecks and admixture occurring 12 generations ago ., Fig 1 shows true and estimated ancestry-specific effective population size ., We see that important aspects of the effective population size trajectory are represented in the estimated trajectories , including the approximate timing of the population bottleneck , the approximate size of the ancestral population , and the effective size of the ancestry-specific fraction of the admixed population after the bottleneck ., The bootstrap confidence intervals do not always cover the true value , but they provide an approximate measure of the precision of the estimates ., In order to keep the simulation from becoming overly complex , the simulated population size decrease associated with migration occurs instantaneously , resulting in a sharp bottleneck in population size ., The effective population size estimation procedure cannot fully capture this sharp change because it applies smoothing by fitting exponential growth curves to groups of 8 generations ., In real life admixture events , such as that associated with the colonization of the Americas , we would not expect population changes to occur instantaneously ., Rather , migration and population decline would have taken place over the course of several generations , resulting in smoother effective size trajectories ., We simulated two admixture scenarios that don’t include bottlenecks ., One has the three populations merging without population size reductions , while the other has continuous migration from two of the populations into the third ., We estimated ancestry specific effective population sizes and show the results for the merging scenario in S1 Fig and for the continuous migration scenario in S2 Fig . Because these scenarios don’t include large population size changes over short time intervals , the estimates closely match the underlying true values , and the bootstrap intervals mostly cover the true values ., We also simulated an admixture scenario with recent population structure , with or without biased sampling across the sub-populations , and show the results in S3 Fig . With unbiased sampling the results are similar to those for the main simulation without structure ( Fig 1 ) , while with biased sampling the ancestry-specific effective sizes are underestimated in the first few generations ., We group individuals in HCHS/SOL into populations based on the reported country-of-origin of their grandparents ., Individuals with missing grandparental origins or grandparents from different countries are omitted from the analysis ., Fig 2 shows estimated ancestry-specific effective population sizes and overall effective population sizes for the HCHS/SOL populations for the past 100 generations ., S1 Table shows average total length of detected IBD segments shared by unrelated pairs of samples , which is a summary of the data used to estimate the overall effective population size ., Table 1 gives sample sizes for each population ., When the overall sample size is low , the amount of data for estimating the overall effective population size is low and the estimates will have a high level of uncertainty ., Similarly , when the average genome-wide ancestry proportion multiplied by the sample size is low , the amount of data for estimating the ancestry-specific effective population size is low ., The bootstrap intervals give approximate measures of precision of the estimates ., Estimates with wide intervals should be disregarded because the bootstrap intervals may not capture the full extent of the uncertainty in these estimates ., The widest intervals occur when the sample size and/or average ancestry proportion is lowest ., Wide pre-bottleneck confidence intervals are also seen for Puerto Rico , despite its high sample size , due to the extremely small bottleneck that occurred in this population ., The small bottleneck means that many of the possible coalescences between haplotypes occurred in the post-bottleneck period , leaving few independent haplotypes to provide information about the pre-bottleneck period ., In consequence , we recommend against drawing conclusions about the pre-bottleneck sizes of the populations ancestral to Puerto Ricans from this analysis ., The results for Puerto Rico show an apparent severe drop in overall effective population size in the most recent couple of generations ., This is an artefact resulting from an excess of relatives in the Puerto Rican sample , particularly at the level of 2nd to 3rd cousins ., Although we exclude IBD from relatives that are half-siblings or closer ( including parent-offspring pairs and full siblings ) , it is not straightforward to exclude IBD from more distant relatives without the use of pedigree information , and the IBDNe program is not designed to allow for the removal of these more distant relatives ., Inclusion of these relatives in the analysis results in an excess of long IBD segments which depresses the estimate of effective population size in the last few generations ., This effect may also somewhat depress estimates of effective population size in the last few generations in the other HCHS/SOL populations , though clearly not to such an extreme as for the Puerto Rican sample ., Most of the populations and ancestries show a clear population bottleneck around 12 generations ago ., Colonization began earlier , around 17 generations ago ( approximately 500 years ago , assuming 30 years per generation ) , but occurred over the course of multiple generations ., Overall bottleneck sizes vary considerably across the populations , ranging from 1 , 000 for Puerto Rico to 60 , 000 for Mexico ., Growth in overall effective population size subsequent to the bottleneck is estimated to have been very high in all the populations , with estimated current effective sizes in the hundreds of thousands or millions ( Table 2 ) ., When interpreting the drop in ancestry-specific effective population size at colonization , one must consider population structure ., Populations are not closed units , since there is always migration between regions ., When one considers the effective size of a “population” , one is considering the effective number of ancestors of the individuals in that population ., For example , if the population is a village in a region with low migration , most of the parents and grandparents ( corresponding to effective size at generations 1 and 2 , respectively ) will be derived from that village and the effective size will reflect the effective size of the village ., However , when one looks back 20 generations , many of the ancestors may have come from nearby villages , and the effective size will reflect the effective size of the region containing those villages ., Thus the effective size for the village may be lower in recent generations than in more distant generations , even if the census population size in the village and region has been stable ., This effect was noted in a previous IBD-based analysis 15 ., In the context of HCHS/SOL , the effective size 20 or so generations before admixture may reflect larger regional effective sizes while the immediate pre-bottleneck sizes reflect smaller sub-populations ., Thus the changes in size between the maximum pre-admixture effective population size and the bottleneck effective population size ( Fig 2 and Table 2 ) reflect these population structure effects as well as the effects of colonization ., Fig 3 shows selected population-ancestry combinations for which the precision is relatively high ., African-American and European-American populations from Memphis are included for comparison ., Considering each ancestry in turn , we see similarities and differences between populations in the estimated pre-admixture effective population sizes ., In the African component , we see smaller estimated pre-admixture effective sizes for Cuba ( 150 , 000 ) and Mexico ( 100 , 000 ) than for the Dominican Republic ( 700 , 000 ) , suggesting that the African ancestors of the former two populations came from smaller sub-populations of Africa than the African ancestors of the latter two populations ., In the European component we see smaller estimated pre-admixture effective sizes for Cuba ( 200 , 000 ) , Mexico ( 150 , 000 ) , and Nicaragua ( 120 , 000 ) than for the Dominican Republic ( 400 , 000 ) ., In the American ancestral component , the estimated pre-admixture effective sizes are similar between Nicaragua ( 400 , 000 ) , Ecuador ( 700 , 000 ) , and Mexico ( 600 , 000 ) ., We can also look at the relative magnitude of the bottleneck population sizes to the pre-admixture sizes , bearing in mind the potential effects of population structure discussed above ., For the African and European ancestral components , the drops in size are presumably mostly related to founder effects induced by migration ., In contrast , for the American ancestral components , the drops in effective population size are likely due to the negative impacts of colonization including war and disease ., Mexico had a relatively smaller estimated reduction in American-specific effective population size compared to the other populations ( Fig 3 and Table 2 ) ., In interpreting these results , it is important to recognize that the sampled individuals were residents of four major cities in the United States ., Thus the results apply to those particular urban populations , and not necessarily to the entire countries-of-origin represented ., If the populations in the US are derived from regional subsets of the countries-of-origin , the estimated effective sizes will be smaller than would be found for the countries as a whole if one had samples of individuals drawn randomly from those countries ., This would be expected to have a significant influence on the estimated effective size of the most recent generations , and less influence on more distant generations due to mixing within the population over time ., In order to further investigate the demographic history of US populations we analyzed data from the Health ABC study , which is comprised of samples from Memphis and Pittsburg ( Table 3 ) ., As in the HCHS/SOL populations , the Health ABC populations show significant growth in the most recent generations , as expected ., The overall bottleneck effective sizes for the Memphis populations and for the Pittsburgh African-American population are 130 , 000–190 , 000 which is more than twice as large as those for any of the HCHS/SOL populations ., The pre-admixture African-specific effective population sizes for the Memphis and Pittsburg populations are 1–2 million , and thus are higher than those for HCHS/SOL populations ( Fig 3 ) ., The pre-admixture European-specific effective sizes for the African-American populations and for the Memphis European-American population are around 1 million , and thus are also higher than those for most of the HCHS/SOL populations ., The estimated ancestry-specific effective population sizes for the African-American populations in these two cities are similar to each other ( Fig 4 ) ., The similarity of the estimated demographic histories of the Memphis and Pittsburgh African-American populations suggests significant historical mixing within the larger African ancestry population that encompasses these cities , so that the two populations have a shared demographic history ., In particular , the similarity of the estimated demographic histories of the European ancestry component is consistent with previous analysis of genetic data from African Americans 19 , which indicates that most of the European admixture in US African American populations occurred in the southern US prior to the Great Migration movement of African Americans from the South to elsewhere in the US ., In contrast , the demographic histories of the European-American populations in Memphis and Pittsburgh differ , both before and after the founding bottleneck ., Prior to the founding bottleneck , the effective population size of the Memphis European-American population was similar to that of the European component of the two African-American populations , suggesting that the European ancestors of these populations were drawn from the same European source , which again is consistent with the European admixture in the African-American populations having occurred in the South ., Memphis European-Americans have a higher estimated current effective size than Pittsburgh European-Americans , which contrasts with a previous report of more long segments of IBD between European Americans in the South than in the Northeast 19 ., Urban areas often differ from the general population in having more immigrants , both domestic and foreign ., During the period 1910–1950 , Memphis grew rapidly , tripling in size from 130 thousand to 400 thousand , while Pittsburgh’s population only increased slowly , from 530 thousand to 680 thousand 20 ., Since 1950 , Memphis’s population has continued to grow , while Pittsburgh’s population has declined ., Thus , it is likely that Memphis’s European American Health ABC population has more diverse geographical origins on average than Pittsburgh’s European American Health ABC population , leading to the larger current effective population size ., A striking difference between the two demographic histories is that the estimated bottleneck size in Pittsburg European-Americans is significantly smaller than that in Memphis , and the timing of the bottleneck appears to be earlier ( > 20 generations ago versus around 10 generations ago ) , which is earlier than the timing of European migration to North America ., Also the decrease in population size approaching the bottleneck is very gradual , which suggests that the ancestors came from sub-populations that had quite slow rates of mixing with the broader European ancestral population ., One possible explanation that could fit both of these characteristics is that many of the ancestors of the European-American population in Pittsburgh may have been members of groups that formed relatively small separated populations within Europe prior to their migration to the US ., An example of such an ancestral group that fits the historical record would be the Anabaptists ( including Mennonites and Amish ) , who separated from other European populations around 500 years ago and migrated to the US in large numbers to escape religious persecution ., Many of these Anabaptists settled in Pennsylvania 21 ., Although this is one possible explanation , it is not the only possibility , and our data do not address the question of origins ., In this paper we presented a method for calculating ancestry-specific recent effective population size by integrating local ancestry calls with inferred IBD segments ., With this approach , one can estimate the demographic history over the past several thousand years of populations ancestral to current-day admixed populations ., We applied our method to data from admixed populations sampled in the United States ., Our method is based on iterated method of moments ., As such , it is not guaranteed to make full use of all information contained in the data , although we show that it is able to make accurate inferences from moderately large samples ., One source of information that we do not incorporate in our method is the observation that any IBD segment that contains a switch in ancestry must necessarily be due to a post-admixture ancestor ., This information could improve the estimation of the number of generations to the most recent common ancestor of the IBD segment if the time of the onset of admixture is known ., However only a small proportion of IBD segments would provide this additional information because most IBD segments will not have a switch in ancestry and could be inherited from either pre- or post-admixture ancestors ., Previous methods for ancestry-specific effective population size estimation have not been able to estimate changes in effective size in the recent past , so the approach presented here opens new avenues for inference of demographic history ., Whereas Gravel et al . 16 estimate constant American-specific effective population sizes over the past ~12 , 000 years , our method allows for estimation of population growth and bottlenecks during the past 500 years ., Our estimates ( Table 2 and Fig 2 ) of the American-specific effective size of Mexico are fairly flat over the past 500 years , and are in agreement with Gravel et al . ’s estimate of 62 , 000 ., We estimate an order of magnitude bottleneck around 13 generations ago for Colombia’s American ancestry population , and a three orders of magnitude bottleneck around 13 generations ago for Puerto Rico’s American ancestry population ., The estimates of American-specific effective size given by Gravel et al . ( 7 , 000 for Colombia and 2 , 000 for Puerto Rico ) are intermediate between our bottleneck and maximal pre-admixture estimated sizes ., A caveat of our approach , and that of other methods based on local ancestry calls , including that of Gravel et al . 16 , is that it depends on the accuracy of the local ancestry calls and inferred IBD segments ., In simulated data that was designed to have similar characteristics to real human data , the results produced with our analysis pipeline had good accuracy , giving confidence that the results presented here are sound ., Our approach requires at least a few hundred samples ., Additional samples are required when considering an ancestral component that forms a relatively small proportion , p , of the overall ancestry of the population ., For most human populations , we recommend the use of sample sizes , n , that are sufficiently large so that np > 100 ., The precision of the estimates depends on the total number of IBD segments detected , which depends not only on np but also on the effective population size that is being estimated , so that a larger sample size will be needed in populations of larger effective size ., In addition , in populations with extremely small bottleneck sizes the pre-bottleneck estimates will have low precision due to a high proportion of haplotypes coalescing around the time of the bottleneck , leaving few haplotypes to provide information about the pre-bottleneck sizes ., When the sample size is small | Introduction, Results, Discussion, Methods | Populations change in size over time due to factors such as population growth , migration , bottleneck events , natural disasters , and disease ., The historical effective size of a population affects the power and resolution of genetic association studies ., For admixed populations , it is not only the overall effective population size that is of interest , but also the effective sizes of the component ancestral populations ., We use identity by descent and local ancestry inferred from genome-wide genetic data to estimate overall and ancestry-specific effective population size during the past hundred generations for nine admixed American populations from the Hispanic Community Health Study/Study of Latinos , and for African-American and European-American populations from two US cities ., In these populations , the estimated pre-admixture effective sizes of the ancestral populations vary by sampled population , suggesting that the ancestors of different sampled populations were drawn from different sub-populations ., In addition , we estimate that overall effective population sizes dropped substantially in the generations immediately after the commencement of European and African immigration , reaching a minimum around 12 generations ago , but rebounded within a small number of generations afterwards ., Of the populations that we considered , the population of individuals originating from Puerto Rico has the smallest bottleneck size of one thousand , while the Pittsburgh African-American population has the largest bottleneck size of two hundred thousand . | Using genome-wide genetic data on several hundred individuals sampled from a population , we can estimate the current effective size of the population and the changes in effective size that have occurred over the past hundred generations ., Many populations in the Americas are admixed , having ancestry from Europe , Africa , and the Americas ., In such cases , one can learn not only about the effective population size history of the admixed population since admixture , but also about the effective population size histories of the contributing ancestral populations ., In this paper we develop methodology for estimating past effective population size and analyze data from Hispanic , African-American , and European-American populations resident in the United States ., We observe differences between populations in their historical effective sizes ., These differences are useful for understanding differences in disease incidence between populations and for identifying populations that will maximize power in genetic association studies . | united states, evolutionary biology, population genetics, geographical locations, genetic mapping, ethnicities, simulation and modeling, north america, effective population size, african american people, population biology, africa, research and analysis methods, people and places, population metrics, haplotypes, population size, heredity, genetics, biology and life sciences, population groupings, europe | null |
journal.pntd.0006251 | 2,018 | Clonorchis sinensis adult-derived proteins elicit Th2 immune responses by regulating dendritic cells via mannose receptor | Clonorchiasis , resulted from Clonorchis sinensis ( C . sinensis ) infection , is a major but surprisingly neglected public health problem in Asia , notably in China and Korea ., About 15 million people are infected with C . sinensis worldwide ., Among which , China has the biggest share with around 13 million people infected with the parasite ., Further , the morbidity rose every year1 ., The histopathology of clonorchiasis is mainly characterized by a hyperplasia of intrahepatic bile-duct epithelium , followed by periductal and liver fibrosis in chronic cases2 ., Clinically , clonorchiasis patients show different severity of the symptoms ., Some patients show only mild or unspecific symptoms , such as asthenia , nausea , indigestion , jaundice , hepatomegaly and liver tenderness ., Nevertheless , chronic C . sinensis infection results in various complications in the liver and biliary systems , mainly cholelithiasis , cholangitis and cholecystitis ., What’s worse , 1 . 5 to 2 million patients with chronic infection develop to the late stage , cirrhosis or cholangiocarcinoma3–5 ., Liver fibrosis is a reversible pathological process for excessive repair and damage of hepatic tissue that characterized by accumulation and activation of various fibroblasts , deposition of extracellular matrix ( ECM ) proteins including collagen ., If the injury is acute or self-limited , these changes are transient ., However , chronic and sustained infection , may cause considerable tissue remodeling and a progressive substitution of liver parenchyma by permanent scar tissue and subsequent cirrhosis6–8 ., Parasites represent a diverse group of pathogens that often trigger highly polarized immune responses that become tightly regulated during chronic infections9 ., Proinflammatory and profibrotic cytokines produced by cells of the innate and adaptive immune systems can trigger fibroblasts and nonfibroblastic cells by transdifferentiation , especially in liver fibrosis caused by parasitic infections8 ., In addition , numerous studies clearly point out that interferon gamma ( IFN-γ ) and interleukin 12 ( IL-12 ) produced by T helper type 1 ( Th1 ) cells have anti-fibrotic effects10 , 11 ., Whereas Th2 cell is strongly pro-fibrogenic and in this setting IL-13 is acknowledged as a pivotal pro-fibrogenic mediator , since it could promote collagen production by three distinct but possibly overlapping pathways12 ., More interestingly , research shows that IL-13 is capable of stimulating collagen deposition directly and independently without the aid of transforming growth factor β 1 ( TGF-β1 ) , which is considered as the most potent pro-fibrogenic cytokine mainly produced by kupffer cells , monocytes , platelets paracrine and hepatic stellate cells 13 ., Fibrosis often develops as a consequence of parasitic infections that is strongly linked with the development of a Th2 CD4+ T-cell response , involving IL-4 and IL-13 production10 , 14 ., Th1 immune responses , which appeared during the acute phase , would shift to Th2 immune reactions accompanied by collagen deposition during long time infection of C . sinensis15–17 ., High concentrations of IgG1 in sera from mice model and patients infected with C . sinensis that suggested the dominant of Th2 immune responses18 , 19 ., Our previous studies reported the markedly elevated production levels of IL-13 in the splenocytes of C . sinensis-infected BALB/c mice15 ., In addition , there are accumulating evidences disclose that parasites drive the development of Th1 or Th2 cells through their effects on dendritic cells ( DC ) which are the most potent antigen-presenting cells ( APC ) 9 , 20 , 21 ., Th2-cell skewing immune responses presented during chronic infection of C . sinensis ., However , the underlying mechanisms remain vague in Th2 immunologic cascade-related reaction following C . sinensis infection ., In this study , we assessed the effects of proteins from C . sinensis on maturation and cytokines production of bone marrow-derived dendritic cells ( BMDC ) and subsequent influence on naive CD4+ T cells ., In addition , we investigated the involved mechanisms ., The conducts and procedures involving animal experiments were approved by the Animal Care and Use Committee of Sun Yat-Sen University ( Permit Numbers: SYXK ( Guangdong ) 2010–0107 ) ., All work with animals were according to the National Institutes of Health on animal care and the ethical guidelines ., 6 to 8 weeks old female BALB/c mice were purchased from the animal center of Sun Yat-Sen University ( Guangzhou , China ) ., Mice were maintained in specific pathogen-free animal facilities with 12 h light/dark cycle and water adlibitum ., C . sinensis adults were collected from hepatobiliary ducts of C . sinensis-infected mice that were infected with 30 living C . sinensis metacercariae through intragastric administration and sacrificed at week 12 ., Freshly collected C . sinensis adults were washed several times in phosphate buffered saline ( PBS , PH 7 . 2 ) with penicillin and streptomycin ( 100 U/ml and 100 μg/ml , Gibco , USA ) ., About 10 to 15 worms were lysed in 1ml of PBS with oscillation frequency of 30 Hz for 10 min ., Supernatant containing CsTPs were harvested and centrifuged at 4000 rpm for 15 min at 4 °C to remove residual tissue ., CsTPs were filtered through sterile 0 . 22 μm syringe filter and stored at -80°C until use ., BMDCs were generated from female BALB/c mice according to standard protocol22 , 23 with minor modifications as follows ., Briefly , bone marrow ( BM ) cells were flushed from tibiae and femurs of 6 to 8 weeks old BALB/c mice with chilled RPMI-1640 medium ( Gibco , USA ) ., Red blood cells were lysed with red blood cell lysing buffer ( Sigma , USA ) ., Then , the total BM cells were counted and resuspended in 4 ml RPMI-1640 medium supplemented with 10% FBS ( NQBB , Australia ) , 100 μg/ml streptomycin and 100 U/ml penicillin , 2 . 5 mM β-mercaptoethanol , 20 ng/ml mouse granulocyte-macrophage colony-stimulating factor ( GM-CSF , R&D Systems , USA ) and 10 ng/ml mouse IL-4 ( R&D Systems , USA ) , then the cells were seeded in 6-well plates ( Nest , China ) with the density of 5 × 106 cells /well ., On day 3 and day 5 , half culture medium was removed and 4 ml fresh RPMI-1640 medium containing the above supplements were added ., Anti-mouse CD11c PerCP-Cyanine5 . 5 ( eBioscience , USA ) was used to detect the phenotypes of BM cells by flow cytometry on day 7 ., The immature BMDCs were collected and 1 × 106 cells /well were seeded in 12-well plates in 1 ml complete RPMI-1640 medium ( containing 10% FBS , 100 U/ml penicillin , and 100 μg/ml streptomycin ) and pulsed with CsTPs ( 20 μg/ml , 40 μg/ml or 80 μg/ml ) or 0 . 5 μg/ml albumin ( Alb , Fitzgerald , USA ) of mouse as a control protein in the presence of 1 μg/ml lipopolysaccharide ( LPS , Sigma-Aldrich , USA ) ., BMDCs were collected after pulsed for 24 h and maturation markers expressed on BMDCs surface were analyzed by flow cytometry ( FACS ) ., The following monoclonal antibodies ( mAb ) were used: PerCP-Cyanine5 . 5-conjugated anti-CD11c , FITC-conjugated anti-CD80 , PE-conjugated anti-CD86 , and APC-conjugated anti-MHC class II ( eBioscience , USA ) ., The cells were respectively incubated with the mAb for 30 min at 4°C in the dark , and then washed twice with PBS containing 0 . 5% BSA and resuspended in PBS ., FACS was performed on a Beckman Coulter Gallios cytometer and analyzed by using Kaluza software ( Beckman Coulter , USA ) ., To assess IL-10 and IL-12p70 levels produced by BMDCs , the culture supernatants were centrifuged and harvested at different time points ( 24 h , 36 h and 48 h ) after stimulation and determined by ELISA using the corresponding mouse ELISA kits ( eBioscience , USA ) referred to the instructions ., CD4+ T cells were isolated from spleens of BALB/c mice on the autoMACS Pro Separator by using CD4+ T Cell Isolation Kit ( Miltenyi Biotec , Germany ) ., 1×105 isolated CD4+ T cells were co-cultured with 1×104 BMDCs pulsed for 24 h in the round-bottomed 96-well plate ( Costar , USA ) in a total volume of 200 μl/well ., 200 ng/ml IFN-γ ( PeproTech , USA , USA ) , 2 ng/ml IL-12 ( R&D Systems , USA ) and 5 μg/ml anti-IL-4 ( R&D Systems , USA ) were added as Th1 controls , while 10 ng/ml IL-4 ( R&D Systems , USA ) , 10 μg/ml anti-IL-12 and 5 μg/ml anti-IFN-γ were supplied with as Th2 controls24 ., On day 3 , 10 U/ml rIL-2 ( PeproTech , USA ) was added and the cultures were expanded for another 7 days ., After 10 days , for analysis of intracellular cytokine production , the primed CD4+ T cells were re-stimulated with 1× Cell Stimulation Cocktail ( plus protein transport inhibitors ) ( eBioscience , USA ) for 6 h ., The cells were collected and stained with PE-Cyanine7-conjugated anti-CD3e ( eBioscience , USA ) and FITC-conjugated anti-CD4 ( eBioscience , USA ) for 30 min at 4 °C before being fixed and permeabilized with Fixation/Permeabilization buffer ( eBioscience , USA ) according to the manufacturer’s protocol ., Finally , the cells were intracellular stained with APC-conjugated anti-IL-4 and PE-conjugated anti-IFN-γ ( eBioscience , USA ) ., FACS was performed on a Beckman Coulter Gallios cytometer and analyzed by using Kaluza software ., Meanwhile , IL-13 , IL-4 , IL-10 and IFN-γ levels in the supernatant of culture were measured by ELISA using their suiting Mouse ELISA kits ( eBioscience , USA ) ., Naive T cells were isolated from spleens of BALB/c mice using a CD4+CD62L+T Cell Isolation Kit II ( Miltenyi Biotec , Germany ) ( S1E and S1F Fig ) ., 5×104 BMDCs pulsed for 24 h and 5×105 naive CD4+ T cells were co-cultured at the conditions as mentioned above ., Productions of IL-13 , IL-4 , IL-10 and IFN-γ in supernatants of the culture were quantified by ELISA after 10 days ., Female BALB/c mice were subcutaneously immunized with 100 μg of CsTPs emulsified in complete Freund’s adjuvant ( Sigma-Aldrich , USA ) at 6 to 8 weeks of age ., Mice similarly administered with an equal volume of PBS were as a negative control group ( n = 15 in each group ) ., Two booster injections were performed with 50 μg of CsTPs or equal volume of PBS emulsified in incomplete Freund’s adjuvant ( Sigma-Aldrich , USA ) at two-week intervals ., The treated mice were sacrificed for isolation of splenocytes and hepatic tissue at 2th , 4th , 7th and 10th week after the first immunization , respectively ., Spleens were extracted from mice and single splenocyte suspensions were isolated by using red blood cell lysing buffer ( Sigma-Aldrich , USA ) and 40 μm cell strainers ( BD Falcon , USA ) ., 5×106/ml splenocytes were stimulated with 1× Cell Stimulation Cocktail ( plus protein transport inhibitors ) ( eBioscience , USA ) in complete RPMI-1640 medium ., The supernatants were removed and the levels of IL-4 , IL-10 , IFN-γ and IL-13 were analyzed by ELISA after 48 h incubation ., Livers were aseptically removed from the mice and stored in TRIzol reagent ( TransGen Biotech , China ) ., Total RNAs were extracted from liver tissues following standard protocols ., cDNAs were synthesized using TransScript All-in-One First-Strand cDNA Synthesis SuperMix for qPCR ( One-Step gDNA Removal ) kit ( TransGen Biotech , China ) from 1μg total RNA as manufacturer protocol described ., Real-time quantitative reverse transcription polymerase chain reaction ( RT-PCR ) reactions were performed on CFX96 Real-Time PCR Detection System ( Bio-Rad , USA ) using TransStart Top/Tip Green qPCR SuperMix ( TransGen Biotech , China ) ., Specific mRNA levels of IL-4 , IFN-γ , IL-10 , IL-12 and IL-13 were analyzed by calculating 2-ΔΔCt and normalized to a housekeeping gene ( β-actin ) ., All primers of RT-PCR were shown in Table 1 ., 1×106 cells/ml immature BMDCs were stimulated with CsTPs ( 20 μg/ml or 40 μg/ml ) or 0 . 5 μg/ml Alb in the presence of 1 μg/ml LPS for 24 h ., Receptors expressed on BMDCs including , toll like receptors ( TLR ) TLR2 and TLR4 , C-type lectin receptors mannose receptor ( MR ) , DC-SIGN and Dectin-2 were analyzed by RT-PCR ., The primer sequences were listed in Table 1 ., MR was also assessed by FACS using FITC-conjugated anti-CD206 antibody ( BioLegend , Canada ) ., To block MR , BMDCs were incubated with 0 . 1 mg/ml or 1 mg/ml mannan ( Absin Bioscience Inc , China ) in complete RPMI-1640 medium for 30 min at 37°C prior to addition of the above indicated reagents ., Statistical analysis was performed by programme Prism 6 . 0 ( GraphPad Software ) ., All data were presented as the mean values ± standard error or mean values ., One-sided paired Student’s t-test were used to analyze differences between two experimental groups , and P value <0 . 05 was considered to be significant ., Statistical analyses of the data were performed by ANOVA for multivariate analyses and only P value < 0 . 05 was considered statistically significant ., After being isolated from BM cells and cultured with 20 ng/ml GM-CSF and 10 ng/ml IL-4 for 7 days , more than 75% of the suspension cells and loosely adherent cells expressed CD11c , among which more than 65% did not express maturation marker CD86 by FACS ( S1A and S1B Fig ) ., The obtained BMDCs were stimulated with different concentrations of CsTPs in the presence of 1 μg/m LPS ., 20 μg/ml or 40 μg/ml CsTPs directly suppressed the classical LPS induced up-regulation of surface markers CD80 , CD86 , and major histocompatibility complex class II ( MHC-II ) expression on BMDCs compared to the control group treated with Alb plus LPS , and the optimum concentration of CsTPs was 40 μg/ml ( Fig 1 ) ., As the effect of CsTPs on LPS-treated BMDCs decreased obviously when the concentration up to 80 μg/ml but not in a dose-dependent manner , we identified the cytotoxic concentration of CsTPs on BMDCs by CCK-8 ., The result illustrated that 80 μg/ml CsTPs had a pronounced cytotoxic effect on BMDCs activity in the presence of LPS , while 20 μg/ml or 40 μg/ml CsTPs hadn’t ( S2 Fig ) ., DCs polarize Th cells mainly through the production of cytokines25 , 26 ., ELISA results demonstrated that CsTPs inhibited IL-12p70 release from LPS-treated BMDCs and the highest inhibition effect was at the concentration of 40 μg/ml ( Fig 2A ) ., IL-10 level increased in a time-dependent manner in LPS-treated BMDCs after incubation with CsTPs and the optimum concentration was also 40 μg/ml ( Fig 2B ) ., IL-12p70 or IL-10 level had no statistical difference in LPS-treated BMDCs following by incubation with Alb compared to those in LPS-treated BMDCs ., Isolated CD4+ T cells from BALB/c mice splenocytes ( S1C and S1D Fig ) were co-cultured with stimulated BMDCs at 10:1 ratio for 10 days ., By intracellular cytokine staining and detecting with FACS , the ratio of IL-4 positive CD4+ T cells to IFN-γ positive CD4+ T cells in group of LPS-activated BMDC with 20 μg/ml or 40 μg/ml CsTPs pulse was close to that in Th2 control group , however , the much lower ratios were showed in Th1 control group ( Fig 3A and 3B ) ., Th2 cytokines , mainly IL-4 and IL-13 , had distinct roles in the regulation of liver fibrosis12 ., We used ELISA to examine Th1/2 cytokine productions in the co-culture system of CD4+T cells and CsTPs-pulsed BMDCs ., The secretions of IL-13 and IL-4 significantly elevated in CsTPs-stimulated BMDCs group compared with those in only LPS-treated group ( P < 0 . 05 or P < 0 . 01 ) ., Whereas , the productions of IL-13 and IL-4 were not influenced by Alb-treated BMDCs and there was no statistic difference in the production of IFN-γ among the groups ( Fig 4A ) ., In co-culture system of BMDCs and naive T cells , IL-13 and IL-4 levels in the supernatant of CsTPs-stimulated BMDCs group were higher than those in only LPS-treated group ( P < 0 . 05 ) by ELISA ., In contrast , Alb-treated BMDCs neither drived significant IL-13 production nor IL-4 compared with those in only LPS-treated group ., There was no difference of IFN-γ level among LPS alone , LPS plus Alb and LPS plus 20 μg/ml CsTPs administrated groups , but LPS plus 40 μg/ml CsTPs treatment negatively regulated IFN-γ level ( P < 0 . 05 ) compared to LPS incubation ( Fig 4B ) ., In vivo , IL-13 level in splenocytes of mice immunized with CsTPs increased dominantly by using ELISA compared with those from naive mice at 7 weeks ( P < 0 . 0001 ) and 10 weeks ( P < 0 . 001 ) post administration ., IL-4 level statistically increased from 2 weeks post immunization and showed significant elevation at 7 weeks ( P < 0 . 0001 ) and 10 weeks ( P < 0 . 001 ) post administration ., IL-10 level also statistically increased at 7 weeks ( P < 0 . 05 ) and 10 weeks ( P < 0 . 05 ) post treatment ., There was no significant effect on the secretion of IFN-γ ( Fig 5A ) ., The mRNA levels of IL-4 and IL-13 in liver tissues of immunized mice showed distinctly enhancements with time dependence ., There were statistical differences ( P < 0 . 05 ) of IL-13 level compared with those in naive mice at all time points ( 2 , 4 , 7 and 10 weeks post treatment ) ., IL-10 transcripts had only a marginal increase compared to those in the control group and presented a statistical elevation at 10 weeks ( P < 0 . 05 ) post immunization ., As to transcripts of IL-12 and IFN-γ , there were no statistical significant between the groups ( Fig 5B ) ., Pattern recognition receptors ( PRRs ) like TLR-2 , DC-SIGN ( CD209 ) , Dectin-2 and MR ( CD206 ) on DC are documented to be related to a more Th2-skewing response27–30 ., Transcripts of TLR2 , DC-SIGN , Dectin-2 and MR on BMDCs were detected by RT-PCR to sift the specific receptor through which CsTPs triggered BMDC-induced polarization of Th2 cell ., MR transcripts remarkably increased on LPS plus CsTPs stimulated BMDCs compared to those on only LPS-treated BMDCs ( P < 0 . 05 ) , but not TLR2 , Dectin-2 or DC-SIGN ( Fig 6A ) ., Conversely , the high expression of TLR4 mRNA had been observed on LPS-stimulated BMDCs as Th1 response control ., For further verification , we used FACS to examine the MR expression ., It was verified that 40 μg/ml CsTPs could activate near fifty percent MR on the surface of LPS-activated BMDCs by FACS in contrast to those of only LPS group ( P < 0 . 0001 ) , however , Alb did not obviously interfere with the expression of MR on LPS-pulsed BMDCs ( Fig 6B and 6C ) ., Soluble mannan was used as a MR blocker via competitive inhibition ., There was no obvious difference of MHC-II , CD80 or CD86 expression among groups of LPS-activated BMDCs with 1 mg/ml mannan plus 40 μg/ml CsTPs , LPS-activated BMDCs and medium by FACS ., No statistical difference in the percentage of cells expressed CD80 or CD86 was observed among groups of LPS-activated BMDCs with 0 . 1 mg/ml mannan plus 40 μg/ml CsTPs , LPS-activated BMDCs and medium ( Fig 7A and 7B ) ., In co-culture system of BMDCs and CD4+ T cells , there was no statistical difference in the ratio of IL-4 positive CD4+ T cells to IFN-γ positive CD4+ T cells between the groups of LPS-treated BMDCs and BMDCs pretreated with LPS and 1 mg/ml mannan by 40 μg/ml CsTPs pulse by FACS ( Fig 8A and 8B ) ., IL-13 level in the supernatants of the groups showed no significant difference ( Fig 8C ) ., Parasites always activate greatly polarized immune responses , especially during chronic infection ., Our previous studies had also confirmed that compare with mechanical obstruction of the worm , the regulation of host immune responses was triggered much earlier and more important in liver fibrosis caused by a chronic infection with C . sinensis ., Infection with C . sinensis has been demonstrated to promote the generation of liver fibrosis by eliciting Th2 immune response of the host15 , 16 , 31 ., Nevertheless , the immune regulatory pathway that could contribute to the pathological processes are currently not well known ., It has been demonstrated that DC is pivotal for the recognition of helminth antigens as well as plays an essential role in regulating immune responses , in particular , priming initial T cell9 , 18 , 32–35 ., DC is increasingly recognized as a key mediator for the direction of Th1/Th2 polarization , which is closely related to the mature situation of DC ., Mature DC is mainly characterized by the up-regulation of co-stimulatory molecules CD80 and CD86 and the translocation of MHC molecules such as MHC-II to the cell surface36 ., Antigens from parasites are able to induce maturation of DC mostly via TLRs pathway ., The mature DC polarizes Th1 responses though the production of IL-12 to contribute to liver inflammation as well as play a protective role against to fibrosis7 , 37 ., In contrast to mature DC , immature DC as a consequence of a fail to classically mature when exposed to antigens derived from parasitic helminthes , does not up-regulate surface molecules such as MHC-II , CD80 and CD86 ., And immature DC has also been found to have the ability to present antigen to CD4+ T cells and involves in triggering Th2 responses by the production of IL-1038 , 39 ., Meanwhile , immature DC cloud be distinguished by their low production of IL-12 , which is also thought to be a prerequisite for their Th2-inducing capacity40 ., We had previously identified that a recombinant protein from C . sinensis could promote Th2 response during the chronic infection via modulating DC maturation , as well as production of IL-12p70 and IL-1018 ., In this study , we showed that natural CsTPs suppressed the classical LPS-induced BMDC maturation by significantly reducing the expression of CD80 , CD86 , and MHC-II ( Fig 1A and 1B ) ., These results were consistent with the function that CsTP has been observed in allergic airway inflammation , as a previous study showed that CsTP interfered with the ability of airway DC to initiate initial T cells in draining lymph nodes ( dLN ) by restraining the secretion of CD80 , CD86 and CD40 in LPS or ovalbumin ( OVA ) -stimulated DC41 ., It is well known that DC is crucial to the differentiation of CD4+ T cell ., Th2 cell as pro-fibrogenic cell has such potential to contribute for liver fibrosis by its effect on type 2 immune response6 , 12 ., We therefore speculated that in C . sinensis-induced liver fibrosis , the modulation of CsTPs-induced DC might be the initiation of the subsequent immunologic cascade as its strong capacity for priming type 2 immune response and CD4+ T cell has a crucial role in orchestrating this immune response ., Indeed , ample evidences determine that the relative balance of Th1 and Th2 immune response has been recognized as a key mediator for regulating the reversible pathological process between infectious disease-induced liver inflammation and liver fibrosis ., The co-administration of the Th1 cell cytokine IL-12 with Schistosoma spp ., decreased the granuloma formation and markedly reduced the fibrosis that are associated with this infection10 ., Our results showed a diminished expression of IL-12 that could prevent the generation of Th1-polarized responses ., Therefore , CsTPs as strongly pro-fibrogenic antigens have been demonstrated from the opposite angle ., Moreover , we found that CsTPs-stimulated BMDC potently triggered the differentiation of T cell toward to a Th2 cell profile ( Fig 2A and 2B ) ., The secretion of IL-13 dramatically increased from a co-culture system of CsTPs-stimulated BMDCs and naive T cells ( Fig 2D ) ., Our previous research suggested that C . sinensis-infected mice could induce Th2 immune response by expressing markedly increased levels of Th2 cytokine IL-4 and IL-1315 , 16 and promote early inflammatory cell infiltration while dense collagen deposition over time in hepatic tissue15 ., In this study , IL-4 , IL-13 and IL-10 not IFN-γ were expressed in a high percentage of splenocytes and hepatic tissue in CsTPs-immunized mice too compared with the control groups ( Fig 3A and 3B ) ., The results suggested that CsTPs were of great immunogenicity and could strongly drive the Th2-type immune responses , especially promote the expression of IL-13 both in vitro and in vivo ., As IL-13 is considered the major pro-fibrotic mediator12 , we speculated that CsTPs-induced high level of IL-13 may contribute primarily to the generation and development of liver fibrosis caused by C . sinensis infection ., A large body of evidences attest to the fact that the activation of specific receptors on DC can promote Th2 responses25 , 36 ., Several receptors on DC , in particular , TLR and CLR that could bind with antigens derived from helminths which are considered to be Th2 stimuli have been identified , including soluble tachyzoite antigens of Toxoplasma gondii binding to MyD88-induced TLR42 , Lewis-x derived from soluable egg antigens of Schistosoma mansoni binding to DC-SIGN43 , lipophosphoglycan of Leishmania mexicana binding to DC-SIGN44 , 45and glycosylated Schistosoma mansoni omega-1 binding to MR30 ., In this study , we screened out MR on BMDC from a number of pattern recognition receptors ( TLR-2 , DC-SIGN , Dectin-2 and MR ) that had been regarded as the potential elements to direct Th2 responses , and identified that it was the specific receptor to CsTPs ( Fig 4 ) ., Studies about schistosome indicated the roles of MR in recognizing glycosylated antigens and initiating Th2 immune responses at different stages of the infection 2846 ., MR , which has extensively been studied in DC , is a member of type I C-type lectin receptor superfamily of homologous proteins and binds glycans in a calcium-dependent manner47 ., MR has a great effect on recognizing and endocytosing variety pathogens including parasites and has been considered as a pattern recognition receptor involved in host immunity ., In mice , immature DC are activated via the TLR-4 ligand LPS to become mature DC by up-regulate the costimulatory molecules CD80 , CD86 and MHC-II48 , 49 ., We found that so simultaneously with the high expression of MR , CsTPs suppressed the production of TLR4 that was stimulated with LPS ( Fig 4A ) ., Besides , the blockade of MR with soluble mannan significantly impaired the inhibitory effect on expression of CD80 , CD86 , and MHC-II by CsTPs ( Fig 5A and 5B ) ., It illustrated that an absence of MR on CsTPs-induced BMDC were neither able to polarize Th2 effectors ( Fig 5C and 5D ) , nor promote the secretion of IL-13 ( Fig 5E ) ., Thus , MR might have a pivotal impact on the ability of DC to regulate pro-fibrogenic cytokine via inducing Th2 polarization ., That how CsTPs-MR interaction contributes to Th2-type immune responses awaited further researches ., In addition , highly glycosylated soluble antigens are generally responsible for Th2 polarization via MR , so that glycoprotein from CsTPs that could bind to MR and subsequent modulate DC function remains to be identified ., In summary , we validated in vitro that CsTPs could suppress the maturation of BMDCs in the presence of LPS via binding MR , and showed that the CsTPs-pulsed BMDCs actively polarized naive T helper cells to Th2 cells though the production of IL-10 instead of IL-12 ., Our findings also illustrated that CsTPs endowed host with the capacity to facilitate a Th2 cytokine production including IL-13 and IL-4 in vitro and vivo , thus possibly promoting the formation and development of liver fibrosis ., Our study underscored a crucial role of CsTPs in immune responses and liver fibrosis during infection of C . sinensis , which might provide useful information for developing potential therapeutic targets against the disease . | Introduction, Methods, Results, Discussion | Clonorchis sinensis ( C . sinensis ) is the most widespread human liver fluke in East Asia including China and Korea ., Clonorchiasis as a neglected tropical zoonosis , leads to serious economic and public health burden in China ., There are considerable evidences for an etiological relation between chronic clonorchiasis and liver fibrosis in human beings ., Liver fibrosis is a highly conserved and over-protected response to hepatic tissue injury ., Immune cells including CD4+ T cell as well as dendritic cell ( DC ) , and pro-fibrogenic cytokines like interleukin 4 ( IL-4 ) , IL-13 have been identified as vital manipulators in liver fibrogenesis ., Our previous studies had a mere glimpse of T helper type 2 ( Th2 ) dominant immune responses as key players in liver fibrosis induced by C . sinensis infection , but little is known about the involved mechanisms in this pathological process ., By flow cytometry ( FACS ) , adult-derived total proteins of C . sinensis ( CsTPs ) down-regulated the expression of surface markers CD80 , CD86 and major histocompatibility complex class II ( MHC-II ) on lipopolysaccharide ( LPS ) induced DC ., ELISA results demonstrated that CsTPs inhibited IL-12p70 release from LPS-treated bone marrow-derived dendritic cells ( BMDC ) ., IL-10 level increased in a time-dependent manner in LPS-treated BMDCs after incubation with CsTPs ., CD4+ T cells incubated with LPS-treated BMDCs plus CsTPs could significantly elevate IL-4 level by ELISA ., Meanwhile , elevated expression of pro-fibrogenic mediators including IL-13 and IL-4 were detected in a co-culture system of LPS-activated BMDCs and naive T cells containing CsTPs ., In vivo , CsTPs-immunized mice enhanced expression of type 2 cytokines IL-13 , IL-10 and IL-4 in both splenocytes and hepatic tissue ., Exposure of BMDCs to CsTPs activated expression of mannose receptor ( MR ) but not toll like receptor 2 ( TLR2 ) , TLR4 , C-type lectin receptor DC-SIGN and Dectin-2 on the cell surface by RT-PCR and FACS ., Blockade of MR almost completely reversed the capacity of CsTPs to suppress LPS-induced BMDCs surface markers CD80 , CD86 and MHC-II expression , and further made these BMDCs fail to induce a Th2-skewed response as well as Th2 cell-associated cytokines IL-13 and IL-4 release in vitro ., Collectively , we validated that CsTPs could suppress the maturation of BMDCs in the presence of LPS via binding MR , and showed that the CsTPs-pulsed BMDCs actively polarized naive T helper cells to Th2 cells though the production of IL-10 instead of IL-12 ., CsTPs endowed host with the capacity to facilitate Th2 cytokines production including IL-13 and IL-4 in vitro and vivo ., The study might provide useful information for developing potential therapeutic targets against the disease . | In China , the morbidity of clonorchiasis resulting from the infection of Clonorchis sinensis ( C . sinensis ) increased every year and 1 . 5 to 2 million patients develop to the late stage—liver fibrosis , cirrhosis or cholangiocarcinoma ., Proinflammatory and profibrotic cytokines produced by cells of the innate and adaptive immune systems can trigger fibroblasts and nonfibroblastic cells by transdifferentiation , especially in liver fibrosis caused by parasitic infections ., T helper type 2 ( Th2 ) -cell skewing immune responses presented during chronic clonorchiasis ., However , the underlying mechanisms remain vague in Th2 immunologic cascade-related reaction following C . sinensis infection ., The present study illustrated that C . sinensis adult-derived proteins ( CsTPs ) inhibited LPS-induced maturation of bone marrow-derived dendritic cells ( BMDC ) via mannose receptor in vitro and led to BMDC-induced differentiation of naive T cells into Th2 cells though the production of IL-10 ., Our findings also confirmed that CsTPs initiated Th2-cell skewing immune responses to markedly elevate the production of IL-13 and IL-4 which are closely associated with the generation of liver fibrosis . | blood cells, t helper cells, invertebrates, innate immune system, medicine and health sciences, immune cells, immune physiology, cytokines, helminths, immunology, animals, liver diseases, trematodes, clonorchis sinensis, developmental biology, gastroenterology and hepatology, molecular development, cytotoxic t cells, immune system proteins, white blood cells, animal cells, liver fibrosis, proteins, flatworms, t cells, immune response, immune system, toll-like receptors, biochemistry, signal transduction, eukaryota, cell biology, physiology, clonorchis, biology and life sciences, cellular types, immune receptors, organisms | null |
journal.ppat.1005019 | 2,015 | Herpesvirus Genome Recognition Induced Acetylation of Nuclear IFI16 Is Essential for Its Cytoplasmic Translocation, Inflammasome and IFN-β Responses | Kaposi’s sarcoma associated herpes virus ( KSHV ) , a γ-2 herpesvirus , is etiologically associated with Kaposi’s sarcoma ( KS ) and primary effusion lymphoma ( PEL ) 1 ., The hallmark of KSHV infection is the establishment of latent infection , reactivation and reinfection , and KS and PEL lesion endothelial and B cells , respectively , carry episomal KSHV latent dsDNA genome 1 ., Human PEL ( B ) cell lines BCBL-1 and BC-3 carry >80 copies of the episomal latent KSHV genome/cell and the lytic cycle can be induced by chemicals ., Purified virions from the supernatants are used for in vitro infection of human dermal microvascular endothelial cells ( HMVEC-d ) and foreskin fibroblast cells ( HFF ) 2 ., During infection of its target cells , KSHV must be coming in contact with the host innate immune system’s pattern recognition receptors ( PRR ) , such as Toll-like receptors ( TLRs ) , RIG-I-like receptors ( RLRs ) , NOD-like receptors ( NLRs ) and absent in melanoma 2 ( AIM2 ) -like receptors ( ALRs ) ., TLRs on the plasma membranes and endosomes as well as the RLRs , NLRs and AIM2 in the cytoplasm recognize pathogen or danger-associated molecular patterns ( PAMP/DAMP ) 3 , 4 , 5 ., KSHV infection of HMVEC-d cells induces inflammatory cytokines including the secretion of IL-1β into the supernatants which are similar to the microenvironments of KS and PEL lesions 6 ., IL-1β , IL-18 and IL-33 are synthesized as inactive proforms , undergo proteolytic processing by activated caspase-1 generated by the cleavage of procaspase-1 via inflammasomes ., Most of these molecular platforms are formed by homotypic interactions of a sensor protein recognizing the danger trigger , adaptor molecule ASC ( apoptosis-associated speck-like protein containing CARD ) , and the effector procaspase-1 ., NLRs are cytoplasmic inflammasome sensors of foreign molecules , including ROS , K++ , alum , bacterial products , RNA and RNA viruses replicating in the cytoplasm , while AIM2 recognizes cytoplasmic DNA including transfected DNA and DNA of pox viruses replicating in the cytoplasm 4 , 7 , 8 , 9 ., They initiate the host defenses by regulating the production of IL-1β , IL-18 , IL-33 or type I interferons ( IFN ) α/β 7 , 8 , 9 , 10 ., Whether innate responses recognize and respond to the presence of foreign episomal genomes of herpesviruses as well as other DNA viruses in the infected cell nuclei leading into the induction of inflammatory responses was not known initially ., Our studies revealed that in vitro KSHV infection of endothelial cells induces caspase-1 activation via the nuclear resident gamma-interferon-inducible protein-16 ( IFI16 ) also known as interferon-inducible myeloid differentiation transcriptional activator ., Colocalization of IFI16 with viral genome in the infected endothelial cell nucleus , induction of IFI16-ASC inflammasomes by UV-inactivated KSHV and the absence of induction by lentivirus vectors expressing KSHV genes demonstrated that, a ) KSHV genes individually do not play a role in IFI16-inflammasome activation ,, b ) the IFI16-inflammasome is not induced against linear integrated foreign DNA , and, c ) episomal KSHV genome is required for IFI16-inflammasome activation 11 ., When we analyzed the gene expression in uninfected and infected HMVEC-d cells , a significant increase in caspase-1 gene expression from 2 to 24 h post-infection ( p . i . ) , significant induction of the ASC gene only at 24 h p . i . , a slight but not significant increase in IFI16 gene expression , and no increase in NLRP-1 , NLRP3 and AIM2 genes were observed 11 ., We have subsequently demonstrated that only the IFI16-inflammasome is constitutively induced in KSHV latently infected endothelial and PEL cells 12 , as well as in B-lymphoma , epithelial and lymphoblastoid cells latently infected with γ-1 Epstein-Barr virus ( EBV ) 13 ., Colocalization of IFI16 with the latent KSHV and EBV genome in the nuclei suggested that continuous sensing of latent genome results in the constitutive induction of IFI16-ASC inflammasomes ., In addition , our studies showed that IFI16 recognizes the α-herpes simplex virus type-1 ( HSV-1 ) genome soon after its entry into the nucleus resulting in the formation of IFI16-inflammasomes 14 ., The 730 aa ( 1–2190 bp ) IFI16 protein consists of an n-terminal ASC interacting PYRIN domain ( 41–261 bp ) , 200-amino-acid HIN I ( 401–895 bp ) and HIN II ( 1043–1541 bp ) domains involved in the sequence independent DNA recognition , and 2 nuclear localizing signals ( NLS; 296–311 and 387–407 bp ) which attribute to its nuclear entry after synthesis in the cytoplasm 15 ., Though IFI16 is a predominately nuclear protein , after recognizing KSHV and HSV-1 DNA during de novo infection , the IFI16-ASC complex initially colocalized in the infected cell nucleus and subsequently localized in the perinuclear areas 11 , 14 ., Similarly , we observed the colocalization of IFI16 and ASC both in the nucleus and cytoplasm of cells latently infected with KSHV and EBV 12 , 13 ., Western blot analysis of de novo KSHV infected HMVEC-d cells showed steady levels of ASC and procaspase-1 in the nuclear fractions ., Infected cells also showed higher levels of both ASC and procaspase-1 in the cytoplasmic fractions which demonstrated that ASC and procaspase-1 undergo subcellular redistribution upon infection ., Active caspase-1 ( p20 ) was detected in the nucleus of infected HMVEC-d cells at 2 and 8 h post-infection demonstrating that the inflammasome is activated upon sensing KSHV in the nucleus , and the majority of activated caspase-1 was subsequently detected in the cytoplasmic fractions at later times of infection probably to prevent caspase-1 mediated adverse activities in the nucleus ., Detection of caspase-1 in the cytoplasm during de novo KSHV and HSV-1 infection as well as in latently infected cells demonstrated that after recognizing viral DNA in the nucleus , the newly formed IFI16-ASC inflammasome complex is transported to the cytoplasm 11 , 12 , 13 , 14 ., However , the mechanism behind the redistribution of this complex is not known ., HSV-1 infection also induced IRF-3 phosphorylation through the IFI16-STING interaction in the cytoplasm ., Even though the recognition of HSV-1 genome in the nucleus via IFI16 is suggested to be the factor behind the cytoplasmic STING-IRF-3 activation and IFN-β production early during infection 16 , the mechanism of post-genome detection signaling from nucleus to cytoplasm resulting in STING activation is not known ., KSHV infection induces only a moderate IFN-β response early during de novo infection which was inhibited by a variety of early lytic and latent gene products at later times of infection 17 ., The role of IFI16 in IFN-β production during KSHV infection is not known ., Using IFI16-EGFP constructs transfection in human osteosarcoma U2OS cells , Li et al . , 15 studies showed that the two NLS motifs of IFI16 ( aa 96–100 and aa 128–131 ) are essential for the entry of newly synthesized IFI16 in the cytoplasm to the normal cell nucleus ., Using a FISH assay , they demonstrated that during HSV-1 ( strain 17+ ) infection of U2OS cells ( 5 PFU/cell ) containing transfected IFI16-EGFP construct , virion DNA colocalized only with full length IFI16-EGFP with intact NLS and not with mutated NLS-IFI16-EGFP that were localized in the cytoplasm ., They also observed that as reported by us for KSHV 11 , 12 , EBV 13 and HSV-1 14 , a subset of wild type IFI16 translocated to the cytoplasm ., In addition , co-IP of HSV-1 DNA-protein complexes followed by qPCR with four HSV-1 primer sets ( UL30 , US6 , RL1 and RS1 ) demonstrated the nuclear IFI16 interaction with viral DNA in the nucleus ., Using uninfected U2OS transfected with DNA , Li et al . , 15 concluded that acetylation at the NLF motifs of IFI16 results in the cytoplasmic retention of newly synthesized IFI16 by prohibiting nuclear import , and the histone acetyltransferase p300 regulated the cytoplasmic IFI16 acetylation during transfection of DNA ., However , the fate of nuclear IFI16 during HSV-1 infection , whether IFI16 undergo acetylation during HSV-1 infection , the role of p300 during viral DNA recognition in the nucleus , and the mechanism behind the IFI16 redistribution into the cytoplasm during infection was not studied 15 ., Here , we demonstrate that the presence of KSHV genome in the nucleus induces the p300 mediated acetylation of IFI16 and this modification is the driving force behind the nuclear to cytoplasmic redistribution of the IFI16-inflammasome which was facilitated by Ran-GTPase ., IFI16 acetylation is required for its interaction with ASC , inflammasome assembly and function ., In addition , cytoplasmic redistribution of acetylated IFI16 is also essential for STING-IRF-3 mediated IFN-β production in KSHV and HSV-1 infected cells ., These studies for the first time demonstrate that IFI16 acetylation is a dynamic post-herpes viral genome recognition event required for the IFI16-mediated innate responses of inflammasome induction ( KSHV , EBV and HSV-1 ) and IFN-β production ( KSHV and HSV-1 ) ., KSHV enters HMVEC-d and HFF cells by a rapid endocytic process which is followed by the transport of genome-containing capsid to the nuclear pore vicinity , capsid disassembly and entry of the linear dsDNA into the nucleus within 15–30 min p . i . , followed by the establishment of a latent infection 18 ., Our studies have shown that IFI16 colocalized with the KSHV genome at 2 h p . i . in the nucleus of HMVEC-d cells 11 ., To determine the earliest time of interaction of IFI16 with KSHV genome , HMVEC-d cells were infected with KSHV containing BrdU-labeled genome ( BrdU-KSHV ) and immunostained with anti-BrdU antibodies ( Fig 1A; Table 1 ) ., IFI16 was predominantly localized in the uninfected cell nucleus ( Fig 1A , top panel ) ., By 15 min p . i . , viral particles were seen in the cytoplasm and near the nuclear periphery ( Fig 1A , red arrows , middle panel ) ., In contrast , significant accumulation of viral DNA was observed at 30 min p . i . in the infected cell nuclei , and most of them colocalized with IFI16 ( Fig 1A , white arrows ) ., In addition , a few IFI16 signal spots were also detected in the cytoplasm at 30 min p . i . ( Fig 1A , yellow arrow ) ., These results suggested that IFI16 senses the KSHV genome soon after its entry into the nucleus during de novo infection with a concomitant redistribution to the cytoplasm ., To determine the kinetics of IFI16 redistribution to the cytoplasm , the cytoplasmic and nuclear fractions from uninfected cells and cells infected with KSHV for various times were analyzed by western blots ( WB ) ., Consistent with the IFA results , a very faint IFI16 band was detected at 30 min p . i . in the cytoplasm which steadily increased during the observed period of 24 h p . i . ( Fig 1B , lanes 9–12 ) with a corresponding decrease in the nuclear IFI16 levels ( Fig 1B , lanes 4–6 ) ., TBP and tubulin proteins were used as markers of nuclear and cytoplasmic preparation purity and as controls for equal loading ( Fig 1B , lanes 1–12 ) ., When IFA was performed to validate the biochemical data , IFI16 was predominantly in the nucleus of uninfected cells ( S1A Fig , top panel ) ., In contrast , at 30 min p . i . , few IFI16 signal spots were visible in the cytoplasm which increased steadily during the observed period of 24 h p . i . ( S1A Fig , red arrows ) ., These results demonstrated that KSHV infection induces IFI16 redistribution from the nucleus to the cytoplasm as early as 30 min p . i . with steady increase thereafter ., IFI16 has been shown to function as a transcriptional modulator via unknown mechanisms 19 ., We theorized that acetylation of IFI16 could be one of the reasons for cytoplasmic transport since acetylation of HMGB-1 ( high-mobility group protein B1 ) protein involved in transcription/ chromatin bending has been shown to result in HMGB-1’s translocation into the cytoplasm 20 ., Furthermore , IFI16 acetylation within the NLS motifs during transfection of DNA in U20S cells promoted cytoplasmic retention by blocking nuclear import of newly synthesized IFI16 15 ., However , the fate of IFI16 during nuclear DNA sensing was not studied ., To investigate the acetylation status of IFI16 during KSHV infection , uninfected and infected cell lysates were immunoprecipitated ( IP-ed ) with anti-acetylated lysine antibody and western blotted for IFI16 ., Compared to the uninfected cells , we observed a robust increase in the acetylation of IFI16 only in the infected cells ( Fig 1C , lanes 1 and 2 ) ., In contrast , equal levels of acetylated tubulin were observed in both uninfected and KSHV infected cells ( Fig 1C , lanes 1 and 2 ) ., The input IFI16 and loading control tubulin were of similar levels ., These results suggested that the acetylation machinery was functional in both uninfected and infected cells and KSHV infection induced increased acetylation of IFI16 ., When we next investigated the kinetics of IFI16 acetylation in the nuclear and cytoplasmic fractions by co-IP experiments , as early as 30 min p . i . an appreciable level of nuclear IFI16 acetylation was observed which steadily increased during the observed 24 h p . i . ( Fig 1D , lanes 2–6 ) ., Correspondingly , we detected a faint band of acetylated IFI16 in the cytoplasm at 30 min p . i . , with steady increase from 2 to 24 h p . i . ( Fig 1D , lanes 9–12 ) , which corroborated the results in Fig 1B , lanes 9–12 ., The faint acetylated IFI16 band detected in the nucleus of uninfected cells probably represents the basal level ( Fig 1D , lane 1 ) ., These detections were not due to nuclear contamination as shown by the absence of TBP and presence of tubulin in these fractions ( Fig 1D ) ., As positive control for nuclear and cytoplasmic acetylation , the proteins were IP-ed with acetylated lysine antibody and western blotted for H3 and tubulin , respectively ( Fig 1D , lanes 1–12 ) ., Total H3 level was also analyzed by western blot as input control ., These results were also validated by IFA using anti-IFI16 and anti-acetylated lysine antibodies ( S1B Fig ) ., In the uninfected cells , IFI16 was detected in the nucleus and acetylated lysine signals were observed both in the nucleus and in the cytoplasm ( S1B Fig , top panel ) ., We also observed some basal level of IFI16 and acetylated lysine colocalization in the nucleus of uninfected cells ( S1B Fig , UI , red arrow ) ., In contrast , KSHV infection significantly increased the colocalization of acetylated lysine and IFI16 in the nucleus as well as in the cytoplasm in a time dependent manner ( S1B Fig ) ., Taken together , these results demonstrated that during de novo KSHV infection , IFI16 recognizes the viral genome with a concomitant increase in its acetylation in the nucleus and redistribution of acetylated IFI16 to the cytoplasm of the infected cells ., The cellular transcriptional coactivator protein p300 functions as a histone acetyltransferase and has been shown to be involved in the cytoplasmic acetylation of IFI16’s NLS domains 15 ., To investigate the significance of nuclear acetylation of IFI16 and its redistribution , we utilized the p300 competitive inhibitor C-646 ., Based on the results in BCBL-1 and HMVEC-d cells incubated with various concentrations of C-646 for 4 and 24 h ( S2A and S2B Fig ) we selected the least toxic 1 μM concentration ( 5–6% cell death ) for all further experiments ., C-646 treatment did not interfere with viral entry or nuclear delivery of viral genome , and equal levels of the characteristic KSHV latent LANA-1 protein dots were detected in the treated and untreated cells ( S2C , S2D , and S2E Fig ) ., Significant increase in acetylation was observed in the KSHV infected cells which was reduced by C-646 treatment ( S2F Fig , lanes 1–4 ) ., The specificity of C-646 was examined by the acetylation level of H2B , one of the target proteins of p300 ., IP with acetylated lysine antibody and WB for H2B showed six fold reduction in H2B acetylation by C-646 compared to the untreated KSHV ( 24 h ) infected cells ( S2G Fig , lanes 1 and 2 ) ., These results demonstrated that de novo KSHV infection induced acetylation , which is in part due to p300 , can be inhibited by C-646 ., To determine the effect of C-646 on IFI16 acetylation , HMVEC-d cells were either uninfected or infected with KSHV in the presence or absence of C-646 , whole cell lysates IP-ed with anti-acetylated lysine antibody and western blotted for IFI16 ., Compared to untreated infected cells , C-646 treatment completely abolished the infection induced IFI16 acetylation ( Fig 1E , lanes 1–6 ) ., Immunoprecipitation of IFI16 followed by WB for IFI16 demonstrated equal pull down; in addition , β-actin levels did not change due to treatment and showed equal loading ( Fig 1E , lanes 1–6 ) ., IP of IFI16 and WB with anti-acetylation antibody also validated these results which showed decreased levels of acetylated IFI16 by C-646 treatment in infected cells ( Fig 1F , lanes 1–3 ) ., To investigate the effect of C-646 on KSHV infection induced acetylation mediated cytoplasmic redistribution of IFI16 , HMVEC-d cells were infected in the absence or presence of C-646 , cytoplasmic and nuclear fractions isolated and western blotted for total IFI16 ., KSHV infection induced redistribution of IFI16 into the cytoplasm was abolished in C-646 treated cells ( Fig 1G , lanes 7–12 ) ., Interestingly , we also observed that the nuclear IFI16 levels decreased at later time points by C-646 ( Fig 1G , lanes 4–6 ) which suggested that acetylation may have a role in the stabilization of IFI16 ., These results demonstrated that IFI16 acetylation during KSHV infection is dependent on p300 and acetylation is required for the redistribution of IFI16 from the nucleus to the cytoplasm after recognition of the KSHV genome in the nucleus ., To validate these results , we performed in situ-PLA which detects endogenous levels of proteins and gives the spatial distribution and localization of a single or multiple proteins ( Fig 2A ) ., PLA uses oligonucleotide-linked secondary antibodies and a fluorescence-based assay to detect closely associated proteins ., If epitopes of a single protein or two protein epitopes are within 40 nm proximity , the antibody-linked oligonucleotides will ligate with adaptor oligonucleotides to form complete circles that are amplified via DNA replication and detected with fluorescent sequence-specific probes which will appear as distinct dots visible under fluorescent microscopy ., HMVEC-d cells were uninfected or infected in the presence or absence of C-646 and subjected to PLA using rabbit and mouse anti-IFI16 antibodies detecting different epitopes , and the detected red dots depict IFI16 ( Fig 2A ) ., IFI16 was predominantly nuclear in both untreated and C-646 treated uninfected cells ( Fig 2A , top 2 panels , yellow arrows ) ., In the absence of C-646 , we observed abundant cytoplasmic IFI16 localization in KSHV infected cells at 24 h p . i . ( Fig 2A , lower panels , white arrows ) , and an uninfected cell in the same field showed predominantly nuclear IFI16 ( Fig 2A , blue arrow ) ., In contrast , while IFI16 was detected in the nucleus of C-646 treated infected cells , we did not observe IFI16 redistribution in the cytoplasm ( Fig 2A , lower panels ) ., These results demonstrated that inhibition of acetylation compromised the cytoplasmic redistribution of IFI16 ., To further elucidate the effect of C-646 on acetylation of IFI16 , PLA was performed using anti-IFI16 and anti-acetylated lysine antibodies and the observed red dots represent the acetylated IFI16 ( Fig 2B ) ., Low levels of nuclear acetylated IFI16 PLA dots were detected both in the treated and untreated uninfected cells ( Fig 2B , top panel , yellow arrows ) ., In contrast , at 30 min p . i . , acetylated IFI16 dots were appreciably increased in the nucleus with few dots visible in the cytoplasm , which increased to numerous acetylated IFI16 spots in a time dependent manner ( Fig 2B , left panels , white arrows ) ., In contrast , with C-646 treatment the acetylated IFI16 dots did not increase either in the cytoplasm or in the nucleus of infected cells ( Fig 2B , lower three right panels ) ., These studies demonstrating the reduction in cytoplasmic IFI16 redistribution by C-646 treatment validated our findings , and confirmed that IFI16 acetylation in the nucleus during KSHV infection is required for its redistribution to the cytoplasm ., We have previously shown that replication incompetent UV treated KSHV ( UV-KSHV ) enters the cells , delivers the viral DNA into the nucleus and induces the IFI16-inflammasome 11 , which demonstrated that the presence of KSHV genome is the requirement for IFI16 recognition and further consequences ., When lysates from HMVEC-d cells infected with KSHV or UV-KSHV for 24 h were IP-ed with anti-acetylated lysine antibody and western blotted for IFI16 , similar to live-KSHV infected cells , acetylation of IFI16 increased in a time dependent manner by infection with UV-KSHV ( Fig 2C , lanes 1–7 ) ., These results suggested that the presence of viral genome is enough to induce the IFI16 acetylation process and viral gene expression is not required ., We next determined whether acetylation of IFI16 and its cytoplasmic redistribution also occur in other cell types ., Compared to uninfected cells , as in HMVEC-d cells , KSHV infected HFF cells ( 24 h p . i . ) showed increased acetylation of IFI16 which was significantly inhibited by C-646 ( S3A Fig , lanes 1–4 ) , and WB for total IFI16 showed a slight reduction in C-646 treated cells ( S3A Fig , lanes 1–4 ) ., In PLA analysis , infected HFF cells in the absence of the inhibitor showed robust acetylation of IFI16 and its redistribution to the cytoplasm , which was significantly abrogated by C-646 ( S3B Fig ) ., Uninfected cells showed only a basal level of acetylated IFI16 in the nucleus ( S3B Fig ) ., Evaluation of the total IFI16 levels by PLA using mouse and rabbit anti-IFI16 antibodies revealed that IFI16 was solely nuclear in the uninfected cells ( S3C Fig ) , while the KSHV infected cells showed IFI16 both in the nucleus and in the cytoplasm ( S3C Fig ) ., However , when the cells were treated with C-646 , IFI16 was only detected in the nucleus ( S3C Fig ) ., These results demonstrated that acetylation of IFI16 is essential for its redistribution to the cytoplasm of KSHV infected HFF cells ., We have shown that IFI16 recognizes the latent KSHV genome and only the IFI16-inflammasome is constitutively induced in endothelial and PEL cells carrying latent genome ., Hence , we determined the acetylation status of IFI16 in these cells ., Whole cell lysates from control BJAB and KSHV ( + ) BCBL-1 cells were IP-ed with anti-acetylated lysine antibody and western blotted for IFI16 ., Compared to BJAB cells , we detected increased IFI16 acetylation in BCBL-1 cells which was significantly reduced by C-646; however , total IFI16 was pulled down equally in each group ( S4A Fig , lanes 1–4 ) ., Examination of total IFI16 in the cytoplasmic and nuclear fractions from untreated or C-646 treated BCBL-1 cells revealed ~6–11 fold less cytoplasmic IFI16 protein levels at 4 and 24 h of drug treatment , respectively , compared to the untreated controls ( S4B Fig , lanes 4–6 ) ., These results demonstrated the acetylation dependent cytoplasmic redistribution of IFI16 in the latently infected cells ., As in de novo infected cells , nuclear IFI16 protein levels also decreased in the presence of C-646 indicating that IFI16 stability in KSHV infected cells may be dependent upon its acetylation ., To validate these results , PLA was performed in BJAB and BCBL-1 cells using anti-IFI16 and anti-acetylated lysine antibodies ( S4C Fig ) ., Compared to the few nuclear acetylated IFI16 PLA dots in the BJAB cells ( S4C Fig , upper left panel ) , we observed a significant increase in the acetylated IFI16 in the nucleus as well as in the cytoplasm of KSHV+ BCBL-1 cells ( S4C Fig , lower left panel , yellow and white arrows , respectively ) ., C-646 treatment resulted in a drastic reduction in acetylated IFI16 ( S4C Fig , right panels ) ., When PLA was done to examine total IFI16 and its redistribution in the absence or presence of C-646 , we did not observe any cytoplasmic IFI16 in the BJAB cells ( S4D Fig , upper panels ) ., Corroborating the biochemical data in S4B Fig , increased nuclear and cytoplasmic IFI16 were observed in untreated BCBL-1 cells whereas IFI16 was mostly nuclear in the C-646 treated cells ( S4D Fig , lower panels , yellow arrows ) ., The KSHV latently infected endothelial ( TIVE-LTC ) and B ( BJAB-KSHV ) cells were also analyzed for IFI16 acetylation ., IP of the whole cell lysates from control endothelial TIVE and BJAB , KSHV ( + ) TIVE-LTC and BJAB-KSHV cells with anti-acetylated antibody followed by IFI16 WB revealed significantly higher levels of acetylated IFI16 in both TIVE-LTC and BJAB-KSHV cells than in the KSHV negative control cells ( S4E Fig , lanes 1–4 ) ., Equal amounts of IFI16 were detected in IP and in WB reactions ( S4E Fig , lanes 1–4 ) ., By PLA for IFI16 acetylation in the presence or absence of C-646 , TIVE cells showed a minimal amount of acetylated IFI16 in both treated and untreated cells ( S4F Fig , upper panels ) ., In contrast , the TIVE-LTC cells showed increased levels of acetylated IFI16 both in the nucleus and in the cytoplasm ( S4F Fig , lower left panel ) ., This cytoplasmic redistribution of acetylated IFI16 was abolished by C-646 ( S4F Fig , lower right panel ) ., Total IFI16 levels in C-646 treated or untreated TIVE and TIVE-LTC cells were also analyzed by PLA using mouse and rabbit anti-IFI16 antibodies ., In untreated and C-646 treated TIVE cells , IFI16 was solely nuclear ( S4G Fig , upper panels ) ., In contrast , TIVE-LTC cells showed robust IFI16 cytoplasmic redistribution ( S4G Fig , lower left panel ) which was significantly reduced by C-646 ( S4G Fig , lower right panel ) ., Taken together , these results demonstrated that similar to de novo infected HMVEC-d cells , p300 mediated acetylation plays an important role in the cytoplasmic redistribution of IFI16 in cells latently infected with KSHV ., As an IFI16-ASC inflammasome is formed during EBV infection of B cells and in latently infected cells , we performed PLA for IFI16 and acetylated lysine in primary human B cells infected with KSHV or EBV as well as in cells latently infected with EBV ( S5 Fig ) ., Compared to uninfected cells , both KSHV and EBV infected primary B cells showed acetylation as well as cytoplasmic redistribution of acetylated IFI16 ( S5A Fig ) ., Compared to EBV negative Ramos cells , EBV latently infected Raji ( latency I ) and LCL ( latency III ) cells showed both nuclear and cytoplasmic acetylated IFI16 ( S5B Fig ) ., These results demonstrated that acetylation of IFI16 and its cytoplasmic redistribution also occur in EBV infected cells ., To determine the specificities of nuclear herpesvirus genome activation of IFI16 acetylation and its cytoplasmic distribution , we next used vaccinia virus replicating its dsDNA exclusively in the cytoplasm ., The acetylation of IFI16 was not induced by vaccinia virus infection of HMVEC-d cells ( S6A Fig ) ., Only similar levels of a few dots representing basal level of acetylation were detected in the nucleus of both uninfected and vaccinia virus infected cells ( S6A Fig ) ., When mouse and rabbit antibodies were used to perform the PLA , IFI16 was predominantly detected in the nucleus of both uninfected as well as vaccinia infected HMVEC-d cells ( S6B Fig ) ., These results demonstrated that vaccinia viral DNA in the cytoplasm was not recognized by nuclear IFI16 , and hence acetylation of the nuclear IFI16 and cytoplasmic translocation were not observed ., These findings clearly supported our observations that the presence of nuclear KSHV , EBV and HSV-1 genomes induced the acetylation of IFI16 in the nucleus which then relocated into the cytoplasm of infected cells ., The dynamic process of exporting molecules of >50-kDa from the nucleus is initiated by exportins binding to cargo and Ran-GTP protein ., The guanine-nucleotide exchange factor ( GEF ) of Ran that converts Ran-GDP to GTP form is in the nucleus and GTPase-activating proteins ( GAPs ) for Ran-GTPase are present in the cytoplasm as well as on the cytoplasmic face of the nuclear pore ., To determine whether Ran is responsible for IFI16 transport from the nucleus to the cytoplasm , the lysates from uninfected or KSHV infected HMVEC-d cells ( 4 h p . i . ) in the presence or absence of C-646 were IP-ed with anti-Ran-GTPase antibodies and WB for IFI16 ., Compared to the uninfected cells that showed a basal level of IFI16-RAN association ( Fig 3A , lanes 1 and 2 ) , KSHV infected cells showed robust association of IFI16 with Ran-GTPase which was inhibited by C-646 ( Fig 3A , lanes 3 and 4 ) ., Comparable levels of IFI16 and Ran proteins were pulled down with their corresponding antibodies ( Fig 3A , lanes 3 and 4 ) ., Higher IFI16-RanGTP association in untreated KSHV infected cells corroborated the higher cytoplasmic redistribution of IFI16 shown in the earlier figures ., When PLA was performed using anti-Ran and IFI16 antibodies , consistent with the IP results , the association between these two molecules increased during KSHV infection , which was abolished by C-646 ( Fig 3B ) ., These results demonstrated that acetylation enhances the association of IFI16 with Ran-GTP during infection facilitating its transport to the cytoplasm and this association is dependent upon acetylation ., The nuclear resident IFI16 translocates to the nucleus after its translation in the cytoplasm via its two NLS domains and acetylation of NLS has been shown to retain IFI16 in the cytoplasm 15 ., To determine whether the cytoplasmic IFI16 detected during KSHV de novo infection and latency represents newly synthesized IFI16 or redistributed from the nucleus , we used 50 nM Leptomycin B ( LPT ) to block nuclear export to the cytoplasm ., This concentration of LPT was not overly toxic ( 6–8% ) to HMVEC-d cells nor did it significantly affect the establishment of KSHV infection ( S7A , S7C , and S7E Fig ) ., When HMVEC-d cells infected with KSHV in the presence or absence of LPT were analyzed , infected cells showed enhanced cytoplasmic redistribution of IFI16 which was abolished by LPT treatment ( Fig 3C , top panel , lanes 5–8 ) ., Compared to untreated cells , nuclear IFI16 increased in LPT treated cells probably due to blocked cytoplasmic redistribution ( Fig 3C , top panel , lanes 1–4 ) ., Reduced cytoplasmic and increased nuclear cyclin-B1 in LPT treated cells confirmed the hampered nuclear to cytoplasmic protein transport ( Fig 3C , second panel , lanes 1–8 ) ., Since IFI16-ASC-procaspase-1 assembly was initiated in the nucleus , we next examined the effect of LPT on the transport of the other components of IFI16-inflammasomes ., Procaspase-1 was detected in the nucleus of untreated uninfected and infected cells ( Fig 3C , third panel , lanes 1 and 3 ) ., The increased cytoplasmic procaspase-1 in untreated infected cells was significantly decreased by LPT with a corresponding increase in the nucleus ( Fig 3C , third panel , lanes 7 and 8 , and 3 and 4 ) ., We have previously observed the presence of cleaved caspase-1 in the nucleus of infected HMVEC-d cells at 2 h and 8 h p . i . and only in the cytoplasm at 24 h p . i . 11 ., Similarly , cleaved caspase-1 was detected in the infected cell cytoplasm at 24 h p . i . which was abolished by LPT treatment with a concomitant increase in the nucleus ( Fig 3C , lanes 3 , 4 , 7 and 8 ) ., When cell lysates of KSHV infected HMVEC-d cells in the presence or absence of LPT were analyzed by IP with anti-acetylated antibody and IFI16 WB , IFI16 was acetylated minimally in uninfected cells and to the same extent in untreated and LPT treated infected cells; however , tubulin was acetylated in both uninfected and infected samples ( Fig 3D , top 2 panel , lanes 1–4 ) ., Similarly , the IFI16 and ASC association was equal in untreated and LP | Introduction, Results, Discussion, Materials and Methods | The IL-1β and type I interferon-β ( IFN-β ) molecules are important inflammatory cytokines elicited by the eukaryotic host as innate immune responses against invading pathogens and danger signals ., Recently , a predominantly nuclear gamma-interferon-inducible protein 16 ( IFI16 ) involved in transcriptional regulation has emerged as an innate DNA sensor which induced IL-1β and IFN-β production through inflammasome and STING activation , respectively ., Herpesvirus ( KSHV , EBV , and HSV-1 ) episomal dsDNA genome recognition by IFI16 leads to IFI16-ASC-procaspase-1 inflammasome association , cytoplasmic translocation and IL-1β production ., Independent of ASC , HSV-1 genome recognition results in IFI16 interaction with STING in the cytoplasm to induce interferon-β production ., However , the mechanisms of IFI16-inflammasome formation , cytoplasmic redistribution and STING activation are not known ., Our studies here demonstrate that recognition of herpesvirus genomes in the nucleus by IFI16 leads into its interaction with histone acetyltransferase p300 and IFI16 acetylation resulting in IFI16-ASC interaction , inflammasome assembly , increased interaction with Ran-GTPase , cytoplasmic redistribution , caspase-1 activation , IL-1β production , and interaction with STING which results in IRF-3 phosphorylation , nuclear pIRF-3 localization and interferon-β production ., ASC and STING knockdowns did not affect IFI16 acetylation indicating that this modification is upstream of inflammasome-assembly and STING-activation ., Vaccinia virus replicating in the cytoplasm did not induce nuclear IFI16 acetylation and cytoplasmic translocation ., IFI16 physically associates with KSHV and HSV-1 genomes as revealed by proximity ligation microscopy and chromatin-immunoprecipitation studies which is not hampered by the inhibition of acetylation , thus suggesting that acetylation of IFI16 is not required for its innate sensing of nuclear viral genomes ., Collectively , these studies identify the increased nuclear acetylation of IFI16 as a dynamic essential post-genome recognition event in the nucleus that is common to the IFI16-mediated innate responses of inflammasome induction and IFN-β production during herpesvirus ( KSHV , EBV , HSV-1 ) infections . | Herpesviruses establish a latent infection in the nucleus of specific cells and reactivation results in the nuclear viral dsDNA replication and infectious virus production ., Host innate responses are initiated by the presence of viral genomes and their products , and nucleus associated IFI16 protein has recently emerged as an innate DNA sensor regulating inflammatory cytokines and type I interferon ( IFN ) production ., IFI16 recognizes the herpesvirus genomes ( KSHV , EBV , and HSV-1 ) in the nucleus resulting in the formation of the IFI16-ASC-Caspase-1 inflammasome complex and IL-1β production ., HSV-1 genome recognition by IFI16 in the nucleus also leads to STING activation in the cytoplasm and IFN-β production ., However , how IFI16 initiates inflammasome assembly and activates STING in the cytoplasm after nuclear recognition of viral genome are not known ., We show that herpesvirus genome recognition in the nucleus by IFI16 leads to interaction with histone acetyltransferase-p300 and IFI16 acetylation which is essential for inflammasome assembly in the nucleus and cytoplasmic translocation , activation of STING in the cytoplasm and IFN-β production ., These studies provide insight into a common molecular mechanism for the innate inflammasome assembly and STING activation response pathways that result in IL-1β and IFN-β production , respectively . | null | null |
journal.pcbi.1005301 | 2,017 | The Potential Role of Direct and Indirect Contacts on Infection Spread in Dairy Farm Networks | The structure of contact networks between individuals from human and animal populations is a key determinant of the dynamics of communicable diseases ., In response to the Bovine Spongiform Encephalopathy crisis , the Council of the European Union implemented in 1997 a system of permanent identification of individual bovine animals enabling reliable traceability from birth to death ., A fundamental part of this system consists in an extensive database to track movements of farmed animals ., Information on between-farm animal movements have been used to reveal the existing contact network structure within livestock systems 1–5 , as it is considered the most effective route of disease transmission 6 ., The study of the disease spread in livestock systems made it possible to fine tune surveillance systems , to address biosafety guidelines and control strategies aimed to reduce the risk of disease outbreaks , and to limit the impact on animal health and economic sustainability of farming systems ., The vast majority of these studies , models and analyses mainly ( if not exclusively ) focuses on direct contacts , i . e . infections following the movement of diseased animals between farms ., Alternative and often more cryptic transmission pathways have received much less attention , even though they can crucially affect the efficacy of disease surveillance and control systems ., For instance , in 2001 foot-and-mouth Disease ( FMD ) epidemic in the UK , although animal movements were banned since late February , the on-set of newly infected farms was reported until the end of the summer 7 , 8 ., Since further studies excluded a strong effect of aerial spread for the FMD virus strain in question , the only alternative route of transmission for the infection was represented by the spread through fomites 9–such as contaminated operators , vehicles , and equipment–potentially capable of carrying pathogens from infected farms to susceptible ones 10 ., In fact , the FMD epidemic stopped only after the implementation of stronger biosecurity measures in UK farms , which mostly targeted the movement of contaminated equipment and personnel 8 ., Between-farm disease transmission through fomites , mediated by operators and personnel external to the farm , is usually defined as indirect transmission 11 ., In recognition of its importance , epidemiological models of disease dynamics in livestock have started to include different pathways of between-farm transmission in addition to cattle movements ( see 12 , and references therein ) ., Unlike animal movements , the role of indirect transmission of livestock diseases is still largely unknown ., The reason for this knowledge gap is twofold: on the one hand , because of the subtle , highly diverse and complex nature of indirect contacts , it is intrinsically difficult to assess the relative importance of alternative transmission pathways for disease risk ., On the other hand , because of privacy reasons , it is much easier to track livestock movements than that of farm operators and personnel ., Indeed , collecting reliable and extensive quantitative data on indirect contacts on a temporal and spatial scale relevant for epidemiological modelling has proved to be very challenging ., Information on farm operator movements has been generally gathered by using voluntary questionnaires on the number and the frequency of farm visits over a given time span 10 , 11 , 13–15 ., Despite the usually low questionnaire response rate and the low number of farms involved in these studies , this approach has been crucial to provide a preliminary rank of categories of indirect contacts by potential disease risk ., However , the information gathered has been often insufficient to fully investigate the network structure of indirect contacts in a given area , and to characterize the contact frequency between farms ., This is why only few studies have applied network analysis techniques on questionnaire-based data ( see e . g . 10 , 16 , 17 ) ., Alternatively , indirect routes of transmission in epidemic models have been represented using risk kernel functions 18 , 19 ., These are functions assigning a probability of between-farm disease transmission on the basis of the inter-farm distance 19 , 20 ., However , the approach based on kernel functions is unable to tease apart the relative importance of different networks of indirect contacts , such as those associated to the movement of different farm operators ., The aim of this work is to present a novel quantitative analysis of the relative importance of indirect and direct contacts in a network of 1 , 349 dairy farms in the Province of Parma in the Emilia Romagna Region ( Italy ) ., The analysis is based on a unique , high-resolution temporal and spatial database of between-farm movements of 50 public officers of the regional veterinary service and 203 private veterinary practitioners ( which represent the potentially infectious indirect contacts ) ., The former visit a large number of farms usually only few times a year; the latter serve a small subset of farms each , and they visit them several times per year ., We thus expected that the structures of their contact networks are substantially different and might result in different risks of disease transmission through fomites ., We used network analysis to characterize the structure of the networks of indirect contacts and contrast them with the structure of the network of direct contacts ( i . e . the one associated to animal movement/trade ) ., Then , we assessed the contribution of both direct and indirect contacts networks in explaining the observed spatial distribution of dairy farms infected by Mycobacterium avium subsp ., paratuberculosis ( MAP ) ., MAP is responsible for Johne’s disease , a chronic gastrointestinal inflammation affecting ruminants and it is endemic in the study area 21 ., It is well documented that animal movements represent the primary route of MAP transmission between farms 22 , 23 ., However , the role of fomites such as footwear 24 and shared farm and veterinary equipment 25 as secondary transmission routes has been highlighted ., Finally , we used advanced techniques in network analysis to characterize the temporal network defined by direct and indirect contacts in order to understand the between-farm transmission for fast spreading diseases where the time scale of epidemics is similar to those of the evolution of the network , such as FMD 26 ., Our study system is represented by a network of 1 , 349 dairy farms operating in the Province of Parma ( Emilia-Romagna region , Italy ) in 2013 ( Fig 1 ) ., For each farm , we extracted from the Italian National Bovine Database ( BDN ) a unique identification code and the related spatial coordinates ., As we were interested in analysing the structure of the cattle movement network on a wider geographical scale and time window as well , we extracted from BDN also information on cattle movement from the 4564 dairy farms operating in the whole Emilia-Romagna region ( which includes also the province of Parma ) between 2010–2013 ., Each individual cattle movement record contained: a unique identification code for the animal , identifier codes of the farms of origin and destination , codes for farm production sector ( dairy or mixed ) , and the movement date ., As the Province of Parma is a strongly oriented dairy area , beef farms were not considered in the present study ., In fact , they represented less than 25% of the total cattle farms area ( 473 over 1 , 822 ) , and the two systems are almost completely separated ., The only unidirectional contact points consist in the shipment of surplus individuals from dairy , mostly male calves , to beef farms ., Beef farms are less involved in veterinarians networks too ., First , they do not receive frequent inspections because they are not included in surveillance plans for most diseases ( i . e . bovine tuberculosis 27 ) ., Second , beef animals receive less care by practitioners , in part because individuals’ life span is shorter ( 2 vs . 5 years for dairies 27 ) , but also because the lower economic value of individuals does not justify intensive health assistance as for dairy cattle ., The network of cattle movements was assembled by creating a directed edge between any two farms ( representing network nodes ) that exchanged animals during the observed period and setting a non-zero value in the corresponding adjacency matrix 28 ., Among the many ways in which edges could be weighted in a time-aggregated cattle movement network 29 , we considered, ( i ) the unweighted case , in which a link value is equal to 1 if at least one contact exists in a given period , and 0 otherwise; and, ( ii ) the case in which links are weighted proportionally to the number of animals exchanged through each contact within a given period ., Data on visits of veterinary officers ( VO ) were provided by the Local Health Unit of Parma Province ( LHU ) ., These visits were scheduled by the LHU for various purposes , including animal health inspection and disease surveillance ., We created a database including all visits on dairy farms during the year 2013 ., For each visit we recorded the farm unique identifier , the identifier of the VO visiting the farm ( in an anonymous form ) , and the visit date ., Data on veterinary practitioners ( VP ) were obtained from three datasets ., The first was the list of drug prescriptions in 2013 , that is , documents that compulsorily need to be, ( i ) signed by a registered practitioner , and, ( ii ) delivered and kept by both the LHU and the farm ., For this reason , each drug prescription corresponds to at least one on-farm visit by a veterinarian ., This dataset contained the unique identifier of the farm where the drug was prescribed , the identifier code of the VP prescribing the drug ( in an anonymous form ) , and the prescription date ., The second dataset was constituted by the records of diagnostic samples submitted to the Istituto Zooprofilattico Sperimentale della Lombardia e dell’Emilia-Romagna ( IZSLER ) , the local veterinary diagnostic laboratory ., These samples are collected on farm by practitioners and delivered to the IZSLER for biological testing ., This dataset contained the unique identifier of the farm where the sample was collected , the identifier of the VP collecting the sample ( in an anonymous form ) , and the date of collection ., The third dataset included the list of on-farm inspections on order of the LHU , but subcontracted to VPs ., For each dataset , the record contained the VP identifier , the farm unique identifier , and the visit date ., To build the VO and the VP contact networks , we assigned a directed edge connecting a given farm to those later visited by the same veterinarian within a given time interval ., This time interval represents the time span in which the veterinarian or her/his equipment can remain contaminated by the pathogenic agent ., We defined it as contamination period , h , and it depends on the pathogen ability to survive in the environment and on the type of contaminated material ., The order by which VO and VP operators visited the farms within a given day was not reported in the dataset ., Thus , to define the contact chain generated by multiple farm visits occurring in any given day , we generated 50 networks with potential itineraries by randomly selecting , for each veterinarian , the first farm visited in that day , so to derive the itinerary that minimized travel distance ( see S1 Text for details on method and software ) ., The analyses of network structure and disease risk were conducted by setting h = 0 days under the conservative assumption that only within day visits can result in infection transmission ., We assessed the effect on network properties of higher values for h in a specific analysis reported in S1 Text ., As for the cattle movement network , we also developed a weighted version of the veterinarian networks , where the weighting coefficients represent the number of contacts within the year 2013 ., To evaluate the potential risk of disease spread in the three different networks ( CM , VO , and VP ) , as per other studies on farm networks 2 , 3 , 30 , we first derived three important metrics for each network , namely:, ( i ) the link density 31 , which is defined as the fraction of observed links over the possible number of links;, ( ii ) the giant strongly connected component ( GSCC ) 2 , which is defined as the biggest portion of the network in which each node is reachable from any other node; and , in the weighted version of the networks ,, ( iii ) the contact frequency , which is defined as the mean frequency of the observed links within the observation period ., The node degree k , a central measure in network theory , is defined as the number of links for each node 28 ., As our networks were characterised by directed links , we derived two node degrees for each farm in each contact network: the i-th farm in-degree , defined as the total number of farms from which farm i receives cattle or farms visited by VO and VP before visiting farms i ( kI ) ; and i-th farm out-degree , defined as the number of farms to which farm i sends cattle or farms visited by VO and VP after visiting farm i ( kO ) ., Consequently , for each contact network , we computed the degree distributions P ( kI ) and P ( kO ) , respectively ., In the case of VO and VP networks , we computed degrees and degree distributions assuming h = 0 days ( analyses based on different assumptions on h are shown in S1 Text ) ., In addition , in the case of weighted networks , we computed the node strengths , which are defined as the sum of all incoming ( in-strength , SI ) and outgoing ( out-strength , SO ) links weights 28 ., As for the degrees , for each weighted contact network , we computed the strength distributions P ( SI ) and P ( SO ) ( see S1 Text for details ) ., A fundamental assumption in the spatial analysis of epidemiological data for communicable diseases is that the contact network is an important driver of the observed disease dynamics and , accordingly , may be able to partially explain the spatial distribution of reported cases of the infectious disease under study ., Here , we wanted to assess the relationship between direct and indirect contact networks and Mycobacterium avium subsp ., paratuberculosis ( MAP ) positive farms at regional scale ( i . e . Emilia-Romagna , for cattle movements only ) and local scale ( i . e . Parma province , for veterinarians and cattle movements ) by using data on the infection status of 2 , 648 dairy farms in Emilia-Romagna region ( whereof 966 in Parma province ) as identified in Ricchi et al . 21 ., The infection state of farms , positive or negative , was evaluated by ascertaining the presence of MAP in bulk tank milk by real-time PCR targeting insertion sequence IS900 , twice per farm 21 ., To avoid detection bias , farms for which bulk tank milk had not been tested for MAP were excluded from the analysis ., Since MAP has a long incubation period 32 and , consequently , a slow infection dynamics , static network measures as those underlying our analysis can be appropriately used to represent the between-farm disease transmission , as shown by 26 ., To assess the relationship between MAP presence and direct/indirect contact structures , we used a network-based model approach similar to that developed in 16 ., Specifically , we defined the mean exposure to infection of MAP positive farms ( EI ) as:, EI=∑j=1n∑i≠jEijδj∑j=1nδj ,, ( 1 ), where δj = 1 δj = 0 if farm j is MAP positive negative; and Eij represents exposure of farm j to farm i , defined as Eij = Aij ( where A represents the adjacency matrix of the weighted network ) if farm i is MAP positive; Eij = 0 otherwise ., Consequently , ∑i ≠ jEij represents the total exposure for farm j ., Analogously , we defined the mean exposure to infection of MAP negative farms ( ES ) as:, ES=∑j=1n∑i≠jEijθj∑j=1nθj ,, ( 2 ), where θj = 1 θj = 0 if farm j is MAP negative positive ., Garcia Alvarez et al . 16 proposed that , if the infection was transmitted from the observed contact networks , we would expect that MAP positive farms were more exposed than MAP negative ones ( i . e . , EI > ES ) ., In addition , we would expect that MAP positive farms were more exposed in the observed contact network than in a random one , while MAP negative farms were expected to be less or similarly exposed compared to a random network ., To test these hypotheses , we generated random networks with the same number of nodes as in the observed contact networks , but randomly allocating the edges between the nodes ., In order to investigate the impact of the network linking pattern ( instead of the in-degree distribution ) on the spread of the disease , the distribution of farm contacts was maintained through a rewiring process in the random networks , as suggested by Kiss et al . 33 ., The node state with respect to MAP infection was maintained fixed in all random networks using the observed bulk tank milk data ., Since MAP can persist in farms for years , we also tested whether our results were robust with respect to the specific year used to derive the contact networks ., In particular , for the cattle movement network , for which data were available for multiple years , we tested whether contact networks built by using data from years 2010–2012 could explain the infectious state by MAP detected in 2013 ., In addition , since in the absence of biosecurity measures MAP can persist in the environment , we tested whether our results at local and regional scale were robust with respect to different assumptions on the contamination period ( specifically , h = 0 and h = 7 days ) ., Moreover , to determine the possible effect of spatial clustering as a driver of the differences in MAP positivity among farms , we used the q-nearest neighbours test ., The q-nearest neighbours is a non-parametric test able to identify a potential spatial clustering in the distribution of cases , by computing the number of cases observed within the q neighbour farms of each positive case 39 ., We set the number of neighbours q from 1 to 10 16 , 34 ., A major focus of our work was to identify which farms could act as super-spreaders in the studied networks ., A super-spreader is defined as a highly connected individual farm able to potentially spread the infection to a very large number of farms in the system 35 ., In the context of diseases spreading through a contact network , centrality measures are often used to identify the super-spreaders 36 ., The most simple but still effective centrality measure is the degree centrality 28 ., However , despite the simplicity and the usefulness of the degree centrality , it is well known that assuming a static network derived by all the contacts that occurred in a given period , 2013 in our case , and ignoring the temporal sequence of connections , can lead to largely overestimate the transmission risk for fast spreading diseases , such as FMD and influenza , where the time-scale of the epidemics is similar to that of the evolution of the network 4 , 26 , 37 , 38 ., To overcome this limit , Dubé and colleagues 3 introduced a risk-based measure that accounts for the temporal sequence of contacts in animal trade networks , called the infection chains ( IC ) ., In particular , for a given farm , the ingoing IC ( IIC ) and the outgoing IC ( OIC ) measure the maximum number of farms connected with it through a sequence of animal movements 3 , 30 ., Konschake et al . 39 extended the infection chain concept by assuming that only contacts occurring within a finite infectious period ( γ ) may act as potential transmission events ., Accordingly , we computed time-dependent ICs referred to a specified date of emergence of the infection in the farm system ( d ) , specifically IIC ( d ) and OIC ( d ) ., From an epidemiological view point , the OIC ( d ) in the i-th farm , OICi ( d ) , provides an upper bound to the size of an epidemic emerging from the i-th farm in day d ., Analogously , the IIC ( d ) in the i-th farm , IICi ( d ) , provides an upper bound for the probability for the i-th farm of getting infected on day d following an epidemic event occurred in any other farm in the network ., This was computed as IICi ( d ) / ( N−1 ) , with N corresponding to the total number of farms in the system ., Following these considerations , we defined the infection potential ρi ( d ) of the i-th farm in a given day d as:, ρi ( d ) =IICi ( d ) N−1OICi ( d ) ., ( 3 ), From expression ( 3 ) , we built two more general epidemiological indicators: the mean infection potential of i-th farm on a given time-period of m days as:, ρi=∑d=1mρi ( d ) m, ( 4 ), and the average infection potential of the system in day d as:, ρ ( d ) =∑i=1Nρi ( d ) N ., ( 5 ), In order to avoid boundary effects due to the limited period of data availability ( 1 year ) , we computed ρi ( d ) for a period of 245 days starting from the beginning of March to the end of October 2013 ., We computed the infection potential for individual CM , VO and VP networks , for the veterinarians total network ( VT = VO + VP networks ) , and for the network of all transmission routes combined ( TN = CM + VT ) ., For VO , VP , VT , and TN networks , the calculation was repeated for each of the 50 simulations ., We defined as super-spreaders the farms in the highest 5th percentile of ρi value distribution ., We assumed a farm infectious period length γ of 14 days ., According to Konschake et al . 39 , this is the threshold value above which the IC measure is stable to variations in γ and , additionally , this infectious period is compatible with rapidly spreading diseases , such as FMD 40 ., However , we performed a sensitivity analysis on the ICs for different values of γ ( from 3 to 28 days ) to assess whether a variation of γ has strong consequences on farm ranking in terms of γ and , thus , on the identification of super-spreaders ., In order to assess the correlation between ρi calculated for CM and VT networks , we used the Kendalls τ , a non-parametric test ., There were 16 , 647 cattle moved in the province of Parma in 2013 , for a total of 1 , 433 between-farm directed links in the yearly aggregated CM network ., Link-density was 0 . 0008 ( see Table 1 ) ., The giant strongly connected component ( GSCC ) included 18 farms , corresponding to the 1 . 14% of the network , while the distribution of the yearly exchanged animal-per-contact was very heterogeneous , with an average of 11 . 62 animals ., The mean farm degree was 1 . 06 , and both kI and kO ranged from 0 to 15 ., The degree distributions P ( kI ) and P ( kO ) are showed in Fig 2 ( red line , a and b panels , respectively ) ., The median of both kI and kO was equal to zero , and this was a consequence of the large number of farms with no incoming ( 688 ) or outgoing ( 719 ) cattle movements within the system during 2013 ., The total number of moved cattle between dairy farms within the Emilia-Romagna region ranged from 50 , 186 to 57 , 276 over the period 2010–2013 ., These movements originated from 4 , 624 to 5 , 094 between-farm contacts ., The veterinary officer ( VO ) dataset included data on 6 , 524 on-farm visits performed by 50 officers ., By setting the contamination period h equal to 0 ( corresponding to assuming possible indirect transmission only for consecutive visits on the same day ) , the median 5th-95th percentile among the 50 simulations link density was 0 . 0049 0 . 0047–0 . 0050; the median 5th-95th percentile giant strongly connected component ( GSCC ) included about 67% 35%-70% of the network , corresponding to 918 480–948 farms ., The median 5th-95th percentile yearly contact frequency was 1 . 19 1 . 15–1 . 22 ., The overall veterinary practitioner ( VP ) dataset included a total of 14 , 053 visits performed by 203 practitioners ., This was the result of joining three data sources: the drug prescription list ( 11 , 611 prescriptions by 181 VP ) , the animal tissue-drop records ( 1 , 085 records by 108 VP ) , and the government subcontracted visits ( 1 , 426 visits performed by 12 VP ) ., As for the VO network , we simulated the same day visit order 50 times ., The median 5th-95th percentile link density was 0 . 0029 0 . 0029–0 . 0029; the median 5th-95th percentile GSCC included about 54% 53%-55% of the networks , corresponding to 732 721–740 farms; and the median 5th-95th percentile yearly contact frequency was 1 . 19 1 . 18–1 . 20 ., VO and VP networks showed more widespread degree distributions with respect to the CM network ( Fig 2 ) ., At h = 0 , the mean degrees were 6 . 55 and 3 . 90 for VO and VP , respectively , and 1 . 06 for CM ., On the other hand , the strength distributions of all three networks showed to be more similarly distributed ( see Figure S1 . 3 . 1 and Table S1 . 2 . 2 in S1 Text ) ., As expected from previous literature on between-farm transmission of Mycobacterium avium subsp ., paratuberculosis ( MAP ) , we found evidence of association between cattle movements and the distribution of MAP positive farms within the Emilia-Romagna region ( see Fig 3 ) ., Specifically , we found that the mean exposure of MAP positive farms ( Fig 3 upper segment: blue dot , EI ) derived by using the CM network at regional scale is:, a ) higher than that of MAP negative farms ( Fig 3 upper segment: red dot , ES ) ;, b ) higher than that in a randomly generated network ( Fig 3 upper segment: blue vertical bars , p = 0 . 005 ) ., Conversely , within Parma province , we did not find a significantly higher mean exposure of MAP positive farms derived by using the CM network ( Fig 3 middle segment: blue dot ) compared to random networks ( Fig 3 middle segment: blue vertical bars , p = 0 . 456 ) ., On the other hand , within Parma province , we found that the mean exposure of MAP positive farms in the veterinary network ( Fig 3 bottom segment: blue dot ) was significantly higher than in random networks ( Fig 3 bottom segment: blue vertical bars , p < 0 . 001 ) ., In addition , we found that our results were robust with respect to the year considered for building the contact networks and the assumptions on the length of the contamination period ( see S1 Text ) ., The spatial analysis showed that no clear spatial clustering could be detected among MAP-positive farms ., Specifically , the q-nearest neighbours test was not statistically significant for neighbour farms from q = 1 to q = 10 ( see Table 2 ) ., This result suggests that between-farm transmission of MAP was not associated to spatial proximity ( see Fig 4 ) ., Infection potential ρi of VT network was poorly correlated with that of CM network ( Kendalls τ = 0 . 08 , p < 0 . 01 ) ., The number of shared super-spreaders for the CM and VT networks ( defined as the 5% of farms with the highest ρi value ) ranged between 4 and 7 over 50 simulations of within day veterinary itineraries ., By using the median value of ρi for each farm as a reference measure , the number of shared super-spreaders between direct and indirect contacts was 4 ( see Fig 5 ) ., To test whether the number of observed shared super-spreaders was significantly higher than in the random case , we computed a permutation test by assigning the observed ρi values in each network to random farms ., Upon 20 , 000 runs ( 1 , 000 for each of the 50 simulations ) , the test turned out to be not statistically significant ( p = 0 . 20 ) ., Fig 6 shows the time trend of the average infection potential of the whole farm system , ρ ( d ) , during the 245-day period ., The average infection potential ρ ( d ) was: 0 . 04 × 10−3 ( sd = 0 . 44 × 10−3 ) for the CM network , 0 . 14 × 10−3 ( sd = 0 . 37 × 10−3 ) for the VO network , and 0 . 16 × 10−3 ( sd = 0 . 69 × 10−3 ) for the VP network ., Combining networks together , the average ρ ( d ) was 0 . 84 × 10−3 ( sd = 2 . 17 × 10−3 ) for the veterinarians total network ( VT ) , while ρ ( d ) was 3 . 13 × 10−3 ( sd = 39 . 23 × 10−3 ) for all the direct and indirect contact networks combined ., Sensitivity analyses showed stable rankings for IIC , OIC and ρ with varying infectious period γ ( see S1 Text ) ., Prevention measures such as targeted surveillance and farm isolation , which are largely used to control livestock epidemics , have been shown to be more effective when directed to those higher risk farms 1 , 4 , also called super-spreaders ., Understanding the structure of the contact network is therefore crucial to derive effective surveillance strategies ., Identification of super-spreaders , however , should be performed by accounting for all transmission pathways and not only the direct ones ., In fact , despite it is well known that indirect contacts are less efficient in transmitting infectious diseases compared to direct ones 6 , 40 , our results showed that they can substantially affect the ability of farms to potentially spread a disease within the network system ., In particular , our analysis showed that direct and indirect transmission routes shared only a handful of super-spreader farms , indicating that direct and indirect transmission risks were independent from each other ., A major consequence of this observation is the need to account for both routes in the definition of contingency plans for the control of potential epidemics where indirect contacts represent an effective route of disease transmission ., By considering only one of these transmission routes , we would miss a substantial part of the spreading pattern ., This conclusion is strongly supported also by the analysis of infectious risk in the direct and indirect contacts networks ., The infection potential was substantially lower for cattle movement ( CM ) compared to the veterinarians total ( VT ) network ., This reflects the fact that farms may receive or move cattle only few times in a year and a large fraction of the farms in our database did not trade any animal during the study period ., On the contrary , each veterinary practitioner visits a small set of farms on a regular basis , while veterinary officers have frequent visits to farms but visit the same farm only once a year on average ., Farm infection potentials derived by using the network of indirect contacts is not correlated with that derived by using the network of direct contacts , thus reinforcing the finding that the transmission pathways of the two contact networks are remarkably different ., The most striking result of our study , however , is that the infection potential derived by combining the networks of direct and indirect contacts is considerably larger than the one computed by using only cattle movements or the veterinary network ( Fig 6 ) ., This suggests a synergistic effect between the networks of direct and indirect contacts: despite being sparse , direct contacts act as a bridge joining different clusters of potentially infectious contacts due to veterinarians ., Despite movements of infected animals usually play a primary role in the spread of livestock epidemics ( since they represent the most efficient transmission route between farms ) , our results highlighted the importance of considering indirect contacts to adequately model between-farm spread of infections ., We showed that the combination of different pieces of information included in the infection potential metric is essential to understand the role of farms within the study system ., In essence , the infection potential is the expression of the two fundamental components of the contact system , the contact structure and the temporal sequence of the potential infectious contacts 3 , 30 , 39 ., In fact , as pointed out in many studies 4 , 37 , 38 , 41–43 , traditional network metrics based on a static representation of the contacts between nodes can hide significant temporal patterns in epidemic network structures of fast spreading diseases , such as FMD ., Conversely , the temporal sequence of contacts is crucial in defining the real risk of epidemic spread in a n | Introduction, Materials and Methods, Results, Discussion, Conclusions | Animals’ exchanges are considered the most effective route of between-farm infectious disease transmission ., However , despite being often overlooked , the infection spread due to contaminated equipment , vehicles , or personnel proved to be important for several livestock epidemics ., This study investigated the role of indirect contacts in a potential infection spread in the dairy farm network of the Province of Parma ( Northern Italy ) ., We built between-farm contact networks using data on cattle exchange ( direct contacts ) , and on-farm visits by veterinarians ( indirect contacts ) ., We compared the features of the contact structures by using measures on static and temporal networks ., We assessed the disease spreading potential of the direct and indirect network structures in the farm system by using data on the infection state of farms by paratuberculosis ., Direct and indirect networks showed non-trivial differences with respect to connectivity , contact distribution , and super-spreaders identification ., Furthermore , our analyses on paratuberculosis data suggested that the contributions of direct and indirect contacts on diseases spread are apparent at different spatial scales ., Our results highlighted the potential role of indirect contacts in between-farm disease spread and underlined the need for a deeper understanding of these contacts to develop better strategies for prevention of livestock epidemics . | Farm-to-farm contacts due to shared operators and vehicles–such as veterinarians , hoof-trimmers , milk and rendering trucks–are generally considered important for the spread of many infectious diseases in livestock systems ., These contacts are usually defined as indirect , as opposed to animal movements which are defined as direct contacts ., The actual significance of indirect contacts is still poorly understood due to study limitations deriving from their highly diverse and complex nature and to privacy issues in data collection ., Thanks to the availability of high-resolution data in space and time on veterinarian on-farm visits in a dairy farm network in Northern Italy , we showed through network analysis techniques that between-farm indirect contacts are more widespread and display significantly different patterns compared to direct contacts ., Using infection data on paratuberculosis , we also found that indirect contacts can support disease spread at local scale , while direct contacts ( due to long distance animals exchanges ) can support disease spread on a larger spatial scale ., Our results corroborate the conclusion that the role of indirect contacts in livestock disease spread is essential and deserves a deeper understanding . | livestock, medicine and health sciences, animal diseases, ruminants, infectious disease epidemiology, vertebrates, animals, mammals, paratuberculosis, farms, veterinarians, zoology, veterinary science, infectious diseases, veterinary diseases, veterinary epidemiology, epidemiology, agriculture, people and places, professions, biology and life sciences, population groupings, cattle, amniotes, bovines, organisms | null |
journal.pgen.1006034 | 2,016 | Discovery of Genetic Variation on Chromosome 5q22 Associated with Mortality in Heart Failure | Heart failure ( HF ) is a common clinical condition in which the heart fails to maintain blood circulation adequate to meet the metabolic demands of the body without increased cardiac filling pressures ., HF is the result of chronic ventricular remodelling initiated by myocardial injury , volume/pressure overload , or intrinsic cardiomyopathic processes ., Progression of HF is a complex process involving many tissues , driven by activation of neurohormonal pathways , which induce gradual myocardial hypertrophy , ventricular dilation , and deterioration of cardiac function , often resulting in death from low cardiac output , arrhythmia , or thromboembolic complications 1 ., Activation of such neurohormonal pathways in the short term increases cardiac output when necessary ., However , long-term activation results in accelerated ventricular remodelling and myocyte death ., Inhibitors of deleterious neurohormonal pathways , including adrenergic 2–4 and renin-angiotensin-aldosterone ( RAAS ) 5–8 pathways have been shown to improve ventricular function and survival in patients with HF and are the mainstay of current pharmacological treatment of HF 9–10 ., Despite advances in therapy with neurohormonal antagonists , mortality after onset of HF remains high 9–13 and continued progress to identify additional therapeutic targets is needed ., Genome-wide association ( GWA ) studies have the potential to identify in an agnostic manner genetic variants related to clinical outcomes in humans and has led to the identification of novel pathways 14 and potential treatments 15 for cardiovascular traits ., Heritable factors have been shown to be predictive of mortality in certain heart failure patients 16 ., We therefore implemented a genome-wide association approach to identify novel molecular determinants of mortality in patients with new-onset HF ., We expanded our previously published GWA study 17 of HF mortality with additional samples and extended follow-up in Stage, 1 . Stage 1 included 2 , 828 new-onset HF patients from five community-based cohorts , thus representative of the general population of HF patients , as part of the Cohorts for Heart and Aging Research in Genomic Epidemiology ( CHARGE ) consortium 18: the Atherosclerosis Risk in Communities ( ARIC ) Study , the Cardiovascular Health Study ( CHS ) , the Framingham Heart Study ( FHS ) , the Health , Aging and Body Composition ( Health ABC ) Study , and the Rotterdam Study ( RS ) ., Cohorts are described in detail in S1 Text ., HF was defined using international published criteria as outlined in S1 Table ., Subjects in Stage 1 cohorts were of European ancestry , predominantly male , and approximately 20–30% had a history of myocardial infarction at the time of HF diagnosis ., Additional characteristics are shown in Table, 1 . During an average follow-up time of 3 . 5 years , 1 , 798 deaths occurred ., The sample-size weighted average 1-year mortality rate was 28% ., Among deaths , 51% were classified as cardiovascular , 19% were due to neoplasms , 10% were respiratory deaths , and the remaining were due to other miscellaneous causes ., Genotyping using high-density Illumina or Affymetrix single nucleotide polymorphism ( SNP ) arrays , followed by imputation to the HapMap CEU release 22 imputation panel was performed in each cohort ., Population stratification was assessed and corrected in each cohort as described in S1 Text ., Association with time to death following HF diagnosis was examined in each cohort using Cox proportional hazards models with censoring at loss to follow-up ., Mild inflation of test statistics was observed only in the Framingham Heart Study ( FHS ) as shown in S1 Fig ( λGC = 1 . 07 , other cohorts ≤ 1 . 03 ) , and genomic control was applied in each individual study ., In the meta-analysis of all cohorts , there was no evidence of inflated test statistics overall ( λGC = 1 . 00 ) as shown in S2 Fig , so no further genomic control was needed ., Results for all SNPs across the genome are plotted in S3 Fig . Single nucleotide polymorphisms ( SNPs ) passing a significance threshold specified a priori as P < 5 . 0x10-7 , as used in our previous article 17 , were carried forward to a second stage of genotyping in independent cohorts ., Five SNPs on chromosome 5q22 and one SNP on chromosome 3p22 passed the pre-specified P-value threshold ., Results for all six SNPs are shown in Table 2 and S3 Table ., The five SNPs on chromosome 5q22 were highly correlated ( pairwise r2 > 0 . 9 ) ., Two sentinel SNPs , rs9885413 and rs12638540 , on chromosomes 5q22 and 3p22 , respectively , were next genotyped in 1 , 870 European-ancestry subjects with new-onset HF from four independent cohorts in Stage 2: Malmö Diet and Cancer , Malmö Preventive Project , Physicians’ Health Study , and the PROSPER trial ., Characteristics of populations in Stage 2 are shown in S2 Table ., During an average sample-size weighted follow-up of 4 . 3 years in Stage 2 samples , 889 patients died ., We observed evidence of association with mortality for rs9885413 on chromosome 5q22 ( P = 0 . 006 ) but not for the SNP rs12638540 ( P = 0 . 18 ) which reached nominal significance in our previous analysis 17 ., Results for both SNPs are shown in Table, 2 . In the combined results from Stages 1 and 2 , rs9885413 was associated with a 36% relative increase in mortality per minor allele ( P = 2 . 7x10-9 ) ., There was no evidence for effect heterogeneity across cohorts in the two stages ( P for heterogeneity = 0 . 39 ) as shown in S4 Table ., The SNP had a similar minor allele frequency ( MAF = 0 . 07 ) across cohorts ., Information on cause-specific mortality was available from death certificates in a subset of cohorts ( S5 Table ) and was explored descriptively due well-known problems with substantial misclassification in death certificate data and low power for agnostic GWAS of individual causes ., The minor allele frequency was slightly higher for several causes of death associated with heart failure , including renal , pulmonary and endocrine mortality and death from ischemic heart disease ., We next examined whether rs9885413 on chromosome 5q22 that was associated with HF mortality was also associated with differences in myocardial structure and function , which could potentially mediate the association ( S6 Table ) ., In 12 , 612 individuals from the EchoGen Consortium 19 , the SNP was not associated with major echocardiographic characteristics ., The SNP rs9885413 was not associated with incident HF in 20 , 926 individuals from the general population in the CHARGE-HF study 20 , or with cardiac endocrine function , as determined by plasma levels of atrial and B-type natriuretic peptides ( all P > 0 . 05 ) , in a GWA study of 5 , 453 individuals from the population-based Malmö Diet and Cancer study 21 ., No association was observed with electrocardiographic measures of cardiac conduction ( n = 39 , 222 ) 22 or repolarization ( n = 74 , 149 ) 23 , which confer risk of ventricular arrhythmia , or with sudden cardiac death in 4 , 496 sudden death cases and over 25 , 000 controls from the general population ( described in S1 Text ) ., The lead SNP rs9885413 on chromosome 5q22 that was associated with mortality is located in an intergenic region , 100 kb downstream of the gene SLC25A46 , 114 kb upstream of TMEM232 , and 230 kb upstream of TSLP as shown in Fig, 1 . The SNP is not in linkage disequilibrium with any known coding SNP in the 1000 Genomes Project database ( no coding SNP with r2 > 0 . 01 to the sentinel SNP ) ., We therefore sought to evaluate gene regulatory functions of this SNP ., In 129 human tissues from the ROADMAP Epigenomics project 24 , we studied whether rs9885413 or strongly correlated SNPs ( a total of 9 at r2 > 0 . 8 ) are located in regulatory regions , as determined by histone modification patterns ., None of the 9 SNPs was located in an active regulatory region in cardiac tissues ( S7 Table ) , but rs9885413 was located in a predicted enhancer in several epithelial or mesenchymal tissues , including keratinocytes , gastrointestinal cell types and adipose cells ( Fig 2 and S7 Table ) ., Regulatory motif annotations in HaploReg indicate that the SNP causes a change in a regulatory motif predicted to bind the transcription factor NHLH1 as shown in S8 Table ., Interestingly , NHLH1-null mice have been shown to be predisposed to premature , adult-onset unexpected death in the absence of signs of cardiac structural or conduction abnormalities , in particular when mice were exposed to stress 25 ., Little is known about the function of NHLH1 , but it is widely expressed in human tissues and has been shown to regulate expression of key inflammatory genes 26 ., To experimentally test the effect of rs9885413 on enhancer activity , the 100 bp region flanking the SNP ( 50 bp on either side ) was cloned into a reporter vector and transfected into HEK293 cells expressing NHLH1 ( S1 Text ) ., Luciferase activity measured after 24 hours was 4-fold higher with a construct corresponding to the risk allele as compared to the wild-type allele ( S4 Fig , P < 0 . 001 ) , indicating that the risk allele of rs9885413 substantially increases enhancer activity ., We next explored the association of rs9885413 with DNA methylation at the locus , providing functional evidence of epigenetic association and regulation of gene expression ., DNA methylation was determined by a microarray assaying in total over 480 000 CpG methylation sites in whole blood samples from 2408 participants of the FHS ., Of the 84 CpG methylation sites on the microarray within +/- 500 kb of the SNP , two were significantly associated with rs9885413: cg21070081 ( beta 0 . 017 per T allele , P = 9 . 0x10-69 ) and cg02061660 ( beta -0 . 015 per T allele , P = 4 . 5x10-40 ) , thus constituting strong methylation quantitative trait loci ( mQTLs ) at the locus ., Other , correlated SNPs at the locus were more strongly associated with each of these mQTLs as shown in S5 Fig: rs244431 for cg21070081 ( P = 6 . 7x10-369 ) and rs72774805 for cg02061660 ( P = 7 . 0x10-85 ) ., The SNP rs72774805 ( perfect proxy SNP rs3844597 used ) but not rs244431 was associated with heart failure mortality ( P = 3 . 3x10-3 and 0 . 08 , respectively ) , indicating that the methylation site cg02061660 is more strongly related to the underlying signal for heart failure mortality ., The association of rs9885413 with lower probability of methylation at cg02061660 was replicated in 731 participants from the Rotterdam study ( beta -0 . 029 per T allele , P = 1 . 7x10-11 ) ., Adjustment for cell types from direct measurement instead of estimates from methylation patterns did not abolish the association ( beta -0 . 029 per T , P = 1 . 2x10-6 ) ., Interestingly , differential methylation at this CpG site was also correlated with a SNP at the locus previously associated with allergic sensitization 27 ( rs10056340 , P = 4 . 7x10-29 for mQTL ) , suggesting a link to inflammatory disease ., This SNP was also modestly correlated with rs9885413 ( r2 = 0 . 28 ) and associated with heart failure mortality ( P = 0 . 01 ) ., The association of cg02061660 with rs9885413 ( P = 0 . 52 ) and rs10056430 ( P = 0 . 87 ) was abolished in analyses conditioning for rs72774805 , for which the association was also markedly attenuated ( P = 7 . 0x10-33 and 2 . 1x10-46 , respectively ) indicating that these correlated SNPs may reflect the same underlying signal ., We further assessed the association of rs9885413 with gene expression ., No gene was significantly associated with rs9885413 in the diverse tissues from the Gene-Tissue Expression ( GTEx ) project 28 after correction for multiple tests ( S1 Text , S9 Table ) , although conclusions were limited by a small sample size ., We next assessed association of the SNP with gene expression in two large datasets with each of the tissues most relevant for the phenotype under study: heart tissue and whole blood ., We observed no convincing evidence of association ( S1 Text , S10 Table ) with gene expression in 247 left ventricular samples from patients with advanced heart failure ( n = 116 ) undergoing transplantation and from unused donors ( n = 131 ) ., Finally , we tested the association of rs9885413 with the expression of genes at the locus in whole blood from 5257 FHS participants 29 , and with DNA methylation at cg02061660 among 2262 FHS participants ., All five genes at the locus ( Fig 1 ) except TMEM232 were expressed in blood ., We did not observe association of the SNP rs9885413 with any transcript , but expression of one gene ( TSLP ) was significantly associated with the methylation status of cg02061660 ( P = 1 . 1x10-4 ) ., The TSLP gene encodes a cytokine released from epithelial cells that induces release of T cell-attracting chemokines from monocytes , promotes T helper type 2 cell responses , enhances maturation of dendritic cells and activates mast cells ., It has also been linked to angiogenesis and fibrosis ., A monoclonal antibody targeting and inhibiting TSLP is currently in clinical phase III trials for asthma and allergic inflammation after a promising phase II trial 30–32 ., In the myocardium , the TSLP gene has very low expression ( S10 Table ) but expression has been described in mature myocardial fibroblasts , which are abundant in the myocardium but of substantially smaller volume than cardiomyocytes and likely contribute little to the overall myocardial RNA pool 31 , 32 ., To examine whether the transcription factor NHLH1 affects the expression of any of the five genes in the locus ( Fig 1 ) , we knocked down NHLH1 in HEK293 cells using siRNAs ., A 50% decrease in NHLH1 mRNA levels was seen 48 hours after transfection , confirming efficient knock down ( p<0 . 05 , S6A Fig ) ., TSLP was the only gene at the locus affected by NHLH1 knock down , showing a 30% decrease compared to cells transfected with negative control siRNA ( p<0 . 05 , S6A Fig ) ., Moreover , we observed a dose-response relation between level of NHLH1 knockdown and expression of TSLP in HEK293 cells ( r2 = 0 . 74 , p<0 . 0001 , S6B Fig ) ., Finally , distribution of the risk allele of rs9885413 in human populations was assessed using data from HapMap phase II ., The derived ( non-ancestral ) T allele ( risk allele for mortality ) was highly differentiated among human populations ( S7 Fig ) having risen to an allele frequency of 0 . 59 in a Nigerian population ( HapMap YRI sample ) but only 0 . 06 in a European population ( CEU sample ) ., The fixation index ( Fst ) , a measure of population differentiation in allele frequencies , for comparison of YRI and CEU was 0 . 48 and more extreme Fst was observed in only 2 . 4% of SNPs in the HapMap phase 2 dataset ., Consistent results were observed for another signature of recent positive selection , based on longer runs of haplotype homozygosity in carriers of the derived allele ( standardized integrated haplotype score -0 . 766 in YRI , where negative score values indicate longer haplotypes on the background of the derived allele ) 33 ., These observations are consistent with positive selection in recent human history , with a selective sweep resulting in high frequency of the derived allele in western African populations ., These findings are of particular interest as HF mortality is well known to be higher in populations of African ancestry , although the current study has not tested for the association with HF mortality in such populations 34 ., We identified a SNP on chromosome 5q22 associated with increased mortality in subjects with HF ., Although previous genome-wide association studies have described hundreds of loci associated with risk of disease onset , few have examined prognosis in subjects with manifest disease ., This approach has the potential to generate targets for novel disease-modifying medications ., Through a series of analyses in silico and in vitro we show that the SNP is located in an enhancer region , and confers increased activity of this enhancer ., Interestingly , mice deficient in the transcription factor NHLH1 predicted to bind a motif in this enhancer region have been reported to be predisposed to premature , adult-onset unexpected death in the absence of signs of cardiac structural or conduction abnormalities ., NHLH1 has also been shown to regulate expression of key inflammatory cytokines such as interleukin-6 and tumor necrosis factor α ., The SNP was not associated with any electrocardiographic , endocrine , or echocardiographic marker of increased risk in the general population , suggesting a mechanism specific to heart failure , an extracardiac pathway of importance in cardiac pathophysiology , or interaction with therapy for heart failure which we were unable to further test given the inception cohort design of this study ., We also did not observe any robust eQTL associations for the SNP in heart ., The SNP was however associated with a DNA methylation signature in whole blood that was also associated with a SNP previously associated with allergy , and with expression of the cytokine TSLP in blood ., Knockdown of NHLH1 also resulted in lower expression of TSLP in HEK293 cells ., This non-coding SNP may thus exert an influence on TSLP expression via altered NHLH1 enhancer function and DNA methylation at the methylation site cg02061660 ., Detailed characterization of causal variants and different association signals at the locus would however require finemapping and sequence data ., The TSLP cytokine is released from epithelial cells and fibroblasts and is considered important in initiation of inflammatory responses to tissue damage , particularly in the type 2 T-helper ( Th2 ) pathways ., Th2 pathways are central in the response to extracellular parasites but also play a key role in the pathophysiology of allergies and hypersensitivity reactions ., A small subset of HF is known to be caused by Th2-mediated inflammation ( eosinophilic cardiomyopathy ) , yet Th2 cells have received limited attention in HF pathophysiology ., Recent experimental work implicates an important role of T-helper cells in HF progression for both systolic and diastolic heart failure , but has mainly focused on type 1 T-helper pathways 35 , 36 ., It remains unclear if the mechanism for rs9885413 is through a specific etiology characterized by high mortality such as eosinophilic cardiomyopathy or a pathway involved in outcomes with manifest disease ., The lack of association with HF incidence suggests that it may not act through incidence of a specific etiology , although firm conclusions are limited by sample size ., We did not observe significant associations of the SNP with gene expression in any tissue ., It is possible that adequately powered samples with a specific cell subtype in a specific context is needed to detect such associations , as illustrated by a recent study which only observed certain eQTLs with single-cell but not across averaged cells 37 ., Indeed , baseline expression of TSLP was low in our samples , and is induced by tissue injury , microbes , viruses and proinflammatory cytokines 38 ., Evidence of recent positive selection in individuals of African descent suggests that the HF risk allele may have been beneficial in some environments in recent human history ., Inflammatory pathways are enriched for signals of recent positive selection , reflecting that infectious disease has been an important cause of mortality throughout recent evolution ., Genes such as HBB and APOL1 have also been reported to have been subject to recent positive selection in Africa by conferring protection against infectious diseases such as Malaria and Trypanosomiasis ( sleeping sickness ) 39 , and APOL1 alleles have also been linked to cardiovascular disease 40 ., As cardiovascular disease and heart failure often presents after reproductive age , increased mortality in such patients would not be expected to exert purifying ( negative ) selective pressure ., Whether SNPs at 5q22 contribute to higher mortality in subjects of African ancestry remains to be shown ., Thus , although additional work is needed to further clarify the tissues and pathways perturbed by this genetic variant and the mechanisms linking it to mortality in HF patients , the current findings implicate rs9885413 as a novel marker of increased risk among patients with HF ., Complementary epigenomic evidence demonstrated candidate regions and genes , which may be mediators in cardiac pathophysiology and potential therapeutic targets to improve prognosis in patients with HF ., A genome-wide association ( GWA ) study was performed in a total of 2 , 828 subjects of European ancestry with HF from seven samples collected within five large community-based prospective cohort studies including the Atherosclerosis Risk in Communities ( ARIC and ARIC2 ) Study , the Cardiovascular Health Study ( CHS ) , the Framingham Study ( FHS ) , the Health ABC ( Health ABC ) study and the Rotterdam Study ( RS and RS2 ) ., Sample characteristics , data collection and clinical definitions have been described previously and are summarized in S1 Text ., 41–46 First diagnosis of heart failure ( new-onset ) was ascertained using a variety of methods based on international published criteria , as detailed in S1 Table ., Mortality was ascertained from telephone contacts with relatives and from medical records , death certificates and/or municipal records ( S1 Text ) ., Genotyping was performed using commercially available assays for genome-wide SNP detection ., Imputation of non-genotyped SNPs was performed using CEU reference panels of SNP correlations from the HapMap project phase II ( S1 Text ) , to characterize a total of 2 . 5 million SNPs ., Imputation quality was assessed for each SNP from the ratio of observed over expected variance of allele dosage ., All-cause mortality following initial HF diagnosis was examined for association with additive allele dosage of each genotyped or imputed SNP using Cox proportional hazards models , with censoring at the end of or loss to follow-up ., Models were adjusted for age at diagnosis , sex , and recruitment site in multicenter cohorts ., In the family-based FHS , Cox models were implemented with clustering on pedigree to account for relatedness ., Genomic control was applied to results from each cohort ., Cohort-specific GWA results were combined using fixed effects meta-analysis with inverse variance weights ., SNPs were excluded from cohort-level analyses if exhibiting implausible beta coefficients ( < -5 or > 5 ) and from the meta-analysis for low heterozygosity ( sample size-weighted minor allele frequency ≤ 0 . 03 , corresponding to < 100 minor allele carriers with an endpoint ) ., SNPs passing a P-value threshold defined a priori as P < 5 . 0x10-7 in the genome-wide stage 1 were carried forward to the second stage with targeted genotyping in 1 , 870 HF patients from four independent cohorts ., For 2 . 5 million tests , this threshold limits the expected number of genome-wide false positives to approximately 1 , assuming statistical independence of tests ., The second stage included four independent cohorts; the Malmö Diet and Cancer Study ( MDCS ) , the Malmö Preventive Project ( MPP ) , the Physicians’ Health Study ( PHS ) and the Prospective Study of Pravastatin in the Elderly at Risk ( PROSPER ) 47–50 ., Heart failure ascertainment and time of death in these cohorts was similar to in stage 1 cohorts , as shown in S1 Table and S1 Text ., Genotyping was performed as outlined in S1 Text ., Association analyses and meta-analysis of results were performed as in the first stage ., Meta-analysis of stage 1 and 2 was performed , and a combined P-value < 5 . 0x10-8 was considered significant ., Heterogeneity was assessed across the combined stage 1 and 2 cohorts using Cochran’s Q test for heterogeneity , computed as the sum of the squared deviations of each study’s effect from the weighted mean over the study variance , and the I2 test , the percentage of total variation across studies that is due to heterogeneity rather than chance ( I2 = Q—df / Q ) 51 , 52 ., The association of replicated SNPs with measures of cardiac structure and function was evaluated from summary results of the following GWA consortia: EchoGen 19 , CHARGE-HF 20 , CHARGE-QRS 22 , natriuretic peptides in 5453 subjects from the Malmö Diet and Cancer study 21 , QT-IGC 23 , and the CHARGE Sudden Cardiac Death consortium ( manuscript in preparation ) ., Each of these consortia is described in S1 Text ., The correlation of replicated SNPs with known coding SNPs was examined in the databases for the 1000 Genomes Project and phase III of the HapMap project , using SNAP 53 ., The location of SNPs in relation to regulatory motifs was explored using histone methylation patterns generated as part of the ROADMAP Epigenomics project 24 ., Enhancers were identified in each of the 129 ROADMAP tissues using the ChromHMM algorithm 54 from patterns of monomethylation ( H3K4Me1 ) of the fourth residue ( lysine ) and acetylation of the 27th residue ( H3K27Ac ) of histone H3 ., The location of SNPs in relation to transcription factor binding sites was assessed in silico using HaploReg version 4 . 1 ( http://www . broadinstitute . org/mammals/haploreg/haploreg . php ) 55 and the UCSC Genome Browser ( http://genome . ucsc . edu ) ., In HaploReg , position weight matrices ( PWMs; probabilistic representations of DNA sequence ) were computed with p-values based on literature sources and ENCODE ChIP-Seq experiments as previously described 55 , and only instances where a motif in the sequence passed a threshold of P < 4−7 were considered ., The NHLH1-binding motif was retrieved into HaploReg from the manually curated TRANSFAC database ., Complementary DNA oligonucleotides corresponding to the 100 bp genomic region flanking rs9885413 ( 50 bp on either side of the SNP ) were cloned into the luciferase reporter vector pGL3-Promoter ( Promega , Madison , WI ) using the MluI and BglII sites ., Two different sets of oligos were cloned , one corresponding to the major allele of rs9885413 ( pGL3P-G ) and one to the minor allele ( pGL3P-T ) ., Oligonucleotide sequences were as following: major allele sense: CGCGTCCTGCCTCACATAATCTTTTTGTTTGTCCCCCTGAAATGGATTCTCAGCTGTTGCCCAAACATTTCATCTTAGCGTTCCAGGTTTGAACTCGCCCTCACGA , minor allele sense: CGCGTCCTGCCTCACATAATCTTTTTGTTTGTCCCCCTGAAATGTATTC TCAGCTGTTGCCCAAACATTTCATCTTAGCGTTCCAGGTTTGAACTCGCCCTCACGA , and the corresponding antisense sequences ., The reporter vectors were co-transfected with the pRL-null vector at a ratio of 10:1 into HEK293 cells using Lipofectamine LTX ( Life Technologies ) according to the manufacturer’s instructions ., 24 hours post-transfection , luciferase activity was assayed using the Dual-Luciferase Reporter Assay System ( Promega ) and Glomax 20/20 Luminometer ( Promega ) ., The signal from the reporter vector was normalized to the signal from the pRL-null vector ., Samples of left ventricular cardiac tissue from patients undergoing cardiac surgery were genotyped for the SNP rs9885413 and for all five transcripts within +/- 500 kb of the SNP ., Samples of cardiac tissue were acquired from patients from the MAGNet consortium ( http://www . med . upenn . edu/magnet/ ) ., Gene expression levels were determined using the Affymetrix ST1 . 1 gene expression array ( Affymetrix , Santa Clara , CA , USA ) in a cohort including 247 heart samples ., Genotyping was performed using the Illumina OmniExpress array ., Left ventricular free-wall tissue was harvested at time of cardiac surgery from subjects with heart failure undergoing transplantation or from unused transplant donors ., In all cases , the heart was perfused with cold cardioplegia prior to cardiectomy to arrest contraction and prevent ischemic damage ., Tissue specimens were then obtained and frozen in liquid nitrogen ., Genomic DNA from left ventricle was extracted using the Gentra Puregene Tissue Kit ( Qiagen ) according to manufacturer’s instruction ., Total RNA was extracted from left ventricle using the miRNeasy Kit ( Qiagen ) including DNAse treatment on column ., RNA concentration and quality was determined using the NanoVue Plus spectrophotometer ( GE Healthcare ) and the Agilent 2100 RNA Nano Chip ( Agilent ) ., For all samples , genome-wide SNP genotypes were generated using the Illumina OmniExpress Array ., Caucasian Ancestry was verified using multi-dimensional scaling of genotypes ., For Gene expression array experiments , the Affymetrix ST1 . 1 Gene array was used ., Data were normalized using the Robust Multi-array Average algorithm and batch effects were adjusted for using ComBat ., Transcript expression levels were considered significantly higher than background noise if expression values from robust multiarray analysis in at least 10% of either cases or controls exceeded of the 80% quantile of expression of genes on the Y-chromosome in female hearts ( 5 . 24 ) ., Associations of expression levels for expressed genes with SNP genotypes were tested using a likelihood ratio test ., Specifically , we fit a linear regression model Y = β0 + β1*D + β2*g + β3* ( g x D ) where Y is the log2 transformed expression level of a given probe , g is the genotype ( coded as 0 , 1 , and, 2 ) of the test SNP , and D is heart failure disease status ( D = 1 for heart failure cases and D = 0 for unused donor controls ) ., Association between the probe and test SNP was assessed by testing H0: β2 = β3 = 0 using a likelihood ratio test ., Significance of the test statistic was evaluated by comparing with a Chi-squared distribution with two degrees of freedom ., All models were additionally adjusted for age , gender , and study site ., The association of the SNP rs9885413 with DNA methylation was examined in 2408 participants from the FHS Offspring cohort ., Methylation at cytosine-guanine dinucleotides ( CpG ) at the 5q22 locus ( +/-500 kb from rs9885413 ) were ascertained from a gene-centric DNA methylation array ( Infinium HumaMethylation450 BeadChip , Illumina , San Diego , CA , USA ) which allows interrogation of 485 , 512 methylation sites across the genome ., The array has coverage of at least one methylation site near 99% of RefSeq genes and 96% of CpG islands ., Briefly , bisulfite-treated genomic DNA ( 1 μg ) from peripheral blood samples underwent whole-genome amplification , array hybridization and scanning according to manufacturer instructions ., Genotyping of rs9885413 was performed as described in S1 Text ., Association of rs9885413 and the methylation probe cg02061660 with expression of the five genes at the locus ( +/-500 kb from rs9885413 ) was examined from microarray data ( Affymetrix Human Exon Array ST 1 . 0 ) in 5257 participants from the FHS Offspring cohort and Third Generation cohort ., Procedures for RNA extraction , processing and analysis have been described previously ( 28 ) ., Linear mixed effect ( LME ) models were fit accounting for familial correlation , cell count heterogeneity and technical covariates to account for batch effects using the pedigreemm package in R 56 ., Specifically , the mQTL model utilized a two-step approach: first , the DNA methylation beta-value ( ratio of methylated probe intensity to total probe intensity ) was residualized with adjustment for age , sex , cell count proportions ( imputed using the Houseman method for granulocytes , monocytes , B-lymphocytes , CD4+ T lymphocytes , CD8+ T lymphocytes and NK cells ) 57 , measured technical covariates ( row , chip , column ) , and the family structure covariance matrix ., Second , DNA methylation residuals were specified as dependent variable , SNP genotype dosage as independent variable with additional adjustment for 558 SVAs ( surrogate variable analysis ) 58 and ten principal components from eigenstrat 59 to account for unmeasured batch effects ., The eQTL models similarly residualized gene expression with adjustment for age , sex , imputed cell count proportions ( imputed in Offspring Cohort participants utilizing gene expression markers of cell count proportions developed from the Third Generation participants with both gene | Introduction, Results, Discussion, Materials and Methods | Failure of the human heart to maintain sufficient output of blood for the demands of the body , heart failure , is a common condition with high mortality even with modern therapeutic alternatives ., To identify molecular determinants of mortality in patients with new-onset heart failure , we performed a meta-analysis of genome-wide association studies and follow-up genotyping in independent populations ., We identified and replicated an association for a genetic variant on chromosome 5q22 with 36% increased risk of death in subjects with heart failure ( rs9885413 , P = 2 . 7x10-9 ) ., We provide evidence from reporter gene assays , computational predictions and epigenomic marks that this polymorphism increases activity of an enhancer region active in multiple human tissues ., The polymorphism was further reproducibly associated with a DNA methylation signature in whole blood ( P = 4 . 5x10-40 ) that also associated with allergic sensitization and expression in blood of the cytokine TSLP ( P = 1 . 1x10-4 ) ., Knockdown of the transcription factor predicted to bind the enhancer region ( NHLH1 ) in a human cell line ( HEK293 ) expressing NHLH1 resulted in lower TSLP expression ., In addition , we observed evidence of recent positive selection acting on the risk allele in populations of African descent ., Our findings provide novel genetic leads to factors that influence mortality in patients with heart failure . | In this study , we applied a genome-wide mapping approach to study molecular determinants of mortality in subjects with heart failure ., We identified a genetic variant on chromosome 5q22 that was associated with mortality in this group and observed that this variant conferred increased function of an enhancer region active in multiple tissues ., We further observed association of the genetic variant with a DNA methylation signature in blood that in turn is associated with allergy and expression of the gene TSLP ( Thymic stromal lymphoprotein ) in blood ., Knockdown of the transcription factor predicted to bind the enhancer region also resulted in lower TSLP expression ., The TSLP gene encodes a cytokine that induces release of T-cell attracting chemokines from monocytes , promotes T helper type 2 cell responses , enhances maturation of dendritic cells and activates mast cells ., Development of TSLP inhibiting therapeutics are underway and currently in phase III clinical trials for asthma and allergy ., These findings provide novel genetic leads to factors that influence mortality in patients with heart failure and in the longer term may result in novel therapies . | death rates, genome-wide association studies, medicine and health sciences, body fluids, demography, genome analysis, epigenetics, dna, population biology, dna methylation, chromatin, cardiology, genomics, chromosome biology, gene expression, chromatin modification, dna modification, heart failure, genetic loci, hematology, people and places, biochemistry, population metrics, blood, cell biology, nucleic acids, anatomy, physiology, genetics, biology and life sciences, computational biology, chromosomes, human genetics | null |
journal.pgen.1003034 | 2,012 | A Dominantly Acting Murine Allele of Mcm4 Causes Chromosomal Abnormalities and Promotes Tumorigenesis | Mouse models have been invaluable tools for studying human cancer ., Many mouse models used for this purpose are reverse genetic , in that they involve genetically modified mice engineered to have lost a specific tumor suppressor gene ( tsg ) or to over-express a specific proto-oncogene ., More rarely , spontaneous or mutagen induced mouse models that result in tumor formation have been used to study tumorigenesis ., Given the contribution of mouse models to understanding tumorigenesis , when a spontaneous mouse mutant that developed T-ALL arose in our colony , we pursued studies to both characterize the disease in these mice and to identify the causal mutation ., The mutation was spontaneous and the phenotype dominant , so we named the mutant Spontaneous dominant leukemia ( Sdl ) ., We have identified a mutation in the Mcm4 gene as the likely causative genetic lesion in these mice ., MCM4 is part of the MCM2–7 heterohexameric complex that is involved in licensing origins of DNA replication prior to S phase ., The MCM complex has ATPase activity and serves as the core of the replicative helicase that unwinds duplex DNA and drives progression of the replication fork 1 ., Improper fork progression can lead to stalled forks , the potential for incomplete DNA replication and even fork collapse which may lead to double strand break ( DSB ) formation 2 ., Therefore , the MCM proteins play important roles in maintaining genomic integrity , however their roles in tumorigenesis are just beginning to be elucidated ., Previous studies of murine Mcm genes have involved hypomorphic or gene-trap null alleles ., Gene-trap alleles are heterozygous viable and homozygous lethal 3 , 4 ., Mice harboring hypomorphic alleles of Mcm2 ( Mcm2IRES-CreERT2 ) 5 or Mcm4 ( Mcm4chaos3 ) 3 show reductions in MCM protein levels and develop tumors only in the homozygous state ., Mcm4chaos3 was discovered in a screen for mutations that cause increased micronucleus formation in reticulocytes and therefore promote chromosome instability ., Mcm4chaos3 results from a Phe345Ile substitution in MCM4 , which is a residue that is involved in the interaction of MCM4 with MCM6 in the heterohexameric complex 3 ., In Mcm4chaos3/chaos3 mouse embryonic fibroblasts ( MEFs ) , total and chromatin bound levels of MCM4 and other MCM proteins are reduced compared to wild-type 3 , 6 ., This leads to a loss of backup origins that normally fire during replicative stress which is hypothesized to be the mechanism by which low levels of MCM proteins promote genomic instability 6 , 7 ., Mcm4chaos3/chaos3 mice develop tumors with long latency ., Although the tumor spectrum varies with genetic background , Mcm4chaos3/chaos3 mice have not been reported to develop T-ALL 3 , 6 ., We have accumulated evidence that the early-onset T-ALL phenotype in Sdl mice results from a novel allele of Mcm4 ( Mcm4D573H ) that in the heterozygous state promotes chromosomal abnormalities that cause highly penetrant tumor formation ., The Sdl mutation arose in our colony in the germline of a breeder on the C57Bl/6 genetic background ., We therefore pursued a recombination mapping strategy by utilizing out-crosses and backcrosses to the FVB/N and 129S1/SvImJ genetic backgrounds ., A whole genome scan using simple sequence length polymorphisms ( SSLPs ) was performed and it was determined that mice of backcross generations that inherited C57Bl/6 markers at D16MIT131 and D16MIT4 on proximal Chr 16 rapidly became moribund ( Figure 1A ) indicating linkage to this chromosomal location ., Therefore , Sdl carriers can be identified by the presence of C57Bl/6 markers at these two SSLPs ., Phenotypically , 94 . 2% ( 180 of 191 ) of moribund Sdl mice necropsied had signs of hematologic malignancy including mediastinal masses , splenomegaly and/or lymphadenopathy ., Histologically , neoplastic cells filled hematopoietic tissues ( Figure 1B ) and infiltration of neoplastic lymphocytes into non-hematopoietic organs was frequently observed ( Figure 1C ) ., Leukemic cells are also found in the blood ( Figure 1C ) and bone marrow ( Figure S1A ) ., Sdl-induced disease was transplantable as tumors ( Figure S1B ) developed with an average latency of 29 days in four of four immunocompromised nude/nude mice that received cells isolated from mediastinal masses from moribund Sdl mice ., Flow cytometry was used to determine the phenotype of hematologic tumors from four Sdl mice ., Three mice developed disease early in life that was phenotypically T-ALL ( Figure 1D–1F and Table S1 ) ., The fourth mouse developed leukemia/lymphoma late in life , which expressed few lineage markers ( Figure 1G and Table S1 ) ., Southern analysis of early-onset leukemias/lymphomas from Sdl mice detects rearrangements of the T cell receptor ( TCR ) β locus 8 ( Figure S1 ) ., The majority of Sdl leukemias/lymphomas express TdT ( Figure S1 ) that , together with the surface phenotypes , indicates that most Sdl mice develop T-ALL with an immature phenotype ., Inter-crosses of Sdl heterozygotes were performed to determine the phenotype of Sdl homozygotes ., No Sdl/Sdl mice were present at weaning , so embryos from timed pregnancies of Sdl inter-crosses were examined ., No Sdl/Sdl embryos were detected even as early as 8 . 5 dpc ( n\u200a=\u200a69; 20 wild-type , 49 Sdl/+ , 0 Sdl/Sdl; p<0 . 0001 Chi square test ) , indicating that Sdl is homozygous lethal early during embryonic development ., Therefore , all carrier mice utilized for the experiments described here are Sdl/+ ., To further characterize the molecular basis of leukemogenesis in Sdl mice , microarray analysis was performed to detect mRNA expression differences between wild-type thymuses and thymuses from pre-leukemic Sdl carriers ., To detect differentially expressed genes , the false discovery rate was controlled at 5% ., Specifically , transcripts with a posterior probability of differential expression >95% and a q-value <0 . 05 were considered to be significantly differentially expressed ( see Methods ) ., No transcripts were found to be significantly differentially expressed between wild-type and pre-leukemic Sdl carrier thymuses ., Tests for common function did identify Gene Ontology ( GO ) sets enriched for differential expression between wild-type and carrier thymuses ( Table 1 ) , indicating some molecular differences between wild-type and carrier thymuses ., However , a similar analysis of the Kyoto Encyclopedia of Genes and Genomes ( KEGG ) failed to detect any differences between wild-type and carriers ., To determine if Sdl impacts T cell development , flow cytometry was performed to characterize T cell developmental stages in Sdl carriers ., Thymocytes were analyzed from Sdl carrier ( n\u200a=\u200a4 ) and wild-type siblings at 3 . 5 weeks of age ( n\u200a=\u200a4 ) ., Analysis of more mature thymocyte populations ( CD4 , CD8 and CD4/8 double positive ) revealed a trend toward decreased levels of CD4+ cells in Sdl mice , however this did not reach statistical significance ( p<0 . 064 Table 2 ) ., Lineage markers as well as CD44 and CD25 were then utilized to further analyze more immature double negative ( DN ) populations ., There was a statistically significant decrease in the percentage of DN cells at the DN1 stage of development in Sdl mice ( Table 2 ) ., Although no statistically significant differences in other DN cell populations were observed , flow cytometry profiles from individual mice revealed inter-animal differences , particularly in the DN3 population , in Sdl mice ( Figure S2 ) ., Taken together , these data indicate that Sdl does cause subtle defects in thymocyte development; with some mice more severely affected than others ., However , it is unlikely that Sdl causes T-ALL by directly promoting a block in thymocyte differentiation ., To identify the affected gene in Sdl mice , the chromosomal location of the Sdl mutation was further narrowed utilizing single nucleotide polymorphic ( SNP ) markers to analyze mice with recombination events in proximal Chr 16 ., Using this approach , the Sdl mutation was mapped to a 1 . 4 Mb candidate region ( Figure 2A ) that contains 30 . 5 kb of annotated protein-coding sequence ., No differences in expression levels of genes in the interval were detected by quantitative RT-PCR ( qRT-PCR ) analysis comparing 21-day-old Sdl carrier thymuses to 21-day-old control thymuses ( Figure S3 ) ., Exon capture followed by re-sequencing was performed on Sdl genomic DNA on the 129S1/SvImJ congenic background ., To ensure complete coverage in the Sdl interval , PCR amplification followed by Sanger sequencing was used to further examine exon and splice site sequences with fewer than 10× coverage following exon capture ( Figure S4 and Table S2 ) ., After eliminating known SNPs between C57Bl/6 and 129S1/SvImJ ( Table S3 ) , only one non-synonymous sequence difference in the Sdl interval was identified ., This difference is a G to C missense mutation that causes a D573H substitution in Mcm4 ( Mcm4D573H ) ., This residue is conserved not only in MCM4 but also across all MCM2–7 subunits in eukaryotes ( Figure S5 ) ., This nucleotide change was present in all confirmed leukemic Sdl mice examined and was not detected in FVB/N , 129S1/SvImJ , or mice of the C57Bl/6 genetic background that were present in the colony at the time that Sdl arose ( Figure 2B ) ., A cross of Sdl/+ to Mcm4chaos3/+ did not produce any Sdl/Mcm4chaos3 viable pups at p1 ( n\u200a=\u200a26; 7 wild-type , 10 Sdl/+ , 9 Mcm4chaos3/+ , and 0 Sdl/Mcm4chaos3; Chi square p value 0 . 0246 ) , indicating non-complementation ., To further investigate if Sdl mice harbor phenotypes indicative of replicative stress , chromosomal aberrations were examined in both reticulocytes and mouse embryonic fibroblasts ( MEFs ) isolated from Sdl mice ., Sdl mice harbor an ∼18-fold increase in spontaneous micronucleated reticulocytes compared to non-carrier siblings ( Figure 2C and 2D ) ., This is similar to the ∼20-fold increase reported for Mcm4chaos3/chaos3 mice studied on a different strain background 3 ., MEFs from Sdl carriers and non-carrier siblings were analyzed cytogenetically for chromosome breaks in the presence and absence of the DNA replication inhibitor aphidicolin ( APH ) ( Figure 2E–2F ) ., More chromosome breaks were found in APH-treated Sdl MEFs compared to wild-type ( p<0 . 02 ) ., Together , these observations indicate that Sdl causes chromosomal aberrations and increased sensitivity to exogenous replication stress , a phenotype that is consistent with MCM dysfunction 3 , 5 ., Therefore , all evidence suggests that the Sdl phenotype is caused by Mcm4D573H ., Previously studied hypomorphic or gene trap null alleles of Mcms have indicated that minimum thresholds of MCM levels are needed for normal development and for tumor suppression in adults; and reductions in protein levels of other members of the MCM2–7 complex have been detected in Mcm2 and Mcm4 hypomorphic mice 3–6 ., Therefore qRT-PCR and Western analyses were utilized to examine Mcm levels in 21-day-old wild-type thymuses and 21-day-old Sdl carrier thymuses ., No reductions in mRNA levels for Mcm2–7 ( Figure 3A ) or total or chromatin bound protein levels for MCM2 and MCM4 ( Figure 3B ) were detected ., MCM4 total and chromatin bound levels were also not reduced in Sdl MEFs compared to wild-type MEFs ( Figure S6 ) ., Therefore Mcm4D573H does not promote tumorigenesis by simply causing a reduction in transcript or protein levels of Mcm4 or other Mcms ., Although genetically it acts dominantly , Mcm4D573H could actually promote tumor formation in a recessive manner if loss-of-heterozygosity ( LOH ) or epigenetic silencing at the Mcm4 locus occurs during tumor formation ., To address these possibilities , RT-PCR followed by re-sequencing was used to examine if both wild-type and mutant Mcm4 alleles are expressed at the mRNA level in Sdl tumors ., Peak heights of Sanger sequencing traces indicated that both alleles are expressed at similar levels in both Sdl tumors and in 21-day-old thymuses from Sdl carrier mice ( Figure 3C ) ., As stromal cells are present in bulk tumors , Mcm4 allele expression was also examined in cell lines that were established from Sdl T-ALLs ., Both alleles were expressed at similar levels as they are in thymuses from pre-leukemic Sdl carrier mice ( Figure 3C ) ., Therefore , tumorigenesis in Sdl mice does not require LOH , and the Mcm4D573H allele acts dominantly to cause T-ALL ., To determine the impact of the Sdl mutation on MCM function , complementation studies in Saccharomyces cerevisiae were performed ., These studies utilized a haploid strain that harbors a deletion of the chromosomal mcm4 locus in which viability is maintained by a URA3-mcm4 plasmid 9 ., This strain was transformed with a TRP1 plasmid harboring mcm4 with the Sdl mutation engineered into the analogous yeast residue ( mcm4D632H , hereafter referred to as mcm4Sdl ) ., Cloning into the TRP1 vector added a HA/10XHis tag , which has been shown to not compromise Mcm4 protein function in complementation tests 9 and allowed verification of Mcm4Sdl protein expression by Western blotting ( not shown ) ., If the mcm4Sdl allele expressed by the TRP1 plasmid complements the mcm4 genomic deficiency , then growth on -TRP+5-Fluoroorotic Acid ( FOA ) ( restrictive conditions ) will occur due to the ability to lose the wild-type mcm4 copy on the URA3 plasmid ., TRP1-mcm4 wild-type and empty TRP1 vectors served as positive and negative controls , respectively ., For each TRP1 vector , multiple individual colonies were analyzed for growth under restrictive conditions ( example shown in Figure 4A ) ., As expected , no empty TRP1 vector colonies ( n\u200a=\u200a37 ) grew while all TRP1-mcm4 colonies ( n\u200a=\u200a37 ) grew ., Surprising , mcm4Sdl showed an intermediate phenotype as 10 of 38 colonies grew ., To further examine this phenomenon , mcm4Sdl colonies were examined for the presence of mcm4Sdl sequences ( Figure 4B ) ., As expected , freshly isolated mcm4Sdl colonies grown under permissive conditions ( −URA −TRP ) harbored both wild-type mcm4 and mcm4Sdl sequences due to the presence of both URA3-mcm4 and TRP1-mcm4Sdl plasmids ., However , mcm4Sdl colonies that grew under restrictive conditions ( −TRP +FOA ) only harbored mcm4 wild-type sequences , indicating that a reversion or gene conversion involving mcm4 sequences on the TRP1-mcm4Sdl plasmid had occurred ., These results indicate that mcm4Sdl generates a biologically non-functional helicase as it cannot complement a mcm4 genomic deletion ., As Mcm4D573H does not reduce MCM protein levels in mice , it is hypothesized that it causes chromosomal abnormalities and promotes tumorigenesis by stably incorporating into MCM heterohexamers and interfering with their normal function ., To further characterize the molecular basis of leukemogenesis in Sdl mice , microarray analysis was performed to compare expression in overt thymic tumors from Sdl mice to wild-type thymus ., Utilizing the same criteria described above for analysis of pre-leukemic Sdl carriers , 3627 genes were found to be differentially expressed , of which 745 had ≥2 fold change in expression levels ., The 20 significantly differentially expressed genes with the largest fold changes of increased and decreased expression in Sdl leukemias compared to wild-type thymuses are outlined in Table 3 ., Microarray data indicated that the Notch1 pathway is activated in Sdl tumors as Notch1 itself and several Notch1 target genes including Myc , Hes1 , Dtx1 , Adam19 , Hey , Heyl and Il2ra 10–13 were transcriptionally up-regulated in Sdl tumors compared to normal thymus ( Table 3 and microarray data available at GEO ) ., Expression levels of the Notch1 targets Hes1 and Myc were investigated by qRT-PCR ( Figure 5A ) , which revealed that they were expressed at equivalent levels in wild-type and carrier thymuses , but were approximately 2 fold up-regulated in tumors ., As NOTCH1 activating mutations are present in >50% of human T-ALL 14; exons 26 , 27 and 34 which are the common sites of Notch1 mutational activation in murine leukemias 15 were sequenced from Sdl tumors ., Only one point mutation in exon 26 was detected out of 13 tumors sequenced ., Therefore , activation of Notch1 by point mutation is not a common mechanism in this model ., To further investigate the mechanism of Notch1 activation in Sdl T-ALL , Notch1 transcript levels were investigated by qRT-PCR with primer pairs spanning several exon-exon boundaries ( Figure 5B ) ., Five of five Sdl tumors examined showed higher levels of expression of 3′ exons than 5′ exons , and in four of five tumors 3′ exons were expressed at higher levels than in normal thymus ., These results were consistent with the presence of intragenic deletions removing 5′ regions of the Notch1 locus that have been recently reported in murine leukemias ., These deletions result in truncated or chimeric transcripts that produce NOTCH1 proteins that are constitutively active 16 , 17 ., Two types of intragenic Notch1 deletions have been reported in murine T-ALLs ., Both types of transcripts were shown to be translated beginning at M1727 in exon 28 , produce intracellular NOTCH1 ( ICN1 ) and activate a Notch1 reporter 16 ., Type 1 were more common , had specific break points that occur immediately adjacent to sequences similar to RAG-signal sequences ( RSSs ) and had features consistent with being driven by RAG activity ., Type 2 deletions were more rare ( 3 of 10 cell lines examined , two of which were sub-clones of the same tumor ) and did not have evidence of RSS-like sequences at their breakpoints 16 ., Type 1 deletions break at specific chromosomal locations , so genomic PCR can be used to detect them ., No such deletions were detected in Sdl tumors ., Type 2 deletions have varying breakpoints as they are not limited to RSS-like sequences , so they are difficult to detect via genomic PCR ., However , RT-PCR can be used to detect the resulting abnormal chimeric transcripts ., Such transcripts were detected in 12 of 15 tumors examined ( Figure 5C ) ., Sequencing of the primary RT-PCR product from three separate tumors revealed splicing from exon 1 to exon 28 ., To attempt to clone the breakpoints in Notch1 in Sdl tumors , genomic PCR on a separate cohort of tumors was performed with various forward primers spanning exon 1 through intron 2 in combination with an exon 27 or exon 28 reverse primer ., In 3 of 13 tumors , products were cloned and it was verified that the breakpoints do not possess evidence of RSS-like sequences ., Two of three breakpoints had 2–4 bp microhomology ( Figure 5D ) ., Therefore , it is hypothesized that the tumor spectrum of Sdl mice is , at least in part , due to the propensity to develop type 2 non-RAG driven deletions at the Notch1 locus ., To determine if Sdl T-ALLs harbor additional genomic aberrations , array CGH was performed on genomic DNA isolated from Sdl thymic tumors compared to DNA isolated from a non-carrier mouse ., Although whole chromosome gains and losses were not detected , many small deletions and amplifications averaging 110 kb in size were present in tumors ( Figure 6 ) ., Therefore , the Sdl mutation promotes focal copy number changes and not aneuploidy ., We have been studying a novel spontaneous mouse cancer model , Sdl , in which an early-onset T-ALL phenotype is inherited in a dominant manner ., We have accumulated evidence that Mcm4D573H is the causative tumor-causing genetic lesion in this model ., The dominant inheritance of the cancer phenotype observed in Sdl contrasts to previous studies of mice harboring Mcm2 ( Mcm2IRES-CreERT2 ) or Mcm4 ( Mcm4chaos3 ) hypomorphic alleles in which tumors were only observed in the homozygous state 3 , 5–7 ., Mcm2IRES-CreERT2/IRES-CreERT2 and Mcm4chaos3/chaos3 mice harbor reductions in MCM levels detectable by Western analysis , which leads to a loss of backup origins that normally maintain genomic instability by firing during times of replicative stress 3–7 ., Gene trap ( GT ) null alleles of Mcm2 and Mcm4 have also been generated ., Mcm2GT/+ mice have been reported to develop tumors , but only after one year of age and with approximately 75% penetrance 4 ., Therefore , it has been proposed that a threshold level of MCM proteins ( between 35 and 50% of normal for MCM2 ) is required for sufficient origin licensing to maintain genomic stability and prevent tumor formation 4 ., We did not detect a reduction in total or chromatin bound MCM levels in Sdl mice , suggesting that the Mcm4D573H allele acts in a different mechanism to cause tumorigenesis ., However , it is also possible that Mcm4D573H mice harbor small reductions in MCM levels that are beyond the detection limits of Western analysis ., Although detailed aging studies were not presented , Mcm4GT/+ mice were reported to be apparently normal 3 ., However , a thorough study of the tumor phenotype of Mcm4GT/+ mice will be required to determine the threshold levels of active MCM4 that are required to maintain genomic stability and prevent tumorigenesis ., The tumor spectrum and latency in Sdl is also very different from that observed in Mcm4chaos3/chaos3 mice 3 , 6 ., The reasons underlying these differences remain to be elucidated ., As Mcm4chaos3/chaos3 and Sdl have been studied on different strain backgrounds , genetic modifiers could contribute to the observed phenotypic differences ., Alternatively , the recessively acting hypomorphic Mcm4chaos3 and the dominantly acting Mcm4D573H may have different consequences on origin licensing and DNA replication ., In addition , a recent report found that MCM 2 , 3 , 5 and 7 regulate HIF1 activity and this function is likely independent from their function in the heterohexamer ., A similar activity was not detected for MCM4 or 6 18 ., Therefore , the reduction in levels of total MCMs seen in Mcm4chaos3/chaos3 mice could also influence HIF1 activity and have phenotypic consequences ., A study of tumor and DNA replication phenotypes for both alleles on the same genetic background will be required to address the reasons for phenotypic differences between the two alleles ., Analysis of T cell differentiation in Sdl carriers revealed subtle defects , with some animals being more severely affected than others ., One potential interpretation is that T cell differentiation is mostly normal in Sdl mice until genomic mutations due to replicative stress start to accumulate ., In support of this , microarray data failed to detect any transcripts that are significantly differentially expressed in Sdl carrier thymuses compared to wild-type thymuses ., Tests for common function did identify differences in genes with common gene functions including protein localization or targeting to mitochondria , chemokine binding or receptor activity and endothelial cell proliferation ., In contrast , many expression differences were detected between wild-type thymuses and Sdl leukemias ., Many of the mostly profoundly down-regulated genes in Sdl leukemias are genes such as Prss16 and Tbata that are expressed in thymic epithelial cells 19 , 20 ., This observation likely results from a lower ratio of T cells to thymic epithelial cells in normal thymus than in thymic lymphoma ., Although non-T lineage cells are the minority of cells in the developing thymus , they nevertheless impacted our ability to identify genes that are down-regulated during T-ALL formation in Sdl mice ., The transcripts with the greatest fold up-regulation in Sdl leukemias compared to normal thymus include genes with unknown function , metabolic genes , genes expressed during T cell activation and Notch1 target genes ., RT-PCR in Sdl tumors demonstrated the presence of an aberrant Notch1 transcript splicing from exon 1 to exon 28 in 12 of 15 Sdl leukemias ., Genomic PCR on a separate cohort of Sdl T-ALLs was able to clone genomic breakpoints in the Notch1 locus in 3 of 13 tumors ., These breakpoints occurred in introns 2 and 27 , introns 1 and 27 , and introns 2 and 26 ., It is possible that the exon 1 to 28 splice is favored even when deletions leave more internal exons intact , or that our RT-PCR conditions failed to robustly amplify transcripts containing other aberrant splice variants ., Alternatively , the genomic re-arrangements present at the Notch 1 locus may be more complex than can be detected by our genomic PCR ., Nevertheless , the detected Notch1 transcript and lack of RSS-like sequences at the cloned breakpoints are both consistent with the presence of type 2 deletions at the Notch1 locus in Sdl T-ALLs ., The vast majority of murine T-ALLs previously examined have harbored type 1 RAG-mediated deletions , while type 2 deletions were more rare ., A predisposition to T-ALL has also been observed for Mcm2 hypomorphic mice 5 , 7 and array CHG detected deletions at the Notch1 locus in 4 of 8 of T-ALLs in Mcm2 mice 21 ., One possibility to explain the tumor spectrum in Sdl mice and Mcm2 hypomorphic mice is that the integrity of the murine Notch1 locus is sensitive to replicative dysfunction in developing T cells and that replicative stress promotes the formation of type 2 deletions at the Notch1 locus ., As the majority of T cell development is completed by young adulthood , Mcm4chaos3/chaos3 mice may not experience sufficient replicative stress to cause Notch1 deletions in developing thymocytes , which would allow them survive longer to develop other late-onset tumor types ., Previous array CGH studies of Notch1-driven mouse T-ALLs failed to detect tumor-specific chromosomal aberrations , indicating that chromosomal instability is not a general characteristic of mouse T-ALL 22 ., In contrast , array CGH data of Sdl tumors did detect small amplifications and deletions but not whole chromosome gains and losses ., This data is consistent with previous observations that an improved growth phenotype found in mcm4Chaos3/Chaos3 diploid yeast is due to mutations in a few genes and not due to aneuploidy 23 ., In addition , recent array CGH experiments on T-ALLs from Mcm2 hypomorphic mice also detected small genomic aberrations 21 ., However , aberrations in T-ALLs in Mcm2 mice were primarily deletions , while both amplifications and deletions were found in Sdl T-ALLs ., It is possible that functional differences between MCM helicase activity in Sdl and Mcm2 hypomorphic mice could explain this difference ., However , it is also possible that strain specific modifiers can impact the types of aberrations generated by replicative dysfunction or selected for during tumorigenesis ., Nevertheless , studies in yeast , Mcm2 hypomorphic mice and Sdl mice all support a model that replicative stress can contribute to tumorigenesis by generating smaller chromosomal aberrations and not by causing aneuploidy ., The residue impacted by the observed Mcm4 mutation in Sdl mice is part of the Walker B box , one of the structural motifs in MCM4 that is an integral part of the ATPase active site formed between MCM4 and MCM7 in the heterohexameric complex 24 ., Engineering the D to H mutation into the analogous residue in yeast mcm4 failed to complement a mcm4 genomic deletion ., Previous studies in yeast where the analogous D residue was mutated to A or T did complement a mcm4 deletion allele 9 ., Mutation of the D and the adjacent E residue to N and Q , respectively , ( DE>NQ ) did however fail to complement 25 ., As the D residue in the Walker B box is believed to be important for coordinating the Mg2+ ion involved in ATP hydrolysis 26 , the substitution of a positively charged H residue could result in a greater impact on MCM4 function than would mutation to an A or T ( Figure S5 ) ., Given the observation that total and chromatin bound MCM levels are not different in Sdl carrier and wild-type mice , this supports a model in which MCM4D573H containing helicases are stable , yet functionally inactive ., The role of MCM proteins in promoting genomic instability during human cancer initiation and progression remains unclear ., Immunohistochemistry detects MCM protein expression in many human tumor samples , as would be expected for rapidly dividing cells 27 ., Knockdown of MCM 2 , 3 or 7 in medulloblastoma cell lines caused inhibition of anchorage-dependent and independent growth; while their over-expression promoted cell migration , invasion and increased anchorage-independent growth 28 ., MCM7 over-expression in epithelial progenitor cells sensitized mice to carcinogen-induced skin tumors but did not itself drive tumors by 1 year of age 29 ., Over-expression of MCM7 alone in the prostatic epithelium did not promote phenotypes ., However , over-expression of MCM7 along with a PTEN-targeting microRNA cluster encoded within the MCM7 human locus did initiate prostate tumorigenesis 30 ., Although mutations in genes involved in DNA damage checkpoints and DNA damage repair are known to contribute to sporadic and hereditary tumorigenesis , it is unclear if genetic changes in the actual components of the replication machinery such as MCM proteins contribute to tumorigenesis in humans ., A few point mutations in MCM subunits have been detected in human tumors 31 ., However , the functional consequences of these mutations are currently unknown ., Given clinical use and preclinical development of compounds that impact replication as cancer chemotherapies , it will be important to elucidate how MCMs contribute to tumor initiation and progression ., Although previous studies have uncovered a tumor suppressive activity for Mcms , our studies of the Sdl model indicate that dominantly acting Mcms alleles can be compatible with viability but cause chromosomal abnormalities and highly penetrant tumor formation ., Therefore , Mcm mutations with different functional consequences on MCM levels and activity have the potential to act as driver mutations during tumorigenesis ., Mouse experiments were performed according to the institutional guidelines for animal care under the approval of the IACUC of the University of Minnesota and the University of Wisconsin ., Sdl arose in the germline of a Rosa-SB11 mouse maintained on the C57Bl/6 background 32 ., Wild-type mice were purchased from Jackson Labs or Charles River ., Non-carrier sibling mice were utilized as controls ., Mcm4chaos3 mice on the FVB/N genetic background were generously provided by Naoko Shima ., The SureSelect XT Mouse All Exon Kit ( Agilent Technologies ) was used to capture exonic sequences from genomic DNA purified from a tail clip of a Sdl carrier ., Sequencing was performed on the Illumina Genome Analyzer 2 platform as paired-end 76-bp reads ., One lane of sequence was generated ., Reads were aligned to the MM9 reference genome with BWA v0 . 5 . 5 33 ., The GATK ( v1 . 0 . 4771 ) 34 was then used to do local realignment of the reads around all indel sites called in the mouse genomes project 35 ., The base qualities of the BAM file were recalibrated with the GATK v1 . 0 . 4771 by masking all SNP and indel positions called in the mouse genomes project ., SNP calling was carried out using SAMtools mpileup/bcftools ( v0 . 1 . 16 ) 36 ., For SAMtools mpileup , the following options were used: -d 500 -C50 -m3 -F0 . 002 -aug ., The raw sequence data is available under ERA accession number ERP000474 ., DNA was purified from thymic tumors from three Sdl mice on the 129S1/SvImJ genetic background ., Tail clip DNA from a non-carrier also on the 129S1/SvImJ genetic background was utilized as reference DNA ., Hybridizations were performed by WiCell research institute according to manufacturers recommendations to the mouse CGH 3×720 K Whole-Genome Tiling Arrays ( NimbleGen ) ., NimbleScan , CGH Fusion ( RBS v1 . 0 ) ( Infoquant ) software was utilized for analysis and data visualization ., Gains and losses were called with the following parameters: average log-ratio threshold of 0 . 2 , a minimum aberration length of 5 probes and maximum p-value of 0 . 001 ., The micronucleus assay was performed essentially as described 37 ., 5 week-old females on the FVB/ | Introduction, Results, Discussion, Materials and Methods | Here we report the isolation of a murine model for heritable T cell lymphoblastic leukemia/lymphoma ( T-ALL ) called Spontaneous dominant leukemia ( Sdl ) ., Sdl heterozygous mice develop disease with a short latency and high penetrance , while mice homozygous for the mutation die early during embryonic development ., Sdl mice exhibit an increase in the frequency of micronucleated reticulocytes , and T-ALLs from Sdl mice harbor small amplifications and deletions , including activating deletions at the Notch1 locus ., Using exome sequencing it was determined that Sdl mice harbor a spontaneously acquired mutation in Mcm4 ( Mcm4D573H ) ., MCM4 is part of the heterohexameric complex of MCM2–7 that is important for licensing of DNA origins prior to S phase and also serves as the core of the replicative helicase that unwinds DNA at replication forks ., Previous studies in murine models have discovered that genetic reductions of MCM complex levels promote tumor formation by causing genomic instability ., However , Sdl mice possess normal levels of Mcms , and there is no evidence for loss-of-heterozygosity at the Mcm4 locus in Sdl leukemias ., Studies in Saccharomyces cerevisiae indicate that the Sdl mutation produces a biologically inactive helicase ., Together , these data support a model in which chromosomal abnormalities in Sdl mice result from the ability of MCM4D573H to incorporate into MCM complexes and render them inactive ., Our studies indicate that dominantly acting alleles of MCMs can be compatible with viability but have dramatic oncogenic consequences by causing chromosomal abnormalities . | Our study investigated a spontaneous mouse model for dominantly inherited T-cell leukemia/lymphoma ., Using genetic methods , we identified a mutant allele of Mcm4 ( Mcm4D573H ) in this model ., Interestingly , this Mcm4 allele promotes the accumulation of focal chromosomal gains and losses , including aberrations at the Notch1 locus that drive the formation of T-cell leukemia/lymphoma ., Previous studies of hypomorphic Mcm alleles have demonstrated that a decrease in MCM levels can cause tumorigenesis ., However , total and chromatin bound MCM levels were similar to wild-type in our model , indicating that Mcm alleles that do not drastically impact MCM levels can cause genomic aberrations that drive tumor formation . | animal genetics, cancer genetics, genetic mutation, genetics, molecular genetics, biology, genetics and genomics | null |
journal.pgen.1003952 | 2,013 | Deletion of an X-Inactivation Boundary Disrupts Adjacent Gene Silencing | Recent annotation of the human and mouse genomes has revealed chromosome domains that are distinguished by sequence and gene content , regulatory-factor binding , replication dynamics , chromatin composition , or nuclear location ., Many of these domains overlap and can functionally segregate active and inactive transcripts 1–4 ., What regulates such extensive genome compartmentalization is not fully understood ., Intriguingly , many boundaries share common features including opposing chromatin marks , active transcription , or binding by the CCCTC binding factor , CTCF 1 , 4–6 ., Whether these elements are essential for segregating domains has not been thoroughly examined , yet boundary deletion can lead to misregulation ( e . g . 7 ) ., An interesting example of partitioned , closely juxtaposed , active and inactive transcripts is found on one X chromosome in female mammals ., This X is largely silenced during early embryonic development in order to balance dosage between the sexes ., X-chromosome inactivation ( XCI ) is mediated by the cis-limited action of Xist , a structural RNA that coats the X chromosome and recruits inactive chromatin modifiers 8 ., Nevertheless , XCI is not chromosome-wide , as some genes “escape” inactivation 9 ., Current understanding of how genes escape XCI on an otherwise silenced chromosome is incomplete , but the answer may reveal novel insights about regulatory sequences not only at XCI boundaries but also at other expression transitions throughout the genome ., Escape and X-inactivated genes are epigenetically and structurally distinct 9 ., Escape genes are depleted in Xist RNA and promoters are marked by active histone modifications and lack silent epigenetic marks associated with X-inactivated transcripts ( e . g . 10–12 ) ., However , long-range regulation is likely involved , as many escape genes , particularly in humans , are physically clustered 13 , 14 ., Further supporting this idea , unique sequence composition distinguishes these domains relative to the rest of the X 15 , 16 ., Distant escapees also frequently interact on the inactive X 17 and can be spatially separated from silent inactive X regions 18 ., To functionally delimit sequences sufficient to confer XCI escape , we previously developed a transgene approach in female mouse embryonic stem ( ES ) cells , a well established ex vivo XCI model 19 ., X-linked BAC transgene lines were isolated that carry the escapee Kdm5c ( previously Jarid1c ) that encodes a histone H3K4 demethylase 20 , 21 ., The BACs also included an adjacent long non-coding RNA ( lncRNA ) AK148627 that escapes XCI 14 , 22 and flanking X-inactivated genes 16 , 19 , 23 ., Endogenous expression patterns examined were maintained including transgenic Kdm5c ( Kdm5c-tg ) escape at four ectopic X-chromosome locations ., Therefore , these BACs must include sequences necessary for Kdm5c to escape XCI ., What features at this locus direct XCI escape ?, Plausible candidates include CTCF and the AK148627 lncRNA , as both CTCF and lncRNAs are found at a number of XCI boundaries 14 , 22 , 24 ., Further , such elements are enriched at other boundaries throughout the genome ( e . g . 1 , 4 , 5 ) , and can function to regulate adjacent genes in cis 25 , 26 ., Intriguing associations notwithstanding , both candidates lack functional validation ., To better understand the role of boundary sequences in inactive X regulation we now extend our analysis of Kdm5c BAC transgenes ., We further narrow sequences necessary for XCI escape and identify a novel role for XCI boundary sequences in regulating inactive X expression ., Previous studies focused on four full-length BAC transgenes that were derived from two overlapping BACs 19 ( Figure 1 ) ., However , by PCR analysis of BAC-backbone sequences , six additional female ES lines carry X-linked integrants of the BAC RP23-391D18 with partial deletions ., We turned to these truncated transgenes to further delimit sequences that dictate XCI states at the Kdm5c locus ., To determine transgene content and copy number , we exploited allele differences between the 129 and M . m . castaneous ( CAST ) X chromosomes in the ES cell line and assayed for the presence or absence of an additional BAC-transgene allele ., Allele ratios for up to 18 SNPs across the region were measured using a quantitative primer-extension assay , qSNaPshot 13 , 19 , with primers that abut each SNP ., The approach was validated with allele ratios of 2∶1 ( ( 129+BAC ) /CAST ) for all SNPs mapping within full-length , single-copy BACs ( e . g . B202 ) 19 ( Figure 1 , Figure S1 ) ., Similar analysis excluded three lines with multi-copy inserts ( Figure S1 ) ., Further , the transgene in the B176 line is severely truncated and deletes the entire Kdm5c-tg ., Breakpoint analysis for two other transgene lines revealed deletions of distal XCI boundary sequences ., ES lines C048 and C138 carry single-copy inserts that retain all or most of Kdm5c-tg ( Figure 1 ) ., The C048 transgene contains the AK148627 lncRNA but deletes a large portion of non-transcribed XCI boundary sequence ., The transgene in C138 is more extensively deleted as all sequences downstream of Kdm5c are removed including the lncRNA ., Additional SNPs narrowed the C138 transgene breakpoint to a small ∼900 bp window and indicate that at least 90% of the Kdm5c genomic locus remains intact ., Further , by RNA fluorescence in situ hybridization ( FISH ) a stable nascent Kdm5c-tg transcript is detected in pre-XCI undifferentiated ES cells ( not shown ) ., Therefore , the C048 and C138 transgenes lack all or part of the intervening region between the 3′ end of escapee Kdm5c and the closest X-inactivated gene and allow the role of sequences within an escape domain and at an XCI boundary to be evaluated ., Prior to examining transgene expression we surveyed the local chromosomal environment flanking the C048 and C138 BAC transgenes ., By inverse PCR and subsequent analysis of an adjacent SNP , the C048 transgene inserted on the CAST X , upstream of the first coding exon of the Mid1 gene ( 166 , 290 , 616 bp , mm9 ) ., Importantly , Mid1 is normally X inactivated on the CAST X 19 ., Additionally , FISH and SNP screening indicate that this transgene insertion was accompanied by a large and likely terminal deletion that removes the entire pseudoautosomal region ( Figure S2A ) ., Similar characterization of the C138 transgene revealed that the BAC integrated on the CAST X at 98 , 065 , 555 bp ( mm9 ) ( Figure S2B ) ., DNA FISH and SNP analysis near the C138 transgene integration site ensured that the BAC insertion was not accompanied by a larger chromosomal rearrangement or deletion ( Figure S2B ) ., This places Kdm5c-tg in an intron near the 3′ end of Tex11 , a gene that functions in male meiosis 27 , 28 ., Although predominantly expressed in testis 29 , we detected a low level of Tex11 expression in somatic tissues by RT-PCR; monoallelic expression of a transcribed polymorphism in female fibroblasts with non-random XCI confirms that Tex11 is normally X inactivated ( Figure S2C ) ., Therefore , both transgenes integrated into regions that are normally silenced by XCI , enabling direct testing of BAC sequence influences on Kdm5c-tg expression ., Will Kdm5c-tg still escape XCI in the absence of distal boundary sequences ?, Expression was examined by sequential RNA and DNA FISH upon ES cell differentiation and concomitant XCI ., Non-denatured cells were hybridized with a Kdm5c BAC probe to detect nascent transcripts from the endogenous and transgenic loci ., Following probe fixation , cells were denatured and hybridized for DNA FISH to demarcate all Kdm5c loci ., In C138 and C048 , three expressed foci were detected in most cells ( Figure 2 ) ., Importantly for each line , nuclei with two RNA signals colocalizing with Xist RNA demonstrate that both endogenous and transgenic loci are expressed on the inactive X . Additional FISH for C138 directly confirmed Kdm5c-tg escape , as one inactive X transcript colocalizes with a DNA signal from a probe at the integration site ( Figure S3A ) ., RNA FISH using a smaller Kdm5c-specific probe ensured results reflect Kdm5c expression ( Figure S3B ) ., Because of genetic background differences in the ES cells , XCI is skewed and the transgene is on the inactive CAST X in ∼25% of cells 19 , 30 ., For both C138 and C048 , the proportion of cells with two expressed Kdm5c foci from the inactive X closely mirrors the frequency that cells inactive the CAST X chromosome ( Figure 2B , Figure S3 ) ., Therefore , these data indicate Kdm5c-tg escapes XCI at a frequency similar to the non-transgenic locus ., To better estimate the level of Kdm5c-tg escape in C138 , we isolated a clonal line that carries the transgene on the inactive X chromosome ., Allelic expression , measured by qSNaPshot , is consistent with Kdm5c-tg and the non-transgenic locus each partially escaping XCI , at levels that are ∼34% of active X expression ( see methods ) ., Such levels are in good agreement with previous reports of partial escape for the endogenous locus 18 , 19 , 31 , 32 ., These data indicate that despite BAC truncation , Kdm5c-tg is expressed from the inactive X chromosome ., Altogether , we conclude that Kdm5c escape does not require distal sequences ., Previous studies of Kdm5c indicate that escape genes preferentially assume an exterior location on the Xist-coated inactive X in interphase nuclei 18 ., This positioning likely facilitates more frequent long-range associations with other escape genes than with X-inactivated genes 17 ., To further confirm the active state of Kdm5c-tg , we asked if transgenes establish similar interactions with distant escapees ., Interactions were evaluated in differentiated post-XCI cells by FISH using three-dimensional deconvolution microscopy ( Figure 3A ) ., Inactive X distances were initially measured between the escapee Ddx3x and a probe detecting either escapee Kdm5c or an X-inactivated gene ( Figure 3A , B ) ., For each comparison , cumulative frequency plots indicate the proportion of nuclei in which two loci are closer than a given nuclear distance ( normalized for area ) ( Figure 3B ) ., This approach was first validated in a non-transgenic line and confirmed that profiles differ for the active and inactive X 17; distant loci are more frequently in close proximity on the inactive X relative to their distance on the active X ( Figure S4A ) ., Further , inactive X escapee associations are also consistent with previous observations 17 ., A higher proportion of nuclei have two escape loci in close proximity as the cumulative frequency plot of nuclear distances between escapees Ddx3x and Kdm5c is significantly shifted to the left relative to profiles comparing Ddx3x and either X-inactivated gene , Tex11 or Mecp2 ( Figure 3B , Figure S5A ) ., All differences were readily apparent regardless of whether or not probe distances were normalized to nuclear area ( Figure S4B ) ., Similar probe comparisons were performed in the transgene lines ., All profiles in line C048 , with the Mid1-integrated transgene , were indistinguishable from the non-transgenic line ( Figure 3B ) indicating that a transgene at a location unrelated to the genes tested is insufficient to alter gene localization and interaction ., In contrast , while C138 cumulative frequency curves comparing Ddx3x to active and inactive non-transgenic loci mirrored the other lines tested , comparison to the Tex11 BAC revealed a significant left shift ( Figure 3B , Figure S5A ) ., Tex11 lies at the C138 transgene integration site and proxies for the transgene in cells that inactivate the transgenic X . Indeed , the Tex11 BAC is frequently located near Ddx3x on the inactive X , with a profile that is more similar to plots comparing two escapees than to curves for genes with differing XCI states , e . g . Ddx3x and Mecp2 ., These data suggest that a transgene can reconfigure associations on the inactive X . To more directly visualize transgene interactions we specifically scored transgenic inactive X associations between Kdm5c-tg and the endogenous Kdm5c locus ., Compared to interactions with X-inactivated Tex11 ( measured on non-transgenic inactive Xs ) , Kdm5c more frequently lies in close proximity to the transgene in C048 , C138 , and the full-length B202 transgene ( Figure 3C , Figure S5B ) ., In contrast , profiles for the severely truncated transgene in B176 resemble those with X-inactivated locus Tex11 ( Figure 3C , Figure S5B ) ., Such a profile likely reflects the absence of Kdm5c-tg transcript in this line and indicates that the partial proximal boundary sequences retained in B176 are insufficient to direct interactions with escape loci ., Importantly , these studies demonstrate that Kdm5c-tg in C138 and C048 structurally interacts in a manner similar to the endogenous locus , further confirming the active state of the transgenes on the inactive X . Therefore , despite truncating the endogenous escape domain , retained sequences are sufficient to induce an altered inactive X conformation even when inserted at a different chromosomal location ., We previously established that the full-length BAC transgenes retain intact XCI boundaries as Kdm5c-tg is expressed , but adjacent transgenic Tspyl2 or Iqsec2 properly undergo XCI 19 ., Therefore , we next sought to determine if transcripts near the integration site would remain silent despite the absence of distal boundary sequences ( Figure 4A ) ., Given the orientation and close proximity of the C048 transgene to the pseudoautosomal boundary ( Figure S2A ) we focused on the C138 line ., C138 proximal transgene sequences and XCI expression boundary are intact and therefore , adjacent genes are predicted to remain X inactivated ., Consistent with this expectation , robust mono-allelic expression from the active X was detected by RNA FISH in both C048 ( used to control for a non-transgenic Tex11 locus ) and C138 ( Figure 4B ) ., These data further establish that transcripts in this region are normally X inactivated and are not altered upon transgene integration ., To examine effects at the C138 distal boundary we queried transcripts included in BAC RP23-263O9 because low Tex11 expression was undetectable on either X by RNA FISH ( Figure S6 ) ., Monoallelic expression from only the active X in C048 confirms that RP23-263O9 transcripts are normally X inactivated ( Figure 4B ) ., However , a heterogeneous pattern was seen in C138 , with inactive X expression in 22% of cells ., This proportion closely approximates the percentage of cells that inactivate the transgenic CAST X ( Figure 4B ) , and argues that distal genes on the transgenic X escape XCI at a high frequency ., Aberrant XCI regulation does not extend further , as adjacent transcripts detected by BAC RP23-295G17 are properly X inactivated ( Figure 4B ) ., To confirm and extend these results , we determined the XCI status of proximal and distal transcripts in differentiated clonal lines that carry the C138 transgene only on the active X or only on the inactive X chromosome ., First , allele-specific expression of cDNA from the C138-derived clonal lines confirmed that the proximal gene Dlg3 is X inactivated ( Figure 4C ) ., Next , Tex11 at the integration site was tested ., While Tex11 is X inactivated in the clonal line that carries the transgene on the active X ( Figure 4C ) , the gene now escapes XCI when interrupted by Kdm5c-tg ., To determine the extent of XCI misregulation , we queried additional genes downstream of Tex11 ., Two additional transcripts , Slc7a3 and Snx12 , aberrantly escape XCI on the transgenic X ( Figure 4C ) ., By qSNaPshot , the level of inactive X escape relative to active X expression is quite similar for all three genes ., However , it is unlikely that absolute inactive X expression is equivalent given that RNA FISH suggests significantly higher Snx12 transcription on both Xs ( Figure 4B , Figure S6 ) ., Altogether these results argue that absence of the distal XCI boundary results in 350 kb expansion of an escape domain ., Recent genome-wide studies have made tremendous strides in uncovering long-range organization and predicting functional domains 33 ., Direct annotation of the inactive X is more challenging , in part because it is masked by its active X counterpart ., Despite recent efforts to catalogue allele-specific epigenetic features ( e . g . 10 , 11 , 34 ) , current understanding of the pivotal sequences and modifications that regulate how a gene responds to XCI remains incomplete ., While inactive X profiling has identified intriguing candidates , functional dissection can reveal unexpected regulatory modes , such as uncovered here at Kdm5c ., These studies have expanded our understanding of the Kdm5c locus ., Because our BAC transgenes carry large inserts encompassing X-chromosome genes that normally are influenced by XCI , effects are expected to recapitulate endogenous regulation and identify candidate sequences that are highly likely to be relevant ., Our previous full-length BAC transgene studies allowed us to conclude that an element ( s ) within the BAC is sufficient to initiate Kdm5c-tg escape 19 ., Such a regulatory element could also explain XCI escape of a human autosomal transgene 35 ., For the Kdm5c locus , this activity was mapped to a 112 kb region defined by BAC overlap ( Figure 1 ) 19 ., Here we examine additional transgenes that further narrow this interval , as Kdm5c-tg still escapes XCI from BAC transgenes lacking distal boundary sequences ( Figure 2 ) ., Because the truncated BACs integrated into X-inactivated regions , we conclude that the remaining transgene sequences must include a dominant element ( s ) sufficient to initiate Kdm5c escape and to structurally remodel the X in a manner that allows preferential association with escape genes ( Figure 3 ) ., Further , our studies of the C138 transgene reveal an additional role for distal XCI boundary sequences , since in contrast to the full-length BACs 19 XCI regulation of adjacent X-inactivated genes was disrupted ( Figure 4 ) ., What sequences are necessary for XCI escape and do these elements also facilitate long-range escapee interactions ?, Sequences orchestrating these activities must map within the C138 transgene and likely reside within the proximal XCI boundary ( Figure 5A ) ., Therefore , the complete escape domain , including the escapee lncRNA , cannot be necessary for directing inactive X expression ., Retained BAC sequences include the Kdm5c promoter and CTCF-binding sites that are proposed to delimit this proximal XCI boundary 24 ( Figure 5A ) ., Nevertheless , CTCF binding alone is not sufficient to confer XCI escape 36 ., Further , whether specific promoter elements alone can drive escape is untested , but large-scale transgenesis likely excludes promoter strength as a sole property 35 ., Sequences within C138 also enable long-distance association with other escape genes ., Yet , the region may be further narrowed as the short B176 transgene , lacking Kdm5c-tg and its promoter , fails to preferentially interact ., Deletion of distal transgene sequences in C138 reveals additional regulation at Kdm5c ., In the absence of an XCI boundary , three normally X-inactivated genes near the BAC integration site now escape XCI ( Figure 4 ) ., We asked whether aberrant distal expression is due to permissive chromatin propagated by read-though transcription from the truncated Kdm5c-tg ., This possibility seems unlikely , as transcription does not extend across the entire escape domain ( Figure S6 ) ., Further any read-through is at most minimal , as no transcription across the Tex11 locus is seen by RNA FISH , even when the transgene is on the active X . Nevertheless , strand-specific RT-PCR within Tex11 detects low-level sense and antisense transcripts from both non-transgenic and transgenic undifferentiated ES cells ( Figure S6 ) ., That these transcripts are not unique to the Kdm5c-tg locus argues that low levels of transcription alone cannot enable escape ., Therefore , while the extent that XCI is disrupted is likely dependent on integration site characteristics , the C138 transgene must lack a regulatory element that normally has an essential role in establishing an XCI boundary at the endogenous Kdm5c locus ( Figure 5B ) ., How this element functions is not clear , but could actively prevent heterochromatin encroachment into active domains or instead block escapee regulators from influencing adjacent silenced genes ., Consistent with the former , a chromatin barrier could act as a boundary if upon deletion other distal elements reposition the XCI boundary ( Figure 5C ) ., CTCF could perform such a function , as sites are found near the distal Kdm5c boundary and are normally present at locations that could delimit the expanded escape domain ( Figure S7 ) ., Moreover , CTCF frequently binds at chromatin boundaries throughout the genome 37 , and can organize and reorganize chromatin loops 38–40 ., This would suggest plasticity at XCI boundaries and could explain tissue differences in some escape genes 11 , 17 , 41 ., Sequences at the distal XCI boundary could instead actively block adjacent genes from escape in a manner that is directional and in cis ( Figure 5C ) ., Deletion of such a boundary could appear as euchromatin spreading , although , to our knowledge , similar effects have not been described elsewhere in the genome ., Yet , elements at other loci could explain this observation ., CTCF functioning as an enhancer-blocking element fits this model 42–44 , particularly since deletion at other epigenetically regulated loci can induce gene reactivation 45 ., Alternatively , transcripts near escape genes may require additional elements to be properly X inactivated 46 ., In this role , the lncRNA could silence by transcriptional interference 47 , although effects extending such distances are not reported ., Further , lncRNAs can recruit chromatin-modifying enzymes in cis ( e . g . 47 , 48 ) ., Supporting recruitment , it is intriguing the AK148627 lncRNA is amongst transcripts immunoprecipitated by the PRC2 polycomb-complex component EZH2 49 ., Finally , we considered the role that inactive X topological structure plays in determining XCI states ., Distant escapee contacts are maintained for Kdm5c-tg at all three ectopic locations tested ., Therefore , long-distance interaction is another inherent property of an escape locus , yet its mechanistic relationship to active transcription remains undefined ., Transgenic loci are likely repositioned at the exterior of the Xist compartment , similar to endogenous Kdm5c 18 ., Such rearrangement would also impact genes adjacent to the transgenes ., While positioning on the inactive X could influence distal gene escape in C138 , it cannot be sufficient since proximal genes remain X inactivated ., Additional factors must be necessary to direct XCI fates ., Epigenomic features may refine the XCI boundary and localize key regulatory sequences ., Using available data sets , H3K27me3 profiles in non-transgenic female lines mirror inactive X expression , with depletion clearly characterizing the expressed Kdm5c locus ( Figure S7A ) ., Intriguingly , while the proximal H3K27me3 transition is quite distinct , the distal boundary appears more diffuse ( Figure S7A ) ., Both H3K27me3 patterns occur at domain boundaries throughout the genome 50 and the distal profile may be indicative of an expression transition 9 ., That this moderate H3K27me3 region contains critical regulatory sequences is supported by our current studies , since the shortest transgene breakpoint directly abuts this region ., Nevertheless , the nature of the boundary makes regulatory element localization more difficult ., If boundary repositioning expands the escape domain , it is intriguing that the novel boundary appears demarcated by H3K27me3 even on non-transgenic chromosomes ( Figure S7B ) ., However , further conclusions will require chromatin profiling on transgenic chromosomes ., We next turned to DNaseI hypersensitivity that demarcates many regulatory elements 51 ., At both the endogenous Kdm5c locus and C138 transgene integration site available data only identify hypersensitive sites at gene promoters and CTCF-binding sites ( Figure S7 ) ., Perhaps this strengthens CTCF as a candidate ., A caveat is that such a function may be developmentally regulated and no female lines have been profiled upon the onset of XCI ., Altogether , work here has defined two separable functions at the Kdm5c locus ., We narrowed sequences required for directing escape and for the first time have assigned a function to an XCI boundary in actively delimiting expression domains ., By defining and demarcating regions responsible for each activity , future experiments can be directed to examine specific candidate elements ., The parental ES line SA13 was derived from a ( 129×CAST ) F1 female 19 ., ES cell lines carrying X-linked BAC transgene RP23-391D18 were described previously 19 ., All cells were cultured using established conditions and were maintained in the absence of drug selection 19 ., For post-XCI experiments , cells were differentiated for ten days following LIF removal ., Clonal C138 lines were isolated by first differentiating ES lines for 10 days ., Cells were replated using conditions that further enrich for differentiated cells 19 and after two days were infected with SV40-VA4554 52 ., Cells were passaged as required and after >20 days plated at very low cell density and allowed to clonally expand ., Monoallelic expression of SNPs within Hprt and/or Pctk1 32 confirmed clonality ., Due to the location of the selectable marker within the RP23-391D18 BAC vector 19 , truncated transgenic lines surviving initial drug selection lack genomic sequences at the distal XCI boundary ., Informative SNPs to delimit these transgene breakpoints were identified ( http://cgd . jax . org/cgdsnpdb ) and are listed in Table S1 ., Allelic ratios were evaluated using a quantitative primer extension assay , qSNaPshot 13 , 19 ., Samples were run on an ABI 3130XL sequencer and peak heights measured using GeneMapper 4 . 0 software with SNaPshot default settings ., Allele ratios in transgenic lines were normalized by comparison with the non-transgenic ES line ., Results were further adjusted as allele ratios for a non-transgenic SNP rs29296320 deviated slightly from an expected ratio of 1 . 0 ( ranging from 0 . 84 to 1 . 07 ) , likely reflecting loss of an X in a small proportion of cells ., Precise transgene integration sites were determined by inverse PCR 53 ., For C048 and C138 , genomic DNA was digested with XbaI or PstI respectively ., Purified DNA was self-ligated in dilute conditions and used as template for PCR with BAC-derived primers ., PCR products were cloned and sequenced ., Similar efforts for B176 failed to isolate integration sequences , consistent with a more complex vector rearrangement upon insertion ., To determine if C138 and C048 transgene integrations resulted in large-scale deletions , genomic SNPs distal to the integration site were analyzed by qSNAPshot ( Table S1 for primers and SNPs ) ., To identify the strain origin of the transgenic Xs in C138 and C048 , SNP alleles were assayed from transgenic X specific PCR products that were generated by anchoring one primer to the BAC backbone ., For C048 , the closest informative SNP was >6 kb away and required initial amplification from a self-ligated template , similar to inverse PCR ( Table S1 for SNP and primer information ) ., Strain origin of the transgenic X in additional lines was inferred by determining the frequency that the BAC is on the inactive X since XCI skewing results in inactivation of the CAST X in 25% of cells 19 ., The normal XCI status of transcripts at the transgene integration site was assayed using qSNaPshot to measure allelic expression in the non-randomly X-inactivated mouse fibroblast lines B120 or B119 13 , 14 ., Mid1 was tested previously in a similar manner 19 ., Mid1 has a unique gene organization and XCI pattern; it straddles the pseudoautosomal ( PAR ) boundary in some strains , but is X-specific in others 54 , 55 ( Figure S2A ) ., Mid1 escapes XCI in domestic mouse strains 10 , 54 , but is X inactivated on the CAST X 19 ., Allelic expression was similarly assayed in the C138 clonal lines ., For genes flanking the transgene , inactive X expression was measured relative to the active X allele and normalized to DNA ., Kdm5c , with three expressed loci in C138 , required the expression ratio to be normalized to non-transgenic DNA ( to account for dye incorporation differences ) and additionally to DNA from the clonal line ( to account for loss of an X in a small subset of cells ) ., However , both Kdm5c-tg and the endogenous locus on the active X are derived from domestic strains and are not distinguishable ., Therefore , levels of Kdm5c-tg escape were estimated from the normalized allele ratios as if equivalent to the endogenous CAST inactive X allele ., This estimate appears justified since both inactive X alleles ( CAST and Kdm5c-tg ) are predicted to partially escape at levels similar to those previously reported 18 , 19 , 31 , 32 ., Further , given the measured allele ratios , estimates of lower Kdm5c-tg escape require concomitant reduction in the endogenous CAST allele to levels below that been previously seen ., FISH probes included Xist ( 7 . 2 kb of exon 1 ) 19 , Kdm5c ( 19 kb spanning exons 5–12 19 ) , DXWas70 , an X-specific repeat 56 , and BACs RP23-391D18 ( includes Kdm5c ) , RP23-330G24 ( Kdm5c ) , RP23-67G4 , RP23-459H14 ( Tex11 ) , RP23-263O9 , RP24-255O24 , RP23-295G17 , RP23-459P19 ( Ddx3x ) , and RP23-378I14 ( Mecp2 ) ., Probes were directly labeled with Alexa Fluors 488 , 594 , or 647 by nick translation using either ARES DNA labeling kits ( Invitrogen ) or ChromaTide Alexa Fluor dUTPs ( Invitrogen ) as indicated by the manufacturer ., Slides were prepared and FISH performed for each specific experiment as follows ., For DNA FISH studies , metaphase spreads were prepared and FISH performed as previously described 19 , 57 ., For all other studies , embryoid bodies were plated on slides at day 3 of differentiation and cultured to day 10 ., RNA FISH was performed on non-denatured slides as described 18 , 58 ., For sequential RNA and DNA FISH , slides were initially processed as for RNA FISH ., Subsequently , signals were fixed in 4% paraformaldehyde in PBS prior to denaturing ( 75°C for 5 minutes ) and processing for DNA FISH 19 ., For association studies , cells were fixed in 4% paraformaldehyde before permeablization to preserve nuclear morphology 17 ., Slides were denatured at 85°C for 4′ or 75°C for 7′ , which allowed sufficient retention of Xist RNA to identify the inactive X chromosome ., Slides were imaged on Nikon TE2000-U microscope with Roper Scientific CCD camera and NIS elements software equipped with a 60× objective ., Alternatively , a DeltaVision Elite microscope was used that is equipped with 60× or 100× objective and CoolSnap HQ2 Photometrics camera ., Deltavision images were acquired across 0 . 2 µm Z stacks , deconvolved , and analyzed using softWoRx software version 5 . 5 . 5 ., In all cases , wavelengths were captured separately and merged and pseudocolored in Adobe Photoshop ., Image manipulation was restricted to overall fluorescent level adjustment applied uniformly across the image ., To ensure optimal hybridization , we adopted specific scoring criteria for each experiment ., For all FISH expression studies , we required hybridization patterns for scored cells to at least reflect known endogenous XCI expression ., That is , for a gene that normally escapes XCI ( Kdm5c ) , all cells included had at least one active X and one inactive X signal; for normally X-inactivated genes , only cells with at least one robust active X signal were scored ., | Introduction, Results, Discussion, Methods | In mammalian females , genes on one X are largely silenced by X-chromosome inactivation ( XCI ) , although some “escape” XCI and are expressed from both Xs ., Escapees can closely juxtapose X-inactivated genes and provide a tractable model for assessing boundary function at epigenetically regulated loci ., To delimit sequences at an XCI boundary , we examined female mouse embryonic stem cells carrying X-linked BAC transgenes derived from an endogenous escape locus ., Previously we determined that large BACs carrying escapee Kdm5c and flanking X-inactivated transcripts are properly regulated ., Here we identify two lines with truncated BACs that partially and completely delete the distal Kdm5c XCI boundary ., This boundary is not required for escape , since despite integrating into regions that are normally X inactivated , transgenic Kdm5c escapes XCI , as determined by RNA FISH and by structurally adopting an active conformation that facilitates long-range preferential association with other escapees ., Yet , XCI regulation is disrupted in the transgene fully lacking the distal boundary; integration site genes up to 350 kb downstream of the transgene now inappropriately escape XCI ., Altogether , these results reveal two genetically separable XCI regulatory activities at Kdm5c ., XCI escape is driven by a dominant element ( s ) retained in the shortest transgene that therefore lies within or upstream of the Kdm5c locus ., Additionally , the distal XCI boundary normally plays an essential role in preventing nearby genes from escaping XCI . | Early in mammalian female development , one X chromosome is largely silenced to equalize X-linked gene expression between the sexes ., Nevertheless , some genes “escape” this silencing and therefore are expressed from both X chromosomes ., Understanding how these escape genes are regulated , particularly when they closely juxtapose silenced genes , may give important insight into regulatory transitions throughout the genome ., To evaluate sequences that are essential for appropriate inactive X expression we analyzed large transgenes that integrated on the X chromosome in mouse embryonic stem cells ., Transgenes that include an escape gene , Kdm5c , but lack all or part of the downstream sequences , including the X-inactivation boundary , still escape X inactivation ., Nevertheless , downstream genes at the transgene insertion site are misregulated and now inappropriately escape X inactivation as well ., These data identify two important regulatory activities at this locus ., First , sequences retained within the truncated transgene are sufficient to direct the Kdm5c gene to escape X inactivation ., Further , we have uncovered a function for an X-inactivation boundary in protecting adjacent genes from escape . | null | null |
journal.pgen.1006750 | 2,017 | TCF21 and the environmental sensor aryl-hydrocarbon receptor cooperate to activate a pro-inflammatory gene expression program in coronary artery smooth muscle cells | Genome-wide association studies ( GWAS ) have identified susceptibility loci and candidate genetic variants that predispose to atherosclerotic coronary artery disease ( CAD ) in humans ., 1–4 Despite significant advances made in mapping the genetic contribution to CAD , there has been limited progress toward understanding molecular mechanisms leading to increased atherosclerosis susceptibility that are mediated through gene-environment ( GxE ) interactions . 5, The difficulty in identifying the role of genetic variation in the differential response to environmental exposure stems from inaccurate quantification of the exposure , the inability to isolate the exposures of interest , and the lack of statistical power . 6, GWA studies have identified variation at 6q23 . 2 to be associated with CAD in Caucasian and Han Chinese populations1 , 7 , and work in this lab has identified TCF21 as the causal gene in this locus . 8 , 9, Mechanistic studies employing lineage tracing in murine disease models have found that Tcf21 expression is localized to the medial and adventitial layers of the coronary vessel wall at baseline , and that Tcf21 expressing cells migrate through the lesion and contribute to the fibrous cap as disease progresses . 10, These data , in combination with in vitro studies indicating that TCF21 inhibits differentiation and promotes SMC proliferation , suggest a role for this transcription factor in the phenotypic modulation of medial SMC in the response to vascular injury . 10 , 11, Further , our RNA-seq and ChIP-seq studies have shown that TCF21 binds and regulates a network of genes associated with CAD . 12, We discovered one of the central components of the TCF21 gene network to be the aryl hydrocarbon receptor ( AHR ) , a transcription factor that mediates the response to environmental toxins and xenobiotics , and is known to regulate the inflammatory cellular response ., 13–17 AHR binds to a complex array of nuclear proteins involved in diverse processes related to signaling through hormone receptor and inflammatory pathways , chromatin remodeling , etc . , and activates a number of target genes , including cytochromes P450 ( CYP1A1 and CYP1B1 ) , and AHR repressor ( AHRR ) . 18 , 19, AHR is active primarily in the liver , however it is also strongly expressed in the cardiovascular system , where it has been described to play a role in the cardiovascular development and vascular remodeling ., 20–22 In the context of environmental stimuli , AHR ligands include a wide range of environmental pollutants , including 2 , 3 , 7 , 8-tetrachlorodibenzo-p-dioxin ( dioxin ) , co-planar polychlorinated biphenyls ( PCBs ) , and polycyclic aromatic hydrocarbons ( PAH ) which are major constituents of tobacco smoke ., 15 , 22–24 The correlation of major cardiovascular risk factors with the AHR pathway relates to epidemiological evidence that dioxin exposure is linked to increased cardiovascular mortality . 25, Furthermore , murine model studies have shown that mice carrying an AHR variant with higher ligand affinity developed more severe atherosclerosis compared to wild-type mice22 , and an increase in disease burden when exposed to dioxin . 26, In humans , a common SNP associated with AHR was found to correlate with the CAD phenotype in a Chinese population . 27, In addition , the expression level of AHR in circulating peripheral mononuclear cells was associated with acute coronary syndromes ( ACS ) , suggesting that greater AHR level might be associated with plaque instability and rupture ., Given the role of AHR in mediating inflammation and atherosclerosis , we postulated that TCF21 may alter the risk of atherosclerosis by modulating the AHR pathway ., We set out to characterize the intersection of these pathways at the genomic level , identify the possible mechanisms of interaction , and to determine the role of TCF21-AHR interactions in the context of inflammation in the vessel wall ., Through these studies , we define the molecular mechanisms by which these two transcriptional pathways interact to regulate the risk of atherosclerosis ., We have previously reported the analysis of an in vitro siTCF21 knockdown RNA-seq study in human coronary artery smooth muscle cells ( HCASMC ) and noted differential expression of a number of inflammatory genes and pathways . 10, Interestingly , this module included the gene encoding AHR which directs an inflammatory program as part of its repertoire of response to xenobiotics13 , 14 , 26 , and the xenobiotic pathway was identified as one of those differentially regulated with TCF21 modulation . 10, Also , ChIP-seq studies in HCASMC have shown TCF21 binding in the AHR locus , suggesting that this gene is regulated in part by TCF21 , and raising our interest in possible interactions between these transcriptional networks . 12, While AHR has not been associated with CAD risk using the statistical criteria employed for GWAS efforts , we have identified a variant within the AHR locus ( rs608646 ) that has a nominal association ( p = 0 . 0047 ) in the CARDIOGRAM+C4D GWAS data in the context of a single SNP study ( S1A Fig ) ., This SNP was also noted to regulate expression of AHR as identified with eQTL studies in GTEx tissues high in SMC content ( S1B Fig ) ., In aortic and coronary artery tissues , the AHR locus ( +/- 1Mb ) was generally enriched with AHR-eQTL signals with multiple peaks uniformly scattered throughout the locus ( S2 Fig ) , indicating that AHR gene expression is genetically regulated in the vasculature and emphasizing the importance of AHR in these tissues ., These data thus validate a previous candidate gene study that found association of CAD with AHR in East Asians . 26, To further investigate overlap of these pathways , we sought to expand the repertoire of TCF21 regulated genes by performing a transcriptome analysis with lentivirus mediated TCF21 over-expression in HCASMC ., Analysis of the top 500 differentially regulated genes with goseq28 ( Fig 1A , S1 Table ) identified a significant number of terms related to embryonic development ( lung morphogenesis , lung vasculature development ) , but also numerous terms related to innate immunity and inflammation ( response to bacterial lipopeptide , response to lipoteichoic acid , CCR chemokine receptor binding , chemokine receptor binding , lymphocyte chemotaxis , chronic inflammatory response ) , most of which were upregulated with TCF21 overexpression ., Employing the DAVID algorithm we found that downregulated genes were primarily associated with SMC development and phenotype ( regulation of blood vessel size , contractile fiber/myofibril ) while upregulated genes were primarily associated with cellular proliferation ( mitosis/cell cycle , positive regulation of DNA replication ) ( Fig 1B , S2 Table ) ., In addition , GO terms enrichment and PCA analysis performed using goseq yielded multiple immune system and atherosclerosis- related GO terms , implicating TCF21 in HCASMC to promote immune and pro-atherosclerotic-responses ( S3 Fig ) ., To look for relationships between AHR and TCF21 transcriptional networks , we investigated correlations among genes that reside in the co-expression modules of both TCF21 and AHR , using publicly available microarray data sets ., We created genome-wide gene expression modules using 4164 human microarray data sets , and used the GeneFriends algorithm that reports the top 5% of co-expressed genes as high order associations , as well as second order indirect , associations . 29, In this analysis , TCF21 and AHR shared a number of indirect associations that link the two modules ( Fig 1C ) ., Gene ontology analysis of associations with p<0 . 05 between TCF21 and AHR networks revealed strong enrichment for inflammation , extracellular matrix modification , and developmental terms ( S3 Table ) ., TCF21 and AHR appeared to be highly related when co-expression network was visualized with all other CAD GWAS implicated genes , localizing in a cluster of extracellular matrix gene COL4A1 and growth factor receptor PDGFR , and distinct from a cluster of lipid genes ( LPA , APOA5 , APOB , APOA1 ) ( Fig 1D ) ., Further , we identified the co-expression module for ARNT , the heterodimer partner of AHR , and found that it also contains genes indirectly connected to AHR and TCF21 modules ( S4 Fig ) , suggesting functional connectivity between AHR-ARNT and TCF21 co-expressed genes ( S3 Table ) ., Experiments were first conducted to determine whether TCF21 modulates expression of AHR and ARNT ., RNA-seq and qPCR analysis in HCASMC showed that the AHR and ARNT mRNA levels were down-regulated by TCF21 siRNA knockdown ( AHR 1 . 0±0 . 07 vs . 0 . 58±0 . 02 , p = 0 . 0037; and ARNT 1 . 0±0 . 04 vs . 0 . 63±0 . 04 , p = 0 . 0031 ) ( Fig 2A and 2B ) , and up-regulated by TCF21 overexpression ( S5 Fig ) ., To further characterize the intersection of TCF21 and AHR transcriptional networks , we investigated the mechanism by which TCF21 regulates downstream genes in the AHR pathway , and chose to first study the dioxin effect on the canonical AHR target gene CYP1A1 ., Dioxin induction of CYP1A1 mRNA levels nearly doubled in HCASMC exposed to both dioxin and TCF21 transfection compared to dioxin alone ( 153 . 5±9 . 7 vs . 61 . 8±5 . 9 fold , P = 0 . 001 ) , and the opposite result was seen with TCF21 knock-down in conjunction with dioxin ( 99 . 5±29 . 8 vs . 247 . 9±64 . 8 , P<0 . 05 ) ( Fig 2C and 2D ) ., Manipulation of TCF21 expression alone did not alter CYP1A1 expression , suggesting that it does not directly affect transcription of this canonical AHR downstream gene , but does alter the response to dioxin most likely through regulation of AHR and ARNT expression levels ., To investigate the possible interaction of TCF21 and AHR in the regulation of target inflammatory pathway genes , additional studies were conducted in HCASMC ., We focused on IL1A and MMP1 genes , as previous studies have found these genes to be representative targets of AHR activation through direct or indirect pathways ., 30–32 mRNA levels were measured for IL1A , and MMP1 genes by RT-PCR in HCASMC with TCF21 expression perturbed by knockdown and over-expression ., Knockdown of TCF21 decreased IL1A expression compared to cells treated with scrambled siRNA ( 0 . 91±0 . 04 vs . 0 . 52±0 . 06 , p = 0 . 013 ) ( Fig 2E ) ., Dioxin treatment significantly increased expression of IL1A ( 0 . 91±0 . 04 vs . 1 . 42±0 . 09 , p = 0 . 007 ) , and co-treatment of siTCF21 blocked this effect ( 1 . 42±0 . 09 vs 0 . 65±0 . 04 , p = 0 . 001 ) ., Similar results were obtained for MMP1 , with siTCF21 alone decreasing gene expression ( 1 . 02±0 . 06 vs . 0 . 65±0 . 05 , p = 0 . 009 ) , dioxin increasing expression ( 1 . 02±0 . 06 vs . 1 . 62±0 . 28 , p = 0 . 10 ) , and siTCF21 knocking down the increased expression of MMP1 seen with dioxin ( 1 . 62±0 . 28 vs . 0 . 64±0 . 12 , p = 0 . 034 ) ( Fig 2F ) ., To begin to investigate the mechanism by which TCF21 regulates AHR expression , we correlated whole genome RNA-seq and genotype information developed in 52 HCASMC lines to evaluate expression quantitative trait locus ( eQTL ) effects at the AHR locus . 33, We identified SNP rs10265174 to be one of the top eQTLs for AHR ( p<9e-5 ) ( Fig 3A and 3B ) ., Also , rs10265174 was consistently found to regulate gene expression in multiple GTEx tissues , including coronary artery , aorta and tibial artery ( S4 Table ) ., Furthermore , the SNP was located in an open chromatin region/enhancer marked by ATAC-seq , H3K27ac ChIP-Seq , JUN and JUND ChIP-Seq peaks , and within a TCF21 ChIP-Seq peak ., We found the rs10265174 variant to alter the PMW scores for AP1 and TCF4 transcription factors ( HaploReg ) ( Fig 3C ) ., Given that TCF4 is a known bHLH binding partner for TCF21 , 34 we evaluated whether TCF21 might directly regulate AHR gene expression at this site ., We surveyed ChIP-seq data previously generated for TCF21 in HCASMC12 and identified ChIP-seq peaks representing TCF21 binding sites in both the AHR and ARNT genes ., We confirmed the binding of TCF21 to both genomic regions in HCASMC with ChIP-qPCR for AHR ( 1 . 02±0 . 29 vs . 3 . 58±0 . 75; p = 0 . 033 ) and ARNT ( 1 . 03±0 . 28 vs . 20 . 04±4 . 80; p = 0 . 017 ) loci ( Fig 3D , S6 Fig ) ., Taken together , these data suggest that TCF21 may directly regulate expression of both the AHR and ARNT genes at the transcriptional level ., To further investigate the overlap of TCF21 and AHR transcriptional networks at the genomic level , we determined the genome-wide relationship between binding sites for AHR-ARNT and TCF21 ., We scanned the human genome sequence with the position weight matrix ( PWM ) for AHR-ARNT , and scanned for the PWM for TCF12 as a surrogate for TCF21 ( JASPAR matrices , Ahr::Arnt—MA0006 . 1; Tcf12—MA0521 . 1 ) . 12 , 35 , 36, TCF12 is the primary heterodimerization partner for TCF21 , binds the same primary sequence as TCF21 , and this composite site is indicated here as TCF12/TCF21 . 12 , 36 Predicted binding sites for TCF12/TCF21 and AHR-ARNT identified co-localization within a broad region of 5kb , with 339 high stringency TCF12/TCF21 and AHR-ARNT sites that directly overlap ( P<2 . 2e-16 , Fisher exact test , using combined ENCODE open chromatin regions as background; 218 sites overlapping within the background ) ( Fig 4A and 4B ) and 11769 lower stringency sites overlapping ( P<2 . 2e-16 , Fisher exact test , ENCODE background; 4833 sites overlapping within the background; S5 Table ) ., Next , we tested the positional orientation of TCF12/TCF21 and AHR-ARNT sites near functional elements , such as promoters , using the collection of 100 , 276 human ENSEMBL transcription start sites ( TSS ) , Hum_ENSEMBL69 from Biomart ., We observed that both matrices show double peaks near the oriented ENSEMBL TSS ( Fig 4C ) ., We also noted that these double peaks are in phase with each other , suggesting conservation of spatial orientation between the two sets of predicted binding sites and possible functional interaction of the two proteins that is preserved by evolutionary constraint near the TSS ., In addition , we observe that the distance between phased peaks corresponds to the position of the +1 nucleosome , with an additional peak corresponding to the +2 nucleosome in the TCF PWM profile , suggesting that functional interaction between TCF21 and AHR-ARNT would be localized at the boundaries defined by nucleosome positioning at functional regions ., To test whether TCF21 in vivo binding sites correlate with AHR-ARNT PWM predictions , we generated the precise locations of TCF21 ChIP-seq summit positions in HCASMC using the MACS ChIP-seq tool ( S6 Table ) . 37, We found that the center of these TCF21 ChIP-seq summits co-localized with the predicted AHR-ARNT PWM sites , further suggesting that AHR-ARNT complexes co-localize genome-wide with TCF21 in vivo binding sites ( Fig 4D ) ., In contrast , control PWMs for kidney/liver specific factors HNF1A and HNF1B showed uniform background distribution near TCF21 summits ( Fig 4E and S7 Fig ) ., In addition , AHR-ARNT PWM profiles showed an increase at summits for open chromatin regions in HCASMC , defined with MACS and ATAC-seq HCASMC data sets ( Fig 4F ) ., Control HNF1A and HNF1B matrices showed a decrease in their frequency near ATAC-seq summits ( Fig 4G and S7 Fig ) ., We further assessed the steric relationship of TCF21 and AHR binding by dividing the co-localized TCF and AHR predicted sites into two categories , rotationally phased and un-phased , as rotational phasing has been shown to be crucial for direct protein binding . 38 , 39, We considered PWM sites to be phased if they occurred at distances of n ( 10bp ) , i . e . 10 , 20 , 30 , and 40bp , in which case due to the DNA helical pitch they will be oriented on the same side of the DNA strand and capable of direct protein-protein interaction ., If separated by distances of 5 , 15 , 25 , 35 , 45 bp they would be expected to be oriented on the opposite sides of the DNA molecule due to the pitch of the DNA major groove , rotationally un-phased and less capable of direct protein-protein interaction ., In the case of un-phased sites , indirect interaction through a protein complex might still be possible , e . g . through intermediate protein interactions ., We extracted genes that are proximal to both categories of sites and calculated GO enrichment using GREAT ( Fig 5 , S7 Table ) . 40, The un-phased sites were enriched in cellular differentiation categories such as: negative regulation of cell fate commitment , but importantly a number of terms were related to inflammatory response ( regulation of cytokine production , regulation of interleukin-6 production , regulation of TNF production ) ., GO terms for phased sites were enriched in cellular and developmental terms ( skeletal system morphogenesis , response to organophosphorous , regulation of cell migration , cell-matrix adhesion , and osteoblast development ) In addition , binomial fold changes for un-phased AHR-TCF sites were ~10 times higher than for phased sites , implicating the indirect interaction of AHR-TCF21 factors as predominant in the human genome ., To further evaluate whether TCF21 and AHR-ARNT complex binding co-localizes in the human genome , we compared ChIP-seq data for these three TFs ., We identified TCF21 ChIP-seq peaks in HCASMC that overlapped with AHR and ARNT ChIP-seq sites identified in MCF-7 cells41 , and obtained a statistically significant co-localization of sites using the Fisher’s exact test ., Overlap of TCF21 and AHR peaks produced odds ratios within confidence intervals CI: 4 . 34–5 . 4 ( p = 1 . 88e-121 ) , for ARNT and TCF21 , CI: 4 . 16–5 . 77 ( p = 1 . 7e-56 ) and for AHR , ANRT and TCF21 , CI: 4 . 91–7 . 05 ( p = 1 . 13e-56 ) ( S8 Table ) ., We obtained in total 322 ( 12 . 4% ) genomic locations that were co-occupied by AHR and TCF21 , out of which 119 sites were also occupied by ARNT ., Similarly , in 143 genomic locations ( 10 . 5% ) ARNT co-localized with TCF21 , out of which 119 were occupied with its binding partner AHR ( Fig 6A ) ., Overlap of AHR and ARNT identified 890 sites ( AHR , 34 . 3%; ARNT , 65 . 8% percent of total sites ) , consistent with the fact the two proteins are known binding partners ., Subsequently , we selected genes that are proximal to the overlapping TCF21-AHR , TCF21-ARNT and TCF21-AHR-ARNT ChIP-seq sites , and performed GO enrichment analysis with GREAT ( Fig 6B , S8 Table ) ., TCF21 and AHR-ARNT overlapping sites classified into GO-terms related to chemokine and cytokine signaling ( positive regulation of cytosolic calcium ion concentration , regulation of cytosolic calcium ion concentration ) , apoptosis ( regulation of apoptotic process , regulation of programmed cell death ) , metabolic processes ( cellular hormone metabolic process , isoprenoid metabolic process ) , and cellular signaling ( cellular response to stimulus ) ., Next , we assessed the binding of AHR , ARNT and TCF21 near lead SNPs from the GWAS Catalog ( version 2016-05-08 ) , expanded by addition of CARDIoGRAM+C4D meta-analysis data2 , 42 , using the binomial test for genomic overlap ., We scanned the lead SNPs using windows of +/-2kb , +/-5kb , and +/-10kb near the ChIP-seq binding sites and calculated the significance using the gwasanalytics tool ( Fig 6C–6F , S8 Fig ) ., Using the +/- 2kb window , TCF21 binding shows general enrichment near a wide range of GWAS SNPs for cardio-metabolic phenotypes ( coronary heart disease , blood pressure and type 2 diabetes ) as well as chronic inflammatory diseases ( Crohns disease , multiple sclerosis , and rheumatoid arthritis ) , as well as skeletal phenotypes ( bone mineral density ) and in certain neurological disorders ( bipolar disorder and schizophrenia ) ., ARNT binding was localized near GWAS SNPs for chronic inflammatory diseases ( lupus erythematosus and ulcerative colitis ) and prostate cancer GWAS SNPs ., AHR binding co-localized with CAD variants ( coronary artery disease , coronary artery calcification ) as well as chronic inflammatory GWAS SNPs ( e . g . , Crohn’s disease ) ., After intersection of AHR with ARNT and TCF21 the only remaining categories were coronary artery disease and coronary artery calcification , narrowing the importance of the interaction of AHR/ARNT and TCF21 factors to pathophysiological processes in cardiovascular disease ., Furthermore , we surveyed the overlap of CARDIoGRAM+C4D GWAS SNPs and AHR-ARNT PWM to consider the potential role of AHR in other CAD associated genes ., In total 456 ARNT-AHR sites overlapped with CARDIOGRAM+C4D SNPs ( lead plus LD r2>0 . 8 ) , comprising 0 . 27 permil ( low stringency ) and 0 . 38 permil ( high stringency ) of total ARNT-AHR PWMs ., In comparison , there were only 7 and 5 HNF1A and HNF1B sites , comprising 0 . 12/0 . 10 permil of total HNF1A/B sites ( p<0 . 005 , comparison of AHR-ARNT and HNF using Z-score test for proportions , S9 Fig ) ., Given these data showing that TCF21 and AHR binding sites are co-localized in the genome ( Figs 4–6 ) , and that TCF21 expression levels directly modulate the AHR response to dioxin ( Fig 2 ) , we investigated the functional interaction of these transcription factors at target loci ., First , we surveyed the genomic region of CYP1A1 for TCF21 in vivo binding in HCASMC ., A TCF21 ChIP-seq binding peak was identified and localized to a region of open chromatin , as defined by ATAC-seq data in HCASMC10 , and binding was confirmed with ChIP-qPCR ( IgG 1 . 0±0 . 18 vs . TCF21 4 . 71±0 . 24 , p = 0 . 001 ) ( Fig 7A and 7B ) ., This peak co-localized in the same region of open chromatin with several predicted AHR-ARNT binding sites , thus suggesting coordinated regulation of CYP1A1 expression ., To confirm that the regulation of CYP1A1 mRNA levels by AHR and TCF21 is mediated at the transcriptional level through the observed ChIP identified binding sites , and to look for evidence of cooperativity at this level , we conducted reporter gene transfection studies ., A 50 bp sequence containing alternating binding motifs for TCF21 and AHR-ARNT binding identified in the CYP1A1 gene was cloned into a luciferase expression plasmid with a minimal promoter sequence ., Dual luciferase-renilla assays revealed enhancer activity when exposed to TCF21 overexpression ( 1 . 0±0 . 06 vs . 3 . 15±0 . 02 , p = 0 . 0024 ) , and TCF21 overexpression further increased the luciferase expression induced by dioxin in these cells ( 3 . 62±0 . 30 vs . 6 . 17±1 . 31 fold , p = 0 . 0005 ) ( Fig 7C ) ., The combined effect was additive with no evidence of synergism that would be suggestive of cooperative binding ., Further , when the TCF21 binding motifs were removed from the reporter construct , TCF21 overexpression failed to further increase the expression of luciferase , suggesting that the transcriptional effect of TCF21 is specific for protein-DNA binding ( Fig 7D ) ., These data indicate that the regulatory effect of TCF21 on AHR target genes can be mediated by direct interaction in these target loci , and requires protein-DNA binding ., We followed up our previous studies showing regulation of inflammatory mediators by AHR and TCF21 with studies investigating possible endogenous mediators of AHR activation ., As shown previously , application of dioxin to HCASMC resulted in up-regulation of IL1A , and this effect was reversed when cells were treated with the AHR antagonist alpha-napthoflavone ( α-NF ) ( Fig 8A ) ., In the same experiments , we tested oxidized-LDL ( ox-LDL ) as a potential endogenous activator of AHR in HCASMC . 43 , 44, The treatment with ox-LDL resulted in the activation of genes that was similarly reduced with α-NF co-treatment , suggesting that the SMC response to ox-LDL is at least partly mediated by the AHR pathway ( Fig 8A ) ., We also found activation of a dioxin response element with oxLDL in luciferase assays ( S10 Fig ) ., Given these data suggesting that AHR targets overlap the TCF21 CAD associated transcriptional network genes , we sought to substantiate the relevance of AHR in vascular disease through expression studies in mouse and human vascular tissues8–10 For in vivo gene expression in mice , we performed microarray analysis of carotid arteries subjected to plaque rupture induced by partial ligation in ApoE-/- animals to compare gene expression between ruptured and non-ruptured plaques . 45, We found Ahr expression to be higher in the ruptured plaques compared to non-ruptured plaques ( 8 . 60±0 . 20 vs . 9 . 58±0 . 16 , FDR q = 0 . 054 ) ( Fig 8B ) ., Furthermore , laser capture microdissection ( LCM ) was performed in atherosclerotic lesions in the aortic sinus of ApoE-/- mice exposed to 12 weeks of high fat diet ., We found the expression level of Ahr to be significantly higher in the intimal plaque when compared to the expression in the adventitia , localizing the expression of AHR to the pathologic intimal thickening ( 1 . 0±0 . 2 vs . 12 . 4±1 . 2 , p = 0 . 0008 ) ( Fig 8C ) ., Next , we validated these findings in human arteries ex vivo , using microarray based expression data from normal arteries and atherosclerotic human carotid lesions from the BiKE repository . 46, Expression levels of AHR along with IL1A , and MMP1 were significantly higher in the diseased lesions ( Fig 8D , AHR 8 . 71±0 . 16 in normal vs . 9 . 27±0 . 05 in plaques , p = 0 . 0065; S11 Fig ) ., Furthermore , we analyzed the proteins present in human carotid plaques using liquid chromatography tandem mass-spectrometry ( LC-MS/MS ) ., Proteomic datasets were constructed from highly phenotyped patients with asymptomatic and symptomatic carotid stenoses , 10 subjects each matched for gender , statin usage and age , with plaques selected on CT and histology criteria ., AHR-TCF21 unique interactors from BIOGRID protein-protein interaction database were used to display clustering patterns ( Fig 8E ) ., AHR is located in a cluster of genes that include immune related genes such as IRAK4 , interleukin-1 receptor-associated kinase 4 , transcription factors like SP1 , and cell cycle regulated genes including XPO1 ., In addition , we selected ChIP-seq co-occupied genes for AHR-TCF21 and AHR-ARNT-TCF21 transcription factors and observed clustering of AHR target protein CYP1B1 with extracellular matrix factors FN1 , COL18A1 and with growth factor receptor IGF1R , implicating AHR and its downstream targets in regulation of extracellular matrix component of the diseased human carotid artery plaque ( S12 Fig ) ., We have identified TCF21 as the causal gene at 6q23 . 2 , characterized its mechanism of association , and shown that binding of this transcription factor is enriched in other CAD associated loci . 8 , 12, To investigate how TCF21 interaction with other CAD loci may regulate disease risk , we have begun to study mechanisms of association in these loci ., For initial studies we have chosen the AHR gene , because it encodes a transcription factor , allowing direct study of its downstream signaling pathway , and because of the well-characterized link between this factor and environmental exposures that are relevant for cardiovascular disease ., This work thus addresses two aspects of CAD that have not been directly approachable with human association studies , investigating both gene-by-gene and gene-by-environment contributions to disease genetic risk ., Although variants in the AHR locus ( rs608646 ) have shown only nominal association with CAD risk in GWAS meta-analyses , this may be due to the technical limitation of the GWAS methodology in the AHR locus or inadequate statistical power ., It remains possible if not likely that AHR functions as a hub or master regulator in CAD without harboring regulatory disease variants ., We did identify a variant within the AHR locus ( rs608646 ) that has a moderate association ( p = 0 . 0047 ) in the CARDIOGRAM+C4D GWAS data ( S1A Fig ) , and this SNP was also noted to regulate expression of AHR as identified with eQTL studies in GTEx tissues high in SMC content ( S1B Fig ) ., In aortic and coronary artery tissues , the AHR locus was enriched with AHR-eQTL signals with multiple peaks across the genomic region ( Fig 3B , and S2 Fig ) , indicating that AHR gene expression is genetically regulated in the vasculature and emphasizing the relevance of AHR expression in these tissues ., Further , we also found genome-wide enrichment of the PWM for AHR-ARNT within CARDIOGRAM+C4D GWAS loci ( S9 Fig ) , suggesting that the effect of AHR on CAD may be partly via genetic variation in protein-DNA interaction near genes related to CAD ., These data support the candidate gene study which found an association of CAD with the AHR locus in East Asians . 26, In our studies , we have pursued numerous approaches to investigate links between these two genes and their related transcriptional networks , and to investigate mechanisms by which they may work together to modulate CAD risk ., First , we have shown with targeted studies that TCF21 binds both the AHR and ARNT loci , and increases expression levels of these genes in HCASMC , confirming previously published genomic studies and RNA-seq studies reported here ., Second , these studies provide evidence for overlap of the TCF21 and AHR transcriptional networks ., Both TCF21 and dioxin were shown to increase expression of disease-related factors such as IL1A , MMP1 and interestingly knockdown of TCF21 was able to almost completely abolish the effect of dioxin , suggesting that the inflammatory activation by AHR is dependent on the presence of TCF21 ., AHR is well known to promote inflammation in a number of situations , and to work with NFkB in this regard . 17, Also , we have previously shown that TCF21 can promote expression of a number of inflammatory genes and we show that this pro-inflammatory program represents an intersection of TCF21 with AHR function , identifying a subset of TCF21 target genes that could create a highly inflammatory cellular profile that would be significantly magnified with relevant environmental exposures ., We also found that oxidized LDL activated the AHR pathway in HCASMC , consistent with previous reports in other cell types . 43 , 47, Further analyses investigated additional mechanisms of interaction between these two pathways ., Using PWMs for both TCF21 and AHR , we found highly significant enrichment for co-localization in regions of open chromatin in HCASMC , and characterized similar organization of these binding sites around transcription factor start sites , suggesting functional interaction between TCF21 and AHR and the basal transcriptional apparatus , as proposed previously for other TFs . 48 , 49, The genomic co-localization was further refined by intersecting summit locations from TCF21 ChIP-seq data with AHR-ARNT PWM positions ., Support for these observations reflecting in vivo associations was provided by co-localization of ChIP-seq peaks for TCF21 , AHR , and ARNT ., These data suggest a role for AHR-ARNT in the functional regulation of coronary SMC phenotype ., Co-localization of TF binding often suggests direct functional interaction , and since TCF21 and AHR may regulate transcription in the same direction , an obvious hypothesis is that they bind cooperatively either through direct protein-protein interaction or through joint recruitment of ancillary adaptor proteins . 50, In addition to the genomic co-localization data , the absence of IL1A response to dioxin in TCF21 knockdown , and our studies investigating the phasing of binding site placement also suggests some form of direct or indirect molecular interaction ., The striking difference between functional annotations for the two categories of steric relationship are consistent with different functional interactions between AHR–ARNT and TCF21 in the context of DNA binding ., GO terms for un-phased sites showed much stronger enrichment and significance compared to those of the phased sites , supporting indirect interaction as the likely functional mechanism ., We investigated this possibility using the CYP1A1 gene as model locus where both transcription factors bind ., The reporter gene transfection studies with constructs containing both T | Introduction, Results, Discussion, Materials and methods | Both environmental factors and genetic loci have been associated with coronary artery disease ( CAD ) , however gene-gene and gene-environment interactions that might identify molecular mechanisms of risk are not easily studied by human genetic approaches ., We have previously identified the transcription factor TCF21 as the causal CAD gene at 6q23 . 2 and characterized its downstream transcriptional network that is enriched for CAD GWAS genes ., Here we investigate the hypothesis that TCF21 interacts with a downstream target gene , the aryl hydrocarbon receptor ( AHR ) , a ligand-activated transcription factor that mediates the cellular response to environmental contaminants , including dioxin and polycyclic aromatic hydrocarbons ( e . g . , tobacco smoke ) ., Perturbation of TCF21 expression in human coronary artery smooth muscle cells ( HCASMC ) revealed that TCF21 promotes expression of AHR , its heterodimerization partner ARNT , and cooperates with these factors to upregulate a number of inflammatory downstream disease related genes including IL1A , MMP1 , and CYP1A1 ., TCF21 was shown to bind in AHR , ARNT and downstream target gene loci , and co-localization was noted for AHR-ARNT and TCF21 binding sites genome-wide in regions of HCASMC open chromatin ., These regions of co-localization were found to be enriched for GWAS signals associated with cardio-metabolic as well as chronic inflammatory disease phenotypes ., Finally , we show that similar to TCF21 , AHR gene expression is increased in atherosclerotic lesions in mice in vivo using laser capture microdissection , and AHR protein is localized in human carotid atherosclerotic lesions where it is associated with protein kinases with a critical role in innate immune response ., These data suggest that TCF21 can cooperate with AHR to activate an inflammatory gene expression program that is exacerbated by environmental stimuli , and may contribute to the overall risk for CAD . | Coronary heart disease is the leading cause of death in the world ., Both genes and the environment are important risk factors for the progression of disease , however , how genes may modulate the harmful response to the disease promoting environment is unknown and difficult to study ., Here , we show that a common heritable variation in the gene TCF21 may regulate coronary heart disease risk by regulating the response of downstream gene activation by the disease environment ., We find that a well-known environmental sensor , aryl-hydrocarbon receptor ( AHR ) , is regulated by TCF21 and also interacts with TCF21 , resulting in regulation of pro-inflammatory gene expression in coronary artery smooth muscle cells ., We further show that oxidized LDL , a well-known driver of atherosclerosis in the plaque can activate the AHR pathway ., This work describes a heritable form of gene-environment interaction identified through genome wide association studies in coronary artery disease , and presents an opportunity to define causal gene-gene and gene-environment interactions . | genome-wide association studies, medicine and health sciences, gene regulation, regulatory proteins, dna-binding proteins, dna transcription, coronary heart disease, genome analysis, transcription factors, cardiology, small interfering rnas, proteins, gene expression, genetic loci, biochemistry, rna, nucleic acids, genetics, biology and life sciences, genomics, non-coding rna, vascular medicine, computational biology, human genetics | null |
journal.pntd.0004983 | 2,016 | Comparative Bioinformatics Analysis of Transcription Factor Genes Indicates Conservation of Key Regulatory Domains among Babesia bovis, Babesia microti, and Theileria equi | The presence of AP2 genes in apicomplexans was initially described by Balaji et al . 22 , who first reported the identification of members of the AP2 gene family in the genomes of Plasmodium , Theileria , Cryptosporidium , and Toxoplasma ., Initial genome characterization in the B . bovis T2Bo strain genome resulted in the annotation of 18 genes encoding for AP2 domain-containing proteins 3 ., However , Oberstaller et al . 8 , using a highly sensitive Hidden Marcov Model ( HMM ) , recently identified four additional genes encoding for AP2 proteins , thus extending the number of genes encoding for AP2 domain-containing proteins to a total of 22 ., General features of the 22 B . bovis genes and their predicted proteins are shown in Table, 1 . Because AP2 proteins may have more than a single AP2 domain , the B . bovis AP2 proteins display a total of 26 known AP2 domains ., Similar to what was found in other apicomplexan genomes , the AP2 genes are not organized in clusters but dispersed throughout the four chromosomes of B . bovis ( Fig 1A and 1B and Table 1 ) ., Bioinformatics analysis performed on the predicted amino acid sequences of the B . bovis AP2 proteins shows that some contain other additional known functional domains ( Table 1 , Fig 1B ) , such as the ACDC domain ( AP2 coincident domain present mostly at the C-terminus of the proteins ) , a conserved PBP1domain ( PAB1-binding protein 1 ) , which is also present in proteins interacting with a poly ( A ) -binding protein , and in the Topoisomerase II-associated protein ( PAT1 ) , a protein that facilitates accurate chromosome separation during cell division ( Table 1 ) ., Consistently , and together with the AP2 domain , all these additional domains are known to function in a nuclear environment ., Predicted intracellular localization and routing of B . bovis AP2 proteins into the cell nucleus is consistent with the lack of signal peptides in all the putative B . bovis AP2 proteins as determined by sequence analysis using the SMART programs ( http://smart . embl-heidelberg . de/smart/set_mode . cgi ? NORMAL=1 ) ., In addition , cellular localization predictions using the program Cello v2 . 5 ( http://cello . life . nctu . edu . tw/ ) predicted an intranuclear subcellular localization for all B . bovis AP2 proteins ., The predicted molecular size and isoelectric points of the B . bovis AP2s are also highly diverse , ranging from ~21 to 103 kDa to 5 . 15 to 11 . 21 kDa ( Table 1 ) ., In general , there appears to be an association between isoelectric points ( pI ) and size of the molecules , and , thus , molecules with higher pI are of a relatively smaller size than the ones with a lower pI ( Table 1 ) ., This association is consistent with a previous study by Kiraga et al . 23 , although its biological relevance remains unknown ., While 19 out of the 22 known B . bovis AP2 proteins contain a single AP2 domain , the genes BBOV_II007120 and BBOV_III004740 contain two AP2 domains , and gene BBOV_I004850 has three AP2 domains ( Table 1 , Fig 1B ) ., Similar to AP2 proteins in plants , two of the three domains in the putative protein encoded by BBOV_I004850 are separated by 25 amino acids in the amino terminal part of the molecule , whereas the third domain is distally localized , separated by 160 amino acids from the second domain and 30 amino acids apart from the C-terminal end of the molecule ., The AP2 protein encoded by gene BBOV_II007120 contains the two AP2 domains separated by 21 amino acids , whereas the two AP2 domains of the protein encoded by gene BBOV_III004740 are just 17 amino acids apart ., It is possible that proteins containing multiple AP2 domains are able to bind to distinct DNA regions either separately or simultaneously , thus adding increasing functional versatility for these molecules ., In general , and consistent with what was found for other AP2 proteins , there is low sequence identity or similarity among the AP2 proteins , and , thus , their similarities are just restricted to the conserved 60 amino acid domain 8 , 22 , 24 ., The percent identities found among the full AP2 proteins after their alignment is shown in S1 Table ., The alignment and the identity results were obtained by using Clustal omega ( http://www . ebi . ac . uk/Tools/msa/clustalo/ ) ., The more significantly related AP2 proteins are BBOV_I004850 and BBOV_II005480 , sharing 25 . 59% identity ( S1 Table ) , followed by BBOV_I000100 and BBOV_III003770 , with 23 . 68% identity ( S1 Table ) ., Overall , these data suggest that , with few exceptions , the B . bovis AP2 proteins are not highly related in sequence outside the AP2 domains ., Alignments of the AP2 domain among all B . bovis revealed 100% identity between the AP2 domains in BBOV_III003770 and BBOV_I003560 , suggesting the possibility of shared DNA binding specificities ., Interestingly , the highly related domains BBOV_I004850 . 3 and BBOV_I004850 . 2 ( sharing 51 . 02% identity ) are both localized in the same protein ( gene BBOV_I004850 ) ., Alignments among all B . bovis AP2 domains ( Fig 2 ) show that certain amino acid residues have a high degree of sequence conservation and may be functionally required in the B . bovis AP2 proteins ., For instance , and similar to what was found for other apicomplexan AP2 proteins , all B . bovis AP2 domains contain highly conserved W and F residues ( labeled with asterisks in Fig 2 ) ., It is known that these positional conserved residues are likely to help stabilize hydrophobic interactions between the AP2 domain and its recognized DNA target 22 ., Consistently , and as described in more detail below , these residues are also conserved in the AP2 domains identified in the B . bovis related intra-erythrocytic apicomplexan B . microti and T . equi ( S1 and S2 Figs ) ., In addition , other amino acids are also highly conserved ( Fig 2 ) among the B . bovis AP2 domains ., Just 20 AP2 genes were annotated as containing AP2 domains in the published T . equi genome 6 ., However , using further bioinformatics analysis , we found that genes BEWA_041620 and BEWA_018840 also contain AP2 domains ., Thus , we propose that T . equi contains at least 22 AP2 genes ., The organization and orientation of such genes into the four nuclear T . equi chromosomes are depicted in Fig 3A and S2 Table ., Similar to what was observed for B . bovis , the T . equi AP2 genes are scattered among all four chromosomes ( Fig 3A ) ., As it was found for B . bovis , the AP2 genes of T . equi may also contain 1 , 2 or 3 AP2 domains ., Similar searches performed on the published B . microti genome 5 resulted in the identification of 21 AP2 genes ( S3 Table ) ., All the Ap2 domain-containing genes present in the B . microti genome were previously annotated as such , except gene BBM_III08920 coding for a protein with a single previously unnoticed Ap2 domain , which is reported here for the first time ., Fig 3B describes the organization as well as the orientation of the 21 AP2 genes into the four chromosomes of B . microti ., Similar to what was found for B . bovis , the T . equi and B . microti AP-2 proteins contain other conserved domains , such as the ACDC and the PBP1 domains ( S2 and S3 Tables ) ., Sequence comparisons among all the Ap2 domains identified in the B . bovis , T . equi , and B . microti putative AP2 proteins ( Table 2 ) revealed high levels of identity among some domains ., The identity reaches 100% among domains from proteins BBOV_III008870 ( B . bovis ) , and BEWA_010510 ( T . equi ) , and BBM_III06770 ( B . microti ) ., Interestingly , the proteins encoded by the B . bovis gene BBOV_I004850 and the T . equi BEWA_011980 gene have three highly similar domains ., They share 100% identity for their first domain , which is also highly conserved in the B . microti protein encoded by gene BBM_III05870 . 1 ( 95 . 74% identity ) ., Additional domain similarities are described in Table, 2 . The functions and DNA-binding specificities of the B . bovis , B . microti , and T . equi AP2 domains remain unknown , and they will need to be defined experimentally ., Remarkably , the specificity of binding of some AP2 proteins to certain short DNA target motifs ( usually six to seven base-pairs long ) appears to be quite conserved among distinct Plasmodium species and , furthermore , among other related apicomplexans 7 , 25 ., These findings suggest that Plasmodium binding specificity data together with bioinformatics analysis on the 5′ upstream gene coding regions could guide the design of future experiments aimed at establishing the DNA binding specificities of the AP2 proteins in the three parasites examined in this study ., Recent research focused on the identification of specific AP2 proteins involved and required for regulating the expression of some stage-specific genes in Plasmodium 9 , 10 , 15 , 16 ., The related malaria parasites start differentiating into gametocytes while the parasites are still replicating inside erythrocytes in mammalian hosts ., This crucial step requires a developmental decision , resulting in parasites that continue to replicate asexually or to differentiate into non-dividing male or female gametocytes , a life cycle event that is required to assure generation of genetic diversity and further transmission of the parasite upon mosquito acquisition ., It was recently demonstrated that this developmental transition in P . falciparum parasites is regulated by the activity of the AP2 protein identified as pfAP2-g ( PFL1085w ) ( Fig 4 Panel A ) ., It was thus postulated that pfAP2-G functions as a transcriptional switch , stimulating the commitment to sexual development in this parasite 26 ., Recent studies also supported the role of AP2 factors as candidate regulators driving the commitment to merozoite production in T . annulata 27 ., Using a combination of techniques including transcriptome analysis and phenotypic characterization of AP2 gene knock outs , Yuda et al . 28 identified the AP2-O transcription factor , which is involved in the formation of invasive kinetes in Plasmodium berghei and P . falciparum ( PB000572 . 01 . 0 and PF11_0442 ) ( Fig 4 , Panel B ) ., Orthologues of the AP2-O gene have been also identified in other Plasmodium spp parasites ., In addition , the same study also defined the sequence of the DNA involved in the binding to the AP2-O as the six-base motif TAGCTA ., In a different study , Yuda et al . 29 also identified AP2-Sp ( PB000752 . 01 . 0 ) ( Fig 4 , Panel C ) , a protein that is required for the regulation of the expression of P . berghei sporozoites and also defined the sequence TGCATG as a cis-acting element that is specific for its binding to DNA ., Interestingly , the Ap2 domains involved in the binding of all these functionally defined Plasmodium AP2s are found to be well conserved in B . bovis , B . microti , and T . equi AP2 proteins , as shown in Fig, 4 . Therefore , and based on the sequence similarities of the AP2 domains shown in Fig 4 , we hypothesize that the proteins encoded by genes BBOV_II005480 ( ~72% identity ) , BBM_I03085 ( ~76% identity ) , and BEWA_022490 ( ~77% ) are functionally equivalent to the Plasmodium G ( AP2-G ) protein ( PFL1085w ) ., This is supported by previous findings demonstrating that the divergent T . annulata AP2-G protein containing AP2 motifs that are orthologous with the P . falciparum AP2-G protein are able to bind identical GxGTACxC motifs 27 ., Data in S4 Table illustrates the orthologous relationships of putative AP2-G motifs of Theileria and Babesia parasites ., The recently identified AP2-G T . annulata TA13515 gene 27 encodes for an AP2 motif that is 77 . 36% identical to the motif encoded by the functionally defined AP-G PFL1085w gene ., However , this motif is more related in identity to the putative AP-G proteins in Theileria parva , Theileria orientalis , T . equi , B . bovis , and B . microti addressed in this study ., These findings further support the testing of these AP2 as candidates for modulators in the transition of these parasites into sexual stages ., Consistently , we also hypothesize that the genes identified as BBOV_I004280 ( ~70% identity ) , BBM_II03250 ( ~79% identity ) , and BEWA_041620 ( ~74% identity ) are the functional equivalents of the Plasmodium AP2 proteins PF11_0442 and PB000572 . 01 . 0 , which are both involved in Plasmodium ookinete development ., Similarly , the AP2 proteins encoded by genes BBOV_II001610 ( ~65% identity ) , BBM_II02455 ( ~68% identity ) , and BEWA_008880 ( ~65% identity ) might also be functional equivalents of AP2- Sp PB00752 . 01 . 0 , which is involved in sporozoite development in malaria ( Fig 4 ) ., These domain homology-driven predictions could help in prioritizing and selecting candidates for functional testing of these hypotheses , leading to define B . bovis , B . microti , and T . equi regulation pathways involved in gametocyte , ookinete , and sporozoite development ., It is possible that the proteins containing these highly conserved domains share similar DNA binding specificities among these three parasites , but this will have to be confirmed experimentally ., Full transcriptome analysis in the life cycle of these organisms is not yet available , and it will be needed in order to perceive the possible role of AP2 proteins influencing life cycle transitions in these parasites ., The Myb proteins , which are highly conserved in eukaryotes , belong to the tryptophan cluster family and are also known to regulate gene expression ., Similar to AP2 factors , Myb proteins are involved in differentiation and growth control by binding to DNA in a sequence-specific manner through a DNA-binding domain 10 , 30 ., Importantly , Myb proteins have been confirmed to be essential for parasite growth , cell cycle regulation , and progression in Plasmodium parasites 18 ., Myb families containing eight genes each are present in the B . bovis , T . equi , and B . microti genomes ( Table 3 ) ., Interestingly , a full set of eight Myb genes appears to be well conserved in sequence among the three parasites , and the Myb proteins of these three parasites appear to have similar domain architectures ( S3 Fig ) ., Their phylogenetic relationships are shown in Fig 5 and their orthologous relationships confirmed by using Bidirectional Best Blast hit analysis 31 ., The orthologous Myb proteins BBOV_II001770 , BEWA_009170 , and BBM_I02995 contain an additional DnaJ motif located at their N-terminus region , while the DNA binding domain typical of the Myb proteins is located in their C-terminus ( S3 Fig ) ., In general , Myb genes are unlinked and dispersed among these three parasites’ chromosomes ., However , this is not the case for the T . equi Myb genes BEWA_008190 and BEWA_008180 , which are contiguous in chromosome 3 of T . equi ., Protein sequence comparisons revealed limited sequence identity among the Myb proteins encoded in each of these three parasites ., The possible ortholog relationships among all Myb genes identified in these three parasites are illustrated in the phylogenetic tree shown in Fig, 5 . The highly conserved gene BBOV_IV003030 encodes for a Myb protein that is 60 . 43% identical to the one encoded by gene BEWA_044120 in T . equi , and 50% identical to the protein encoded by gene BBM_III01265 found in in the B . microti genome ., It is thus possible that these three proteins are functional homologues ., In conclusion , these relationships indicate that a core of eight Myb genes is conserved among these three parasites , and perhaps this is also the case in other related apicomplexan parasites as well ., Consistently , searches performed on the genome of T . annulata , T . parva , and T . orientalis revealed full conservation of the set of eight Myb genes in these classical Theileria parasites ( S5 Table ) ., The complement of eight Myb genes from B . bovis , B . microti , and T . equi grouped in the phylogenetic tree together with the three classical Theileria parasites is shown in S4 Fig . It is possible to infer from these data that an ancestor organism existing previous to speciation among Babesia and Theileria also contained an eight Myb gene family ., The high mobility group box proteins ( HMG ) is a group of DNA-binding transcription factors required for the maintenance of structural alterations in DNA during transcription ., The HMG superfamily is divided into three families of proteins according to their functional motifs , known as HMGA , interacting with the AT hook; HMGN , involved with nucleosomes; and HMGB , containing one or several copies of HMG box DNA binding domain 20 ., In contrast to the AP2 and Myb proteins , the HMG proteins have the ability to bind A-T—rich regions of DNA rather than sequence-specific targets , in a process mediated by basic amino-acid residues of the proteins 31 ., There appears to be just one HMG gene in B . bovis ( BBOV_IV001910 ) in chromosome, 4 . This HMG gene has been previously cloned and characterized in yeast and B . bovis 32 , 33 ., The size of the predicted protein , domain and secondary structure predictions , and sequence comparisons indicate that the B . bovis BBOV_IV001910 gene is similar to the Pf HMGB genes 20 and , thus , it can be considered as a member of the HMGB family ., The binding specificity of the P . falciparum HMGB proteins to four-way DNA junctions was also previously established 20 ., In addition , a single HMG gene copy in T . equi BEWA_012790 was found on chromosome, 4 . The B . bovis BBOV_IV001910 and the T . equi BEWA_012790 predicted proteins are 65% identical and contain just 92 amino acids and a single HMG domain , lacking the typical acidic C-terminal tail 20 , 33 ., This putative HMG gene is well conserved among apicomplexans 20 and in other cells but was not annotated as such in the B . microti genome ., However , BLAST analysis of the B . microti genome with the BBOV_IV001910 sequence demonstrated the occurrence of a gene present in an unannotated region of the genome ( http://protists . ensembl . org/Babesia_microti_strain_ri/Tools/Blast ? db=core ) , encoding for a homologous HMG protein ., This novel putative HMG gene is located in the ~2829bp non-coding region between bp 676455 and 676744 of chromosome 1 of B . microti ( Fig 6A upper part ) ., Furthermore , synteny among B . microti , B . bovis , and T . equi in genomic regions encoding this gene was identified ( Fig 6A bottom part ) ., Similar to B . bovis and T . equi , the non-coding region of B . microti , which contains the HMG domain , was found to be followed by gene BBM_I01880 ( Fig 6A bottom part ) encoding for a protein containing an AAA domain cd00009 , an ATP binding motif present in ATPases ( Fig 6A bottom part ) ., Furthermore , we also found conservation and consistent synteny of the HMG gene in other related apicomplexa ( T . annulata , T . parva , P . falciparum , Plasmodium knowlesi , and Plasmodium vivax ) ( S5 ) ., In Fig 6B , the defining amino acids for the HMG domain are shown , as well as a sequence alignment of three putative HMG proteins and the predicted secondary structures of B . microti , B . bovis , and T . equi ., Interestingly , the predicted secondary structures for the in silico translated HMG proteins of B . microti , B . bovis , and T . equi shows three identical alpha-helixes comprising all amino acids involved in the HMG domain ( Fig 6B ) , identical to what was described for their Plasmodium HMGB homologues 20 ., It is likely that this conserved secondary structure is essential for access of the HMG proteins to its DNA binding target and for effecting protein function ., In P . falciparum , the HMG proteins are present in the nucleus and induce DNA bending 20 ., However , the binding targets and exact functions of the Babesia and Theileria HMG proteins remain to be defined ., Considering these observations , together with the facts that gene BBOV_IV001910 is relatively highly expressed in B . bovis erythrocyte stages , as described below and shown in Fig 7C , and that key residues defining the HMG domain are also fully conserved in the B . microti putative protein ( Fig 6A and 6B ) , we propose that the region in chromosome 1 of B . microti represented in Fig 6A represents a novel HMG gene ., If the presence of an HMG gene in B . microti is confirmed experimentally , then annotation in this region of chromosome 1 of B . microti should be revised ., Studies in Plasmodium , T . annulata , and Toxoplasma indicated that most AP2 genes are differentially expressed during the life cycle of the parasites 27 ., B . bovis parasites have a complex life cycle involving at least two distinct hosts , the mammal bovine and arthropod tick hosts ., B . bovis parasites developing in the bovine hosts only invade and reproduce in erythrocytes , and it remains unclear whether they start committing into gametogenesis while residing in the erythrocyte ., However , the life stages of the parasite developing in the definitive tick vector appear to be more diverse and complex , including sexual stages and sexual reproduction , in addition to the development of kinete and sporozoite stages ., Furthermore , because of their trans-ovarian mode of transmission , Babesia parasites are able to survive in additional stages of the tick host ( adult , egg , larva , and nymph , with each of these tick stages occurring in dramatically distinct physical surroundings ) ., We propose that this feature reflects a high degree of plasticity for this parasite , which enables radical adaptive morphological transitions during changing temperatures , surviving the non-adaptive immune system of the tick and other variable environmental factors while replicating in the tick ., Based on the known role of AP2 proteins in related apicomplexans , it is possible that these changes are correlated with unique patterns of expression of AP2 proteins , in order to fulfill their role as stage-specific transcriptional regulators ., Analysis of the currently available transcriptome of B . bovis in the blood stages supports this notion , as , at least , expression of two of the AP2 genes , such as BBOV_II005480 and BBOV_II004230 , are significantly elevated in blood stage parasites of attenuated and virulent B . bovis T2Bo strains , while some of the AP2 genes are silenced ( Fig 7A ) ., Interestingly , and as shown in Fig 4 , sequence comparisons suggest that the AP2 gene BBOV_II005480 , highly transcribed in blood stages of B . bovis , is a possible correlate of the P . falciparum gene AP2-G ( PFL_1085w ) , which was shown to be involved in the transition of P . falciparum blood stage parasites into sexual forms 26 , 34 ., It was recently shown that PFAP2-G functions as a master regulator controlling sexual-stage differentiation decision in Plasmodium parasites 26 ., It is currently unknown whether the B . bovis AP2 gene BBOV_II005480 is also involved in the regulation of the expression of genes involved in sexual stage transitions and whether such stage transition also occurs in blood-stage parasites of B . bovis ., However , the general currently accepted paradigm is that commitment of B . bovis to sexual forms might start with the formation of pre-gametes while the parasites reside in the bovine hosts 35 , 36 , which would be associated with the high level of expression of the AP2 gene BBOV_II005480 gene in the blood stages of the parasite ., It is possible that B . bovis blood-stage parasites need to be primed before developing into sexual stage while still developing into the mammalian host , but this remains unknown ., Alternatively , it is also possible that the expression of the gene BBOV_II005480 in blood stages is required for functions unrelated to sexual stage development ., Other AP2 genes found to be highly expressed in blood stage parasites include BBOV_II004230 , BBOV_III008870 , BBOV_I002320 , and BBOV_III009600 ., Interestingly , levels of transcription for the putative gene AP2-O ( BBov_I004280 ) are negligible in the blood stage , whereas the levels of transcript for the putative AP2-Sp gene , although higher than AP2-O ( BBov_II001610 ) , are also significantly lower than AP2-G ( BBOV_II005480 ) ., It could be predicted that the levels of expression of both genes are elevated in tick stages of B . bovis , as its differentiation to kinete and sporozoite stages occurs in the tick ., Comparative multistage global transcriptome analysis , together with proteomic analysis , remains to be performed in order to fully understand the patterns of expression of the Babesia AP2 genes among its different life stages ., Taken together , these studies should provide a framework for deciphering the gene regulation networks operating during the life cycle of B . bovis and may also contribute to the design of novel methods for the control of this parasite ., Myb transcript analysis performed on two distinct B . bovis strains ( T2bo attenuated and virulent ) shows that seven out of the eight gene members are transcribed at relatively low levels in B . bovis blood stages ( Fig 7B ) , whereas the Myb gene BBOV_IV011350 appears to be expressed at significantly higher levels , and , thus , members of this family are also differentially expressed by the parasite ., In addition , the HMG gene BBOV_IV001910 is also consistently and relatively highly expressed in the two distinct B . bovis strains analyzed ( T2bo attenuated and virulent strains ) ( Fig 7C ) ., The relative high levels of expression of the AP2 , Myb , and HMG genes in B . bovis blood stages can be compared in Fig 8 ., Transcripts of the AP2 gene BBOV_II005480 were detected at levels that are at least an order of magnitude higher than the Myb gene and at twice the levels of the highest expressed HMG gene BBOV_IV001910 ., The functional significance of these observations remains unknown and requires further study ., However , microarray data does not show significant differences in the level of expression of the genes analyzed in this study among the attenuated and virulent strain pairs so far analyzed ., There is no experimental evidence supporting the hypothesis that differential expression of these regulatory genes has any correlation with the virulence phenotype of Babesia strains ., Described here is the structure of the AP2 genes of B . bovis as well as the general organization of this family in the related T . equi and B . microti parasites ., AP2 genes that are differentially expressed during the blood stages were identified and , based on domain sequence similarities , correlated with already functionally characterized Plasmodium AP2 proteins ., A previously unknown gene family with an eight-gene core encoding for proteins , including the DNA binding domain that is characteristic for the transcription factors , known as Myb , was found conserved in B . bovis , B . microti , and T . equi ., Remarkably , a conserved HMG gene was also described in these three parasites for the first time , although expression of the B . microti HMG gene identified in this study remains to be confirmed experimentally ., The Myb and HMG genes of B . bovis might also be differentially expressed in the blood stages of the parasite ., The pattern of expression of AP2 , Myb , and HMG genes in multiple B . bovis , T . equi , and B . microti parasite stages should also be compared in order to start unraveling mechanisms involved in the regulation of gene expression in these parasites ., Overall , the findings described in this study suggest conservation of regulatory genes in the face of large divergence of genome size , content and organization , and host specificities among these three apicomplexan parasites ., Taking advantage of transfection and gene editing techniques , it is now possible to design KO and overexpression studies aimed at defining the resulting phenotype of mutated or genetically altered transfected parasites , leading to a correlation between gene and protein function for the AP2 , HMG , and Myb proteins ., In addition , experiments leading to the identification of the binding specificities for each of the B . bovis , B . microti , and T . equi AP2 proteins , as well as the Myb and HMG transcription factors , should also be performed ., Finally , the ability to genetically manipulate genes encoding for transcription factors should result in a better understanding of the biology of these parasites and to the rational design of attenuated and non-tick transmissible parasite strains that can be used for the development of the next generation of live attenuated vaccines and chemotherapeutics ., Conservation of key gene regulation mechanisms may lead to future development of novel converging control strategies that can be applied to apicomplexan parasites . | Introduction, Concluding Remarks | Apicomplexa tick-borne hemoparasites , including Babesia bovis , Babesia microti , and Theileria equi are responsible for bovine and human babesiosis and equine theileriosis , respectively ., These parasites of vast medical , epidemiological , and economic impact have complex life cycles in their vertebrate and tick hosts ., Large gaps in knowledge concerning the mechanisms used by these parasites for gene regulation remain ., Regulatory genes coding for DNA binding proteins such as members of the Api-AP2 , HMG , and Myb families are known to play crucial roles as transcription factors ., Although the repertoire of Api-AP2 has been defined and a HMG gene was previously identified in the B . bovis genome , these regulatory genes have not been described in detail in B . microti and T . equi ., In this study , comparative bioinformatics was used to:, ( i ) identify and map genes encoding for these transcription factors among three parasites’ genomes;, ( ii ) identify a previously unreported HMG gene in B . microti;, ( iii ) define a repertoire of eight conserved Myb genes; and, ( iv ) identify AP2 correlates among B . bovis and the better-studied Plasmodium parasites ., Searching the available transcriptome of B . bovis defined patterns of transcription of these three gene families in B . bovis erythrocyte stage parasites ., Sequence comparisons show conservation of functional domains and general architecture in the AP2 , Myb , and HMG proteins , which may be significant for the regulation of common critical parasite life cycle transitions in B . bovis , B . microti , and T . equi ., A detailed understanding of the role of gene families encoding DNA binding proteins will provide new tools for unraveling regulatory mechanisms involved in B . bovis , B . microti , and T . equi life cycles and environmental adaptive responses and potentially contributes to the development of novel convergent strategies for improved control of babesiosis and equine piroplasmosis . | The tick-borne apicomplexan parasites Babesia and Theileria are responsible for costly and devastating diseases globally ., Improved control is needed , but the biology of these parasites remains poorly understood ., Significant gaps include better understanding of the mechanisms involved in control of gene expression and the events leading to parasite development among hosts , including the production of sexual stages in their definitive tick vector hosts ., Similar to other better-studied eukaryotic cells , it is likely that regulatory genes coding for DNA binding proteins such as members of the Api-AP2 , HMG , and Myb families play crucial roles as transcription factors in these processes , but these genes remain uncharacterized in these three related parasites ., In this study , we describe the presence and genomic organization of these three types of genes in Babesia bovis , Babesia microti , and Theileria equi , highlighting the importance of the conservation of these genes and their possible contributions to parasite development through their different life stages ., We also describe the occurrence of a previously unreported HMG gene in B . microti , an important emerging human pathogen; define the repertoire of eight conserved Myb genes; and describe the pattern of transcription of the regulatory AP2 , HMG , and Myb genes in B . bovis intra-erythrocytic stages for the first time ., It is expected that these findings will elicit additional research in this field and contribute to the development of converged intervention strategies for the improved control of these devastating and generally under-studied diseases . | sequencing techniques, parasite groups, plasmodium, gene regulation, regulatory proteins, dna-binding proteins, parasitic protozoans, parasitology, developmental biology, apicomplexa, review, protozoans, transcription factors, sequence motif analysis, molecular biology techniques, research and analysis methods, sequence analysis, proteins, gene expression, life cycles, molecular biology, biochemistry, babesia, protein domains, genetics, biology and life sciences, organisms, parasitic life cycles | null |
journal.pcbi.1004819 | 2,016 | Mechanical Stress Induces Remodeling of Vascular Networks in Growing Leaves | Organismal development relies on both the progressive differentiation of cells according to specific spatial patterns and the growth of tissues and organs towards their target shapes ., On the one hand , numerous studies have addressed differentiation mechanisms , leading to a framework where differentiation patterns depend on the establishment of biochemical gradients , see e . g . 1 ., On the other hand , it has been shown that simple growth rules can lead to complex morphologies , such as for tumors 2 or ruffled leaves 3–5 ., However , the coordination between patterning and growth has received much less attention 6–8 ., Are patterns passively stretched by growth like drawings on an inflated rubber balloon , or do patterns remodel during tissue growth ?, This question is central to the present study ., As growth entails dynamic changes in the structural elements that define shape , such as the cytoskeleton or the extra-cellular matrix , it is essential to address the physical properties of these elements and how these properties are controlled at the cellular level 9–17 ., In this framework , cell mechanics would provide a direct link between biochemical activity and growth ., Accordingly , the question above can be reformulated as follows ., Do the patterns of cell differentiation correspond to patterns of changes in mechanical properties ?, If so , do changes in mechanical properties predict the geometry of the patterns when the organ reaches its target shape ?, In addition , what would be the functional role of such changes in the geometry of patterns ?, Here we use a combination of experiments and mechanical modeling of growth to address these questions within the context of leaf vasculature ., The leaves of dicotylodonous flowering plants and their vasculature provide a fitting context for the study of patterns on growing tissues ., Leaves grow manyfold from a sub-millimetric size to several centimeters 18 , 19 ., They are amenable to genetic 20 or physical manipulation; finally , they can be analyzed quantitatively , being almost two dimensional 21 , 22 ., Vasculature in dicotyledons is an elaborate reticulated network with striking geometrical and statistical properties , as revealed by advanced mathematical quantification 23–29 ., Throughout the leaf’s growth , the network multiplies its size by orders of magnitude while maintaining its crucial structural and functional properties 30 , 31: due to their rigidity 32 , veins are the main carriers of mechanical loads in the mature leaf ., On the other hand , veins are responsible for the transport of nutrients and water ., With this respect , the leaf’s ability to withstand damage of one vein is often ensured by redundancy: the network is reticulated ( featuring loops ) , allowing for alternative routes ., Consequently , the venation network , through its topology and geometry , is thought to optimize both its mechanical 33 and transport properties 34 ., Finally , vasculature and leaf development appear to be tightly coupled 35 , 36 ., In many species , the differentiation of ground cells into provascular cells is completed when the leaf is millimetric in size 30 ., This process of differentiation is dependent on a biochemical field: the distribution of the phytohormone auxin ., The canalization model 37 suggests that the salient features of venation networks are due to instabilities of this field—an initially homogeneous concentration field evolves into a hierarchical network of localized concentrated flow of transported auxin , which eventually becomes the vein system ., Canalization has received genetic and molecular support 38–40 , while numerical simulations showed that the model accounts for many features of vasculature 41–43 ., However , additional hypotheses on transport or on auxin production are needed to account for loops 44 , 45 ., An alternative model 46 proposed that the mechanical stress field regulates differentiation into provascular cells , motivated by the resemblance between the vascular network seen in leaves and the network of cracks in drying mud , which is known to be created by instabilities of the stress field ., Numerical simulations of this model 47 , 48 reproduced many features of the network geometry ., However the stress field model of differentiation has not received mechanistic support so far ., Here , we do not investigate the process of differentiation of veins , but rather how the vascular network reaches its final geometry ., Indeed , after vein formation has ceased the leaf may continue to grow in area by an order of magnitude ., Plant growth is driven by the osmotically generated turgor pressure and restrained by cell walls ( the extracellular matrix ) ; therefore , mechanical stress can accumulate: for instance , slits made in stems tend to open , indicating that the epidermis is in tension ., In leaves , since veins are stiffer than their surrounding environment 32 , the vascular network is expected to carry most of the accumulated stress , which might lead to geometrical deformations of the network ., This led to the ‘force model’ describing the final geometry of junctions in vasculature 23: each vein pulls with a force that is proportional to its diameter , and the requirement of local equilibrium at vein junctions leads to a statistical correlation between veins’ diameter and the angles between veins; this correlation was found to hold in the leaves of many cotyledons 23 ., More recently , a cell-based mechanical model was developed to describe the time-evolution of the vascular network 49 ., The tissue was modeled as a network of viscoelastic cell walls , and vein cells were distinguished from ground cells by their higher rigidity ., This yielded realistic venation patterns and reproduced the experimental findings of 23 , in line with the force model ., However , these studies remain correlative and do not prove that mechanical forces shape the vascular network ., Here , we probe the force model by perturbing mechanically a growing leaf and making predictions about the effect of such a perturbation on the vascular network ., We use the texture tensor 50 to quantify this effect; we simulate networks on a tissue that grows anisotropically and predict how leaf vasculature is affected by stretching; we apply external forces to growing leaves after veins have differentiated , and compare observations with predictions ., We consider situations in which a leaf grows anisotropically as the result of the application of external forces ., More generally , we are interested in the evolution of patterns , here vascular networks , on a growing tissue ( Fig 1A ) : how does the pattern change with growth ?, Is it merely stretched passively or does its geometry change in a more complex manner ?, This question is reminiscent of the nature of deformations in elastic solids; in homogeneous solids , elastic deformations are affine , i . e . the local strain is the same as the large-scale strain , whereas in heterogeneous solids , elastic deformations are non-affine , i . e . the local strain differs from large-scale strain 51 ., Among biological materials , non-affinity was observed for collagen fibers 52 ., Our question therefore amounts to whether growth ( an irreversible deformation ) is affine or not ., Fig 1B shows a portion of a leaf that was subject to external mechanical stress during two weeks of growth ., It is clear qualitatively that the network in the region of the leaf that was grown under tension looks stretched while on the other side it is unaffected ., Yet one needs a mathematical method to quantify the strength and orientation of this deformation ., Deformation is a tensor , meaning that at each point of the leaf , deformation can occur in many directions: imagine that we draw small circles on the leaf ., After growth , each circle will become an ellipse ., In order to fully characterize growth , one needs to quantify the orientations and areas of the ellipses , as well as their anisotropies ( i . e . how elongated the ellipse is ) ., Since we are interested in the quantification of the geometrical properties of a network , we use the texture tensor ., The time-derivative of this tensor was proposed as an equivalent of the elastic strain tensor for the quantification of local deformations in materials with a cellular-like structure 50 and has been used to analyse epithelial morphogenesis 53 ., It measures the local geometry of a network , and its time-evolution is a measure of the network’s deformation ., Using the texture tensor enables capturing both the averaged , continuum-like deformation as well as the local , discrete deformation of the network’s elements ., We give here a qualitative description of the tensor’s definition and properties ( see Materials and Methods for details ) ., The texture tensor , which we denote by M , is defined for materials that have a network-like structure , and therefore has a natural application in our case ., A network ( a graph , in mathematical language ) is composed of nodes and links that connect between them ., In this paper we define the graph by using the areoles ( areas surrounded by veins ) as the nodes , and we define two areoles as linked ( neighbors ) if they share a common vein on their boundary ., The local texture tensor is defined from the vectors linking the center of an areole to the centers of its neighboring areoles , as sketched geometrically in Fig 1C ( see Eq 1 in Methods for the exact definition ) ., Thus , the texture tensor contains information not only about the geometry of a single areole , but also about the local topology ., In order to obtain properties averaged at the scale of a few areoles , we also define the averaged texture tensor from a spatial smoothing ( with a constant Gaussian weight ) of the local tensor ., Since the texture tensor is a symmetric 2nd order tensor , it describes an ellipse , which is a measure of the local shape of the network ., The area of the ellipse ( the determinant of texture tensor det ( M ) ) quantifies the size of areoles , while anisotropy corresponds to the ratio of the greater axis to smaller axis of the ellipse ( ratio of eigenvalues of M ) ; note that in this definition anisotropy is always larger than unity ., We now turn to testing the model in experiments ., To do so , we chose to work with leaves in which the vein network has already formed , so as to avoid a direct coupling with differentiation mechanisms ., We sought a species such that, ( i ) a large number of veins would improve the statistics and, ( ii ) veins are apparent on photographs to allow for a non-perturbative time-lapse analysis of the geometry of the network ., It turned out that bay laurel ( Laurus nobilis ) was appropriate as seen in Fig 1A ., Each leaf was loaded by a U-shaped spring , glued to two points on its edge , typically 3mm apart ( Fig 1 ) ., The loaded leaf was allowed to grow for 15 days , during which it multiplied its area by about one order of magnitude ., The vascular system of the entire leaf was repeatedly photographed ., The images were processed and the geometry and topology of the vascular network were extracted ., The unstretched half of the leaf was considered as a control ., The robust qualitative results were observed in a dozen bay leaves ., The detailed mathematical analysis was preformed on three bay leaves ., Qualitatively similar results were obtained with tobacco leaves ( Nicotiana benthamiana , S2 Fig ) ., We sought to reconcile simulations with experimental data ., The broad distribution of non-affinity ( q ) with no stretching ( Fig 6C ) suggests that the venation network is affected by noise ., Indeed , the ‘force model’ was observed to hold only approximately and vein thickness is broadly distributed 23 ., We therefore modified the initial state of the rod network by adding noise in rod thickness; each value of thickness was multiplied by a random number uniformly distributed between 1 − r and 1 + r ., We started the simulations from this state and observed non-affine growth ., The distribution of q is shown in Fig 7A for a noise of r = 40% ( ratio of standard deviation of thickness to its average ) , a value that was chosen to match the observed distribution on the unstretched side of the leaf ( Fig 6C ) ., Consequently , a frozen noise in vein thickness is sufficient to retrieve observations of non-affinity in unstretched leaves ., Finally , non-affinity decreases with vein thickness ( S3 Fig ) meaning that areoles surrounded by thick veins tend to grow less than their neighborhood in the presence of noise ., However , when we added external stress to the simulation , we found again that the distribution of q was broadened ( Fig 7A ) , in contrast with the experimental trend ., Thus , we hypothesized that the growth equation , according to which vein elongation rate is proportional to vein tension , was not sufficient to model the system ., The narrowing of non-affinity distribution in stretching experiments suggests that high tensions have relatively more effects on vein growth ., Accordingly , elongation rate should be a concave function of vein tension ., We then recalled that the commonly accepted plant growth law , the Lockhart equation 10 , 16 , 56 , is nonlinear and concave: elongation occurs only above a threshold stress and is then an affine function of stress ., We incorporated this into our model and added a threshold to the growth equation ( Eq 3 in Methods ) in the form, ν h i l i 0 d l i 0 d t = max 0 , T i - h i η, where ν is the effective viscosity , l i 0 is the rest-length of the i-th rod , Ti is the tension in the i-th rod , and η is the threshold stress for elongation ., We first chose the value of η equal to η0 = 6 that corresponds to the average vein stress at the first step of simulations ., We repeated the stretching simulations with this new growth law and we found that the non-affinity ( q ) distribution narrowed under unidirectional external stress ( Fig 7B ) , as in experiments ., Moreover the distributions of q for σx = 0 and σx = 2Ptur are quite similar to experimental distributions ., In addition , we found that other growth laws ( quadratic , with a maximum , with a saturation ) yield a broadening of the distribution of q when tensile external stress is applied ( S4 Fig ) ., Finally we investigated the robustness of the model by studying the sensitivity of this behavior to the value of the stress threshold ., The control parameter was the normalized stress threshold η⋆ = η/η0 , η⋆ = 1 corresponding to our first successful trial ., We did not consider values of η⋆ < 0 . 1 as the model converges to the initial model with no threshold , as well as η⋆ > 1 . 3 as the tissue stopped growing because the tension in all rods remains below the threshold ., With no external stress , increasing the threshold broadens the non-affinity distribution , as shown in Fig 7C ., With high external stress ( σ = 2Ptur ) , the distribution of q is insensitive to α as the behavior of the system is then dominated by external stress ., Importantly , the distribution of q is broader with no stress , as in experiments , when η⋆ > 0 . 7 ., We therefore conclude that our model reproduces experimental observations when including noise and a Lockhart-like growth law as long as the mean vein tension is not much higher than the threshold tension ., In order to address the coordination between patterns and growth during the course of organismal development , we studied the response of leaf vasculature to external stress ., More specifically , we investigated whether leaf vasculature is merely dilated by growth , like a drawing on a balloon that is inflated , or whether growth is non-affine ., We combined the simulations of a two-dimensional mechanical model of vasculature with the experimental manipulation of leaves in which veins have formed ., The main assumption of the model was that veins are much stiffer than ground tissues ., The application of anisotropic external mechanical stress resulted in elongated areoles in simulations; on average , the long axis of the areole corresponded to the direction of the maximal stress ., To quantify this effect , we used the texture tensor , which is a good measure of the local geometry of the network ., We found that , overall , the anisotropy of the texture tensor increased with the level of stress ., We then used the texture tensor to quantify experiments on leaves ., While measuring the geometry of leaves before stress application , we retrieved known features of leaf geometry and growth ., On the one hand , areoles are bigger and more anisotropic near the base of the leaf , which might be ascribed to an enhanced growth , reflecting the gradient in maturation along the leaf axis that occurs at the later stages of leaf development 22 , 57 ., On the other hand , the anisotropy of areoles follows the left-right symmetry of the leaf and its local geometry; the major axis of areoles parallels secondary veins and the margin , while on average , it is aligned with the leaf axis ., When external mechanical stress is applied to the leaf , areoles become elongated in the direction of the largest stress , as in simulations ., Nevertheless , the elongation of areoles might only be a passive consequence of the largest growth in the direction of external force ., To test this possibility , we used the texture tensor to quantify non-affinity ., We found that , both in simulations and experiments , the local change in texture tensor differed from the average change , demonstrating that growth is heterogeneous and non-affine ., However the simulated distributions differed in behavior from experimental distributions ., Therefore we modified the model by incorporating noise in thickness and a threshold in the growth law ., Both this model and experiments featured heterogeneity in growth , which was reduced upon external stretching ., A threshold in the growth law was introduced by Lockhart 56 to describe experimental data showing that a minimum turgor pressure was needed for growth to occur ., This model is well-supported in situations with growth along one axis , as in single cells or in cylindrical plant organs 10 , 16 ., Our results further support this model in a two-dimensional setting ., While we cannot exclude more complex hypotheses involving biochemical feedbacks , it is more parsimonious to ascribe our observations to the vein mechanics that induce non-affine growth ., It is still left to find out whether they can be explained by a simple viscoelastic behavior of the veins as implemented in the model , or whether they also involve a more sophisticated regulation process ., In the former case , it suffices that veins have a specific ‘mechanical identity’ , being stiffer than ground tissues , as is obvious in mature leaves 32 ., If additional regulation existed , it might be manifested , for example , by softening of cell walls in correlation with stress , or by preferential thickening of veins that carry higher loads ., However none of the growth laws that we tried yields results that agreed with observations , except the one with a threshold ., In this context , one should note that the effect of stress exists only when the leaf is growing: we did not observe any measurable effect when we applied stress to mature leaves that do not grow in area , or to areas that stopped growing within a growing leaf ., Pursuing this direction , we wondered whether non-affinity was also applicable to the earlier stages of leaf development ., We thus examined leaf primordia in Arabidopsis thaliana ., While this species does not fulfill the requirements stated above for an experimental investigation of the effects of external forces , many molecular and genetic resources are available , such as a reporter for early vascular identity ( pVH1::GUS , see Materials and Methods ) ., Using this reporter , we visualized veins in dissected leaf primordia ( S5 Fig ) ; the midvein appears to be smooth and almost straight before tertiary veins have formed , while at later stages it features kinks at the junctions with secondary veins ( S5 Fig ) ., This observation indicates that the shape of the midvein does not change according to a simple dilation of the leaf but rather that growth is inhomogeneous and influenced by the local geometry of vasculature , consistently with our observations on older leaves ., This might seem at odds with the work in 8 , showing that growth fields in early leaves can be accounted for by the affine dilation of an initial polarity pattern , but this work considered younger primordia: tertiary veins appear only at the end of the periods monitored there ., To conclude , we showed that , in leaves in which the vasculature has formed , veins reorient in the direction of applied external forces , and that the geometry of the midvein suggests that this also applies to leaves in which vasculature is differentiating ., It would be interesting to investigate whether this is relevant to vasculature in animals 58 , 59 , to veins in insect wings , or more generally to netted patterns of differentiation in other growing tissues ., Our results further support the force model 23 , 49 , according to which most of the mechanical load is carried by the veins ( or equivalently , that the veins are stiffer ) and that the tension in each vein is proportional to its thickness ., Our results may imply that the network changes so as to become reinforced in the direction of the main stress ., This reinforcement would be reminiscent of Wolff’s law according to which bone remodels so as to resist changes in mechanical stress , or of the reorientation of cortical microtubules in plant cells according to the direction of highest stress 60–62 ., Similarly to these studies , applying external stress helped us identify a response to internal stress , which can result from differential growth ., However , we note that the reduction in growth heterogeneity with higher anisotropy of mechanical stress differs from work in the shoot apex showing that the reorientation of cortical microtubules according to external forces induces growth heterogeneity at the cell scale 63 ., These mechanisms operating at different scales might reflect a form of homeostasis , in which the tissue becomes anisotropically stiffer so as to resist the effect of external forces , and which would also underlie the coordination between patterning and tissue growth ., Deformations and growth are associated with the mathematical concept of a second rank tensorial field ., The texture tensor was proposed in 50 for quantifying local geometry in materials with cellular-like geometry; the time-derivative of the texture tensor allows the quantification of geometry ., In this paper we define a graph by taking the areoles to be the graph’s nodes , and defining two areoles as connected if they share a common vein ., One can also define the dual graph , whose nodes are the vein junctions , linked by veins ., This method gives similar , yet more noisy , results ., For each node i , located at r → i , the local texture tensor is defined as, M i = 1 N ∑ k ( r → k - r → i ) ⊗ ( r → k - r → i ) ( 1 ), where the summation runs over all the neighbors if the site i , N is the number of r → i’s neighbors , and ⊗ denotes the standard 2D tensor product , defined by ( u → ⊗ v → ) α β = u α v β where α , β are Cartesian coordinate indices ., In experiments , the texture tensor is undefined for areoles that are on the boundary of the leaf ., The process is sketched geometrically in Fig 1C ., This gives the local texture tensor , which is defined only on the graph’s nodes r → i ., To get the averaged texture tensor , M ( r → ) , which is a continuous field defined everywhere on the leaf , the local tensor is averaged over the whole leaf with a Gaussian weight centered at r → ., The width of the Gaussian , σ , is chosen so that the area πσ2 is 30 times the mean area of an areole ., This value of 30 was determined to reveal general trends , but the results were insensitive to the width of the Gaussian , in a range around this value ., When the averaged tensor is used for an areole , we take its value M ( r → i ) at the areole center r → i ., At each point the texture tensor field describes an ellipse , which is a measure of the local shape of the network ., The determinant measures the area of the ellipse and the directions of the tensor’s eigenvectors indicate the ellipse’s orientation ., The eigenvalues are the lengths of the ellipse’s axes , and we define the anisotropy of the tensor to be the ratio of the larger to the smaller axis ., Note that by definition the anisotropy is always larger than 1 . At each time step , we compare the local texture tensor Mi ( t ) of each areole i to the averaged texture tensor M ( r → i , t ) , using the ratio of their determinants ., If dilation were locally homogeneous , or equivalently if growth were affine , this ratio would be independent of time , because the geometry of the network would be the same up to a magnification factor ., Therefore we define the non-affinity index of areole i between time t1 and t2 as, q i = det M i ( t 2 ) det M ( r → i , t 2 ) / det M i ( t 1 ) det M ( r → i , t 1 ) ., ( 2 ), If the network was affinely dilated , then q = 1 in all areoles because the ratio of detMi ( t ) to det M ( r → i , t ) would be time-independent ., The deviation of q from unity quantifies the differences between the local and averaged behavior of the areole ., An equivalent index can be defined using areal growth 64 , see S6 Fig , that is related to the coefficient of variability of growth introduced in 63 ., As described earlier , the simulations were built upon the work of F . Corson et al 49 , 54 ., We give here a brief description of the model , and refer the reader to 49 for details ., Corson’s model consists of an array of interconnected viscoelastic rods , modeling the cell walls , in a two-dimensional periodic boundary condition space ., The difference between cell walls of the background tissue and cell walls of the vascular tissue is manifested in their elastic properties—vein cell walls are stiffer when oriented with the direction of the vein ., The simulations were divided to two stages: In the creation stage , a ‘reference’ network was created using Corson’s model , which yields networks statistically similar to real venation networks 49 ., In the reorganization stage , creation of new veins was arrested , and the network was transformed into an ‘effective’ network , where each vein was replaced by a viscoelastic rod , with the same thickness hi and rest length l i 0 , given by those of the vein that it represents ., The background tissue was erased ., The process is shown in S7 Fig . In order to have an ideal initial configuration , we further optimized vein thickness so that the tension carried by each vein is proportional to its thickness ., The linear viscoelastic behavior of the rods is manifested in the change of the rods’ rest length , given by, T i = μ h i l i l i 0 - 1 = ν h i l i 0 d l i 0 d t ( 3 ), where Ti is the tension in the i-th rod , μ is the vein’s Young modulus , ν is its viscosity , and l i , l i 0 are its length and rest-length , correspondingly ., The network was grown in quasi-static conditions , at each time step minimizing the elastic energy of the network , which is given by, E e l = ∑ i ∈ veins 1 2 μ h i l i l i 0 - 1 2 - P t u r S - E a n i , ( 4 ), where is the turgor pressure , and S is the total area of the network ., While Corson’s model was restricted to isotropic stress , we introduced an external stress by an anisotropic term in the energy:, E a n i = W H ϵ i j σ i j = H 0 ( W - W 0 ) σ x x + W 0 ( H - H 0 ) σ y y ( 5 ), where W , H , W0 , H0 are the network’s width and height , reference width and reference hight , respectively ., The definition of the reference width and hight is done by calculating the equilibrium configuration of the network without the term Eq ( 5 ) in the energy equation ., The rod model was implemented in C . The energy is minimized according to the BFGS algorithm using the NLopt library ., The system of ordinary differential equations is solved using the GNU Scientific Library ., All parameters were set to 1 except for μ = 300 ., Thus the typical strain was around 0 . 02 in the initial conditions ., The energy was minimized every Δt = 10−5 ., The experimental set-up consists of attaching a U-shaped steel wire stretcher to a growing leaf , using epoxy glue ., After polymerization , the glue was attached to the leaves’ trichomes ., The leaves showed no pathologic behavior in response to the glue as could be checked in leaves where two glue drops were deposited with no spring ., The applied stress is of same order of magnitude as the turgor pressure ., We present a rough estimation: The order of magnitude of the stress is σ ≈ F/S where F ≈ 1 grams ≈10N and S is the surface of the stretched area , perpendicular to the applied force ., We estimate the affected area to be about 1 cm wide ., The leaf thickness is of the order of 1mm ., Therefore we have, σ = F S ≈ 10 N 10 - 3 m 10 - 2 m = 10 6 P a = 10 atm, which is of the same order of magnitude as the turgor pressure ., In the numerical model , the external stress was in the range 0 < σ/Ptur < 2 . In order to quantify the vascular network , the leaf was photographed using a commercial digital camera ( Nikon CoolPix 8800VR ) , with strong back-light ., The different optical properties of the vascular network allow it to be easily distinguishable from the rest of the leaf ., The vascular network was then extracted from the image either by semi-automated image processing methods ( written in Matlab ) or manually ., During several repetitions we noticed that the effect of external force is much more pronounced when stretching close to the base of the leaf , which might be ascribed to the fact that in later stages of development , growth is concentrated near the base of the leaf 22 , 57 ., We used Arabidopis thaliana Col-0 transgenic plants expressing β-glucuronidase under the control of the promoter of VASCULAR HIGHWAY 1 ( pVH1::GUS ) , an early vascular marker 65 ., Plants were grown in soil in long day ( 16hrs day/8hrs night ) conditions and at 20–22°C and harvested two weeks after sowing ., The plants were stained for GUS activity in 10mM sodium phosphate buffer ( pH 7 ) , 10mM EDTA , 0 . 1% Triton X-100 , 0 . 5g/L X-glucuronic acid , and 10mM ferri- and ferro-cyanide for 24hrs at 37°C and subsequently cleared in 70%-100% ethanol for 2 days ., Leaves were dissected and mounted in 70% glycerol and pictured with a Zeiss Axiophoto microscope and Axiovision software . | Introduction, Results, Discussion, Methods | Differentiation into well-defined patterns and tissue growth are recognized as key processes in organismal development ., However , it is unclear whether patterns are passively , homogeneously dilated by growth or whether they remodel during tissue expansion ., Leaf vascular networks are well-fitted to investigate this issue , since leaves are approximately two-dimensional and grow manyfold in size ., Here we study experimentally and computationally how vein patterns affect growth ., We first model the growing vasculature as a network of viscoelastic rods and consider its response to external mechanical stress ., We use the so-called texture tensor to quantify the local network geometry and reveal that growth is heterogeneous , resembling non-affine deformations in composite materials ., We then apply mechanical forces to growing leaves after veins have differentiated , which respond by anisotropic growth and reorientation of the network in the direction of external stress ., External mechanical stress appears to make growth more homogeneous , in contrast with the model with viscoelastic rods ., However , we reconcile the model with experimental data by incorporating randomness in rod thickness and a threshold in the rod growth law , making the rods viscoelastoplastic ., Altogether , we show that the higher stiffness of veins leads to their reorientation along external forces , along with a reduction in growth heterogeneity ., This process may lead to the reinforcement of leaves against mechanical stress ., More generally , our work contributes to a framework whereby growth and patterns are coordinated through the differences in mechanical properties between cell types . | The development of an organism involves a coordination between the differentiation of cells in well-defined spatial patterns and the growth of tissues towards their target shapes ., While extensive research has addressed each of these key processes , their coordination has received less attention ., In particular , when a pattern has formed and the tissue continues growing , is the pattern passively dilated like a drawing on an inflated balloon , or does the pattern remodel during tissue expansion ?, We address this question in the context of leaf vasculature and examine the role of mechanics in leaf growth ., We model the growing vascular network and identify quantities that compare network growth to background tissue growth ., We apply this quantification to mature leaves that are stretched mechanically; we find that vasculature does not dilate passively and that veins reorient in the direction of external forces ., This is reminiscent of the reinforcement of bones or of the cytoskeleton so as to resist to mechanical stress ., In a developmental context , this might be an essential process to match patterns and growth . | infographics, plant anatomy, ellipses, classical mechanics, condensed matter physics, mechanical stress, anisotropy, geometry, simulation and modeling, plant science, mathematics, materials science, damage mechanics, research and analysis methods, leaf veins, computer and information sciences, deformation, leaves, physics, data visualization, graphs, biology and life sciences, physical sciences, material properties | null |
journal.pcbi.1003830 | 2,014 | Baseline CD4+ T Cell Counts Correlates with HIV-1 Synonymous Rate in HLA-B*5701 Subjects with Different Risk of Disease Progression | The clinical course of HIV-1 infection is characterized by considerable variability in the rate of disease progression among patients with different genetic background 1–3 ., It has been shown that the likelihood of progressing to AIDS for subjects with baseline viral load ( VL ) around or lower than 10 , 000 copies/mL is dependent on baseline CD4+ T cell counts 4 ., Subjects with baseline CD4+ T cell counts <750 cells/mm3 are at significantly higher risk for progression to AIDS ( high-risk progressors , HRPs ) than those with CD4+ T cell counts >750 cells/mm3 ( low-risk progressors , LRPs ) ., There is also evidence that HIV-1 genome controls virulence; however , the mechanisms underlying differential risk of progression to AIDS are not fully understood and likely involve both viral dynamics and host immune system 5 ., CD8+ T cell responses play an important protective role in HIV-1 infection ., HIV-1 replication in vivo is temporally associated with the appearance of CD8+ T lymphocyte responses 6 , and the rate of disease progression is dependent on human leukocyte antigen ( HLA ) class I alleles 7 , 8 ., HLA-B*5701 is the host factor most strongly associated with slow HIV-1 disease progression 1 , 9 and , in subjects with this allele , the CD8+ T cell responses target several epitopes in the gag p24 gene 10–12 ., This often results in the evolution of viral variants that escape CD8+ T cell responses 13 , 14 , although there is evidence that some escape mutations in HLA-B*5701-restricted epitopes in p24 might occur at the expense of viral fitness 15–17 ., HLA-B*5701 subjects with detectable viral load are ideal patients to investigate how the interaction between on-going viral intra-host evolution and immune system relates to risk of disease progression ., It has recently been shown that HLA-B*5701 LRP subjects have a larger fraction of polyfunctional cells – i . e . cells producing two or more immune mediators ( such as gamma interferon , interleukin-2 , macrophage inflammatory protein 1 β , and Perforin ) in response to specific HLA-B*5701-restricted epitopes in p24 – than HRPs 5 ., At the same time , the study found that HIV-1 evolutionary rate is lower in LRPs compared to HRPs 5 ., However , the exact mechanism , evolutionary meaning and clinical implications of these observations are still unclear ., The rate of evolution estimated by molecular clock analysis of longitudinally sampled viral sequences is a compounded parameter , which depends on different factors , such as viral mutation ( error ) rate per generation , generation time ( i . e . the viral replication rate ) , as well as the interplay between neutral genetic drift and positive or purifying selection ., In molecular adaptation studies , investigating the ratio of nonsynonymous and synonymous substitutions ( dN/dS ) has often proved to be useful 18 , although evaluating the absolute nonsynonymous and synonymous substitutions rates separately can sometimes provide greater insights 19–21 ., In HIV-1 intra-host evolution , for example , differences in synonymous substitution rates may reflect differences in mutation rate or generation time ( i . e . viral replication rate ) , while different nonsynonymous rates may be linked to changes in selective pressure and effective population size 21 ., Lemey et al . ( 2007 ) showed that HIV-1 disease progression seems to be predicted by synonymous substitution rates , which are indicative of the underlying viral replication dynamics 20 ., By using a different method , Lee et al . ( 2008 ) also showed that the rate of intra-host HIV-1 evolution was not constant , but rather slowed down at a rate correlated with the rate of CD4+ T cell decline 19 ., However , these studies were performed on patients of unknown HLA type , which makes it difficult to assess the potential impact of the host immune response on viral evolution and disease progression ., The mechanism relating evolutionary rates and disease progression may also involve factors such as replication capacity 16 of the infecting virus or T cell activation 22–25 ., Moreover , virus generation times and the ability of the viral strains to replicate in different environments could be affected by the virus population dynamics in latently infected cells 26 , 27 ., The present work focuses on a cohort of six untreated HIV-1 infected subjects , all carrying the HLA-B*5701 allele , followed longitudinally from early infection up to seven years ., Bayesian molecular clock estimates , based HIV-1 gag p24 sequences , were analyzed in combination with in vitro viral replication capacity and immune activation data ., The integration of experimental data with coalescent-based estimates allowed to develop , for the first time , a possible explanation for the correlation between HIV-1 in vivo replication rate and different risk of disease progression in HLA-B*5701 subjects ., Analyses were performed using longitudinal gag p24 sequence data from six HIV-1 infected subjects ( P1-P6 ) carrying the HLA-B*5701 allele 5 ., The subjects had different risk of progression toward AIDS based on CD4+ T cell count at baseline 10–11 weeks post infection ( wpi ) 4 ., Three of these subjects ( P1-P3 ) were classified as high-risk progressors ( HRPs ) and three ( P4-P6 ) as low-risk progressors ( LRPs ) 5 ., The average baseline viral load ( VL ) was 4 , 250 copies/mL for HRPs and 4 , 229 copies/mL for LRPs , while average baseline CD4+ T cell count was 458 cells/mm3 for HRPs and 1 , 129 cells/mm3 for LRPs ., The presence of molecular clock signal in each data set was first investigated by regression between root-to-tip divergence and sampling date on ML trees , which showed high correlation ( r2>0 . 6 ) for each data set ., HIV-1 evolutionary rate , estimated by molecular clock analysis of longitudinally sampled viral sequences , has been shown to be lower in LRPs than in HRPs 5 ., However , rate estimates can be biased due to potential differences in internal and external branches of the phylogenetic tree ., HIV-1 high mutation rate is expected to lead to a considerable number of deleterious mutations in the viral population , such that the most recent mutations segregating on external branches of HIV-1 phylogenies are likely to be transient 20 , 28 , 29 , 30 ., Deleterious mutations are rapidly purified and their inclusion can bias nucleotide divergence and evolutionary rate estimates , while mutation along internal branches are usually fixed ., Internal and external branches were , thus , defined for 200 trees randomly sampled from the posterior distribution of HIV-1 gag p24 genealogies inferred with a Bayesian framework under a relaxed molecular clock ( Figure 1 ) , and mean evolutionary rates were estimated separately for each branch subset ( Figure 2 ) ., In longitudinally sampled genealogies it is also possible to define the subset of branches connecting the root node with the most recent common ancestor of the sequences sampled at the last time point , which represent the surviving viral population successfully propagating over time through sequential bottlenecks driven by either positive selection or neutral genetic drift 28 ., However , in HIV-1 intra-host genealogies , the last sampled sequences may not be monophyletic and different sets of backbone branches can be defined by a simple rotation around an internal branch ., Therefore , a weighted average of the evolutionary rate was also calculated for the rates estimated along all the possible backbone paths of a genealogy ( one example of such paths is shown by the branches highlighted in orange in Figure 1 ) ., For most patients , evolutionary rates in internal branches and backbone paths of the viral genealogies were very similar , while rates for external branches were higher ( for all patients except P6 ) due , as expected , to an increased amount of deleterious mutations ., However , the difference in mean substitution rates between HRPs and LRPs was still significant ( p<0 . 05 ) in each analysis ., Gag p24 evolutionary rate differences between HRPs and LRPs were investigated in more detail by disentangling nonsynonymous ( dN ) and synonymous ( dS ) rates ., Absolute dN and dS rates for all , internal , and external branches , as well as average rates for the backbone paths of the viral genealogies were estimated for each patient ., The virus infecting HRPs displayed significantly higher dN rates along internal branches ( Mann-Whitney U-test , p\u200a=\u200a0 . 024 ) compared to the LRPs ( Figure 3 ) ., There was also a trend toward higher dN rates in HRPs compared to LRPs when backbone paths ( Figure 3 ) , external or all branches ( Figure S1 ) of the viral genealogies were analyzed ., HIV-1 dS rates were significantly higher in HRPs than LRPs ( Figure 3 ) for both internal branches ( p\u200a=\u200a0 . 024 ) and backbone paths ( p\u200a=\u200a0 . 024 ) ., A significant difference ( p\u200a=\u200a0 . 024 ) between the two groups of patients was also observed when external or all branches were analyzed ( Figure S1 ) ., Similarly , plots of HIV-1 dN and dS divergence over time within each patient were estimated for all and internal branches and along backbone paths ., For internal branches , as well as along backbone paths , the virus infecting HRPs displayed a faster accumulation of dN substitutions over time compared to the LRPs for all patients except P5 ( Figure 4 ) ., Analogous results were observed for all branches ( Figure S2 ) ., There was a clear separation in dS divergence over time between the two groups of patients ., The viruses infecting HRPs showed , overall , a higher number of dS substitutions over time for both internal branches and backbone paths compared to LRPs ( Figure 4 ) ., Interestingly , during the first year ( up to 400 days ) of the infection , the accumulation of dS substitutions seemed to happen at the same rate between the two groups of patients ., After the first year , the two groups began to diverge , and the virus populations in LRPs appeared to accumulate dS substitutions more slowly than those in HRPs ., The observed difference in dN and dS substitution patterns between the two groups of patients could be due to strong site-to-site rate variation , which has the potential to bias the estimates 31 ., To investigate this possibility within the p24 gene we estimated the coefficient of variation ( CoV ) of substitution rates across dS and dN sites ., As expected , the analysis revealed significant across-site variation in viral dS for all data sets , although the values were lower compared to the ones estimated for the HIV-1 env data set analyzed in Lemey et al . 20 ., For both dN and dS , CoVs were similar among all six patients and there was no significant difference between the HRPs and LRPs ( Table 1 ) ., Therefore , it is likely that the presence of significant rate heterogeneity across dS and dN sites equally affected HIV-1 evolutionary rate estimates in both groups of patients and does not account for the observed differences ., Differences in mean dS between HRPs and LRPs could also be the result of different levels of purifying selection ., For each branch set ( all , internal , external and backbone paths ) of the viral genealogies , dN/dS ratios were calculated and compared ., In general , correlation of dN versus dS rates was weak ( 0 . 17–0 . 39 ) , and slopes were <1 , indicating signal for purifying selection rather than neutral genetic drift or positive selection ( Table S1 ) ., No significant difference was observed between LRPs and HRPs ( Table S2 ) indicating that differential purifying selection was also an unlikely explanation for the observed substitution patterns ., Since dS substitutions are neutral or nearly neutral 32 , HIV-1 dS is expected to be proportional to the virus replication rate 20 ., The higher dS in HRPs may be the consequence of an infection with fitter viral variants characterized by faster replication ., We examined viral replication capacity ( RC ) for all six HLA-B*5701 subjects ( 1–4 time points ) by using a Phenosense Gag-Pro assay ( see Methods ) ., As expected , a trend toward increased RC over time was observed in all patients likely linked to the progressive fixation of fitter variants driven by ongoing selection 33–35 ., However , no differences in RC measurements ( 10–332 wpi ) were apparent between LRPs and HRPs ( Table 2 ) , suggesting that the observed difference in dS may not be related to fitter ( high replicative ) variants infecting the HRPs ., Finally , CD38 expression on CD4+ and CD8+ T cells was measured at baseline ( 13–17 wpi ) ., Two HRPs ( P1 and P2 ) displayed the highest values of CD38 expression on CD4+ T cells ., Differences in CD38 expression on CD4+ or CD8+ T cells between the two groups of patients were not statistically significant ( Table S3 ) ., Therefore , T cell activation during early infection is also an unlikely explanation for the observed difference in dS between HRPs and LRPs ., In order to identify other potential mechanisms behind the observed differences in viral evolutionary rate , correlation between mean dN or dS for different branch sets ( all , internal , external and backbone paths ) and clinical parameters for each patient ( baseline CD4 count , baseline VL , CD4 slope , VL slope and baseline T cell activation ) were investigated ( Table S4 ) ., The only strong correlation found ( r2\u200a=\u200a0 . 9 ) was between weighted average of dS estimated along possible backbone paths and baseline CD4+ T cell counts ( Figure 5 ) ., In particular , higher baseline CD4+ T cell counts ( 10–11 wpi ) were correlated with lower dS , indicative of lower replication rates and longer viral generation times in the LRPs compared to HRPs ., The inverse correlation was highly significant ( p\u200a=\u200a0 . 002 ) , even after Bonferroni correction ( p\u200a=\u200a0 . 09 ) ., The present work investigated in depth the relationship between dS and dN viral evolutionary rate and risk of disease progression in HIV-1-infected subjects carrying the HLA-B*5701 allele ., A recent study carried out on the same cohort has shown that HRPs have significantly lower polyfunctional CD8+ T cell responses , as well as higher viral evolutionary rate than LRPs 5 ., The study also noticed that dS and dN changes , calculated by pairwise comparisons , were higher in HRPs than LRPs ., However , the exact mechanism driving HIV-1 faster evolutionary rate in HRPs and its clinical implications remained obscure ., Herein , absolute rates of dS and dN substitutions were estimated by Bayesian molecular clock analysis from longitudinally sampled HIV-1 gag p24 sequences along different branches of the viral genealogies ., The evolutionary analyses and the comparison of dN and dS with various clinical , immunological and virological parameters resulted in three major and novel findings , which provide a potential mechanism for the initial observations reported in Norstrom et al . ( 2012 ) ., First , it was shown that the virus infecting LRPs exhibited significantly lower dN and dS divergence over time compared to HRPs ., Second , no significant difference in site-to-site variation of dS or in dN/dS ratios along different branches of the HIV-1 genealogies was observed between the two groups of patients ., This indicates that differences in rate heterogeneity across synonymous sites or purifying selection were unlikely to be the cause of lower viral dS in LRPs ., Third , the analysis detected a strong inverse correlation between HIV-1 dS , which is directly proportional to the virus replication rate 33 , 36 , and baseline ( 10–11 wpi ) CD4+ T cell count ., Changes in absolute HIV-1 dN and dS rates have been investigated previously 34 , by analyzing env sequences from the Shankarappa et al . ( 1999 ) data set – nine HIV-1 infected patients followed longitudinally from the time of seroconversion 34 ., However , seven out of nine patients in that data set received antiretroviral treatment during the follow-up time , their HLA-type was unknown and no data on in vitro viral RC or immune activation were available , making it difficult to disentangle the different factors that may have contributed to the interplay between viral evolution and disease progression ., On the other hand , the present study examined untreated HLA-B*5701 subjects with different risk of disease progression , where HIV-1 evolutionary patterns could be compared to in vitro viral RC data , using a novel Gag-pro phenotypic assay , as well as immune activation data and a number of clinically relevant parameters ., Using the Shankarappa data set 34 , Lemey et al . ( 2007 ) provided some evidence that slow HIV-1 disease progression can be predicted by lower dS rates , which are indicative of the underlying viral replication dynamics ., In agreement with our finding , they did not detect significant differences in rate heterogeneity across synonymous sites between patients with different rates of disease progression ., Rate heterogeneity , however , was generally higher than the one estimated for the viruses infecting the subjects enrolled in the present study , which likely reflects the higher diversity in env gp120 compared to gag p24 region ., Lemey et al . ( 2007 ) also suggested that the slower replication dynamics of HIV-1 in patients with slow disease progression could depend on the state of immune activation of the host ., Indeed , T cell activation ( defined as the expression of CD38 on the T cells ) is one of the strong predictors of progression to AIDS 22 , 23 , 25 , 37 , 38 ., Nevertheless , within the HLA-B*5701 subjects studied herein no significant differences in T cell activation was observed ., In addition , our analysis showed no differences in viral RC between the HRPs and LRPs ., A trend toward increasing RC over time was observed in viruses sampled from patients with RC longitudinal data available , which is expected as a result of the continuous emergence and fixation of fitter viral variants over time 33 ., HRPs displayed significantly higher dN rate along internal branches of the viral genealogies compared to the LRPs , which may be indicative of a higher rate of adaptation in the HRPs ., It is important to notice , however , that the small sample size of our cohort requires a certain caution before drawing firm conclusions and no samples from earlier than 11 wpi were available to compare whether RC differed between HRPs and LRPs during primary infection ., Moreover , the RC assay tested only one part of the viral genome that may not fully capture viral replication capacity ., Yet , the data suggest that neither T cell activation nor an initial infection with fitter viral variants would explain the difference in dS substitution patterns between HRPs or LRPs carrying the HLA-B*5701 allele ., An intriguing alternative can be hypothesized by considering the highly significant inverse correlation between baseline CD4+ T cell count and average dS rates along branches representing lineages effectively propagating through time ., The finding suggests a mechanistic link between CD4+ T cell count and the virus replication rate 33 , 36 , by indicating that HLA-B*5701 subjects with CD4+ T cell counts >750 cells/mm3 within the first 10–11 weeks of the infection will keep HIV-1 replication under better control during the subsequent years ., This observation is in agreement with earlier results showing that subjects with a stronger immune system during early infection exhibit more constrained viral evolution , probably linked to a more robust HLA-B*5701-specific CD8+ T cell response 5 ., In other words , the higher polyfunctional responses observed in these subjects 5 coupled with a larger number of CD4+ T cells during early infection may ultimately result in an overall slower in vivo replication rate of the virus ., There is evidence that emergence of escape mutations in p24 , as a consequence of CD8+ T cell responses , can negatively affect viral fitness 16 , and thereby be indirectly responsible for control of viral replication , longer generation times , and lower risk to progress to AIDS ., Replication rates can also depend on the ability of the viral strains to replicate in different environments 16 ., Differences in the contribution of latent HIV-1 reservoirs , such as resting memory CD4+ T cells , to the circulating virus population can impact mean generation times and replication rates even though the may produce only a fraction of circulating viruses 26 , 27 ., Further work will be necessary to clarify the relationship between HIV-1 generation times and replication dynamics in different viral reservoirs ., Even though we included more HLA-B*5701 patients than in previous studies , our sample size remains small and conclusions need to be taken with caution ., Regardless , our findings provide , for the first time , a possible evolutionary mechanism for different risk of disease progression in HLA-B*5701 subjects ., They indicate that subjects who maintain high CD4+ T cell counts in early infection are more likely to control HIV-1 replication for an extended time and that synonymous substitution rates , which are proportional to in vivo replication rates , could be used as a novel evolutionary marker of disease progression ., The University of California , San Francisco ( UCSF ) Committee on Human Research , the Regional Ethical Council in Stockholm , Sweden ( 2008/1099-31 ) , and the University of Florida review board approved this study ., All patients provided written informed consent and all clinical investigations were conducted according to the principles expressed in the Declaration of Helsinki ., The study included six untreated HIV-1 subtype B infected men ( P1-P6 ) , all carrying the HLA allele B*5701 , from the OPTIONS cohort 39 ., Patients were enrolled within six months of HIV seroconversion and followed longitudinally ., Patients details have been described in a previous study 5 ., Briefly , five of them were men who have sex with men ( MSM ) and one ( P5 ) was an injecting drug user ( IDU ) ., Each patient-specific data set included HIV-1 gag p24 sequences obtained by single genome sequencing of longitudinal plasma samples as previously described 5 , 40 ., GenBank accession numbers for the sequences analyzed in this study are: JX234575-JX235332 ., All sequences included in the present study were non-recombinant , as previously described 5 ., The presence of molecular clock signal in each patient data set was investigated by regression between root-to-tip divergence and sampling date using ML likelihood trees inferred with the best fitting nucleotide substitution model , chosen by a hierarchical likelihood ratio test , as previously described 5 ., For each data set , the Markov chain Monte Carlo ( MCMC ) sampler implemented in BEAST 1 . 7 41 , was used to obtain a posterior distribution of trees under a relaxed molecular clock model with the best fitting population prior ( according to estimated Bayes Factors ) 5 , 42 ., The selected population prior for each data set was the Bayesian skyline plot ., The approach to infer synonymous and nonsynonymous substitution rates , and to explore how these rates change through time , is an empirical extension of the coalescent-based Bayesian relaxed clock models 20 , 42 ., Briefly , a subsample of 200 trees was randomly selected from posterior distribution and used to re-estimate branch lengths proportional to either nonsynonymous or synonymous substitutions according to the method described in Lemey et al 20 ., For each clock-like genealogy , the rate of absolute nonsynonymous and synonymous substitutions was estimated including all branches in the genealogy , as well as internal and external branches only ., The weighted average of the evolutionary rate was also calculated for the rates estimated along all the possible backbone paths of a genealogy ( weighted by the number of branches along each path ) ., Backbone paths represent lineages propagating ( i . e . effectively surviving ) from root node to sequences sampled at the last time point through sequential population bottlenecks ., For each data set , HIV-1 among-site nonsynonymous and synonymous rate variation was analyzed by comparing two nested models with the likelihood ratio test: the constant rate variation model , which assumes that neither nonsynonymous nor synonymous rates vary across sites , and the dual random effects likelihood ( REL ) model , where site-specific nonsynonymous or synonymous rates are drawn from independent general discrete distributions with three rate categories 31 , 43 ., The dual REL model estimates the coefficient of variation ( CoV ) , defined as standard deviation/mean for nonsynonymous or synonymous rates across sites ., A large CoV and a low p-value for the test comparing the dual model with the null hypothesis ( constant model ) that CoV =\u200a0 indicate significant rate variation from codon to codon in the alignment ., Viral replication capacity ( RC ) was measured in vitro using the PhenoSense Gag-Pro assay 44 ., Sequences of gag and protease genes were amplified from patients plasma by RT-PCR and transferred into a resistance test vector ( RTV ) containing a luciferase reporter gene ., Transfections of HEK293 cells with patient-derived gag-pro RTVs and an amphotropic murine leukemia virus envelope expression vector were performed to generate pseudovirus stocks for infection of HEK293 cells ., Gag-pro mediated RC was determined by measuring the viral infectivity ( luciferse activity ) of patient-derived pseudoviruses relative to NL3-4 , the reference control , and expressed as a percentage ., Immune activation data were also available for all subjects ., The proportion of CD4+ and CD8+ T cells expressing CD38 was measured at 13–17 weeks post infection ( wpi ) , as previously described 23 ., A one-tail Mann-Whitney U-test was carried out with an online calculator using the normal approximation ( http://elegans . som . vcu . edu/leon/stats/utest . html ) to assess whether substitution rates in HRPs were significantly higher than in LRPs ., Slopes of CD4+ T cell counts and viral load ( VL ) were obtained from least squares regression of log-transformed CD4 counts and VL over time ( years ) ., Model coefficients were back transformed and converted from proportions to percentage effect by subtracting one and multiplying by 100 to obtain individual estimates of percent change over time ., Mean nonsynonymous and synonymous rates for different set of branches ( all , internal , external and backbone paths ) were compared to the corresponding clinical parameters of each patient ( baseline CD4 count , baseline VL , CD4 slope , VL slope and baseline T cell activation ) using Pearsons linear correlation to calculate the associated t-values and assess significance ., All p-values obtained from applying any test statistic multiple times were adjusted with the Bonferroni correction . | Introduction, Results, Discussion, Materials and Methods | HLA-B*5701 is the host factor most strongly associated with slow HIV-1 disease progression , although risk of progression may vary among patients carrying this allele ., The interplay between HIV-1 evolutionary rate variation and risk of progression to AIDS in HLA-B*5701 subjects was studied using longitudinal viral sequences from high-risk progressors ( HRPs ) and low-risk progressors ( LRPs ) ., Posterior distributions of HIV-1 genealogies assuming a Bayesian relaxed molecular clock were used to estimate the absolute rates of nonsynonymous and synonymous substitutions for different set of branches ., Rates of viral evolution , as well as in vitro viral replication capacity assessed using a novel phenotypic assay , were correlated with various clinical parameters ., HIV-1 synonymous substitution rates were significantly lower in LRPs than HRPs , especially for sets of internal branches ., The viral population infecting LRPs was also characterized by a slower increase in synonymous divergence over time ., This pattern did not correlate to differences in viral fitness , as measured by in vitro replication capacity , nor could be explained by differences among subjects in T cell activation or selection pressure ., Interestingly , a significant inverse correlation was found between baseline CD4+ T cell counts and mean HIV-1 synonymous rate ( which is proportional to the viral replication rate ) along branches representing viral lineages successfully propagating through time up to the last sampled time point ., The observed lower replication rate in HLA-B*5701 subjects with higher baseline CD4+ T cell counts provides a potential model to explain differences in risk of disease progression among individuals carrying this allele . | The clinical course of HIV-1 infection is characterized by considerable variability in the rate of progression to acquired immunodeficiency syndrome ( AIDS ) among patients with different genetic background ., The human leukocyte antigen ( HLA ) B*5701 is the host factor most strongly associated with slow HIV-1 disease progression ., However , the risk of progression to AIDS also varies among patients carrying this specific allele ., To gain a better understanding of the interplay between HIV-1 evolutionary rate variation and risk of disease progression , we followed untreated HLA-B*5701 subjects from early infection up to the onset of AIDS ., The analysis of longitudinal viral sequences with advanced computational biology techniques based on coalescent Bayesian methods showed a highly significant association between lower synonymous substitution rates and higher baseline CD4+ T cell counts in HLA-B*5701 subjects ., The finding provides a potential model to explain differences in risk of disease progression among individuals carrying this allele and might have translational impact on clinical practice , since synonymous rates , which are proportional to in vivo viral replication rates , could be used as a novel evolutionary marker of disease progression . | infectious diseases, phylogenetics, medicine and health sciences, aids, molecular evolution, biology and life sciences, viral diseases, evolutionary biology, evolutionary systematics | null |
journal.pgen.1005584 | 2,015 | IBR5 Modulates Temperature-Dependent, R Protein CHS3-Mediated Defense Responses in Arabidopsis | Plant growth and development are continuously affected by various environmental stresses , including low temperature and pathogen infection ., Emerging evidence has shown that the defense responses of plants are regulated by temperatures 1 ., Temperature modulates the defense responses induced by certain types of resistance ( R ) /R-like proteins , including Toll/Interleukin–1 receptor ( TIR ) - nucleotide-binding ( NB ) - leucine-rich repeat ( LRR ) proteins N ( Resistance to tobacco mosaic virus ) in tobacco; RPP1 ( Recognition of Peronospora parasitica 1 ) -like , RPP4 , RPS4 ( Resistance to P . syringae 4 ) and SNC1 ( Suppressor of npr1-1 , Constitutive 1 ) in Arabidopsis , CC-NB-LRR proteins Rx in tomato; RPM1 ( Resistance to Pseudomonas syringae pv . maculicola 1 ) and RPS2 in Arabidopsis , LRR-TM ( transmembrane-domain ) proteins ( Cf–4 and Cf–9 in tomato ) , TM-CC proteins RPW8 ( Resistance to Powdery Mildew 8 ) in Arabidopsis 2–7 , and the TIR-NB protein CHS1 ( chilling sensitive 1 ) in Arabidopsis 8 ., A recent study showed that low temperatures ( 10°C to 23°C ) elevate R protein-mediated effector-triggered immunity ( ETI ) , and higher temperatures ( 23°C to 32°C ) lead to a shift in pattern-triggered immunity ( PTI ) signaling in plants 9 ., These studies suggest that temperature largely affects the function of R proteins ., Recent studies have revealed that a number of components regulate the activities of R proteins , which in turn finely tune defense signaling ., Chaperone and co-chaperone proteins , such as the HSP90-SGT1b ( Suppressor of the G2 allele of skp1 ) -RAR1 ( Required for MLA12 resistance 1 ) complex , involve in multiple R protein-mediated defense pathways 10 ., Earlier reports have indicated that these complexes are required for the correct folding and/or stability of R proteins 11–13 ., However , several recent studies have suggested that both SGT1b and HSP90 play positive roles in the degradation of R proteins , including RPM1 , RPS2 and SNC1 , by the SCF complex 14–16 ., The dual functions of R protein regulators ensure that the plant immunity system rapidly and properly responds to pathogen invasion ., In addition to these chaperones , many other regulators are also involved in R protein regulation ., A series of regulators of SNC1 were identified by screening suppressors and enhancers of snc1-1 , have been shown to regulate the chromatin , transcription and protein levels of SNC1 ., These proteins include E1 and E4 ligases , U-box proteins , acetyltransferases , RNA binding proteins , nuclear pore complex components 13 , 17–22 ., These results suggest that SNC1 and/or other R proteins are regulated by multiple biological processes including nucleo-cytoplasmic trafficking , transcriptional reprogramming , RNA processing and protein modification ., Previous studies have shown that low temperature activates defense responses in plants harboring mutations in R/R-like genes , including CHS1 , CHS2/RPP4 and CHS3 7 , 8 , 23 ., Arabidopsis CHS3 encodes a TIR-NB-LRR-type R protein harboring a C-terminal LIM domain 23 , 24 ., The chs3-1 mutant exhibits chilling-sensitive phenotypes , including small stature and increased disease resistance ., The SGT1b and RAR1 proteins are required for R protein stability 25–27 ., The chs3 chilling-sensitive phenotypes are suppressed in sgt1b and rar1 mutants 23 ., However , the molecular regulatory mechanism of the temperature-dependent defense responses through CHS3 remains elusive ., In the present study , we identified ibr5-7 as a suppressor of the chilling-sensitive phenotypes of chs3-1 ., IBR5 ( Indole-3-Butyric Acid Response 5 ) encodes a dual-specificity MAPK phosphatase , which acts as a positive regulator of plant responses to auxin and ABA 28 ., IBR5 physically interacts with MPK12 , and activated MPK12 is dephosphorylated and inactivated by IBR5 , thereby negatively regulating auxin signaling 29 ., Here , we observed that ibr5 suppresses the chilling-sensitive phenotypes of chs3-1 independently of MPK12 ., Biochemical data showed that IBR5 complexes with CHS3 and HSP90-SGT1b to to stabilize CHS3 ., Moreover , IBR5 is involved in the R-gene mediated resistance specified by SNC1 , RPS4 and RPM1 ., Thus , IBR5 plays an important role in regulating different R protein-mediated defense responses ., The chs3-1 plants are dwarfed and have small , curly leaves ., The defense responses in chs3-1 are constitutively active at 16°C , but this phenotype is alleviated at higher temperatures ( 22°C ) 23 ., To understand the molecular mechanism underlying the temperature-dependent cell death in the chs3-1 mutant , we performed a genetic screen to identify suppressors of chs3-1 ( suc ) ., The chs3-1 seeds were mutagenized with ethyl methylsulfonate ( EMS ) , and the M2 population was screened for mutants with wild-type morphology at 16°C ., Among the suppressors screened , most of the variations were second-site , loss-of-function mutations in CHS3 , thereby rescuing the chs3 chilling-sensitive phenotype ., One suppressor harbored a mutation in RAR1 , and two suppressors harbored mutations in SGT1b; these genes are important regulators required for CHS3 function 23 ., These results indicate that the genetic screen was effective ., Here , we characterized suc5 as a new suppressor of chs3-1 ., The chs3 suc5 mutant plants largely resembled wild-type plants when grown at 16°C , except these plants exhibited a slightly smaller stature compared with the wild type and had serrated true leaves ( Fig 1A ) ., Previous studies have indicated that extensive cell death and strong defense responses occur in chs3-1 mutants grown at 16°C 23 ., To determine whether the suc5 mutation affects these cell death-related phenotypes , chs3-1 plants were grown at 16°C , followed by staining with trypan blue and 3 , 3’-diaminobenzidine ( DAB ) ., The suc5 mutation dramatically reduced the extensive cell death observed in chs3-1 mutants grown at 16°C ( Fig 1B ) ., Furthermore , the accumulation of hydrogen peroxide ( H2O2 ) in chs3 suc5 plants grown at 16°C was dramatically reduced compared with chs3-1 ( Fig 1C ) ., The chs3-1 mutant also accumulates high levels of salicylic acid ( SA ) 23 ., To determine whether suc5 inhibits SA accumulation in chs3-1 at 16°C , the endogenous SA level in chs3 suc5 plants was measured ., Both the free and total SA levels were dramatically reduced in chs3 suc5 plants grown at 16°C compared with the chs3 mutant ( Fig 1D ) ., Because PR genes were highly expressed in chs3-1 , we further examined the expression of the PR genes in chs3 suc5 plants grown at 16°C ., Quantitative real-time PCR ( qRT-PCR ) analysis showed that the expression of PR1 , PR2 and CHS3 was significantly reduced in chs3 suc5 plants grown at 16°C ( Fig 1E and 1F ) ., Compared with wild-type plants , chs3-1 plants grown at 16°C exhibit enhanced resistance to a virulent pathogenic strain of Pseudomonas syringae pv tomato ( P . s . t . ) , DC3000 23 ., To investigate the role of SUC5 in the chs3-mediated basal defense response , the response of chs3 suc5 seedlings to P . s . t . DC3000 was analyzed ., The suc5 mutation fully suppressed the chs3-conferred constitutive resistance to P . s . t . DC3000 , resulting in wild-type-like susceptibility ( Fig 1G ) ., Taken together , these results demonstrated that , under chilling stress , the suc5 mutation largely suppresses all known autoimmune phenotypes of chs3 ., To map the suc5 mutation , chs3 suc5 in Columbia ( Col ) was crossed with Landsberg erecta ( Ler ) to generate a mapping population ., Among the F2 progeny , 50 plants homozygous or heterozygous at the chs3-1 locus and exhibiting a wild-type morphology at 16°C were used for rough mapping ., The suc5 mutation was initially mapped to the top of chromosome II ( Fig 2A ) ., Fine mapping using approximately 200 plants refined the mutation to a 500-kb region between markers F3C11 and F5G3 ( Fig 2A ) ., Further sequencing analysis of this region in chs3 suc5 revealed a G-to-A substitution in the first exon of At2g04550 , resulting in an amino substitution from Arg to Lys ( Fig 2A ) ., At2g04550 was previously identified as IBA RESPONSE5 ( IBR5 ) , which encodes a putative dual-specificity protein phosphatase 28 ., To determine whether the mutation in IBR5 is responsible for the suppression the phenotypes of chs3 , a chs3 ibr5-3 double mutant was generated by crossing chs3-1 with ibr5-3 29 ., The ibr5-3 mutant largely restored the growth defects of chs3-1 mutant plants grown at 16°C ( Fig 2B ) ., Furthermore , a wild-type genomic IBR5 fragment containing 5 . 0 kb of the 5’-promoter region and the 3’ untranslated region was transformed into the chs3 suc5 mutant ., All eight T1 transgenic plants displayed chs3-conferred morphological and cell death phenotypes ( Fig 2B and 2C ) ., Taken together , these results indicate that SUC5 is indeed IBR5 ., Therefore , suc5 was renamed ibr5-7 ., The ibr5-7 single mutant was isolated by crossing chs3 ibr5-7 with wild-type Col plants ., Immunoblot analysis showed that the IBR5 protein level in ibr5-7 was lower than that in the wild-type Col plants , whereas no IBR5 protein was detected in ibr5-3 ( Fig 2D ) ., Intriguingly , IBR5 protein accumulated in the chs3-1 mutant ( Fig 2D ) , implying that IBR5 might stabilize CHS3 protein ., Consistent with a previous study on ibr5 loss-of-function mutants 28 , ibr5-7 displayed serrated leaves and was resistant to high concentrations of exogenous auxins , including IAA , IBA and 2 , 4-D ( S1 Fig ) ., These results indicate that ibr5-7 is a novel null allele of IBR5 ., To further investigate whether IBR5 physically interacts with CHS3 in CHS3-mediated signaling , a yeast two-hybrid assay was performed ., As a TIR-NB-LRR-LIM-containing protein , the full-length CHS3 protein is approximately 185 kD and is difficult to express in yeast ., Therefore , truncated constructs individually carrying the TIR , NB , LRR , unknown and LIM domains of CHS3 were generated and transformed into yeast ( Fig 3A ) ., IBR5 directly interacted with the TIR domain of CHS3 in yeast ( Fig 3B ) ., Previous studies have shown that CHS3 localizes to the nucleus 24 , consistent with the results of the present study ( S2B Fig ) ., We also observed that IBR5 localized to both the cytosol and the nucleus ( S2A and S2B Fig ) ., To determine whether CHS3 interacts with IBR5 in vivo , 35S:HA-Flag-IBR5 ( HF-IBR5 ) and the Myc-tagged TIR domain of CHS3 driven by the Super promoter ( TIR-Myc ) were transiently expressed in Arabidopsis mesophyll protoplasts , and subsequently , a co-immunoprecipitation ( co-IP ) assay was performed ., IBR5 precipitated the TIR domain of CHS3 ( Fig 3C ) ., Moreover , HF-IBR5 interacted with full-length CHS3-1-Myc ( the mutated form of CHS3 containing the same mutation as the chs3-1 mutant ) when expressed in N . benthamiana ( Fig 3D ) ., These results indicated that CHS3 interacts with IBR5 through the TIR domain of CHS3 in vivo ., IBR5 belongs to a family of dual specificity protein phosphatases ( DSPs ) , and the conserved cysteine in the conserved motif ( VxVHCx2GxSRSx5AYLM ) of DSPs is necessary for catalytic activity in many DSP proteins , including IBR5 and its homolog DsPTP1 30 ., To examine whether the phosphatase activity of IBR5 is required for interactions with CHS3 , we generated the mutated form of IBR5 ( IBR5C129S ) ., The yeast two-hybrid assay showed that the mutation did not affect the interaction of IBR5 and CHS3 ( Fig 3B ) , suggesting that the catalytic activity is not necessary for the interaction between IBR5 and CHS3 ., We next introduced Super:IBR5-Myc and Super:IBR5C129S-Myc into the chs3 ibr5 mutant background ( Fig 3E , 3F and 3G ) ., As a control , IBR5-Myc fully recovered the chs3 ibr5 phenotype and PR1 expression ., However , the morphological phenotypes and PR1 expression of chs3 ibr5 were partially rescued in the mutated IBR5C129S-Myc plants ( Fig 3F and 3G ) ., These results suggest that the catalytic activity of IBR5 is required for full function in the CHS3-mediated defense response ., Another gain-of-function chs3 mutant , chs3-2D , was dwarfed and exhibited a constitutively active defense phenotype at 22°C 24 ., Moreover , chs3:chs3-2D-GFP transgenic plants were generated , exhibiting chs3-2D-like phenotypes 24 ., To examine whether IBR5 affects the accumulation of CHS3 in chs3-2D-GFP plants , ibr5-3 chs3-2D-GFP plants were generated by crossing ibr5-3 with chs3-2D-GFP plants ( Fig 4A ) ., The dwarf phenotype of chs3-2D-GFP plants was fully restored by ibr5-3 ( Fig 4B ) , and GFP signals were observed in the nuclei of chs3-2D-GFP transgenic plants ( Fig 4C ) ., However , no obvious GFP signal was detectable in ibr5-3 chs3-2D-GFP plants ( Fig 4C ) ., Consistently , the expression of PR genes in chs3-2D-GFP was also suppressed in ibr5-3 plants ( Fig 4D ) ., The expression of CHS3 in ibr5-3 chs3-2D-GFP plants was also examined ., As shown in Fig 4E , CHS3 expression in ibr5-3 chs3-2D-GFP plants was dramatically decreased compared with chs3-2D-GFP plants , consistent with the result obtained in chs3 ibr5-7 ( Fig 1F ) ., Moreover , the expression of CHS3 was down-regulated in the ibr5-3 mutant ( Fig 4F ) ., These results suggest that IBR5 positively regulates the expression of CHS3 , which might at least be partially responsible for the decreased CHS3-2D-GFP signals in ibr5-3 chs3-2D-GFP ., To dissect the influence of IBR5 on CHS3 at the protein level , we examined the effect of IBR5 on the CHS3 protein level in Arabidopsis protoplasts ., The protein level of CHS3-1-Myc in protoplasts co-expressing CHS3-1-Myc and HF-IBR5 was much higher than that in protoplasts expressing CHS3-1-Myc and HF ( Fig 4G ) ., This result suggests that IBR5 also promotes the accumulation of CHS3 protein in plant ., HSP90 plays an important role in the stability of R proteins 31 ., A previous study showed that the hsp90 . 3–1 mutant was a suppressor of the chs2/rpp4-1d mutant 32 ., Therefore , we examined whether hsp90 . 3–1 rescues the chilling-sensitive phenotype of chs3-1 ., The chs3-1 hsp90 . 3–1 double mutant was generated and largely showed wild-type morphology but was smaller at 16°C ( Fig 5A ) , and cell death was dramatically suppressed ( Fig 5B ) ., PR1 expression was also significantly inhibited in the double mutant ( Fig 5C ) ., Further analysis showed that the F1 progeny of ibr5 chs3 and hsp90 . 3 chs3 could partially rescue the morphology , cell death and PR1 gene expression of chs3 ( Fig 5A , 5B and 5C ) ., Moreover , the chs3 hsp90 . 3 ibr5 triple mutant more closely resembled wild-type plants than the chs3 hsp90 and chs3 ibr5 mutants in terms of growth , cell death and PR1 gene expression ( Fig 5A , 5B and 5C ) ., These data indicate that HSP90 and IBR5 synergistically modulate the chs3-conferred growth and defense responses ., HSP90 functions as a complex with SGT1b and RAR1 11 , 33–35 and we previously showed that sgt1b and rar1 suppressed the phenotypes of chs3-1 23 , similar to ibr5 ( Fig 1 ) ., Next we determined whether IBR5 physically interacts with HSP90 and SGT1b in plants ., To this end , a luciferase complementation imaging ( LCI ) assay was performed in Nicotiana benthamiana ., IBR5 interacted with HSP90 and SGT1b in N . benthamiana leaves ( Fig 6A and 6B ) ., Furthermore , co-IP assays showed that endogenous HSP90 successfully immunoprecipitated IBR5-Myc in transgenic plants expressing IBR5-Myc , but not in Myc transgenic plants ( Fig 6C ) ., Similarly , SGT1b could be co-immunoprecipitated with IBR5 in transgenic plants expressing IBR5-Myc and SGT1b-FLAG , but not in transgenic plants expressing Myc and SGT1b-FLAG ( Fig 6D ) ., We also examined the association between SGT1b and CHS3 ., Co-IP assays showed that SGT1b associated with the TIR domain of CHS3 and full-length CHS3-1 in vivo ( S3 Fig ) ., Furthermore , we examined the interaction of CHS3-1 with IBR5 and SGT1b in N . benthamiana leaves ., As shown in Fig 6E , CHS3-1 could simultaneously pull down IBR5 and SGT1b in plants ., These results indicate that IBR5 forms complex ( es ) with CHS3 , HSP90 and SGT1b in vivo ., We subsequently investigated whether IBR5 protects proteins in vitro using a thermal aggregation assay with citrate synthase ( CS ) as a model substrate 36 ., CS aggregation was examined after measuring the absorbance at 500 nm under thermally denaturing conditions ( 40°C or above ) ., Heat-induced aggregation of CS was inhibited by HSP90 ( Fig 6F ) , consistent with the results of previous study 37 ., IBR5 showed a weaker but more significant effect than HSP90 on the inhibition of CS aggregation ., When IBR5 was incubated with CS at 43°C , the aggregation of CS was partially suppressed in a dose-dependent manner ( Fig 6F ) ., This result indicates that IBR5 protects proteins against aggregation in vitro ., The TIR domain of CHS3 shares amino acid similarity with TIR-NB-LRR R proteins SNC1 and RPP4 ( S4 Fig ) ., Gain-of-function mutants of SNC1 ( snc1-1 and bal/snc1-2 ) activate the defense response in a temperature-dependent manner 6 , 38 ., The loss of BON1 function in Arabidopsis leads to temperature-sensitive growth and autoimmune phenotypes resulting from the activation of SNC1 6 ., Phenotype analyses showed that the bon1-1 ibr5-3 ( bon1 ibr5 ) and bal ibr5-3 ( bal ibr5 ) double mutants were slightly larger than the bon1-1 and bal single mutants ( Fig 7A and 7B ) ., Moreover , the cell death in bon1 ibr5 and bal ibr5 was reduced ( Fig 7C ) ., Consistently , the expression of PR1 and PR2 genes in bon1 ibr5 and bal ibr5 was also lower than that in the bon1-1 and bal/snc1-2 mutants ( Fig 7D ) ., We also examined the basal defense response of bon1 ibr5 and bal ibr5 to P . s . t . DC3000 ., The pathogen resistance of bon1-1 and bal/snc1-2 mutants to P . s . t . DC3000 was slightly suppressed in the ibr5-3 mutant ( Fig 7E and 7F ) ., These genetic data suggest that IBR5 is partially implicated in the SNC1-mediated defense response ., To further dissect the function of IBR5 on SNC1 , we examined the expression of SNC1 in the ibr5-3 mutant ., As shown in Fig 4F , the SNC1 transcript levels in the ibr5-3 mutant were much lower than those in Col , indicating that SNC1 expression is positively regulated by IBR5 ., We next explored whether IBR5 physically interacts with SNC1 ., A yeast two-hybrid assay showed that IBR5 interacted with the TIR domain of SNC1 ( Fig 8A ) ., The interaction of IBR5 and full-length SNC1-1 ( the mutated form of SNC1 containing the same mutation as the snc1-1 mutant ) was confirmed by a co-IP assay in N . benthamiana leaves ( Fig 8B ) ., Moreover , the protein level of SNC1-1-Myc increased when co-expressed with HF-IBR5 in Arabidopsis protoplasts ( Fig 8C ) ., Taken together , these data suggest that IBR5 might also promote SNC1 protein accumulation ., Our previous studies showed that the mutations in R protein CHS2/RPP4 or R-like protein CHS1 induce plant sensitivity and activate defense responses under chilling stress 7 , 8 ., To investigate whether ibr5 also suppresses the chilling sensitivity of chs1-2 and chs2-1/rpp4-1d , we generated chs1-2 ibr5-3 ( chs1 ibr5 ) and chs2-1 ibr5-4 ( chs2 ibr5 ) double mutants ., In terms of morphology and cell death , chs1 ibr5 and chs2 ibr5 mutants showed chs1-2 and chs2-1 single mutant phenotypes under chilling stress ( S5A and S5B Fig ) ., However , PR1 expression in chs1 ibr5 was partially suppressed , whereas the expression of this protein in chs2 ibr5 was not obviously affected ., A yeast two-hybrid assay showed that IBR5 did not interact with RPP4 ( Fig 8A ) ., We also examined the pathogen resistance of ibr5 mutants to RPP4-specific Hyaloperonospora arabidopsidis ( H . a . ) Emwa1 ., As controls , Col was completely resistant to H . a . Emwa1 , whereas rpp4-r26 , which contains a mutation in RPP4 resulting in the introduction of a stop codon in front of the LRR domain 32 , was susceptible to H . a . Emwa1 ( S6 Fig ) ., The ibr5-3 and ibr5-7 mutants showed complete resistance to H . a . Emwa1 ( S6 Fig ) , suggesting that IBR5 might not be involved in RPP4-mediated oomycete resistance ., To examine whether basal defense was affected in the ibr5 single mutant , we infected wild-type Col and ibr5 mutants with virulent P . s . t . DC3000 ., The bacterial growth in ibr5-3 and ibr5-7 was comparable to that in wild-type plants , whereas as the control , pad4-1 , supported 100 times more bacterial growth than the wild-type plants ( Fig 9A ) , suggesting that IBR5 does not play an important role in basal defense ., As the role of CHS3 is compromised in the ibr5 mutant , we further investigated whether IBR5 is required for other R-gene-mediated disease resistance ., The ibr5 mutants were inoculated with P . s . t . DC3000 carrying either avrRpm1 , avrRps4 , or avrRpt2 ., The ibr5 mutants showed enhanced susceptibility to P . s . t . DC3000 ( avrRpm1 ) and P . s . t . DC3000 ( avrRps4 ) ( Fig 9B and 9C ) ., In contrast , no remarkable difference in the growth of P . s . t . DC3000 ( avrRpt2 ) was detected between the ibr5 mutants and wild-type Col ( Fig 8D ) ., As a control , pad4-1 showed enhanced disease susceptibility to all three avirulent bacterial strains ( Fig 9B , 9C and 9D ) ., These results suggest that IBR5 might also contribute to disease resistance mediated by RPM1 and RPS4 ., We next examined the expression of RPM1 and RPS4 in the ibr5-3 mutant ., The transcription levels of RPM1 and RPS4 were slightly down-regulated in the ibr5-3 mutant compared with wild type plants ( Fig 4F ) ., Furthermore , we examined whether IBR5 interacts with TIR-NB-LRR protein RPS4 or CC-NB-LRR protein RPM1 ., The results of a yeast two-hybrid assay showed that neither wild-type IBR5 nor mutated IBR5C129S interacted with the TIR domain of RPS4 or CC domain of RPM1 ( S7A Fig ) ., However , interestingly , we observed the direct interaction of IBR5 and IBR5C129S with RPS4-interacting protein RRS1 39 in yeast ( S7B Fig ) ., The results of additional co-IP assays showed that IBR5 could pull down RPS4 ( S7C Fig ) , suggesting that IBR5 might form a complex with RPS4 and RRS1 ., In contrast , no interaction between IBR5 and RPM1 was observed in plants ( S7D Fig ) ., Furthermore , no obvious change in the RPM1 protein level was detected when IBR5 and RPM1 were co-expressed ( S7E Fig ) ., CHS3 is involved in temperature-dependent defense responses 23 ., In the present study , we identified ibr5-7 as a suppressor of chs3-1 ., IBR5 interacts with CHS3 and HSP90/SGT1b chaperones to stabilize the CHS3 protein , thereby modulating temperature-dependent defense responses ., In addition , IBR5 is invovled in defense responses mediated by several other R genes , including SNC1 , RPS4 and RPM1 ., IBR5 is a MAP kinase phosphatase ( MKP ) that dephosphorylates activated MAPKs 40 , 41 ., Emerging evidence has shown that MKPs play important roles in modulating plant defense responses 41 ., For example , the mkp1 null mutation in the Col accession exhibits constitutive stress response phenotypes , causing a dwarf stature dependent on SNC1 42 ., However , it remains unclear whether the substrates of MKP1 , MPK3 and MPK6 42 , 43 are involved in the regulation of SNC1 ., Additional studies have revealed that MKP1 functions as a negative regulator of MPK6-mediated pathogen-associated molecular pattern ( PAMP ) responses and disease resistance 44 ., The mkp2 null mutant displays delayed symptoms of disease induced by Ralstonia solanacearum , and shows increased susceptibility to Botrytis cinerea 45 ., MKP2 might regulate MPK3/MPK6 activity during different pathogen defense responses ., Moreover , MKP2 reverses hypersensitive-like responses triggered by MPK6 upon plant treatment with fungal elicitors 45 , 46 ., It is likely that these MKPs have negative functions in defense signaling pathways through their MPK substrates ., The data obtained in the present study showed that the loss of IBR5 function suppresses chs3-conferred , temperature-dependent defense responses and slightly weakens the autoimmunity of bal/snc1-2 , a gain-of-function mutant of a TIR-NB-LRR protein SNC1 ., Moreover , resistance mediated by R proteins , such as RPS4 and RPM1 , is compromised in the ibr5 mutants ., These results suggest that IBR5 plays a positive role in the ETI pathway mediated by multiple R proteins ., Therefore , different MKPs play either positive or negative roles in plant immunity ., As IBR5 is a MAP kinase phosphatase , we also examined whether the dephosphorylation activity of IBR5 is required for the regulation of CHS3 ., The inactive form of IBR5 ( IBR5C129S ) partially rescues the phenotype of chs3 ibr5 , suggesting that the dephosphorylation activity of IBR5 is necessary for the complete function of this protein in the CHS3-mediated defense response ., MPK12 is a substrate of IBR5 29 , and the reduced expression of MPK12 partially complements auxin , but not ABA insensitivity , in the ibr5 mutant , suggesting that there might be other substrate ( s ) of IBR5 involved in IBR5-mediated ABA responses 29 ., In the present study , the loss-of-function mutant mpk12-3 displayed an auxin-sensitive phenotype ( S8A , S8B and S8C Fig ) , similar to that of mpk12 RNAi lines 29 ., However , mpk12-3 cannot rescue the phenotype of chs3 ibr5 ( S8D Fig ) , indicating that the CHS3-mediated defense response does not require MPK12 ., Moreover , the mutant of auxin receptor tir1 enhances the auxin insensitive phenotype of ibr5 , implying that IBR5 is involved in the auxin response independently of the TIR1-mediated auxin signaling pathway 30 ., Therefore , the role of IBR5 in R-mediated resistance might not occur through the function of IBR5 in the auxin response ., ibr5 mutant is shown to be insensitive to ABA , but the underlying mechanism is completely unknown 28 ., ABA signaling plays a role in defense responses in a complicated manner ., Recent studies have reported that the overexpression of ABA receptors maintains stomatal closure during P . s . t . DC3000 infections , leading to enhanced resistance after dipping , but enhanced susceptibility to the pathogen after infiltration 47 ., In contrast , several ABA signaling mutants , such as abi1 and abi4 , do not exhibit prominent phenotypes under the snc1-1 background or during pathogen infection 48 ., The ABA-insensitive mutant cpr22 , containing a deletion on two cyclic nucleotide-gated ion channel genes , shows constitutive PR gene expression , enhanced pathogen resistance and SA accumulation 49 ., WRKY40 , WRKY18 , and WRKY60 play negative roles in ABA signaling 50 ., The single and triple mutants show diverse phenotypes to different pathogens , as these mutants are more resistant to P . syringae but more susceptible to B . cinerea compared with the wild type 50 ., Whether the role of IBR5 in the ABA response is involved in CHS3-mediated defense pathway remains unknown ., In plants , the TIR domain is indispensable for defense signal transduction 51 ., For example , the transient expression of the TIR domain of RPS4 in N . benthamiana leaves triggers cell death in a manner dependent on both SGT1 and HSP90 13 , 51 ., The TIR domain of L6 , a TIR-NB-LRR R protein in flax , forms a homodimer required for immune signaling 52 ., A recent study showed that TIR domain heterodimerization is necessary to form a functional RRS1/RPS4 effector recognition complex 9 , indicating the importance of the TIR domain of R proteins in signal recognition and transduction ., Moreover , the TIR domain of R proteins interacts with other proteins 39 , 53–56 ., In the present study , we observed that the TIR domain of CHS3 interacts with IBR5 in vitro and in vivo ., The mutation of IBR5C129S does not affect this interaction , suggesting that the dephosphatase activity of IBR5 is not a prerequisite for the interaction between IBR5 and CHS3 ., We also examined the interaction between IBR5 and four other TIR-NB-LRR R proteins: SNC1 , RPP4 , RPS4 and its interacting R protein RRS1 39 , and CC-NB-LRR , RPM1 ., Under these conditions , IBR5 interacts with SNC1 and RRS1/RPS4 in plant , but not with RPP4 and RPM1 ., Consistently , ibr5 partially suppresses the cell death and PR gene expression in bal/snc1-2 but does not suppress the expression of these proteins in chs2/rpp4-1d ., These data suggest that IBR5 might specifically interact with certain TIR-NB-LRR proteins to regulate defense signaling ., However , the mechanism underlying the involvement of IBR5 in defense responses mediated by the CC-type R protein remain unclear ., Increasing evidence has shown that HSP90 , SGT1 and RAR1 form a protein complex with chaperone activity required for the proper folding and stabilization of R proteins 11 , 23 , 33 , 34 , 57–60 ., In this complex , HSP90 and RAR1 increase R protein levels 57 ., The phosphatase PP5 interacts with HSP90 and the tomato R protein I–2 , and together with HSP90 , PP5 acts as a co-chaperone 61 ., In the present study , we found an interaction between the phosphatase IBR5 and the HSP90/SGT1B/RAR1 complex in vivo , suggesting that IBR5 complexes with these chaperone proteins and stabilizes CHS3 ., Increasing evidence further supports the notion: ( 1 ) The in vitro holdase activity of IBR5 indicates that this enzyme markedly inhibits temperature-dependent CS degradation ., ( 2 ) The F1 progeny of chs3 ibr5 and chs3 hsp90 partially inhibits the chs3 phenotype ., Moreover , the chs3 ib5 hsp90 triple mutant is larger than both chs3 hsp90 and chs3 ibr5 and is reminiscent of the wild type ., We speculate that different complexes comprising IBR5 and HSP90 might play roles in CHS3-mediated signaling ., This involvement might reflect the dosage effect of IBR5 and HSP90 ., A similar mechanism was observed for the COI1 ( CORONATINE INSENSITIVE, 1 ) and HSP90 proteins in regulating RPM1-mediated disease resistance 62 ., ( 3 ) chs3-dependent phenotypes are suppressed by sgt1b , rar1 23 and hsp90 . 3–1 , indicating that HSP90 , RAR1 and SGT1b are required for CHS3 activation ., ( 4 ) IBR5 interacts with the HSP90 , SGT1b and CHS3 proteins in vivo ., Collectively , these data show that IBR5 associates with HSP90/RAR1/SGT1b to stabilize the CHS3 protein ., Because SGT1b interacts with a core component of the SKP1-CULLIN1-F-box ( SCF ) E3 ligase complex , S-phase kinase-associated protein 1 ( SKP1 ) , SGT1b has been considered as a subunit of the SCF complex 63 , 64 ., Recent studies have shown that SNC1 is degraded by the SCFCPR1 complex 65 , 66 ., In a protein-protein interactome study , IBR5 was also shown to interact with SKP1 67 ., These results suggest that IBR5 , in concert with HSP90/SGT1B proteins , might affect the function of SCF E3 ubiquitin ligase complexes , thereby regulating the ubiquitination and degradation of some R proteins ., Further studies will elucidate the molecular mechanisms underlying the roles of IBR5 in R-mediated defense responses ., Arabidopsis thaliana Col–0 , chs3-1 23 , ibr5-3 29 , pad4-1 68 , hsp90 . 3–1 , rpp4-r26 32 , and mpk12-3 ( SAIL_543_F07 ) were used in this study ., Plants were grown in soil or on Murashige and Skoog ( MS ) medium containing 2% ( w/v ) Suc and 0 . 8% ( w/v ) agar at 16°C or 22°C with a 16-h light/8-h dark cycle under white light ., Salicylic acid measurement and were described previously 23 ., The suppressors of chs3-1 were screened and mapped as described previously 23 ., Approximately 200 plants with chs3-1 background with wild-type morphology at 16°C were used for mapping to a 500-kb region between markers F3C11 and F5G3 ., Full length genomic DNA of candidate genes was amplified by PCR and sequenced to find the mutant site ., To confirm the mutated gene , genomic complementation and T-DNA insertion mutant were used ., Trypan blue and DAB staining was performed as previously described 69 , 70 ., The pathogen resistance assay on Pseudomonas syringae pv tomato ( P . s . t . ) strain DC3000 was performed as described previously 32 ., B | Introduction, Results, Discussion, Materials and Methods | Plant responses to low temperature are tightly associated with defense responses ., We previously characterized the chilling-sensitive mutant chs3-1 resulting from the activation of the Toll and interleukin 1 receptor-nucleotide binding-leucine-rich repeat ( TIR-NB-LRR ) -type resistance ( R ) protein harboring a C-terminal LIM ( Lin-11 , Isl-1 and Mec-3 domains ) domain ., Here we report the identification of a suppressor of chs3 , ibr5-7 ( indole-3-butyric acid response 5 ) , which largely suppresses chilling-activated defense responses ., IBR5 encodes a putative dual-specificity protein phosphatase ., The accumulation of CHS3 protein at chilling temperatures is inhibited by the IBR5 mutation ., Moreover , chs3-conferred defense phenotypes were synergistically suppressed by mutations in HSP90 and IBR5 ., Further analysis showed that IBR5 , with holdase activity , physically associates with CHS3 , HSP90 and SGT1b ( Suppressor of the G2 allele of skp1 ) to form a complex that protects CHS3 ., In addition to the positive role of IBR5 in regulating CHS3 , IBR5 is also involved in defense responses mediated by R genes , including SNC1 ( Suppressor of npr1-1 , Constitutive 1 ) , RPS4 ( Resistance to P . syringae 4 ) and RPM1 ( Resistance to Pseudomonas syringae pv . maculicola 1 ) ., Thus , the results of the present study reveal a role for IBR5 in the regulation of multiple R protein-mediated defense responses . | Resistance ( R ) genes play central roles in recognizing pathogens and triggering plant defense responses ., CHS3 encodes a TIR-NB-LRR-type R protein harboring a C-terminal LIM domain ., A point mutation in CHS3 activates the defense response under chilling stress ., Here we identified and characterized ibr5-7 , a mutant that suppresses the chilling-induced defense responses of chs3-1 ., We observed that the enhanced defense responses and cell death in the chs3-1 mutant are synergistically dependent on IBR5 and HSP90 ., IBR5 physically interacts with CHS3 , forming a complex with SGT1b/ HSP90 ., Moreover , IBR5 is also involved in the R-gene resistance mediated by SNC1 , RPS4 and RPM1 ., Thus , IBR5 plays key roles in regulating defense responses mediated by multiple R proteins . | null | null |
journal.pntd.0004384 | 2,016 | Mitochondrial Genome Sequence of the Scabies Mite Provides Insight into the Genetic Diversity of Individual Scabies Infections | The scabies mite is an ectoparasitic arachnid that causes an itchy skin infection , known as scabies ., Each year around 300 million people worldwide are affected by scabies 1 ., Scabies is responsible for a significant disease burden in affected populations through its obligate parasitic lifecycle , which facilitates secondary infections by other pathogens ., A severe , but more rare form of scabies , known as crusted scabies , is characterised by hyper-infestation ., It generally occurs in immune-compromised individuals 2 , although it can occur in patients with no overt immunological deficiency 3 ., Cases of crusted scabies can play a significant role in transmission 4 ., The mite also infects more than a hundred species of mammals , creating an animal welfare and economic burden in primary industry 5–7 ., Scabies represents a major health problem in many remote Indigenous communities in Australia and particularly affects children ., Up to 25% of adults and 50% of children acquire scabies infections each year , and 7 out of 10 children under 1 year contract scabies with first presentation peaking at 2 months of age 4 , 8 ., Scabies is associated with pyoderma ( skin sores ) in Indigenous communities , but also in many other circumstances of disadvantage globally 9 ., In tropical regions , the major pathogens of pyoderma are Group A streptococcus ( Streptococcus pyogenes; GAS ) and Staphylococcus aureus; with S . pyogenes considered the dominant and usually primary pathogen 4 , 10 , 11 ., This is also the case for remote Indigenous communities in northern and central Australia 4 , 10–12 ., Sequelae of infection with GAS include acute post-streptococcal glomerulonephritis , which can be clustered ( epidemic ) or sporadic , and acute rheumatic fever 4 , 13 , 14 ., Rheumatic heart disease , characterised by heart valve damage , occurs as a consequence of acute rheumatic fever and repeated episodes of it can result in cumulative heart valve damage , with consequent heart failure and death 5 , 13 ., The prevalence of rheumatic heart disease in Indigenous communities is amongst the highest in the world 4 , 15 ., Several recent studies provide molecular evidence of the scabies mite itself promoting streptococcal growth in pyoderma through complement inhibitors 1 , 16 , 17 ., Association of scabies with these long-term health problems makes it an important factor to consider in Indigenous health ., Despite this , there is a relative paucity of genetic and genomic information on scabies 1 ., The scabies mite belongs to the subclass Acari ( Arthropoda: Chelicerata: Arachnida: Acari ) , which contains around 48 , 000 species of ticks and mites 18 , and order Sarcoptiformes ., Currently , the scabies mite is classified taxonomically as a single species with different varieties based on host specificity 19 ., Reportedly , cross infectivity is rare and when it happens it is generally of temporary nature and self-limiting infestation 19 , 20 ., However , evidence regarding host specificity is conflicting , with some studies suggesting cross infectivity is possible between certain animal and human varieties 21 , 22 , while others suggest mono-specificity of scabies mite varieties 4 , 5 , 23–26 ., For example , genetic and phylogenetic studies that used hypervariable satellite markers , 16S rRNA and cytochrome oxidase subunit I of mitochondria ( cox1 ) , have shown that human and dog mites are genetically distinct in north Australian sympatric populations and that gene flow between the scabies mite population is extremely rare 19 , 27; while another study , using the cox1 gene showed that dog mites from China , USA and Australia are genetically similar to with certain human mites from Australia 21 , 22 ., Choice of the genetic marker may play a role in conflicting outcomes ., Additionally , it has been postulated that each variety , when infesting a non-native host , can acquire morphological and innate characteristics suited to the host through selection if it is allowed to persist due to immunodeficiency or malnutrition of the host 20 ., Host specificity has been an area of ongoing investigation because potential cross infectivity , in particular between domestic and companion animals and humans , would have important implications for disease control programs ., There are no morphological differences between mites from different host species 20 , but multiple failed experimental cross infestation attempts indicated that physiological differences may exist 1 , 27 , 28 ., Mites from dogs have successfully established long term infestations in rabbits 28 ., Microsatellite studies in wild animal mite populations also indicated a limited gene flow between mites from sympatric host populations 29 , but a further study suggested a prey to predator transfer of mites may be possible 30 ., Taken together , available data suggests that a limited gene flow occurs between host-associated populations of scabies mites , however a strict ‘host taxon’ law cannot be assumed ., Here , we describe the in silico isolation , de novo assembly and analysis of the mitochondrial genome of scabies mites from a variety of complex metagenomic samples ., The samples consisted of thousands of whole mites collected from two clinical isolates from different regions of Australia and replicates from a laboratory porcine model of scabies 31 ., We assembled the mitochondrial genome by applying a bespoke , iterative bait-and-assemble strategy to the massively parallel sequencing data from each mixture—demonstrating the utility of the general approach on complex metagenomics mixtures ., Additionally , we identified 6 mitochondrial haplotype clusters in the human ( Sarcoptes scabiei var . hominis ) and pig ( Sarcoptes scabiei var . suis ) scabies/mange mite populations sampled ., This allowed us to examine the genetic diversity within and between isolates and suggests an extremely low level of diversity overall ., To the best of our knowledge , these findings provide the first view of the genetic diversity of individual scabies infestations ( intra-host diversity ) based on whole mitochondrial genome sequences ., The collection of human patient samples was approved by the Human Research Ethics Committee of the Northern Territory Department of Health and Menzies School of Health Research ( approval 13–2027 ) and informed consent was obtained in writing from each participant ., Animal care and handling procedures used in this study followed the Animal Care and Protection Act , in compliance with the Australian code of practice for the care and use of animals for scientific purposes , outlined by the Australian National Health and Medical Research Council ., The study was approved by the QASP and the QIMR Berghofer MRI Animal Ethics Committees ( DEEDIAEC SA2012/02/381 , QIMR A0306-621M ) ., Skin scrapings were collected from two unrelated patients with severe crusted scabies ( patient A and patient B ) from different regional areas of Northern Territory , Australia ., The collections from individual patients were made 14 months apart and were likely independent ., On both occasions , scabies mites ( S . scabiei var . hominis ) were individually picked from the skin ., Each sample contained >1000 mites ., Two samples of pig mites ( S . scabiei var . suis ) were collected from an inbred population of mites from a pig model 31 ., The samples were taken from different pigs from consecutive cohorts ( where infections are passed on to new piglets from a previous group ) at different time points ., The first sample consisted of >1000 whole adult mites ( pig unwashed ) ., The second sample also consisted of >1000 mites , but was split into three subsamples , which were washed using different protocols to reduce the amount of bacteria present on the surface of the mites due to the wound micro-environment ( pig washed 1 , 2 and 3 respectively ) ., The protocol entailed: ( 1 ) 15 min wash at room temperature in 4% paraformaldehyde in water 32; ( 2 ) 1 hour incubation at 37°C in 150 mM NaCl , 10 mM EDTA , pH8 . 0 , 0 . 6% SDS , and 0 . 125 ug/ul lysozyme adapted from 33 and; ( 3 ) 1 hour incubation at 37°C in 1% bleach ( Sodium hypochlorite ) in water ., Mites were subsequently rinsed twice in water ., Between wash steps mites were centrifuged at 10000 rpm for 2 min ., Whole mites were crushed and DNA was extracted from each sample using a Blood and Cell culture DNA Kit QIAGEN and a modified procedure , adapted from the manufacturer’s protocol ., Washed mites were submerged in 1 ml of ice-cold lysis buffer ( 20 mM EDTA , 100 mM NaCL , 1% TritonX-100 , 500 mM Guanidine-HCl , 10 mM Tris pH7 . 9 ) and homogenized with stainless steel beads of 2 . 8mm diameter at 6800rpm , 3 cycles , 30 sec per cycle , 30 sec between cycles ., The suspension of lysed mites was supplemented with DNase free RNase A to 0 . 2 mg/ml and with Proteinase K to 0 . 8 mg/ml and incubated at 50°C for 1 . 5 h ., After centrifugation at 4000g for 10 min to pellet insoluble debris the genomic DNA was isolated on the QIAGEN genomic tip as instructed in the manufacturer’s protocol ., Six DNA libraries were constructed and 100 nucleotide ( nt ) long paired-end reads were generated on an Illumina HiSeq 2500 ., Additionally , 54 S . scabiei var ., suis eggs were collected from the pig model ., To reduce any surface bacteria , these were washed washed twice in 4% paraformaldyde and rinsed twice in water ., DNA was extracted separately from 51 individual eggs , and from 3 pools of 5 eggs and 1 pool of 16 eggs ., Sequencing libraries were constructed using the Nugen Ovation SP Ultralow DNA kit for 3 single eggs , and pools of 5 and 16 eggs , and sequenced using an Illumina HiSeq 2500 with 2x100 nt reads ., Raw data is available via ENA accession PRJEB12428 ., Sequence read quality was assessed using FASTQC 34 ., Adapter and quality ( Q≥20 ) trimming was performed using TrimGalore !, ( v0 . 3 . 1 ) 35 ., For the human and unwashed pig samples , reads were de novo assembled using Velvet ( v1 . 2 . 08 ) 36 ., To establish the best k-mer size , we initially assembled all reads from patient B using k-mer values of k = 61 , 63 , 65 , 67 , 69 , 71 , 73 , 75 , 79 , 85 , 89 and 95 ., Based on the quality of these assemblies , we used k = 69 , 75 , 77 , 79 , 81 , 83 , 85 , 89 and 95 for patient A and the unwashed pig samples ., Metagenomic profiling of the patient B mite assembly was carried out using PhymmBL ( v4 . 0 ) 37 ., We augmented the bundled PhymmBL model database with interpolated Markov models trained on the spider mite , Tetranychus urticae ( strain London ) ( available at https://bioinformatics . psb . ugent . be/gdb/tetranychus/ ) 38 as a proxy for scabies mite ., To estimate the species abundance , reads were aligned back to contigs using Bowtie2 ( v2 . 2 . 3 ) in local alignment mode 39 and the number of reads aligning to each contig were counted ., For the two human samples and the unwashed pig sample , the mitochondrial genomes were assembled individually using a bait-and-reassemble strategy ( see Fig 1 for overview ) ., First , contigs from the whole genome assemblies were aligned to European house dust mite ( EHDM ) , Dermatophagoides pteronyssinus mitochondrial reference genome ( NCBI GenBank: EU884425 ) 40 using LASTZ ( v1 . 02 . 00 ) 41 with default settings ., For patient B , contigs from the k = 65 assembly were used for alignment; for patient A , contigs from k = 77 , 79 and 89 were used; and for the unwashed pig sample , contigs from the k = 69 and 81 assemblies were used ., These different k-mer value assemblies varied in their N50 values , largest contigs sizes and median read coverage ., The aligned contigs were then filtered against the National Center for Biotechnology Information ( NCBI ) NT database for scabies mite mitochondrial contigs using BLASTN ( v2 . 2 . 29 ) 42 to remove likely false positives due to homology with other mitochondrial genomes ( e . g . host mitochondrial genome ) ., After the filtering step , contigs from patient B k = 65 assembly gave the best coverage of EHDM mitochondrial reference genome ., Adapter and quality trimmed reads from the patient B sample were then aligned back to the filtered patient B k = 65 contigs using Bowtie2 ( v2 . 2 . 3 ) in local alignment mode ., Aligned reads were then used to de novo assemble the patient B scabies mite mitochondrial genome using Velvet ( v1 . 2 . 08 ) ., Assemblies were run using k = 69 , 79 , 87 , 89 , 91 and 95 ., A k-mer size of 91 gave the longest single contig and was selected as the reference mitochondrial genome of scabies mite ., To generate the mitochondrial reference for patient A , adapter and quality trimmed reads from the patient A sample were aligned to the patient B mitochondrial reference genome , and matching reads were de novo assembled using Velvet ., The final contigs were scaffolded and gaps filled with Ns ., This scaffold was then used to realign the patient A reads and reassemble ., This process was run iteratively ., The second iteration gave the minimal number of Ns in the contig and this was retained as the patient A mite mitochondrial genome ., The same process was used for the unwashed pig sample , but in this case the first iteration gave the best contig with minimal Ns ., Mitochondrial protein-coding genes were predicted on patient B mitochondrial reference genome using the MITOS pipeline ( accessed online , August 2014 ) 43 using the invertebrate mitochondrial genetic code ( with default settings ) ., To verify the annotations , open reading frames ( ORFs ) between stop to stop codons were extracted from the assembled patient B mite genome using the GETORF program from the EMBOSS package ( v6 . 6 . 0 ) 44 using the invertebrate mitochondrial codon table ., The extracted 774 ORFs were then used to search the NCBI NR protein database using BLASTP ( v2 . 2 . 29 ) ( E-value threshold = 0 . 1 ) ., For further verification , 13 protein-coding genes from EHDM were aligned to the patient B mite reference genome using TBLASTN ( v2 . 2 . 29 ) ., 12S and 16S ribosomal RNA were annotated using covariance models for those genes using Infernal ( v1 . 1rc4 ) 45 , 46 ., The covariance models were built from multiple sequence alignments of 12S and 16S rRNA genes from three sarcoptiformes , Steganacarus magnus ( NCBI RefSeq: NC_011574 ) , Dermatophagoides pteronyssinus ( NCBI GenBank: EU884425 ) and Dermatophagoides farinae ( NCBI RefSeq: NC_013184 ) using Clustal-omega ( run on the web version on 15/12/2013 ) 47 ., MITFI ( within the MITOS pipeline ) was used to identify 21 of 22 standard mitochondrial tRNA genes ., For each of the six samples , reads from each sample were aligned to the patient B reference genome using the Bowtie2 aligner in local mode ., Pileups were generated using SAMtools ( v0 . 1 . 19-44428cd ) 48 mpileup ., Varscan ( v2 . 3 . 6 ) 49 was then used to call SNPs with the mpileup2snp command ( Min Coverage: 300; Min Variant Frequency: 0 . 01; otherwise default parameters ) ., SNPs called within the 100 nt boundary at both ends of the genome ( positions 1–100 and 13 , 820–13 , 919 ) were manually filtered out due to lack of reliability of alignments at those low complexity regions ., For each sample , SNP frequency was estimated using the ratio of reads supporting the variant to total read depth at the SNP ., Thirty-five predicted genes were identified in the scabies mite ( var . hominis ) mitochondrial genome ( Fig 2 ) ., These include 13 protein coding genes , two ribosomal RNA genes , and 20 tRNA genes ., All of the expected mitochondrial genes for a typical metazoan mitochondrial genome were identified , except for two tRNAs ( Alanine and Tyrosine ) ., Search with BLASTP of extracted stop-to-stop ORFs from the mitochondrial genome also verified 12 of the 13 protein-coding genes ( except the ATP8 gene ) and TBLASTN alignment of 13 protein coding genes from the EHDM mitochondrial genome to the scabies mite mitochondrial genome also confirmed 12 of the 13 protein coding genes ( except the ND4L gene ) in the scabies mite mitochondrial genome ., Two tRNA genes identified by tRNAscan-SE were considered ambiguous due to their overlap with other genes ( A is overlapping with C and Y is overlapping with NAD4L ) ., However , the overlap is not on the same strand ., The gene order and strand specification of the protein coding and ribosomal RNA genes are the same as that of EHDM ., All identified tRNA genes , except tRNA-C and tRNA-V also maintain the same gene order and strand as EHDM ., The tRNA genes that are not syntenic with EHDM ( tRNA-C , tRNA-V ) are also on the opposite strand to that of EHDM ., To identify genetic polymorphisms in the sequenced mite pools , reads were mapped back to the reference Mt genome ( patient B ) ., The average depth of coverage was 2914 across samples ( average per sample ranging 1698–4299 ) ., A total of 665 single nucleotide polymorphisms ( SNPs ) were identified across all samples relative to the reference Mt genome ( patient B assembly ) : 601 SNPs in the patient A sample , 598 SNPs in the patient B sample , and 102 SNPs in the unwashed pig sample , while the washed pig samples ( w1 , w2 , w3 ) contained 102 , 100 , 102 SNPs respectively ( S2 Table ) ., The four pig mite samples are effectively replicates ( biological and technical ) and were highly concordant ., Within each sample , SNP allele frequencies , estimated from the ratio of reads supporting the variant to the total coverage , were tightly grouped into a small number of clusters ( Fig 3A ) , suggestive of the presence of just a few mitochondrial haplotypes in each sample ., To estimate the number and frequencies of haplotypes , and to infer their sequences , we used k-means clustering ( S3 Table ) ., SNPs common to all haplotypes within a sample have a frequency of 1 ., Additional haplotypes are defined by the presence of extra SNPs with mean frequency less than 1 ., We also examined reads supporting SNPs located close to each other ( <100 nt ) for evidence that SNPs were on the same haplotype ( supported by the same reads ) or distinct haplotypes ( never co-occurring on one read ) ., Only 4 pairs of SNP were located within 100 nucleotides of each other and in two different frequency clusters ., In each sample , we gave the haplotype with the highest frequency the prefix H1 ., Haplotypes with successively lower frequencies are labeled H2 and H3 , in that order ., In the patient B sample , a single clear cluster in the SNP frequencies was observed ( Fig 3A ) ., This implies the presence of two haplotypes ., The dominant haplotype , H1_B_REF , has an estimated frequency of 0 . 98 and corresponds to the consensus obtained from de novo assembly ., The second haplotype ( H2_B ) is defined by the presence of 598 SNPs and has an estimated frequency of 0 . 02 ., The patient A sample contains a single clear cluster , containing 593 SNPs with a frequency of 1 . 0 ., An additional 8 SNPs do not cluster well with the dominant group ., These fall into four other clusters identified by k-means ., We chose to ignore the three smallest clusters , resulting in just two closely-related haplotypes: H1_A , which has a frequency of 0 . 98 , and H2_A , which has a frequency of 0 . 02 and is separated from H1_A by just 4 SNPs ., The lack of replication of these SNPs makes the significance of these closely related haplotypes unclear ., All four pig samples contain three clear clusters , forming three haplotypes in each sample ., The haplotypes with the highest frequencies ( H1_u , H1_w1 , H1_w2 , H1_w3 ) have estimated frequencies of 0 . 89 , 0 . 91 , 0 . 89 and 0 . 90 respectively ., Additional haplotypes are composed of combinations of the clusters ( S3 Table ) and are concordant between samples ., The second haplotypes have estimated frequencies of 0 . 10 , 0 . 07 , 0 . 09 and 0 . 09 for the unwashed and three washed samples respectively and we label these H2_u , H2_w1 , H2_w2 , and H2_w3 ., The third haplotypes , which we label H3_u , H3_w1 , H3_w2 and H3_w3 for the unwashed and three washed samples respectively , have estimated frequencies 0 . 02 , 0 . 01 , 0 . 02 and 0 . 01 respectively ., The H1_u , H1_w1 , H1_w2 , H1_w3 haplotypes have 82 , 82 , 81 , 81 SNPs; H2_u , H2_w1 , H2_w2 , H2_w3 have 82 , 82 , 81 , 81 SNPs and H3_u , H3_w1 , H3_w2 , H3_w3 have 87 , 87 , 85 , 87 SNPs respectively ., Similar analyses of DNA sequencing data from a limited number of individual scabies mite eggs or small pools did not identify clusters in the SNP allele frequencies ( S2 Fig ) , suggesting that multiple haplotypes provide evidence for genetic diversity rather than heteroplasmy ., To understand the relationship between the 16 inferred haplotypes , we constructed a phylogenetic tree based on the SNPs present in each haplotype sequence using MEGA5 ( v5 . 2 . 2 ) 51 with a distance measure based on the number of differences between haplotype sequences ( S4 Table ) ., The tree shows that the haplotypes fall into two broad clades and six haplogroups ( Fig 3B ) ., Three haplogroups are found in clinical isolates ( human mite haplogroups 1–3 ) , while the pig mites comprise three haplogroups ( pig mite haplogroups 1–3 ) ., The average difference between haplotypes within each of the pig mite haplogroups is 1–2 SNPs ., The two haplotypes in human mite haplogroup 3 are almost identical and very similar to human mite haplogroup 2 , forming one of the clades , while human mite haplogroup 1 is distinct from the other human mite haplogroups , and more similar to the pig mite haplogroups , forming the second clade ., Scabies is responsible for significant morbidity in Indigenous Australians in many remote communities of northern and central Australia ., Effective control and prevention of scabies epidemics in those communities is of paramount importance as scabies has a long-term effect on the life expectancy and quality ., We have sequenced , assembled and annotated the mitochondrial genome of the scabies mite using a bespoke bait-and-assembly approach; we identified SNPs in multiple isolates from patients and a laboratory pig model , and inferred the haplotype structure and diversity of individual infections ., We used these tools to investigate the genetic diversity within individual infestations ., Larger samples are now needed ., The development of genomics resources for studying the scabies mite will accelerate research into this parasite , just as genome sequences have for other neglected parasitic diseases ., For example , in malaria genomic resources provided means to identify drug resistance causing mutations 59 or in schistosomiasis it helped suggesting new approaches to preventions and strategies for control 60 ., The scabies mite mitochondrial genome sequence will facilitate further population genetics research in this area . | Introduction, Methods, Results, Discussion | The scabies mite , Sarcoptes scabiei , is an obligate parasite of the skin that infects humans and other animal species , causing scabies , a contagious disease characterized by extreme itching ., Scabies infections are a major health problem , particularly in remote Indigenous communities in Australia , where co-infection of epidermal scabies lesions by Group A Streptococci or Staphylococcus aureus is thought to be responsible for the high rate of rheumatic heart disease and chronic kidney disease ., We collected and separately sequenced mite DNA from several pools of thousands of whole mites from a porcine model of scabies ( S . scabiei var . suis ) and two human patients ( S . scabiei var . hominis ) living in different regions of northern Australia ., Our sequencing samples the mite and its metagenome , including the mite gut flora and the wound micro-environment ., Here , we describe the mitochondrial genome of the scabies mite ., We developed a new de novo assembly pipeline based on a bait-and-reassemble strategy , which produced a 14 kilobase mitochondrial genome sequence assembly ., We also annotated 35 genes and have compared these to other Acari mites ., We identified single nucleotide polymorphisms ( SNPs ) and used these to infer the presence of six haplogroups in our samples , Remarkably , these fall into two closely-related clades with one clade including both human and pig varieties ., This supports earlier findings that only limited genetic differences may separate some human and animal varieties , and raises the possibility of cross-host infections ., Finally , we used these mitochondrial haplotypes to show that the genetic diversity of individual infections is typically small with 1–3 distinct haplotypes per infestation . | The scabies mite is a skin parasite that infects humans and other animal species , causing scabies , a contagious disease characterized by extreme itching ., Scabies infections are a major health problem in developing countries and in indigenous Australian populations , where scabies is associated with pyoderma ( skin sores ) and linked to rheumatic fever and rheumatic heart disease ., Little is known about the genetics of the scabies mite ., We have assembled the mitochondrial genome of scabies mites obtained from human patients in Australia and from a pig model ., While investigating the genetic diversity of each infestation , we found that mitochrondial genomes clustered into two broad clades and showed limited genetic diversity within each infestation ., Remarkably , one closely related clade included both human and pig mites , suggesting that mite transmission from pig to human may be possible ., This could have major implications in the management of porcine mange and human scabies . | invertebrates, medicine and health sciences, pig models, population genetics, tropical diseases, computational biology, parasitic diseases, animals, invertebrate genomics, animal models, model organisms, ectoparasitic infections, sexually transmitted diseases, genome analysis, neglected tropical diseases, mitochondria, bioenergetics, population biology, cellular structures and organelles, research and analysis methods, infectious diseases, genomics, scabies, biological databases, mites, animal genomics, arthropoda, biochemistry, haplotypes, cell biology, database and informatics methods, genetics, biology and life sciences, energy-producing organelles, evolutionary biology, genomic databases, organisms, human genetics | null |
journal.pcbi.1000285 | 2,009 | A Genome-Scale Metabolic Reconstruction of Mycoplasma genitalium, iPS189 | Genome-scale metabolic reconstructions are already in place or under development for a growing number of organisms including eukaryotic , prokaryotic and archaeal species 1 ., Metabolic pathway reconstructions are increasingly being queried by systems engineering approaches to refine the quality of the resulting metabolic models 2 ., Curated metabolic models are indispensable for computationally driving engineering interventions in microbial strains for targeted overproductions 3–6 , elucidating the organizing principles of metabolism 7–10 and even pinpointing drug targets 11 , 12 ., Currently , over 700 genomes have been fully sequenced 13 whereas only about 20 organism-specific genome-scale metabolic models have been constructed 1 , 14 , 15 ., Figure 1 pictorially demonstrates , in logarithmic space , the widening gap between organism-specific metabolic models and fully sequenced genomes over the past twelve years ., It appears that metabolic model generation can only keep pace with about 1% of the fully sequenced genomes ., In response to this flood of present and future genomic information , automated tools such as Pathway Tools 16 and SimPheny ( Genomatica ) have been developed that , using homology comparisons , allow for the automated generation of draft organism-specific metabolic reconstructions that can subsequently be upgraded into metabolic models ., All of these models remain to some extent incomplete as manifested by the presence of unreachable metabolites 17 and some growth inconsistencies between model predictions and observed in vivo behavior 2 ., In particular , optimization-based techniques for automatically identifying metabolites disconnected from the rest of metabolism ( i . e . , GapFind ) and hypotheses generators ( i . e . , GapFill ) for reconnecting them have recently been introduced 17 ., In order to resolve substrate utilization prediction inconsistencies , Reed et al . 2 introduced a novel approach for identifying what reactions to add to the genome-scale metabolic models of E . coli to correct some of the in silico growth predictions ., In our group , we have taken the next step for gene deletion data by attempting to correct all such growth inconsistencies by allowing not just additions but also eliminations of functionalities in the model ( i . e . , GrowMatch ) ( Satish Kumar and Maranas , submitted ) ., As outlined in Figure 2 , in this work , we describe the application of these automated methodologies during the Mycoplasma genitalium model construction process ( as opposed to an a posteriori mode of deployment ) ., M . genitalium has received considerable attention as it is the smallest organism that can be grown in pure culture , having a genome size of ∼580 kb and approximately 480 protein coding regions 18 , 19 ., An examination of its genome content revealed limited metabolic capabilities 20 , leading researchers to suggest it may be a close approximation to the minimal set of genes required for bacterial growth 19 , 21 ., Several researchers have carried out genomic and proteomic analysis of M . genitalium to quantify this minimal set ., For example , Mushegian and Koonin have carried out a detailed comparison of M . genitalium and H . influenzae proteins to derive a set of 256 genes that they suggested are necessary for viability 22 ., Further , genomic analyses of these species revealed that Mycoplasma genes encode for several catabolic and metabolite transport proteins but for only a limited number of anabolic proteins suggesting that Mycoplasma species need to scavenge for the required nutrients from the surrounding environment 20 ., More recently , Glass and co-workers performed global transposon mutagenesis and established that 382 of the 482 protein coding sequences are essential genes for this minimal bacterium 19 ., These gene sets and essential gene analyses , however , have not been put into context of a complete functional metabolic model ., Mycoplasma genitalium is not only the closest known approximation of a minimal cell but also an important sexually transmitted human pathogen ., It is a cause of nongonococcal urethritis in men and is associated with genital tract inflammatory diseases in women , including endometritis , cervicitis , pelvic inflammatory disease , and tubal factor infertility ( for a recent review see 23 ) ., Additionally , evidence suggests that M . genitalium infection increases the risk of contracting HIV-1 24–26 ., Mycoplasmas , the generic name for the bacteria that comprise the Mollicutes taxon , evolved from the low G+C Gram positive bacteria through a process of massive genome reduction 27 ., Their salient characteristics in addition to small genomes are a lack of a cell wall , and an almost complete inability to synthesize the building blocks of DNA , RNA , proteins , and cell membranes ., The above underlines the importance of investigating the molecular biology of mycoplasma and M . genitalium in particular ., However , a major hindrance to M . genitalium research and laboratory diagnosis of infection has been their cultivation in vitro ., While defined media are present for some mycoplasmas 28–30 , researchers have often had to resort to complex media to cultivate most mycoplasmas , including M . genitalium ., M . genitalium , and many other mycoplasmas are cultured in vitro in SP-4 medium ., This extremely rich medium contains several undefined additives including peptones , yeast hydrolysate , yeast extract and 17% fetal bovine serum 31 ., The use of complex undefined growth media has interfered with the molecular definition of mycoplasma metabolic pathways , genetic analyses , estimation of growth requirements , characterization of auxotrophic mutants and examining the nutritional control of bacterial pathogenecity ., In this paper , we highlight the development of an in silico model of metabolism of M . genitalium ., It was subjected to network connectivity gap detection and reconnection as well as restoration of consistency with in vivo gene essentiality experiments 19 ., We subsequently used the model to pinpoint components in the growth medium that are needed for the production of all components of biomass in an effort to eventually eliminate the need for non-defined components such as serum in the growth medium ., The metabolic reconstruction of M . genitalium was carried out in a series of successive refinements ( see Figure 2 and Materials and Methods ) ., Of the 482 predicted open reading frames ( ORFs ) , 113 ( 23% ) only have annotations of ( conserved ) putative or hypothetical proteins ., Of the remainder , 369 ORFs have well-defined annotations , with functions either shown biochemically or predicted for 272 of them ( 42% ) 18 , 19 ., From these well-annotated genes , 82 ( 17% ) are not involved in specific metabolic transformations , but rather encode proteins whose roles include DNA/RNA polymerization , DNA repair , protein folding and adhesion ., The model construction process started with the application of an automated procedure for creating a draft metabolic reconstruction from the genome sequence of M . genitalium ( see Materials and Methods ) 32 , 33 ., This auto-generated model contained 150 genes and 249 unique metabolites associated with 167 reactions ( see Table 1 ) ., The 150 genes comprise 31% of the ORFs present in the genome and provided a solid starting point with very little manual effort ., Additional homology searches of genes not included in the auto-model against the NCBI database increased the number of model components to 187 genes and 263 distinct metabolites associated with 179 reactions ( Table 1 ) ., These genes comprise 39% of the ORFs present in the genome ., These reactions enable , for example , the uptake of glycerol into the cell , thymidine kinase , and ribonucleotide diphosphate reductase ( Table 2 ) , as well as the remaining annotated ribosomal proteins that were not previously incorporated ., As indicated in Table 2 , the bidirectional protein-protein BLAST ( i . e . , BLASTp ) expectation values exhibited by these genes when compared to the biochemically-characterized counterpart in other organisms provided strong support for their inclusion in the model ., In addition , we also included nine nucleoside di- and tri-phosphate kinase associated reactions based on the observation that the kinase pool of M . genitalium has relaxed substrate specificity 34 ., As part of the initial model generation , we also checked the BLASTp scores , gene annotations , and the cluster of orthologous groups ( COGs ) ontology 35 of all genes in the automodel , to guard against the erroneous inclusion of functions in the model ( see Materials and Methods ) ., A metabolic reconstruction has been described as a 2-D annotation of a genome 32 ., The generation of a computations-ready model requires the complete assignment of metabolites to reactions , inclusion of exchange reactions , resolution of gene-enzyme associations , and derivation of biomass equations ., Here , we largely follow the steps put forth by 33 in the latest E . coli metabolic reconstruction ., The computations-ready model along with the biomass description allows for the use of optimization-based techniques for testing and correcting for the presence of connectivity gaps ( step 3 ) and growth prediction inconsistencies ( step 4 ) ., The initial model was constructed almost exclusively based on homology searches within model libraries ., This procedure led to the presence of many network gaps 17 preventing 177 reactions ( 99% of total ) from carrying flux under all uptake conditions ( i . e . , they were blocked ) ., As a consequence , these blocked reactions precluded the formation of some of the biomass components ., Using GapFind 17 we found that a total of 175 ( 70% ) cytoplasmic metabolites could not be produced inside or transported into the intracellular space ., These metabolites included a number of biomass precursor metabolites ( e . g . , some amino acids , cofactors and metal ions ) that had not been assigned uptake reactions ., Of all the blocked metabolites , thirteen were involved in nucleotide metabolism and eight were metal ions without an identified transporter ., We also note that 40 of these metabolites are charged/uncharged tRNA molecules , which are active in closed reaction cycles used in forming the protein component of biomass ., Through the use of GapFill 17 we subsequently sought to bridge these network gaps through the addition of reactions , transport pathways and relaxation of irreversibilities of reactions already in the model ., Reactions known not to be present in M . genitalium ( e . g . , an incomplete TCA cycle ) were excluded as gap filling candidates ., We first applied GapFill to unblock constituents of biomass guided by the known components in the growth medium ., We unblocked biomass production by adding 65 reactions , for which most ( i . e . , 43 ) were involved in metabolite transport , such as for the uptake of amino acids ( 14 ) , folate , riboflavin , metal ions ( 8 ) , and cofactors such as CoA ., Among the remaining reactions were those responsible for the hydrolysis of dipeptides ( 15 ) and eight reactions involving other biotransformations ., We performed an additional round of BLASTp comparisons of genes annotated with these reactions against the M . genitalium genome to determine if we could associate any of these reactions with specific genes in M . genitalium ., We found five proteins catalyzing these reactions that had BLASTp scores smaller than 10−5 ( see Table 2 ) ., For example , GapFill suggested the addition of reaction glutamyl-tRNA ( Gln ) amidotransferase in the model to allow the formation of the gln-tRNA molecule ., BLASTp searches allowed us to link this activity with the genes encoding for the three subunits ( MG098 , MG099 and MG100 ) ., Note that these three genes ( and others added during this step ) were not added earlier ( steps 1 and 2 ) on account of their ambiguous functional characterization ., By bringing to bear both homology ( though BLASTp ) and connectivity restoration ( through GapFill ) , here we rely on multiple pieces of evidence when appending a new functionality and corresponding genes to the model ., Even after unblocking biomass formation , 43 metabolites remained blocked and were subsequently analyzed by GapFill ., The results from GapFill are summarized in Figure 4 ., We were able to reconnect three metabolites by treating three reactions as reversible ., We also found that the originally assigned ( based on the auto-model ) directionality of 1-acyl-sn-glycerol-3-phosphate acyltransferase was incorrect ., It was subsequently reversed and found to be in accordance with both KEGG and MetaCyc entries ., An additional 21 metabolites were reconnected by adding 18 reactions from the KEGG and MetaCyc databases ( see Materials and Methods ) ., The addition of these 18 reactions also introduced an additional nine metabolites ( three of which were involved in glycerolipid metabolism ) to the model ., Finally the incorporation of uptake/transport reactions reconnected an additional four metabolites ., We performed an additional round of BLASTp comparisons and we were able to associate three out of 22 reactions with specific genes ( see Table 2 ) ., We found that the associated gene ( i . e . MG066 ) for 1-deoxy-D-xylulose 5-phosphate synthase was already included in the model but with a different functionality ( i . e . , transketolase ) ., The secondary synthase functionality , revealed by GapFill/BLASTp , was subsequently associated with gene MG066 in the model ., A similar situation occurred with MG053 , which was already associated with phosphomannomutase in the model ., In addition , gene MG259 ( annotated as “modification methylase , HemK family” in the Comprehensive Microbial Resource , http://cmr . jcvi . org ) was added to the model to carry out the glutamine-N5 methyltransferase activity elucidated by GapFill/BLASTp ., The model statistics after correcting for network gaps are summarized in Table 1 ., Based on in vivo gene essentiality data 19 we deduced that there are 174 essential genes and 19 non-essential genes among the 193 genes provisionally present in the model ( after steps 1 , 2 , and 3 ) ., We note that the in vivo gene essentiality experiments were performed using non-defined medium containing serum and yeast hydrolysate among other rich components ., During the in silico model predictions/comparisons , we allowed the uptake of all extracellular metabolites with transport reactions , except for sugars other than glucose , in order to computationally approximate this medium ., Using a recently proposed diagnostic of the percentage of correctly-identified essential genes 38 , 39 , we found that the model correctly identified 137 out of a total of 174 essential genes ( i . e . , specificity of 79% ) and 16 out of a total of 19 non-essential genes ( i . e . , sensitivity of 84% ) ., This implies that the model ( after steps 1 , 2 , and 3 ) was 79% correct in its overall accuracy in growth predictions ( i . e . , 153 of 193 ) ., Most of the mismatches ( 92% ) were over-predictions of the metabolic capabilities ( i . e . , predicting growth when none is observed in vivo ) instead of under-predictions ( i . e . , predicting no growth when growth is observed in vivo ) ., We subsequently deployed the GrowMatch method ( Satish Kumar and Maranas , submitted ) to rectify as many as possible of the erroneous essentiality predictions by the model ., GrowMatch functions by identifying the minimal number of model modifications required to restore consistency between growth predictions and gene essentiality experiments ( see Materials and Methods ) ., Model under-predictions include mutants ( MG410 and MG411 ) , which encode the subunits for the phosphate transporter , preventing in both cases the uptake of phosphate ., This implies that M . genitalium must have an additional uptake route of phosphates ., Even though GrowMatch suggested a number of phosphate uptake alternatives to resolve this conflict and Glass and coworkers 19 had posited the activity of a putative phosphonate transporter ( MG289 , MG290 , and MG291 ) , we decided not to add them to the model as no direct evidence exists to ascertain their presence ., For instance , the putative phosphonate transporter might be nonspecific thus also enabling uptake of phosphate ., Alternatively , the unidentified phosphonate substrate might be catabolized to yield phosphate through a number of reactions ., The other incorrect under-prediction involved MG138 ( homologous to elongation factor 4 in E . coli ) , which had been associated with macromolecule formation during the automodel construction ., We observed that deletion mutants of the homolog in E . coli ( lepA ) are viable 40 ., Based on this information , we removed this gene and its erroneous association as an essential component of the biomass equation from the model ., Interestingly , three of the 37 erroneous over-predictions were corrected by adding three membrane components to the biomass equation ( see Table 3 ) ., These components were not added during the initial model construction because it was not clear which ( if any ) of this class of metabolites were essential ., An additional three erroneous predictions of non-essentiality were corrected by suppressing two reactions ., One of these reactions , inosine kinase , was added during GapFill but not linked to an associated gene ., Suppression of this reaction corrected two over-predictions but did not invalidate any correct model predictions , suggesting that the reaction activity is unlikely to be present in vivo , at least under the experimental conditions , and perhaps is not an activity encoded by M . genitalium ., An additional six over-predictions involved two metal ion ABC transporters ., GrowMatch identified each transporter to be essential when the other one was suppressed ., We rejected co-regulation of the two transporters as a model restoration mechanism ., Instead , we restored consistency for three of the six genes by assigning the cobalt uptake to the complex with a better homology to characterized cobalt transporters ( MG179 , MG180 , MG181 ) ., The remaining three genes were removed from the model ., An alternative interpretation of the GrowMatch results is that some other ion uptake reaction ( s ) are uniquely associated with these transporters and are thus responsible for the in vivo phenotype ., Overall , the application of GrowMatch to the metabolic model led to the generation of a number of testable hypotheses regarding the presence or absence of specific functionalities and emphasized the importance of determining the substrate specificity of the transporters ., We also identified reactions that had to be inactivated only for certain knock-outs suggesting their dependence on the genetic background in addition to the specific environmental conditions ., For example , MG112 ( ribulose-phosphate 3-epimerase ) had to be suppressed in conjunction with two single gene deletions ( i . e . , conditional suppressions ) to restore consistency with the in vivo data , suggesting possible regulation events ., Figure 5 summarizes the complete GrowMatch results ., Considering only those changes that could be repaired with global model adjustments conservatively raised the overall percent accuracy of the model ( versioned as iPS189 ) from 79 to 87% ., By recalculating the diagnostics of the percentage of correctly- identified essential genes 38 , 39 we found that the model is now 87% ( i . e . , 149 of 171 ) correct in its essentiality predictions ( specificity ) and 89% ( i . e . , 16 of 18 ) correct in its non-essentiality predictions ( sensitivity ) ., The iPS189 model predicts that M . genitalium uptakes fructose via a PTS system ., The fructose is converted to fructose 1 , 6-bisphosphate ( fdp ) and finally enters the glycolytic pathways to produce lactate via lactate dehydrogenase ., Neither fructose nor glycerol uptake was found to be essential as glucose could be efficiently taken up and converted ., Specifically , glucose is transformed to glucose 6-phosphate and finally to fdp via phosphofructokinase ., As expected , the model also indicates that co-enzyme A ( CoA ) is taken up , since M . genitalium has no coA biosynthesis genes ., Additionally , accetal-CoA ( accCoA ) is not formed via pyruvate formate lyase but rather by pyruvate dehydrogenase ., Interestingly , we find that should acetate be taken up , it is converted to acetyl phosphate ( actp ) and finally to accoa by phosphotransacetylase ., Sources for acyl-CoA ( aCoA ) and CDP-glucose are also required for lipid production ., The metabolites riboflavin and nicotinic acid ( niacin ) are taken up for synthesis of the cofactors FAD and NAD , respectively ., In addition , both spermidine and putrescene are directly imported as biomass components ., Similarly , we also found that D-ribose ( rib-D ) is needed to fuel the truncated pentose phosphate pathway ., Examination of fluxes indicated that the uptake of rib-D results in production of 5-phospho-α-d-ribose 1-diphosphate , which enables the conversion of adenine to amp ., We also deduced that only adenine and cytidine are precursors to nucleotides and nucleosides ( CTP , dCTP , UTP , dUTP , dTTP ) ., Interestingly , the model required the direct uptake of GTP and could not be produced through the uptake of guanine ., Model modifications that eliminate this requirement using GrowMatch resulted in a number of incorrect gene essentiality predictions ., The need for the direct uptake of GTP is consistent with the fact that in M . mycoides the guanine nucleotide pathways depend on transport of preformed guanine derivatives 34 , and that a number of other Mycoplasmas are not able to grow on medium that only contains guanine as a nucleobase 41 ., In addition , all amino acids are imported directly from the environment as either monomers or dipeptides ., Unlike many other mycoplasmas , M . genitalium is an arginine nonfermenting species , and not surprisingly arginine deiminase activity was not present in the model ., Furthermore , in iPS189 , the only participation of the amino acid arginine is its direct incorporation into biomass ., Finally , flux predictions revealed that lactate is the main product of M . genitalium fermentation ., A key targeted application of the iPS189 metabolic model is to drive the development of a defined growth medium ., As noted above , gene essentiality experiments were performed using a non-defined medium , SP-4 , which contains beef heart infusion , peptone supplemented with yeast extract and fetal bovine serum ., The use of an undefined medium can confound the characterization of gene essentiality , as the exact environmental conditions are not fully specified ., Furthermore , the lack of a defined growth medium complicates the understanding of nutritional control and regulation of pathologies , evaluation of drug susceptibility , characterization of auxotrophic mutants and performing genetic analysis ., Using trial-and-error researchers have already attempted to formulate defined media by systematically deleting components from an undefined or complex media 28 ., For example , defined media have been constructed for the growth of Mycoplasma capricolum 42 , Acholeplasma laidlawii 43 , Spiroplasmas 44 , and a semi-defined medium was recently formulated for two Mycoplasma mycoides subspecies 45 ., However such approaches do not take into account the balance and availability of chemical species in cellular metabolic pathways to systematically guide medium design ., Genome-scale models of metabolism provide maps for tracing missing components needed for biomass formation , redox potential and ATP maintenance 46–48 ., These models have already been successfully employed to establish minimal reaction sets needed for growth under several uptake environments 49 , elucidate substrate uptake requirements for several microbial organisms such as Helicobacter pylori 50 and Haemophilus influenzae 50 and more recently design complete growth media 51–53 ., Motivated by these medium-associated shortcomings , we used the iPS189 metabolic model as a roadmap of the available transporters , metabolites and internal interconversions to seek out the minimum number of growth medium components necessary for biomass production ., We used as a starting point the components of the C5 medium ( Rodwell , 1983 ) , which is used as a component of SP-4 , with the addition of folate and biotin and the replacement of thiamine by the four individual deoxynucleosides ( see Table S4 ) ., By minimizing the total number of additional components that are needed for growth ( see Materials and Methods ) , a number of additional components were identified as required ( see Table 4 ) ., The purported reduced bio-availability of the amino acids in their monomeric form might be , in part , the reason for M . genitaliums requirement of yeast extract ., The listed dipeptides were found to be needed components consistent with the presence of dipeptide transporters that were found to be essential genes both in vivo and in iPS189 ., In addition to components identified based on the metabolic model , we identified that some of the enzymes present in the model required cofactors not included present in the model ., In addition , a number of additional carbon source supplements could be investigated ., We note that these medium predictions generate a necessary but not a sufficient list of components needed in the medium ., For instance , a number of components of biomass such the lipids are not fully specified in the model ., Furthermore , additional signaling molecules might be necessary signals for allowing growth of M . genitalium in a defined medium ., We anticipate that the iterative process of testing and refining a defined medium followed by updates to the model will successively help pinpoint the precise metabolic capabilities and requirements of M . genitalium ., In this paper , our focus was two-fold:, ( a ) to construct the metabolic model for the minimal organism and pathogen M . genitalium , and, ( b ) to introduce and bring to bear automated procedures that streamline the construction of metabolic models ., The procedure does not require a fully annotated genome and can serve to complement existing annotations 2 by generating testable hypotheses of functionality ., We made use of BLASTp to associate functionality to ORFs in the multiple stages , but alternate methods of determining enzymatic function , such as profile-based approaches 54 , 55 could provide additional or alternative assignments ., Hypothesizing novel pathways 56 will also likely become increasingly important as metabolic reconstructions for more diverse organisms are carried out ., Many genome annotation errors are caused 57 by the use of non-specific reaction compound associations or partially qualified Enzyme Commission numbers 58 ., For instance , a comparative study identified an 8% difference in ORFs across three different functional annotations of M . genitalium 59 ., Therefore , the direct use of genome-annotation derived reconstructions ( e . g . , KEGG and Pathway Tools generated models ) can lead to inaccurate descriptions of metabolic behavior 60 ., Specifically , a recent study has shown that a permissive inclusion of pathways from these reconstructions can lead to models that overpredict the metabolic capabilities of Lactococcus lactis 61 ., To safeguard against this issue , we have used manually curated metabolic models as libraries of biotransformations ., We note that this procedure allows for the straightforward incorporation of reactions that are charge and elementally balanced , which is not the case with many reactions in KEGG 62 ., Earlier efforts have examined the general metabolism of Mycoplasmas 63 or targeted some specific subsections such as purine and pyrimidine metabolism 41 , 64 ., Although there is an overlap between the reaction set in our reconstruction and those available in previous studies , developing a M . genitalium specific model with growth requirement as a constraint revealed novel uptake and non-gene associated reactions that previous studies were unable to identify ., The identification of which metabolites are produced internally or transported directly from the extracellular environment was complicated by the lack of a defined medium for M . genitalium and its fastidious growth-requirements ., Notably , no other metabolic model reconstruction efforts to date were complicated by the lack of both a well-defined biomass composition and a defined growth medium ., Here , we allowed the uptake of all metabolites known to be present in the current medium ., We also included metabolites either with identified transporters or those necessary for reconnecting blocked metabolites/reactions ., Even though missing metabolites and pathways still exist in iPS189 , we were able to achieve a high degree of agreement between the model predictions and in vivo gene essentiality data ( 87% ) ., We note that the most recent iteration of the metabolic model for E . coli , an organism which has both a well-defined biomass composition as well as chemically defined growth media , has an overall agreement with in vivo gene essentiality data of 91% under aerobic glucose conditions 33 ., Becker and Palsson 38 have recently reported that most in silico models correctly predict less than 45% of essential genes ( called specificity in 39 ) ; for the most-recent E . coli model this diagnostic was 66% ., iPS189 has similar performance on both the overall accuracy in growth predictions and specificity ( both 87% ) because of , in part , the much higher percentage of essential genes in M . genitalium but also its careful construction ., Additional in vivo gene essentiality studies using a fully defined medium could usher a more accurate elucidation of the true metabolic capabilities of M . genitalium , as well as suggest improvements to the reconstruction ., The metabolic model iPS189 is smaller than the 256 genes suggested as a minimal gene set based on comparison of M . genitalium and H . influenzae proteins 22 ., In large part , this seeming discrepancy results from the intentional exclusion from the model of many genes that are essential ( e . g . , those encoding DNA and RNA polymerases ) though not directly related to metabolic processes ., If such genes were included , the model size would increase by ( at least ) 59 genes to 248 ., In addition , 68 genes that are essential in vivo have unknown function , and thus cannot ( yet ) be incorporated into the model ., It is possible that they could , in part , carry out some of the non-gene associated reactions that were proposed during the GapFill procedure ., Determining their metabolic function through biochemical and molecular biology techniques , as well as determining the substrate specificity for non-characterized transporters , would improve subsequent metabolic models ., Looking to the future , we note that it was recently shown that it is feasible to transplant the genome from one mycoplasma species to another 65 , thus opening the door to the transplantation of a perhaps completely synthetic genome ., Furthermore , the recent announcement of the de novo synthesis and assembly of the complete M . genitalium genome 66 brings closer to reality the ab initio design of microbes from scratch that are exquisitely tuned for specific biotechnological applications ., The constructed metabolic model iPS189 could serve as a core of metabolic functions to add upon so as to bring about the desired biological functionalities and/or production capabilities ., The first step in our reconstruction of the genome-scale model of metabolism of M . genitalium involved analyzing the annotated M . genitalium G-37 genome sequence with the SimPheny automated model generation platform developed by Genomatica ( San Diego , CA ) ., This au | Introduction, Results, Discussion, Materials and Methods | With a genome size of ∼580 kb and approximately 480 protein coding regions , Mycoplasma genitalium is one of the smallest known self-replicating organisms and , additionally , has extremely fastidious nutrient requirements ., The reduced genomic content of M . genitalium has led researchers to suggest that the molecular assembly contained in this organism may be a close approximation to the minimal set of genes required for bacterial growth ., Here , we introduce a systematic approach for the construction and curation of a genome-scale in silico metabolic model for M . genitalium ., Key challenges included estimation of biomass composition , handling of enzymes with broad specificities , and the lack of a defined medium ., Computational tools were subsequently employed to identify and resolve connectivity gaps in the model as well as growth prediction inconsistencies with gene essentiality experimental data ., The curated model , M . genitalium iPS189 ( 262 reactions , 274 metabolites ) , is 87% accurate in recapitulating in vivo gene essentiality results for M . genitalium ., Approaches and tools described herein provide a roadmap for the automated construction of in silico metabolic models of other organisms . | There is growing interest in elucidating the minimal number of genes needed for life ., This challenge is important not just for fundamental but also practical considerations arising from the need to design microorganisms exquisitely tuned for particular applications ., The genome of the pathogen Mycoplasma genitalium is believed to be a close approximation to the minimal set of genes required for bacterial growth ., In this paper , we constructed a genome-scale metabolic model of M . genitalium that mathematically describes a unified characterization of its biochemical capabilities ., The model accounts for 189 of the 482 genes listed in the latest genome annotation ., We used computational tools during the process to bridge network gaps in the model and restore consistency with experimental data that determined which gene deletions led to cell death ( i . e . , are essential ) ., We achieved 87% correct model predictions for essential genes and 89% for non-essential genes ., We subsequently used the metabolic model to determine components that must be part of the growth medium ., The approaches and tools described here provide a roadmap for the automated metabolic reconstruction of other organisms ., This task is becoming increasingly critical as genome sequencing for new organisms is proceeding at an ever-accelerating pace . | genetics and genomics/bioinformatics, computational biology/metabolic networks, computational biology/systems biology | null |
journal.pntd.0002900 | 2,014 | Risk Factors for Active Trachoma and Ocular Chlamydia trachomatis Infection in Treatment-Naïve Trachoma-Hyperendemic Communities of the Bijagós Archipelago, Guinea Bissau | Trachoma is caused by ocular infection with Chlamydia trachomatis and is the leading infectious cause of blindness worldwide ., It manifests as distinct clinical syndromes beginning with an acute self-limiting keratoconjunctivitis , which following repeated episodes may progress to a more chronic inflammatory and immunofibrogenic process leading to conjunctival scarring and blinding sequelae ., Trachoma is endemic in 50 countries , with 325 million people at risk of blinding disease 1 ., Trachoma is responsible for visual impairment in 1 . 2 million people and 3% of blindness globally 1 ., The highest prevalence of active trachoma ( trachomatous inflammation-follicular ( TF ) and/or trachomatous inflammation-intense ( TI ) ) is in sub-Saharan Africa and the distribution of disease is heterogeneous 2 ., Ocular C . trachomatis is probably transmitted between individuals through direct spread from eye to eye during close contact , direct or indirect spread of infected nasal or ocular secretions on fingers or cloths ( fomites ) and indirect passive transmission by eye seeking flies ., There is no known animal reservoir of C . trachomatis in endemic environments , the primary reservoir being young children ., Blinding trachoma is usually found in hot , arid , dusty regions ., A recent systematic review examined studies reporting higher trachoma prevalence in savannah areas and areas of lower rainfall , and found weak but consistent evidence supporting anecdotal findings that trachoma is associated with semi-arid environments 3 ., This study was conducted on the Bijagós Archipelago , a remote group of islands off the coast of Guinea Bissau with a total population estimated at 24 , 000 4 , where trachoma is hyperendemic ., The climate and environment are not typical of trachoma-endemic areas ., The islands are covered with subtropical forest and altitude does not exceed 50 m ., The climate is tropical , hot and humid ., The islands are surrounded by mangroves and mudflats ., There is significant rainfall ( average 400 mm/month ) from May to November 5 ., Many studies have suggested that the prevalence of trachoma is associated with environmental risk factors such as poor sanitation , access to water and latrine use 6 , 7 ., Eye-seeking flies ( Musca sorbens ) have also been associated with trachoma as passive vectors 8 but significant disease exists in areas where fly populations are scarce and are therefore less likely to contribute to trachoma transmission 9 ., M . sorbens preferentially breeds in human faeces and there may be association between fly populations and lack of latrine access or use 6 , 8 ., Social risk factors such as migration events and crowded living conditions have also been shown to be important in transmission of C . trachomatis and the appearance of active trachoma 10 , 11 ., Clustering of disease at the community , household and bedroom levels has been noted and is likely to reflect the dynamics of transmission between family members with prolonged close contact 6 , 10–12 ., Most transmission events have been shown to occur at the household level with more gradual spread within the community 13 ., The World Health Organization ( WHO ) advocates the implementation of the SAFE strategy ( Surgery for trichiasis , Antibiotics for active infection , Facial cleanliness to prevent disease transmission and Environmental improvement to increase access to water and sanitation ) for trachoma elimination ., The WHO recommends annual mass treatment of entire communities with oral azithromycin for three years if the prevalence of TF in 1–9 year olds within a district or community exceeds 10% ., Mass antibiotic treatment aims to clear infection from communities such that transmission ceases to be a public health concern 14 ., Following this , an assessment is made of A , F and E interventions and a decision is taken to continue or cease treatment 15 ., Despite their inclusion in the SAFE strategy , local environmental factors are not well understood , though many are potentially modifiable risk factors for infection and disease ., The relative importance of these risk factors is not clear and may differ between communities ., Fewer studies have investigated risk factors for disease and infection simultaneously 16–19 ., Understanding risk factors associated with trachoma and C . trachomatis infection may increase our understanding of disease and transmission dynamics allowing for optimization of community-specific interventions ., We examined household and individual-level risk factor associations with ocular C . trachomatis infection and active trachoma in this unique environment , where trachoma is a significant public health problem ., Prior to these surveys , these communities were treatment-naïve and had not been exposed to any trachoma control interventions ., This study was conducted in accordance with the declaration of Helsinki ., Ethical approval was obtained from the Comitê Nacional de Ética e Saúde ( Guinea Bissau ) , the LSHTM Ethics Committee ( UK ) and The Gambia Government/MRC Joint Ethics Committee ( The Gambia ) ., Verbal consent was obtained from community leaders ., Written informed consent was obtained from all study participants or their guardians on their behalf if participants were children ., A signature or thumbprint is considered an appropriate record of consent in this setting by the above ethical bodies ., We conducted a cross-sectional population-based trachoma prevalence survey on four islands of the Bijagós Archipelago of Guinea Bissau ( Bubaque , Canhabaque , Soga and Rubane ) in January 2012 ., Trachoma survey methodology has been described previously 20–22 ., We randomly sampled one in five households , representing a one stage probability sample design satisfying desired criteria for population-based prevalence surveys 20 , 21 ., A sample size of 1500 ensured adequate power with conservative correction ( using a design effect of 4 ) to account for anticipated household clustering ., The sample size provides over 90% power to detect an odds ratio ( OR ) of 2 associated with a risk factor found in 20% of subjects without disease or infection , or an OR of 3 for a risk factor present in 5% of subjects without disease or infection with 95% confidence ., The sample size also provides good precision for an estimated TF prevalence of >25% in 1–9 year olds on the four islands of Bubaque and Canhabaque ( ±4% ) , Soga ( ±6% ) and Rubane ( ±10% ) , which is adequate to determine whether these communities require mass drug treatment with azithromycin in line with WHO policy ., A census of persons resident in randomly selected households was conducted prior to the household survey ., Residency was defined as living within the household for longer than the preceding month or intending to stay resident in the household for longer than one month ., This was updated to reflect the de facto population ( those present in the household on the previous night ) to limit absenteeism ., Demographic , socio-economic and environmental information was collected at household and individual levels ., Household-level risk factor data were obtained using questionnaires administered to the household head or an appropriate responsible adult and included items on the level of education of the household head , their socio-economic status , whether the household had been exposed to any health education or promotion within the community , household access to and use of latrines , access and use of water and measures of sanitation , waste and presence of flies in the environment ., The questionnaire was supported through observational data collected on water use , latrine use and environmental sanitation ., Household size ( measured as number of members of all ages ) and number of children under the age of 10 years within the household was recorded ., Researchers were masked to trachoma status of household members at the time of the household survey ., Following the household risk factor survey all individuals from study households were invited to attend for clinical examination and conjunctival sampling ., Individuals age , sex and ethnic group and data on facial cleanliness ( the presence of ocular and/or nasal discharge and whether or not there were flies on the face ) were collected at the time of examination ., A single trained examiner assessed each participant using the WHO simplified grading system where TF ( trachomatous inflammation – follicular ) and/or TI ( trachomatous inflammation – intense ) constitute active trachoma and TS ( trachomatous scarring ) , TT ( trachomatous trichiasis ) and CO ( corneal opacity ) are trachomatous sequelae which may lead to blindness 23 ., A trachoma grade was assigned to the upper tarsal conjunctivae of each consenting participant using adequate light and a 2 . 5× binocular magnifying loupe ., Two sequential samples were taken from the left upper tarsal conjunctiva of each participant with Dacron swabs ( Fisher Scientific , UK ) using a standardised procedure 24 , 25 ., The first swab was collected into transport medium for other studies ., The second dry swab was collected into a microcentrifuge tube ( Simport , Canada ) and used in this study ., Previous work using the Roche Amplicor CT/NG assay ( Roche Molecular Systems , NJ USA ) in a population-based study has shown that there was good agreement between first and second swabs with respect to C . trachomatis DNA positivity by PCR 26 ., Swabs were kept on ice in the field and frozen to −80°C within 8 hours of collection ., Measures were taken to avoid cross-contamination in the field ., Control swabs ( pre-marked swabs drawn at random from the swab dispenser and passed 10 cm in front of the eye ensuring no contact between the swab tip and participant ) were taken to ensure field and laboratory quality control ., After survey completion all communities on the study islands were treated with a single height-based dose of oral azithromycin in accordance with WHO and national protocols ., Each swab was suspended in 400 µl sterile phosphate buffered saline ( PBS ) after thawing at room temperature ., DNA was extracted from the swab/PBS suspension using an adapted whole blood protocol on the QIAxtractor ( Qiagen , Crawley , UK ) automated instrument and eluted into a final volume of 50 µl DX Elution Buffer ( Qiagen ) ., C . trachomatis DNA was detected using the Roche Amplicor CT/NG assay ( validated for use with ocular swabs 27 ) ., Required reaction buffer conditions were obtained as described previously and used in the standard assay 28 ., Positive and negative samples were assigned according to the manufacturers instructions ., In this study , C . trachomatis infection is defined as the presence of C . trachomatis DNA by Amplicor PCR ., Data were double entered into a customised database ( MS Access 2007 ) ., Discrepancies were resolved through reference to original data forms ., Data were further cleaned prior to analysis in STATA 13 ( Stata Corporation , College Station , Texas USA ) ., Random effects logistic regression models were used to assess the variability between villages and households assuming a three tier hierarchy to the data ( at village , household and individual levels ) ., Null models were used to examine the effect of cluster variables on the outcome using the likelihood ratio test ( LRT ) , which if significant , provided strong evidence that between-village and household variance was non-zero ., The log likelihood and the LRT were used to compare models ., Univariable associations with active trachoma ( TF/TI ) and infection with C . trachomatis were examined using two-level hierarchical random effects logistic regression , accounting for between-household variation ., Covariates associated with active trachoma or C . trachomatis infection with p<0 . 10 ( using the Wald test ) were sequentially added to the multivariable model after a priori adjustment for age and gender ( as categorical variables ) ., Covariates were retained in the final model if the Wald p-value≤0 . 05 unless otherwise specified ., Further exploration of environmental predictor variables was conducted using logistic and hierarchical random effects logistic regression models as appropriate using the same criteria ., As C . trachomatis infection is on the causal pathway between several risk factors and active trachoma , models with and without C . trachomatis infection were fitted ., The model including C . trachomatis infection provides estimates of independent associations of other risk factors with active trachoma which are not mediated through C . trachomatis infection ., All statistical analyses were carried out using STATA 13 ., Statistical significance was determined at the 5% level ., From an estimated total rural population of 5 , 613 inhabitants on the four study islands 4 , 1 , 511 individuals from 293 randomly selected households across 39 villages were enrolled ., Of these , 1 , 508 had an ocular assessment and conjunctival swabs were obtained from 1 , 507 ., The median age of participants was 13 years ( range 1 month–88 years ) and 57% were female ., The majority of participants were of the Bijagós ethnic group ( Table 1 ) ., The prevalence of active trachoma in 1–9 year olds was 22 . 0% ( 95% Confidence Interval ( CI ) 18 . 9–25 . 5% ) ( 136/618 ) ., The prevalence of active trachoma was highest in children under the age of 5 years ( 27 . 3% ( 95% CI 23 . 1–31 . 9% ) ( 113/416 ) ) ., Overall , 11 . 1% ( 95% CI 9 . 4–12 . 6% ) ( 167/1508 ) of the study population had active trachoma ., The relationship between trachoma and infection is shown in Table, 2 . C . trachomatis DNA was detected in 18 . 0% overall ( 269/1507 ) and 25 . 4% of 1–9 year olds ( 157/618 ) ., All 15 ( ∼1% of total ) control swabs were negative for C . trachomatis DNA ., Null models for both active trachoma and C . trachomatis infection adjusted for age and gender showed significant clustering at island , village and household levels ., For active trachoma , the variance estimated due to between-household clustering was 1 . 11 ( standard error ( SE ) 0 . 17 , p<0 . 0001 ) ., The between-village clustering variance was 0 . 75 ( SE 0 . 16 , p<0 . 0001 ) and between-island clustering variance was 0 . 50 ( SE 0 . 28 , p\u200a=\u200a0 . 0100 ) ., For C . trachomatis infection , the variance estimated due to between-household clustering was 1 . 37 ( SE 0 . 15 , p<0 . 0001 ) , between-village clustering was 0 . 89 ( SE 0 . 14 , p<0 . 0001 ) and between-island clustering was 0 . 40 ( SE 0 . 18 , p\u200a=\u200a0 . 0005 ) ., The clustering effect was strongest at household level and models adjusting for clustering at household level only were a better fit than those including adjustment for village and island clustering ., Adjusting for clustering at household level significantly improved the model versus standard logistic regression analyses ( p<0 . 0001 ) ., Two-level hierarchical regression models with adjustment for household level clustering are presented in this analysis ., Univariable associations with active trachoma are presented in Table, 3 . The final multivariable model showed that active trachoma was strongly independently associated with C . trachomatis infection ( OR\u200a=\u200a11 . 2 ( 95% CI 6 . 9–18 . 1 ) ) , ocular ( OR\u200a=\u200a2 . 0 ( 95% CI 1 . 0–4 . 0 ) ) and nasal ( OR\u200a=\u200a2 . 5 ( 95% CI 1 . 5–4 . 3 ) ) discharge , male gender ( OR\u200a=\u200a1 . 9 ( 95% CI 1 . 2–2 . 9 ) ) and being aged 0–5 years ( OR\u200a=\u200a10 . 2 ( 95% CI 5 . 1–20 . 4 ) compared to being >15 years of age ) ( Model 2 , Table 4 ) ., There was also a strong independent association between household water access and active trachoma , such that households with access only to a traditional natural spring as a water source had an increased risk of active trachoma compared to households with access to multiple water sources ( OR\u200a=\u200a1 . 9 ( 95% CI 0 . 9–3 . 9 ) ) ., The model without C . trachomatis infection shows stronger associations , indicating that some effect of these factors is mediated through C . trachomatis infection ( Table 4 ) ., Comparison of the two models suggests that some of the effect of younger age and water source is partly mediated through C . trachomatis infection , but these remain independently associated with trachoma beyond this effect ., Univariable associations with C . trachomatis infection are presented in Table 5 ., In the final multivariable model C . trachomatis infection was strongly independently associated with being aged ≤10 years ., The presence of ocular discharge ( OR\u200a=\u200a2 . 3 ( 95% CI 1 . 3–4 . 4 ) ) and household access only to a traditional natural spring ( OR\u200a=\u200a6 . 6 ( 95% CI 2 . 8–15 . 2 ) ) and or access to a single water source only ( OR\u200a=\u200a3 . 9 ( 95% CI 1 . 9–8 . 0 ) ) ( rather than households who had access to multiple water sources ) were strongly associated with infection ( Table 6 ) ., The presence of flies around a latrine was also independently associated with infection ( OR\u200a=\u200a2 . 1 ( 95% CI 1 . 1–3 . 8 ) ) ., The presence of flies around a latrine were strongly associated with the presence of flies in the environment surrounding the household ( OR\u200a=\u200a8 . 3 ( 95% CI 5 . 4–12 . 7 ) , p<0 . 0001 ) and the presence of visible faeces within the latrine ( OR\u200a=\u200a46 . 7 ( 95% CI 28 . 5–76 . 6 ) , p<0 . 0001 ) ., There was no association between flies in the environment ( OR\u200a=\u200a1 . 1 ( 95% CI 0 . 4–3 . 0 ) , p\u200a=\u200a0 . 91 ) nor flies around the latrine ( OR\u200a=\u200a0 . 5 ( 95% CI 0 . 1–2 . 6 ) , p\u200a=\u200a0 . 43 ) with flies on the face at the time of examination ., We have described individual and household-level risk factor associations with active trachoma and ocular infection with C . trachomatis on the Bijagós Archipelago to improve our understanding of the relationship between disease and infection in this remote treatment-naïve trachoma-hyperendemic population ., These data suggest that in this environment household-level risk factors relating to fly populations , hygiene behaviours and water usage are likely to be important in the transmission of ocular C . trachomatis infection ., Education about cleanliness , sanitation and hygiene practices is likely to be important in reducing transmission of infection in these communities ., Ensuring the provision of water sources which allow adequate water to be allocated for hygiene may assist this , and further studies examining specific hygiene practices may be useful ., Reducing fly populations around the latrines where they exist may be of benefit ., These findings may be important in the implementation of the F and E components of SAFE in this population ., In order to fully understand the factors associated with active trachoma and ocular C . trachomatis infection in these communities , further epidemiological studies examining transmission and clustering of C . trachomatis infection are required ., These studies should focus on pathogen factors such as the role of infection intensity and strain diversity , and socio-behavioural factors such as specific hygiene behaviours . | Introduction, Methods, Results, Discussion | Trachoma , caused by ocular infection with Chlamydia trachomatis , is hyperendemic on the Bijagós Archipelago of Guinea Bissau ., An understanding of the risk factors associated with active trachoma and infection on these remote and isolated islands , which are atypical of trachoma-endemic environments described elsewhere , is crucial to the implementation of trachoma elimination strategies ., A cross-sectional population-based trachoma prevalence survey was conducted on four islands ., We conducted a questionnaire-based risk factor survey , examined participants for trachoma using the World Health Organization ( WHO ) simplified grading system and collected conjunctival swab samples for 1507 participants from 293 randomly selected households ., DNA extracted from conjunctival swabs was tested using the Roche Amplicor CT/NG PCR assay ., The prevalence of active ( follicular and/or inflammatory ) trachoma was 11% ( 167/1508 ) overall and 22% ( 136/618 ) in 1–9 year olds ., The prevalence of C . trachomatis infection was 18% overall and 25% in 1–9 year olds ., There were strong independent associations of active trachoma with ocular and nasal discharge , C . trachomatis infection , young age , male gender and type of household water source ., C . trachomatis infection was independently associated with young age , ocular discharge , type of household water source and the presence of flies around a latrine ., In this remote island environment , household-level risk factors relating to fly populations , hygiene behaviours and water usage are likely to be important in the transmission of ocular C . trachomatis infection and the prevalence of active trachoma ., This may be important in the implementation of environmental measures in trachoma control . | Trachoma , caused by ocular infection with Chlamydia trachomatis , is the leading infectious cause of blindness worldwide ., The World Health Organization elimination strategy includes community mass treatment with oral antibiotics , education regarding hygiene and facial cleanliness and environmental improvements ., Population-based trachoma prevalence surveys are essential to determine whether community interventions are required ., Knowledge of risk factors associated with trachoma and C . trachomatis infection in a particular setting may help prioritise trachoma elimination activities ., We conducted a trachoma prevalence survey to establish the prevalence of active ( follicular and/or inflammatory ) trachoma and C . trachomatis infection on the Bijagós Archipelago of Guinea Bissau ., We also collected household risk factor data from survey participants ., Active trachoma prevalence was 11% overall and 22% in children aged 1–9 years ., C . trachomatis infection prevalence was 18% overall and 25% in children aged 1–9 years ., Active trachoma and the presence of C . trachomatis infection were strongly correlated ., Risk factors for disease and infection were similar ., In this environment , measures of facial cleanliness ( ocular and nasal discharge ) and household-level risk factors relating to fly populations , hygiene behaviours and water usage are likely to be important in C . trachomatis transmission ., This may have implications in the implementation of trachoma elimination activities . | public and occupational health, infectious diseases, medicine and health sciences, global health, epidemiology | null |
journal.pcbi.1003054 | 2,013 | Simultaneous Identification of Multiple Driver Pathways in Cancer | Cancer is a disease driven in part by somatic mutations that accumulate during the lifetime of an individual ., The declining costs of genome sequencing now permit the measurement of these somatic mutations in large numbers of cancer genomes ., Projects such as The Cancer Genome Atlas ( TCGA ) and International Cancer Genome Consortium ( ICGC ) are now undertaking this task in hundreds of samples from dozens of cancer types ., A key challenge in interpreting these data is to distinguish the functional driver mutations important for cancer development from random passenger mutations that have no consequence for cancer ., The ultimate determinant of whether a mutation is a driver or a passenger is to test its biological function ., However , because the ability to detect somatic mutations currently far exceeds the ability to validate experimentally their function , computational approaches that predict driver mutations are an urgent priority ., One approach is to directly predict the functional impact of somatic mutations using additional biological knowledge from evolutionary conservation , protein structure , etc . and a number of methods implementing this approach have been introduced ( see 1–4 ) ., These methods are successful in predicting the impact of some mutations , but generally do not integrate information across different types of mutations ( single nucleotide , indels , larger copy number aberrations , etc . ) ; moreover , these methods are less successful for less conserved/studied proteins ., Given the declining costs of DNA sequencing , a standard approach to distinguish driver from passenger mutations is to identify recurrent mutations , whose observed frequency in a large cohort of cancer patients is much higher than expected 5 , 6 ., Nearly all cancer genome sequencing papers , including those from TCGA 7–10 and other projects 5 , 11 , 12 , report a list of significantly mutated genes ., However , driver mutations vary greatly between cancer patients – even those with the same ( sub ) type of cancer – and this heterogeneity significantly reduces the statistical power to detect driver mutations by tests of recurrence ., One of the main biological explanations for this mutational heterogeneity is that driver mutations target not only individual genomic loci ( e . g . nucleotides or genes ) , but also target groups of genes in cellular signaling and regulatory pathways ., Consequently , different cancer patients may harbor mutations in different members of a pathway important for cancer development ., Thus , in addition to testing individual loci , or genes , for recurrent mutation in a cohort of patients , researchers also test whether groups of genes are recurrently mutated ., Since exhaustive testing of all groups of genes is not possible without prohibitively large sample sizes ( due to the necessary multiple hypothesis testing correction ) , current approaches focus on groups of genes defined by prior biological knowledge , such as known pathways ( e . g . from KEGG 13 ) or functional groups ( e . g . from GO 14 ) , and methods have been introduced to look for enrichment in such pre-defined groups of genes ( e . g . 15–17 ) ., More recently , methods that identify recurrently mutated subnetworks in protein-protein interaction networks have also been developed , such as NetBox 18 , MeMO 19 , HotNet 20 , and EnrichNet 21 ., Knowledge of gene and protein interactions in humans remain incomplete , and most existing pathway databases and interaction networks do not precisely represent the pathways and interactions that occur in a particular cancer cell ., Thus , restricting attention to only those combinations of mutations recorded in these data sources may limit the possibility for novel biological discoveries ., Thus algorithms that do not make this restriction – but also avoid the multiple hypothesis testing problems associated with exhaustive enumeration – are desirable ., Recently , the RME 22 and De novo Driver Exclusivity ( Dendrix ) 23 algorithms were introduced to discover driver pathways using combinatorial constraints derived from biological knowledge of how driver mutations appear in pathways 24 , 25 ., In particular , each cancer patient contains a relatively small number of driver mutations , and these mutations perturb multiple cellular pathways ., Thus , each driver pathway will contain approximately one driver mutation per patient ., This leads to a pattern of mutual exclusivity between mutations in different genes in the pathway ., In addition , an important driver pathway should be mutated in many patients , or have high coverage by mutations ., Thus , driver pathways correspond to sets of genes that are mutated in many patients , but whose mutations are mutually exclusive , or approximately so ., We emphasize that the driver pathways exhibiting patterns of mutually exclusivity and high coverage are generally smaller and more focused than most pathways annotated in the literature and pathway databases ., The latter typically contain many genes and perform multiple different functions; e . g . the “cell cycle” pathway in KEGG contains 143 genes ., It is well known that co-occurring ( i . e . , not exclusive ) mutations are observed in these larger , multifunctional biological pathways 25 ., The RME and Dendrix algorithms use different approaches to find sets of genes with high coverage and mutual exclusivity: RME builds sets of genes from pairwise scores of exclusivity , while Dendrix computes a single score for the mutual exclusivity of a set of genes , and finds the highest scoring set ., The aforementioned MeMO algorithm 19 also considers mutual exclusivity between mutations , but only for pairs of genes that have recorded interactions in a protein-protein interaction network ., Thus , MeMO does not attempt to identify driver pathways de novo and can only define subnetworks in existing interaction networks ., While many of the strongest signals of mutual exclusivity are between genes with known interactions , below we show examples in cancer data of mutual exclusive mutations between genes with no known direct iterations ., The two existing de novo algorithms , RME and Dendrix , consider the detection of only a single driver pathway from the pattern of mutual exclusivity between mutations ., However , it is well known that mutations in several pathways are generally required for cancer 26 ., There is little reason to assume that mutations in different pathways will be mutually exclusive , and in contrast may exhibit significant patterns of co-occurrence across patients ., Multiple pathways may be discovered using these algorithms by running the algorithm iteratively , removing the genes found in each previous iteration , and such an approach was employed for Dendrix 23 ., However , such an iterative approach is not guaranteed to yield the optimal set of pathways ., Here we extend the Dendrix algorithm in three ways ., First , we formulate the problem of finding exclusive , or approximately exclusive , sets of genes with high coverage as an integer linear program ( ILP ) ., This formulation allows us to find optimal driver pathways of various sizes directly – in contrast to the greedy approximation and Markov Chain Monte Carlo algorithms employed in Dendrix ., Second , we generalize the ILP to simultaneously find multiple driver pathways ., Third , we augment the core algorithm with additional analyses including: examining gene sets for subtype-specific mutations , summarizing stability of results across different number and size of pathways , and imposing greater exclusivity of gene sets ., We apply the new algorithm , called Multi-Dendrix , to four somatic mutation datasets: whole-exome and copy number array data in 261 glioblastoma ( GBM ) patients from The Cancer Genome Atlas ( TCGA ) 7 , whole-exome and copy number array data in 507 breast cancer ( BRCA ) patients from TCGA 8 , 601 sequenced genes in 84 patients with glioblastoma multiforme ( GBM ) from TCGA 7 and 623 sequenced genes in 188 patients with lung Adenocarcinoma 27 ., In each dataset Multi-Dendrix finds biologically interesting groups of genes that are highly exclusive , and where each group is mutated in many patients ., In all datasets these include groups of genes that are members of known pathways critical to cancer development including: Rb , p53 , and RTK/RAS/PI ( 3 ) K signaling pathways in GBM and p53 and PI ( 3 ) K/AKT signaling in breast cancer ., Multi-Dendrix successfully recovers these pathways solely from the pattern of mutual exclusivity and without any prior information about the interactions between these genes ., Moreover , Multi-Dendrix also identifies mutations that are mutually exclusive with these well-known pathways , and potentially represent novel interactions or crosstalk between pathways ., Notable examples include mutual exclusivity between: mutations in PI ( 3 ) K signaling pathway and amplification of PRDM2 ( and PDPN ) in glioblastoma; mutations in p53 , GATA3 and cadherin genes in breast cancer ., Finally , we compare Multi-Dendrix to an alternative approach of iteratively applying Dendrix 23 or RME 22 , two other algorithms that search for mutually exclusive sets ., We show that these iterative approaches typically fail to find an optimal set of pathways on simulated data , while Multi-Dendrix finds the correct pathways even in the presence of a large number of false positive mutations ., On real cancer sequencing data , the groups of genes found by Multi-Dendrix include more genes with known biological interactions ., Moreover , Multi-Dendrix is orders of magnitude faster than these other algorithms , allowing Multi-Dendrix to scale to the latest whole-exome datasets on hundreds of samples , which are largely beyond the capabilities of Dendrix and RME ., Multi-Dendrix is a novel and practical approach to finding multiple groups of mutually exclusive mutations , and complements other approaches that predict combinations of driver mutations using biological knowledge of pathways , interaction networks , protein structure , or protein sequence conservation ., The Multi-Dendrix algorithm takes somatic mutation data from cancer patients as input , and identifies multiple sets of mutations , where each set satisfies two properties: ( 1 ) the set has high coverage with many patients having a mutation in the set; ( 2 ) the set exhibits a pattern of mutual exclusivity where most patients have exactly one mutation in the set ., We briefly describe the Multi-Dendrix algorithm here ., Further details are provided in the Methods section below ., We assume that somatic mutations have been measured in cancer patients and that these mutations are divided into different mutation classes ., A mutation class is a grouping of different mutation types at a specific genomic locus ., In the simplest case , a mutation class corresponds to a grouping of all types of mutations ( single nucleotide variants , copy number aberrations , etc . ) in a single gene ., We represent the somatic mutation data as an binary mutation matrix , where the entry is defined as follows: ( 1 ) More generally , a mutation class may be defined for an arbitrary genomic locus , and not just a gene , and may distinguish different types of mutations ., For example , one may define a mutation class as single-nucleotide mutations in an individual residue in a protein sequence or in a protein domain ., Or alternatively , one may separate different types of mutations in a gene ( e . g . single-nucleotide mutations , deletions , or amplifications ) by creating separate mutation classes for each mutation type in each gene ., We will use this later definition of mutation classes in the results below ., For ease of exposition we will assume for the remainder of this section that each mutation class is a gene ., Vandin et al . 23 formulate the problem of finding a set of genes with high coverage and high exclusivity as the Maximum Weight Submatrix Problem ., Here the weight of a set of genes is the difference between the coverage , the number of patients with a mutation in one of the genes in , and the coverage overlap , the number of patients having a mutation in more than one gene in ., Vandin et al . 23 introduce the De novo Driver Exclusivity ( Dendrix ) algorithm 23 that finds a set of genes with maximum weight ., While finding single driver pathways is important , most cancer patients are expected to have driver mutations in multiple pathways ., Dendrix used a greedy iterative approach to find multiple gene sets ( described below ) , that is not guaranteed to find optimal gene sets ., Identification of multiple driver pathways requires a criterion to evaluate possible collections of gene sets ., Appealing to the same biological motivation as above , we expect that each pathway contains approximately one driver mutation ., Moreover , since each driver pathway is important for cancer development , we also expect that most individuals contain a driver mutation in most driver pathways ., Thus , we expect high exclusivity within the genes of each pathway and high coverage of each pathway on its own ., One measure that satisfies these criteria is to find a collection of gene sets whose sum of weights is maximized ., We define the Multiple Maximum Weight Submatrices problem as the problem of finding such a maximum weight collection ., We solve the Multiple Maximum Weight Submatrix problem using an integer linear program ( ILP ) , and refer to the resulting algorithm as Multi-Dendrix ( see Methods ) ., In addition , the ILP formulation used in Multi-Dendrix uses a modified weight function , where is a parameter that adjusts the tradeoff between finding sets with higher coverage ( more patients with a mutation ) versus higher coverage overlap ( greater non-exclusivity between mutations ) ., We use this parameter in the breast cancer dataset below ., In contrast , Dendrix was limited to ., We compare Multi-Dendrix to iterative versions of Dendrix 23 and RME 22 on simulated mutation data with both driver mutations implanted in pathways in a mutually exclusive manner and random passenger mutations ., The goal of these simulations is to compare Multi-Dendrix to other algorithms that identify mutually exclusive genes on straightforward datasets that contain multiple mutually exclusive sets ., We generate mutation data for patients and genes as follows ., We select a set of four pathways with each containing four genes ., We select the coverage uniformly from the following intervals: , , , , respectively ., The size of this dataset and the varying coverages of the pathways model what is observed in real data ( see § Somatic Mutation data ) and is consistent with models of mutation progression where driver mutations accumulate in pathways 28 ., For each pathway , we select patients at random and add a driver mutation to exactly one gene from the set ., Thus , the driver mutations in each pathway are mutually exclusive ., We then add passenger mutations by randomly mutating genes in each patient with probability , , the passenger mutation probability . We used values of similar to our estimates for on the TCGA GBM and Lung cancer data sets ( in § Somatic Mutation data below ) , which were and , respectively ., We emphasize that these simulations do not model all of the complexities of somatic mutations in cancer e . g . gene-specific and patient-specific mutation rates , genes present in multiple pathways , etc ., Since the Dendrix and RME algorithms are designed to find single pathways , we compared Multi-Dendrix to iterative versions of these methods that return multiple gene sets ., For Dendrix we used the iterative approach described in 23: apply Dendrix to find a highest scoring gene set , remove those genes from the dataset , and apply Dendrix to the reduced dataset , repeating these steps until a desired number of gene sets are found ., We will refer to this algorithm as Iter-Dendrix ., Thus , Iter-Dendrix returns a collection of gene sets such that ., We implemented the analogous iterative version of RME , and will refer to this algorithm as Iter-RME ., We compared the collection of gene sets found by each algorithm to the planted pathways , computing the symmetric difference between and as described in Methods ., Table 1 shows a comparison of Multi-Dendrix , Iter-Dendrix , and Iter-RME on simulated mutation data for different values of ., Note that we do not show comparisons to Iter-RME for as Iter-RME did not complete after 24 hours of runtime for any of the 1000 simulated mutation data sets ., While the RME publication 22 analyzed mutation matrices with thousands of genes and hundreds of patients , this analysis ( and the released RME software ) required that mutations were presented in at least 10% of the samples , greatly reducing the number of genes/samples input to the algorithm ., In fact , a threshold of 10% will remove nearly all genes in current whole-exome studies ( see § Comparison of Multi-Dendrix and RME ) ., For , Multi-Dendrix identifies collections of gene sets that were significantly closer ( ) to the planted pathways than the collections found by either Iter-Dendrix and Iter-RME ., These results demonstrate that Multi-Dendrix outperforms other methods , even when the passenger mutation probability is more than 15 times greater than the value estimated from real somatic mutation data ., For , the differences between Multi-Dendrix and Iter-RME were not significant ., We also compared the runtimes of each algorithm on the simulated datasets ., Multi-Dendrix was several orders of magnitude faster than Iter-Dendrix and Iter-RME on all datasets ( Table 2 ) ., Note that as the passenger mutation probability increases , the number of recurrently mutated passenger genes increases ., Multi-Dendrix scales much better than Iter-RME and maintains a significant advantage over Iter-Dendrix , completing all simulated datasets in less than 5 seconds ., We evaluated how the runtime of Multi-Dendrix scales to larger datasets ., Using the same passenger mutation probabilities listed above , we calculated the average runtime in seconds of Multi-Dendrix for ten simulated mutation matrices with genes and patients , more than the number of patients to be measured in any cancer study from TCGA ., In each case , we run Multi-Dendrix only on the subset of genes that are mutated in more than the expected number of samples ., For the largest dataset with genes , the average number of genes input to Multi-Dendrix for the highest and lowest passenger mutation probabilities are and , respectively ., ( Table S1 shows the average number of input genes for varying and . ), The average runtime for this largest dataset is under one hour ( average of 54 . 4 minutes ) ., Figure S1 shows the runtimes for varying and ., We incorporate the Multi-Dendrix algorithm into a larger pipeline ( Figure, 1 ) that includes several additional pre- and post-processing tasks including: ( 1 ) Building mutation matrices for input into Multi-Dendrix; ( 2 ) Summarizing Multi-Dendrix results over multiple values for the parameters , the number of gene sets , the minimum size of a gene set , and the maximum size of a gene set; ( 3 ) Evaluating the statistical significance of results; ( 4 ) Examining Multi-Dendrix results for mutually exclusive sets resulting from subtype-specific mutations ., We describe these steps briefly below , with further details in the Methods and Supporting Information ., First , we build mutation matrices from somatic mutation data ., We use several steps to process single-nucleotide variant ( SNV ) data , copy number variant ( CNV ) data , and to combine both types of data ., Second , in contrast to simulated data , on real data we do not know the correct values of the parameters , , and ., Thus , we consider a reasonable range of values for these parameters and summarize the results over these parameters into modules ., We build a graph , where the nodes are individual genes ( or mutation classes ) and edges connect genes ( respectively mutation classes ) that appear in the same gene set for more than one value of the parameters ., We weight each edge with the fraction of parameter values for which the pair of genes appear in the same gene set ., The resulting edge-weighted graphs provide a measure of the stability of the resulting gene sets over different parameter values ., By choosing a minimum edge weight , we partition the graph into connected components , or modules ., One may choose to use these modules as the output of Multi-Dendrix ., Third , we evaluate the statistical significance of our results using two measures ., Since the collection with high weight may not be surprising in a large mutation matrix , the first measure evaluates the significance of the score maximized by Multi-Dendrix ., We evaluate whether the weight of the maximum weight collection output by Multi-Dendrix is significantly large compared to an empirical distribution of the maximum weight sets from randomly permuted mutation data ., We generate random mutation data using the permutation test described in 19 ., This test permutes the mutations among the genes in each patient , preserving both the number of mutated genes in each patient and the number of patients with a mutation in each gene while perturbing any patterns of exclusivity between mutated genes ., Note that this permutation test requires running Multi-Dendrix many times to determine statistical significance for a single parameter setting ., Thus , the runtime advantages of Multi-Dendrix compared to Iter-Dendrix and Iter-RME are very important in practice on real datasets ., Next , we evaluate whether the collection output by Multi-Dendrix contains more protein-protein interactions than expected by chance by applying our direct interactions test on a PPI network constructed from the union of the KEGG and iRefIndex PPI networks ., The direct interactions test computes a statistic of the difference in the number of interactions within and between gene sets in , and compares the observed value of to an empirical distribution on 1000 permuted PPI networks ( full details of the test are in Evaluating known interactions ) ., These permuted networks account for the observation that many genes that are frequently mutated in cancer also have large degree in the interaction network – either due to biological reasons or ascertainment bias ., We use an interaction network to assess biological function rather than known pathways ( e . g . KEGG pathways or GSEA sets ) because most of these pathways are relatively large , while the gene sets found by Multi-Dendrix that exhibit exclusivity tend to be much smaller , each containing only a few genes ., Finally , we examine possible correlations between the mutually exclusive sets reported by Multi-Dendrix and particular subsets of samples ., A number of cancers are divided into subtypes according to pathology , cytogenetics , gene expression , or other features ., Since mutations that are specific to particular subtypes will be mutually exclusive , disease heterogeneity is an alternative explanation to pathways for observed mutually exclusive sets ., For example , 29 report four subtypes of GBM based on gene expression clusters , and show that several mutations – including IDH1 , PDGFRA , EGFR , and NF1 – have strong association with individual subtypes ., Unfortunately , if the subtypes are unknown there is no information for Multi-Dendrix , Dendrix , RME , or other algorithms that analyze mutual exclusivity to distinguish between mutual exclusivity resulting from subtypes and mutual exclusivity resulting from pathways or other causes ., If subtypes are known , two possible solutions are to analyze subtypes separately , or to examine whether patterns of mutual exclusivity are associated to these subytpes ., We annotate results by known subtypes as a post-processing step in Multi-Dendrix ., We applied Multi-Dendrix and Iter-Dendrix to four somatic mutation matrices: ( 1 ) copy number variants ( CNVs ) , small indels , and non-synonymous single nucleotide variants ( SNVs ) measured in 601 genes in 84 glioblastoma multiformae ( GBM ) patients 7; ( 2 ) indels and non-synonymous single nucleotide variants in 623 sequenced genes in 188 Lung Adenocarcinoma patients 27; ( 3 ) CNVs , small indels , and non-synonomous SNVs measured using whole-exome sequencing and copy number arrays in 261 GBM patients 7; and ( 4 ) CNVs , small indels , and non-synonymous SNVs measured in 507 BRCA patients ., We will refer to these datasets as GBM ( 2008 ) , Lung , GBM , and BRCA below ., We removed extremely low frequency mutations and known outliers from these datasets as described in Methods ., After this processing , the GBM ( 2008 ) dataset contained mutation and CNV data for 46 genes in 84 patients; the Lung dataset contained somatic mutation for 190 genes in 163 patients; the GBM dataset contained mutation and CNV data for 398 genes in 261 patients; and the BRCA dataset contained mutation and CNV data for 375 genes in 507 patients ., We focus here on presenting results from the latter two datasets because they are the latest whole-genome/exome datasets and most representative of the datasets that are now being produced and will be analyzed now and in the coming years ., Results with the first two older and smaller datasets from targeted sequencing are described in the Supporting Information ., We compute gene sets , each of minimum size and maximum size ranging from ., We summarize the results over these 9 different parameter values into modules using the procedure described above ., We applied Multi-Dendrix and Iter-Dendrix to the GBM dataset , considering EGFR amplification as a separate event ( see Methods ) ., The algorithms report the same results over all values of the parameters except , where Iter-Dendrix includes the IRF5 gene in a gene set with RB1 , CDK4 ( A ) , and CDKN2A/CDKN2B ( D ) , and MSL3 ., However , Multi-Dendrix is significantly faster running in 142 seconds compared to 37 , 786 seconds ( over 10 hours ) for Iter-Dendrix ., We summarize the results of these different parameter choices by connecting genes that appear in the same gene set at least twice , resulting in four modules ( Figure 2 ) ., These four modules include all the genes ( except ERBB2 ) that are: ( 1 ) members of the three signaling pathways highlighted in the TCGA GBM study 7 , and ( 2 ) are mutated in of the samples ., The weight of all collections found by Multi-Dendrix on the GBM dataset are significant ) and the direct interactions statistic of these four modules is also significant ( ) ., Three of the four modules also contain a significant number of interactions ( ) ., In addition to these four modules , two additional mutation classes , CNTNAP2 and deletion of 10q26 . 3 , each appear in one choice of parameters for Multi-Dendrix ., Since these are not part of a larger module , they are not analyzed further ., Figure S2 shows a combined mutation matrix with all four modules ., The first module includes the amplification of CDK4 , mutation of RB1 , and a deletion that includes both CDKN2A and CDKN2B ., This module is mutated in 87 . 7% ( 229/261 ) of the samples , and are discovered for all parameter choices ., These four genes are members of the RB signaling pathway ( as annotated in 7 ) involved in G1/S progression ( by Bonferonni-corrected hypergeometric test ) : CDKN2A and CDKN2B inhibits CDK4 , which in turn inhibits RB1 ., In addition for 7/9 parameter choices , this module includes mutations in MSL3 ., MSL3 is a member of the MSL ( male-specific lethal ) complex that has a major role in dosage compensation in Drosophila ., While this complex is conserved in mammals , the specific function of human MSL3 is unknown ., However , the MSL complex also includes the histone acetyltransferase MOF which is involved in cell cycle regulation of p53 and may play a role in cancer 30 ., Thus , the mutual exclusivity of mutations in MSL3 and the other well-known members of the RB signaling pathway is intriguing and deserves further study ., This module contains two interactions ( ) ., The second module includes mutations and deletion of PTEN , mutations in PIK3CA , mutations in PIK3R1 , mutations in IDH1 , and an amplification that includes PDPN and PRDM2 ., The module is mutated in 62 . 8% ( 164/261 ) of the samples ., PTEN , PIK3CA , and PIK3R1 are all members of the RTK/RAS/PI ( 3 ) K signaling pathway ( as annotated in 7 ) involved in cellular proliferation ( ) ., IDH1 is not a known member of this pathway; moreover , IDH1 is preferentially mutated in the proneural subtype of GBM 29 ., Deletions in PTEN are also associated with the proneural subtype of GBM , although they are not considered a defining feature of this subtype ( as IDH1 mutations are ) and do not result in a gene expression signature 29 ., However , there are no reports that PTEN , PIK3CA , or PIK3R1 mutations are subtype specific , and thus the mutual exclusivity of IDH1 and the remaining genes in this set is not simply explained by subtypes ., PRDM2 is not known to be part of the RTK/RAS/PI ( 3 ) K signaling pathway ., PRDM2 is a member of the histone methyltransferase superfamily , interacts with the RB protein 31 , and is proposed as a tumor suppressor in colorectal cancer 32 ., PDPN is used as a molecular marker for glioma , due to its association with clinical outcomes 33 ., Our results suggest that PDPN and PRDM2 may have an undiscovered role in GBM as well ., This module contains three interactions ( ) ., The third module includes mutations in TP53 , the amplification of MDM2 , the amplification of MDM4 , mutations in NLRP3 , and the deletion involving AKAP6 and NPAS3 ., This module is mutated in 57 . 8% ( 151/261 ) of the samples , and appears for every parameter choice for ., TP53 , MDM2 , and MDM4 are members of the p53 signaling pathway ( ) , a critical and frequently altered pathway in GBM involved in senescence and apoptosis ., NPAS3 is a transcription factor expressed in the brain and implicated in psychiatric disorders including schizophrenia 34 , 35 ., In addition , NPAS3 was recently shown to act as a tumor suppressor in astrocytomas , with a possible role in glioblastoma progression and proliferation 36 ., This module contains three interactions ( ) ., The fourth module includes mutations in EGFR , the amplification of PDGFRA , and the deletion of RB1 ., This module is mutated in 45 . 6% ( 119/261 ) of samples , and appears for ., EGFR and PDGFRA are members of the RTK/RAS/PI ( 3 ) K signaling pathway ( ) , and RB1 is a member of the RB signaling pathway ., While EGFR and PDGFRA both interact with RAS , there are no reported direct interactions between these three proteins ., In addition , mutations in these three genes are significantly associated with two of the expression subtypes reported in 29: mutations in EGFR and the deletion of RB1 are associated with the Classical GBM subtype , and the amplification of PDGFRA is significantly associated with the Proneural subtype ., Thus , it appears that the mutual exclusivity discovered by Multi-Dendrix is a result of subtype-specific mutations , despite PDGFRA and EGFR being a member of the same biological pathway ., In summary , we see that subtype-specific mutations provide an alternative explanation for observed mutual exclusivity and confound the identification of driver pathways ., However , on the GBM data subtype-specific mutations are a minor feature in the data , and Multi-Dendrix successfully identifies de novo portions of three critical signaling pathways in GBM ., We applied Multi-Dendrix and Iter-Dendrix to the BRCA dataset ., We found that for most values of the parameters and , the results combined the most frequently mutated genes into a single gene set despite the fact that these genes had high coverage overlap ( Figures S4 and S5 ) ., That is , for a gene set , high coverage was outweighing a high coverage overlap in the weight function optimized by Multi-Dendrix ., To enforce g | Introduction, Results, Discussion, Methods | Distinguishing the somatic mutations responsible for cancer ( driver mutations ) from random , passenger mutations is a key challenge in cancer genomics ., Driver mutations generally target cellular signaling and regulatory pathways consisting of multiple genes ., This heterogeneity complicates the identification of driver mutations by their recurrence across samples , as different combinations of mutations in driver pathways are observed in different samples ., We introduce the Multi-Dendrix algorithm for the simultaneous identification of multiple driver pathways de novo in somatic mutation data from a cohort of cancer samples ., The algorithm relies on two combinatorial properties of mutations in a driver pathway: high coverage and mutual exclusivity ., We derive an integer linear program that finds set of mutations exhibiting these properties ., We apply Multi-Dendrix to somatic mutations from glioblastoma , breast cancer , and lung cancer samples ., Multi-Dendrix identifies sets of mutations in genes that overlap with known pathways – including Rb , p53 , PI ( 3 ) K , and cell cycle pathways – and also novel sets of mutually exclusive mutations , including mutations in several transcription factors or other genes involved in transcriptional regulation ., These sets are discovered directly from mutation data with no prior knowledge of pathways or gene interactions ., We show that Multi-Dendrix outperforms other algorithms for identifying combinations of mutations and is also orders of magnitude faster on genome-scale data ., Software available at: http://compbio . cs . brown . edu/software . | Cancer is a disease driven largely by the accumulation of somatic mutations during the lifetime of an individual ., The declining costs of genome sequencing now permit the measurement of somatic mutations in hundreds of cancer genomes ., A key challenge is to distinguish driver mutations responsible for cancer from random passenger mutations ., This challenge is compounded by the observation that different combinations of driver mutations are observed in different patients with the same cancer type ., One reason for this heterogeneity is that driver mutations target signaling and regulatory pathways which have multiple points of failure ., We introduce an algorithm , Multi-Dendrix , to find these pathways solely from patterns of mutual exclusivity between mutations across a cohort of patients ., Unlike earlier approaches , we simultaneously find multiple pathways , an essential feature for analyzing cancer genomes where multiple pathways are typically perturbed ., We apply our algorithm to mutation data from hundreds of glioblastoma , breast cancer , and lung adenocarcinoma patients ., We identify sets of interacting genes that overlap known pathways , and gene sets containing subtype-specific mutations ., These results show that multiple cancer pathways can be identified directly from patterns in mutation data , and provide an approach to analyze the ever-growing cancer mutation datasets . | algorithms, computer science, biology, genomics, computational biology | null |
journal.pgen.1000986 | 2,010 | siRNA–Mediated Methylation of Arabidopsis Telomeres | Telomeres safeguard the stability of eukaryotic chromosomes by protecting natural chromosome ends from triggering DNA damage responses ., Chromosome termini consist of telomeric and subtelomeric repeats that are bound by a specific set of telomere binding proteins as well as nucleosomes that exhibit features of pericentric heterochromatin 1 ., These regions are usually devoid of functional genes , and transgenes integrated in the vicinity of telomeres are subjected to transcriptional silencing , a phenomenon known as telomere position effect 2 ., Studies in mammals indicate that telomeric heterochromatin plays an important function in chromosome end protection and telomere length regulation ., Inactivation of the SIRT6 histone deacetylase in human cells causes hyperacetylation of telomeric histone H3 , telomere dysfunction and premature cell senescence 3 ., Deficiency in histone methyltransferases or the retinoblastoma tumor suppressor leads to disruption of telomeric heterochromatin and aberrant telomere elongation in mouse cells 4–6 ., Another important hallmark of heterochromatin in mammals is DNA methylation ., Although vertebrate telomeric DNA does not appear to be methylated due to the lack of canonical CG sites , subtelomeric repeats are heavily methylated 7 ., Interestingly , inactivation of DNA methyltransferases in mouse cells decreases 5-methylcytosine at subtelomeres and leads to increased telomeric recombination , without a concomitant change in histone modifications 7 ., These data indicate a functional interaction between subtelomeric and telomeric chromatin ., Heterochromatin was thought to be transcriptionally inactive , but this view has been challenged by discoveries of numerous non-coding ( nc ) transcripts derived from heterochromatic loci ., Some of these transcripts directly contribute to the assembly of heterochromatin at defined chromosomal domains and their biogenesis is vital for processes such as X chromosome inactivation , genomic imprinting , transposon silencing and centromere function 8 ., Thus , it is not surprising that although telomeres possess marks of repressive heterochromatin , they are not transcriptionally silent ., Recent studies revealed the presence of telomeric repeat-containing RNAs ( TERRA ) that are transcribed from subtelomeric regions in yeast and vertebrates 9–11 ., TERRA are removed from telomeres either through Rat1p-dependent degradation in budding yeast or through non-sense mediated RNA decay ( NMD ) in human; deficiencies in these RNA processing pathways have dramatic effects on telomere maintenance 9 , 10 ., Hypomethylation of subtelomeric regions in mammalian cells lacking DNA methyltransferases leads to the overproduction of TERRA 11 , 12 ., This suggests that the epigenetic status of subtelomeres and telomeres influences TERRA expression ., The discovery of TERRA raised the question of whether ncRNAs contribute to the establishment of telomeric heterochromatin ., This hypothesis gained support in a recent study in which downregulation of TERRA by exogenous short interfering RNAs ( siRNAs ) in human cell lines led to depletion of histone heterochromatic modification from telomeres 13 ., In many organisms , RNA-mediated chromatin silencing relies on small RNA molecules that guide effector complexes to target sites 8 , 14 ., However , involvement of small RNAs in chromatin formation at canonical telomeres has not been shown yet ., In this study , we investigate chromatin organization and transcription at chromosome ends in the model plant Arabidopsis thaliana ., We detect the presence of transcripts containing telomeric repeats and show that some of these transcripts are processed into ∼24 nt siRNAs ., These transcripts are produced from telomeres as well as from intrachromosomal telomeric loci that are mainly located at centromeres ., The 24 nt siRNAs are generated through the RNA-dependent DNA methylation ( RdDM ) pathway , which is a plant-specific mechanism that utilizes siRNAs to guide DNA methyltransferases to asymmetric cytosines ( CNN ) 15 , 16 ., We demonstrate that RdDM is responsible for methylation of telomeric DNA that contains cytosines exclusively in asymmetric sequence contexts and hence for reinforcement of heterochromatic marks at telomeres ., Gene organization at chromosome ends in Arabidopsis appears to be unique ., In contrast to the majority of organisms with known telomere/subtelomere sequences , 8 of the 10 Arabidopsis subtelomeres have no repetitive DNA , and predicted genes are annotated in the immediate vicinity of telomeres 17 ( Figure 1A ) ., We experimentally confirmed that sequences annotated as chromosome ends are indeed associated with telomeres for 7 chromosome arms with the exception of the right arm of chromosome 3 18 ., The two remaining chromosome termini contain clusters of ribosomal RNA genes ( NORs ) 19 ., We performed reverse transcription ( RT ) PCR analysis to verify that all the predicted terminal genes are expressed and that they do not represent pseudogenes ( Figure 1B ) ., The genes showed distinct tissue-specific expression patterns and the size of the RT-PCR products corresponded to the predicted size of the spliced mRNAs ., There was no obvious correlation between the level of expression and promoter distance from telomeres , and even the At2g48160 gene , with a promoter immediately adjacent to telomeric DNA , was robustly expressed ., These data indicate that , in contrast to yeast and mammals , Arabidopsis telomeres do not silence genes located in their vicinity ., The high transcriptional activity near telomeres raised questions about the chromatin structure of chromosome termini in Arabidopsis ., We investigated the distribution of histone modification marks typical for plant euchromatin ( tri-methylation of histone H3 at Lys4 , H3K4me3 ) and heterochromatin ( di-methylation of H3 at Lys9 , H3K9me2; and mono-methylation of H3 at Lys27 , H3K27me1 ) at telomere-associated regions by chromatin immunoprecipitation ( ChIP ) ., The ∼600 bp region immediately adjacent to the telomere on the right arm of chromosome 2 ( 2R ) represents the promoter of the At2g48160 gene ( Figure 2A ) and carries typical euchromatic histone marks ( Figure 2B ) ., The H3K4me3 euchromatin mark was also dominant at the promoter of the At1g01010 gene that is located ∼3 . 5 kb from the telomere on the left arm of chromosome 1 ( region 1L-3 , Figure 2A and 2B ) , although we could detect a weak H3K27me1 signal that is usually typical of heterochromatin ., Histone heterochromatic marks ( H3K9me2 and H3K27me1 ) became more pronounced at the 1L-2 and 1L-1 regions that are located on the same arm ∼1 . 5 kb and 1 kb from the telomere , respectively ( Figure 2A and 2B ) ., The 1L telomere contains a recent 104 bp insertion of mitochondrial DNA embedded within the centromere-proximal region of telomeric repeats 20 ( Figure 2A ) ., Using this insertion to design primers that span the centromere-proximal part of the 1L telomere ( 1L-0 , Figure 2A ) , we were able to demonstrate that this region also displays heterochromatin marks ( Figure 2B ) ., Nevertheless , the 1L-0 region still possessed clearly detectable H3K4me3 , which is atypical of classical heterochromatin where the H3K4me3 modification is strongly reduced in comparison to H3K27me1 and H3K9me2 ., A similar histone-modification pattern was also observed in telomere-adjacent regions of five other chromosome arms ( Figure 2B ) ., To further examine chromatin at telomeres , we analyzed ChIP fractions by dot-blot hybridization with a telomeric probe ( Figure 2C ) ., The Arabidopsis genome is enriched for intrachromosomal degenerated telomeric repeats that are mainly localized at centromeres ( Figure S1 ) ., To specifically assay for chromatin at telomeres , we used stringent hybridization conditions at which the centromere-derived signal is eliminated to less than 2% of the total telomeric signal ( Figure S1 ) ., We readily detected H3K27me1 and H3K9me2 modifications , and a weaker but still clearly detectable H3K4me3 signal ., This hybridization pattern was reminiscent of the results obtained by ChIP analysis of telomere-adjacent regions by PCR ( Figure 2B ) ., Thus , our ChIP data show that Arabidopsis telomeres form chromatin that is enriched for H3K9me2 and H3K27me1 heterochromatic marks , but still retains the euchromatic H3K4me3 modification ., We found that the heterochromatin marks extend ∼1 . 5 kb into the subtelomeric region of 1L ., A survey of a high-resolution genome-wide map of H3K9me2 distribution indicates that H3K9me2 also spreads up to 1 . 5 kb from telomeres at chromosome arms 1R , 3L , 4R and 5L 21 ( http://epigenomics . mcdb . ucla . edu/H3K9m2/ ) ., However , detecting the prominent H3K4me3 signal side by side with the heterochromatic marks ( Figure 2B and 2C ) strongly indicates that Arabidopsis telomeres exhibit features of intermediate heterochromatin that is characterized by retention of opposing histone H3 methylation marks 22 ., We next asked whether Arabidopsis telomeres are transcribed by assaying for the presence of TERRA by Northern hybridization with a CCCTAAA probe ., We readily detected two types of TERRA: heterogeneous transcripts which ranged from high molecular weight strands that migrated at the limits of gel resolution to hundreds of nucleotides , and several distinct bands ( Figure 3A ) ., We also detected antisense telomeric transcripts ( ARRET ) that gave a similar hybridization pattern as the TERRA by the complementary TTTAGGG probe ( Figure 3A ) ., These signals disappeared after pretreatment of the samples with RNaseA ( Figure 3B and data not shown ) demonstrating that they do not represent remnants of DNA in RNA preparations ., Expression of TERRA varied between RNA samples extracted from different tissues of Arabidopsis ( Figure 3C ) ., Interestingly , remarkable variation in expression was also detected between different Arabidopsis accessions , as the levels of TERRA in seedlings of Zur and Ws ecotypes were almost two orders of magnitude higher than in Col and Ler ( Figure 3C ) ., Arabidopsis TERRA and ARRET can originate at telomeres or arise from transcription of degenerated intrachromosomal telomeric sequences localized at centromeric regions ( Figure S1 ) ., The bulk of centromeric DNA consists of 177–179 bp satellite repeats ( CEN180 ) , a subset of which is transcribed 23 ., Sequential hybridization of a Northern blot with probes detecting TERRA and CEN180 resulted in an almost identical hybridization pattern , characterized by five distinct bands ( Figure 3A ) ., Hybridization of the blots with probes detecting sequences immediately adjacent to telomeres did not produce any detectable signal ( data not shown ) ., These results suggest that TERRA and ARRET transcripts detected by Northern analysis mainly arise from centromeric regions that contain remnants of telomeric DNA and not from the transcription of telomeres ., To examine whether telomeres are transcribed at levels non-detectable by Northern hybridization , we analyzed expression of subtelomeric regions adjacent to telomeric DNA by strand-specific RT-PCR in flowers ., We could distinguish expression of TERRA and ARRET by using either telomeric or subtelomeric arm-specific primers for reverse transcription ( Figure 3D ) ., We detected expression of both TERRA and ARRET at four out of eight analyzed chromosome arms ., We failed to detect any transcription at chromosome arms 1R and 5R ., Interestingly , only the TERRA but not ARRET transcripts were detected at 1L ., The RT-PCR data demonstrate that at least five Arabidopsis telomeres are indeed transcribed , albeit at a low level ., To gain further insights into telomere transcription , we cloned a ∼500 nt promoter of the At2g48160 gene , which is located next to the telomere ( Figure 1 ) , in front of a reporter β-glucuronidase ( GUS ) gene in both sense and antisense orientations ., We could detect GUS transcripts in transgenic plants carrying both constructs , although the expression in the antisense direction was much weaker than in the sense orientation ( Figure S2 ) ., This experiment further supports the idea that telomere adjacent regions can drive transcription into a telomere ., The presence of centromeric and telomeric TERRA and ARRET indicated that telomeric transcripts are able to form partially double stranded ( ds ) intermediates that could be processed by a Dicer into siRNA ., In support of this hypothesis , siRNAs corresponding to both strands of telomeric DNA were detected in wild-type plants ( Figure 4A ) ., We estimate the size of the telomeric C-rich strand siRNAs ( C-siRNA ) to be 24–25 nt , and the size of G-siRNAs to be 23–24 nt ( Figure S3 ) ., The formation of 24 nt siRNAs in Arabidopsis is mediated by RNA-processing enzymes of the RdDM pathway 24 ., This pathway is specific to plants and mediates methylation of cytosine residues in an asymmetric sequence context ( CNN ) ., The absence of telomeric 23–25 siRNAs in plants lacking RNA-dependent RNA polymerase 2 ( RDR2 ) , Dicer-like 3 ( DCL3 ) or subunits of RNA Polymerase IV ( NRPD1 or NRPD2 ) and their reduction in two other RdDM mutants ( drd1 and nrpe1 ) further demonstrated that telomeric siRNAs belong to the category of 24 nt heterochromatic siRNAs ( Figure 4A ) ., These siRNAs are usually derived from heterochromatic loci and form the most abundant fraction of plant small RNAs 25 , 26 ., They typically associate with Argonaute 4 ( AGO4 ) that is part of the effector complex that , together with Polymerase V , mediates CNN methylation 27 , 28 ., To determine whether telomeric siRNAs associate with AGO4 , we surveyed published datasets containing ∼600 , 000 Argonaute ( AGO1 , AGO2 , AGO4 and AGO5 ) -bound small RNAs 29 ., We identified a total of 133 small RNAs containing at least 12 nucleotides with a perfect telomeric repeat ( Table S1 ) ., As expected , the majority of these small RNAs were associated with AGO4 ( Figure 4B ) ., Surprisingly , the AGO4-associated telomeric siRNAs were almost exclusively G-siRNAs and only a few C-siRNAs containing no more than 14 nt of the CCCTAAA repeat sequence were found in the dataset ( Figure 4C ) ., Since the levels of total G- and C-siRNAs are similar ( Figure 4A ) , this bias may be caused by a selective incorporation of the G-siRNAs into the AGO4 complex ., As TERRA transcripts are produced from telomeres as well as from centromere-located telomeric DNA , the siRNAs may be of either telomeric or centromeric origin ., To determine whether telomere-derived transcripts are processed into siRNAs , we aligned Argonaute-associated siRNAs with telomere-adjacent sequences ., We found abundant siRNAs corresponding to both strands of subtelomeric DNA at chromosome arms 1L , 1R , 3L , 4R and 5L ( Figure 5 , Table S2 ) ., Since these regions are formed by unique sequences , the origin of the siRNAs can be unambiguously traced to these loci ., Interestingly , AGO4-associated siRNAs were particularly enriched at the chromosome ends that also exhibited expression of TERRA and ARRET ( 1L , 3L , 4R , 5L; Figure 5 ) ., These data strongly argue that telomeric TERRA and/or ARRET are processed into siRNAs ., Plants can methylate cytosines in all sequence contexts , and DNA methylation at asymmetric positions relies largely on 24 nt siRNAs and on the RdDM pathway ., The presence of telomeric siRNAs prompted us to ask whether telomeric DNA , which contains cytosines exclusively in the CNN context , can be methylated ., We took advantage of the unique insertion in the 1L telomere that allowed us to design primers spanning 13 CCCTAAA repeats located in the centromere-proximal part of the 1L telomere ( region 1L-0; Figure 2A ) ., Bisulfite sequencing of the 1L-0 region in wild-type plants revealed that over 40% of cytosines in these telomeric repeats are methylated ( Figure 6 ) ., In contrast , the 1L and 2R subtelomeric regions are devoid of DNA methylation ( Figure S4 ) ., The telomeric methylation in 1L-0 is non-randomly distributed , with preferential enrichment at the third cytosine in the CCCTAAA sequence ( Figure 6A and 6B ) ., A similar observation was recently made through whole genome bisulfite sequencing that also revealed methylation of telomeric repeats , albeit at a lower total frequency than reported here 30 ., The level of 5-methylcytosine in all sequence contexts was dramatically reduced in rdr2 mutants , arguing that methylation of the 1L-0 region primarily depends on the RdDM mechanism ( Figure 6A and 6C ) ., We next examined whether cytosine methylation and its dependence on the RdDM pathway is a general feature of telomeric DNA ., We sequentially hybridized bisulfite-treated total genomic DNA to oligonucleotide probes that first detected fully converted telomeric DNA ( probe AAAATTT ) , then unconverted , and thus completely methylated DNA ( probe TTTAGGG ) , and finally the complementary cytosine-free strand ( probe CCCTAAA ) as a control for loading ( Figure 6D ) ., A strong hybridization AAATTTT signal suggested that the bulk of telomeric DNA is only weakly methylated ., Nevertheless , a portion of wild-type DNA was resistant to bisulfite conversion as hybridization with the TTTAGGG oligo probe showed a signal that was ∼4-fold higher than a background signal from a corresponding amount of non-methylated bisulfite-converted telomeric DNA cloned in a plasmid ( Figure 6D and 6E ) ., These data further indicate the presence of some heavily methylated CCCTAAA sequences in wild-type plants ., Importantly , this CCCTAAA signal was reduced to a background level in rdr2 and nrpd2a mutants ( Figure 6D and 6E ) ., To further investigate whether methylation occurs at telomeres , we performed high-stringency hybridization of the bisulfite-converted samples with a long telomeric TTTAGGG probe ( Figure 6C ) ., Under these conditions , converted plasmid-cloned telomeric DNA produces a high background hybridization signal that is likely caused by sufficiently stable interactions between longer fragments of the ( TTTTAAA ) n converted telomeric DNA and the ( TTTAGGG ) n probe ., Nevertheless , wild-type DNA samples still produced a signal that was significantly higher than the background hybridization ( Figure 6F ) ., These data , together with the bisulfite sequencing of the 1L-0 telomeric region , strongly argue that DNA methylation is a general characteristic of Arabidopsis telomeres and that its maintenance requires the RdDM pathway ., Loss of DNA methylation is often accompanied by chromatin remodeling ., However , the decrease in telomeric DNA methylation did not result in a significant loss of heterochromatic histone marks , and both H3K9me2 and H3K27me1 remained enriched at the bulk of telomeric DNA in rdr2 mutants ( Figure 7A ) ., However , analysis of histone modifications at the 1L-0 locus by ChIP and quantitative PCR ( Figure 7B and 7C ) showed a decrease in H3K9me2 and H3K27me1 ( Figure 7C ) in rdr2 mutants ., These data indicate that although the RdDM-dependent mechanism is not solely responsible for heterochromatin formation at telomeres , it contributes to its maintenance by mediating methylation of telomeric DNA , thereby reinforcing heterochromatic histone modifications ., Disruption of telomeric heterochromatin or demethylation of subtelomeric sequences leads to increased telomere elongation and recombination in mouse 7 ., Our analysis of telomere length and intrachromatid recombination at chromosome ends did not reveal any differences between RdDM mutants and wild-type plants ( Figure S5 and Figure S6 ) ., This observation further corroborates our finding that despite reduced DNA methylation , the bulk of telomeric chromatin in rdr2 mutants still retains heterochromatic features ., Heterochromatin is a universal characteristic of chromosome termini in a variety of organisms , including yeast , flies and mammals ., Subtelomeric regions in these organisms are gene-poor and enriched for middle to highly repetitive sequences that contribute to the formation of a heritably repressed chromatin structure at chromosome termini that shares similarities with pericentromeric heterochromatin 1 , 31 , 32 ., Nevertheless , some aspects of chromatin organization appear to be unique at telomeres as telomeric chromatin in humans and plants display unusually short nucleosomal spacing ( ∼160 nt ) in comparison with the ∼180 nt periodicity at the bulk of chromatin 33–35 ., In contrast to many other organisms , telomeres in Arabidopsis are directly adjacent to transcriptionally active genes ., This situation is more similar to silenced transposons inserted in gene-rich regions than to pericentromeric heterochromatin ., This is also reflected in the organization of telomeric chromatin that exhibits features of intermediate heterochromatin that is characterized by the presence of both active and repressive histone H3 marks ., Such chromatin was described to be associated with some Arabidopsis transposons and transgenic loci 22 , 36 ., Chromatin analysis of the 1L subtelomere demonstrates that repressive histone H3 modifications are most pronounced immediately next to telomeres and that their presence gradually recedes with growing distance from telomeres ., Data on whole-genome distribution of H3K9me2 indicate that this also holds true for telomere-associated regions of several other chromosome arms 21 ., These data infer that repressive histone marks are primarily established at telomeres and spread only a limited distance within adjacent subtelomeric sequences ., The existence of such relatively small clusters of repressive chromatin ( 2–5 kb ) next to otherwise large gene-rich regions suggests a functional importance for the heterochromatization of telomeres in Arabidopsis ., It further suggests the existence of mechanisms that specifically maintain repressive histone modifications at telomeres ., Assembly of heterochromatin at chromosome ends in budding yeast is partially dependent on tethering Sir proteins to telomeres via the Rap1 telomere-binding protein 31 ., Human SIRT6 histone deacetylase preferentially associates with telomeres , although how it is recruited to chromosome termini is not known 3 ., A recent study in mice overexpressing TRF2 indicates that , similar to the situation in yeast , heterochromatin formation at telomeres in mammals may also involve telomere-binding proteins 37 ., The discovery of TERRA provides another attractive model that involves targeting the chromatin remodeling machinery to chromosome termini through ncRNA 38 , 39 ., This suggestion was recently corroborated by the finding that downregulation of TERRA by RNAi in human cells causes a decrease in histone H3K9 methylation 13 ., It was proposed that TERRA facilitates heterochromatin formation by stabilizing interactions between heterochromatin factors and telomeric DNA ., In this study , we demonstrate expression of telomeric transcripts in Arabidopsis and describe a mechanism by which telomeric repeats-containing RNAs affect telomeric chromatin through siRNA ., In contrast to the situation in mammals , where only UUAGGG telomeric transcripts were detected 10 , 11 , both telomeric strands appear to be transcribed from some telomeres in Arabidopsis ., This indicates that canonical telomeric DNA may , under certain circumstances , act as a promoter and initiate transcription ., Two lines of observations further corroborate the link between transcription and telomeric DNA in Arabidopsis ., Firstly , short stretches of a telomeric sequence were found in numerous Arabidopsis promoters and it has been shown that these interstitial telomere motifs are required for transcription 40 ., Secondly , several transcription factors have been identified in Arabidopsis that specifically bind to telomeric DNA in electromobility shift assays ( reviewed in 41 ) ., Thus , it is possible that some of these transcription factors localize to telomeres and promote their expression ., In addition to transcripts that originated at telomeres , we detected TERRA and ARRET that are apparently generated by transcription of centromere-associated telomeric loci ., We cannot currently determine the exact identity of telomere- or centromere-derived TERRA/ARRET that is processed by DCL3 and degraded to telomeric siRNAs ., The requirement of RDR2 for siRNA formation indicates that the predicted dsRNA intermediate is not a simple annealing product of complementary TERRA and ARRET , but is dependent on additional RNA-dependent RNA synthesis ., Thus , even relatively low level transcripts can yield significant amounts of siRNA ., In fact , direct detection of precursor transcripts in the RdDM pathway has been so far reported only in a special transgene system 42 ., In plants , heterochromatic siRNAs serve to guide DNA methylases to specific asymmetric CNN positions in a mechanism that relies on AGO4 28 ., Interestingly , AGO4 appears to retain telomeric G-siRNAs , and not the complementary C-siRNAs , although these data should still be verified by Northern analysis of AGO4 co-immunoprecipitated siRNAs ., It is unknown whether the bias towards G-siRNAs is of biological significance , but it is interesting that the AGO4 complex appears to specifically retain siRNAs complementary to the telomeric strand to be methylated ., Our data , showing methylation of bulk telomeric DNA as well as heavy methylation of the centromere-proximal region of the 1L telomere , together with data from whole genome bisulfite sequencing 30 , argue that telomeric heterochromatin in Arabidopsis is not only defined by histone modifications , but also by DNA methylation ., Although mammalian telomeres lack CG sites , and are , thus , believed to be unmethylated , at least two proteins linked to DNA methylation ( SMCHD1 , MBD3 ) have been found in purified fractions of human telomeric chromatin 43 ., Additionally , the recent discovery of CNN and CNG methylation in human embryonic stem cells warrants the re-examination of DNA methylation at human telomeres 44 ., We demonstrate that the maintenance of telomeric DNA methylation depends , to a large extent , on heterochromatic siRNA and the RdDM machinery ., Intriguingly , loss of telomeric DNA methylation only has a slight effect on histone methylation at bulk telomeres , indicating that assembly of Arabidopsis telomeric heterochromatin relies on several reinforcing mechanisms that recruit histone methyltransferases such as SUVH4 to telomeres 45 ., Loss of DNA methylation has a more profound effect on histone methylation at the centromere-proximal part of the 1L telomere ., This indicates that RdDM may play a role in maintaining heterochromatin at the boundary between telomeres and adjacent euchromatic genes ., The involvement of siRNA in modulation of telomeric heterochromatin may not be restricted to plants ., Our data in Arabidopsis are reminiscent of the situation in fission yeast where heterochromatin in subtelomeric regions is established by two independent pathways , one of which relies on the telomere-binding protein Taz1 , while the other involves RNA-induced transcriptional silencing ( RITS ) 46 ., However , in contrast to the situation in Arabidopsis where siRNA targets canonical telomeric repeats , RITS in fission yeast is directed at centromere-like sequences that are located ∼15 kb from telomeres ., In humans , TERRA has been proposed to act as a scaffold , reinforcing interactions between telomere-binding proteins and heterochromatin factors such as ORC1 and HP 1 13 ., Nevertheless , human TERRA could also promote heterochromatin formation through an siRNA-mediated pathway ., This notion is supported by the observation that enrichment of Argonaute-1 at human telomeres is correlated with increased H3K9 methylation and HP1 association 47 , and by the discovery of telomere-derived human siRNAs 48 ., Arabidopsis mutants carrying the following alleles were used in this study: dcl3-1 ( dcl3 ) , rdr2-1 ( rdr2 ) , nrpd1a-4 ( nrpd1 ) , nrpd1b-1 ( nrpe1 ) , sgs2-1 ( rdr6 ) , drd1-1 ( drd1 ) and nrpd2a-1 ( nrpd2 ) ., Plants were grown in soil under long-day conditions ( 16 h light/8 h dark ) at 22°C ., Total RNA was extracted using TriReagent solution ( Sigma ) ., For Northern blot analysis , 10 µg aliquots were separated on 1 . 2% formaldehyde agarose gels , blotted onto a nylon membrane and hybridized with 32P 5′ end-labeled ( TTTAGGG ) 4 ( TTTAGGG probe ) or ( TAAACCC ) 4 ( CCCTAAA probe ) oligonucleotides ., Oligo hybridizations were carried out at 55°C as previously described 49 ., Centromeric transcripts were detected by hybridization with a 32P-labeled CEN180 repeat unit amplified from Arabidopsis genomic DNA using primers CEN1 and CEN2 ( Table S3 ) ., For RT-PCR analyses , ∼2 µg of total RNA was reverse transcribed by using oligo dT for gene expression ., The ( CCCTAAA ) 3 oligo or subtelomere-specific primers ( Table S3 ) were used for RT of TERRA and ARRET , respectively ., The respective cDNAs were amplified by 25–35 cycles of PCR with specific primers ( Table S3 ) ., Small RNAs were isolated from inflorescences using the mirVana miRNA isolation kit ( Ambion ) , separated on 15% polyacrylamide gels and electroblotted onto a nylon membrane ., Telomeric siRNAs were detected by hybridization with either ( TTTAGGG ) 4 or ( TAAACCC ) 4 oligo probes in ULTRAhyb-Oligo hybridization buffer ( Ambion ) at 42°C ., The artificial 25 and 23 nt siRNAs were synthesized by in vitro transcription using T7 RNA polymerase ( MBI ) ., The T7-TOP oligonucleotide ( 10 µM ) was annealed to a template oligonucleotide ( 10 µM ) as indicated in Figure S3 ., In vitro transcription was carried out with 30U of T7 RNA polymerase ( MBI ) and the annealed oligos ( 0 . 5 µM ) in 50 µL of 1× Transcription buffer ( MBI ) supplemented with NTPs ( 10 mM ) and RiboLock RNase inhibitors ( MBI ) for 60 min at 37°C ., 25 µL of the reaction was separated on a 15% polyacrylamide gel , electroblotted onto a nylon membrane and analyzed by Southern hybridization ., Genomic DNA was extracted from 4 week old plants with the DNAeasy Plant Maxi Kit ( Qiagen ) ., Bisulfite modification was performed using the EpiTect Bisulphite Kit ( Qiagen ) according to the manufacturers instructions ., The completeness of the conversion was tested by PCR amplification of a non-methylated genomic region 50 ., Modified DNA was used as a template for PCR amplification with the primers indicated in Table S3 ., The PCR products were cloned into the pCR2 . 1 TOPO cloning vector ( Invitrogen ) and sequenced using a BigDye terminator and an ABI310 sequencer ( Applied Biosystems ) ., The sequence of the clones was analyzed with the software CyMATE 50 ., The efficiency of cytosine conversion in the 1L-0 region in these samples was further controlled by either spiking genomic DNA with a bacterial plasmid containing a region that partially overlaps with 1L-0 or by sequence analysis of other genomic loci that are devoid of 5-methylcytosines ., For methylation analysis at bulk telomeric DNA , bisulfite-modified genomic DNA was transferred onto a nylon membrane by vacuum-blotting ., As a control , a bisulfite-modified plasmid containing 750 bp of plant non-methylated telomeric DNA was blotted onto the membrane in an amount that roughly corresponded to the total amount of telomeric DNA present in genomic samples ( 1 ng of the plasmid contained telomeric DNA equivalent to ∼260 ng of genomic DNA ) ., The membrane was hybridized with the 32P 5′ end-labeled ( TTTAAAA ) 4 oligo ( AAAATTT probe ) in a standard hybridization buffer 49 at 40°C ., The membrane was washed twice for 10 min at 40°C in 2× SSC followed by a 40 min wash in 1× SSC at 40°C ., The membrane was exposed to a Kodak Phosphor screen ( Biorad ) and scanned with Molecular Imager FX ( Biorad ) ., The membrane was then stripped and sequentially rehybridized with the TTTAGGG and CCCTAAA oligo probes at 55°C as described 49 ., The final rehybridization was performed at 65°C with a strand-specific ( TTTAGGG ) n probe that was obtained by labeling of a 750 bp fra | Introduction, Results, Discussion, Materials and Methods | Chromosome termini form a specialized type of heterochromatin that is important for chromosome stability ., The recent discovery of telomeric RNA transcripts in yeast and vertebrates raised the question of whether RNA–based mechanisms are involved in the formation of telomeric heterochromatin ., In this study , we performed detailed analysis of chromatin structure and RNA transcription at chromosome termini in Arabidopsis ., Arabidopsis telomeres display features of intermediate heterochromatin that does not extensively spread to subtelomeric regions which encode transcriptionally active genes ., We also found telomeric repeat–containing transcripts arising from telomeres and centromeric loci , a portion of which are processed into small interfering RNAs ., These telomeric siRNAs contribute to the maintenance of telomeric chromatin through promoting methylation of asymmetric cytosines in telomeric ( CCCTAAA ) n repeats ., The formation of telomeric siRNAs and methylation of telomeres relies on the RNA–dependent DNA methylation pathway ., The loss of telomeric DNA methylation in rdr2 mutants is accompanied by only a modest effect on histone heterochromatic marks , indicating that maintenance of telomeric heterochromatin in Arabidopsis is reinforced by several independent mechanisms ., In conclusion , this study provides evidence for an siRNA–directed mechanism of chromatin maintenance at telomeres in Arabidopsis . | Telomeres are protein–DNA structures that protect the ends of eukaryotic chromosomes ., A failure in this protective structure can lead to chromosomal instabilities and contribute to cancer and aging ., The protective nature of telomeres relies on complex interactions between repetitive telomeric DNA and associated proteins ., One major question is how telomeric proteins , including telomere-associated nucleosomes , are modified in order to achieve this protection ., In this study , we have discovered that Arabidopsis telomeric nucleosomes contain a unique mixture of both active and inactive chromatin marks ., Additionally , the telomeric DNA itself is modified by methylation of cytosines within the telomeric repeat ., Regulation of DNA methylation is achieved by telomeric repeat–containing small RNAs , which are derived from the processing of telomeric transcripts by the RNA–dependent DNA methylation pathway ., From these data , we infer that the formation of a proper telomere structure is partly regulated by non-coding telomeric RNAs . | genetics and genomics/epigenetics, genetics and genomics/nuclear structure and function, genetics and genomics/plant genetics and gene expression, genetics and genomics/chromosome biology | null |
journal.pcbi.1004643 | 2,015 | Cultured Cortical Neurons Can Perform Blind Source Separation According to the Free-Energy Principle | Blind source separation is a problem of separating independent sources from a complex mixture of inputs without knowledge about sources 1–4 and is the computation underlying the cocktail party effect––a phenomenon by which one is able to listen to a single person’s speech in a noisy room 5–8 ., Understanding the basis of blind source separation , as well as other learning and memory processes , requires characterization of the underlying functional network architecture ., Presumably , this can be directly accomplished by measuring the activity of individual neurons during blind source separation processing to establish the role of each neuron in the network ., In practice , this is enormously challenging , given both the large number of neurons that may reside in a network and the technical limitations encountered in attempting to distinguish the activity of neurons that perform blind source separation from others throughout the network ., As a result , most studies of blind source separation rely on simulations and on computational models , and the possible electrophysiological basis for any such information processing in real neurons remains poorly understood ., Theoretically , blind source separation is classed as unsupervised learning , a type of learning that does not require teacher signals 9–11 ., Blind source separation is modeled as principal component analysis ( PCA ) 12 , as independent component analysis ( ICA ) 13 , 14 , or as sparse coding 15 , 16 ., These are widely used for signal processing where separation of sources from a complex mixture of inputs is desired ., Neural network models that include neurons with linear firing rates can perform PCA , a model that describes how neurons in artificial networks can strengthen or weaken their interconnections over time 12 ., In contrast , ICA , which can be represented using model neurons with nonlinear firing rates 13 , 14 , maximizes Shannon entropy among outputs in order to detect several independent sources , thus separating a multivariate signal into individual components ., The sparse coding model detects independent sources 15 , 16 using a calculation similar to that proposed by the predictive coding hypothesis of the cerebral cortex 17 ., What all these models of unsupervised learning have in common is that they can be implemented with a form of Hebbian or associative plasticity 18 and that they are instances of the free energy principle––a candidate unified theory of learning and memory 19 , 20 ., Moreover , blind source separation , whether by PCA , ICA , or sparse coding , is one of the simplest problems that the free-energy principle addresses ., Additionally , numerous computational studies have demonstrated that simulated neural networks can perform blind source separation ., PCA , ICA , and sparse coding have been demonstrated in both firing-rate models and spiking-neuron models 21–27 ., However , although early studies indicated that cortical neurons might use an ICA-like signal processing strategy for sensory perception 5–8 and described the relationship of sparse- and predictive coding to biological properties 28 , 29 , examinations of the neural basis of ICA-like learning are few ., Experimental studies on in vivo or in vitro networks have demonstrated that neural networks can perform learning and memory tasks , when learning is defined as the process of changing activity or behavior by experiencing something , as it is in this study ., One of the simplest networks can be constructed from actual cultured neurons , and such real neural networks can exhibit stimulation-dependent synaptic plasticity 30 , 31 , supervised learning 32 , adaptation to inputs 33 , associative memory 34 , aspects of logical operation 35 , 36 , short-term memory 37 , and homeostatic plasticity 38 , 39 ., However , it is uncertain whether these biological neural networks can perform blind source separation ., Previously , we have used the microelectrode array ( MEA ) to simultaneously stimulate and record from multiple neurons over long periods 30 , 40 ., The MEA enables random electrical stimulation from 64 electrodes in parallel and the recording of evoked spikes immediately after each stimulation ., Thus , by varying probabilities during stimulation trains , the MEA makes it possible to apply spatiotemporal inputs synthesized from hidden sources while measuring the response evoked from the entire neural network ., Through this capability , we demonstrate here that cultured rat cortical neurons receiving multiple inputs can perform blind source separation , thereby providing an in vitro model of neural adaptation ., In brief , our approach consisted of two parts ., First , we tried to establish whether single neuron responses preferred mixtures of sources or the individual sources per se ., To address this , we examined the Kullback-Leibler divergence 11 between the probabilities of neuronal responses conditioned upon one of two sources ., We hoped to see that neurons were able to discriminate between sources rather than mixtures , because this would imply a blind source separation––or the inversion of a generative model of stimulation patterns ( i . e . , sources ) ., We were able to show that neurons preferred hidden sources , as opposed to mixtures of sources ., This then allowed us to quantify the probabilistic encoding of sources by assuming that the expected amplitude of each hidden source was encoded by the mean activity of neuronal populations preferring one source or the other ., By assuming a rate coding model , where mean firing rates encode the mean of a mixture of Gaussians , we were able to compute the variational free energy of the neuronal encodings in terms of energy and entropy ., Crucially , the free energy principle suggests that with learning , energy should decrease and entropy should increase ( where the free energy is the difference ) 19 , 20 ., In this instance , the energy can be thought of as level of prediction error ., Conversely , the entropy refers to the average uncertainty of the encoding ., According to Jaynes’ maximum entropy principle 41 , 42 , entropy should increase to ensure a generalizable inference that is in accordance with Occam’s principle ., In short , we hoped to see an increase in the entropy of the probabilistic encoding that was offset by a decrease in energy ( an increase in accuracy ) ––producing an overall decrease in free energy ., Rat cortical cells were cultured on MEAs ( Fig 1A and 1B ) and electrical stimulation and recordings were conducted ., Typical stimulus-evoked responses of cultured neural networks recorded at the stimulated electrode are shown in Fig 1C ., In accordance with previous studies , we observed tri-phasic responses 30 , 40 ., To study ICA-like learning in networks created in these neuronal cultures , we designed a generative process constructed from two independent binary sources u, ( t ) = ( u1, ( t ) , u2, ( t ) ) T ∈ {0 , 1} , 32 inputs produced by the MEA s, ( t ) = ( s1, ( t ) , … , s32, ( t ) ) T , and a 32×2 matrix A , where ( Ai1 , Ai2 ) = ( a , 1–a ) for i = 1 , … , 16 and ( Ai1 , Ai2 ) = ( 1–a , a ) for i = 17 , … , 32 ( Fig 2A ) ., Note that t s is discrete time ( a natural number ) between 1 and 256 ., In brief , we had an array of ( 8×8 ) 64 recording electrode sites of which half ( 32 ) were stimulated ., The remaining 32 were for recording neural activities at non-stimulated electrodes ., The detailed neural response properties are discussed in the next section ., The stimuli were formed by mixing two underlying patterns , or hidden sources , to create stochastic stimulus patterns ., These were mixed separately for each of two groups of 16 stimulation electrodes , such that the stimulation pattern comprised of probabilistic mixtures of the underlying sources ., The responses from the 64 electrodes and 23 cultures were pooled , yielding over 1000 electrode responses to various mixtures of hidden sources ., In other words , u, ( t ) was generated from the stationary Poisson process , while s, ( t ) obeyed the non-stationary Poisson process with the time varying intensity of A u, ( t ) ., The generative model ensured that the two sources contributed to the stimuli with an equal probability ρ ., We used mixtures of these sources to produce stimulus patterns that contained no signal , one of the two sources , and a fully mixed source ., Unless specifically mentioned , we used ρ = 1/2 and a = 3/4 ., Electrical stimulations with 256-s pulse trains were applied at 1-s intervals for 100 trials ., A schematic image of how inputs s, ( t ) were obtained from sources u, ( t ) is shown in Fig 2B and 2C ., A detailed description is provided in the Fig 2 legend and the Methods section ., Neural responses evoked by the input trains were recorded using a 64-electrode MEA ., We used 23 cultures for a training group and a total of 37 cultures as control groups ., We performed 100 trials ( 500 s for 1 trial; about 14 h in total ) for each culture ., An overview of the experimental paradigm is shown in Fig 3 and S1 and S2 Movies ., A raster plot and post stimulus time histogram ( PSTH ) detailing the spike timing of evoked response recorded at a representative electrode ( xi ( τ ) ; τ , continuous time ) is shown in Fig 4A and 4B ., Evoked response increased immediately after each stimulation for both stimulated and non-stimulated neuron groups ., The peak of evoked responses was observed 10-to-20 ms after each stimulation in all trials ., Compared to the results of the first trial ( Fig 4A ) , the evoked response for the hidden source of u = ( 0 , 1 ) ( blue curve ) decreased after the training stimulation ( Fig 4B ) , indicating that neurons recorded at this electrode tuned their activity to only respond to the ( 1 , 0 ) and ( 1 , 1 ) states , i . e . , only to u1 ., According to previous studies , the directly evoked responses occur immediately after stimulation and their jitters are relatively small; thus , large numbers of spikes that appear more than 10 ms after stimulation are generated by synaptic inputs 43 ., Therefore , the change in number of evoked spikes generated 10–30 ms after each stimulation , defined as evoked response , occurred gradually over training ( Fig 4C left ) ., The center and right panels in Fig 4C illustrates a typical transition of a conditional probability distribution of evoked responses , i . e . , the number of evoked spikes recorded at the electrode before and after training ., In this case , a typical shift of a peak of the ( 0 , 1 ) type ( blue curve ) is presented ., Fig 4D shows the transition of responses over training at another stimulated electrode ., In contrast to Fig 4C , a shift of a peak of the ( 1 , 0 ) type ( red curve ) is shown ., The transition of response at each electrode can be found in S1 Dataset ., These results suggested that neurons near stimulated electrodes had preferences to one of the two hidden signals , but not the other ., Specifically , most neurons from electrodes 1–16 ( x1 , … , x16 ) preferred u1 signals ( neurons were activated more largely when u = ( 1 , 0 ) than when u = ( 0 , 1 ) ) , most neurons from electrodes 17–32 ( x17 , … , x32 ) preferred u2 signals , and most neurons at electrodes 33–64 ( non-stimulated; x33 , … , x64 ) showed no preference ( Fig 5A and 5B ) ., Note that xiu indicates the conditional expectation with the source state u and xiu¯ is its over-trial average ., Neurons near stimulated electrodes exhibited larger responses compared to these near non-stimulated electrodes ., In u1-preferring neurons , the increase in response strength was larger when the state of the source was u = ( 1 , 0 ) than when it was u = ( 0 , 1 ) ( Fig 5C and 5D ) , while the exact opposite alteration profile was observed in u2-preferring neurons ( Fig 5E and 5F ) ., Moreover , at 50 electrodes out of 371 u1-preferring electrodes , xi1 , 0¯ was 3 times larger than xi0 , 1¯ , and at 44 electrodes out of 345 u2-preferring electrodes , xi1 , 0¯ was 3 times larger than xi1 , 0¯ as all trial average ( S1A Fig ) ., Additionally , the number of such electrodes increased during training ( S1B Fig ) ., If a neuron responded to si ( i = 1 , … , 16 ) , xi1 , 0¯ should be 3 times as large as xi0 , 1¯ by the relationship between si and u , while if a neuron responded to si ( i = 17 , … , 32 ) , xi0 , 1¯ should be 3 times as large as xi1 , 0¯ ., Therefore , this indicates that at approximately 13% of u1- or u2-preferring electrodes , neural responses ( xi ) were more likely to be determined by the state of hidden sources, ( u ) rather than by induced stimulation itself ( si ) in the strict sense of the word ., Taken together , these results suggest that neural responses were more likely determined by the state of hidden sources estimated based on inputs from multiple electrodes , termed source-coding , rather than the input from an electrode , e . g . , the nearest electrode ., The difference between the probability distribution at u = ( 1 , 0 ) and ( 0 , 1 ) is a well-established criterion to evaluate response preference , which in information theory is often defined by the Kullback-Leibler divergence ( KLD ) 11 ., We calculated KLD of the evoked response at each electrode under the assumption that these conditional probabilities conformed to a Poisson distribution ., We observed a significant change in KLD ( represented as DKLi , where i = 1 , …… , 64 is the index of electrodes ) between distributions given the ( 1 , 0 ) state and ( 0 , 1 ) state ( P ( xi, ( t ) | u = ( 1 , 0 ) ) and P ( xi, ( t ) | u = ( 0 , 1 ) , respectively ) ., The values of DKLi were increased in some electrodes after the training period ( red circles in Fig 6A ) , where trained neuron cultures are labeled as TRN ., Moreover , the mean values for DKLi averaged across all recording electrodes increased after training ( Fig 6B and 6C ) ., The increase in the value of DKLi in trained neuron cultures in the presence of 20 μM 2-Amino-5-phosphonopentanoic acid ( APV ) , an N-methyl-D-aspartic acid ( NMDA ) -receptor inhibitor , was significantly smaller than in nontreated TRN cultures ( black circles in Fig 6B; **** , p < 10−5 ) ., We confirmed that the alterations in KLD were maintained for a long time by comparing continuously stimulated trained neurons to partially trained ( PRT ) neurons ., PRT neurons were trained for only 10 trials , then went unstimulated for 18–24 h ( i . e . the resting period ) , and then went through 10 additional training trials ., In PRT cultures , the values of DKLi at trial 91 ( i . e . , first trial after the resting period ) were significantly larger than that at trial 1 ( Fig 6D ) ; however , the difference was significantly smaller than the difference in DKLi observed between trial 1 and 91 in TRNs ( white circles in Fig 6B; **** , p < 10−4 ) ., Interestingly , the values of DKLi at trial 100 in PRTs were almost same level as that at trial 100 in TRNs ( p = 0 . 268 ) ., The transition of KLD at each electrode can be found in S1 Dataset ., KLD was affected by the merged balance of inputs ( a ) and the frequency of inputs ( ρ ) ., Specifically , we varied input balance by comparing the change of the a:1–a = 3/4:1/4 balance condition with that of the 0:1 and 1/2:1/2 balance conditions and the source condition with a ρ = 1/2 probability with a 1/4 and 3/4 probability ( Fig 6E and 6F ) ., Compared to the initial values ( trial 1 vs . trial 100 ) , KLD was not altered by inputs with 1/2:1/2 ratio of merged balance ( ( a , ρ ) = ( 1/2 , 1/2 ) ) ( black circles in Fig 6E; p = 0 . 515; n = 147 from 4 cultures ) , suggesting that input variance was necessary to elicit these changes ., As both PCA and ICA rely on variations of input , these results are consistent with the hypothesis that cultured neural networks use ICA-like signal processing ., When sources summed to one with a probability of 1/4 , i . e . , ( a , ρ ) = ( 3/4 , 1/4 ) ( red circles in Fig 6E ) , KLD increased after training ( *** , p < 10−3; n = 139 from 4 cultures; trial 1 vs . trial 100 ) ., Similarly , when ( a , ρ ) = ( 3/4 , 3/4 ) ( white circles in Fig 6E ) , KLD increased after training ( **** , p < 10−7; n = 234 from 6 cultures; trial 1 vs . trial 100 ) ., The change in KLD with ( a , ρ ) = ( 3/4 , 1/4 ) was slightly smaller than when ( a , ρ ) = ( 3/4 , 1/2 ) ( p = 0 . 469 , at trial 100 ) , while the change in KLD with ( a , ρ ) = ( 3/4 , 3/4 ) was slightly larger than when ( a , ρ ) = ( 3/4 , 1/2 ) ( p = 0 . 166 , at trial 100 ) ., When the input balance was 1:0 ( not merged; ( a , ρ ) = ( 1 , 1/2 ) ) , a large increase of KLD was observed ( Fig 6F; **** , p < 10−4; n = 161 from 4 cultures; trial 1 vs . trial 100 ) , which is an analog of conventional pattern separation 34 ., Note that to calculate Fig 6F , when the change in KLD form trial 1 was larger than 10 or smaller than –10 , it was shifted to 10 or –10 , respectively ., We then set out to build a population-based model of neural network assembly based on our experimental paradigm ., We defined the population model as x˜= ( x˜1 , x˜2 ) T , where x˜1 and x˜2 represent mean evoked responses of neurons in u1- and u2-preferring neuron groups in each culture preparation ., Distribution of x˜, ( t ) at trial 1 and 100 are shown in Fig 7A and 7B , which represents the recognition density 19 , 20 of x˜ , q ( x˜ ) ., Alterations observed in q ( x˜ ) over the trial periods are show in S3 Movie ., Notably , the total evoked response from all available electrodes ( x˜1+x˜2 ) was almost proportional to the total input ( i . e . , the number of stimulated electrodes ) ( S2A Fig ) ., Early computational studies proposed several learning models ( recognition models ) employing blind source separation ., These models can be roughly separated into two types: the inverse recognition model 12–14 , 44 and the feed-forward recognition model 15–17 , 19 , 20 ., Considering the fact that inputs s were instantaneously induced in cultured neural networks and evoked responses recorded at stimulated electrodes decreased 20–30 ms after each stimulation ( Fig 4A and 4B ) , the feed-forward recognition model was not suitable in this situation , as it requires the dynamics of neural networks to converge towards an equilibrium state for learning ., Moreover , large populations of neurons that we observed were state-coding and correlated with sources, ( u ) ( 96 . 2% of electrodes were corr ( xi , u1 ) > 0 . 4 or corr ( xi , u2 ) > 0 . 4 ) , while only a small population of neurons were correlated with estimation errors ( e1 or e2 , where e1 and e2 are estimation errors of xi from u1 and u2; only 1 . 8% of neurons were |corr ( xi , e1 ) | > 0 . 4 or |corr ( xi , e2 ) | > 0 . 4 ) ( Fig 7C ) ., Therefore , our results indicated that the recognition model used by cultured neural networks is more consistent with the inverse model , as the inverse model does not require the equilibrium state of x˜ or the existence of error-coding neurons ., Based on this evidence , we generated an inverse recognition model of cultured neural networks , as we show in Fig 7D ., Schematic images of the model’s dynamics are shown in Fig 7E ., Taken together our results indicated that cultured neural networks implement ICA-like learning and that their dynamics can be described by an inverse recognition model ., Estimations of effective connectivity help in understanding neural dynamics 45 , 46 ., To estimate parameters of the inverse model from observed evoked responses , we calculated the maximum likelihood estimator of connectivity W ( a 2×2 matrix ) to analyze the averaged synaptic connection strengths within and between assemblies ., Changes in estimated connection strengths are shown in Fig 8A ., After training ( relative to trial 1 ) , intrinsic connection strengths ( W11 , W22 ) increased significantly , while connectivity between different neuron groups ( W12 , W21 ) tended to decrease ( Fig 8B ) ., Notably , if we assumed a constraint on total synaptic strengths with a γ-norm ( the 1/γ power of the γ power sum of synaptic strengths ) , and if γ was between 2 and 4 , the γ-norm of the connection strengths maintained almost same value during the latter part of the training period ( S2B Fig ) ., As the model and connection parameters are well defined , we could calculate the internal energy and the Shannon entropy for these neural networks ., To do this , we assumed that q ( x˜ ) obeys a Gaussian mixture model with four peaks corresponding to the four states of u ., Internal energy , U\u2009=\u2009U ( s˜ , x˜ , W ) , is defined as the negative log likelihood function of prediction error at a moment , where s˜ and x˜ are input and output , respectively ., Shannon entropy , H , is defined by H\u2009=\u2009Hq ( x˜ ) ., Friston’s free energy , F , is defined as the difference between 〈U〉 and H 19 , 20 , where 〈•〉 is an expectation under q ( x˜ ) ., Therefore , F is represented as F ( s˜ , x˜ , W ) =\u2009〈 U ( s˜ , x˜ , W ) 〉\u2009−\u2009Hq ( x˜ ) ., Generally , free energy gives an upper bound on ‘surprise’ of inputs , so the decrease of free energy implies that the system is changing to adapt to ( or learn ) its environment 19 , 20 ., The full details of these calculations are fully described in the Methods ., These components of free energy changed dramatically over training trials ( Fig 8C ) ., We found that the expectation of internal energy 〈U〉 decreased , Shannon entropy H increased significantly , and free energy F decreased significantly after training ( Fig 8D ) , which is consistent with the principle of free-energy minimization 19 , 20 ., These data thus indicate that connectivities in neural networks were established such that they minimize free energy ( F ) ., As expected , as learning proceeds over trials , the implicit entropy of the probabilistic encoding increases in accord with Jaynes’ maximum entropy principle 41 , 42 ., Crucially , this is accompanied by a profound decrease in energy ( i . e . , the amount of prediction error ) ., Therefore , the decrease in the energy and the increase in the entropy both contributed to produce an overall reduction in free energy––that can only be attributed to learning or plasticity ., This assertion was verified empirically by quantifying free energy changes in the presence of APV ., Remarkably , free energy did not change at all during training under APV ( S3 Fig ) ., The changes in KLD and free energy we observed are indicative of synaptic plasticity and suggested that cultured neural networks are capable of performing blind source separation ., These findings further suggested the existence of a transformation matrix ( W ) in cultured neural networks , which transforms merged inputs to independent outputs 12–14 , 44 ., However , it is unclear whether the blind source separation is realized only by Hebbian learning 18 ., To estimate the learning rule of cultured neural networks , we first considered a simple Hebbian plasticity model , where a learning efficacy αu becomes 0 for u = ( 0 , 0 ) and α for other states ( α-model; see also the Methods ) ., We then estimated α for each culture sample ., The estimated values of α are shown in Fig 9A left and the Bayesian information criterion ( BIC ) 47 in α-model is shown in Fig 9B ., In this α-model , connections between different neuron groups ( W12 , W21 ) were expected to increase substantially , because Hebbian learning operates by simply increasing the correlation among neurons that fire together ( Fig 9C ) ., However , we did not observe substantial increases between neuron groups , indicating that a simple Hebbian rule could not explain our experimental results ., These results therefore suggested that blind source separation in our cultured neural networks required another mechanism ., We thus considered a modified version of Hebbian plasticity ( β-model ) , where a learning efficacy βu depends on the state of u , 0 for u = ( 0 , 0 ) , β1 for u = ( 1 , 0 ) , ( 0 , 1 ) , and β2 for u = ( 1 , 1 ) ., β1 and β2 were estimated for each culture ., Interestingly , we found that estimated values of β2 were significantly smaller than the estimated values of β1 ( approximately 27% of β1; Fig 9A right ) ., Moreover , the BIC was significantly smaller than in the α-model ( Fig 9B ) ., Accordingly , the β-model successfully explained the increase of intrinsic connections within neuron groups ( W11 , W22 ) , and the absence of increases inter-connections between different groups ( W12 , W21 ) ( Fig 9C ) ., Furthermore , as an additional Bayesian model comparison , we showed that Hebbian plasticity with state-dependent efficacy ( the β-model ) is better than Hebbian plasticity with γ-norm constraint on total synaptic strength ( the α’-model ) to explain our experimental results ( see S1 Note and S4 Fig ) ., These results suggest that cultured neural networks do not use the simplest form of the Hebbian plasticity rule ( the α-model ) , but rather a state-dependent Hebbian plasticity rule ( the β-model ) in which learning efficacy is modified according to the state of sources ., A conceptual conclusion is that the depression in inter-connections between different groups and the formation of cell assemblies are crucial to achieve blind source separation ., Generally , the potentiation in connections makes the correlation between a neuronal group and a source stronger , while their depression makes the correlation between the neuronal group and the other source weaker ., In our analysis , because the β-model encouraged stronger depression in connections from the other source and induced stronger competition between different neuronal groups , the β-model was better able to explain the results than the α-model ., Moreover , this result supports the hypothesis that neurons render their activity independent of each other ., This is consistent with early work on decorrelating or lateral interactions in PCA/ICA learning rules , which , importantly , can be formulated as variational free energy minimization 48 ., In this study , we discovered that cultured neural networks were able to identify and separate two hidden sources ., We found that the distinct classes of neurons learned to respond to the distinct hidden sources and that this was reflected in differences in the Kullback-Leibler divergence ( KLD ) ., We then sought to determine how connection strength is determined between cultured neurons and found that connectivities are established such that they minimize free energy ., Finally , we integrated these data to construct a model of learning in cultured neural networks and determined that learning is established by a modified Hebbian plasticity rule ., Taken together these data indicate that cultured neural networks can infer multivariate hidden signals through blind source separation ., Although cultured neural networks are random and may not have functional structures for signal processing before training , our data indicated that the process of training enables them to self-organize and obtain functional structures to separate two hidden signals though activity-dependent synaptic plasticity , such as spike-timing dependent plasticity ( STDP ) 49–51 ., This process was a clear example of unsupervised learning 9–11 in cultured neural networks ., Previous studies have reported that the response electrode almost agrees with the stimulating electrode 43 and that the increase in response strength at stimulated electrode is larger than in the non-stimulated electrodes 52; our results are consistent with these findings ., Synaptic plasticity and inputs with different merged points of balance are necessary for learning to occur ., As spikes observed less than 10 ms after stimulation in our culture system corresponded to responses directly evoked by electrical stimulation and artifacts ( switching noise ) , we only assessed spikes more than 10 ms after stimulation ., This allowed the analysis of changes in neural activity related to mechanisms of synaptic plasticity ., Indeed , we observed that changes in KLD were inhibited by APV , strongly suggesting that learning mechanism was mediated by long-term synaptic plasticity regulated by NMDA-receptor signaling ., As a further indication of a role for long-term synaptic plasticity , assays of partially stimulated cultures indicated the changes brought about by neural activation were maintained after 18–24 h without stimulation ., Additionally , our results indicated that differences in the size of inputs were necessary for blind source separation in cultured neurons ., Neurons with larger initial states of KLD tended to exhibit greater changes , suggesting that learning is nuanced by the initial input strengths as would be consistent with most forms of Hebbian learning 18 ., Although the specificity of the neuronal response to hidden sources increases significantly , there remains a possibility that the neurons merely responded to their neighbor input stimulation ., In fact , responding to neighbor stimulation might be enough to increase the response specificity in the current stimulation design ., Indeed , in a large portion of electrodes , neural responses were affected by the input from an electrode ., However , we found that at least at 13% of u1- or u2-preferring electrodes , neural responses were more likely to be determined by the state of hidden sources rather than by the input from an electrode , typically the nearest one , in the strict sense of the word ., Moreover , the number of such electrodes increased during training ., In short , this means we might be observing the superimposition of the response to the input from an electrode and the response corresponding to the state of hidden sources ., Hence , to reduce the effect of neighbor stimulation site and emphasize the response determined by the state of hidden sources , we should search the optimal stimulation design for investigating blind source separation as future work ., Even in the presence of APV , KLD increased slightly ., One explanation is that this is the result of an NMDA-R-independent form of learning ., For example , it is known that synaptic plasticity independent of NMDA-R activity occurs at GABAergic synapses 53 , 54 , and could alter the neural network state to some degree ., However , it could also be related to the drug’s imperfect blockade of NMDA-Rs ., In our experiments , evoked activities of cultured neurons were only synchronously generated immediately after each stimulation ., This would be expected for both forward and inverse recognition models , given that the input was synchronous and instantaneous ( discrete-time system ) , but the dynamics did not reach an equilibrium as is required for learning of a feed-forward model ., Moreover , most neurons we observed were highly correlated with one of two sources ( source-coding neurons ) ., Taken together , these findings suggest that for our experimental protocol , the structure of cultured neural networks can be represented as a two-layer feed-forward network constructed from input and output layers and functioning as an inverse recognition model ., However , it remains unclear which model applies to cultured neural networks with non-synchronous input ., Although some ICA models use information via non-local connections , several studies have proposed local rules that ICA can be constructed only using biologically plausible local connections 48 , 55 ., Internal energy , or negative log likelihood , also decreased after training , indicating that our culture neural networks also performed a maximum likelihood estimation or a maximum a posteriori estimation ., Consequently , the free energy of the population model decreased significantly after training as predicted by the free energy principle 19 , 20 , which can also be regarded as an increase in mutual information between input and output ( infomax principle ) 56 , 57 ., Taken together these results suggest that in response to synchronous input , cultured neural networks perform ICA-like learning using an inverse recognition model constructed from local c | Introduction, Results, Discussion, Methods | Blind source separation is the computation underlying the cocktail party effect––a partygoer can distinguish a particular talker’s voice from the ambient noise ., Early studies indicated that the brain might use blind source separation as a signal processing strategy for sensory perception and numerous mathematical models have been proposed; however , it remains unclear how the neural networks extract particular sources from a complex mixture of inputs ., We discovered that neurons in cultures of dissociated rat cortical cells could learn to represent particular sources while filtering out other signals ., Specifically , the distinct classes of neurons in the culture learned to respond to the distinct sources after repeating training stimulation ., Moreover , the neural network structures changed to reduce free energy , as predicted by the free-energy principle , a candidate unified theory of learning and memory , and by Jaynes’ principle of maximum entropy ., This implicit learning can only be explained by some form of Hebbian plasticity ., These results are the first in vitro ( as opposed to in silico ) demonstration of neural networks performing blind source separation , and the first formal demonstration of neuronal self-organization under the free energy principle . | The ‘cocktail party’ effect is a phenomenon by which one is able to pick out and listen to a single person’s speech in a noisy room ., In information engineering , this is termed blind source separation ., Numerous computational studies demonstrate that simulated neural networks can perform blind source separation ., However , if or how a living neural network learns to perform blind source separation remains unknown ., Using a microelectrode array ( MEA ) system that allowed us to apply composite inputs and record responses from neurons throughout a cultured neural network , we discovered that even neurons in cultures of dissociated rat cortical cells can separate individual sources from a complex mixture of inputs in the absence of teacher signals ., Given these findings , we then determined that the neural networks adapted to reduce free energy , as predicted by the free energy principle and Jaynes’ principle of maximum entropy ., These results provide evidence that cultured neural networks can perform blind source separation and that they are governed by the free-energy principle , providing a compelling framework for understanding how the brain identifies and processes signals hidden in complex multivariate information . | null | null |
journal.pntd.0004680 | 2,016 | The Role of Serotype Interactions and Seasonality in Dengue Model Selection and Control: Insights from a Pattern Matching Approach | With a 30-fold increase in incidence over the last five decades , dengue poses an increasing threat to about two thirds of the world population 1 ., Dengue , caused by a group of viruses belonging to the Flavivirus genera , circulates in four major serotypes ( DENV 1–4 ) 2 , and manifests in a wide spectrum of clinical forms , from subclinical to classic dengue fever to the more serious forms of the disease , namely , dengue haemorrhagic fever ( DHF ) and dengue shock syndrome ( DSS ) ., In the absence of treatment , dengue can be highly fatal in subjects with DHF or DSS , with a case-fatality rate of 15% , which may be reduced to 1% with adequate medical intervention 3 ., Despite on-going efforts , no effective antiviral drugs are available against the disease and the potential impact of the recently licenced vaccine has yet to be determined ., This limits control efforts primarily to vector control 4 ., Dengue dynamics are characterized by highly seasonal , multi-annual fluctuations , with replacement of serotypes occurring at varying intervals ., An example of these patterns arising in a newly emerging dengue setting is illustrated in ( Fig 1 ) 5 , 6 ., This is thought to result from a complex interplay between environmental factors , vector ecology and host-pathogen dynamics 7 ., Various hypotheses have been proposed to uncover the main drivers of dengue dynamics and to reveal how such drivers interact among themselves to govern infection and disease patterns in the field ., Emphasis has been on unravelling the roles that cross-immunity ( CI ) , cross-enhancement between serotypes , and seasonal variation in the transmission rate , play in capturing the complex dynamics of dengue 8 ., Cross-enhancement is believed to be caused by antibody-dependent enhancement ( ADE ) , where heterotypic antibodies facilitate cell entry through the formation of virion-antibody complexes , ultimately leading to increased viral titers upon secondary infection 9 , 10 ., This is thought to result in increased susceptibility to a secondary heterologous infection and , upon these secondary infections , in a more serious form of disease and increased infectiousness ., Enhanced disease severity is however believed to have minor impact on the dynamics as the proportion of DHF and DSS cases is substantially small ( 1% of confirmed cases 11 ) ., By contrast , including sufficiently high levels of enhanced infectiousness or susceptibility ( 60–130% ) in simulation models has been found to induce asynchronous outbreaks of different serotypes 12 , 13 , an outcome which has been indicated to underlie the manifestation of the 3–5 year epidemic cycles observed for dengue dynamics in Thailand 14 , 15 ., Decomposing ADE into both enhanced infectiousness and susceptibility has further been shown to mimic this effect at lower , more realistic values of ADE , while also reducing the magnitude of oscillations to more plausible levels and decreasing the risk of stochastic extinction 15 ., Similarly , relaxing the common assumption of complete immunity after two heterologous infections results in asynchronous , multi-annual outbreaks at lower levels of ADE and R0 16 ., While most modelling endeavours have assumed serotypes to have identical characteristics , allowing for a small amount of asymmetry in the transmission rate is found to increase serotype persistence in the presence of ADE 17 ., Furthermore , the inclusion of short-lived cross-immunity in models was found to be sufficient to reproduce the observed out-of-phase , irregular oscillations and 3-year cycles 18–21 ., An alternative hypothesis has been proposed by Lourenço et al . , who demonstrated that spatial segregation between human hosts and its vectors can be sufficient to capture the semi-regular dengue patterns observed , even in the absence of immune interactions 22 ., By contrast , to mimic the distinct seasonal signature of dengue dynamics , the incorporation of seasonal forcing into the vector population dynamics or transmission rate has been found to be essential 19 , 22 , 23 ., The above results hint at the complexity of dengue transmission and suggest that multiple mechanisms could underlie disease dynamics in any particular site ., A key question in understanding dengue dynamics and control , therefore , is how best to use observed data in order to identify the processes governing the transmission of the disease in a given location ., Recently , there has been increasing recognition that for complex systems , such as dengue , model matching to single or a few patterns is not sufficient to narrow down the range of possible explanatory mechanisms 24 , and that matching to multiple patterns observed at various scales and hierarchical levels is required for identifying the mechanisms that generate such patterns , and hence are likely to be key elements of the system’s structure ., Tying ecological models to multiple system patterns concurrently may also aid in detecting the right level of complexity and improve the predictive ability of such models for replicating local dynamics 24 ., Methods such as Pattern Oriented Modelling ( POM ) allow for such a multi-scope approach by facilitating the design , selection , and calibration of models of complex systems 25–30 ., This study applied a POM approach to modelling global dengue infection data in order to determine whether the above proposed mechanisms related to serotype interactions and seasonal forcing of the transmission rate were able to explain all of the observed dynamical patterns in the field ., We further used the modelling results to investigate the vulnerability of dengue to interruption in transmission as a result of vector control , and examined how such vulnerability was related to the identified processes governing disease transmission ., We demonstrate that model selection is largely driven by the seasonality of the system , with CI being a preferred mechanism in the case of low , and ADE in the case of highly seasonal transmission regimes ., At similar levels of transmission rate , resistance to control efforts was found to increase in dengue systems with CI ., The results highlight the utility of the POM approach for detecting and fitting of appropriately structured disease transmission models based on observed data ., In addition , they also reveal challenges in structural and parameter identifiability that would remain unnoticed when guided by individuals patterns used in isolation ., Five characteristic dengue patterns were used to filter out unrealistic model structures and reduce parameter uncertainty ., The patterns were selected to reflect the breadth of characteristics used in single pattern matching approaches 12 , 15 , 16 , 18 , 22 , include strong and weak patterns that are common across endemic regions and those which are relatively stable over time and encompass different levels of organization 24 ., The patterns ( i . e . mean duration between peaks , multi-annual fluctuations , frequent replacement of one circulating serotype by another , serotype co-dominance and asynchronous serotype cycling ) were derived from literature describing dengue case data and serotype epidemiology from different endemic regions across the world 5 , 6 , 31–42 ., The observed patterns are described in Table 1 ., We used a deterministic Susceptible-Infected-Recovered ( SIR ) modelling framework to describe the circulation of four different dengue serotypes ( DENV1-4 ) in a population 13 ., The full system of ordinary differential equations is shown in ( Fig 2 ) ., The model consists of 26 compartments , each of which represents a fraction of the population ., The population size is modelled to be stationary; hence births and deaths occur at an equal rate ( μ ) ., New-borns are assumed to be immunologically naïve to all serotypes and are born into the class of susceptibles ( S ) ., Although the presence of maternal antibodies is shown to affect the risk of infection , the impact on the overall dynamics is believed to be minimal and thus not taken into consideration 43 ., Susceptibles become primarily infected by serotype i ( Ii ) at rate βSIi and αTRANSβSIji proportional to the number of primarily and secondarily infectious individuals respectively ., The parameter αTRANS>1 indicates enhanced transmissibility of secondarily infected individuals ., A seasonal change in the transmission rate ( β ( t ) ) is incorporated through a sinusoidal function with a forcing period of one year: β ( t ) = β0 ( 1−β1 cos ( 2πt ) ) where β0 indicates the mean transmission rate and β1 the strength of seasonal fluctuation and t time in years ., The transmission rate ( β ( t ) ) is assumed to be equal across serotypes ., Individuals remain infectious for a period of 1/γ ., After recovery from a primary infection , individuals become immune to all serotypes ( Ci ) for a period 1/ρ after which they move to the partially immune stage ( Pi ) ., The P-class individuals are assumed to experience full immunity against the serotype i and enhanced susceptibility ( αSUS>1 ) to all other serotypes ., They acquire secondary infection ( Iij ) at rates αSUSβPiIj and αTRANSαSUSβPjIkj proportional to the number of cases respectively primarily and secondarily infectious to a different serotype ( with k≠j and j≠i ) ., The duration of the infectious period is assumed to be equal upon secondary and primary infection ., To account for imported cases and prevent the ODE-models to simulate unrealistically low levels of infections , individuals ( susceptible or partially immune ) can also acquire infection through an infectious contact with an individual from an external population at rate βδ , where δ signifies the import rate 23 ., As tertiary and quaternary infections are rarely observed 44 , we assume that after recovery from a secondary infection , individuals become life-long immune to all serotypes ., An adaptive time step fourth and fifth -order Runge-Kutta solver was used with initial conditions for I1-4 1x10-7 , 2x10-7 , 3x10-7 and 4x10-7 and S=1−∑1−4iIi ., All other state variables were initialized at zero ., The implementation of the model , as well as the analysis of its simulation results were carried out in the Matlab , version 2014b ( www . mathworks . com ) ., In this analysis we assume the following hypotheses ( see Table 2 ) ., H1: The most parsimonious hypothesis is represented by the base-model with neither ADE ( αSUS = 1 and αTRANS = 1 ) nor CI ( individuals upon recovery from primary infection go straight to the P-class ) ., H2: The base-model with CI ., H3: The base-model with enhanced susceptibility , further referred to as ADE ( αSUS>1 and αTRANS = 1 ) ., H4: H3 with CI ., H5: The base-model with both enhanced susceptibility and transmissibility ( i . e . ADEx2 with αSUS>1 and αTRANS>1 ) but no CI ., H6: H5 with CI ., In all models , an annual seasonal forcing in the transmission rate is assumed ., The variables that we estimated from the simulated data to contrast the dynamics of each model against the characteristics of dengue dynamics are:, 1 ) Mean inter-peak period;, 2 ) Presence of a multi-annual signal;, 3 ) Duration of serotype replacement;, 4 ) Intensity of single-serotype emergence; and, 5 ) Serotype phase-locking ., The mean inter-peak period ( MIPP ) is defined as:MIPP=YN , where Y is the number of years analysed and N the number of peaks occurring during that period ., To ensure comparability of the simulated estimates with reported observations on the inter-epidemic period , peaks were defined to have a minimum proportion of infectious people of 1/4000 ., To assess the presence of significant multi-annual signals in addition to the near yearly MIPP , a spectral density approach was used ., To reduce the confounding effect of very low amplitude fluctuations , the time series were smoothed using a moving average filter ., The power spectral density of the smoothed time series was assessed with the Welch’s overlapped segment averaging estimator 45 ., To evaluate the significance of the periodic signals , the signals were compared to the null-continuum ., The null-continuum is a greatly smoothed version of the raw periodogram , encapsulating the underlying shape of the distribution of variance over frequency 46 ., A signal was assessed to be significant if the lower bound of the 90% confidence interval of the raw periodogram exceeded the null continuum 46 ., The duration of serotype replacement is defined as the mean number of years before a dominant serotype during a peak is replaced by another serotype in a subsequent peak ., The intensity of single serotype emergence ( ε ) was defined as by Recker et al . 47:ε=1N∑iNγmaxi−γsubiγmaxi , where N defines the number of peaks occurring during the analysed number of years , γmaxi the prevalence of the dominant serotype and γsubi the prevalence of the serotype with the second-highest peak ., Model runs with either complete co-dominance ( ε<0 . 01 ) ( i . e . there are multiple serotypes present at any point in time ) or complete single serotype dominance ( ε>0 . 99 ) were omitted ., Lastly , serotype phase-locking here is defined as the perfect synchronization of serotypes and is detected by comparing the MIPP of serotype i to the aggregated MIPP ., Simulations in which MIPP = MIPPi are discarded based on the presence of perfect phase-locking ., To determine which of the hypotheses or models capture the observed dengue dynamics and at which parameter values , we used a pattern oriented modelling approach ( Fig, 3 ) 25–28 ., Model performance was assessed based on the extent to which a model captured all the 5 characteristics of dengue simultaneously , as defined above ( Table 1 ) ., Models were assessed using the following steps ., First , Latin hypercube sampling 48 was employed to select a sample of Ω ( = 5 , 000 ) parameter vectors from a conjoint parameter distribution , encompassing the transmission rate ( β0 ) , the level of seasonal forcing or seasonality ( β1 ) and , depending on the model , a combination of enhanced susceptibility ( αSUS ) , enhanced transmissibility ( αTRANS ) and the rate of loss of CI ( ρ ) ( Table 3 ) ., Uncertainty in the values of these parameters was addressed by assigning uniform distributions from their ranges deemed realistic according to literature ( Table 3 ) ., The resulting ensemble of models ( Model 1–6 with Ω parameter vectors ) was run for 1400 years ., The model outputs for the last 400 years were considered to determine whether the model mimicked all five dengue characteristics ( a model is assumed to match a characteristic if the simulated response falls within the range of that characteristic pattern given in Table 1 ) ., The resulting set of passing ( good ) parameters G ( where G ⊂ Ω ) was retained as a multivariate distribution for further analysis ., To assess the impact of simplifying model assumptions on pattern-matching , we repeated the POM exercise for two distinct scenarios ., One , we allowed for transmission rates to be uneven between serotypes ( the asymmetric model ) ., More specifically , serotype-specific transmission rates were drawn from a normal distribution with standard deviation 0 . 15 17 ., Two , we used a model variant that allows for four heterologous infections prior to acquiring complete immunity ( the 4-infection model , equations are provided in S1 Text 52 ) ., We used logistic regression to assess the sensitivity of pattern-matching ( binary response variable ) to the parameters ( independent variables ) ., We normalised the independent variables on a 0 to 1 scale to obtain comparable regression coefficients: coefficients larger than|3| indicate strong sensitivity while parameters with small coefficients ( <<|1| ) have little impact on the model matching the patterns 53 ., Two-way interactions were included in the construction of the logistic regression models: logit ( p ) = b0+b1β0+b2β1+b3αSUS+b4αTRANS+b5ρ+interactions , with p being the probability of a pattern-match , b0 the intercept and b1-n the regression coefficients ., Additionally , the identifiability of each of the parameters was examined using a principal component analysis ( PCA ) 54 , 55 ., The identifiability of a parameter is a function of dependence , prior uncertainty and the model’s sensitivity to the parameter and defines how well one can estimate a parameter ., We assessed the parameter identifiability for the full model ( ADEx2+CI ) , using its passing distribution ( G ) ., First , the variance-covariance matrix ( Σ ) was constructed from the log-transformed G . Next , the principal components ( PCs ) were derived from Σ ., The PCs of Σ define the 5-dimensional ellipsoid that approximates the population of passing parameter values ., The eigenvalues ( λi ) denote the respective radii and the eigenvectors representing how much each parameter contributes to the direction of each radius ., As such , λi gives an indication of the variance explained by the ith PC ., The overall variance of all PCs was defined as ∑i=15λi=trace ( Σ ) , thus the proportion of the total variation in G that was explained by the ith PC is was estimated by:λtrace ( ∑ ) ., We interpret these results as follows: A smaller λi indicates that the model is more sensitive to changes in the direction described by the ith component , whereas a larger λi signifies that the model is less sensitive to changes in the direction of the component ., Parameters contributing most to a large λi are responsible for a big portion of the variation in the parameter space and are thus considered less identifiable ., We examined the vulnerability of the models to sudden reductions in the transmission rate that may be brought about by vector control ., The models were run for all parameter sets in G for a burn-in period of 1000 years after which the system was perturbed by a reduction in the transmission rate ( i . e . β0 is reduced by 90% ) for a control period of w weeks per year ., We varied w from 1 week to 52 consecutive weeks , starting at the valley of the sinusoidal function , which mimics the onset of the rainy season ., After the control period of w weeks , β0 returns to its original value ., These control runs were performed for 30 years after the burn-in period ., The intervention of w weeks was assumed to be successful if no more than one peak occurred over the time-course of the model simulation ., We assessed the probability of control for model i , where i represents 1 to 6 , by calculating the proportion ( Pwi ) of Gi presenting successful control as a function of the number of weeks the transmission was disrupted ., Here , Pwi=NwiGi with Nwi being the number of parameter vectors out of Gi that showed successful control for model i given w weeks of interruption in transmission ., A composite average ( Pw ) for each control period w was derived by weighing the individual probability values of the models by the sizes of their passing parameter distributions ( Gi ) , such that:Pw=∑i=16NwiGi ., Lastly , we estimated the values of the basic reproduction rate ( R0 ) for each of the parameter vectors in G to assess the relation between transmission potential and the models’ vulnerability ., The R0 of the model was derived using the next generation method 56–58 ( Proof provided in S2 Text ) and is defined as: R0=β0γ+μ , where β0 defines the transmission rate , 1/γ the duration of the infectious period and 1/μ the average life expectancy of the human host 59 ., Fig 4 demonstrates the accepted parameter distributions ( G ) for the 2-infection models ., While some parameters demonstrate broad distributions indicating limited uniqueness and abundant parameter interactions , others show clear preferential values and ranges that are sensitive to the structural components of the model ., Overall it appears , as can be expected , that the more complex models fit the patterns at a wider parameter range ., Fig 4A shows that models with CI selected for relatively higher transmission levels relative to models with ADE only ., For low transmission levels , the full model outcompeted all the other models , indicating that more complex models may be necessary to fit dengue dynamics at lower values of R0 ., These results are insensitive to the assumption of low levels of asymmetry in transmission rates ( S2aA Fig ) ., In contrast to this , the 4-infection models display similar fits at lower transmission levels ( S2bA Fig ) ., Seasonality appeared to be the most prominent driver of model fit and selection in the 2-infection model ( Fig 4B ) ., Models with CI showed a marked shift towards lower seasonal forcing relative to the base-model ., In fact , at low seasonality ( β1<0 . 06 ) there is a strong preference for the inclusion of CI , as is especially notable from the elevated density levels of the ADE+CI and ADEx2+CI models ., At high seasonality ( β1>0 . 17 ) only the more complex models provided an adequate fit ., At intermediate levels of seasonality ( β1: 0 . 1–0 . 15 ) multiple models were equally proficient at replicating the dynamics , indicating a region of large model uncertainty ., The model’s structural sensitivity to seasonality persisted when asymmetry in transmission rates was assumed ( S2aB Fig ) ., However , when we allowed for tertiary and quaternary infections , the medians and shapes of the passing parameter distributions for β1 were similar across the models ( S2bB Fig ) ., The addition of CI to models with ADE results in higher levels of αSUS ( Fig 4C ) , yet had minor impact on the median levels of αTRANS ( Fig 4D ) ., While previous publications suggested reduced estimates of αSUS and αTRANS upon the inclusion of decomposed ADE , analysis of the 2-infection model does not support this observation 15 ., We did , however , observe this pattern in the 4-infection and asymmetric 2-infection model ( S2aD and S2bD Fig ) ., The inclusion of ADE to the models with CI profoundly affects the estimated duration of cross-immunity by allowing for the selection of a much wider range of ρ ( Fig 4E ) ., Whereas the CI-model by itself only captures the characteristics at durations of cross-immunity shorter than half a year , the inclusion of ADE allows for cross-immune periods of up to 2 years , which is in line with the previous estimates 21 ., Interestingly , in the case of 4-infection , the CI-only model performed well for a wider range of durations of cross-immunity , including estimates from Reich et al . 21 ., Exploring the behaviour of the models in terms of MIPP and duration of serotype replacement ( Table 4 ) reveals as to why there are differences in model fits across the range of seasonal forcing ( S1aAA–S1aAF and S1aCA–S1aCF Fig ) ., Increased levels of seasonal forcing are associated with longer MIPP ., Temporary CI introduces a lag before a secondary infection can be acquired and thus generates a necessary build-up time period during which susceptible individuals accumulate in sufficient number to fuel the next outbreak ., Thus , while an increase in seasonal forcing is characterized by longer inter-epidemic periods , at similar levels of seasonal forcing , the models with CI demonstrate a longer MIPP than the models without CI ( S1aAA–S1aAF Fig ) ., This allows the CI-only models capture the characteristic MIPP at lower seasonal levels than the models with just ADE ., At higher levels of seasonal forcing , CI contributes to MIPPs that are longer than are characteristic to dengue ., This effect is less pronounced in the 4-infection models ., The overall immune population is smaller in the 4-infection models and therefore of less influence on the frequency of outbreaks ., The same can be observed for the duration of serotype replacement ( S1aCA–S1aCF Fig ) ., In contrast to CI , the inclusion of ADE to the model results in shorter cycles , thus successful fits are observed at higher levels of seasonal forcing ( S1aAA–S1aAF Fig ) ., Lastly , we observe a prominent impact of seasonal forcing on the occurrence of phase-locking ., S1aEA–S1aEF Fig demonstrate a threshold-like value of β1 above which the system is forced into synchronized serotype dynamics ., This threshold is relatively stable across the simple model structures ( see also Fig 4B ) and unaffected by the value of R0 ., Only the addition of decomposed ADE disrupts this behaviour , thereby being a possible driver of irregular serotype behaviour at higher seasonal regions ., These phase-locking thresholds are stable to some level of asymmetry in transmission rates ( S1bEA–S1bEF Fig ) , however they completely vanish in the case of 4-infection models ( S1cEA–S1cEF Fig ) ., The logistic regression coefficients for the full-model given in Table 5 illustrate the differential roles each of the parameters play in explaining the dengue characteristics ., β0 is found to be an important driver of the multi-annual signal ., And in conjunction with β1 and αTRANS , it is the dominant factor for the absence of phase-locking ., As can be expected , β1 is the main driver for reproducing a seasonal signature ., The parameter for CI ( ρ ) interacts with β1 in reproducing this pattern and is thus also an important determining factor in fitting the MIPP ., The R2-values for each of the regression models illustrate that the separate parameter values provide reasonable information about whether a characteristic is met or not ., However , when assessing the simultaneous fit , the predictive power of the parameters is negotiated by interactions between the parameters and the separate characteristics ., In particular the interactions between β1 and ρ govern simultaneous fitting ( S3a Fig ) ., These interactions are conserved when fitting the asymmetric 2-infection and symmetric 4-infection model ( S3b and S3c Fig ) ., Strong , multi-level parameter interactions typically result in limited parameter identifiability ., Indeed , the PCA reveals that , in particular the estimates for β1 and ρ are found to be little constrained by the characteristic patterns ( Fig 5 ) ., The parameters β1 and ρ dominate the first two components , which explain the largest portion of the total variance in the passing parameter space ( Gfull ) ( 55% ) ., While this observed lack of uniqueness may result from the limited influence the parameters have on replicating the dynamics and the substantial width of the criteria , complex interactions between patterns and parameters can also underlie this phenomenon ., Indeed , as observed earlier , β1 and ρ are correlated with each other as well with other model parameters , which substantially impedes parameterization efforts ( S3a Fig ) ., Parameters β0 , αSUS and αTRANS contribute equally to the smallest component , indicating that these are more constrained by the examined characteristics and the level of uncertainty and are less affected by dependence to other parameters ( Fig 5 ) ., Allowing for asymmetry in transmission or tertiary and quaternary infections reduces the contribution of seasonality to the first component , leaving the duration of cross-immunity as the most important factor in explaining the variance in the passing parameter distributions ( S5a and S5b Fig ) ., Fig 6 depicts the probability of achieving successful control ( ≤ 1outbreak in 30 years ) as a function of w weeks of reduced transmission ( e . g . due to implementation of vector control ) ., The duration of control required to reach a desired probability of successful control can be used to quantify the level of resistance or vulnerability of a dynamical transmission system ., The inclusion of ADE or ADEx2 reduces the resistance of the model to perturbations ( dark blue and pink lines ) , provided no CI is assumed ( Fig 6 ) ., Including CI to the model offsets this effect and demonstrates a resistance profile similar to the base-model at longer control efforts , yet shows larger vulnerability at shorter durations of control ., The exception is the full-model , which converges with the ADE-model at longer control durations ., The large resistance to control in the base-model is a consequence of the high values of R0 required for this model to meet the criteria ( R0>2 . 2 ) ( Fig 7A ) ., At those levels of R0 the ADE-model demonstrates higher vulnerability to control as a result of decreased persistence ( Fig 7C ) ., The enhanced vulnerability of the ADE-model relative to the base-model as seen in Fig 6 is a consequence of low transmission rates ., The inclusion of CI to either model enhances the resistance of the model especially at lower values of R0 ( Fig 7D ) ., Longer durations of cross-immunity are associated with greater resistance ( S7DE Fig ) , while increased enhancement results in decreased resistance ( S7CC and S7DC Fig ) ., This differential vulnerability is in part due to low infection persistence levels , a typical property of models with ADE only 12 , 15 , 23 ., The addition of CI counters this effect with and without ADE ( Fig 7C , 7D and 7F ) ., This difference in infection persistence between CI and ADE systems , however , diminishes at high levels of seasonal forcing and R0 ., At these high transmission levels , both the models with CI ( ADEx2+CI ) and without CI ( ADEx2 ) represent extreme fluctuations and long periods of non-persistent dynamics ( S4aF and S4aG Fig ) ., Thus , the differential model preference affects predicted control efforts more substantially in lower than higher seasonal scenarios ., We used a pattern-oriented modelling approach to test a range of multi-serotype models and parameter values for their ability to simultaneously replicate multiple dengue fever patterns derived from literature ( Table, 1 ) and case data from Trinidad and Tobago ( Fig 1 ) ., Despite using such a multiple-pattern data fitting approach , we show that all the investigated model structures were effective at fitting each of the characteristic dengue patterns across some part of the model parameter space , suggesting the occurrence of equifinality , i . e . that observed infection patterns can be reproduced by more than one mechanism or combinations of mechanisms 60 ., This implies that there could be multiple acceptable models for describing globally observed dengue dynamics , none of which can easily be rejected and therefore should all be considered in assessing the mechanisms determining disease transmission 61–63 ., Three major efforts that would help disentangle the dominant drivers of dengue are:, 1 ) better estimates of model parameters , in particular the duration of cross-immunity and the strength of seasonal forcing;, 2 ) improved understanding on the contribution of post-secondary infections to dengue transmission dynamics; and, 3 ) additional , more detailed patterns , such as, ( i ) time series of serotype-specific dengue cases and, ( ii ) levels of sero-prevalence in populations ., Some of these patterns may well differ across geographic regions ., Based on the sizes of the passing parameter distributions , a preference for the most complex 2-infection model was apparent ( Table 4 ) ., Remarkably , the model that performs best across all models is the 4-infection model with CI only ., This indicates that , in some instances , the use of multiple patterns for model selection can help filter out overly specialized models and fetch simple , more generalized models that perform better across different scales ., Additionally , it helps reveal the impact of simplifying assumptions on model selection and parameterization , i . e . allowing for quaternary infections enables us to reveal a simpler model framework that outcompetes its 2-infection equivalent ., Also , it sheds new light on the need for ADE in replicating dengue dynamics ., The role of ADE is not supported when allowing quaternary and tertiary infections while it is preferred in the 2-infection case , with and without asymmetry in transmission rates ., The performance of the base-model is noteworthy , given that it does not include the explicit serotype interactions deemed necessary to replicate asynchronous serotype oscillations ., However , there are two implicit serotype interactions that li | Introduction, Methods, Results, Discussion | The epidemiology of dengue fever is characterized by highly seasonal , multi-annual fluctuations , and the irregular circulation of its four serotypes ., It is believed that this behaviour arises from the interplay between environmental drivers and serotype interactions ., The exact mechanism , however , is uncertain ., Constraining mathematical models to patterns characteristic to dengue epidemiology offers a means for detecting such mechanisms ., Here , we used a pattern-oriented modelling ( POM ) strategy to fit and assess a range of dengue models , driven by combinations of temporary cross protective-immunity , cross-enhancement , and seasonal forcing , on their ability to capture the main characteristics of dengue dynamics ., We show that all proposed models reproduce the observed dengue patterns across some part of the parameter space ., Which model best supports the dengue dynamics is determined by the level of seasonal forcing ., Further , when tertiary and quaternary infections are allowed , the inclusion of temporary cross-immunity alone is strongly supported , but the addition of cross-enhancement markedly reduces the parameter range at which dengue dynamics are produced , irrespective of the strength of seasonal forcing ., The implication of these structural uncertainties on predicted vulnerability to control is also discussed ., With ever expanding spread of dengue , greater understanding of dengue dynamics and control efforts ( e . g . a near-future vaccine introduction ) has become critically important ., This study highlights the capacity of multi-level pattern-matching modelling approaches to offer an analytic tool for deeper insights into dengue epidemiology and control . | The fluctuations of multi-serotype infectious diseases are often highly irregular and hard to predict ., Previous theoretical approaches have attempted to disentangle the drivers that may underlie this behaviour in dengue dynamics with variable success ., Here , we examine the role of such drivers using a pattern-oriented modelling ( POM ) approach ., In POM , multiple patterns observed at different scales are used to test a model’s proficiency in capturing real-world dynamics ., We examined dengue models with combinations of cross-immunity , cross-enhancement , seasonal fluctuations in the transmission rate , and with sensitivity analyses of asymmetric transmission rates between serotypes as well as the possibility for four subsequent heterologous infections ., We demonstrate the ability of POM to model dynamical drivers that have gone unnoticed in single pattern or synthetic likelihood approaches ., Further , our results present a determining role of seasonality in the selection and operation of these processes in governing dengue dynamics , in particular when full , heterologous immunity is assumed to occur after a secondary infection ., We show that this structural model uncertainty can have important practical significance , as demonstrated by the differences in control efforts required to disrupt transmission ., These results highlight the importance of localised model selection and calibration using multiple data-matching , as well as taking explicit account of model uncertainty in predicting and planning control efforts for multi-serotype diseases . | medicine and health sciences, immune physiology, infectious disease epidemiology, immunology, tropical diseases, simulation and modeling, multivariate analysis, mathematics, statistics (mathematics), neglected tropical diseases, infectious disease control, antibodies, research and analysis methods, immune system proteins, infectious diseases, proteins, dengue fever, epidemiology, mathematical and statistical techniques, principal component analysis, biochemistry, immunity, disease dynamics, physiology, biology and life sciences, viral diseases, physical sciences, statistical methods | null |
journal.ppat.1002146 | 2,011 | Widespread Endogenization of Genome Sequences of Non-Retroviral RNA Viruses into Plant Genomes | Events of horizontal gene transfer ( HGT ) have been identified between various combinations of viruses and their eukaryotic hosts ., HGT can occur during evolution in 2 inverse directions: “from host to virus” or “from virus to host . ”, In the host to virus direction , viral acquisition of host genes is observed as insertion of cellular genes for proteases ( see 1 for review ) , ubiquitin 2 , chloroplast protein 3 and heat-shock proteins 4 , 5 into viral genomes ., The virus to host direction involves endogenization of viral genes ., Fossil sequences of viral origin , mostly from retroviruses , have been detected in many animal genomes ., However , retrovirus sequences have not been identified in plants; instead , reverse-transcribing DNA viruses ( pararetroviruses ) have been identified ., Although pararetroviral sequences have been found in some plant nuclear genomes 6 , 7 , 8 , 9 , only a limited number of integrated sequences are exogenized to launch virus infection; however , their cellular functions remain unclear in other examples ., In contrast , the sequences of non-retroviral RNA viruses were considered not to integrate into host chromosomes ., However , recent reports identified endogenized genes of non-retroviral elements in mammals 10 , 11 , 12 , 13 ., Examples include the nucleocapsid protein ( N ) and nucleoprotein ( NP ) genes of bornaviruses and filoviruses , members of the negative-strand RNA virus group in the order Mononegavirales 11 , 12 , 14 ., While some integrated N genes are expressed , their biological significance is unclear ., Identification of these sequences contrasts with the lack of evidence for negative-strand RNA virus genome integration into plant genomes ., Furthermore , RNA-dependent RNA polymerase ( RdRp ) and capsid protein ( CP ) coding domains from a group of monopartite dsRNA viruses have been identified in yeast chromosomes , and while some of these viruses appear to be expressed , their biological significance has not been explored 15 , 16 , 17 ., The white root rot fungus Rosellinia necatrix is a soil-borne phytopathogenic ascomycetous fungus that causes damages to perennial crops ., An extensive search of a large collection of field fungal isolates ( over 1 , 000 ) was conducted to identify dsRNA ( mycoviruses ) that may serve as virocontrol ( biological control ) agents ., Approximately 20% of field isolates were infected with known or unknown viral strains 18 , 19 , 20 ., During molecular characterization of these viruses , we identified a novel partitivirus termed Rosellinia necatrix partitivirus 2 ( RnPV2 ) in an ill-defined R . necatrix strain ., The family Partitiviridae contains members with small bi-segmented dsRNA genomes 21 that infect plants , fungi or protozoa ., They are thought to replicate using virion-associated RdRp in the host cytoplasm , which are phylogenetically related to those from the picorna-like superfamily 22 ., Surprisingly , the RnPV2 CP showed the highest level of sequence identity to an Arabidopsis thaliana gene , IAA/LEU resistant 2 ( ILR2 ) , which was previously shown to regulate the activity of the phytohormone auxin 23 ., Combined with information regarding integrated mononegaviral sequences in animals , this finding generated significant interest in searching currently available genome sequence data for not only dsRNA but also negative-strand viral sequences ., In October 2010 , Liu et al . 24 reported similar results based on an extensive search conducted in 2009 ., This group identified sequences in the chromosomes of diverse organisms that may have been acquired from monopartite ( totiviruses and related unclassified viruses ) and bipartite dsRNA viruses ( partitiviruses ) ., We further examined plant genome sequences available as of December 10 , 2010 for integrated sequences of not only partitivirus genomes but also negative- , and positive-strand RNA viruses ( Table S1 ) ., Combining database searches and molecular analyses led to the identification of multiple endogenized sequences related to partitiviruses , cytorhabdoviruses , varicosaviruses and betaflexiviruses in the genomes of a variety of plants including those from the families Solanaceae and Brassicaceae ., For example , while some partitivirus-related sequences are conserved on the orthologous locus across some genera , e . g . , Arabidopsis , Capsella , Turritis , and Olimarabidopsis within the family Brassicaceae , others are retained in only a few species within a single genus , Arabidopsis ., A similar integration pattern was observed for a rhabdovirus-related sequence in the family Solanaceae ., These profiles of occurrence can potentially resolve unclear phylogenetic relationships between plants ., Our study demonstrates widespread endogenization of non-retroviral RNA virus sequences ( NRVSs ) including sequences of plant positive- and negative-strand RNA viruses for the first time ., We have proposed a model of viral gene transfer , in which NRVSs are suggested to be a factor constituting plant genomes ., We determined the complete nucleotide ( nt ) sequence of the genome segments ( dsRNA1 and dsRNA2 ) of a novel partitivirus , RnPV2 , from the white root rot fungus Rosellinia necatrix , a soil-borne phytopathogenic ascomycetous fungus ., DsRNA2 was found to be 1828 nt long , encoding a polypeptide of 483 amino acids ( aa ) ( CP , 54 kDa ) ., Low-level sequence similarities among CPs from Partitiviridae family members were observed using a BLASTP search with RnPV2 CP against non-redundant sequences available in the NCBI database ( http://www . ncbi . nlm . nih . gov/ ) ., Surprisingly , RnPV2 CP showed the highest degree of sequence similarity to ILR2 from Ar ., thaliana ., Notably , sequence similarities between RnPV2 CP and ILR2 were greater than those between the CP sequence from another mycovirus , Sclerotinia sclerotiorum partitivirus S ( SsPV-S ) and ILR2 noted previously 24 ., ILR2 is known to regulate indole-3-acetic acid ( IAA ) -amino acid conjugate sensitivity and metal transport ., An Ar ., thaliana mutant with a single amino acid substitution in ILR2 , known as ilr2-1 , was shown to exhibit normal root elongation in the presence of a high concentration of exogenous IAA-leucine conjugates , which represses root elongation in wild-type lines 23 ., Magidin et al . 23 identified 2 alleles of ILR2 in Ar ., thaliana accessions ( a long and a short allele ) ( Figure 1A ) ., Although the authors confirmed ILR2 expression for only the WS ecotype ( short allele ) , they determined that both short and long versions of ILR2 were functional ., Given the similarity between ILR2 and RnPV2 CP sequences , we hypothesized that HGT occurred between the 2 organisms ., Therefore , we assessed the extent to which ILR2 is conserved in plants ., We used 3 approaches: BLAST search , genomic PCR , and Southern blot analyses ., We first conducted an exhaustive BLAST ( tblastn ) search against genome sequence databases as described in the Materials and Methods ., This search identified ILR2 homologs in Ar ., lyrata and Mimulus guttatus ( yellow monkey flower ) , which included both short and long versions of ILR2 homologs with modest levels of aa sequence identities ( over 20% ) to RnPV2 CP ( Table S2 , Figure 1A ) ., Furthermore , a variety of partitivirus CP-related sequences with low-levels of aa sequence identities ( approximately 20% ) to RnPV2 CP were also detectable from genome sequences from other 17 plant species ( Table 1 ) ., These sequences were classified into a total of 8 subgroups based on relatedness to best matched extant partitiviruses ( Table 1 ) ., Their nomenclature is: AtPCLS1 ( ILR2 ) is from Arabidopsis thaliana partitivirus CP-like sequence ( PCLS ) 1 ., Differently numbered PCLSs , referring to proteins potentially encoded by PCLSs , show the highest level of aa sequence identities to CPs encoded by different partitiviruses ., Genomic PCR analysis with primers corresponding to highly conserved 240-bp portions revealed that ILR2 homologs were retained in genera closely related to Arabidopsis , such as Capsella , Turritis , and Olimarabidopsis , but not in members of distantly-related genera , Brassica , Thellungiella , Crucihimalaya , Sisymbrium , and Thlaspi within the Brassicaceae family ( Figure 1B ) ., Genomic PCR fragments covering the entire ILR2-like domains of the plants shown in Table S4 were sequenced directly or after cloning into a plasmid ., It should be noted that PCLS1s of closely related genera reside in an orthologous position 25 , i . e . , in a convergent configuration with the gene for the transmembrane Golgi matrix protein AtCASP , which shares a high degree of sequence similarity across kingdoms 26 ., This notion was confirmed by genomic PCR in which a primer pair allowed detection of 0 . 75- to 1-kb fragments spanning the CASP gene ., Previous comparative genomics studies proposed a hypothesis that the Brassicaceae genomes consist of 24 ( A to X ) conserved genome blocks 27 ., The ILR2 locus is on block F which is considered to be duplicated in B . rapa ., A search against the Brassica database ( BRAD ) confirmed the absence of a PCLS1 on the 2 B . rapa loci that flank the CASP gene ., Southern blotting with members of the Brassicaceae , Cucurbitaceae , Solanaceae , and Leguminosae families indicated that PCLS1 ( ILR2 ) is present in Ar ., thaliana and Cap ., bursa-pastoris , but absent in the other plants ( Figure 1C ) , consistent with BLAST results and genomic PCR analyses ., Furthermore , the absence of ILR2 in Crucihimalaya lasiocarpa , Sisymbrium irio and B . rapa was confirmed by sequence analysis of genomic PCR fragments covering the entire ILR2 region and its flanking regions ( Figure 1D ) ., Genome sequences with low levels of similarities to RnPV2 CP included a number of PCLSs from various plants spanning more than 17 species from 8 families ( Table 1 ) ., Most PCLSs confirmed to be present on their chromosomes of these organisms were identified by genomic PCR and/or Southern blotting and sequencing ( Tables 1 , S4 ) ., For instance , AtPCLS2 and Ar ., lyrata PCLS3 ( AlPCLS3 ) are retained on non-orthologous loci of ILR2s of Ar ., thaliana and Ar ., lyrata , respectively ( Figure 2A ) ., AtPCLS2 ( At4g14104 ) resides between the genes for COP9 ( constitutive photo-morphogenic-9 , COP9 ) and an F-box protein , while AlPCLS3 is between 2 coding sequences for F-box domains corresponding to At4g02760 and At4g02740 25 ., AtPCLS2 and AlPCLS3 from 2 closely related plant species show the highest sequence identities to the CPs from 2 different partitiviruses: Raphanus sativus cryptic virus 2 ( RSCV2 ) and Fragaria chiloensis cryptic virus ( FCCV ) ( dsRNA2 ) 28 ., The PCLS retention profile was revealed by genomic PCR using 2 primer sets ., A primer set designed to amplify internal AtPCLS2 sequences provided DNA fragments of an expected size of 470 bp in Ar ., thaliana accessions Col-0 , Ler , and Shokei , but not in Ar ., lyrata , Ar ., Arenosa , or Cap ., rubella ( Figure 2B , top panel ) ., A different primer set specific for AtPCLS2 and the F-box protein gene ( At4g14103 ) gave the same amplification pattern ( Figure 2B , second panel ) as shown in the top panel ., Using the same approach with 2 sets of primers , PCLS3 was detected by genomic PCR in Ar ., lyrata and Ar ., arenosa , while no such sequence was observed in Ar ., thaliana ecotypes or Cap ., rubella ( Figure 2B , third and fourth panels ) ., Although the COP9 and the F-box protein genes are conserved on the corresponding loci of Ar ., lyrata , no counterpart of AtPCLS2 was identified between the genes ( Phytozome ) ., Similarly , no AlPCLS3 homolog was observed on the corresponding chromosomal position of Ar ., thaliana 25 ., PCLS4 and PCLS5 were found in the genome sequence databases of B . rapa ( BrPCLS4 and 5 ) , Solanum phureja ( wild species of potato ) ( SpPCLS5 ) ( Figure 3A , S2 ) , and Nicotiana tabacum ( NtPCLS5-1 and -2 ) ( Figure S1A ) ., These sequences commonly exhibited greater sequence similarity to CPs of previously reported plant partitiviruses than to RnPV2 CP ( Tables 1 ) ., The 3 PCLS5s from the Solanaceae family were very similar to each other ( approximately 60% aa sequence identity ) , and showed high sequence identity ( over 45% ) ( Table S2 ) to CP of Raphanus sativus cryptic virus 1 ( RSCV1 , plant partitivirus ) 29 ., Two PCLSs , BrPCLS4 ( Bra021820 ) and BrPCLS5 ( Bra020160 ) , which are detected on different scaffolds , were determined to not flank the CASP gene of B . rapa as AtPCLS1 ( ILR2 ) does ., BrPCLS4 and 5 show much greater aa sequence identities to CPs of RSCV1 and carrot cryptic virus 1 ( CaCV1 , plant partitivirus ) 30 than it does to RnPV2 CP ( Table S2 ) ., Molecular analyses were performed to determine how widely these PCLS4 and PCLS5 are conserved ., Genomic PCR using a primer set specific for BrPCLS4 detected related sequences in all Brassica species tested , but not in other plants including members of the family Solanaceae or genera other than Brassica in Brassicaceae , such as Ar ., thaliana , Cru ., lasiocarpa , Thellungiella parvula , Thl ., arvense and Sis ., irio , and Raphanus sativus ( Figure 3B , top panel ) ., For BrPCLS5 , the primer set , PC5a-1 and PC5a-2 enabled detection of expected PCR fragments in all Brassica plants in addition to R . sativus , while no PCR fragments were amplified in the other plant species ( Figure 3B , second panels ) ., A different detection profile was obtained by genomic PCR with a primer set specific for SpPCLS5 in which PCLS5-related sequences were detectable only in Sol ., tuberosum and Sol ., lycopersicum ( Figure 3B , third and fourth panels ) ., We failed to yield amplification from all other tested plants in the families Brassicaceae and Solanaceae including Sol ., melongena ., Interestingly , PCLS5 , but not PCLS4 fragments , were detected in R . sativus ., Moreover , the presence or absence of PCLSs was confirmed by genomic Southern analysis ., As expected from the genomic PCR results , hybridization signals were detected with a BrPCLS4- or a BrPCLS5-specific probe in the Brassica species such as B . rapa and B . oleracea ( Figure 3C , top and second panels ) ; however , the numbers and signal positions differed between the 2 blots ., The StPCLS4-specific probe allowed detection of 2 and 1 hybridization signals in Sol ., tuberosum and Sol ., lycopersicum , respectively , but not in any other plants examined in this study ( Figure 3C , fourth panel ) ., In addition to PCLS1 to PCLS5 , 2 other subgroups of PCLSs ( PCLS6 and PCLS7 ) were observed in the GSS database of N . tabacum and showed an interesting detection pattern in Nicotiana species ( Figure S1 ) ., NtPCLS6 and NtPCLS7 showed moderate aa sequence identities to CPs encoded by FCCV dsRNA3 ( 38% ) 28 and RSCV3 dsRNA2 ( 30% ) 29 , respectively ., Sequencing of genomic PCR fragments and Southern blotting ( Figure S1B , E ) suggested that NtPCLS5-1 and NtPCLS5-2 are retained only in N . tabacum , but not in other Nicotiana species examined , such as N . benthamiana and N . megalosiphon , whereas PCLS6 was detected in both N . tabacum and N . megalosiphon ( Figure S1B ) ., In contrast , PCLS7 is conserved in all 4 Nicotiana plants tested , although sequence divergence was observed among the PCLS7s ., Other PCLSs from 2 legume plants , MtPCLS7 and LjPCLS8 were identified on their nuclear genomes by PCR ( Figures S1A , C , D ) ., An expanded BLAST ( tblastn ) search against the EST sequence libraries ( in NCBI ) helped detect many related sequences of possible plant partitiviruses that shared moderate levels of sequence similarity ., Some representative EST sequences , PCLSs and partitivirus CPs , whose entire sequences are available , were aligned using the MAFFT program ., Three relatively well-conserved motifs are located on the N- terminal , central , and C-terminal regions of partitivirus CPs and PCLSs , and are represented by PGPLxxxF 31 , F/WxGSxxL and GpfW domains ( Figure S2 ) ., As expected from sequence similarities , phylogenetic analysis of partitivirus CPs and PCLSs identified in plant genomes clearly show that members of each PCLSs subgroup ( PCLS1 , 2 , 4 , 5 , 7 ,, 8 ) clusters together with the CP of the respective partitivirus that shows the highest sequence similarities ( Figure 4 , Table 1 ) ., For example , RnPV2 CP ( in red ) , MgPCLS1 , and ILR2 homologs ( PCLS1s ) from Arabidopsis-related genera ( in green ) constitute one group in the tree ., The MgPCLS1 clade includes an assembled sequence in the EST database from meadow fescue ( Festuca pratensis ) ( in purple ) believed to be from a plant partitivirus ., Another group includes PCLS5s from the families Brassicaceae and Solanaceae ( in green ) , CPs of fungal ( in red ) and plant partitiviruses ( in blue ) are grouped together ., Within this group , PCLSs from the families Brassicaceae ( BrPCLS5 , BoPCLS5 , and BnPCLS5 ) and Solanaceae ( StPCLS5 , SpPCLS5 , SlPCLS5 , and NtPCLS5-1 ) comprised 2 subgroups that included CPs encoded by RSCV1 ( CP ) and RSCV1 dsRNA3 ( Figure 4 ) , respectively , which are considered to be from two different partitiviruses ., PCLS4s from members of the genus Brassica clustered together with CPs of other plant partitiviruses including white clover cryptic virus 1 ( WCCV1 ) 32 , CaCV1 , beet cryptic virus 1 ( BCV1 ) 33 , and vicia cryptic virus ( VCV ) 34 ., The tree topology shown in Figure 4 was similar to that reported by Liu et al . 24 ., The current study used more PCLSs detected in various plants but not partial PCLSs such as PCLS3 and NtPCLS5-2 , 6 and 7 ( Tobacco Contig-2 , -3 and -4 ) analyzed phylogenetically by Liu et al . 24 ., Because negative-strand RNA viral sequences are found in animal chromosomes , we searched for negative-strand RNA viral sequences ( Table S1 ) in plant genomes as described in the Materials and Methods ., This search identified sequences related to the N protein in members of the genus Cytorhabdovirus ( Lettuce necrotic yellows virus , LNYV , Lettuce yellow mottle virus , LYMoV , and northern cereal mosaic virus , NCMV ) and a CP of the genus Varicosavirus ( Lettuce big-vein associated virus , LBVaV ) in the genomes of a variety of plants such as Populus trichocarpa , N . tabacum , and B . rapa ( Figures 5 , S3 , Table 2 ) ., While varicosaviruses have bipartite genomes replicated in the cytoplasm of infected plant cells , they are phylogenetically closely related to cytorhabdoviruses with monopartite genomes 35 , 36 ., Varicosavirus CP is phylogenetically and functionally equivalent to rhabdovirus N . Thus , these plant nuclear sequences were designated as rhabdovirus N-like sequences ( RNLSs ) and classified into 4 subgroups ( RNLS1 to RNLS4 ) based on the sequences of presently existing viruses with the highest levels of sequence similarities ( Table 2 ) ., Their potentially encoding proteins were designated as RNLSs as in the case for PCLSs ., To confirm the presence of the RNLSs in plant chromosomes , we conducted genomic PCR and Southern blot analyses ., Interestingly genomic PCR with primers specific for an RNLS1 from B . rapa ( BrRNLS1 ) detected RNLS1s in R . sativus and all tested plants within the Brassica genus , but not in members in other genera ( Figure 5C ) , in a pattern similar to that of PCLS5s from the family Brassicaceae ( Figure 3B ) ., Consistent with these results , Southern blotting detected hybridization signals in 3 Brassica plants ( Figure 5D ) with a probe specific for BrRNLS1 ., The NtRNLS2 sequence was detected in N . tabacum , while no fragments were generated from other Nicotiana species using genomic PCR ( Figure 5E ) ., Southern blotting results supported this detection profile ( Figure 5F ) ; N . tabacum , but not N . benthamiana , was shown to carry an NtRNLS2-related sequence ( Figure 5F , left panel ) ., All other RNLSs discovered through the similarity search of genome sequence databanks ( Table 2 ) , except for PtRNLS4 from Pop ., trichocarpa and TcRNLS1 from Theobroma cacao , were shown to be retained on respective plant genomes by genomic PCR and subsequent sequencing ( Figure S3 ) ., RNLS1s molecularly analyzed included those from Aquilegia flabellata ( a close relative of Aq . coerulea ) ( AfRNLS1 ) , Lotus japonicus ( LjRNLS1 ) , Malus x domestica ( MdRNLS1 ) and Cucumis sativus ( CsRNLS1 ) ( Figure S3B–H ) ., The AqfRNLS1 sequence defined in this article showed approximately 98% nt sequence identity to AcRNLS1 whose sequence is available in the database ( Phytozome ) ., LjRNLS1-1 from L . japonicus line B129 and CsRNLS1 from 3 cucumber varieties ( Hokushin , Suyo , and ‘Borszcagowski’ line B10 ) were identical to the reported RNLS1 sequences for line MG-20 ( Kazusa DNA Research Institute ) and ‘Chinese long’ line 9930 37 , respectively ., Approximately 97% nucleotide sequence identity was found between MdRNLS1s of cultivars ‘Sun-Fuji’ and ‘Golden Delicious . ’ ‘Golden Delicious’ is currently used in the apple genome sequence project 38 ( http://www . rosaceae . org/projects/apple_genome ) ., These examined RNLS sequences are listed in Table S5 ., Several sequences found through searching plant EST databases ( Table S6 , Figure S4 ) were included in our phylogenetic analysis ., Deduced amino acid sequences of plant RNLSs , the N ( CP ) proteins of negative-strand RNA viruses , and related EST entries were aligned using the MAFFT program ( Figure S5 ) ., Pair-wise similarities between selected RNLSs and viral N ( CP ) sequences are shown in Table S7 ., Two amino acid segments , GmH and YaRifdxxxfxxLQtkxC are relatively well-conserved among these sequences ., A dendrogram generated on the basis of alignment showed 4 major groups containing plant RNLSs ( Figure 6 ) ., RNLS1s are separated into two major groups ., The first group includes varicosavirus CPs and RNLS1s from apple , cucumber and Brassica plants ( MdRNLS1 , CsRNLS1 , BoRNLS1 , and BrRNLS1 ) in addition to a few ESTs ., The second group accommodates RNLS1s from Aquilegia and Lotus ( AqfRNLS1 , AqcRNLS1 , LjRNLS1 ) , together with an RNLS2 from Mim ., guttatus ( MgRNLS2 ) and EST sequences from Cichorium intybus and B . oleracea ., The placement of MgRNLS2 in this group may be explained by low-level sequence identity to its most closely related extant varicosavirus , LNYV ( Table 2 ) ., NtRNLS3 , PtRNLS4 , and Ns of cytorhabdoviruses ( LNYV , LYMoV , and NCMV ) form the third group ( Figure 6 ) ., A dichorhabdovirus ( orchid fleck virus , OFV ) and nucleorhabdoviruses ( PYDV and SYNV ) , replicating in the nuclei of host plants , are placed into an independent clade ., Whether most of the analyzed ESTs originated from viruses or plant chromosomes is unknown ., However , an EST from F . pratensis is presumed to originate from a plant virus in our preliminary experiment not only because the N ( CP ) - but also the L ( RdRp ) -derived ESTs were detected in the same EST library of F . pratensis ., This suggests a presently existing virus more closely related to RNLSs of the genus Brassica than LBVaV , because both N- and L-related sequences are rarely found in a single plant genome ( Table 2 ) ., Extensive searches of genome sequence databases for plant plus-strand RNA viral sequences were conducted using genome sequences of various plus-strand RNA viruses representing the major virus genera and families Potyviridae , Luteoviridae , Tombusviridae , and Bromoviridae ( Table S1 ) ., Compared to searches for double- or negative-strand RNA viral sequences , the search for plus-strand RNA virus sequences yielded a much smaller number of hits ., The Medicago truncatula database ( HTGS ) contains sequences of 320 and 475 nts with over 98% sequence identity to the capsid and movement protein genes of cucumber mosaic virus , a member of the family Bromoviridae ., However , this sequence was not amplified in Med . truncatula line A17 used in the genome sequence project by genomic PCR with different sets of internal and external primers ., A sequence similar to replication-related genes of citrus leaf blotch virus ( CLBV ) 39 belonging to the family Betaflexiviridae , is identified in the complete genome databases for the cucumber ‘Chinese long’ line 9930 37 and termed Cucumis sativus flexivirus replicase-like sequence 1 , CsFRLS1 ( Figure 7A ) ., The GSS database of cucumber ‘Borszczagowski’ line B10 also contains CsFRLS1 ( http://csgenome . sggw . pl/ ) , but its available sequence is fragmented ( Figure 7A , dashed purple bar ) and shorter than that in the complete genome sequence data base ., Two independent cucumber genome databases for 2 different lines strongly suggest the presence of CsFRLS1 in the cucumber chromosome ., We confirmed this by genomic PCR using different sets of primers corresponding to methyltransferase ( Met ) and RNA helicase ( Hel ) domains , the inter-domain region ( FR1-3 and FR1-4 ) and the entire CsFRLS1 region ( Figure 7B ) ., DNA fragments of expected sizes were amplified on genomic DNA from the ‘Borszczagowski’ line B10 , but not from watermelon , Citrullus lanatus ( Figure 7B ) ., Furthermore , genomic PCR fragments covering FRLS1 and its flanking putative open reading frames ( ORFs ) were amplified , strongly suggesting that FRLS1 resides on the nuclear genome as shown in Figure 7A and B . The phylogenetic tree containing CsFRLS1 potentially encoded by CsFRLS1 and its counterparts from related viruses shows that CsFRLS1 is closely related to the genus Citrivirus within the family Betaflexiviridae ( Figure 7C ) ., The distance between CsFRLS1 and citriviruses are similar to intra-genus distances in the genera Carla- , Fovea- , Viti- and Potexviruses ., The finding that the CP of a novel partitivirus , RnPV2 from a fungal phytopathogen matched a plant gene product , ILR2 from Ar ., thaliana initiated a comprehensive search of the plant genomic sequence data available as of December 10 , 2010 for non-retroviral RNA virus sequences ( NRVSs ) in plant genomes ., While this study showed a variety of sequences related to the N ( CP ) genes of negative-stranded RNA viruses ( cytorhabdoviruses and varicosaviruses ) in members in the plant families including Solanaceae , Leguminosae , Brassicaceae and Phrymaceae , only one plus-sense RNA virus-related sequence ( betaflexivirus replication-related gene ) was found to be present in the cucumber genome ., Furthermore , this survey detected sequences related to CP from dsRNA viruses ( partitiviruses ) ( PCLSs ) in various plants in addition to PCLSs reported by Liu et al . 24 ., These authors performed a thorough search of eukaryotic genomic sequences available as of September 2009 for NRVSs and showed multiple dsRNA virus-related sequences not only in plants but also animals ., Importantly , many of the NRVSs revealed by BLAST searches in this study were subsequently identified in plant genomes by Southern blotting , genomic PCR and sequence analyses ( Figures 1–3 , 5 , 7 , S1 , S3 ) ., These findings provide interesting insights into plant nuclear genome evolution , plant phylogeny and virus/host interactions ., Horizontal gene transfer , HGT , can occur “from virus to plant” or “from plant to virus . ”, A retention profile of PCLS1 among plants strongly suggests that HGT may have involved the former direction ., The family Brassicaceae of the order Brassicales includes the genus Arabidopsis , which is believed to have diverged after the split of the families Phrymaceae and Solanaceae , accommodates the genera Mimulus and Solanum and belong to different orders , Lamiales and Solanales , respectively ( Figure 8 ) ., No PCLS1 homologs are found in Vitis vinifera or Carica papaya , and that this gene resides on non-orthologous chromosomal positions of Mim ., guttatus ( data not shown ) and Arabidopsis-related species ( Figure 1A ) ., This strongly suggests that independent HGT events from virus to the Arabidopsis and Mim ., guttatus lineages may have occurred ( Figure 8 ) ., This observation is also true for other PCLSs ., The families Solanaceae and Brassicaceae contain PCLS5s , while their counterparts are not found in other plants whose complete genome sequences are available ( Figure 8 ) ., The observation that a relatively widely conserved gene PUX_4 is disrupted in Sol ., phureja by SpPCLS5 ( Figure 3A ) provides additional evidence for its insertion into the PUX_4 locus ., The HGT direction “from virus to plant” was further confirmed by phylogenetic analysis showing that plant PCLSs and partitivirus CPs are placed in a mixed way ( Figure 4 ) ., Viral sequences are basal in each of the three major clades , supporting the direction of transfers from virus to plant ., The divergence time of plant lineages is estimated through a classical approach using fossils and mutations rates of some particular genes ., Alternatively , if we assume that cellular genes evolve at a constant rate , their divergence time can be calculated from the genome-wide , spontaneous mutation rate determined on a generation basis in the laboratory 40 ., Together with the patterns of occurrence of the non-retroviral integrated RNA virus sequences , these values allow us to estimate time of some , if not all , HGTs identified in this study ., For example , the integration of PCLS1 ( ILR2 ) may have post-dated the split of the lineages containing the genera Arabidopsis and Brassica ( 16 . 0–24 . 1 million years ago ) and pre-dated the speciation of Arabidopsis spp ., , or more accurately the divergence of Arabidopsis and its closely related genera ( Figure 8 ) ( 10–14 million years ago ) 40 , 41 , 42 ., The phylogenetic relation among PCLS1s from Arabidopsis and its close relatives within the tribe Camelina ( Capsella , Olimarabidopsis , and Turritis ) agrees with the phylogeny of the family Brassicaceae deduced from systematic analyses 43 ., Moreover , assuming that the Ar ., thaliana and Ar ., arenosa separated 10 million years ago , the mutation rates calculated for PCLS1s between the 2 plants are estimated to be 6 . 8×10−9 base substitutions per site per year , a value close to the genome-wide base substitution rate , 7×10−9 , reported for Ar ., thaliana by Ossowski et al . 40 ., These observations suggest that endogenized PCLS1s accumulated mutations in a manner similar to those of other nuclear sequences during the course of evolution after a single HGT event in an ancestral Arabidopsis plant ., The genome of B . rapa in the family Brassicaceae retained 2 PCLSs ( BrPCLS4 and BrPCLS5 ) with low-level similarities to RnPV2 CP on chromosomal positions different from each other and from that of the PCLS1 ( ILR2 ) homologs of Arabidopsis-related genera ., No PCLS1 homolog was identified on the orthologous positions of the B . rapa genome , and no BrPCLS4 or BrPCLS5 homologs were found on the corresponding locus of the Ar ., thaliana or Ar ., lyrata genome ., Therefore , BrPCLS4 and 5 may have been introduced into the B . rapa genome separately from each other and from PCLS1 ( ILR2 ) after the divergence of the Brassica and Arabidopsis lineages ( Figure 8 ) ., Similarly , the detection profile of AtPCLS2 and AlPCLS3 ( Figure, 2 ) shows that they may have been introduced into Ar ., thaliana and Ar ., lyrata chromosomes independently after the separation of 2 plant species ( 3 . 0–5 . 8 million years ago ) ( Figure 8 ) ; these are more recent HGT events than the PCLS1 integration into the Arabidopsis lineage ., PCLS integrations into the Solanaceae lineage were slightly complex ., Relatively high or moderate levels of aa sequence identities ( 47–68% ) are shared within the PCLS5s from the family Solanaceae ., However , a lack of information regarding genome sequences flanking the PCLS5s caused difficulty in determining whether a single event or multiple HGT events may have occurred within the lineage ( Figure 8 ) ., Gene sequences related to rhabdovirus or varicosavirus N ( CP ) genes ( RNLSs ) are detected in many genera including Brassica , Raphanus , Mimulus , Nicotiana , Lotus , Malus , Cucumis , Populus , Theobroma | Introduction, Results, Discussion, Materials and Methods | Non-retroviral RNA virus sequences ( NRVSs ) have been found in the chromosomes of vertebrates and fungi , but not plants ., Here we report similarly endogenized NRVSs derived from plus- , negative- , and double-stranded RNA viruses in plant chromosomes ., These sequences were found by searching public genomic sequence databases , and , importantly , most NRVSs were subsequently detected by direct molecular analyses of plant DNAs ., The most widespread NRVSs were related to the coat protein ( CP ) genes of the family Partitiviridae which have bisegmented dsRNA genomes , and included plant- and fungus-infecting members ., The CP of a novel fungal virus ( Rosellinia necatrix partitivirus 2 , RnPV2 ) had the greatest sequence similarity to Arabidopsis thaliana ILR2 , which is thought to regulate the activities of the phytohormone auxin , indole-3-acetic acid ( IAA ) ., Furthermore , partitivirus CP-like sequences much more closely related to plant partitiviruses than to RnPV2 were identified in a wide range of plant species ., In addition , the nucleocapsid protein genes of cytorhabdoviruses and varicosaviruses were found in species of over 9 plant families , including Brassicaceae and Solanaceae ., A replicase-like sequence of a betaflexivirus was identified in the cucumber genome ., The pattern of occurrence of NRVSs and the phylogenetic analyses of NRVSs and related viruses indicate that multiple independent integrations into many plant lineages may have occurred ., For example , one of the NRVSs was retained in Ar ., thaliana but not in Ar ., lyrata or other related Camelina species , whereas another NRVS displayed the reverse pattern ., Our study has shown that single- and double-stranded RNA viral sequences are widespread in plant genomes , and shows the potential of genome integrated NRVSs to contribute to resolve unclear phylogenetic relationships of plant species . | Eukaryotic genomes contain sequences that have originated from DNA viruses and reverse-transcribing viruses , i . e . , retroviruses , pararetroviruses ( DNA viruses ) , and transposons ., However , the sequences of non-retroviral RNA viruses , which are unable to convert their genomes to DNA , were until recently considered not to be integrated into eukaryotic nuclear genomes ., We present evidence for multiple independent events of horizontal gene transfer from a wide range of RNA viruses , including plus-sense , minus-sense , and double-stranded RNA viruses , into the genomes of distantly related plant lineages ., Some non-retroviral integrated RNA viral sequences are conserved across genera within a plant family , whereas others are retained only in a limited number of species in a genus ., Integration profiles of non-retroviral integrated RNA viral sequences demonstrate the potential of these sequences to serve as powerful molecular tools for deciphering phylogenetic relationships among related plants ., Moreover , this study highlights plants co-opting non-retroviral RNA virus sequences , and provides insights into plant genome evolution and interplay between non-reverse-transcribing RNA viruses and their hosts . | plant science, plant evolution, virology, plant pathogens, plant biology, plant pathology, biology, microbiology, viral evolution | null |
journal.pgen.1000100 | 2,008 | An Evolutionarily Conserved Sexual Signature in the Primate Brain | Many primates are sexually dimorphic in a variety of characteristics including overall body size , tooth dimensions , color and pattern of fur and skeletal features 1 ., Additionally , there are behavioral differences between the sexes including reproductive behavior , performance of spatial tasks , domination behavior and aggression 1–5 ., In apes and old world monkeys such morphological and behavioral differences are often extensive ., In contrast , most new world monkeys are more sexually monomorphic 6 ., Much less is known about sexual dimorphism in the primate brain ., Physical and hormonal dimorphism have been described , including brain size and weight , size of specific anatomical regions , grey and white matter content , and hormonal profiles 7 ., However , little information is available about sex differences in gene expression patterns in the brain and their possible functional consequences ., Two earlier studies have investigated sex differences in the brains of adult humans and they focused on genes encoded in the sex chromosomes 8 9 ., Their results led to the suggestion that there are only limited sex-biased gene expression in the adult brain 10 ., More recently , a genome wide survey was performed in many somatic tissues in mice , and hundreds of genes were found to be sexually dimorphic in whole brain 11 ., Since striking physiological differences occur between sexes in specific brain regions 12 , and since the cortex is responsible for higher behavioral functions , we decided to investigate specifically this tissue in our studies ., Here we present the first genome-wide comparison of sex differences in gene expression in a specific brain region in primates ., We hypothesized that molecular variation in sexual dimorphism may exist in the primate cortex , and that the number of gene expression differences between males and females may reflect this molecular dimorphism ., We also speculated that if gene expression differences between the sexes are essential for key male and female characteristics , then these regulatory differences between the sexes may be evolutionary conserved ., In order to determine whether genes are differently expressed in the brains of males and females within primate species , and whether there are any fundamental primate-wide differences in cortex gene expression between the sexes , we compared gene expression levels in the occipital cortex of four male and four female individuals in each of three primate species: humans ( Homo sapiens; a great ape ) , macaques ( Macaca fascicularis; an old world monkey , mainly polygamous 13 ) , and marmosets ( Callithrix jacchus; a new world monkey , mainly monogamous 14 ) ., The evolutionary relationships among the primates are illustrated in Figure S1 ., To identify genes in occipital cortex that are differentially expressed between the sexes , we hybridized cDNA from each sample ( n\u200a=\u200a24 ) of the three primate species to human cDNA microarrays containing probes for 14 , 621 HUGO annotated genes ( KTH Human 46k cDNA , http://www . biotech . kth . se/molbio/microarray/ ) ., We used a loop hybridization study design restricted to within-species comparisons , in which we co-hybridized samples from the opposite sex on each array ( Figure 1 and Materials and Methods ) ., As expected 15 , samples from all species hybridized well to the human cDNA array ., This is illustrated by a comparison of overall absolute intensity levels ( raw data is available at Array Express database under accession E-MEXP-1182 ) ., Since we used cDNA microarrays and performed all competitive hybridizations between females and males from the same species , our results are not expected to be biased by the effect of sequence mismatches on hybridization intensity 15 ., To determine the degree of sexual dimorphism within each of the three primates , the data from each species was analyzed independently and genes expressed differently between the sexes were identified ., We applied single channel normalization 16 to acquire absolute intensities for each clone and individual sample ., To identify genes that are differentially expressed between the sexes , we used a linear model to analyze gene specific expression levels from each species , with the penalized F-ratio ( PenF ) for a sex difference as our ranking statistic ( see Materials and Methods for details ) ., By this approach , the analyzed genes in each species were ranked according to the size and reproducibility of the expression difference ( Table S1 ) ., We found several hundreds of genes ( Table 1 ) to be differentially expressed in the occipital cortex of males and females in human and macaque , while fewer than ten were sexually dimorphic in marmoset ., Table 1 lists the number of clones above three threshold values of the ranking statistic ( PenF: 3 . 0 , 4 . 0 and 5 . 0 ) in each species , along with an estimated false discovery rate ( FDR ) associated with the threshold ., The volcano-plots in Figure 2 show an overview of the data for each species with the thresholds corresponding to Table 1 represented by three horizontal lines ., The differences in population of sex-biased genes between marmoset and the catarrhine species is striking ., The observation of a few prominent genes in marmoset , having both high PenF and fold change values , also indicates that reaching a detectable signal level in this species was not a concern ., However , a caveat to our analysis is that sequence mismatches could result in reduced power to detect differentially expressed genes using cross-species hybridizations 17 ., To investigate this issue , we studied sequence identity between marmoset genes and human cDNA clones present on the microarrays ., Sequence identities between human cDNAs on the microarray and marmoset genes were calculated ( see Materials and Methods ) ., As shown in Figure 3 , the 4 highest ranked clones in marmoset ( Table 1 and Figure 2 ) do not have higher sequence identity than a random sample ( n\u200a=\u200a185 ) of low ranked genes ., We conclude that sequence divergence did not seriously contribute to the exceptional differences in numbers of sexually dimorphic genes between marmoset and the catarrhines ., However , an effect of sequence divergence on some sexually dimorphic genes in each species cannot be ruled out ., In order to identify possibly conserved sexual expression differences in the primate occipital cortex , the differences between the sexes in samples from all combinations of species was determined ., To do this , the gene specific expression levels of 16 individuals at a time ( for two species comparisons ) , or all 24 individuals at a time ( for three species comparisons ) , were analyzed simultaneously with the linear model used to analyze single species , adding the effect for species and the interaction effect between species and sex ., Conserved sexually differentiated genes were defined as genes with a large ( and reproducible ) average difference over the analyzed species , and a relatively small difference in sexual dimorphism between species ( see Materials and Methods for details ) ., Using this approach , we identified 85 genes with sexually dimorphic expression profiles in the same direction in both humans and macaques ( FDR≤0 . 05 , Figure 4 ) ., This provides the first observation of conserved sexually dimorphic gene expression signature in primate brains ., Further , 2 genes , X inactivation-specific transcript ( XIST ) and Heat shock factor binding protein 1 ( HSBP1 ) , were consistently sex-biased in all three species ( FDR ≤0 . 05 , Figure 4 ) , both of these genes were upregulated in females ., These two genes were also the only genes identified in the combinations human-marmoset and macaque-marmoset ., Figure 4 also shows that absolute intensities for marmoset samples were not lower than intensities for the other species ( left panel red color ) , indicating that hybridization intensities were not biased due to an effect of sequence mismatches between primate RNA samples and human cDNA probes on the microarrays ., It is possible that the genes that were identified as conserved sex-biased in cortex of human and the other primates are sex-biased not only specifically in brain , but also in other tissues ., It is also possible that the sex-bias observed is more the result of selective constraints that operate on other tissues as opposed to brain ., Many genes that are expressed in gonad tissues are sex-biased 18 , and we therefore investigated if the genes in the conserved sex signature in primate occipital are highly expressed in sexual tissues and/or in nervous tissue ., We used publicly available expression data for human tissues from SOURCE 19 , where tissues are ranked according to their normalized expression of each gene ( see Materials and Methods for details ) ., We found that a majority of the 85 genes in the conserved sex signature , 55 ( 65% ) , had nervous tissues ranked equal to or higher than 5 ( Figure 5 ) , and that only 23 genes , ( 27% ) were highly expressed in sexual tissues ., These results show that many genes in the conserved signature of sex-biased expression are highly expressed and are therefore likely to be functional in the nervous system ., Since a smaller fraction of the genes are expressed highly in sexual tissues it is possible that selection of sex-biased expression of many of the genes has been on the nervous system ( or other tissues ) rather than on sexual tissues ., We hypothesized that if the conserved signature of sexually dimorphic gene regulation in the brains of human and macaque is functionally important , the genes included in this signature may also be more conserved at the protein level ., To test this , we studied the rate of protein evolution in the human lineage by estimating the ratio of the rates of amino acid changing ( non-synonymous , dN ) to silent ( synonymous , dS ) substitutions ( dN/dS ) ( see Materials and Methods for details ) ., As can be seen in Figure 6 , we found that dN/dS ratios for genes that show conserved sexually dimorphic expression profiles are significantly lower ( median\u200a=\u200a0 . 06 ) compared to dN/dS ratios of other human genes ( median\u200a=\u200a0 . 16 ) ( permutation test for the difference between medians; p\u200a=\u200a0 . 0003 ) ., However , genes expressed in brain are known to be under strong evolutionary constraint 20 and we therefore also compared the conserved sex signature with human brain-expressed genes only ., We find a tendency , although not statistically significant , of higher conservation of sexually dimorphic genes when comparing them with the brain-expressed genes in general ( median\u200a=\u200a0 . 08 ) ., Genes with male-biased expression are expected to have higher dN/dS ratios than female-biased genes 21 ., We investigated whether this holds true for genes recognized as sex-biased in our primate dataset ., We compared dN/dS ratios between genes that were identified as male-biased and female-biased in human and/or macaque in the single species analysis ( genes with PenF≥5 . 0 , Table 1 , Figure 2 , Table S1 ) ., We found dN/dS ratios of male-biased genes to be three times higher than dN/dS ratios of female-biased genes ( Figure 7 , median of male-biased\u200a=\u200a0 . 09 , median of female-biased\u200a=\u200a0 . 03 , permutation test of medians; p\u200a=\u200a0 . 006 ) ., When comparing this data with a set of genes that are non-sexually-biased ( the brain-expressed genes used in Figure 6 ) , we found that non-biased brain expressed genes have intermediate dN/dS values ( median\u200a=\u200a0 . 08 ) ., This is interesting , since the pattern seen here in primates has earlier been observed in Drosophila 22 ., We evaluated three male-biased and three female-biased genes in the conserved sex signature using RT q-PCR in order to confirm the microarray results ., Primer pairs were designed on conserved regions , forward primer and reverse primer located on different exons , for the following genes: FGF12 , NKIRAS1 , AOF1 , SLC6A1 , EPHX1 , MAP1A ( Table S2 ) ., We measured transcript levels in four males and four females in each of the three primate species ., We employed non-template controls ( NTCs ) for each individual sample and gene to control for DNA contaminations ., Actin-β ( ACTB ) was used as reference transcript ( see Materials and Methods for details ) ., Transcript levels in five of the six transcripts could be quantified; FGF12 , NKIRAS1 , SLC6A1 , EPHX1 , MAP1A ( Figure 8 ) ., Contributions from DNA contaminations were negligible ., Typical cycle threshold values ( CT ) of the NTCs were at least 15–20 PCR cycles higher than those of cDNA samples ., AOF1 could not be quantified ( NTCs in the same ranges as cDNA samples ) ., The expression patterns in species and sex could be statistically confirmed and were consistent with the microarray results for each of the five quantified genes except for MAP1A , in which we did not find a difference between male and female expression in human ., To evaluate whether certain biological categories are overrepresented among the conserved sexually dimorphic genes shown in Figure 4 , we categorized the genes according to their functional annotations ( DAVID 23 ) ., We then assessed overrepresented ontology classes in the conserved sex-biased genes compared to the genes on the microarray overall ., The choice of reference set in analysis of overrepresented gene ontology classes can affect the results ., We therefore also compared the conserved sex-biased genes with the 50% of genes on the microarray with highest expression ( i . e . highest intensity values ) ., In Table 2 , we report overrepresented ontology classes with p-values consistently lower or equal to 0 . 05 , using both reference sets ., Notably genes involved in metabolism , polyamine biosynthesis and ubiquitin cycle , were overrepresented ., Sex hormones , such as estrogens and androgens , have been considered to have great influence on the differences between the male- and the female brain 24 ., As a first step , to identify potential conservation of sex hormonal regulation , we decided to investigate the presence of estrogen- and androgen response elements in genes with conserved sexual dimorphic expression in the two catarrhine species ., A search for estrogen alpha and androgen cis-response elements ( ESR1 and AR ) was performed in genes within the conserved sexual expression signature in human and macaque ( genes in Figure 4 ) ., Of these 85 genes , 61 orthologs genes pairs could be identified in the two species ., Sequences from the closely related species Macaca mulatta available in Ensembl ( http://ensembl . org ) were used instead of Macaca fascicularis ., Prometheus system 25 was used to analyze the presence of ESR1 and AR in the regions; 10000 bases upstream the transcription initiation site , introns and 5000 bases downstream the 3′ end of the genes ., The transcription factor binding sites were identified using weight matrices from JASPAR Database 25 ., Only regions with 95% of sequences similarity between human and macaque were considered in the analysis ., The threshold for positive identification of binding sites based on the matrix model similarity was 80% ., We find that 34 ( 56% ) of the 61 genes contained at least one ESR1 , and 40 ( 66% ) contained at least one AR ., 16 ( 26% ) of the 61 genes contained neither ESR1 nor AR in the investigated regions ., Table S3 contains the annotations of the 61 genes investigated here and the locations of ESR1 and AR elements in these genes ., We identified genes with conserved sex differences in mRNA expression in occipital cortex among three primates: human ( Homo sapiens ) , macaque ( Macaca fascicularis ) and marmoset ( Callithrix jacchus ) ., This finding establishes the existence of biological sex differences in gene expression the human cortex , and further , it unveils the existence of conserved sexual signatures in the primate cortex with possible importance during primate evolution ., The obvious question to follow is whether or not these signatures of sex in the brain have physiological significance for brain physiology and/or behavior ., In Drosophila , sex-dependent selection may drive changes in expression of many of the most rapidly evolving genes and sex specific transcriptome differences may be the driving force behind speciation events 26 27 ., This example shows that molecular sex differences may be important during evolution because sex-specific genes may be subjected to different selective pressures on each sex ., Not only the fruit fly , but also many other species including many primates show great morphological sexual dimorphism ., Physical dimorphism goes along with social and behavioral dimorphism , which may be reflected in gene expression in the brains of these species ., Variation in brain gene expression has been observed even between closely related species 28 ., Our results suggest that variation in expression of genes in the brain may be an important component of behavioral variation within as well as between species ., Conservation of gene expression patterns alone may be inadequate to suggest that genes included in the conserved signature are important for physiological differences between the sexes in the brain ., However , when estimating non-synonymous and synonymous substitution rates ( dN/dS ratios ) in the coding region of the genes in the conserved sexual expression signature , we find that these genes evolve under more selective constraint in the human lineage compared with a genome-wide control-set of genes ( p\u200a=\u200a0 . 0003 ) ., Brain expressed genes are known to be under strong evolutionary constraint 20 ., We find a tendency of lower dN/dS ratios even when comparing to other brain genes , although not statistically significant ( p\u200a=\u200a0 . 16 ) ., This observation is consistent with the hypothesis that these genes have important function ( s ) in the brain 29 , possibly of fundamental importance to sex differentiation ., An observation that reinforces this hypothesis is that most of the genes included in the conserved signature are highly expressed in nervous tissues ( 65% ) , while fewer are expressed highly in sexual tissues ( 27% ) , as shown in Figure 5 ., It is therefore probable that the sex differences in expression in the brain are not just a reflection of their functional significance in gonads , but may suggest that they are physiologically relevant for sex differences in the brain itself ., It is consequently possible that selection of sex-biased expression of many of the genes that were identified has been on the nervous system ( or other tissues ) rather than on sexual tissues ., Also important for evolutionary discussions , we observed among the sex-biased genes that are not conserved during evolution a significantly higher constraint in evolutionary rates of coding sequences in female-biased genes as compared to male-biased and non-biased genes ( Figure 7 ) ., Interestingly , the same pattern has recently been observed in a large study of sexually dimorphic gene expression in Drosophila 22 ., This could suggest that the female-biased genes are subject to stronger purifying selection than the male-biased genes , which corresponds well with the idea that natural selection is more important than sexual selection in female mammals ., This pattern could also or alternatively be explained by positive selection in the male biased genes 26 , which may be more exposed to labile and species specific sexual selection ., We do not know what regulatory mechanisms are controling the sexually dimorphic expression of the genes that were identified in our study ., It is possible that the expression of some of these genes is under sex hormonal regulation , but this is yet to be decided ., As a first step to identify potential conservation of sex hormonal regulation , we investigated the presence of estrogen alpha- and androgen response elements in conserved regions in human and macaque in the genes identified in out study ., The results are presented in Table S3 ., It is too early to make any general statements based on this data and further studies in these issues should be done ., To avoid the effect of sequence divergence on the evaluation of sex differences in gene expression 17 , we restricted the microarray analysis to intra-species comparisons between the sexes ., This approach is valid in confirming the conservation of expression differences during evolution , since the effect of sequence deviation between probes on the microarray and the primate RNA should be identical in males and females of the same species ., On the other hand , the strategy has limitations ., First , because of sequence divergence it is possible that some differently expressed genes were not identified in marmoset and/or macaque ., For this reason , it is possible that the conserved cortex sex dimorphism across primates may actually be more extensive than here described ., The method may also be imperfect in its ability to investigate divergence of sex differences between humans and the other primates because of the effect of sequence divergence ., Nevertheless , the comprehensive dissimilarity in the total number of genes with sexually dimorphic expression in the marmoset occipital cortex ( 7 clones ) compared to that of human ( 1349 clones ) and macaque ( 486 clones ) ( PenF≥3 . 0 , Table 1 , Figure 2 ) is substantial enough to suggest that there may be essential biological differences in the prevalence of sex differences in the brain of marmosets compared to that observed in the two strongly sexually dimorphic species ., Moreover , the sequence comparisons and the strength with which all species hybridized to the microarrays indicate that sequence divergence does not explain the extreme differences in the number of dimorphic genes between these species ., However , sequence divergence cannot be totally ruled out as an underlying factor ., The observation of minute sex differences in the marmoset brain is interesting since it correlates with the overall trend in the marmosets , which are relatively sexually monomorphic in their physical constitution as compared to the more strongly sexually dimorphic humans and macaques 6 ., In primates , a general rule is that many of the sexual physical and behavioral dimorphisms that are so pronounced in the polygamous species , such as macaques , are typically less distinct or lacking in monogamous species , such as the marmosets 1 , 14 ., Our observation suggests that this correlation may also be reflected in the degree of sex-biased gene expression in the brain ., Two sexually dimorphic genes were conserved across all three of the primates , suggesting that these genes may have essential functions related to key male and female characteristics ., One of these genes is on a sex chromosome ( XIST on the X chromosome ) and is key in the inactivation of one of the two X chromosomes in mammalian females ., The second is an autosomal heat shock binding protein ( HSBP1 on chromosome 16 ) that has been shown to bind and negatively regulate heat shock factor 1 ( HSF1 ) 30 , which in turn is known to be regulated by estrogen 31 ., The HSBP1 gene is important for regulating the physiological reactions to stress 30 , raising the intriguing possibility of a conserved sexual difference in stress response in primates 32–34 ., In conclusion , our observations suggest that some sexual differences in the occipital cortex at the gene expression level may be conserved during the evolution of primates ., Multiple lines of research have observed sex differences in behavioral and cognitive abilities in humans 35–37 and other primates 4 , 5 , 38 ., However , whether these differences are caused by biological changes present in the brain is not yet known ., The study of sexual differences in gene expression in the primate brain is important not only to increase our understanding of sex differences in normal behavior , but also to explain differences between the sexes in prevalence of psychiatric diseases and response to drug treatments 39 ., Our findings should thus fuel future investigations on the precise role of sexually distinct expression profiles and their possible involvement in physiology , behavior and cognition ., Tissues were obtained from the occipital cortex from macaques , marmosets and humans ., Four males and four females from each species were included in the experiments ., The brain samples from macaques ( Macaca fascicularis ) were controls from a previous research program conducted by Dr . Diana Radu at the Karolinska Institute , Stockholm ., The animals had been housed at The Swedish Institute for Infectious Disease Control , in Stockholm ., The marmoset ( Callithrix jacchus ) samples were a donation from IMANET , Uppsala , where the animals had been used in the development of PetScan analyses and they were housed at the facility for laboratory animals , Uppsala University ., The human samples were previously described occipital cortex control samples 40 ., The brains were stored at −70°C prior to analysis ., Occipital cortex was dissected by applying a vertical cut at the back of the frozen brains and splinters of cerebellum were separated from the cortex slices with a scalpel , keeping the samples at all times on dry ice and inspecting the brain structures with a magnifying glass ., Weights of samples were in the range of 0 . 28–0 . 42, g . Nucleic acids were extracted from frozen tissues which were homogenized in TRIzol and RNA was extracted according to the manufacturers instructions ( Invitrogen™ , Life Technologies ) as in 40 , 41 ., The concentration of extracted RNA was measured using a NanoDrop ND-1000 instrument ( NanoDrop Technologies , USA ) and ranged between 2860–5870 ng/µl for macaques , 3420–4330 ng/µl for marmoset and 1900–3240 ng/µl for humans ., Electrophoresis ( 1% agarose , 0 . 5 x Tris-Acetate-EDTA buffer , 90 V , 30 min , 6 µl sample , 3 µl 3 x loading buffer ) was run for RNA quality inspection ., The gel was stained in an ethidium bromide bath ., 30 µg of total RNA from each male and female sample were reversely transcribed to cDNA with Cy3/Cy5-3DNA catch sequences incorporated in the primers , using Genisphere 3DNA Array 900 labeling kit ( Genisphere Inc . , USA ) and Superscript II RT enzyme ( Invitrogen , Life Technologies ) with supplied buffers and following the manufacturers instructions ., cDNA was concentrated in Microcon YM-30 columns ( Micron Bioseparations Millipore Corporation ) ., The microarray experiments were set up in a loop design ( Figure 1 ) ., Each individual sample was hybridized twice , each time labeled with a different fluorescent dye ( Cy3 or Cy5 ) and coupled with a sample of the opposite sex ., This design resulted in a total of 24 co-hybridizations ( eight hybridizations for each of three species ) ., Hybridizations were performed on preheated microarray slides printed with 46 , 128 cDNA clones ( KTH Human 46k Batch11 , Microarray Resource Centre , Royal Institute of Technology , Sweden ) , using the buffers and procedures given in the Genisphere 3DNA Array 900 labeling kit ., Hybridizations were conducted in humidity chambers ( Corning Inc . ) at 42°C for 18, h . The microarray slides were subsequently washed in glass slide holders shaking at 320 rpm: 2 min in 2 x sodium chloride-sodium citrate buffer ( SSC ) , 0 . 2% sodium dodecyl sulfate ( SDS ) at 65°C; 10 min in fresh 2× SSC , 0 . 2% SDS at 65°C; 10 min in 2× SSC at room temperature; 10 min in 0 . 2× SSC at room temperature ., Arrays were dried by centrifugation at 1000×, g . Arrays , cover slips and humidity chambers were preheated and 3DNA Cy3/Cy5 hybridization mix added onto the arrays in accordance with the Genisphere protocol ., 3DNA-hybridization was conducted in humidity chambers at 42°C for 5, h . The slides were washed again as described above , but with an additional final wash for 5 min in 0 . 1× SSC at room temperature prior to centrifugation and scanning ., The 24 microarrays were scanned at 10 µm resolution using a GenePix 4100A scanner ( Axon Instruments Inc . ) ., Spots on the resulting images were quantified with the software package GenePix Pro 5 . 0 ( Axon Instruments Inc . ) ., No background correction was applied to the data ., However intensity values after mean background subtraction was calculated to evaluate the quality of each array ( Figure S3 ) ., The resulting files are publicly available on the EMBL-EBI ArrayExpress database ( ArrayExpress accession E-MEXP-1182 , conforming to the MIAME guidelines ) ., Analysis was done with SAS software ( SAS/STAT , version 9 . 1 . 3 , SAS institute Inc . , Cary , NC ) ., The log2 transformed mean intensities of Cy5 ( R ) and Cy3 ( G ) for each spot were used to calculate the log-transformed ratio M\u200a=\u200aR-G ., A robust scatter plot smoother ( Proc Loess , SAS version 8 . 02 ) was used to remove systematic intensity dependent dye-bias from M . This within slide normalization was done for 12 sub-array blocks separetly giving Msa , with the smoothing parameter set to 40% ( Figure S2 ) ., Next , quantile normalization 42 was used to adjust the signal intensities between slides ( within species ) ., We applied quantile normalization to the intensity A\u200a=\u200a½ ( R+G ) , giving Aq ., The fully normalized R and G values were then defined as: RMsaAq\u200a=\u200a½ ( Msa+2Aq ) , GMsaAq\u200a=\u200a½ ( Msa-2Aq ) 16 ., All spots with a mean spot intensity below the local median background were excluded from subsequent analysis ., To further evaluate the quality of the data from all arrays , we plotted the average of the background subtracted intensity values for each array versus the number of missing spots for that array ., We noted that the arrays that included a human female called “F4” were of low quality compared to all other arrays , yielding 50 . 2% and 35 . 6% of missing probes while the average for all the other 22 arrays was 6 . 4% , and having lower mean spot intensity values ( Figure S3 ) ., Therefore , this individual was removed from all further analysis ., However , the data from this individual is still shown in Figure 4 , for completeness ., In order to identify genes that are differentially expressed between the sexes we determined whether the average expression difference over the four males and females was significant compared to inter-individual variation for each gene ., This was done by analyzing the microarray data with the following mixed linear model ( single channel analysis ) with Procedure Mixed ( nind\u200a=\u200a8 , ny\u200a=\u200a16 ) , in which the gene labels have been suppressed ., Here yijkl is the log transformed and normalized intensity ( RMsaAq or GMsaAq ) of the l:th replicate of the k:th individual of sex j , incorporated with dye, i . Dye and sex were considered fixed factors , where as individual was considered a random factor ., Differentially expressed genes were ranked by the F-ratio for the factor sex , which was penalized by adding a constant ( a0 ) to the denominator ., We chose a0 to be the 90th percentile of the mean square for individual of all analyzed genes ., A false discovery rate ( FDR ) for each species list was determined empirically for a range of penalized F-ratios ( PenF ) , by permuting sex within species ., P-values were defined as the fraction of simulations that yielded at least the number of observed genes ., For each species there were 35 unique permutations , ( 7C4 for human , and 8C4/2 for macaque and marmoset ) , yielding a minimum p-value of 0 . 029 ., In order to identify conserved sex expression differences across primate species , we combined samples from two respectively all three species ., To do this , the gene specific expression levels of 16 individuals at a time ( for two species comparisons ) , or all 24 individuals at a time ( for three species comparisons ) , were analyzed with the following mixed linear model , where the annotation is the same as for the single species analysis ., Two addition | Introduction, Results, Discussion, Materials and Methods | The question of a potential biological sexual signature in the human brain is a heavily disputed subject ., In order to provide further insight into this issue , we used an evolutionary approach to identify genes with sex differences in brain expression level among primates ., We reasoned that expression patterns important to uphold key male and female characteristics may be conserved during evolution ., We selected cortex for our studies because this specific brain region is responsible for many higher behavioral functions ., We compared gene expression profiles in the occipital cortex of male and female humans ( Homo sapiens , a great ape ) and cynomolgus macaques ( Macaca fascicularis , an old world monkey ) , two catarrhine species that show abundant morphological sexual dimorphism , as well as in common marmosets ( Callithrix Jacchus , a new world monkey ) which are relatively sexually monomorphic ., We identified hundreds of genes with sex-biased expression patterns in humans and macaques , while fewer than ten were differentially expressed between the sexes in marmosets ., In primates , a general rule is that many of the morphological and behavioral sexual dimorphisms seen in polygamous species , such as macaques , are typically less pronounced in monogamous species such as the marmosets ., Our observations suggest that this correlation may also be reflected in the extent of sex-biased gene expression in the brain ., We identified 85 genes with common sex-biased expression , in both human and macaque and 2 genes , X inactivation-specific transcript ( XIST ) and Heat shock factor binding protein 1 ( HSBP1 ) , that were consistently sex-biased in the female direction in human , macaque , and marmoset ., These observations imply a conserved signature of sexual gene expression dimorphism in cortex of primates ., Further , we found that the coding region of female-biased genes is more evolutionarily constrained compared to the coding region of both male-biased and non sex-biased brain expressed genes ., We found genes with conserved sexual gene expression dimorphism in the occipital cortex of humans , cynomolgus macaques , and common marmosets ., Genes within sexual expression profiles may underlie important functional differences between the sexes , with possible importance during primate evolution . | The contribution of genetics versus environment to behavioral differences between the sexes is a fundamental question in neuroscience ., We hypothesized that some differences between the sexes might be partially explained by sexually dependent gene expression differences in the brain ., We further speculated that if differences in gene expression between males and females are functionally important , they may be conserved in the evolution of primates ., To test these hypotheses , we measured gene expression in the brains of male and female primates from three species: humans ( Homo sapiens ) , macaques ( Macaca fascicularis ) , and marmosets ( Callithrix jacchus ) ., Our results point to a conserved signature of sexual gene expression dimorphism in the brains of primates ., Interestingly , we found that genes with conserved sexual gene expression dimorphism in the brain also evolve under more evolutionary constraint , compared with other genes , suggesting that they may have important roles during evolution of sex in primates ., Moreover , we found higher evolutionary constrains in the coding regions of female-biased genes as compared to both male-biased and non sex-biased brain expressed genes ., The study of sex dimorphic genes may in the future shed light on the basis of psychiatric diseases with differences in prevalence between the sexes . | neuroscience, evolutionary biology/animal genetics, genetics and genomics/gene expression, evolutionary biology/evolutionary and comparative genetics | null |
journal.ppat.1005032 | 2,015 | MiR-21 in Extracellular Vesicles Leads to Neurotoxicity via TLR7 Signaling in SIV Neurological Disease | HIV-associated neurocognitive disorder ( HAND ) is a central nervous system ( CNS ) associated neurological disease where neurodegeneration is a consequence of CNS inflammation ., The pathological characteristics of the most extreme form of this disease include astrogliosis , microgliosis , presence of multinucleated giant cells , and loss of dendrites and synapses 1–3 , collectively termed HIV encephalitis ( HIVE ) ., These features are recapitulated in its nonhuman primate equivalent rhesus macaque model , simian immunodeficiency virus encephalitis ( SIVE ) 4 ., In the CNS , HIV primarily infects microglia and macrophages but not the neurons ., However , inflammatory molecules , as well as HIV gene products that are released from infected cells , have damaging affects on neurons 5–8 ., Previously , others and we identified that SIV/HIV infection upregulated microRNAs ( miRNAs ) in macaque and human brains 9–11 ., These studies have shown that upregulation of miRNAs can also lead to neuronal dysfunction by targeting crucial genes and by repressing their expression in the CNS ., Further , we also identified that some of these miRNAs can be released extracellularly in extracellular vesicles ( EVs ) 12 ., EVs are small membrane-bound structures ., They play a significant role in cell-cell communication 13–16 , in progression of cancer 17 and in viral infections 18–20 ., In the brain , astrocytes 21 , microglia 22 and neurons 23 have been shown to release EVs such as exosomes under physiological conditions ., There is growing evidence for intercellular EV transfer within the CNS ., EVs have been repeatedly discussed as potential carriers in the dissemination of disease pathology in neurodegenerative disorders , as they harbor proteins and RNA that can be transferred from the originating cell to a target cell 24 ., We have previously identified that miR-21 is significantly upregulated during SIV/HIV infection in the brain 11 ., Thus , we hypothesized that miR-21 may be present within EVs during SIV/HIV associated neuroinflammation and therefore , can be damaging to neurons ., Intriguingly , a recent study indicated that certain extracellular miRNAs could bind to toll-like receptors ( TLRs ) in neurons and cause neurodegeneration 25 ., These miRNAs had a G/U rich region capable of activating TLR7/TLR8 ., Interestingly , miR-21 is one such miRNA ., The overall goal of this study was to investigate whether miR-21 was significantly enriched in EVs in SIVE pathogenesis and if such an increase induces deleterious signaling pathways downstream ., Here , for the first time , we report the miRNA profiling of EVs from the brain ., We find that miR-21 is increased in EVs during SIVE pathogenesis and that it is deleterious to neurons by activating TLR7 dependent downstream cell death pathways ., Hence , our data provide insight into the evolving EV-biology field and further expands our knowledge on understanding the molecular mechanism underlying the cause for neuronal damage during SIV/HIV-infection of the brain ., Previously , we determined that miR-21 is upregulated in SIVE and HIVE 11 ., Recent studies reported that certain miRNAs such as miR-21 , if present extracellulary or in extracellular vesicles ( EVs ) could trigger TLR signaling pathways by acting as a ligand leading to cell injury 25–27 ., Hence , we questioned whether miR-21 in association with EVs in SIVE neuropathology and whether this EV miR-21 ( EV-miR-21 ) causes neuronal damage ., EVs were isolated from SIVE and uninfected macaques brain regions using a sucrose gradient protocol 28 ., Transmission electron microscopy ( TEM ) was used to characterize the EVs ., The results revealed a size of ~100–150 nm with an appearance ( cup-like ) of vesicles that were previously described as exosomes ( Fig 1A , left ) ., Western blotting confirmed the presence of proteins associated with EVs: Flottilin , CD9 ., CD63 , CD81 , HSP70 and TSG101 ( Fig 1A , right ) ., Next , we extracted RNA from EVs , and small RNA sequencing was conducted ., The results revealed that miR-21 was significantly upregulated in EVs derived from the SIVE brain samples when compared to uninfected animals , as well as to SIV infected animals that did not have CNS disease ( Fig 1B and S1 Table ) ., Additionally , we also found two other miRNAs to increase at much lower levels of change and significance , miR-100-5p and miR-146-5p , and one miRNA to be decreased , miR-126-5p ., The change in expression of miR-21 was then validated by quantitative real time polymerase chain reaction ( qRT-PCR ) on the EV samples for miR-21 , revealing significantly elevated expression of miR-21 in SIVE samples ( Fig 1C ) ., Our initial studies found that in SIVE miR-21 is upregulated in neurons 11 ., Trans migration of cargo from EVs has been shown to enter neurons from non-neuronal cells such as macrophages , microglia and astrocytes 16 ., During HIV and SIV infection , macrophages infiltrate the brain , and activated macrophages as well as microglia and astrocytes are found ., In order to examine whether such non-neuronal cells in the brain express miR-21 during infection , we performed fluorescence in situ hybridization ( FISH ) coupled with immunofluorescent ( IF ) labeling on brain tissue sections of SIVE and uninfected macaques ., As a positive control , U6 , a noncoding snRNA , showed abundant signals in most cells in the tissue; as a negative control , a scrambled miRNA probe did not show any hybridization in these sections ., Interestingly , miR-21 signal was seen in CD163 positive macrophages/activated microglial cells and cells with the phenotypic appearance of neurons , whereas minimal signaling is seen in GFAP positive astrocytes ( Fig 2 , SIVE-Mag panel ) ., In uninfected controls , miR-21 expression was below the detection limit , although U6 could still be detected ( Fig 2 , Uninfected panels ) ., Therefore , it is possible that during infection macrophages could secrete EVs containing miR-21 that could then affect neurons ., Given the prime role of macrophages in neuropathogenesis of HIV/SIV and the presence of miR-21 in macrophages in the infected brain , we used macrophages as the cellular model for EV release in our experiments ., Recent studies have found that certain microRNAs containing a GU-rich sequence could activate TLR7 ., Neurotoxicity and neuronal and non-neuronal cell activation has been found with such free microRNAs and with synthetic EVs of lipid-encapsulated microRNAs 25–27 ., First , we asked if the presence of extracellular miR-21 could render neurotoxicity ., To do so , we used miRNA oligonucleotides ( oligos ) of wildtype miR-21 ( miR-21-WT ) , a mutant miR-21 ( miR-21-Mut ) containing a point mutation in one of the uridine residues in a small G/U sequence in the TLR binding motif ( U to G , since uridines are more crucial ligands to TLRs 29 ) ., Another characterized microRNA , the TLR7 ligand let-7b , was used as a positive control ., First , we added the free “naked” oligos directly to the hippocampal neuronal cultures ., Results indicated no significant cell death observed either in miR-21-WT , miR-21-Mut , or let-7b , assessed with NeuN counting or LDH assay ( Fig 3A , middle and right ) ., Next , we tested whether these microRNAs , when encased in EV-like vesicles , could have an effect on neurons ., Interestingly , when the neuronal cultures were treated with these synthetic EVs , significant neuronal cell death was observed with miR-21-WT and let-7b but not with miR-21-Mut , again demonstrated by both NeuN cell counting assay and LDH assay ( Fig 3B ) ., Staining with the neuronal marker MAP2 also revealed a loss in neurites ( Fig 3C ) ., In clear distinction to what we saw with free miR-21 , the delivery of miR-21 in EV-like vesicles is essential to elicit neurotoxicity ., To further examine whether EV-miR-21 activates the TLR7 pathway , we isolated EVs from bone marrow derived macrophage cultures prepared from wildtype ( WT ) and miR-21-/- mice and used these , differing in the presence of miR-21 , to examine potential neurotoxicity ( Fig 4A ) ., Indeed , there is a significant increase in neuronal cell death when cultures were treated with EVs derived from WT than from miR-21-/- macrophage cultures ( Fig 4B ) ., In order to examine if this neurotoxicity is dependent on TLR7 , we performed the neurotoxicity studies on neurons derived from TLR7-/- animals ., To confirm that TLR7 -/- neurons do not respond to ligands , we treated the hippocampal neurons isolated from WT and TLR7 -/- mice with TLR7 agonist CL075 ., Quantitative RT-PCR on confirms the expression of pro-inflammatory cytokine genes such as IL6 and TNFα only in WT neurons confirming that TLR7 -/- neurons did not respond to TLR7 ligand stimulation ( Fig 4C ) ., Treating the TLR7 -/- neurons with WT-EVs and miR-21-/- EVs demonstrated that toxicity depended not only on the presence of miR-21 in the EVs but also upon the presence of TLR7 in the neurons ( Fig 4D ) ., These results clearly indicate that both miR-21 and TLR7 are required for the activation of neurotoxic pathways ., Since miR-21 is increased in EVs from the brains of monkeys with SIVE , and EV associated miR-21 ( EV-miR-21 ) is associated with neurotoxicity , we then assessed whether EVs isolated from the SIVE ( SIVE-EV ) and uninfected ( control-EV ) brains would show differences in neurotoxicity ., Indeed , treatment of neuronal cultures with SIVE-EV significantly increased neuronal death as compared to control-EV ( Fig 5A ) ., Next , we asked if the TLR7 pathway is activated by EV-miR-21 ., Using HEK ( human embryonic kidney ) cell lines that expressed , or not , TLR7 , in addition to a reporter gene ( secreted alkaline phosphatase ) , we first examined the signaling of the EVs derived from SIVE brains ( as well as use of CL264 , a TLR7 agonist ) ., The results indicated a dose dependent signaling with TLR7 , which was not seen with EVs from uninfected brains ( Fig 5B and 5C ) ., In order to determine if the miR-21 induced TLR7 signaling , HEK-TLR7 cells were treated with EV-like vesicles ., Results indicate that miR-21 induced signaling but not the vehicle control or the miR-21 mutant ( miR-21-Mut ) ( Fig 6A ) ., We next examined if the cell death observed in the EV-miR-21 treated neuronal cultures occurs via apoptosis ., Since the trigger of apoptosis involves activation of the mitogen activated protein kinase ( MAPK ) signaling pathway , that transduces signals to the nuclear transcription factor NF-κB , we first looked at the expression of these proteins ., Western blot analysis revealed that none of the signaling proteins such as p-ERK1/2 , p-JNK and p-p38 changed by treatment with miR-21 WT EVs ( Fig 6B ) ., Next , we treated the hippocampal neurons with a pan-caspase inhibitor , z-VAD-fmk , which have been shown previously to the neurotoxicity resulting from let-7b treatment 25 ., However treatment of hippocampal neuronal cultures with z-VAD-fmk did not prevent neuronal cell death ( Fig 6C ) ., A caspase-independent form of programmed cell death , termed necroptosis , has been recently identified to play a role in disorders of the central nervous system and elsewhere 30 ., Necroptosis occurs through a signaling cascade dependent upon receptor interacting protein kinase-1 ( RIPK-1 ) ., To determine if necroptosis was involved in the neurotoxicity induced by miR-21 , we treated the cultures with necrostatin-1 , which specifically inhibits RIPK-1 ., Indeed the LDH assay results indicate that Nec-1 was able to prevent EV-miR-21 induced neurotoxicity in hippocampal neurons ( Fig 6D ) ., Hence the necroptotic , rather than apoptotic , pathway is active in EV-miR-21 induced neurotoxicity ., In this present study , we showed that EV-miR-21 could activate the TLR7 signaling pathway thus leading to neurotoxicity in SIV neuropathogenesis ., Through RNA sequencing on EVs isolated from control and SIVE brains , we found differences in several miRNAs , the most striking being miR-21 ., Previously , we showed that miR-21 is significantly increased in neurons ., Here , we significantly expand this to reveal the presence of miR-21 in brain EVs from macaques with SIV neuropathogenesis ., In the diseased brain , microglial/macrophages express miR-21; and in vitro , macrophage produces EVs containing miR-21 ., We found that miR-21 when associated with EVs exhibit neurotoxicity , and this neurotoxicity is dependent upon neuronal expression of TLR7 ., Furthermore , we also discovered that neurotoxicity by EV-miR-21 is not caused by an apoptotic mechanism but through the activation of a programmed necrotic pathway termed necroptosis ., Brain macrophages are the most likely source for EV-miR-21 , although we cannot exclude the possibility that neurons to secrete miR-21 associated EVs as well ., Several lines of evidence suggest that miR-21 is upregulated during inflammation in the brain 24 , 31 , 32 ., For the first time , we report that miR-21 is upregulated in EVs in the diseased brain and can activate TLR signaling in neurons during SIV infection ., TLR7 , similar to other TLRs , is a pattern recognition receptor , and plays a role in pathogen recognition as part of the innate immune system ., TLR7 is endosomally located and recognizes single stranded RNA ( ssRNA ) in mice and humans; TLR8 also recognizes ssRNA in humans ., TLR7 and TLR8 are related phylogenetically and functionally and have been identified as important sensors of ssRNA from the viral genomes of influenza and vesicular stomatitis virus as well as HIV itself 29 , 33 , 34 ., These sequences can specifically activate TLR7 in mice and TLR7/TLR8 in humans 33 ., A number of studies have revealed that several miRNAs , such as miR-21 , miR-29a and let-7b , can even serve as physiological ligands of the ssRNA-sensing 25–27 ., Ours is the first study so far that has tested this possibility in the context of SIV infection in the brain ., Through the repression of its targets , miR-21 was shown previously to act as both pro-apoptotic 35 and anti-apoptotic miRNA 36 ., Previously , we showed that miR-21 causes alterations in neuronal physiology by acting through its target gene MEF2C 11 ., Expanding upon its pathogenic actions , in this study we found that miR-21 is released via EVs and that it can directly activate neurotoxic signaling pathways by activating TLR7 receptors in the neuron ., Using in vitro constructed EVs , EVs from mouse macrophages , and EVs isolated from primate brains , we provide multiple lines of evidence revealing EV-miR-21 signaling through TLR7 , resulting in neuronal demise ., Previously , it was shown that “naked” let-7b synthetic oligonucleotide elicited neurotoxicity 25 , 27 ., However , in our cultures , we could not see significant neurotoxicity by naked let-7b ( Fig 3A ) ., Enclosing let-7b in DOTAP as an EV-like particle , however , resulted in neurotoxicity ., A recent study on the role of let-7b in activation of nociceptor dorsal root ganglion ( DRG ) neurons indicated that cell surface expression of TLR7 and another receptor ( TRPV ) were necessary for the effect 27 ., Hence , the localization of the TLR7 in the cells , and its interaction with other receptors , might be important for miRNA-mediated activation of signaling pathways such as neurotoxicity and the potential actions of free versus EV-miRNA ., Additionally , several other factors present in EVs were shown to mediate inflammatory responses and neurotoxic pathways , and EVs may contain proinflammatory mediators that could contribute to pathogenesis and progression of HAND 37–39 ., In neurodegenerative diseases such as Prion disease , Parkinson’s and Alzheimer’s , toxic factors such as prions , tau , amyloid β , α-synucleins , aggregates of superoxide dismutase 1 were shown to be present in EVs eliciting neurotoxicity 40 ., It is also unclear as to why miR-21 is localized to specific cell types in the brain , either through its production or its uptake from EVs ., Intriguingly , temporal differences in expression patterns have been detected in neurons and astrocytes after ischemic injury , where the miR-21 increase in neurons was much later when compared to astrocytes , which occurred 12 hr post injury 41 ., Given the more chronic nature of SIV infection , such temporal differences in the response could not be detected in our experiments ., To study pathways potentially activated upon treatment with EV-miR-21 leading to neurotoxicity , we first looked at changes in the phosphorylation of signaling proteins such as ERK , JNK and p-38 in the MAPK pathway ., The MAPKs are a family of kinases that transduce signals from the cell membrane to the nucleus in response to a wide range of stimuli , including stress ( reviewed in 42 ) ., Interestingly , we did not find any significant changes in the protein expression of signaling proteins belonging to the MAPK pathway ., MAPK activation is linked to apoptosis accompanied by caspase activation , in parallel with not finding activation of MAPK members treatment with a pan caspase inhibitor , z-VAD-fmk , did not rescue the neurons from undergoing death indicating that EV-miR-21 caused neurotoxicity by activating a different cell death pathway ., Intriguingly , a novel cell death pathway has been reported recently that causes cell death by a regulated necrosis , termed necroptosis 43–46 ., Death receptors 47 , interferons , toll-like receptors ( TLRs ) 48 , intracellular RNA and DNA sensors 49 , and probably other mediators induce this pathway ., Necroptosis is a programmed necrosis that requires a number of regulatory proteins and a key protein , RIPK1 ., RIPK1 has important kinase-dependent and scaffolding functions that inhibit or trigger necroptosis and apoptosis ., The development of the RIPK1 inhibitor Nec-1 has been a major breakthrough in research on necroptosis , and the first disease model in which the role of necroptosis was investigated was ischemic brain injury 50 ., Studies in several other disease models revealed that Nec-1 was able to prevent cell death in cells undergoing necroptosis 30 ., Hence , we tested to see if Nec-1 will be able to rescue neuronal death triggered by EV-miR-21 ., Indeed we observed that pretreatment with Nec-1 was able to prevent neurons from undergoing death ., Hence for the first time we report that a miRNA ( miR-21 ) in EVs could cause cell death through a necroptotic cell death pathway ., Further studies need to be conducted to ascertain the pathway components activated or involved in initiating necroptosis , and whether necroptosis inhibitors may be useful in vivo to lead to clinical studies ., In the era of combination antiretroviral therapy , HAND continues to be a common morbidity among individuals infected with HIV ., While the severity of the disease has decreased dramatically , it is still poorly understood as to why the milder forms of HAND are prevalent in HIV-1 infected individuals ., The inflammatory condition in the brain due to the continued viral presence is one possible explanation for CNS damage 51 ., It is interesting that a significant change in miR-21 levels was not seen in animals without CNS disease , which is in support with studies referring to miR-21 as a critical player in inflammation ., It was shown previously that miR-21 levels markedly increased during tissue injury and inflammation in the heart 52 , spinal cord 53 , neurons and astrocytes 41 , and in traumatic brain injury 54–58 ., Furthermore , it has been already shown that pro-inflammatory cytokine signaling , such as IL6 via the activation of STAT3 promoter , increases miR-21 59 ., In SIVE brains , there is a marked inflammatory cytokine response to the presence of the virus; and therefore , up regulation in miR-21 levels could be expected ., In summary , our study for the first time provides evidence of differences in EV derived miRNAs in CNS disease ., We found increased miR-21 expression in EVs derived from SIVE brains when compared to controls ., We also report for the first time that EV-miR-21 causes neurotoxicity by activating necroptosis , a novel cell death pathway ., The studies presented here are novel findings in neuroAIDS research , and the results implicate EVs as crucial communicators between various cells in the brain ., In the context of HIV infection , they are mediators of many neurotoxic factors , miR-21 , being one of them ., This study will further form a premise for therapeutic studies for prevention of long-term neuronal damage as seen in HAND ., Materials used in these studies were from animal work performed under Institutional Animal Care and Use Committee approval ( Protocol #: 08-034-07-FC and 11-032-05-FC ) from the University of Nebraska Medical Center ., Animal welfare was maintained by following the National Institutes of Health Guide for the Care and Use of Laboratory Animals ( National Research Council of the US National Academy of Sciences ) and US Department of Agriculture policies by trained veterinary staff and researchers under Association for Assessment and Accreditation of Laboratory Animal Care certification , insuring standards for housing , health care , nutrition , environmental enrichment and psychological well-being ., Primary enclosures consisted of stainless steel primate caging provided by a commercial vendor ., Animal body weights and cage dimensions were regularly monitored ., Overall dimensions of primary enclosures ( floor area and height ) met the specifications of The Guide for the Care and Use of Laboratory Animals , and the Animal Welfare Regulations ( AWR’s ) ., Light cycle was controlled at 12/12 hours daily ., All animals were fed standard monkey chow diet supplemented daily with fruit and vegetables and water ad libitum ., Social enrichment was delivered and overseen by veterinary staff and overall animal health was monitored daily ., Animals showing significant signs of weight loss , disease or distress were evaluated clinically and then provided dietary supplementation , analgesics and/or therapeutics as necessary ., These met or exceeded those set forth in the Guide for the Care and Use of Laboratory Animals from the National Research Council of the US National Academy of Sciences ., Archived tissue used in these studies was from animal ( Macaca mulatta ) studies performed under Institutional Animal Care and Use Committee approval from the University of Nebraska Medical Center ., Animal welfare was maintained by following the National Institutes of Health Guide for the Care and Use of Laboratory Animals ., All efforts were made to ameliorate suffering of the animals , including the use of anesthesia with ketamine , xylazine and phenobarbital at necropsy ., The following oligoribonucleotides were synthesized by Integrated DNA Technologies ( Coralville , IA , USA ) using all phosphorothioate linkages to protect from degradation , and methyl groups on the 5’ and 3’ nucleotides ., The changed base in miR21-mut ( U to G at position 20 ) is underlined ., All were used following HPLC purification ., miR21-WT: 5- UAG CUU AUC AGA CUG AUG UUG A -3; miR21-Mut: 5- UAG CUU AUC AGA CUG AUG UGG A -3; and let-7b , 5’- UGA GGU AGU AGG UUG UGU GGU U -3′ ., Necrotstatin-1 , a necroptosis inhibitor and z-VAD-fmk , pan-caspase inhibitor , were purchased from Enzo lifesciences ( Farmingdale , NY , USA ) ., miR-21−/− and Tlr7−/− mice were purchased from Jackson Laboratories ( Bar Harbor , Maine ) and bred in the UNMC animal facility ., Pregnant WT mice were purchased from Charles River ( Wilmington , MA , USA ) ., HEK-Blue TLR7 cells designed for studying the stimulation of TLR7 by monitoring the activation of NF-κB and AP-1 were cultured in DMEM supplemented with 10% FBS , normocin ( 50 μg/ml ) , blasticidin ( 10 μg/ml ) , zeocin ( 100 μg/ml ) ( InvivoGen , San Diego , CA ) ., Cells were grown at 37° C in humidified air with 5% CO2 ., Control HEK-Blue Null cells were cultured similarly except without zeocin ., Samples from SIV-infected rhesus monkeys that developed SIVE , and from uninfected control monkeys , were obtained from previous studies ., For animals used in this study , the infection was allowed to follow its natural course , and animals were euthanized when they showed signs of simian AIDS ., At necropsy , all animals were perfused with PBS containing 1 U/ml heparin to remove blood-borne cells from the brain , and samples were taken and stored at -80° C . Those in which pathological examination revealed multinucleated giant cells , microglial nodules and infiltration of macrophages into the brain were classified as having SIVE ., Samples from these animals , as well as uninfected control animals were prepared in a similar manner , were used for this study ., FISH and IF were performed as described previously 60 ., First , formalin-fixed paraffin-embedded sections were deparaffinized ., For combined FISH and IF , this was followed by antigen retrieval using 0 . 01 M citrate buffer and postfixation using 0 . 16 M l-ethyl-3- ( 3-dimethylaminopropyl ) carbodiimide ( EDC; Sigma-Aldrich , St . Louis , MO , USA ) to prevent loss of small RNAs ., The sections were incubated with hybridization buffer ( 50% formamide; 10 mM Tris-HCl , pH 8 . 0; 200 μg/ml yeast tRNA; 1× Denhardts solution; 600 mM NaCl; 0 . 25% SDS; 1 mM EDTA; and 10% dextran sulfate ) for 1 hr at 37° C in a humidified chamber for prehybridization ., They were then incubated overnight at 37° C with locked nucleic acid ( LNA ) -modified DNA probes , all labeled with digoxigenin at the 5′- and 3′-termini ( Exiqon , Woburn , MA , USA ) ., Probes were used at a concentration of 4 pmol of probe per 100 μl of hybridization buffer ., The sequences of the probes are; U6: CAC GAA TTT GCG TGT CAT CCT Y; miR-21: 5’- TCA ACA TCA GTC TGA TAA GCT A -3’; Scramble ( Scr ) 5’- GTG TAA CAC GTC TAT ACG CCC A -3’ ., Stringency washes were performed with 2× and 0 . 2× SSC ( Invitrogen , Carlsbad , CA , USA ) at 42° C . The hybridization and wash temperatures were optimized in preliminary experiments ., The sections were then blocked with a solution of 1% BSA , 3% normal goat serum in 1× PBS for 1 hr at room temperature , followed by incubation with anti-digoxigenin peroxidase antibody ( 1:100 in blocking buffer; Roche Applied Science , Mannheim , Germany ) overnight at 4° C . For combined FISH and IF , co-incubation with either anti-CD163 ( 1:100; Vector Labs , Burlingame , CA , USA ) or anti-glial fibrillary acidic protein ( GFAP; 1:2000; Dako , Glostrup , Denmark ) was performed at this step ., The following secondary antibodies were used: 568 donkey anti-rabbit and 488 goat anti-mouse IgG ( 1:400; Invitrogen ) ., This was followed by signal amplification using tyramide signal amplification Cy5 kit ( Perkin Elmer , Waltham , MA , USA ) according to the manufacturers protocol ., The slides were mounted in Prolong gold antifade reagent with DAPI ( Invitrogen ) ., The sections were imaged in Zeiss Observer . Z1 microscope equipped with a monochromatic Axiocam MRm camera using Axiovision 40 v . 4 . 8 . 0 . 0 software ( Carl Zeiss , Oberkochen , Germany ) ., The following colors were assigned to the fluorescent signals using the Axiovision software: Green for CD163 , Red for GFAP , Magenta for Cy5 , Blue for DAPI ., EV isolations from the brains were carried out as described previously with modifications 28 ., Previously , dissected and frozen macaque brain tissues ( weighing approximately 500 mg each ) were dissected and treated with 20 units/ml papain ( Worthington , Lakewood , NJ ) in Hibernate A solution ( 5 ml/hemi-brain; BrainBits , Springfield , IL , USA ) and rocked for 15 min at 37° C . The brain tissue was gently homogenized in 10 ml/brain of cold Hibernate A solution ., The brain homogenate was sequentially filtered through a 40 μm mesh filter ( BD Biosciences , San Jose , CA ) , a 5 μm filter ( Pall Corporation , Port Washington , NY ) and a 0 . 2 μm syringe filter ( Thermo Scientific , Waltham , MA ) ., EVs were isolated from the filtrate as described previously 15 , 28 ., Briefly , the filtrate was sequentially centrifuged at 300 × g for 10 min at 4° C , 2000 × g for 10 min at 4° C , and 10 , 000 × g for 30 min at 4° C to discard cells , membranes and debris ., The supernatant was centrifuged at 100 , 000 × g for 60 min at 4° C to pellet EVs ., The EV pellet was resuspended in 37 ml of cold PBS ( Thermo Scientific , Waltham , MA ) , and the EV solution was centrifuged at 100 , 000 × g for 60 min at 4° C . The washed EV pellet was resuspended in 2 mL of 0 . 95 M sucrose solution and inserted inside a sucrose step gradient column ( six 2 ml steps starting from 2 . 0 M sucrose down to 0 . 25 M sucrose in 0 . 35 M increments , with the 0 . 95 M sucrose step containing the EVs ) ., The sucrose step gradient was centrifuged at 200 , 000 × g for 16 hr at 4° C . A 1 ml fraction was collected from the top of the gradient and discarded , and 6 mL of the gradient were collected in the EV rich layers containing material with density higher than 1 . 07 ( 0 . 60 M sucrose layer ) and lower than 1 . 17 ( 1 . 30 M sucrose layer ) 28 ., Pooled fractions were diluted to 30 ml with cold PBS ., 25 ml of this volume was taken for RNA extraction , and 5 ml used for Western blot studies ., Sample volumes were brought up to appropriate volumes with cold PBS and centrifuged at 100 , 000 × g at 4° C for 60 min ., PBS was pipetted off both pellets ., Protein pellet was suspended in 50 to 100 μl of PBS depending on pellet size ., EV isolations from BMDM preparations were carried out by Exoquick ( SBI ) according to manufacturer’s instructions ., For transmission electron microscope ( TEM ) , a 10 μl drop of EV sample was placed on the grid ( 200 mesh copper grids coated with Formvar and silicon monoxide ) and allowed to sit for 2 min ., The excess solution was drawn off by filter paper , and the remaining thin film of sample was allowed to dry for 2 min ., A drop of NanoVan negative stain was placed on the grid for 1 min ., The excess negative stain was then drawn off by filter paper and allowed to dry for at least 1 min before being placed in the TEM ., Grids were examined on a Tecnai G2 Transmission Electron Microscope ( built by FEI , Hillsboro , Oregon , USA ) operated at 80Kv ., Small RNAseq was performed by LC Sciences ( Houston , TX , USA ) ., Using the RNA isolated from EVs , a small RNA library was generated using the Illumina Truseq Small RNA Preparation kit following the manufacturer’s guidelines ., The cDNA library was purified and used for cluster generation on Illumina’s Cluster Station and then sequenced on the Illumina GAIIx ( Ilumina , San Diego , CA ) ., Raw sequencing reads were obtained using Illumina’s Sequencing Control Studio software ( version 2 . 8 ) following real-time sequencing image analyses and base-calling by Illuminas Real-Time Analysis ( version 1 . 8 . 70 ) ., A pipeline script , ACGT101-miR v4 . 2 ( LC Sciences ) , was used for sequencing data analyses 61–63 ., Sequences were then mapped to miRbase ( version 20 . 0 ) 64 ., 636 unique sequences mapped to both Macaca mullata mirs in miRbase and the Macaca mullata genome ., Many of these had very low normalized counts ( median/mean for Control , SIV , and SIVE were 6 . 34/444 . 28 , 5 . 17/435 . 8 , and 6 . 04/554 . 50 respectively ) ; thus , only those with >500 counts in any one group ( comprising 75 mirs ) were chosen for statistical analyses ., To assess differences between the groups , normalized sequence counts were subjected to a Bayes-regularized one-way ANOVA using analysis conducted using the Cyber-T web server ( http://cybert . ics . uci . edu ) 65 , 66 ., The sliding window size was set at 101 , the Bayesian confidence value was 11 , and analysis was performed on the natural logarithm of the values ., Significant changes were assigned if the Bonferroni corrected p value was <0 . 05 ., For quantification of miRNA in EVs by qRT-PCR , TaqMan mature miR assays ( Applied Biosystems , Carlsbad , CA , USA ) were used according to the manufacturers protocol ., The relative amount of mi | Introduction, Results, Discussion, Materials and Methods | Recent studies have found that extracellular vesicles ( EVs ) play an important role in normal and disease processes ., In the present study , we isolated and characterized EVs from the brains of rhesus macaques , both with and without simian immunodeficiency virus ( SIV ) induced central nervous system ( CNS ) disease ., Small RNA sequencing revealed increased miR-21 levels in EVs from SIV encephalitic ( SIVE ) brains ., In situ hybridization revealed increased miR-21 expression in neurons and macrophage/microglial cells/nodules during SIV induced CNS disease ., In vitro culture of macrophages revealed that miR-21 is released into EVs and is neurotoxic when compared to EVs derived from miR-21-/- knockout animals ., A mutation of the sequence within miR-21 , predicted to bind TLR7 , eliminates this neurotoxicity ., Indeed miR-21 in EV activates TLR7 in a reporter cell line , and the neurotoxicity is dependent upon TLR7 , as neurons isolated from TLR7-/- knockout mice are protected from neurotoxicity ., Further , we show that EVs isolated from the brains of monkeys with SIV induced CNS disease activates TLR7 and were neurotoxic when compared to EVs from control animals ., Finally , we show that EV-miR-21 induced neurotoxicity was unaffected by apoptosis inhibition but could be prevented by a necroptosis inhibitor , necrostatin-1 , highlighting the actions of this pathway in a growing number of CNS disorders . | HIV associated neurocognitive disorder ( HAND ) are neurological disorders caused due to the entry of HIV infection in the brain ., HIV-1 does not directly infect central or peripheral neurons , however , virus-infected cells of the monocyte/macrophage lineage maintain a low-level HIV infection in the CNS ., Indirect effects of macrophage activation–such as dysregulation of cytokines and chemokines , free-radical ( oxidative stress ) injury , and secretion of soluble factors that are potently neurotoxic–have been implicated as effectors of nervous system injury in HIV ., Here , we report that extracellular vesicles released from macrophages can enhance neurotoxicity ., Using a nonhuman primate model of HAND , simian immunodeficiency virus encephalitis ( SIVE ) , we find that exosomes isolated from SIVE brains contain , microRNAs , including miR-21 , that can serve as ligands to the key immune regulatory receptors , toll-like receptors , and can elicit neurotoxicity ., We provide in vitro evidence for such an effect , and that the toxicity can be mediated by necroptosis ., Thus , our study provides insights into other potential neurotoxic mechanisms by which HIV infection in the brain could harm neuronal health . | null | null |
journal.pcbi.1005120 | 2,016 | Template-Based Modeling of Protein-RNA Interactions | About three quarters of the human genome could be transcribed into RNA , including 4 , 693 miRNAs 1 and 105 , 255 long noncoding RNAs 2 ., The function of most of these RNAs is unknown ., RNAs never act alone ., One hypothesis is that the long noncoding RNA are molecular scaffolds for protein binding 3 , 4 ., Several hundreds of novel RNA-binding proteins ( RBP ) were discovered by high-throughput sequencing 5 , 6 ., Protein-RNA complexes play an important role in gene regulation , mRNA degradation and many other biological processes ., High-throughput experimental techniques ( HITS-CLIP 7 , PAR-clip 8 , RIP-chip 9 ) and computational methods 10–18 have been developed to characterize protein-RNA interactome ., These methods identify and characterize protein-RNA interactions , but do not provide the structure of protein-RNA complexes , which is important for understanding the molecular function ., An increasing number of experimentally determined protein-RNA structures in PDB are still a fraction of all identified protein-RNA interactions , due to the inherent limitations of the experimental techniques ., Thus this gap has to be filled by computational approaches 19 ., The principles of protein-RNA interaction are based on structural and physicochemical complementarity 20–22 , and are similar to those of protein-protein interactions 23 ., Thus the fundamental paradigms of structure prediction should be similar as well: free docking , for protein-protein 23 and protein-RNA complexes 24–29 , and the template-based docking , for protein-protein 30 and protein-RNA complexes ( investigated in this report ) ., The accuracy of the template-based models is determined by the quality of the selected template , identified by sequence or structure alignment ., Whereas the template-based paradigm in protein-protein modeling has been extensively studied and systematically validated/benchmarked 31 , similar investigation of template-based approach to protein-RNA complex structure prediction is still lacking ( although the approach has been applied to predicting RNA binding sites on proteins in SPOT-Struct-RNA18 ) ., We performed such investigation on a representative set of protein-RNA complexes ., The analysis of all-to-all alignments in the set revealed a transition point between random and correct binding modes ., The results showed that structural alignment significantly outperforms sequence alignment in identifying good templates , suitable for generating protein-RNA complexes with the ligand RMSD from the native structure < 10 Å ., A template-based protein-RNA modeling protocol was developed and benchmarked on a representative set of complexes ., The study provides a way for protein-RNA structure modeling on a genome scale ., Co-crystallized protein-RNA structures were downloaded from PDB ( 1 , 619 complexes in 2014-05-13 release ) ., Structures with resolution better than 3 . 0 Å were retained ., Multimeric complexes were split into binary ones , defined as one protein chain and one RNA chain ., The minimal lengths of the protein and the RNA were 30 and 20 residues , respectively ., The interface was defined by < 5 Å distance between any heavy atom of the protein and any heavy atom of the RNA ., The minimal numbers of protein and RNA residues at the interface were 5 each ., This resulted in 2 , 951 binary complexes , including 563 RNA chains and 2 , 721 protein chains ., The RNA redundancy was removed by BLASTClust 32 with sequence identity cutoff 0 . 99 and coverage cutoff 0 . 99 ., The 563 RNA chains were grouped into 288 clusters ., The structure with the highest resolution in a cluster was designated as representative ., This resulted in 633 binary complexes , which still included some short identical RNAs due to limitation in the default word size for nucleotides in BLASTClust ., Thus CD-hit package 33 was used to further filter the RNA chains with sequence identity cutoff 0 . 99 ., Finally , 439 non-redundant binary complexes ( NRBC439 ) were kept for all-to-all alignment and benchmarking ., To determine the predictive power of our program , we split the NRBC439 set into two parts: 80% with an older deposit date were designated as the templates ( NRBC349 ) , and 20% with a newer deposit date were designated as targets ( bound set , NRBC90 ) ., The performance of the template-based and free docking was also tested on the protein-RNA docking benchmark set 34 ., To avoid modeling of targets on themselves , 26 complexes that were also part of the template set were excluded ., Since in our implementation the template-based protocol can deal only with single-chain proteins and RNAs , the benchmark set was restricted to complexes with single-chain monomers ., The length of the RNA chain was ≥ 10 nt according to the alignment procedure ( SARA 35 ) ., Although the minimal 20 nt length was used previously 35 , in our study successful models were generated with the ≥ 10 nt threshold ., The resulting set contained 49 complexes ( unbound set ) ., In NRBC439 set all-to-all pairwise alignment was performed by three approaches ., The first approach was local sequence alignment by fasta35 with default parameters 36 ., Sequence identity of a complex was defined as the smaller sequence identity of the two monomers ., The coverage of the complexes alignment was defined as the lowest coverage of the four chains in the two aligned complexes ., The second approach was global sequence alignment by needle in the EMBOSS package 37 , also with the default parameters ., The complex sequence identity and the coverage were defined as in the first approach ., The third approach was structural alignment ., For the structure alignment of RNA we chose SARA 35 , based on the reported performance characteristics 38 and availability ., A newer version , SARA-coffee , is a structure-based multiple RNA aligner , which integrates SARA with R-coffee framework ., For pairwise alignment , used in our study , the results of SARA-coffee and SARA are the same ., For the structure alignment of proteins , we used TM-align 39 , following our previous studies of protein-protein complexes 31 , 40–44 ., The output of TM-align is TM-score , which varies from 0 for completely dissimilar structures , to 1 for identical structures ., The output of SARA is a score , which depends on the RNA size ., To establish a similar description of structural similarity of proteins and RNAs , the SARA score was normalized by the score value of the RNA aligned to itself , resulting in the score interval 0–1 , similar to the protein alignment ., As with the complex sequence identity , the complex structural score was defined as the minimum of TM-score and the normalized SARA score ., The aligned atoms ( Cα in protein and C3 in RNA ) were used to calculate interaction RMSD ( IRMSD ) similarly to the one proposed for protein-protein complexes 45 , which numerically characterizes binding mode similarity of complexes of different monomers ., It was shown previously to correlate well with the traditional ligand and interface RMSDs for complexes of same monomers in different binding modes ( cannot be applied to the complexes of different monomers ) 31 ., The three alignment approaches were applied to NRBC439 to test the ability to detect a good template ., Binary complexes in NRBC90 were queries for the template set NRBC349 ., After a template was selected , the target protein was superimposed on the template protein by TM-align and the transformation matrix was saved ., The target RNA was superimposed on the template RNA by SARA ., Since SARA does not output the transformation matrix , it was reproduced by superimposing the RNA from SARAs output onto the original query RNA ., The ligand RMSD ( RMSD of RNA C3 atoms ) between the model and the native structure was calculated ., The quality of the model was measured by the ligand RMSD ., In protein-RNA docking , a prediction was defined as acceptable 28 ( elsewhere called native-like 26 , 29 ) for the ligand RMSD ≤ 10 Å from the native structure of the complex , and a more accurate medium for the ligand RMSD ≤ 5 Å ., These definitions correlate with the ones in protein-protein docking field 46 , and the corresponding docking models are generally considered within the intermolecular energy funnel 47 and thus subject to refinement by local optimization ., A previous study on template-based protein-protein docking determined strong dependence of the binding mode similarity on the structural similarity of the participating proteins , with the phase transition from dissimilar modes to the similar ones at TMm = 0 . 4 31 ., In the current study we asked a question: do protein-RNA complexes behave in a similar way ?, We performed all-to-all pairwise comparison of protein-RNA binary complexes in NRBC439 set ., The similarity of the monomers was measured by the sequence alignment ( fasta35 and needle for local and global alignment , correspondingly ) and by the structure alignments ., Fig 1 shows the results of such comparison for local and global sequence alignments ., For the local alignment , the 0 . 3 coverage threshold is used ., The 0 . 3 value was the optimal , minimizing the noise from the lower threshold alignments ( results with no threshold for the coverage were largely random ) , while retaining 420 of 438 binary complexes for the analysis ., The dip in cumulative fractions near 0 . 8 threshold value may be random , due to low sampling at this data range ., One can also speculate that some of RBPs may have close homologs , with sequence ID near this value , whereas recent analysis showed that most RBPs are more diverse 48 ., As the figure shows , the transition to similar binding modes occurs near the complex sequence identity 0 . 3 ., The results of such comparison obtained by the structure alignment approach are shown in Fig 2a ., The transition point on the alignment distributions was used as a cutoff for selecting good templates ., S1 Fig shows that the success rate of detecting templates begins to decrease near the transition point ( complex structural score 0 . 45 ) ., To distinguish the role of the protein in detecting a good template for a protein-RNA complex , the target/template similarity was also measured only for the protein component ( Fig 2b ) ., This distribution is similar to the one in Fig 2a , indicating an important role of the protein ., However , the role of the RNA is evident at the higher end of the structural similarity ( > 0 . 7 ) , where it eliminates multiple alternative binding modes ., Thus the similarities of both protein and RNA are needed for an accurate identification of a good template for the complex ., Overall , correlation of the protein-RNA structural similarity with the binding mode is weaker than that of the protein-protein complexes 31 because of the greater RNA flexibility 49 , 50 ., Structural similarity vs . sequence identity of the protein-RNA complexes is plotted in Fig, 3 . The plot is divided into four areas by the lines x = 0 . 45 ( transition point for structural similarity ) , and y = 0 . 25 ( transition point for sequence similarity ) ., The correlation of structure and sequence similarity in protein-RNA is similar to that in protein-protein complexes 31 ., The structure and sequence are dissimilar in the lower left quadrant , which contains 98 . 4% of the alignments ., This points to the diversity of sequences and structures in NRBC439 set ( supported by observation that 1 , 542 RBPs formed 1 , 111 families in human RBPome 48 ) ., The upper right quadrant contains 1 . 02% of the alignments , and 69 . 53% of those with the structural score ≥ 0 . 45 , where structure and sequence are similar , suggesting that both approaches can find a good template ., The alignments with similar structure and dissimilar sequence are in the lower right quadrant , containing 0 . 45% of alignments , and 30 . 47% of those with the structural score ≥ 0 . 45 ., This suggests that structural alignment approach could find good templates for about 1/3 of cases when sequence alignment cannot ., Last , the top left quadrant shows similarity detected by the sequence , but not the structural alignment ., It is almost empty , which means that the structural alignment finds most templates detectable from the sequence ., A structure alignment-based docking was implemented in a procedure PRIME ( Protein-RNA Interaction ModEling ) ., Fig 4 shows the outline of the approach ., Docking was systematically benchmarked on NRBC90 targets using NRBC349 templates ., For each target docking models were generated by PRIME , ranked separately by the complex structural score and by the TM-score ., The success rates of different approaches are shown in Fig 5 ., The success rate for predicting acceptable model almost reaches the highest value after top, 4 . This suggests that for the docking , the complex structural score , which accounts for both TM-score for proteins and SARA score for RNA , is better than just the TM-score for proteins in top 1 , top 2 , and top, 3 . The TM-score outperformed or tied with the complex structural score when considering more top models ., The TM-score detected the template for three complexes , for which the complex structural score could not ., The reason was that when the normalized SARA score was counted in , the complex structural score decreased below the cutoff ( score values 0 . 37 , 0 . 016 , and 0 . 15 ) ., The improvement of the success rates for top 1 , top 2 , and top 3 predictions with the complex structural score was largely due to the reduction of noise after the transition point in Fig 2 ( by moving it to the left of the transition point ) ., For example , the alignment of the target complex 3umy , chains A and B , and the template complex 2hw8 , chains A and B , had IRMSD = 28 . 04 Å , but the TM-score 0 . 90 , ranked 2 by the TM-score alone ., At the same time , the corresponding complex structural score is 0 . 29 , ranked 51 , moving the complex to the left of the transition point , and thus reducing the noise for the high scored complexes ., Fig 6 shows the distribution of the best models according to ligand RMSD ., The distribution is bimodal , pointing to the existence of alternative binding modes , similar to protein-protein complexes 31 ., The high-quality predictions ( 0–2 Å ) correspond to 30 targets ( 33% ) ., Benchmarking of PRIME suggests that 65% of target models can be built successfully ( structural score-10 . 0 for top 10 predictions in Fig 5 ) ., Ranked by the complex structural score , most models with acceptable quality are ranked at top, 4 . Similar to protein-protein modeling , the template-based protein-RNA docking has a clear advantage over the free docking method , where scoring functions typically are struggling to pick the correct model from the multitude of docking poses 29 ., The template-based method of course cannot be applied when a template is not found , in which case the free docking should be used ., In our benchmark , templates were detected for 69 out of 90 targets ., Fig 7 shows an example of the target with low protein sequence identity to the template , successfully modeled by the structure alignment ., Still , structure similarity does not guarantee correct predictions ., The alternative binding modes were observed in nine targets with high structural similarity to the templates ., Although the complex structural scores of their alignment to the templates were larger than the transition point , the ligand RMSD of the models built on these templates were > 10 Å ., For example , the TM-score , normalized SARA score and the complex structural score between the target 4lgt , chains A and E , and the template 2i82 , chains A and E , were 0 . 543 , 0 . 524 and 0 . 524 , respectively ., However , the binding mode is different , with the model/native ligand RMSD 22 . 45 Å ., To compare the performance of template-base and free docking method , we tested template-based PRIME and free docking RPDock on the unbound set ( see Methods ) ., RPDock 29 is a protein-RNA rigid docking protocol , which takes into account protein/RNA geometric and electrostatic complementarity , and stacking interaction in the base of nucleotides with the aromatic rings of charged amino acids ., All PRIME models were ranked by the complex structural score , and RPDock models were ranked by DECR-RP 29 ., Fig 8 shows the docking results ., Success rate is defined the number of those with at least one acceptable model divided by the total number of targets ., The results show that the success rate of the template-based protein-RNA docking is significantly higher than that of the free docking , similarly to the previous results in protein-protein docking 41 ( although a broader assessment of the protein-protein category is still on-going 51 , 52 ) ., The detailed data on benchmarking ( S1 Table ) indicates that the template-based approach significantly outperforms free docking , successfully predicting complexes where the free docking fails , including cases of larger bound/unbound RMSD ( see S2 Table , and an example of a successful template-based prediction of a complex with a significant conformational change on the protein component in S3 Fig ) ., PRIME also runs ~ 5 times faster than RPDock ( S2 Fig ) , which is especially important for genome-scale studies ., PRIME currently does not include a refinement protocol , which is still a challenging task in macromolecular docking 46 ., The development of a dedicated refinement protocol is in our future plans ., However , even a standard minimization by GROMACS ( v5 . 0 . 7 ) 53with AMBER99 force field reduced the number of clashes in most complexes ( S4 Fig ) ., Sequence and structure alignment approaches were compared in template-based modeling of protein-RNA complexes ., All-to-all alignment of protein-RNA complexes detected a phase transition from random to similar binding modes , according to the degree of monomers similarity ., The structure alignment showed to be significantly better than the sequence alignment in identifying correct templates ., In systematic benchmarking , structure alignment-based docking had far better success rate than the free docking , successfully predicting complexes where the free docking failed , including interactions with significant conformational change upon binding ., The findings are qualitatively similar to those observed earlier in structural modeling of protein-protein complexes 31 ., Applicability of the prediction protocols to complexes of modeled monomers , rather than to experimentally determined structures of monomers , which typically have higher accuracy than models , was previously established for protein-protein interactions in systematic benchmarking studies on specifically designed sets of protein models54 , 55 ., Similar studies are needed to determine such applicability to modeled RNAs 56 ., The structure alignment-based approach for protein-RNA modeling is implemented in PRIME software , publicly available at http://rnabinding . com/PRIME . html . | Introduction, Methods, Results and Discussion | Protein-RNA complexes formed by specific recognition between RNA and RNA-binding proteins play an important role in biological processes ., More than a thousand of such proteins in human are curated and many novel RNA-binding proteins are to be discovered ., Due to limitations of experimental approaches , computational techniques are needed for characterization of protein-RNA interactions ., Although much progress has been made , adequate methodologies reliably providing atomic resolution structural details are still lacking ., Although protein-RNA free docking approaches proved to be useful , in general , the template-based approaches provide higher quality of predictions ., Templates are key to building a high quality model ., Sequence/structure relationships were studied based on a representative set of binary protein-RNA complexes from PDB ., Several approaches were tested for pairwise target/template alignment ., The analysis revealed a transition point between random and correct binding modes ., The results showed that structural alignment is better than sequence alignment in identifying good templates , suitable for generating protein-RNA complexes close to the native structure , and outperforms free docking , successfully predicting complexes where the free docking fails , including cases of significant conformational change upon binding ., A template-based protein-RNA interaction modeling protocol PRIME was developed and benchmarked on a representative set of complexes . | Structures of protein-RNA complexes are important for characterization of biological processes ., The number of experimentally determined protein-RNA complexes is limited ., Thus modeling of these complexes is important ., Reliable structural predictions of proteins and their complexes are provided by comparative modeling , which takes advantage of similar complexes with experimentally determined structures ., Thus , in the case of protein-RNA complexes , it is important to determine if similar proteins and RNAs bind in a similar way ., We show that , similarly to the earlier published results on protein-protein complexes , such correlation of the protein-RNA binding mode and the monomers similarity indeed exists , and is stronger when the similarity is determined by structure rather than sequence alignment ., The data shows clear transition from random to similar binding mode with the increase of the structural similarity of the monomers ., On the basis of the results we designed and implemented a predictive tool , which should be useful for the biological community interested in modeling of protein-RNA interactions . | sequencing techniques, protein interactions, protein structure prediction, protein structure, molecular biology techniques, rna alignment, research and analysis methods, sequence analysis, rna structure, protein structure determination, sequence alignment, proteins, molecular biology, protein structure comparison, biochemistry, rna, nucleic acids, biology and life sciences, macromolecular structure analysis | null |
journal.pcbi.1006121 | 2,018 | Identifying robust hysteresis in networks | In cell biology , the power of a network model as an organizational principle of complex regulation rests on the premise that there is a predictive relationship between the network structure and the network dynamics 1–4 ., A network model only requires specifying the character of the interactions between genes , proteins and signaling molecules , which can be inferred with relative ease compared to the parameters governing these interactions ., If the premise of a predictive relationship holds , then the network approach to complex regulation is highly advantageous , since the phenotype of the cell encoded in its dynamics can be deduced only from the interaction data ., The firm bridge between network structure and the dynamics of the corresponding nonlinear system remains elusive for the fundamental reason that it cannot exist in the suggested generality ., The dynamics will always depend on the state of the cell , which in the models is represented by the parameters and initial data ., Some partial results in terms of motif theory have been suggested 1 , but these are limited to small networks and their applicability to the dynamics of larger networks is questionable 5 , 6 ., Furthermore , there is currently no mathematical theory that suggests that understanding of dynamics of a small motif that is embedded in a larger network informs our knowledge of the dynamics of the larger network ., In fact , the classical theory of dynamical systems lacks tools that describe dynamics when parameters are unmeasured , or , if measured , carry large uncertainty ., In this paper we report on a new approach 7–9 referred to as Dynamic Signatures Generated by Regulatory Networks ( DSGRN ) that provides a queryable global characterization of dynamics over large regions of parameter space ., This is based on a new , still developing , computationally efficient perspective of nonlinear dynamics 10–12 ., The philosophy of this approach has already seen applications in other settings 13–16 ., Novel features of DSGRN include the following:, ( i ) DSGRN does not use an explicit functional form for the nonlinearities governing the dynamics ,, ( ii ) the decomposition of parameter space reflects the representation of the nonlinear dynamics , and, ( iii ) the decomposition of parameter space is determined by information local to each node of the regulatory network , and this local determination is computed a priori ., For the sake of clarity we discuss DSGRN in the specific , but important biological context of resettable bistability and hysteresis , especially as they relate to cell cycle restriction point dynamics ., A key decision for each cell is when to replicate DNA and initiate proliferation ., This decision is based on multiple factors , but once the process has started DNA replication must be finished ., Therefore the influence of these factors must be uncoupled at the moment of the decision , called the restriction point of the cell cycle 17–19 ., The requirement of irreversibility and decoupling suggest that phenotypically a bistable switch may underlie the restriction point ., The simplest model of a bistable switch involves a hysteresis curve as indicated in Fig 1, ( a ) where the curve indicates the equilibria for a differential equation x ˙ = f ( x , λ ) and λ is a parameter ., Parameter space naturally divides into three intervals , low λ and high λ for which there exists a single stable fixed point denoted Off and On , respectively , and medium values of λ for which there exist two stable fixed points ( B ) ., Assume the system is in the On state ., In the setting of Fig 1, ( a ) or 1, ( b ) if the value of λ is decreased by a sufficient amount ( beyond the left hash mark ) then the internal dynamics of the system will drive it to the Off state ., This is not the case in the setting of Fig 1, ( c ) ., Observe that the global structure indicated in Fig 1, ( a ) allows for the occurence of hysteresis , i . e . the ability to repeatedly reset the system from On to Off and from Off to On by changing the value of λ and a region of parameter space , the medium values of λ , at which the direction of of the change in λ ( increasing or decreasing ) determines whether the system is in the on or off state ., While this simple model of a bistable switch provides intuition for the analysis performed in this paper , experimental data leads us to entertain the possibility that the dynamics of switches in biological systems may be more complex ., For example , the lac operon is among the most carefully studied regulatory networks that exhibits bistability ., Associated experimental data 20 , Fig 2b leads to a blurred version ( with measurement on the vertical axis presented using a logarithmic scale ) of the simple single valued curve of Fig 1 ., With this in mind , the hysteresis phenomenon detected by DSGRN consists of identifying well defined regions that contain the attractors associated with Off and On states ., As is made clear in the Materials and Methods section , whether these attractors are stable fixed points or not depends on details of the particular differential equation used in the model ., A network that may be responsible for the restriction point dynamics in mammalian cells was suggested by 21 and then further elaborated by Yao et al . 4 ., The essential elements of the restriction point network is a family of E2F transcription factors which are sequestered in a heterodimer by Rb in non-proliferating cells in G1 phase ., Release of E2F by phosphorylation of Rb results in initiation of S phase of the cell cycle ., The principal controls of Rb are cyclin/kinase complexes CycD/Cdk4 , 6 and CycE/Cdk2 ., CycD/Cdk4 , 6 is up-regulated by Myc which responds to the cell growth; the initial phosphorylation of Rb by CycD/Cdk4 , 6 releases E2F , which up-regulates the second kinase CycE/Cdk2 , which then completes the phosphorylation of Rb and finishes the release of E2F 4 , 17–19 , 21 ., Since one of the hallmarks of cancer is sustained proliferation in cells that are immune to external signals that would prevent proliferation in normal cells , it is not surprising that dysregulation of this network is observed in the majority of cancers 22 ., For recent comprehensive reviews on the connection between retinoblastoma protein ( Rb ) , a key member of this network , and cancer , see 23–26 ) ., This system exhibits resettable bistability 4 if , as the growth factor input is reduced to zero , bistability vanishes and the cell returns to a non-proliferating phenotype with E2F sequestered , e . g . in Fig 1 as λ is reduced the system moves from bistability to the monostable state Off ., Observe that resettable bistability , and hysteresis are physiological phenomena that can only be expressed via an understanding of global dynamics over paths in parameter space ., Yao et . al . 4 executed a modeling study of the mammalian cell cycle restriction point with the goal of identifying “the basic gene circuit underlying resettable Rb-E2F bistable switch by the criterion of robustness” where robustness is defined in terms of the ability to maintain functionality against perturbations ., Note that even an idealized description of the restriction point network has multiple variables and a multitude of parameters ., Thus , from the mathematical perspective to rigorously carry out the program proposed in 4 requires mathematical and efficient computational techniques capable of addressing at least three fundamental challenges: The aim of this paper is to demonstrate that DSGRN is capable of meeting these challenges for moderate sized networks ., As is discussed in detail below , DSGRN provides information about the global dynamics for all parameter values , and to the best of our knowledge , is unique in these capabilities ., There are similarities between DSGRN and a variety of other approaches ., To the best of our knowledge the novel aspects of DSGRN are that we ( 1 ) approximate continuous system by a discrete system ( the state transition graph ) and ( 2 ) via our computations we obtain knowledge about the global dynamics for all parameters associated with the model ., Other approaches , for instance CPSS ( Continuous parameter space search ) 27 chose a particular nonlinearity which in turn determines the parameter space ., A query based on existence of stable equilibria is established , regions of parameter space are non-uniformly sampled , and the differential equation is integrated at the chosen parameter values to identify existence or lack of existence of equilibria ., In contrast DSGRN searches for attracting regions ( a more robust concept than stable equilibria ) and thus the set of nonlinearities for which the computations are provably valid is much larger 7 ., Furthermore , there is no sampling of parameter space , instead the dynamics is reported for all possible parameter values ., We view DSGRN as an algorithm; the input is a regulatory network and the output is a queryable database of the global dynamics for all parameters ., This algorithm is based on four essential concepts: The DSGRN database consists of the parameter graph along with an association of a valid Morse graph to each node of the parameter graph ., Before turning to the Rb-E2F network we use the toggle switch , which consists of two constitutively expressed repressors that repress each other , in an attempt to focus on the general philosophy and novel concepts associated with DSGRN ., For more detailed descriptions see the Methods section and 8 ., The regulatory network of the toggle switch has the form of Fig 2, ( a ) ., DSGRN requires that a regulatory network be a network for which each node has at least one outgoing edge and there is at most one edge from one node to another node ., To each node n in regulatory network , DSGRN associates a real value , e . g . , concentration , xn ≥ 0 and a parameter γn > 0 representing the rate of degradation of xn ., To each edge m → n ( denoting activation ) or m ⊣ n ( denoting repression ) in a regulatory network , DSGRN assigns three parameters ℓn , m , un , m and θn , m where ℓn , m and un , m represent low and high levels of growth of xn , respectively , ( in particular ℓn , m < un , m ) that are determined by the value of xm relative to the threshold θn , m ., Observe that for a regulatory network with N nodes and E edges the dimension of the space of parameters is D = N + 3E and is a subset of 0 , ∞ ) D ., To construct a state transition graph DSGRN uses the thresholds θn , m to decompose the phase space into rectangular regions ( see Fig 2, ( b ) ) ., DSGRN assigns a vertex to each region ( solid dot ) and to each face between regions ( circle ) ., Furthermore , each solid dot is labeled as a vector where the n-th entry indicates the number of thresholds θm , n with values less than xn for any xn in the associated region ., For each fixed set of parameter values the state transition graph represents the dynamics of the regulatory network via a directed graph based on the above mentioned vertices ., As is shown in Fig 2, ( c ) the edges go from circles to solid dots , from solid dots to circles , and potentially there are self edges on solid dots ., Fig 2, ( c ) shows that the edges in a state transition graph are parameter dependent ., Since the size of the state transition graph grows rapidly with the size of the regulatory network , DSGRN stores a minimal representation of the global dynamics using a Morse graph , which is an acyclic directed graph ( see Fig 2, ( d ) ) ., Morse graphs are capable of encoding potentially complicated dynamics ( see 16 ) , however , since the focus of this paper is on robust switch behavior , we restrict our discussion accordingly ., In the state transition graph the most natural representative of a stable fixed point is a vertex with a unique out edge that is a self edge ., In the associated Morse graph this is a terminal vertex labeled FP ., Furthermore , since only solid dots have self edges , we identify each such terminal vertex by the vector label of the associated solid dot ., The directed edges in the state transition graph are determined by affine multilinear inequalities involving the parameters ., These inequalities are defined at each node in the regulatory network RN according to the out edges , in edges , and the logic governing the interaction of in edges ., For each node n in the regulatory network the inequalities are organized via a node-graph PG ( n ) where edges indicate change of a single inequality ., For the toggle switch the node-graphs and their associated inequalities are, P G ( 1 ) : u 12 < γ 1 θ 21 − l 12 < γ 1 θ 21 < u 12 − γ 1 θ 21 < l 12 , P G ( 2 ) : u 21 < γ 1 θ 12 − l 21 < γ 1 θ 12 < u 21 − γ 2 θ 12 < l 21 ., A node in PG ( n ) is called low ( high ) if all the associated ℓ ( u ) values are less ( greater ) than all the associated γθ values ., The remaining nodes are called intermediate nodes ., The parameter graph for a regulatory network is the product of the node-graphs , i . e . P G = ∏ n = 1 N P G ( n ) ., A graphical representation of the parameter graph of the toggle switch is given in Fig 2, ( d ) ., Observe that the inequalities associated with each node provides a subdivision of parameter space ., For the toggle switch the parameter graph provides a representation of parameter space , which is an unbounded region of ( 0 , ∞ ) 8 , via nine regions ., Furthermore , it is guaranteed that for every parameter in a given region the associated state transition graph is the same , and therefore , the Morse graph is constant over each region ., Because the parameter graph is a product graph , if we fix a node n in the regulatory network , then we can decompose the parameter graph into a collection of subgraphs each one of which is isomorphic to PG ( n ) ., We denote this collection of subgraphs by PG ( ¬n ) ., As shown in Fig 2, ( d ) , for the toggle switch , PG ( ¬1 ) = {G1 , G2 , G3} ., The fact that any G ∈ PG ( ¬n ) is isomorphic to PG ( n ) implies that a path in PG ( n ) defines a path in G . We call the path in G the lift of the path in PG ( n ) ., We now describe implemented queries to the DSGRN database that are relevant to the analysis of switching behavior ., A Morse graph exhibits ( p , q ) bistability if it contains terminal nodes FP = p and FP = q ., Fix a node n in the regulatory network and let G be a element of PG ( ¬n ) ., The subgraph G exhibits: We demonstrate the biological relevance of DSGRN on the synthetic toggle switch implemented in Gardner et . al . 28 using Lac repressor lacI and a temperature sensitive phage λ repressor ( cIts ) , with externally supplied IPTG as a control ., Since IPTG binds directly to the Lac repressor and inactivates it , IPTG effectively lowers the available concentration of the Lac repressor ., Let x1 represent the concentration of cIts and x2 represent the concentration of lacI ., The state where cIts ( x1 ) is fully expressed and lacI ( x2 ) is repressed is designated as an ON state , and the state where x1 is low and x2 is high is designated as an OFF state ., In the DSGRN database ( Fig 2, ( d ) ) FP ( 1 , 0 ) and FP ( 0 , 1 ) correspond to ON and OFF , respectively ., Increase in the concentration of IPTG decreases the values of ℓ12 and u12 and thus is quantified by a path in PG ( 1 ) from the high node to the low node ., Visual inspection of Fig 2, ( d ) , shows that G2 is the only subgraph in PG ( ¬1 ) that exhibits ( ON , OFF ) bistability ., Furthermore , moving monotonically along the path defined by G2 from node 4 to node 6 results in hysteresis between ON and OFF states ., Thus the DSGRN database analysis suggests that if the toggle switch is operating at the bistable regime ( node 5 ) , then sufficiently strong IPTG treatment phenotype leads to FP ( 1 , 0 ) , which represents the ON state ., This agrees with the experimental observations 28 ., In the parameter graph there are parameter nodes , called inessential parameter nodes , for which at the associated parameter values there is a node n in the regulatory network such that the inequalities that determine the state transition graph do not vary as a function of xn ., At inessential parameter nodes the network dynamics is identical to that of a subnetwork of the regulatory network ., In the computations presented in the remainder of the paper we only consider essential subgraphs EPG ( ¬n ) ⊂ PG ( ¬n ) , i . e . subgraphs of the parameter graph that do not contain any inessential parameter nodes ., The E2F-Rb network regulates the restriction point of the mammalian cell cycle , i . e . the point where the progression through the cell cycle decouples from the growth signals 4 , 17–19 ., The E2F-Rb network exhibits two essential phenotypes: when the growth signal is absent , the transcription factor E2F is sequestered in the heterodimer with Rb ., This is the quiescent state ( QS ) ., On the other hand , when the growth signal is present at high level , E2F disassociates from the E2F-Rb dimer and activates numerous downstream processes ., This proliferative state ( PS ) initiates entry into S-phase of the cell cycle ., Yao et . al . 4 executed a modeling study of the mammalian cell cycle restriction point with the goal of identifying “the basic gene circuit underlying resettable Rb-E2F bistable switch by the criterion of robustness” where robustness is defined in terms of the ability to maintain functionality against perturbations ., In particular , they start with a large network from 17 , 18 that , as is indicated in Fig 3, ( a ) , they coarse-grain into a system with three nodes and a variety of possible edges ., By considering connected subnetworks of Fig 3, ( a ) they construct a library of 768 mathematical models of networks ., They assume that interactions between the nodes are governed by Hill-functions , thus producing a model consisting of a three dimensional system of ordinary differential equation model with up to 26 parameters ., To evaluate the models they generate 20 , 000 parameter sets by randomly sampling from reasonable parameter ranges for each of the 26 parameters ., Each of the 768 networks is given a score based on the percentage of this collection of parameters at which the differential equations exhibits a particular switching characteristic like resettable bistability or hysteresis ., There are several mathematical objections that can be made to this procedure ., First , if one were to generate the random parameters by insisting on at least two independent choices for each parameter , then one would need to consider 226 ≈ 7 × 107 parameter sets ., It is insufficient to sample dynamics at distinct parameter values as a proxy for its prevalence without a priori bounds on the sensitivity of this dynamics to changes in the parameters ., Second , it is not clear how well a Hill function approximates the nonlinear behavior of the system ., Third , as indicated in the introduction , the phenomena of resettable bistability and hysteresis are both a function of a continuous change of parameters ., Therefore it is insufficient to sample dynamics at distinct values of the input variable S as a proxy for presence of these phenomena ., We now show that DSGRN can efficiently replicate the efforts of 4 ., We begin with a discussion of how it avoids the above mentioned mathematical concerns ., First , DSGRN allows one to identify any point in parameter space with a node in the associated parameter graph; in turn , each node in the parameter graph is identified with a region of parameter space for which the dynamics can be described via a Morse graph ., Therefore , with DSGRN one does not restrict the analysis to finite collections of parameters; the analysis is valid for all parameter values ., Second , the DSGRN analysis is based on representing dynamics via state transition graphs and , for the purposes of this paper , interpreting the dynamics via terminal nodes in the Morse graph ., The terminal nodes represent regions in phase space that are trapping regions for broad classes of nonlinearities 7 ., Hence , with DSGRN one is not restricted to a specific analytic representation of the dynamics ., Third , as is demonstrated in the toggle switch example , paths through the parameter graph represent continuous paths through parameter space , thus questions of resettable bistability or hysteresis can be rigorously addressed ., In addition , because there are finitely many lifts of parameter paths in the parameter graph we can quantify the number of lifts for which the desired switching phenomenon does occur ., There are 49 regulatory subnetworks of Fig 3, ( a ) , such that every node has at least one out-edge , and there is no more than one edge from one node to another ., For each of these subnetworks we compute EPG ( ¬MD ) , where node MD is singled out since the input signal S impacts the network at the node MD ., Varying the input of the network corresponds to a path in EPG ( ¬MD ) ., We assume that a monotone change in the strength of the signal S acts monotonically on MD , but we do not necessarily assume that we know the range of S . This leads us to consider two cases ., In the first case we assume that the network parameters are “aligned” with the range of the signal ., In this case as S ranges from its lowest value to its highest value , MD moves from lowest quantifiable level , i . e . being below all the thresholds associated with MD , to the highest quantifiable level , i . e . being above all the thresholds associated with MD ., In terms of paths in EPG ( ¬MD ) , this is associated with a full path , that is , any path that starts at the parameter node where all outputs of the node MD are below all thresholds of the nodes MD connects to , and finishes at the parameter node where all outputs of the node MD are above all thresholds of the nodes MD connects to ., In the second case we do not assume that the range of the signal S is matched to range of MD ., In terms of paths in EPG ( ¬MD ) this is modeled by a partial path , i . e . any subpath of the full paths ., Note that the extreme case of a partial path is a constant path , that is , a path consisting of a single node in EPG ( ¬MD ) ., Physically , this implies that the variance of the input signal is not sufficiently large to impact the dynamics of the regulatory network ., We remark that given a regulatory network it is straightforward to determine the collection of monotone full and partial paths of EPG ( ¬MD ) ; however , the total number of paths is network dependent ., It is important to note that in order to exhibit resettable bistability ( hysteresis ) the parameter graph EPG ( ¬MD ) must contain monotone paths with at least two ( three ) nodes , respectively ., This , in turn , implies that the parameter graph for MD must contain at least two ( three ) nodes ., Therefore networks where MD has a single outgoing and no incoming edge cannot exhibit hysteresis under our approach ., We view this as a technical failing of DSGRN induced by our insistence on only using coarse measurements defined in terms of the thresholds ., To circumvent this problem , we note that the assumption that in the regulatory network MD has a single outgoing and no incoming edge implies that while MD talks to the network , it is not affected by the network ., In particular , returning to our assumption that MD behaves monotonically with respect to the signal S , this implies that we can without loss of generality assume that the signal acts in a monotone fashion on the unique node that MD acts on ., Of course , whether we assume that the signal acts on this node in a monotone increasing or decreasing manner depends on whether the out edge from MD represents activation or inhibition ., For the regulatory subnetworks of Fig 3 we order the nodes by ( MD , RP , EE ) ., We interpret the quiescent and proliferative states of the restriction point network in terms of dynamic signatures of DSGRN ., In the quiescent state QS , E2F is sequestered in the heterodimer with Rb and therefore the levels of free E2F are low ., In the 3-node network in Fig 3, ( a ) free E2F is represented as EE ., Following 4 we associate the quiescent state with low levels of EE and the proliferative state with high levels of EE ., In the DSGRN database the precision to which we can search for attractors is limited by the number of thresholds ., In other words , we can identify if coordinates associate with an attractor are bounded between consecutive threshold values of that variable , but any finer identification requires choice of a particular nonlinear differential equation and a choice of particular parameter values ., The number of thresholds of a variable is determined by the number of edges emanating from a node that corresponds to the variable , since each such edge is associated to a single distinct threshold ., We characterize quiescent state QS as a minimal mode in the Morse graph with labeling FP ( * , * , 0 ) ., Similarly , the proliferative state PS is characterized by high levels of free E2F ( represented by EE ) , and therefore a PS phenotype will be represented by an attractor that has the EE coordinate above at least one threshold ., Since the number of thresholds of EE changes depending on the subnetwork that we analyze we characterize this attractor as a minimal mode in the Morse graph with labeling FP ( * , * , m ) , for some m ≥ 1 ., We are now in a position to compute statistics that measure the robustness of the subnetworks with regard to the phenotypic behaviors of resettable bistability and hysteresis ., In particular given a subnetwork , we define its prevalence of full path resettable bistability and prevalence of full path hysteresis to be the number of full paths in EPG ( ¬MD ) that exhibit resettable ( QS , PS ) bistability to QS and hysteresis normalized by the number of full paths in EPG ( ¬MD ) , respectively ., Similarly , we define prevalence of partial path resettable bistability and prevalence of partial path hysteresis to be the number of partial paths in EPG ( ¬MD ) that exhibit resettable ( QS , PS ) bistability to QS and hysteresis normalized by the number of partial paths in EPG ( ¬MD ) , respectively ., Note that although a full path is also a partial path , the different normalizations do not allow one to make a priori conclusions on the relative values of full path prevalence and partial path prevalence for either phenotype ., These numbers provide different information about the networks ., As indicated above , prevalence based on full paths assesses the ability to achieve a given phenotype when the input range matches the range of MD , while prevalence based on partial paths provides information about the behavior of the network where no assumption is made about these ranges ., Fig 3, ( b ) –3 ( e ) provides histograms indicating the number of subnetworks of Fig 3, ( a ) for which there is positive prevalence of full and partial path hysteresis and resettable bistability ., Focussing on the full path hysteresis , there are five networks ( Fig 3, ( f ) –3, ( j ) ) that exhibit hysteresis for every full path in EPG ( ¬MD ) ., More generally , the networks ( Fig 3, ( f ) –3, ( j ) ) are also the top five networks with respect to prevelance of partial path hysteresis ., Network in Fig 3, ( f ) shows partial path hysteresis in 20% of paths , networks Fig 3, ( g ) –3, ( i ) in 16 . 66% , and Fig 3, ( j ) in 15 . 49% of all the partial paths ., Furthermore , all the networks Fig 3, ( f ) –3, ( j ) are among the top 8 networks that exhibit the greatest prevalence for partial path resettability ., Interestingly , none of these networks rank among the top 8 networks for full path resettable bistability and one of them , network in Fig 3 ( h ) , has no full path with resettable bistability ., The best two subnetworks found in 4 appear among the top 8 in full path resettable bistability and among top 10 in full path hysteresis , but not among the top eight in either partial path hysteresis or resettable bistabilty ., It is worth contemplating why the results using DSGRN do not agree exactly with the results of 4 ( aside from the obvious fact that we only consider networks that involve all three nodes ) and the biological significance of these differences ., There are at least three fundamental differences in the approaches ., The 3-node networks of the previous section were derived as simplification of 4 , Fig 1 ( A ) ., With this in mind we return to 4 , Fig 1 ( A ) and consider less radical simplifications that result in the 5-node networks indicated in Fig 4, ( a ) ., In particular , we replace the MD node by two nodes Myc and CycD representing cyclin/kinase complex CycD/Cdk4 , 6 , with the assumption that Myc up-regulates CycD , and the EE node by E2F and CycE , representing cyclin/kinase complex CycE/Cdk2 , with the assumption that E2F up regulates CycE ., The node Rb represents free and active form of Rb proteins ., We also include all the potential edges from Fig 3, ( a ) with appropriate modifications of beginning and ending nodes ., We repeat the bistability , resettable bistability and hysteresis queries from the previous section on EPG ( ¬MD ) for the regulatory network subnetworks indicated in Fig 4, ( b ) ., The unlabeled edges are included in all computations with additional edges listed in first column of table in Fig 4, ( b ) ., We organize the networks into three groups: networks that have edge 7 , networks that have edge 8 , and networks that have neither ., The prevalence of partial path and full path hysteresis , as well as partial path and full path resettable bistability is indicated in the columns of Fig 4, ( b ) ., In each column we highlight top three or four values ., Note that the best three networks are the same under the measures of partial and full path hysteresis and partial path resettable bistability ., The network that is in top three in every category is network that does not have either edge 7 , nor 8 and does not have either 2a , nor 2b ., On the other hand , full path resettable bistability orders networks differently ., The top network is still the same ., Existence of edges 7 and 8 is still undesirable , but a network with the edge 2a , a network with edge 2b alone , and some networks with both 2a and 2b rank very highly ., The subtle difference between partial path and full path resettable bistability stems from the fact that full path resettable bistability requires that there are only two Morse graph types along the entire path: either the bistable state and the terminal state to which the bistable state resets ., In the context of E2F-Rb network this corresponds to a set of states ( QS , … , QS , … , B , … , B ) where B is a bistable state ( QS , PS ) ., A full path hysteresis does not correspond to a full path resettable bistability , since there are different states at the ends of the path ., In E2F-Rb networks the full hysteresis corresponds to states ( QS , … , QS , … , B , … , B , … , PS , …PS ) ., However , a full path hysteresis gives rise to a partial path resettable bistability by considering only first half of the full path ., For comparison , we study the yeast cell cycle initiation network ( START ) , see Fig 5, ( b ) 29 , 30 ., The START network of the budding yeast cell cycle has the same topology as E2F-Rb networks , yet there is no homology among the protein and transcription factors in the two networks 29 , 31 ., A transcription factor SBF is sequestered by Whi5 during G1 ., The cell growth leads to accumulation of cyclin/kinase complex Cln3/Cdk1 which phosphorylates Whi5 and as a result , releases SBF from the complex ., Released SBF promotes expression of another cyclin Cln2 , which is part of a cyclin/kinase complex Cln2/Cdk1 ., This complex in turn finishes phosphorylation of Whi5 and completes the release of SBF 30 , 32 , 33 ., The analogy with the mammalian restriction point network in Fig 4, ( a ) is striking ., The results for the START network are in the table in Fig 4, ( b ) ., We | Introduction, Results, Discussion, Materials and methods | We present a new modeling and computational tool that computes rigorous summaries of network dynamics over large sets of parameter values ., These summaries , organized in a database , can be searched for observed dynamics , e . g . , bistability and hysteresis , to discover parameter regimes over which they are supported ., We illustrate our approach on several networks underlying the restriction point of the cell cycle in humans and yeast ., We rank networks by how robustly they support hysteresis , which is the observed phenotype ., We find that the best 6-node human network and the yeast network share similar topology and robustness of hysteresis , in spite of having no homology between the corresponding nodes of the network ., Our approach provides a new tool linking network structure and dynamics . | To summarize our understanding of how genes , their products and other cellular actors interact with each other , we often employ networks to describe their interactions ., However , networks do not fully specify how the underlying biological system behaves in different conditions , nor how such response evolves in time ., We present a new modeling and computational approach that allows us to compute and collect summaries of network dynamics for large sets of parameter values ., We can then search these summaries for all observed behavior ., We illustrate our approach on networks that govern entry to the cell cycle in humans and yeast ., We rank networks based on how robustly they exhibit the experimentally observed behavior of hysteresis ., We find similarities in network structure of the best ranked networks in yeast and humans , which are not explained by a common ancestry ., Our approach provides a tool linking network structure and the behavior of the underlying system . | engineering and technology, cell cycle and cell division, protein interaction networks, cell processes, signaling networks, fungi, systems science, mathematics, network analysis, computer and information sciences, regulatory networks, proteomics, dynamical systems, graph theory, yeast, biochemistry, toggle switches, eukaryota, cell biology, biology and life sciences, physical sciences, organisms, electronics engineering | null |
journal.pcbi.1002368 | 2,012 | Dynamic Energy Landscapes of Riboswitches Help Interpret Conformational Rearrangements and Function | Riboswitches are RNAs in the untranslated ( UTR ) regions of messenger RNAs ( mRNAs ) that can undergo a structural transition in response to a highly specific intracellular ligand 1–4 ., Once bound to the riboswitch , the ligand induces a rearrangement on the secondary structure level ., The new conformation can turn on or off transcription 5–7 or translation 8–11 ., An additional mechanism for gene control has been recently discovered in which eukaryotic riboswitches control sequestration or opening of key alternative mRNA splice sites 12 , 13 ., Currently , more than twenty classes of riboswitches are known and classified according to their cognate intracellular metabolite 4 ., This list of ligands that bind riboswitches has expanded from small molecule metabolites to include second messengers such as cyclic di-guanosine monophosphate ( cdGMP ) 14–16 , other RNAs 17 , and possibly hormones 18 ., Riboswitches are composed of two major RNA domains: an aptamer domain , which binds the ligand , and an expression platform , which controls gene expression ( Figure 1a ) ., The aptamer is the first portion of the riboswitchs sequence and is defined by its ability to fold into a higher-ordered structure that can bind the ligand ., As the aptamer is transcribed , fast base-pairings occur , forming a specific structure to which the ligand may bind called the “ligand-competent” or “pre-organized” state ( Figure 1b , first row ) ., However , non-ligand-competent structures are also possible ( Figure 1b , second row ) ., In the event that the ligand-competent structure docks its target ligand , a specific structure in the downstream expression platform forms ( Figure 1a , b ) ., One of the most common forms of gene control by the expression platform is the transcription terminator hairpin ., As illustrated in Figure 1 , the binding of thiamine pyrophosphate ( TPP ) to the ligand-competent structure of the aptamer domain forces a transcription terminator hairpin to form in the expression platform , which inhibits RNA polymerase from proceeding ., If TPP does not bind to the aptamer structure , the expression platform forms a different structure , termed antiterminator , which allows RNA polymerase to transcribe the downstream gene ( Figure 1a , right; Figure 1b , bottom row ) ., Another example of gene control by the expression platform is the folding of the Shine-Dalgarno sequence ., The Shine-Dalgarno sequence is a section of the expression platform and the ribosome binding site in prokaryotes ., In the presence of ligand , the Shine-Dalgarno sequence forms a double-stranded RNA ( anti-SD ) , which prevents the ribosome from binding and precludes translation of the gene ., Transcription termination and Shine-Dalgarno sequence sequestration are both mechanisms that riboswitches use to control gene expression; however , they accomplish this by acting on two different processes within the cell 19 ., To understand riboswitch gene control in vivo , the RNA folding process must be investigated ., RNAs begin to fold as they are transcribed in the cell and are efficiently directed toward a stable conformation through fast base-pairing interactions ( ∼100 ms ) 20 , 21 ., Thus , meta-stable folded structures of the available sequence fraction are thought to form quickly and differ from the native states of the full length RNA ( Figure 1b ) ., A meta-stable folded structure is any combination of base-pairings for a shorter-than-full length RNA sequence ., RNA elongation also fluctuates due to pause sites and variations in polymerase speed , affecting the fraction of sequence available for folding 22 ., Meta-stable intermediates may not rearrange to the full length native conformation , because dissociation of structural elements might be energetically costly , resulting in a kinetic stabilization ( “trapping” ) ., All these intrinsic properties of transcription affect RNA folding in vivo 23–26 ., Recently , studies have elucidated two mechanisms of ligand binding in riboswitches: thermodynamic and kinetic 27 ., The mechanism of ligand binding involves a two-step chemical reaction , as follows . As with any reaction proceeding toward equilibrium , time is needed for reactants to be consumed and for products to be formed ., However , the process of in vivo folding places limits on the time permitted for RNA-ligand equilibration ., First , in the absence of transcriptional pause sites , RNA polymerase transcribes nucleotides quickly; ligand binding occurs before the polymerase reaches the end of the expression platform ( Figure 1b , right side ) ., If the ligand cannot bind in time , proper folding of the RNA will not occur , and gene regulation cannot occur ., The second limitation to RNA-ligand equilibrium is formation of meta-stable intermediates , which hamper or eliminate ligand binding to the aptamer domain by altering the structure of the ligand binding pocket ( Figure 1b , bottom ) ., Work has shown that high concentrations of ligand are required for gene regulation to occur in vivo and that these concentrations surpass the in vitro dissociation constant ( KD ) 22 , 28–30 ., This setting is the hallmark of kinetic control of ligand binding 22 ., Kinetic control primarily relies on the rate of ligand binding and RNA transcription ., A high ligand concentration drives the above mentioned equilibrium toward the RNA-ligand complex ., In contrast , thermodynamic control occurs when the ligand greatly stabilizes the RNA and reaches equilibrium in a time frame shorter than the time of transcription ., In this case , the KD of the aptamer-ligand complex is generally near the cellular concentration of the ligand 30 ., A riboswitch may use both strategies , as shown for the pbuE riboswitch 30 ., When more time is permitted for transcription , as through use of transcription pausing , the riboswitch can reach equilibrium with ligand ., However , when transcription time is shortened , greater concentrations of ligand are required for gene regulation to occur , and the riboswitch operates under kinetic control ., Presumably because of differences in the mechanism of gene control , ligand binding affinities vary widely among riboswitch classes ( Table 1 ) ., These variations are related to the concentration of ligand needed to elicit gene control in vivo ., For example , although both pbuE and add riboswitches bind adenine , pbuE demonstrates kinetic control , while add shows thermodynamic control 31 ., Riboswitch folding is a multi-step hierarchal process , involving interactions between base-pairs ( Watson-Crick A-U , G-C , and G-U wobble ) , base stacking , hydrogen bonding , and tertiary interactions between distant or proximal nucleotides ., While gene control is affected by changing secondary structure , local changes also occur to adopt a binding pocket specific for a small ligand ., Modeling RNA interactions on the global and local levels is thus required to fully grasp the switching process ( for a review of RNA modeling see 32 , 33 ) ., RNA secondary structure can be predicted from a single sequence or multiple aligned sequences to produce the base pairing arrangement that yields the minimum free energy structure as well as nearby low-energy states ., Algorithms may use thermodynamic models to predict structures with low Gibbs free energy 34 , use prior knowledge of validated structures to predict probable structures 35 , 36 , or search for a structure common to multiple sequences 37–40 ., However , predicting 2D structures is limited by thermodynamic parameters , which are subject to inaccuracies measured experimentally and simplified functional forms used ., Sampling multiple , suboptimal structures provides a more global view that addresses in part parameter uncertainties ., In addition to the platform provided by secondary structure , tertiary contacts further stabilize specific conformations ., Programs developed over recent years take different approaches to the problem of RNA folding; see recent perspectives 32 , 33 ., One of the first programs to accurately predict the structure of RNA was FARNA , an energy-based program that simplifies each base as a single bead representation ., The program uses prior knowledge of solved rRNA structures and secondary structure input to predict the conformation of the RNA being analyzed ., Using this method , FARNA reached an average RMSD∼30 Å in predicting the structure of the Tetrahymena ribozyme 41 ., Another interesting approach , used by the programs MC-Sym and NAST , involves the input of secondary and tertiary structure constraints to produce 3D RNA structures ., The MC-Fold and MC-Sym pipeline use both base pairing and base stacking interactions to build sets of nucleotide cyclic motifs that define RNA structure 42 ., Using experimental data on the tertiary contacts of the HDV ribozyme , Reymond et . al . used MC-Sym to map out individual folding intermediates 43 ., NAST was recently developed to employ molecular dynamics sampling of a coarse-grained model based on knowledge-based statistical potentials 44 ., For example , with some tertiary contact information , compact states of the Tetrahymena ribozyme could be predicted 45 ., A comparative evaluation of some of these approaches has been made in 33 , and a recent review 32 also discusses many limitations ., Previous modeling studies have explored two aspects of aptamer folding: folding in the presence of ligand , and self-directed folding ( without ligand ) ., It is believed that most of the structural scaffolding , which includes secondary and tertiary interactions , is quickly formed , while the addition of the ligand only causes specific tertiary contacts ., For example , Stoddard et . al . 46 revealed that an ensemble of ligand-competent conformations occurs for the SAM aptamer , distinguished only by large-scale relative motion of helices ., Therefore , SAM captures a ligand-competent conformation with most of the structure pre-organized , and this is followed by local adjustments to reach the fully “native” state ., In addition , dynamics simulations have revealed that in the process of SAM binding , a core portion of the aptamer region is stabilized significantly , indicating that the majority of the binding pocket is pre-formed 47 ., Furthermore , Villa and colleagues 48 found a two-step process in the guanine sensing aptamer: A primary screening step for purine molecules is followed by highly discriminative selection for guanine , suggesting that the pocket forms in the absence of guanine ., In the related adenine riboswitch , Sharma et . al . 49 show a similar stepwise mechanism for ligand binding ., In cooperation with the pre-organized aptamer , key tertiary interactions form when the ligand binds , and prior simulations have also shown how this response to the ligand occurs ., An atomic-level computer simulation of the S-adenosylmethionine ( SAM ) aptamer 50 showed that the fully folded structure is formed only after binding of the ligand , which reduces the barrier to folding and triggers helix formation ., In support of these computational results , Wilson et . al . have shown by NMR that certain conformations form exclusively in the presence of SAM 51 ., Similar results have been obtained for multiple aptamer classes including the preQ1 52 and adenine aptamers 53 ., In addition , SAM stabilizes a key subset of tertiary interactions distant from the binding pocket , functioning to collapse the aptamer and control secondary structure switching 54 ., To better interpret the folding process of RNA , we use the perspective of the “new view” of protein folding , which relies on the concept of a free energy landscape 55 ., The free energy landscape is defined by the ensemble free energies of all conformations where each conformation is associated with an energy and distance measure with respect to all other conformations 56 ( Figure 1c ) as evaluated by our computational approach ( see Materials and Methods ) ., Here , we use the base pair distance as a generalization of distance measure between RNA conformations , akin to the root mean square distance ( RMSD ) in protein structure ( Figure 1d ) ., This base pair distance is essentially the difference in Watson-Crick base-pairs between two structures 57 , 58 ., In general , biological molecules take advantage of a funnel-shaped landscape representing many high-energy ( denatured ) conformations and few low-energy states ., This arrangement permits the sequence to search the astronomical number of conformations directly and efficiently ., In a “smooth” energy landscape , there are few low-energy structures in the lowest energy portion of the funnel , whereas a “rough” energy landscape has more low-energy structures with barriers between them ., In the latter , each of the low-energy structures has a smaller funnel leading to it ., If the landscape is smooth and has a single minimum , the minimum free energy occurs near the native state ., This situation is called “downhill folding 59 . ”, In downhill folding , there is little or no free-energy barrier , and folding occurs quickly ( Figure 1c , middle ) ., In contrast , “barrier-limited folding” landscapes are “rougher” or “frustrated” and are marked by the presence of one or more low-energy barriers , which slow transition times and affect pathways to the minimum energy structure ( Figure 1c , left ) 59 ., Feng et . al . previously demonstrated this type of energy landscape for the preQ1 riboswitch , in which stability of individual structures was linked to the rate of folding 52 ., For proteins , an energy landscape is typically computed at the full sequence length ., Here , we compute many landscapes at 1 nt increments to mimic folding as the sequence is transcribed ., We then group similar landscapes into one landscape that captures behavior at that sequence range ., For the entire elongation process , we have distinguished at most three different windows or landscapes of behavior ., We use this procedure to analyze ten riboswitches from seven different classes , by the technique we developed in 57 for the tenA TPP riboswitch ., The nature of the unbound state , the change in secondary structure , and the effects of the expression platform on folding are all questions we address here by deriving a novel energy landscape model and validating our predictions with experimental measurements ., Studies on the full riboswitch , aptamer and expression platform , are still lacking ., Here , we simulate in vivo formation of structures by calculating the energy landscape of secondary structures sequentially from short to full length sequence , without any ligand , at 1 nt increments ., Prediction of individual RNA secondary structures at different lengths is performed with a set of programs from the Vienna RNA folding package 60 as well as pknotsRG 61 for pseudoknot-containing riboswitches ., These programs essentially predict structures on the basis of a set of nearest-neighbor approximations , assigned to the various motifs in RNA structures 34 , 62 , as described above ., While secondary structure predictions do not account for all interactions , these predictions approximate the general structural scaffold and serve as a first-level approximation ., As described above , most of the architecture is thought to be formed in the absence of ligand ., Thus , our 2D energy landscapes provide an approximate picture of the available folding states accessible to the riboswitch during elongation ., This folding as the sequence elongates to full length has not been examined computationally as far as we are aware ., Our analysis reveals that three main types of landscapes exist depending on the sequence length transcribed ., The sensing window encompasses the lengths at which the riboswitch adapts to different structures , including the ligand-competent form ., Overall , the ligand-competent and non-ligand-competent structures are inherent to the energy landscape ( Figure 1b , c , left panels ) ., At this length range these two states can interchange , regardless of the presence of ligand ., Ligand binding induces folding toward the active conformation by shifting the equilibrium ., At other specific sequence lengths , the energy landscape displays a downhill folding window , which favors a low-energy structure with a specific function on gene control ( Figure 1b , c middle panels ) ., This sequence range essentially determines whether the riboswitch will turn the gene on or off ., Finally , at yet another stage of transcription , two alternative pathways are present on the landscape as two separate clusters ( Figure 1b , c right panels ) ., We term this portion of transcription the functional window ., These energy landscapes demonstrate an irreversible decision point: Once one cluster is accessed , switching between states is not likely to occur ., By extending the landscape analysis in 57 for the TPP riboswitch to many other riboswitches , we find that although the overall features are similar , the order of these energy landscape windows varies and can suggest whether the ligand binding mechanism is governed by kinetic or thermodynamic control ., That is , when the sensing window occurs early during the transcription process , as for the tenA riboswitch , landscape analysis suggests kinetic control; when the sensing window occurs at the end of the expression platform , as for the add riboswitch , thermodynamic control reigns ., These energy landscape views thus help interpret riboswitch action by connecting structure to function ., Implications to riboswitch design naturally arise ., Our ten riboswitch examples in seven families consist of six from the Rfam database 63 plus the recently discovered cyclic-di-guanosine monophosphate riboswitch family 64 , 65 ( Table 1 ) ., We expand on our earlier computational approach 57 because two classes of riboswitches ( PreQ , SAM ) fold via pseudoknots ( intertwined base-pair interactions ) ., These classes require further analysis with pknotsRG 61 ( Materials and Methods ) , which predicts pseudoknot formation as well as pseudoknot-containing suboptimal structures ., We exclude riboswitch classes longer than 240 nt , since the accuracy of RNA folding markedly decreases at such lengths and the number of suboptimal foldings concomitantly increases exponentially ., In all riboswitches studied ( Table 1 ) , we found that three broad sequence length ranges displayed similar energy landscapes patterns ., We term the three sequence ranges as the sensing , downhill folding , and functional windows , respectively ., We found that the order of the windows predicts the mechanism of ligand binding ( Table 1 ) ., The sensing window refers to the state at which the RNA is intrinsically able to sense or detect the presence of ligand ., For all the sequence lengths within the sensing window , the energy landscape demonstrates that ligand-competent forms are separated from functionally opposing , non-ligand-competent structures by a small energy barrier , which creates a pathway between the two states ( Figure 1c , left ) ., These landscapes mimic a barrier-limited folding description ., In contrast , the downhill folding window favors a single minimum free energy structure ( mfe ) ., Low barriers and a funnel-shape toward the minimum facilitate an efficient isomerization to the mfe ( Figure 1c , middle ) ., Lastly , the functional window displays compact clusters of structures , a high ( >10 kcal/mol ) energy barrier , and two opposing states ( Figure 1c , right ) ., In the following sections , we analyze our riboswitches according to the order of windows ., We find that the main determinant of kinetic or thermodynamic control is whether the sensing window occurs early or late in transcription ., However , both kinetically and thermodynamically-controlled riboswitches can vary the order of downhill folding and functional windows ., Figures 2–4 show resulting landscapes for the tenA riboswitch from Bacillus subtilis , thiM riboswitch from Escherichia coli , GEMM riboswitch from Candidatus Desulforudis audaxviator , moaA riboswitch from Escherichia coli , and metI riboswitch from Bacillus subtilis ., All riboswitches undergo conformational changes by binding specific ligands ( Table 1 ) ., At the beginning of transcription in the sensing window , the ligand-competent aptamer is the mfe but non-ligand-competent structures are also present on the landscape ., In the downhill folding window , an immediate change occurs as the mfe switches to the non-ligand-competent antiterminator ( tenA , GEMM , metI ) or anti-SD ( thiM , moaA ) ., In this time frame , the energy landscapes describe a spontaneous isomerization to the stable antiterminator/anti-SD form ., We propose that this window decides the ultimate fate of the riboswitch: If the RNA is ligand-bound , it does not isomerize to antiterminator/anti-SD form , and without ligand , the RNA forms the thermodynamically-favored antiterminator/anti-SD form ., In the functional window , the final set of nucleotides of the expression platform form the terminator hairpin/antiterminator or sequester/open ribosome binding site , which are energetically favored ., For the tenA and thiM thiamine pyrophosphate ( TPP ) riboswitches , each energy landscape window correlates with several interesting experimental properties ., First , the RNA favors the ligand-competent form in the sensing window ( Figure 2 a , b , top ) ., In good agreement with our computational results , pre-organization into a ligand-competent form occurs in vitro in the presence of relevant Mg+2 concentrations 66 and binds TPP with high affinity 8 , 66 ( Figure S1 ) ., During the downhill folding window , the TPP riboswitch favors an antiterminator/anti-SD structure , which results in aptamer misfolding ( Figure 2a , b , middle panel ) ., Lang et al . 8 note that shorter-than-full length thiM riboswitch constructs , precisely at those lengths that occupied the downhill folding window , displayed hampered TPP binding ., The authors conclude that alternative folds prevent TPP binding by obliterating the ligand-competent forms ., Our view supports this behavior by relating the poor TPP affinity to formation of non-ligand-competent anti-SD structures ., In the full length riboswitch , experiments have shown that both tenA and thiM recognize TPP with the same affinity as the aptamer domain alone 8 , 66 , 67 ., We also find that in the functional window , the full length TPP riboswitch favors a fully formed aptamer domain ( Figure 2a , b , bottom ) and has less competition from alternative folds ., This stability is due to the high energy-barrier and clustering exhibited in the energy landscape ., Structures distant in the thiM functional window ( Set 2 in Figure 2b ) correspond to the non-ligand-bound TPP riboswitch found experimentally ., Thus , as reported by Rentmeister and colleagues 10 , in the TPP-free form of thiM , stems P2 and P3 form , the Shine-Dalgarno sequence is unpaired , and P1 is mispaired ( Figure S1 ) ., Overall , this order of windows is characteristic of kinetic control , where the choice of folding pathway occurs early in transcription ., High concentrations of ligand both stabilize the ligand-competent aptamer soon after it is transcribed and exclude non-ligand-competent forms 19 ., The concentration at which transcription termination occurs 68 is much greater than the apparent KD ( ∼50 nM ) 69 ., The hallmark of kinetic control is that the concentration of ligand required for in vivo gene regulation is greater than the binding affinity found in vitro ( KD ) ., The GEMM riboswitch from Candidatus Desulforudis audaxviator belongs to a novel class of riboswitches found to bind the second messenger cyclic di-guanosine monophosphate 15 , 16 ., Similar to tenA and thiM , the sensing window contains ligand-competent and non-ligand-competent structures together on the energy landscape , separated by a small energy barrier ( Figure 3a ) ., Only minor differences between our predicted ligand-competent structures and the known structure can be noted ( Figure S1 ) ., In the downhill folding window , the non-ligand-competent , antiterminator structure is the mfe , suggesting that the antiterminator would form if the ligand is not present to stabilize the ligand-competent structure ., Similar to tenA , terminator and antiterminator form in the functional window ., The window pattern suggests kinetic control , in agreement with experimental evidence by Sudarsan et al . 15 , 16 ., The molybdenum-cofactor binding moaA riboswitch 70 which follows the same order ( Figure 3b ) , can bind either Molybdenum-cofactor ( Moco ) or Tungsten-cofactor ( Tuco ) ., Akin to thiM , moaA causes suppression of translation through sequestration of the ribosome binding site ( anti-SD ) ., The folding pathway starts with a sensing window , where ligand-competent and non-ligand-competent structures are in equilibrium ., The downhill folding window that follows shows a tendency to isomerize to the anti-SD structure ., Finally , in the functional window , the anti-SD forms a separate cluster from functionally-opposing structures , which have open Shine-Dalgarno sequences ., This functional window does not display a clear separation of clusters as in thiM , though it has a high energy barrier between sets of conformations ( ∼12 kcal/mol ) ., Experimental studies on kinetic or thermodynamic control of ligand binding are not yet available , though our predicted structures contain all conserved features of the ligand-bound structure ( Figure S1 ) ., The metI leader from Bacillus subtilis binds S-adenosylmethionine ( SAM ) and exhibits dramatic gene silencing in the presence of ligand ( ∼12%→75% termination in presence of ligand ) 71 ., The S-box aptamer requires a pseudoknot interaction for proper folding ., A meta-stable , non-ligand-competent pseudoknot structure forms alongside the SAM-competent structure in our sensing window ( Figure 4 ) ., However , in the downhill folding window , this non-ligand-competent structure is highly stable as the mfe , while the SAM-competent structures are unfavorable and not present in the energy landscape ., In the functional window , the energy landscape demonstrates two structures , corresponding precisely to those predicted by Breaker et al . 72 ., When fully formed , the terminator or antiterminator structure is essentially irreversible , as evident by high energy barriers between structures ., In agreement with the irreversible structures of the functional window , Hennelly et . al . have shown that the full length SAM I antiterminator is essentially irreversible by ligand alone without refolding 54 ., Both experimental 73 and computational 47 results agree with the pattern of landscape window suggesting kinetic control , because the sensing window occurs early in transcription ., The mgtE riboswitch from Bacillus subtilis 74 is a longer RNA characterized by the presence of a terminator hairpin adjacent to the aptamer domain ., As the longest riboswitch studied ( 230 nt ) , the purpose of the early downhill folding window ( Figure 5 ) likely serves to quickly fold the long sequence into a compact , ligand-competent structure ., Later in the sensing window , the ligand-competent structure exchanges with the non-ligand-competent structure ., In the functional window that follows , the validated terminator and antiterminator structures exist 75 ( Figure 5 , S1 ) ., Although the order of windows differs from the five riboswitches above , we also propose a mechanism of kinetic control for mgtE ligand binding because the sensing window occurs during sequence-lengths shorter than full length ., No kinetic studies have yet been performed on this riboswitch to the best of our knowledge ., The pbuE riboswitch alters its structure in response to adenine only at short lengths 31 , 76 ., In agreement with NMR investigations , we predict that pbuE favors an adenine-binding-competent fold at short lengths , in the sensing window , where loops L2 and L3 and stem P1 forms 77 ( Figure S1 ) ., However , the adenine-competent folds are higher in energy and thus buried within the clusters ( Figure 6a ) ., As a result , the pbuE riboswitch differs from all other classes , because the ligand-competent structure is not the mfe at any point in the windows ., In strong agreement with optical trapping assays of the pbuE aptamer domain 78 , we find that the RNA in the sensing window is in rapid equilibrium between unfolded and P1-folded ( i . e . , ligand-competent ) states ., The sensing window in pbuE is followed by a functional window , in which two pathways become possible ., Both Set 1 and 2 structures favor terminator hairpins ( i . e . , non-ligand-competent structures ) ( Figure 6a ) ., Ligand-competent , antiterminator forms are buried in Set 2 , and are in equilibrium with terminator structures , while Set 1 consists of non-ligand-competent structures ., Thus , Set 2 represents the possible pathway in the presence of ligand , while Set 1 represents the pathway in its absence ., The mfe structure of Set 2 in the functional window corresponds to a form that binds and is cleaved by RNAse P 79 ., We suggest that adenine binding may signal or trigger the RNAse P interaction , since the two structures occur in the same cluster within the functional window ., The energy landscape for the full length pbuE RNA highly favors non-ligand-bound states as indicated by a downhill folding window toward the non-ligand-competent mfe ., Adenine-competent structures exist on the landscape , but are much higher in energy ., This suggests that the ligand must stabilize the RNA to prevent isomerization to more energetically favorable non-ligand-competent structures ., This behavior agrees with experimental studies 76 ., The full length pbuE riboswitch is not responsive to ligand , meaning that the RNA does not fold into a ligand-competent structure when adenine is subsequently added to solution ., Since the sensing window occurs early in transcription , pbuE suggests kinetic control ., This finding is also in agreement with experimental results 31 , although some investigators suggest that thermodynamic control may be possible through use of transcriptional pause sites and variations in temperature 30 ., While the xpt-pbuX guanine-sensing riboswitch has a similar structure and sequence to pbuE , specific nucleotides in its aptamer domain bind guanine ., Once the xpt aptamer domain is transcribed , it forms the ligand-competent structure ( Figure 6b , S1 ) 28 , 80 ., Association kinetics experiments reveal that high ligand concentrations induce a unimolecular step prior to ligand binding 28 , this suggests that the RNA interconverts between two isomers until the ligand-competent structure is stabilized ., This result agrees with the sensing window of the xpt aptamer domain ( Figure 6b ) ; the mfe is ligand-competent and coexists with alternative low-energy non-ligand-competent structures on the landscape , separated by a small energy barrier ., The functional window directly follows the sensing window with the functionally-opposing terminator and antiterminator structures forming in separate clusters ., The terminator structure is ligand-competent and the antiterminator structure favors breakage of the crucial P1 stem , forming a non-ligand-competent structure ., Later , at the start of the downhill folding window , the mfe favors a ligand-competent , terminator form ( Figure 6b ) ., The downhill folding window at full length transcription supports isomerization to this structure , regardless of whether guanine is bound or not ., However , as we argue below , isomerization is not likely to occur because of the excessive time required ., For gene regulation to occur , we propose a model of kinetic control ., The structures in the sensing window likely exchange at equilibrium , much like in pbuE ., We propose that the structure forme | Introduction, Results, Discussion, Materials and Methods | Riboswitches are RNAs that modulate gene expression by ligand-induced conformational changes ., However , the way in which sequence dictates alternative folding pathways of gene regulation remains unclear ., In this study , we compute energy landscapes , which describe the accessible secondary structures for a range of sequence lengths , to analyze the transcriptional process as a given sequence elongates to full length ., In line with experimental evidence , we find that most riboswitch landscapes can be characterized by three broad classes as a function of sequence length in terms of the distribution and barrier type of the conformational clusters: low-barrier landscape with an ensemble of different conformations in equilibrium before encountering a substrate; barrier-free landscape in which a direct , dominant “downhill” pathway to the minimum free energy structure is apparent; and a barrier-dominated landscape with two isolated conformational states , each associated with a different biological function ., Sharing concepts with the “new view” of protein folding energy landscapes , we term the three sequence ranges above as the sensing , downhill folding , and functional windows , respectively ., We find that these energy landscape patterns are conserved in various riboswitch classes , though the order of the windows may vary ., In fact , the order of the three windows suggests either kinetic or thermodynamic control of ligand binding ., These findings help understand riboswitch structure/function relationships and open new avenues to riboswitch design . | Riboswitches are RNAs that modulate gene expression by ligand-induced conformational changes ., However , the way that sequence dictates alternative folding pathways of gene regulation remains unclear ., In this study , we mimic transcription by computing energy landscapes which describe accessible secondary structures for a range of sequence lengths ., Consistent with experimental evidence , we find that most riboswitch landscapes can be characterized by three broad classes as a function of sequence length in terms of the distribution and barrier type of the conformational clusters: Low-barrier landscape with an ensemble of conformations in equilibrium before encountering a substrate; barrier-free landscape with a dominant “downhill” pathway to the minimum free energy structure; and barrier-dominated landscape with two isolated conformational states with different functions ., Sharing concepts with the “new view” of protein folding energy landscapes , we term the three sequence ranges above as the sensing , downhill folding , and functional windows , respectively ., We find that these energy landscape patterns are conserved between riboswitch classes , though the order of the windows may vary ., In fact , the order of the three windows suggests either kinetic or thermodynamic control of ligand binding ., These findings help understand riboswitch structure/function relationships and open new avenues to riboswitch design . | biology, computational biology | null |
journal.pgen.1002007 | 2,011 | The Genome Sequence of the Leaf-Cutter Ant Atta cephalotes Reveals Insights into Its Obligate Symbiotic Lifestyle | Ants are one of the most successful insects on earth , comprising up to 20% of all terrestrial animal biomass and at least 25% of the entire animal biomass in the New World Tropics 1 ., One of the most conspicuous and prolific Neotropical ants are the leaf-cutters ( Tribe: Attini ) , so-called because of their leaf-cutting behavior 2 ., Leaf-cutters are unique among ants because they obligately farm a specialized , mutualistic fungus that serves as their primary food source 3 ., Using a complex system of trails , foraging ants seek out and cut leaves ( Figure 1A ) that they use to manure a fungal crop in specialized subterranean fungus gardens ( Figure 1B ) within their colonies ., Fungus farming by ants is exclusive to the New World and is thought to have evolved once 50 million years ago 4 , culminating in the leaf-cutter ants ., A single mature colony of the genus Atta can fill a volume of up to 600 m3 and their fungus gardens can support millions of workers capable of harvesting over 400 kg of leaf material ( dry weight ) annually 1 ., These ants are thus one of the most widespread and important polyphagous insect herbivores in the Neotropics ., The importance of leaf-cutter ants in Neotropical rainforest ecology lies in their ability to substantially alter arboreal foliage through their extensive leaf-cutting activities ., Estimates suggest that leaf-cutter ants remove 12–17% of the total leaf production in tropical rainforests 1 ., As a group , they harvest more plant biomass than any other Neotropical herbivore including mammals and other insects ., As a result , leaf-cutter ants are a major human agricultural pest , responsible for billions of dollars in economic loss each year 5 ., These ants do , however , have a positive impact on rainforest ecosystems , as they contribute to rapid soil turnover through their nest excavation activities 6 , stimulate plant growth by cutting vegetation 7 , and help to recycle organic carbon 1 ., In addition to their importance in Neotropical ecosystems , leaf-cutter ants also serve as a model for understanding the ecology and evolution of host-microbe symbioses 8 ., In return for receiving a continuous supply of leaf-material , protection from competitors , and dispersal , the fungus these ants grow provide nutrients in the form of specialized hyphal swellings called gongylidia ., Gongylidia , which contain a mixture of carbohydrates , amino acids , proteins , lipids , and vitamins 9 , is the sole food source for developing larvae ., The fungus garden is also known to harbor other microbial symbionts including nitrogen-fixing bacteria that provide both fungus and ants with nitrogen 10 , and a diverse community of fungus garden bacteria that appear to help the fungus degrade plant biomass 11 ., The complexity of the leaf-cutter ant symbiosis is further highlighted by the presence of a specialized microfungal pathogen that exploits the ant-fungus mutualism 12 , 13 ., As a result , the leaf-cutter ant symbiosis comprises at least three established mutualists and one specialized pathogen ., With the reported presence of additional microbial symbionts from Acromyrmex leaf-cutter ants 14–19 , and the isolation of numerous microbes from other fungus-growing ants 20–22 , this ant-microbe symbiosis is perhaps one of the most complex examples of symbiosis currently described ., Leaf-cutter ants in the genus Atta are also known for their morphologically diverse caste system ( Figure 1C ) , which reflects their complex division of labor 23 , 24 ., For example , the overall body size of Atta cephalotes workers varies tremendously ( i . e . , head widths ( HW ) ranging from 0 . 6 mm to 4 . 5 mm 23 ) , and these differences correspond to the tasks performed by workers ., The smallest workers ( HW 0 . 8–1 . 6 mm ) engage in gardening and brood care as their small mandibles allow them to manage the delicate fungal hyphae and manipulate developing larvae ., Some of these workers are also responsible for processing plant material collected by foragers by clipping large pieces of leaf material into smaller fragments to manure the fungus ., Larger workers ( HW >1 . 6 mm ) are responsible for foraging , as they have mandibles powerful enough to cut through leaves and other vegetation 24 ., The largest workers form a true soldier caste , which are involved primarily in nest excavation and colony defense 23 , 24 ., To gain a better understanding of the biology of leaf-cutter ants , we sequenced the genome of Atta cephalotes using 454 pyrosequencing technology 25 and generated a high-quality de novo assembly and annotation ., Analysis of this genome sequence reveals a loss of genes associated with nutrient acquisition and amino acid biosynthesis ., These genes appear to be no longer required because the fungus may provide these nutrients ., With the recent reports of genomes from other social hymenopterans 26 , 27 and insects that engage in microbial mutualisms 28 , 29 , the A . cephalotes genome contributes to our understanding of social insect biology and provides insights into the interactions of host-microbe symbioses ., Three males from a mature Atta cephalotes colony in Gamboa , Panama were collected and sequenced using 454-based pyrosequencing 25 with both fragment and paired-end sequencing approaches ., A total of 12 whole-genome shotgun fragment runs were performed using the 454 FLX Titanium platform in addition to two sequencing runs of an 8 kbp insert paired-end library , and one run of a 20 kbp insert paired-end library ., Assembly of these data resulted in a genome sequence of 290 Mbp , similar to the 300 Mbp genome size previously estimated for A . cephalotes 30 ., The genome is spread across 42 , 754 contigs with an average length of 6 , 788 bp and an N50 of 14 , 240 bp ( Table 1 ) ., Paired-end sequencing ( 8 kbp and 20 kbp inserts ) generated 2 , 835 scaffolds covering 317 Mbp with an N50 scaffold size of 5 , 154 , 504 bp ., The disparity between contig and scaffold size may be accounted for by the number of repeats present in this genome ( see below ) leading to an inflated assembly size due to chimeric contigs ., Based on the total amount of base pairs generated and its predicted genome size , we estimate that the coverage of the A . cephalotes genome is 18-20X ., To determine the completeness of the A . cephalotes genome sequence , we performed three analyses ., First , we compared the A . cephalotes genome annotation against a set of core eukaryotic genes using CEGMA 31 , and found that 234 out of 248 core proteins ( 94% ) were present and complete , while 243 ( 98% ) were present and partially represented ., Second , we analyzed the cytoplasmic ribosomal proteins ( CRPs ) in the A . cephalotes genome and identified a total of 89 genes ( Text S1 ) ., These encode the full complement of 79 CRPs known to exist in animals , nine of which are represented by gene duplicates ( RpL11 , RpL14 , RpS2 , RpS3 , RpS7 , RpS13 , RpS19 , RpS28 ) or triplicates ( RpL22 ) ., The presence of a complete set of these numerous genes , which are widely distributed throughout the genome , confirmed the high-quality of the A . cephalotes genome sequence ( Text S2 ) ., Finally , we found that the genome of A . cephalotes contains 66 of the 67 known oxidative phosphorylation ( OXPHOS ) nuclear genes in insects ( Text S3 ) ., The only OXPHOS gene missing , cox7a , we found to also be missing in the two ants Camponotus floridanus and Harpegnathos saltator and the honey bee Apis mellifera ., The presence of this gene in the jewel wasp Nasonia vitripennis ( along with other holometabolous insects ) , suggests an aculeate Hymenoptera-specific loss , rather than a lack of genome coverage for A . cephalotes ., We also generated an annotation for the A . cephalotes genome using a combined approach of electronically-generated annotations followed by manual review and curation of a subset of gene models ., Expressed Sequence Tags ( ESTs ) generated from a pool of workers consisting of different ages and castes from a laboratory-maintained colony of A . cephalotes was used in conjunction with the MAKER 32 automated annotation pipeline to generate an initial genome annotation ., This electronically-generated annotation set ( OGS1 . 1 ) contained a total of 18 , 153 gene models encoding 18 , 177 transcripts ( See Materials and Methods ) , 7 , 002 of which had EST splice site confirmation and 7 , 224 had at least partial EST overlap ., The MAKER-produced gene annotations were used for further downstream review and manual curation of over 500 genes across 16 gene categories ( Table S1 ) ., Significant findings from this annotation are highlighted below , with additional details of our full analysis described in Text S1 , S2 , S3 , S4 , S5 , S6 , S7 , S8 , S9 , S10 , S11 , S12 , S13 , S14 , S15 , S16 , S17 , S18 , S19 , S20 ) ., In addition to the A . cephalotes genome sequence , we also recovered an 18-20X coverage complete and circular mitochondrial genome , which showed strong whole sequence identity to the mitochondrial genome sequence reported for the solitary wasp Diadegma semiclausum 33 ., A synteny analysis of the predicted genes on the A . cephalotes mitochondrial genome showed near-identical gene order with that of A . mellifera 34 ( Text S4 ) ., The A . cephalotes assembly contains 80 Mbp of repetitive elements , which accounts for 25% of the predicted assembly ( Table S2 ) ., The large majority of these are interspersed repeats , which account for 70 Mbp ( 21% ) ., Many of these repeats are transposable elements ( TEs ) , with DNA TEs the most abundant and accounting for 14 . 3 Mbp ( 4 . 5% ) ., A large number of retroid element fragments were also identified , with Gypsy/DIRS1 and L2/CR1/Rex as the most abundant ., However , the majority of interspersed elements ( 51 . 8 Mbp ) were similar to de novo predictions that we could not be classified to a specific family ( Table S2 ) ., Improvements to the assembly , integration of repeat annotation evidence , and manual curation will be necessary to determine if these elements represent new TE families or complex nests of interspersed repeats ., Given the obligate association between A . cephalotes and its fungal cultivar , we investigated the possibility that the A . cephalotes genome might contain transposable elements commonly found in fungi ., This was done by re-analyzing the genome using a TE library optimized for the detection of Fungi and Viridiplantae ., We did not find evidence for any high-scoring or full-length retroid or DNA TEs from either of these taxa present in the A . cephalotes genome ., Our estimate that 25% of A . cephalotes assembly contains repetitive elements may be ambiguous because our assembly spans 317 Mbp and the estimated genome size for A . cephalotes is 300 Mbp 30 ., These predictions are , however , more similar to other ant species 27 and N . vitripennis 35 than to A . mellifera 28 , which lacks the majority of retroid elements and other transposable elements ( TE ) found in A . cephalotes ., Eukaryotic genomes can be understood from the perspective of their nucleotide topography , particularly with respect to their GC content ., Previous work has shown that animal genomes are not uniform , but are composed of compositional domains including homogeneous and nonhomogeneous stretches of DNA with varying GC composition 36 ., A global composition analysis was performed for A . cephalotes and the compositional distribution was compared to those of other insect genomes , as described in Text S5 ., This analysis revealed that A . cephalotes has a compositional distribution similar to other animal genomes , with an abundance of short domain sequences and few long domain sequences ., A . cephalotes also has the largest number of long GC-rich domain sequences when compared to other insect genomes , with over six times the number of long GC-rich domain sequences than the N . vitripennis genome ., When genes are mapped to compositional domains in the A . cephalotes genome , we find that they are uniformly distributed across the entire genome , in contrast to N . vitripennis and A . mellifera , which have genes occurring in more GC-poor regions of their genomes ., The methylation of genes has been reported for other hymenopterans including A . mellifera 37 and N . vitripennis 35 ., In insects , it is thought that this process contributes to gene silencing 37 , but recent reports suggest a positive correlation between DNA methylation and gene expression 38 , 39 ., DNA methylation is thought to involve three genes: dnmt1 , dnmt2 , and dnmt3 40 , although the precise role of dnmt2 remains unresolved ., We found all three genes as single copies in A . cephalotes , which is similar to the other ants 27 but in contrast to A . mellifera and N . vitripennis where dnmt1 has expanded to two and three copies , respectively 35 ( Text S6 ) ., Dnmt3 is known to be involved in caste development in A . mellifera 41 , and the presence of this gene in A . cephalotes may therefore indicate a similar role ., RNA interference is a mechanism through which the expression of RNA transcripts is modulated 42 ., We annotated a total of 29 different RNAi-related genes in A . cephalotes , including most of the genes involved in the microRNA pathway , the small interfering RNA pathway , and the piwi-interacting RNA pathway ( Text S7 ) ., All detected RNAi genes were found as single copies except for two copies of the gene loquacious ., One of these contains three double-stranded RNA binding domains characteristic of loquacious in D . melanogaster 43 , whereas the other contains only two ., It is not known what role this second loquacious-like gene plays in A . cephalotes and future work is needed to deduce its role ., The insulin signaling pathway is a highly-conserved system in insects that plays a key role in many processes including metabolism , reproduction , growth , and aging 44 ., An analysis of the insulin signaling system in A . cephalotes reveals that it has all of the core genes known to participate in this pathway ( Text S8 ) ., One of the hallmarks of A . cephalotes biology is its complex size-based caste system and , although virtually nothing is known about the genetic basis of caste development in this ant , it is currently thought that it is intrinsically linked to brood care and the amount of nutrients fed to developing larvae 1 ., Given the importance of the insulin signaling system in nutrition , it is likely that this pathway is involved in caste differentiation in A . cephalotes , as has been shown for A . mellifera 45 ., The yellow/major royal jelly proteins are encoded by an important class of genes and in A . mellifera they are thought to be integral to many major aspects of eusocial behavior 46 ., For example , members of these genes are implicated in both caste development and sex determination ., An analysis of this gene family in A . cephalotes revealed a total of 21 genes , 13 of which belong to the yellow genes and 8 of which encode major royal jelly proteins ( MRJP ) ( Text S9 ) ., In general , the yellow genes display one-to-one orthology with yellow genes in other insects like Drosophila melanogaster and N . vitripennis ., With eight members in the MRJP subfamily , which is restricted to Hymenoptera , the number of MRJP genes in A . cephalotes is similar to the number reported for other Hymenoptera 35 , 46 ., However , five of the eight genes in A . cephalotes are putative pseudogenes ., This may indicate that a high copy number of MRJPs may be an ancestral feature and that Atta is in the process of losing these genes ., The loss of MRJPs may be a common theme among ants , as the recently reported genome sequences for C . floridanus and H . saltator revealed only one and two MRJP genes , respectively 27 ., Wing polyphenism is a universal feature of ants that has contributed to their evolutionary success 1 ., The gene network that underlies wing polyphenism in ants responds to environmental cues such that this network is normally expressed in winged queens and males , but is interrupted at specific points in wingless workers 47 ., We therefore predict that the differential expression of this network between queens and workers may be regulated by epigenetic mechanisms as has been demonstrated in honey bees 41 ., In A . mellifera , developmental and caste specific genes have a distinct DNA methylation signature ( high-CpG dinucleotide content ) relative to other genes in the genome 48 ., Because A . cephalotes has more worker castes than other ant species 23 ( Figure 1C ) , we predict that the DNA methylation signature of genes underlying wing polyphenism will also be distinct relative to other genes in its genome ., To test this prediction , we analyzed the sequence composition of wing development genes in A . cephalotes , and found that they exhibit a higher CpG dinucleotide content than the rest of the genes in the genome ( Text S10 ) ., Previous experiments have shown that genes with a high-CpG dinucleotide content can be differentially methylated in specific tissues or different developmental stages 49 ., Therefore , DNA methylation may facilitate the caste-specific expression of genes that underlie wing polyphenism in A . cephalotes ., This may be a general feature of genes that underlie polyphenism ., An important aspect of the eusocial lifestyle is communication between colony members , specifically in differentiating between individuals that belong to the same colony and those that do not ., Nestmate recognition in many ants is mediated by cuticular hydrocarbons ( CHCs ) 50 , and nearly 1 , 000 of these compounds have been described ., In ants , CHC biosynthesis involves Δ9/Δ11 desaturases , which are known to produce alkene components of CHC profiles 51 ., We analyzed the Δ9 desaturases in the genome of A . cephalotes and detected nine genes localized to a 200 kbp stretch on a single scaffold in addition to four other Δ9 desaturase genes on other scaffolds ( Text S11 ) ., In contrast , the seven genes found in D . melanogaster are more widely distributed along one chromosome ., The number of Δ9 desaturase genes in A . cephalotes is similar to the 9 and 16 found in A . mellifera and N . vitripennis , respectively ., A phylogenetic analysis of these genes supports their division into five clades , with eight Δ9 desaturase genes falling in a single clade suggesting an expansion of these genes possibly related to an increased demand for chemical signal variability during ant evolution ( Text S11 ) ., Interestingly , the phylogeny also supports an expansion in this type of Δ9 desaturase genes within N . vitripennis but not in A . mellifera ., All insects have innate immune defenses to deal with potential pathogens 52 and A . cephalotes is no exception with a total of 84 annotated genes found to be involved in this response ( Text S12 ) ., These include the intact immune signaling pathways Toll , Imd , Jak/Stat , and JNK ., When compared to solitary insects like D . melanogaster and N . vitripennis , A . cephalotes has fewer immune response genes and better resembles what is known for the eusocial A . mellifera 53 ., The presence of other defenses in A . cephalotes , such as antibiotics produced by metapleural glands 54–56 , may account for the paucity of immune genes ., Furthermore , social behavioral defenses may also participate in the immune response , as has been suggested for A . mellifera 53 ., A set of shared orthologs was determined among A . cephalotes , A . mellifera , N . vitripennis , and D . melanogaster ( Figure 2 ) ., A total of 5 , 577 orthologs were found conserved across all four insect genomes , with an additional 1 , 363 orthologs conserved across the three hymenopteran genomes ., A further , 599 orthologs were conserved between A . cephalotes and A . mellifera , perhaps indicating genes that are specific to a eusocial lifestyle ., We also found 9 , 361 proteins that are unique to A . cephalotes , representing over half of its predicted proteome ., These proteins likely include those specific to ants or to A . cephalotes ., We then analyzed the proteins that were found to be specific to A . cephalotes and determined those Gene Ontology ( GO ) 57 terms that are enriched in these proteins , relative to the rest of the genome ( Table S3 ) ., We found many GO terms that reflect the biology of A . cephalotes and ants in general ., For example , we find proteins with GO terms that reflect the importance of communication ., These include proteins associated with olfactory receptor activity , odorant binding function , sensory perception , neurological development , localization at the synapse , and functions involved in ligand-gated and other membrane channels ., To focus on Hymenoptera evolution , we compared the A . cephalotes genome to 4 other hymenopterans including the ants C . floridanus and H . saltator , the honey bee A . mellifera , and the solitary parasitic jewel wasp N . vitripennis ., We used the eukaryotic clusters of orthologous groups ( KOG ) ontology 58 to annotate the predicted proteins from all of these genomes and performed an enrichment analysis by comparing the KOGs of the social insects A . cephalotes , C . floridanus , H . saltator , and A . mellifera against the KOGs of the non-social N . vitripennis as shown in Table S4 ., A detailed analysis of KOGs within each over- and under-represented category is highly suggestive of A . cephalotes biology ( Table S5 ) ., One of the most over-represented KOGs in A . cephalotes includes the 69 copies of the RhoA GTPase effector diaphanous ( KOG1924 ) ., In contrast , all of the other hymenopteran genomes have substantially less copies of this gene ., RhoA GTPase diaphanous is known to be involved in actin cytoskeleton organization and is essential for all actin-mediated events 59 ., The large number of these genes in A . cephalotes may relate to the extensive cytoskeletal changes that occur during caste differentiation ., One of these genes ( ACEP_00016791 ) was found to exhibit high single nucleotide polymorphism ( SNPs ) ( Text S13 ) ., Given that genes involved in caste development in other social insects like A . mellifera also have high SNPs 60 , 61 , this may indicate that this gene is important for caste determination in A . cephalotes ., A . cephalotes is also significantly over-represented in the dosage compensation complex subunit ( KOG0921 ) , the homeobox transcription factor SIP1 ( KOG3623 ) , the muscarine acetylcholine receptor ( KOG4220 ) , the cadhedrin EGF LAG seven-pass GTP-type receptor ( KOG4289 ) , and the calcium-activated potassium channel slowpoke ( KOG1420 ) , relative to N . vitripennis ., Many of these genes have been implicated in D . melanogaster larval development , specifically during nervous system formation 62 , 63 ., As a result , an over-representation of these genes in A . cephalotes relative to N . vitripennis may indicate their association with a eusocial lifestyle , and in particular , caste and subcaste differentiation ., Genes that were found to be under-represented in A . cephalotes relative to N . vitripennis include core histone genes , nucleosome-binding factor genes , serine protease trypsins , and cytochrome P450s ( Table S5 ) ., These findings were confirmed by a domain-based comparison between A . cephalotes and all other sequenced insects ( Text S14 ) ., One of the most under-represented KOGs is trypsin , a serine protease used in the degradation of proteins into their amino acid constituents ., Trypsins in N . vitripennis are known to be part of the venom cocktail injected into its host , which helps necrotization and initiates the process of amino acid acquisition for developing larvae 35 , 64 ., In contrast to the protein-rich diet of N . vitripennis , A . cephalotes feed on gongylidia produced by their fungus , which represents a switch to a carbohydrate-rich ( 60% of mixture ) diet 65 ., These differences in diet may explain the under-representation of trypsin in A . cephalotes , as trypsin is likely not the primary mechanism used to digest nutrients obtained from the fungal cultivar ., Our analysis also revealed a reduction of trypsin genes in the other social insects relative to N . vitripennis , and this may also reflect their diets ., For example , honey dew is a major component of the diet of C . floridanus and contains primarily sugars 1 , while the honey/pollen diet of A . mellifera is composed primarily of carbohydrates , lipids , carbohydrates , vitamins , and some proteins 66 ., Because this under-representation of trypsin is consistent across social insects when compared to other sequenced insects ( Table S5 , Text S14 ) , this reduction may reflect the specific dietary features of these insects , or could indicate a loss of these genes across eusocial insects ., In addition to trypsin , cytochrome P450s were also found to be under-represented in both A . cephalotes and A . mellifera , relative to N . vitripennis , with reductions in both CYP3- and CYP4-type P450s ( Table S5 ) ., P450s in insects are important enzymes known to be involved in a wide range of metabolic activities , including xenobiotic degradation , and pheromone metabolism 67 ., We identified a total of 52 and 62 P450s in A . cephalotes and A . mellifera , respectively , which is similar to the low numbers reported for another insect , the body louse Pediculus humanus 29 ., These values represent some of the smallest amounts of P450s reported for any insect genome , and may represent the minimal number of P450s required by insects to survive ., Comparison of the A . cephalotes P450s against those of A . mellifera and P . humanus reveals that while there are some shared P450s , many are specific to each insect ( 15 ) ., In A . mellifera , the paucity of P450s is thought to be associated with the evolutionary underpinnings of its eusocial lifestyle 68 , although an enrichment of P450s in the ants C . floridanus and H . saltator 27 would seem to contradict this prediction ., It is therefore unclear why A . cephalotes has a small number of P450s relative to other ants , and future work will be necessary to provide insight into this apparent discrepancy ., A SNP analysis of the P450 genes in A . cephalotes did reveal that one of these , ACEP_00016463 , has 20 SNPs/kbp ( Text S13 ) ., Since P450s are known to undergo accelerated duplication and divergence 67 , the high number of SNPs in this particular P450 may reflect positive selection for new functions ., Given the tight obligate association that A . cephalotes has with its fungal mutualist , one might predict that it acquires amino acids from its fungus in a manner similar to that of the pea aphid Acyrthosiphon pisum , which obtains amino acids from its bacterial symbionts 28 ., To test this , we performed a metabolic reconstruction analysis using the Kyoto Encyclopedia of Genes and Genomes ( KEGG ) 69 ., A . cephalotes contains a nearly identical set of amino acid biosynthesis genes as A . mellifera , C . floridanus , H . saltator , and N . vitripennis , all of which are incapable of synthesizing histidine , isoleucine , leucine , lysine , methionine , phenylalanine , threonine , tryptophan , and valine de novo ., The only exception is arginine , and only A . cephalotes was found to lack the genes necessary for its biosynthesis ( Figure 3 ) ., Arginine , which is produced through the conversion of citrulline and aspartate 70 , 71 , is predicted to be synthesized at levels too low to support growth in insects 72 ., In A . cephalotes the 2 genes that catalyze the synthesis of arginine , argininosuccinate synthase ( EC 6 . 3 . 4 . 5 ) and argininosuccinate lyase ( EC 4 . 3 . 2 . 1 ) , were not found ( Figure 3 ) ., The loss of these two genes suggests a dependence on externally-acquired arginine , which we hypothesize , is provided by their fungus ., In the carpenter ant C . floridanus , arginine is thought to be synthesized from citrulline provided by its endosymbiont Blochmannia floridanus 73 , and this dependency is predicted to play an essential role in maintaining the carpenter ant-bacteria mutualism ., An extreme case has been reported for the pea aphid , which has lost its urea pathway and depends entirely on its endosymbiont , Buchnera aphidicola , for arginine 28 ., The loss of arginine biosynthesis in Atta may similarly be important for maintaining the leaf-cutter ant-fungus mutualism ., In line with this prediction , the fungus the ants cultivate contains all of the amino acids that A . cephalotes can not synthesize , including arginine 65 ., In addition to arginine biosynthesis , A . cephalotes may have also lost the need to rely on hexamerins as a source of amino acids during development ., In many insects , hexamerin proteins are synthesized by developing larvae and used as amino acid sources during development into the adult stage 74 ., Four hexamerins are commonly found across insects , including hex 70a , hex 70b , hex 70c , and hex 110 ., Comparison among the hymenopteran genomes reveals the presence of all hexamerins in varying copy number across all genomes except for A . cephalotes , which is missing hex 70c ( Figure 4 ) ( Text S16 ) ., In A . mellifera , hexamerins are expressed at different times , with hex 70a and hex 110 expressed during the larval , pupal and adult stage of workers , and hex 70b and hex 70c only expressed during the larval stage 74 ., The specific expression of hex 70b and hex 70c in larvae may reflect the increased need for these nutrients during early development ., Given that A . cephalotes larvae feed primarily on gongylidia , it is possible that amino acids supplemented by the fungus over the millions of years of this mutualism has relaxed selection for maintaining larval-stage hexamerins , and thus hex 70c may have been lost ., Future expression analyses of these genes at different life stages , in different castes , and under different nutritional conditions will likely confirm and elucidate their role ., Here we have presented the first genome sequence for a fungus-growing ant and show that its genomic features potentially reflect its obligate symbiotic lifestyle and developmental complexity ., An initial analysis of its genome reveals many characteristics that are similar to both solitary and eusocial insect genomes ., One hypothesis , based on the obligate mutualism of Atta cephalotes and its fungus , is that its genome exhibits reductions related to this relationship ., We have provided some evidence that A . cephalotes has gene reductions related to nutrient acquisition , and these losses may be compensated by the provision of these nutrients from the fungus ., For example , the extensive reduction in serine proteases may reflect the lack of proteins in its diet since the fungus primarily provides nutrients in the form of carbohydrates and free amino acids ., Furthermore , the loss of the arginine biosynthesis pathway in A . cephalotes may indicate the obligate reliance that it has on the fungus , as arginine is part of the nutrients that it provides to the ant ., This type of relationship appears to be conserved in other insect-microbe mutualisms , specifically in the pea aphid 28 and the carpenter ant 73 ., Finally , A . cephalotes appears to have lost a hexamerin protein that is conserved across all other insect genome sequences reported to date ., Loss of this protein , which is associated with amino acid sequestration during larval development , may be tolerated because larvae have a ready source of amino acids from the fungus ., These genomic features may serve as essential factors that have stabilized the mutualism over its coevolutionary history ., The sequencing and analysis of this genome will be a valuable addition to the growing number of insect genomes , and in particular will provide insight into both host-microbe symbiosis and eusociality in hymenopterans ., Three males from a single mature Atta cephalotes colony were collected in June 2009 in Gamboa , Panama ( lati | Introduction, Results/Discussion, Materials and Methods | Leaf-cutter ants are one of the most important herbivorous insects in the Neotropics , harvesting vast quantities of fresh leaf material ., The ants use leaves to cultivate a fungus that serves as the colonys primary food source ., This obligate ant-fungus mutualism is one of the few occurrences of farming by non-humans and likely facilitated the formation of their massive colonies ., Mature leaf-cutter ant colonies contain millions of workers ranging in size from small garden tenders to large soldiers , resulting in one of the most complex polymorphic caste systems within ants ., To begin uncovering the genomic underpinnings of this system , we sequenced the genome of Atta cephalotes using 454 pyrosequencing ., One prediction from this ants lifestyle is that it has undergone genetic modifications that reflect its obligate dependence on the fungus for nutrients ., Analysis of this genome sequence is consistent with this hypothesis , as we find evidence for reductions in genes related to nutrient acquisition ., These include extensive reductions in serine proteases ( which are likely unnecessary because proteolysis is not a primary mechanism used to process nutrients obtained from the fungus ) , a loss of genes involved in arginine biosynthesis ( suggesting that this amino acid is obtained from the fungus ) , and the absence of a hexamerin ( which sequesters amino acids during larval development in other insects ) ., Following recent reports of genome sequences from other insects that engage in symbioses with beneficial microbes , the A . cephalotes genome provides new insights into the symbiotic lifestyle of this ant and advances our understanding of host–microbe symbioses . | Leaf-cutter ant workers forage for and cut leaves that they use to support the growth of a specialized fungus , which serves as the colonys primary food source ., The ability of these ants to grow their own food likely facilitated their emergence as one of the most dominant herbivores in New World tropical ecosystems , where leaf-cutter ants harvest more plant biomass than any other herbivore species ., These ants have also evolved one of the most complex forms of division of labor , with colonies composed of different-sized workers specialized for different tasks ., To gain insight into the biology of these ants , we sequenced the first genome of a leaf-cutter ant , Atta cephalotes ., Our analysis of this genome reveals characteristics reflecting the obligate nutritional dependency of these ants on their fungus ., These findings represent the first genetic evidence of a reduced capacity for nutrient acquisition in leaf-cutter ants , which is likely compensated for by their fungal symbiont ., These findings parallel other nutritional host–microbe symbioses , suggesting convergent genomic modifications in these types of associations . | organismal evolution, evolutionary ecology, genome evolution, genome sequencing, coevolution, genome complexity, forms of evolution, comparative genomics, biology, evolutionary genetics, animal evolution, genomics, evolutionary biology, genomic evolution, genetics and genomics | null |
journal.pgen.1006985 | 2,017 | CTCF counter-regulates cardiomyocyte development and maturation programs in the embryonic heart | The coordinated deployment of genetic programs during lineage commitment and differentiation is a hallmark of developmental processes ., Cell specification and maturation are coordinated by controlled activation and repression of specific gene expression programs ., In the heart , the first functional organ in the embryo , activation of a core set of cardiogenic transcription factors controls specification of cardiac progenitor cells 1 ., Shortly after , high expression of genes encoding sarcomeric components defines the contractile cardiomyocyte as early as embryonic day ( E ) 8 . 5 ., Cardiomyocytes then mature by further sarcomere assembly 2 , and increased mitochondrial biogenesis 3 , 4 , and finally exit the cell cycle and become binucleated at early postnatal stages 5 ., Even though the genes and regulatory networks controlling morphogenesis and function in the heart are well characterized 6 , the events that coordinate the progression from differentiation to maturation are not understood ., Recent studies using both mouse and human pluripotent cells have revealed that epigenomic landscapes and chromatin signatures dynamically change during cardiomyocyte differentiation 7 , 8 , suggesting that chromatin structure might control cardiogenesis ., Chromatin conformational changes allow physical interaction of distal regulatory elements in the genome ., However , the chromatin interactions controlling expression of cardiac development and maturation are poorly understood ., The study of genome function during the last decade 9 , 10 has provided an initial understanding of how functional elements scattered throughout the genome act coordinately to control gene activity ., The advent of tools to analyze interactions between distal regions of chromatin 11 has allowed detailed mapping of the three-dimensional genome structure 12 , 13 and its organization in distinct regulatory domains 14 ., However , how these domains are established , and their function in gene expression regulation are poorly understood ., CTCF ( CCCTC-binding factor ) is one of the best described architectural proteins with a role in chromatin structure organization ., Through sequence specific binding to DNA , CTCF acts as a barrier for heterochromatin spreading , establishes boundaries between adjacent topologically associating domains ( TADs ) , defines insulator elements that block enhancer activity on promoters , and contributes to enhancer-promoter interactions 15 , 16 ., Loss of function studies using knock-out and knock-down approaches have shown that CTCF is essential in early embryo development 17–19 ., Conditional deletion of Ctcf in different developmental contexts leads to defects in cell cycle progression 17 , increased apoptosis 20 , 21 , and its deletion in postmitotic neurons leads to decreases in the expression of clustered protocadherin genes 22 ., Yet , we still do not fully understand how CTCF controls chromatin structure to coordinate gene expression ., Here , we have studied how CTCF regulates gene expression in the context of the developing mammalian heart ., We deleted Ctcf in a population of cardiac progenitor cells , which results in cardiac malformations and embryonic death ., Analysis of global transcriptional changes preceding morphological defects caused by loss of CTCF showed downregulation of the cardiac developmental program , and concomitant upregulation of programs involved in cardiomyocyte maturation ., This suggests that Ctcf deletion causes a premature arrest of cardiac development and concomitantly promotes cardiomyocyte maturation ., Thus , CTCF mediates local chromatin interactions to coordinate transcriptional programs that control developmental transitions in the heart ., To address the role of CTCF during development , we deleted Ctcf in cardiac progenitor cells and their derivatives by using a floxed allele 17 and the Nkx2 . 5-Cre driver , which starts acting as early as E7 . 5-E8 . 0 in cardiomyocyte precursors in the cardiac crescent 23 ., E10 . 5 or E11 . 5 mutant ( Ctcffl/fl;Nkx2 . 5-Cre ) embryos appeared normal and showed no gross morphological alterations in the heart ( Fig 1A–1D ) ., At E12 . 5 , mutant embryos presented pericardial edema ( Fig 1E ) and the cardiac chambers did not expand properly ( Fig 1F ) ., Histological examination showed no defects in E9 . 5 mutant hearts as compared to controls ( Fig 1G and 1H ) ., In E10 . 5 mutants , the four chambers and the atrioventricular canal formed properly , although the interventricular septum appeared slightly disorganized ( Fig 1I and 1J ) ., This defect was exacerbated by E11 . 5 , when thinning of the myocardial wall was also evident ( Fig 1K and 1L ) ., No mutant embryos were recovered beyond E12 . 5 ( S1 Table ) ., Control Nkx2 . 5-Cre and compound Ctcffl/+;Nkx2 . 5-Cre heterozygotes showed normal morphology at these stages ( S1 Fig ) ., To understand the effect of Ctcf deletion in the developing heart , we determined the time point when CTCF protein was lost in cardiomyocytes ., We performed co-immunostaining for CTCF and the cardiomyocyte marker TNNT2 at different stages of heart development ( S2 Fig ) ., In E9 . 5 mutant hearts , 46% of cardiomyocytes still had detectable nuclear CTCF , albeit at lower levels than in controls , in which all nuclei were double positive ( S2A and S2B Fig; S2 Table ) ., However , by E10 . 5 we were not able to identify any cardiomyocyte expressing CTCF in mutant hearts , although expression was present in endocardium ( S2C and S2D Fig ) ., The same pattern was observed at E11 . 5 ( S2E and S2F Fig ) ., Persistent CTCF protein in cardiomyocytes at E9 . 5 , despite that Nkx2 . 5-Cre is active from E7 . 5-E8 . 0 23 , could be explained by protein long half-life 24 , 25 ., It has been recently shown that 15% of CTCF is sufficient for proper function 26 ., Therefore , the remaining protein we observe can explain why morphological defects are not detected until E10 . 5 ., We assessed whether cardiac defects were due to decreased cell proliferation or increased apoptosis ., Numbers of cardiomyocytes positive for TUNEL staining ( S3A–S3F , S3M and S3O Fig; S3 Table ) or phosphorylated histone H3 ( S3G–S3L , S3N and S3P Fig; S3 Table ) were not altered in mutant hearts at E10 . 5 and E11 . 5 , indicating that apoptosis and proliferation were unaffected ., Accordingly , we did not observe a difference in the number of cardiomyocytes between mutants and controls at these stages ( S3 Table ) ., Our results differ from others showing that CTCF loss in other developmental systems cause increased apoptosis 20 , suggesting context-specific effects of CTCF ., Our results indicate that Ctcf is required for cardiac morphogenesis ., To understand the function of CTCF as a transcriptional regulator in the developing heart , we analyzed the global effects of CTCF loss on gene transcription ., We performed RNA-seq analysis on hearts homozygous for cardiac-specific deletion of Ctcf ( Ctcffl/fl;Nkx2 . 5-Cre ) ; heterozygotes , with deletion of only one allele ( Ctcffl/+;Nkx2 . 5-Cre ) ; and control heterozygotes for the floxed allele but not carrying the Nkx2 . 5-Cre driver ( Ctcffl/+ ) ., To identify the earliest transcriptional effects of Ctcf deletion this analysis was performed at E10 . 5 , when we first observed a complete loss of CTCF in the mutants ., Comparison between homozygous Ctcf-deleted hearts and heterozygotes or controls yielded approximately 2 , 000 differentially expressed genes in each case , of which roughly half were upregulated and half downregulated ( S4 Table ) ., Interestingly , comparison between heterozygotes and controls returned only 24 differentially expressed genes , including Ctcf itself and11 pseudogenes ( S4 Table ) ., This suggests that , at least in the developing heart 27 , one functional Ctcf allele is sufficient for correct regulation of gene expression ., Gene-ontology analysis 28 of all genes differentially expressed upon Ctcf deletion showed enrichment in terms related to developmental processes , including heart development , contractile fibers , translation and mitochondria ( Fig 2A; S5 and S6 Tables ) ., When we analyzed upregulated and downregulated genes separately , we found a clear distinction in the functional categories enriched in each case ., Numerous upregulated genes were enriched in categories related to translation and mitochondrial function ., In contrast , the downregulated genes were over represented in categories related to heart development and the sarcomere ( S5 and S6 Tables ) ., Detailed analysis revealed that more than 300 genes related to mitochondrial function were up- and down regulated in Ctcf mutant hearts ., Many of such genes encode subunits of mitochondrial respiratory complexes I , III , IV and V/ATP synthase , Sdhd in Complex II , and the large and small mitochondrial ribosome subunits ( S4A Fig; S6 Table ) ., This could suggest that CTCF controls expression of core transcriptional regulators of the mitochondrial gene program ., However , none of these factors , such as PGC-1α , ERRs , or NRF-1/2 29 , where dysregulated in mutant hearts ( S4 Table ) ., Other upregulated genes are involved in translation and encode most cytoplasmic ribosomal proteins , various initiation and elongation factors , and members of the spliceosomal complex ( S4B Fig; S6 Table ) ., CTCF organizes chromatin structure and contributes to the establishment of regulatory domains in the genome 30 ., We found that genes misregulated in Ctcf mutant hearts do not cluster in specific genomic regions ( S5A Fig ) , suggesting that CTCF does not control gene expression in large genomic regulatory domains similarly to what has been recently shown in embryonic stem cells 26 ., We next examined CTCF chromatin binding near genes whose expression changed upon Ctcf deletion in the heart by mapping the distance between the transcriptional start site ( TSS ) and the nearest CTCF ChIP-seq peak obtained from published datasets on adult 8 week hearts 10 ., We found that the up- and downregulated genes are closer to a CTCF binding site than genes whose expression does not change upon Ctcf deletion ( S5B Fig ) ., Arbitrarily analyzing 10 or 20 kb windows based on the above distribution , we found that TSS of down- and up-regulated genes are surrounded by CTCF binding sites more frequently than genes whose expression did not change in mutants ( Fig 2B ) ., These CTCF binding sites are conserved across multiple tissues , and we did not observe enrichment for heart-specific CTCF peaks near genes deregulated in the mutant ., These results suggest that CTCF regulates gene expression mainly by binding nearby genomic regions , possibly by mediating local chromatin interactions ., CTCF could define gene regulatory domains by shielding genes from the influence of nearby enhancers or by facilitating enhancer-promoter interactions 15 ., In the first scenario , Ctcf loss would lead to upregulation of gene expression and to downregulation in the second ., To distinguish between these two possibilities , we determined the distance between dysregulated genes and the nearest heart enhancer 10 ., We found that downregulated , but not upregulated , genes are significantly closer to a heart enhancer than expressed genes with no change in Ctcf mutants ( S5B Fig ) ., Again , this pattern is preserved when analysis is restricted to a 10 or 20 kb window surrounding the TSS of differentially expressed genes ( Fig 2B ) ., As downregulated genes are enriched in developmental regulators , these results suggest that CTCF promotes enhancer-promoter interactions in genes controlling cardiac progenitor establishment and differentiation ., The previous results suggest specific genomic features of up- or downregulated genes in Ctcf mutant hearts ., To further explore these features , we analyzed in more detail the distribution of CTCF binding peaks on the same dataset used above surrounding the TSS of dysregulated genes ., Up- and downregulated genes showed enrichment in CTCF binding immediately upstream of the TSS , and the binding signal was higher than that in genes whose expression did not change in mutants ( Fig 2C ) , which agrees with the previous result ( Fig 2B ) ., However , when we analyzed only the dysregulated genes belonging to development , or mitochondria and translation categories ( S6 Table ) we observed a clear difference ., Mitochondrial and translation genes showed increased CTCF binding near the TSS , but in developmental genes CTCF binding spread over more distal regions ( Fig 2C ) ., We searched promoter-proximal and distal sites for de novo and known sequence motifs to address the possibility that the presence of CTCF sites or other motifs would underlie the differences in distribution between categories ., However , we only identified binding motifs for CTCF itself ( S7 Table ) ., Our results suggests that Ctcf binds near the TSS to repress genes acting in mitochondria and regulating translation , both crucial for cardiomyocyte maturation 29 ., In contrast , CTCF binding to genomic regions more distal to TSS promotes expression of genes located near heart enhancers and controlling cardiac development ., Dramatic changes to the nuclear-encoded mitochondrial transcriptome , particularly of proteins involved in oxidative phosphorylation ( OXPHOS ) system , in Ctcf mutant hearts prompted us to analyze the components of this pathway in mutant and control embryonic cardiomyocytes ., In agreement with the RNA-seq ( S4 Table ) , Western blot revealed increase in Complex IV subunit I ( Cox I , encoded in mitochondrial DNA ) and Complex IV subunit IV ( Cox IV , encoded by Cox4i1 ) in Ctcf mutant hearts at E10 . 5 and E11 . 5 , as compared to controls ( Fig 3A ) ., Similarly increased was Tom20 ( encoded by Tomm20 ) ( Fig 3B ) , which is the mayor receptor of the mitochondrial outer membrane translocase ., The abundance of other mitochondrial proteins encoded by genes whose expression did not change in Ctcf mutants ( S4 Table ) , such as Grp75 , a key stress chaperone regulating mitochondrial protein translocation , folding and functions 31; and Tfam , the transcriptional regulator of mitochondrial DNA 32 , was comparable between control and mutant hearts ( Fig 3B ) ., We asked whether upregulation of OXPHOS components favors functional assembly of respiratory complexes and supercomplexes on the mitochondrial inner membrane ., Blue-native gel electrophoresis revealed assembled complexes I , IV and V , and supercomplex I+III2 in both control and mutant hearts at E10 . 5 and E11 . 5 ( Fig 3C ) ., The mitochondrial voltage-dependent anion channel ( Vdac ) , whose assembly is independent of respiratory complexes and supercomplexes , and whose encoding gene ( Vdac1-3 ) levels did not change in mutants ( S4 Table ) , was used as loading control ., In control hearts , Complex I , both in the free form I or in the form of I+III2 , and Complex V substantially increased from E10 . 5 to E11 . 5 ( Fig 3C and 3D ) , consistent with maturation of mitochondrial OXPHOS 4 ., In contrast , levels of I+III2 and I did not increase from E10 . 5 to E11 . 5 in Ctcf mutant hearts ., Complex V was similarly increased between E10 . 5 and E11 . 5 in control and Ctcf mutant hearts ( Fig 3C and 3D ) ., This agrees with maturation/stabilization of the electron transport chain components being regulated independently of their production 33 ., Our results suggest that despite increased transcription of subunits of CI and CIII that may lead to more complexes and supercomplexes assembled at the mitochondrial inner membrane , maturation of the respiratory chain is blunted in the Ctcf mutant heart ., Transmission electron microscopy ( TEM ) analysis revealed immature , but overall normal mitochondria with healthy cristae packaging in Ctcf mutant cardiomyocytes at E10 . 5 ( Fig 3E ) ., Mitochondria in control E11 . 5 cardiomyocytes have a more electro-dense matrix containing more cristae than E10 . 5 , and are embedded in newly assembled sarcomere ., This ultrastructure change agrees with blue native gel electrophoresis data and is consistent with cardiomyocyte maturation occurring between E10 . 5 and E11 . 5 3 , 4 ., Mitochondria in Ctcf mutant cardiomyocytes at E11 . 5 are swollen and larger than controls , and are disorganized and scattered through the cytoplasm ( Fig 3E; S6A Fig ) ., Immunohistochemistry , targeting the mitochondrial outer membrane component Tom20 , revealed disorganized mitochondria in Ctcf mutant cardiomyocytes at E10 . 5 ( S6B Fig ) ., TEM analysis also revealed that E10 . 5 mutant , but not control , cardiomyocytes have long and continuous sarcomeres , which were also visible at E11 . 5 ( Fig 3E ) ., Quantification on images of cardiomyocytes from E10 . 5 hearts immunostained for α-actinin revealed comparable numbers of sarcomeric Z-bands between control and Ctcf mutants ( S6C and S6D Fig ) ., These results suggest that sarcomere assembly , but not sarcomeric component synthesis , is premature in the Ctcf mutant embryonic heart ., The set of genes downregulated in Ctcf mutant hearts that are enriched in heart development functional categories include key transcription factors and members of several signaling pathways controlling cardiac development ( Fig 4A ) ., In situ hybridization confirmed downregulation of the transcription factors Nkx2-5 and Hopx in the Ctcf mutant heart at E10 . 5 ., ( Fig 4B and 4C ) ., This analysis also showed reduced expression of Nppa to a more restricted domain in the left ventricle , and loss of Pitx2 expression in the right ventricle ( Fig 4D and 4E ) ., Our RNA-seq showed reciprocal up and downregulation of genes from the Tnnt1/Tnni3 and Tnni2/Tnnt3 troponin clusters ( Fig 4F and 4G ) ., Accordingly , in situ hybridization revealed strong downregulation of Tnnt1 and Tnni2 , and upregulation of Tnni3 in atria and ventricles and of Tnnt3 in atria in the Ctcf mutant heart ( Fig 4H–4K ) ., These changes in the expression pattern of troponin genes , which are arranged in clusters in the genome , suggest that CTCF coordinates their expression during development ., Irx4 , which encodes a transcription factor critical for heart development 34 , 35 was downregulated in Ctcf mutants ., We analyzed this gene and its genomic context in more detail as a means to understand the mechanisms through which CTCF regulates transcription in vivo ., Irx4 forms part of the 1 . 5 Mb IrxA cluster , which also contains the related Irx1 and Irx2 genes 36 ., All three IrxA genes are expressed in the developing heart , in distinct but partially overlapping patterns ., Whereas Irx1 and Irx2 express at low levels in the interventricular septum , Irx4 is strongly expressed throughout the ventricles 37 ., Previous studies have shown that the IrxA cluster is regulated through long-distance gene-specific interactions 38 ., Furthermore , our RNA-seq analysis revealed significant upregulation of the three genes closest to Irx4 outside the IrxA cluster: Ndufs6 , encoding a subunit of mitochondrial Complex I; Mrpl36 , encoding a mitochondrial ribosomal protein; and Lpcat1 , involved in phospholipid metabolism ( Fig 5A; S4 Table ) ., To confirm and extend these observations , we examined the expression of the three genes in the IrxA cluster , and of their immediate neighbor Ndufs6 , in control and mutant hearts by in situ hybridization ., Expression of Irx4 in mutant hearts was indistinguishable from controls at E9 . 5 ( Fig 5B ) , but expression levels were strongly reduced in mutants at E10 . 5 ( Fig 5C ) and E11 . 5 ( Fig 5D and 5J ) ., The RNA-seq analysis showed that Irx1 and Irx2 expression was not significantly different in mutants ., However , in situ hybridization showed that their expression domains expanded from the interventricular septum to the adjacent trabecular myocardium in the right and left ventricle of Ctcf mutants ( Fig 5E , 5F , 5H and 5I ) ., In control hearts , Ndufs6 was expressed ubiquitously but with higher intensity in the ventricles , similar to Irx4 , but its expression was subtly increased in mutant hearts ( Fig 5G ) ., Together , these observations suggest that loss of CTCF leads to overall dysregulation of the IrxA cluster and its neighboring genes , perhaps through modification of its 3D structure ., To uncover the function of CTCF in regulating the IrxA chromatin structure we performed chromosome conformation capture followed by deep sequencing ( 4C-seq ) 39 , using as viewpoints the promoters of Irx4 and Ndufs6 in E11 . 5 control and homozygous mutant ( Ctcffl/fl;Nkx2 . 5-Cre ) hearts ( Fig 6A and 6B; S7 Fig ) ., In controls , the promoter of Irx4 interacted strongly with the Irx2 promoter and with specific CTCF binding sites located upstream and downstream in the Irx2/Irx4 and Irx4/Ndufs6 intergenic regions , respectively ( asterisks in Fig 6A ) ., The CTCF binding sites were previously identified by ChIP-seq in E14 . 5 and 8-week hearts 10 ) ., In mutants , the interaction of the Irx4 promoter with the Irx2/Irx4 intergenic CTCF site was lost , and new interactions appeared upstream and 350 kb downstream in the Clptm1l locus ( Fig 6B ) ., The Ndufs6 promoter established interactions with the promoters of Irx4 , Lpcat1 and Clptm1l as well as with the two intergenic CTCF sites in control hearts ., In CTCF mutants , interactions of the Ndufs6 promoter with both CTCF binding sites and the Irx4 promoter were lost , and only contacts with the promoters of the downstream genes Lpcat1 and Clptm1l were maintained ( Fig 6B ) ., Finally , we used a viewpoint for 4C-seq the Irx2/Irx4 intergenic CTCF site , which interacts with the promoters of Irx4 and Ndufs6 ( Fig 6B , S7 Fig ) ., In controls , the Irx2/Irx4 intergenic CTCF site interacts upstream and downstream of and extended region spanning Irx4 , Ndufs6 and Mrpl36 , and the downstream Clptm1l locus ( Fig 6B ) ., Interaction between the CTCF site and the Irx4 promoter was greatly reduced in Ctcf mutants , reciprocating the pattern observed when using Irx4 as viewpoint ., Furthermore , novel interactions appeared that extend to Irx2 ( Fig 6B ) ., Overall , these results suggest that CTCF plays a central role in organizing chromatin in the Irx4 regulatory domain by mediating the interaction of several CTCF-bound sites with various promoters in the region ., Removal of CTCF from the developing heart re-structures the local 3D organization of the extended IrxA cluster; this results in loss of the interaction between Irx4 and Ndufs6 with flanking CTCF sites ., These results also suggest that CTCF limits chromatin interactions domains , as its loss causes an expansion of local contacts ., To unambiguously demonstrate that the CTCF binding site located in the Irx2/Irx4 intergenic region is necessary for proper expression of Irx4 , we generated a mouse line in which we deleted such CTCF binding site using the CRISPR/Cas9 system 40 ( S8 Fig ) ., Homozygote mice for the deletion are viable and fertile , as expected since Irx4 mutant mice are also viable in homozygosity and only show mild hypertrophy and compromised contractility as adults 34 ., We analyzed the expression of Irx4 by in situ hybridization in E10 . 5 embryos from this line ., Irx4 was slightly reduced in the ventricles of the mutant line as compared with controls ( Fig 6C and 6D ) ., Irx4 was also ectopically expressed in the oral-esophageal region ( Fig 6E and 6F ) , in which Irx1 is normally expressed ( Fig 6G ) ., Therefore , deletion of the Irx2/Irx4 intergenic CTCF site leads to expansion of the expression domain of Irx4 , perhaps by allowing Irx1 regulatory elements to contact and activate Irx4 ., We have thus shown how CTCF is critical to maintain the correct chromatin structure across the IrxA cluster and neighboring genes , and that specific CTCF binding sites are essential for the proper regulation of gene expression ., Recent years have seen substantial advances in our understanding of the relationship between chromatin structure and gene expression ., It is now clear that the spatial organization of the genome sets constraints that determine how different functional elements ( promoters , enhancers , and boundaries ) interact with one another 14 ., Nevertheless , we still do not fully understand how this 3D structure is maintained or the role played by chromatin-bound factors in this process ., In this study we have explored how one of these factors , CTCF , regulates gene expression and genome structure , by analyzing the effects of its loss during the development of the mammalian heart ., Deletion of Ctcf in the developing heart rapidly leads to cardiac defects and embryonic death ., However , transcriptomic analysis shows that loss of CTCF does not lead to dysregulation of gene expression across large chromosomal domains; rather , changes appear to be local , as described in other developmental settings 20 ., Upregulated and downregulated genes are both more likely to have CTCF binding sites in their vicinity , but only downregulated genes are closer to heart enhancers 10 ., Downregulated genes include major regulators of the cardiac developmental program , strongly suggesting that CTCF facilitates enhancer-promoter interactions for these genes in a tissue-specific fashion ., In contrast , upregulated genes are highly enriched for genes involved in mitochondrial function and protein translation , which interestingly show higher levels of CTCF binding close to their promoters ., Cardiomyocyte maturation involves an increase in the demand for energy and protein production , which is accompanied by increased transcription of mitochondrial and ribosomal genes 8 ., Cardiomyocytes lacking CTCF prematurely activate these programs , but fail to maintain functional mitochondria despite the increase in transcription ., Therefore , we observe a premature maturation of cardiomyocytes lacking CTCF , accompanied by the shutting down of developmental and patterning processes ., Nevertheless , there is a lack of coordination of this precocious differentiation , leading to embryonic lethality at E12 . 5 ., To gain insight into the relationship between CTCF , genome structure and the regulation of gene expression , we analyzed the IrxA gene complex ., This complex is a paradigm of gene clustering 36 , with genes expressed in overlapping but distinct domains in the developing heart 37 and separated into distinct structural and regulatory domains 38 ., We observed changes in the levels and pattern of IrxA gene expression in Ctcf mutants , and interestingly detected similar changes in unrelated genes neighboring Irx4 ., This strongly suggests that the changes in the extended IrxA cluster are caused by the loss of CTCF binding in the region , and are not a secondary effect of changes in other genes ., Analysis by 4C-seq in wild type and mutant hearts showed that when CTCF is lost , the Irx4 promoter forms fewer contacts with CTCF-flanking sites and gains interactions with regions situated outside of its regulatory domain ., Furthermore , the promoter of the mitochondrial Ndufs6 gene also losses interactions with these CTCF sites ., These changes in promoter interactions in Ctcf mutants were mirrored by 4C analysis of the Irx2/Irx4 intergenic CTCF site ., Consequently , deletion of this CTCF site leads to reduced Irx4 cardiac expression accompanied by gain of expression in novel territories ., Therefore , in this context CTCF is acting both as an insulator to separate adjacent regulatory domains , and as a facilitator of promoter-enhancer interactions to ensure proper gene expression within these domains ., The structure we have defined for Irx4 is reminiscent of the recently described super-enhancer domains ( SD ) , where regions of a few hundred kilobases located between CTCF sites are organized as insulated neighborhoods within large-scale topological domains 41 ., The domain we identify would contain tissue specific enhancers together with their target genes ., As is the case with SDs , loss of CTCF leads to downregulation of the gene central to this domain ( Irx4 ) and upregulation of flanking genes ( Irx1 and Irx2 on one side; Ndufs6 , Mrpl36 , and Lpcat1 on the other ) ., Overall , our analysis suggests that chromatin structure is relatively stable during development and that the deletion of CTCF does not cause a marked disassembly of this organization , in line with recent reports 42 ., However , local intra-TAD domains loops 16 , 43 are affected , leading to dysregulation of genes important in the cardiac developmental program as we observe for the IrxA genes ., More puzzling is the observation that only a limited set of genes is altered by Ctcf deletion in the heart ., CTCF binds to thousands of sites throughout the genome , most of which are shared between different tissues and cell lines 10 , 12 , 44 ., An essential role for CTCF in determining and maintaining chromatin structure and organization was therefore anticipated 30 ., Accordingly , constitutive loss of CTCF results in very early embryonic death 17–19 and its selective deletion in different developmental contexts leads to profound defects in the targeted organ or tissue , usually through increased apoptosis 20–22 ., However , in all of these cases the same pattern is observed: despite wide distribution of CTCF binding , only a fraction of expressed genes is dysregulated ., Furthermore , there is little overlap among genes regulated by CTCF in different systems , indicating a context-specific role of this factor ., In the developing heart , we observe the concomitant upregulation and downregulation of maturation and developmental programs , suggesting that the role of CTCF here is to maintain the coordination of expression transitions ., In this scenario , only genes subject to dynamic regulation at the time of Ctcf deletion would show changes in expression ., The description of different models of neural-specific deletion of Ctcf is compatible with this interpretation ., When deleted in precursors during neural development , CTCF loss leads to apoptosis and subsequent death 21 , 22 ., However , deletion in postnatal neurons results in long-term survival of Ctcf mutant neurons , but activity-induced changes of gene expression are altered 45 ., Together , these observations suggest that CTCF , and possibly local chromatin structure , are not necessary for basal gene activity but essential for dynamic transitions in expression ., Mice were bred in the core animal facility in the Centro Nacional de Investigaciones Cardiovasculares in accordance with national and European legislation ., All procedures were approved by the CNIC Committee of Animal Welfare and by the Madrid Autonomous Government Regional Ministry of the Environment and Territorial Organisation ( reference number PROEX 196/14 ) ., The Ctcf floxed allele and Nkx2 . 5-Cre line have been previously described 17 , 23 ., Primers used for genotyping are detailed in S8 Table ., Ctcffl/+ or Ctcffl/fl embryos were used as controls ., Mice were bred in the core animal facility in the Centro Nacional de Investigaciones Cardiovasculares in accordance with national and European legislation ., Some of the experiments were performed in Toronto , and they were approved by the Toronto Centre for Phenogenomics Animal Care Committee ., Whole mount embryos were dissected in cold PBS and imaged using a Nikon SMZ1500 microscope with NIS-Elements BR 4 . 12 . 01 imaging software ., For sections , embryos were collected in cold PBS and fixed in 4% PFA overnight at 4°C , dehydrated in an ethanol series , embedded in paraffin , and sectioned at 5 μm for immunostaining amd hematoxylin and eosin , and at 7 μm for in situ hybridization ., Sections were observed under an Olympus BX51 microscope and photographed with an Olympus DP71 digital camera ., 5 μm paraffin sections were incubated with CTCF 1:1500 ( Bethyl labs A300-543A ) and CT3 1:10 ( Hybridoma Bank ) or processed for histological analysis by hematoxylin and eosin staining ., For TUNEL the Terminal Transferase recombinant kit ( Roche 03 333 574 001 ) and biotin-16-dUTP ( Roc | Introduction, Results, Discussion, Materials and methods | Cardiac progenitors are specified early in development and progressively differentiate and mature into fully functional cardiomyocytes ., This process is controlled by an extensively studied transcriptional program ., However , the regulatory events coordinating the progression of such program from development to maturation are largely unknown ., Here , we show that the genome organizer CTCF is essential for cardiogenesis and that it mediates genomic interactions to coordinate cardiomyocyte differentiation and maturation in the developing heart ., Inactivation of Ctcf in cardiac progenitor cells and their derivatives in vivo during development caused severe cardiac defects and death at embryonic day 12 . 5 ., Genome wide expression analysis in Ctcf mutant hearts revealed that genes controlling mitochondrial function and protein production , required for cardiomyocyte maturation , were upregulated ., However , mitochondria from mutant cardiomyocytes do not mature properly ., In contrast , multiple development regulatory genes near predicted heart enhancers , including genes in the IrxA cluster , were downregulated in Ctcf mutants , suggesting that CTCF promotes cardiomyocyte differentiation by facilitating enhancer-promoter interactions ., Accordingly , loss of CTCF disrupts gene expression and chromatin interactions as shown by chromatin conformation capture followed by deep sequencing ., Furthermore , CRISPR-mediated deletion of an intergenic CTCF site within the IrxA cluster alters gene expression in the developing heart ., Thus , CTCF mediates local regulatory interactions to coordinate transcriptional programs controlling transitions in morphology and function during heart development . | Properly regulated gene expression in time and space during development and differentiation requires not only transcriptional inputs , but also specific structuring of the chromatin ., CTCF is a DNA binding factor that is believed to be critical for this process through binding to tens of thousands of sites across the genome ., Despite the knowledge gained in recent years on the role of CTCF in genome organization , its functions in vivo are poorly understood ., To address this issue , we studied the effect of genetically deleting CTCF in differentiating cardiomyocytes at early stages of mouse development ., Surprisingly only a fraction of genes change their expression when CTCF is removed ., Importantly , misregulated genes control opposing genetic programs in charge of development and patterning on one hand , and cardiomyocyte maturation on the other ., This imbalance leads to faulty mitochondria and incorrect expression of cardiac patterning genes , and subsequent embryonic lethality ., Our results suggest that CTCF is not necessary for maintenance of global genome structure , but coordinates dynamic genetic programs controlling phenotypic transitions in developing cells and tissues . | medicine and health sciences, cardiovascular anatomy, gene regulation, cardiac ventricles, developmental biology, mitochondria, epigenetics, bioenergetics, embryos, mammalian genomics, cellular structures and organelles, chromatin, embryology, genomics, chromosome biology, gene expression, animal genomics, biochemistry, anatomy, cell biology, genetics, biology and life sciences, energy-producing organelles, heart | null |
journal.pgen.1002064 | 2,011 | Genome of Herbaspirillum seropedicae Strain SmR1, a Specialized Diazotrophic Endophyte of Tropical Grasses | Soil bacteria can interact in many ways with plant partners ranging from beneficial to pathogenic ., Among beneficial interactions the rhizobia play a central role , forming symbioses with legume species to produce nitrogen-fixing nodules , which supply most of the required fixed nitrogen to many agriculturally important crops such as soybean , pea , beans and clover ., A now well-characterized class of diazotrophic bacteria capable of establishing endophytic associations and promoting plant-growth of important cereal and forage grasses such as wheat , rice and maize has been investigated in recent years ., Among such well-known species are Azospirillum brasilense , Gluconacetobacter diazotrophicus and H . seropedicae 1 ., The colonization of plant tissues by these bacteria may involve the interplay of many as yet unidentified biochemical signals and gene products from both partners ., H . seropedicae is an aerobic , prototrophic , endophytic nitrogen-fixing , plant-growth promoting bacterium , of the Betaproteobacteria found inside tissues of important crops such as corn , sugar-cane , rice , wheat and sorghum without causing disease to the plant partner 2–9 , and has a low survival rate in plant-free soil 5 ., It fixes nitrogen under conditions of ammonium and oxygen limitation 5 and can express nif genes in planta 6–11 ., Moreover , H . seropedicae is an active plant colonizer and has been shown to promote plant growth and increase grain production 4 , 9 , 12 ., Aluminum tolerant varieties of rice were shown by the 15N2 dilution technique to incorporate significant amount of nitrogen derived from nitrogen fixation 4 , 9 ., Ecological , agronomic , physiological , genetic and biochemical aspects of this organism have been reviewed 1 , 12–14 ., The genome of H . seropedicae strain SmR1 , a spontaneous streptomycin resistant mutant of strain Z78 15 ( ATCC 35893 ) was sequenced and annotated by the Paraná State Genome Programme ( Genopar Consortium , www . genopar . org ) ., Reads from the Sanger automatic sequencing ( 125 , 000 ) and from a full 454 FLX Titanium Roche Pyrosequencer run ( 1 , 220 , 352 ) , corresponding to 100 times the coverage of the estimated genome size , were assembled to produce the genome sequence ., End-sequencing of approximately 700 cosmids with an average insert of 40 kb was used to validate the final assembly ., The genome consists of a single circular chromosome of 5 , 513 , 887 base pairs with 63 . 4% G+C content ( Table, 1 ) and a total of 4 , 735 potential ORFs , encoding 3 , 108 proteins with assigned functions , 497 with general function prediction only and 1 , 130 with no known function , covering 88 . 3% of the genome ., Coding sequences for 55 tRNA representing all 20 protein amino acids were also identified ., The genome has 3 complete rRNA operons , one in the positive and two in the negative strand , all containing a pair of Ile-tRNA/Ala-tRNA genes in the intergenic region between the 16SrRNA and 23SrRNA genes ( Figure 1 ) ., Genes for 19 of the 20 aminoacyl-tRNA synthetases are present with the exception of a gene coding for asparaginyl-tRNA synthetase ., The biosynthesis of aspartyl-tRNAAsn or glutamyl-tRNAGln occurs via transamidation catalysed by an Asp-tRNAAsn/Glu-tRNAGln amidotransferase , an enzyme coded by the gatBAC operon as in most Bacteria 16 ., These genes are widely spread among bacteria and are found in the genomes of other closely-related Betaproteobacteria such as Herminiimonas arsenicoxydans , bacteria of the Burkholderia genus , and Minibacterium massiliensis ( Janthinobacterium species Marseille ) ., The probable origin of replication was identified based on the GC skew 17 and the positions of the genes dnaA , dnaN and gyrB ., It maybe contained in the dnaA-dnaN intergenic region or upstream dnaA , where DnaA binding sequences were found ., The region upstream of dnaA is unique , since instead of the rpmHrnpA operon present in most Proteobacteria it contains a probable glutamine amido transferase type II gene ., Downstream from the dnaA , dnaN and gyrB genes there is a low G+C content ( 52% ) region spanning 16 . 6 kbp of probable lateral transfer origin containing a reverse transcriptase gene of bacterial retrotransposons ( RT_Bac_retron_I ) ., In the H . seropedicae genome 18 regions of probable lateral transfer origin , such as insertion sequences and phages were found ., The two largest regions contain genes of bacteriophage origin ., Region 1 ( 213 , 067 to 238 , 374 ) has a higher G+C content ( 66 . 4% ) than the genome and contains 33 ORFs related to phage capsid assembly , regulation and phage transcription ., Region 2 ( 967 , 869 to 1 , 006 , 417 ) has a lower G+C content ( 58 . 1% ) with 52 ORFs , many related to phage P2 ., One of the low G+C content ( 53 . 9% ) regions contains a plasmid addiction module ( operon phd/doc ) coding for the PHD ( prevents-host-death ) and DOC ( death-on-curing ) proteins constituting a toxin-antitoxin ( TA ) module 18 ., There are 3 , 412 PHD ( http://www . ebi . ac . uk/interpro/IEntry ? ac=IPR006442 ) and 707 DOC ( http://www . ebi . ac . uk/interpro/IEntry ? ac=IPR006440 ) protein sequences described in the Domain Bacteria , suggesting the widespread occurrence of this protection mechanism ., ‘Toxin–antitoxin ( TA ) modules’ have been recognized as playing important roles in bacterial stress physiology and genome stabilization 18–20 ., Genes coding for two partial and two complete transposases and 5 phage-related ( three complete and two partial ) and two genomic ( xerC and xerD ) recombinases/integrases were found in the H . seropedicae genome ., The relative few number of genes related to mobile elements seems to be a common feature in all the genomes of endophytic bacteria ( http://www . expasy . ch/sprot/hamap/interactions . html#Plant_endophyte ? ) sequenced to date ., The exception is G . diazotrophicus Pal5 , with 138 transposases and 223 insertion sequences 21 ., The paucity in the number of putative transposable elements may suggest a low recombination/rearrangement in the genome of H . seropedicae SmR1 , possibly reflecting its evolutionary adaptation to an endophytic lifestyle , and indicating a low rate of recent gene transfer that is presumably due to adaptation to a stable microenvironment , as suggested by Krause et al . 22 for the genome of Azoarcus sp . strain BH72 ., H . seropedicae strain SmR1 is capable of growing on mono-saccharides such as D-glucose , D-fructose , D-galactose and L-arabinose , with sugar alcohols and organic acids such as L-malate or L-lactate but failed to grow on oligo- or polysaccharides 5 , 15 ., Accordingly , the genome of H . seropedicae contains the complete set of genes for the Entner-Doudoroff and pentose phosphate pathways ., The Embden-Meyerhoff-Parnas ( EMP ) pathway lacks the gene coding for the classical 6-phosphofructokinase ( PFK , E . C . 2 . 7 . 1 . 11 ) , suggesting that H . seropedicae probably requires the involvement of the Entner-Doudoroff and the pentose phosphate pathways to metabolize D-glucose , D-fructose or D-mannose to pyruvate via the EMP pathway ., Several ABC-type sugar-transport systems and one PEP/PTS transport system are present in the genome of H . seropedicae , consistent with its capacity to grow on a large number of monossaccharides 5 , 15 ., H . seropedicae has all the genes needed for gluconeogenesis: the EMP pathway plus those coding for fructose-1 , 6-biphosphatase , phosphoenolpyruvate dikinase , D-lactate and L-lactate dehydrogenases , and from two-carbon substrates such as ethanol via the glyoxalate cycle ., All genes necessary for the metabolism of D-galactose via 2-dehydro-3-deoxy-D-galactonate-6-phosphate leading to pyruvate and D-glyceraldehyde-3-phosphate are present in the genome ., Subsequent conversion of pyruvate to acetyl-CoA is via the pyruvate dehydrogenase complex , while lactate dehydrogenase serves as an entry point of lactate during lactate-dependent growth ., The conversion of 2-dehydro-3-deoxy-L-arabinonate to 2-keto-glutarate involves the sequential action of a dehydrase and NAD ( P ) -dehydrogenase ., No such specific enzymes were found although several dehydrases and dehydrogenases are present in the genome of H . seropedicae SmR1 ., The pathway for L-arabinose metabolism was shown to involve non-phosphorylated intermediates to produce 2-ketoglutarate 23 ., This pathway probably involves the enzymes of the D-galactose breakdown pathway due to the identical configuration of C-2 , C-3 and C-4 to those of L-arabinose ., The genome of H . seropedicae has all the genes for the citric acid cycle ., Pathways replenishing intermediates of the cycle include the glyoxylate cycle ( isocitrate lyase and malate synthase ) , the complete fatty acid β-oxidation pathway , the malic enzyme , phosphoenolpyruvate carboxykinase , phosphoenolpyruvate carboxylase and , from the degradation of the L-aminoacids alanine , glutamate , aspartate , asparagine and glutamine ., H . seropedicae grows in ethanol-containing media via alcohol dehydrogenase and aldehyde dehydrogenase to yield acetyl-CoA which can feed into the citric acid cycle ., H . seropedicae is an aerobic bacterium capable of fixing nitrogen under conditions of oxygen limitation ., The genome of H . seropedicae has genes for four terminal oxidases: cytochrome c oxidase aa3 and the three alternative terminal oxidases bd , cbb3 and o , suggesting a branched respiratory chain ., It has all the genes for the synthesis of NADH dehydrogenase , succinate dehydrogenase , cytochrome c reductase and also the complete set of genes for ATP synthase ., The high affinity terminal oxidase cbb3 presumably supports ATP synthesis under the limiting oxygen conditions essential for nitrogenase synthesis and activity , as in other aerobic diazotrophs 24 ., H . seropedicae SmR1 synthesizes poly ( 3-hydroxybutyrate ) under diazotrophic growth conditions and , as in other bacteria , it can reach up to 60% of the cell dry weight 25 ., In silico analysis of the genome of H . seropedicae revealed 13 genes potentially involved in poly ( 3-hydroxybutyrate/alkanoate ) synthesis and degradation ., A main cluster containing phbF , phbB and phbC coding respectively for a transcription regulator , acetoacetyl-CoA reductase and poly ( 3-hydroxybutyrate ) synthase was found between bases 3 , 411 , 979 and 3 , 415 , 628 ., In addition there are three phbA ( acetyl-CoA acyltransferase ) , one phbC ( poly ( 3-hydroxybutyrate ) synthase ) , two phaC ( poly ( 3-hydroxyalkanoate ) synthase ) , one phaB ( 3-keto-acyl-CoA reductase ) , two phaP ( phasin ) and two poly3-hydroxyalkanoate depolymerase ( phaZ ) genes ., The data suggests the presence of two systems for the synthesis of poly ( 3-hydroxyalkanoate ) and one specific for poly ( 3-hydroxybutyrate ) in H . seropedicae strain SmR1 , which is consistent with the isolation of poly ( 3-hydroxybutyrate ) and poly ( 3-hydroxybutyrate/valerate ) co-polymer from strain Z67 25 ., The genome of H . seropedicae contains genes coding for the synthesis of all 20 protein amino acids ., However , it has limited ability to grow on amino acids as carbon sources ., It can grow on L-proline , L-tyrosine , D/L-alanine , β-alanine , L-isoleucine and L-glutamate but failed to grow on L-phenylalanine , L-histidine , L-arginine or L-lysine 5 , 15 , 26 , 27 ., In silico analysis of the genome content suggested that the pathway for the degradation of L-histidine and L-lysine is incomplete ., No specific L-arginine transporter was found , supporting the observation that this molecule cannot serve a sole N-source for H . seropedicae growth 26 ., On the other hand , endogenously synthesized L-arginine can be catabolised to agmatine , putrescine and to 4-aminobutanoate which could be further converted to succinate in H . seropedicae ., A strain of H . seropedicae carrying a Tn5-lacZ insertion in the speB gene coding for arginase is induced under low ammonium conditions 28 , suggesting the presence of a second pathway for arginine degradation under conditions of ammonium limitation ., H . seropedicae is capable of synthesizing and degrading urea ., Genes coding for the complete urea cycle enzymes , the probable pathway for arginine biosynthesis in this bacterium , using proline and carbamoyl phosphate as a precursors were found ., Urea is degraded by urease ., The urease operon contains the structural genes ureA , ureB and ureC and the accessory genes ureD , ureE , ureF , ureG and ureJ ., This operon is very similar to that of Janthinobacterium sp ., Marseille , although it is lacking in the Herminiimonas arsenicoxidans genome ., A complete ABC-type urea transport operon ( urtABCDE ) was found upstream from the ure gene cluster in both the H . seropedicae and Janthinobacterium genomes , similar to that of Corynebacterium glutamicum 29 ., Analysis of a mutant strain of H . seropedicae Z78 , containing a Tn5-lacZ insertion in the urtE gene and obtained by random Tn5-lacZ insertion and screening for differential expression under N-limiting conditions , led to the suggestion that both the urt and ure genes are expressed under N deprivation 28 and are probably controlled by the Ntr system since a σ54-dependent promoter is located upstream of urtA ., The nitrogen fixation genes ( nif ) of H . seropedicae , including nifA , nifB , nifZ , nifZ1 , nifH , nifD , nifK , nifE , nifN , nifX , nifQ , nifW , nifV , nifU and nifS were found in a region spanning 37 , 547 bp interspersed with fix , mod , hes , fdx , hsc and other genes ., The 46 ORFs of this cluster are organized in 7 NifA- , σ54-dependent operons ., This cluster is flanked by two 348 bp fragments 93% identical ( 325 out of 348 bp ) , probably derived from a partial duplication of the gloA gene , corresponding to the region coding for the 99 aminoacid residues of the C-terminus of GloA ., Just upstream the nif cluster a sequence reminiscent of a transposase gene is present , as in the nif cluster of Burkholderia vietnamiensis strain G4 chromosome 3 ., These are suggestive that H . seropedicae acquired the nif cluster by lateral transfer ., Two globin-like genes expressed from a putative NifA-regulated promoter are present in the nif cluster of H . seropedicae , while a single globin-like gene is found in this cluster in Burkholderia xenovorans LB400 chromosome 2 and Burkholderia vietnamiensis G4 chromosome 3 ., Since NifA in H . seropedicae is transcriptionally active only under limiting oxygen tensions , these globin-like proteins may support the delivery of oxygen to energy production under nitrogen-fixing conditions in both the free-living and the endophytic state ., The nif cluster carries all the genes necessary for nitrogenase synthesis and activity , including molybdenum uptake , electron transport and metal cluster synthesis , and the nif operon regulatory gene , therefore a cluster capable of endowing an organism with the full capacity to fix nitrogen ., No genes for alternative nitrogenases nor for both hydrogenase types were found in the genome of H . seropedicae ., H . seropedicae is capable of growing aerobically with nitrate as sole N source , but is unable to denitrify anaerobically 15 ., In silico analysis revealed that the genome of H . seropedicae contains the genes for an assimilatory and a dissimilatory nitrate reductase ., The genes for nitrate assimilation are located in two genomic regions: the first contains the genes for the ABC-type nitrate transport ( nasFED ) and the second contains the gene narK , a nitrate/nitrite transporter , nirBD coding for the assimilatory nitrite reductase and nasA the structural gene for the assimilatory nitrate reductase ., In the same operon , upstream of nasA , a gene coding for a probable FAD-dependent pyridine nucleotide-disulphide oxidoreductase could fulfill the function of NasC in H . seropedicae ., This latter operon organization is common in the Ralstonia eutropha H16 , R . solanacearum and R . metallidurans genomes ., The complete set of narGHJI genes coding for a respiratory nitrate reductase is present in H . seropedicae located downstream from two nitrate/nitrite transporters narK1 and narU and upstream of the regulatory pair narXnarL ., No genes coding for dissimilatory nitrite reductase , nitric oxide reductase or nitrous oxide reductase are present in H . seropedicae SmR1 ., This is consistent with the observation by Baldani et al . 15 who found no evidence of denitrification as the release of N2O by H . seropedicae compared with that from Azospirillum lipoferum ., Presumably the nitrite formed by this respiratory nitrate reductase can only be converted to ammonium ions and not dissimilated to N2 ., The role of this dissimilatory nitrate reductase in H . seropedicae is not clear , however , it may be involved in NO production and in survival under hypoxia as described for Mycobacterium tuberculosis 30 ., A gene coding for a nitric oxide dioxygenase was found in the genome of H . seropedicae transcribed in the opposite direction to the norR gene located immediately upstream ., Nitric oxide mediates plant defense responses against pathogens and is used as a signaling molecule 31–34 and the role of this nitric oxide dioxygenase may be NO detoxification during the initial stages of the H . seropedicae endophytic colonization of plants ., H . seropedicae is capable of the rapid colonization of several Gramineae 11 ., Monteiro et al . 35 showed H . seropedicae in cortical cell layers of maize roots 12 hours after inoculation and xylem occupation after 24 hours ., Three important aspects of the H . seropedicae beneficial association with plants are its ability to invade and colonize plant hosts , to thrive on plant exudates and to benefit associated plants ., Interestingly , genes coding for plant cell wall degradation enzymes such as glycosidases , cellulases and hemi-cellulases associated with bacterial penetration were not found in the H . seropedicae genome ., It is likely therefore that this organism relies only on natural discontinuities of the plant root epidermis for penetration as suggested by Olivares et al . 6 ., During the annotation of the H . seropedicae genome a large number of Blast returned hits with high levels of identity to ESTs and genomic sequences of Ricinus communis ., A total of 686 of such sequences was found: these varied in size from 100 to 2000 bp and were distributed randomly on the genome ., This result suggests that the ricinus plant used to construct the libraries had an active Herbaspirillum endophyte ., Recently we showed that H . seropedicae can colonize Phaseolus vulgaris 36 , and Herbaspirillum lusitanum was isolated from Phaseolus nodules 37 ., Together these results indicate that Herbaspirillum species may have a broader host range than previously described ., The genome of H . seropedicae has genes involved in Sec-dependent and Sec-independent protein export systems ., The Sec-dependent secretion systems present are type II ( T2SS ) , type V ( auto-transporters; T5SS ) and the type IV pili , while the Sec-independent secretion systems are type I ( ABC transporters ) , type III ( T3SS ) and the type VI ( T6SS ) ., T3SS , type IV pili and T6SS have been implicated in delivering toxic effector proteins directly into the cytoplasm of eukaryotic cells by pathogenic bacteria ., In non-pathogenic bacteria , such as H . seropedicae , the latter secretion systems may be involved in plant–bacterial recognition ., In addition to the Sec translocase system , twin arginine translocase ( tat genes ) are also present in the H . seropedicae SmR1 genome ., Genes for the type IV secretion system are absent from the H . seropedicae genome ., Effector proteins delivered by the T3SS of pathogenic bacteria can circumvent plant defense mechanisms and control host metabolism to their advantage ., However , the T3SS system may also optimize beneficial host-bacteria interactions , a phenomenon first demonstrated for Rhizobium NGR234 which secrete effector proteins via the T3SS in response to flavonoids exudated by the plant host roots ., The effect of the secreted effector can either enhance or diminish nodulation depending on the host legume 38–40 ., In the H . seropedicae genome the T3SS gene region , potentially involved in plant/bacterial interactions , spans a 22 kb region of DNA which contains 7 hrp ( hypersensitive response and pathogenicity ) , 8 hrc ( hypersensitive response conserved ) , and 11 hypothetical ORFs ( Figure 2 ) ., Two protein T3SS related genes hrpG , coding a transcription activator , and hpaB , that codes for a chaperone involved in protein secretion , are found at 10 kb downstream from the hrp/hrc cluster ., The G+C content of the hrp/hrc region ( 66 . 1% ) is slightly higher than the chromosomal average of 63 . 4% ., Furthermore , no transposition elements flanking this region are present , suggesting that this region is not a recent acquisition by H . seropedicae or was laterally transferred from a closely related species ., Gram negative bacteria that contain the hrp genes are divided into two main groups , according to the regulatory circuitry controlling T3SS gene expression and organization ., In group I hrp genes are regulated by HrpL , a member of the ECF family of alternative sigma factors 41–43 ., Induction of the hrpL gene requires the σ54 activator HrpS ( Erwinia spp . , Pantoea stewartii ) , or HrpS and HrpR ( P . syringae ) ., In organisms of group II the hrp genes are activated by an AraC-like activator , HrpB ( R . solanacearum ) or HrpX ( Xanthomonas spp ) 44–46 , and the hrpX and hrpB genes are activated by the HrpG protein 46 , 47 ., H . seropedicae contains a gene for the ECF-like sigma factor HrpL resembling group I bacteria such as Pseudomonas syringae , Erwinia amylovora , and Pantoea stewartii ., In contrast , H . seropedicae contains a gene for the HrpG protein , a transcriptional activator characteristic of group II bacteria , suggesting a hybrid regulatory system , involving regulatory elements from both groups ., In addition , hrp-box motifs were found upstream of the hrp/hrc operons ., Contiguous to the hrp/hrc cluster were found the genes pilNOPgspEbfpEpilSVTdapAglsA ( Figure 2 ) ., These code for proteins of the type IV pili , a system responsible for processes such as attachment to surfaces , twitching motility , biofilm formation , virulence and protein secretion 48–50 ., In this region a gene coding for a lytic transglycosylase was also found ., This protein is probably involved in partial degradation of the peptidoglycan to allow the efficient assembly and anchoring of supramolecular transport complexes such as T2SS , T3SS and type IV pili to the cell envelope ., Interestingly , downstream from the genes of the type IV pili a methionyl-tRNA gene is present , suggesting that the hrp/hrc-type IV pili genes may form a genomic island ., A proteomic investigation of the secretome of H . seropedicae grown in minimal medium indicated a large number of proteins involved in cellular processes ( 45 . 4% ) , metabolism ( 36% ) , and hypothetical and conserved hypothetical ( 14 . 1% ) proteins 51 ., However , no type III proteins were detected among the secreted proteins , suggesting that specific physiological conditions may be required for expression and activity of the T3SS and synthesis of effector proteins in H . seropedicae ., A probable operon involved in the synthesis and degradation of a homopolymer of D-glucose , composed by the genes glgA ( glycogen synthase ) , glgB ( 1 , 4-alpha-glucan branching enzyme ) , glgX ( glycogen debranching enzyme ) , treZ ( malto-oligosyltrehalose trehalohydrolase ) , malQ ( 4-alpha-glucanotransferase ) and treY ( malto-oligosyl trehalose synthase ) , is located in the complementary strand spanning bases 2 , 843 , 031 to 2 , 856 , 518 ., Neighbor gene analysis using the String server ( http://string-db . org ) revealed a similar gene organization in the Alphaproteobacteria Rhizobiales , in the Betaproteobacteria Burkholderia spp ., and Gammaproteobacteria Xanthomonas spp ., Synthesis of an amylopectin-like polysaccharide may be related to osmotic stress protection and energy storage ., This may reflect the potential of these bacteria to interact with plants in an endophytic or pathogenic mode ., Environmental Oxalobacteraceae such as Janthinobacterium sp ., ( strain Marseille ) ( Minibacterium massiliensis ) and Heminiimonas arsenicoxidans , of the same family as H . seropedicae , lack these genes ., There are genes for two trehalose synthesis systems in the genome of H . seropedicae ., One of these involves otsA coding an alpha , alpha-trehalose-phosphate synthase ( UDP-forming ) and otsB , coding a trehalose-6-phosphate phosphatase , and a glucoamylase gene ., The other system involves an alpha-amylase ( Hsero_2325 ) , trehalose synthase ( Hsero_2326 ) , and a 1 , 4-alpha-glucan branching enzyme ( Hsero_2327 ) and constitute an operon ., Furthermore , four Na+ ( K+ ) /H+ antiporter ( nhaA , nhaP , arsB and Hsero_3967 ) genes are present in the genome of H . seropedicae which may contribute to the defense against osmotic/saline stress ., Polyphosphates are involved in the response of bacteria to extreme stress conditions of salinity , osmolarity , desiccation , N-starvation , UV radiation , barometric pressure , pH , and temperature 52 ., Two genes coding for polyphosphate kinase ( Hsero_0611 and ppk ) , the enzyme responsible for the synthesis of polyphosphate , and one coding for an exopolyphosphatase ( ppx ) , are present in the genome of H . seropedicae ., These systems may constitute adaptative defense mechanism for the endophytic life style of H . seropedicae ., The rhizosphere and the rhizoplan are highly competitive areas for bacterial survival and development; the capacity to acquire siderophores complexed with Fe3+ in Fe-limited soils would be advantageous in such competition ., H . seropedicae has at least 27 genes involved in iron transport and metabolism ., A very large gene ( 27 , 483 bp ) coding for a modular peptide synthase is the only protein of H . seropedicae probably involved in siderophore synthesis ( Hsero_2343 ) ., This gene is located downstream from cirA , a TonB-dependent siderophore receptor , and prfI , an ECF sigma factor ., The genome has 17 TonB-dependent siderophore receptors and one ABC-type hydroxamate-type ferric siderophore uptake system ., Presumably iron uptake is via active transport involving an ABC-type system and TonB/ExbB/ExbD ., The rice endophyte Azoarcus also contains a plethora of TonB dependent siderophore receptors 22 , 53 ., This large number of iron receptors may endow organisms such as H . seropedicae and Azoarcus with a high competitiveness in iron-limited environments and may confer the ability to out-compete other bacteria ., Also present in the genome is the global iron regulator gene fur ., The plant growth-promoting bacteria probably owe some of their ability to the production and secretion of phytohormones 54 ., There are four possible pathways in H . seropedicae for the production of indoleacetic acid ( IAA ) from tryptophan ., The most probable route is via indolepyruvate , to indole-acetic acid catalysed by tryptophan transaminase and indolepyruvate ferredoxin oxidoreductase ., Genes for the other possible metabolic routes are also present:, 1 ) tryptophan to indoleacetate via indoleacetamide;, 2 ) from indoleacetamide to indoleacetate via indoleacetonitrile and, 3 ) tryptophan to indoleacetate via tryptamine and indoleacetaldehyde ., Ethylene is a known plant hormone synthesized from S-adenosylmethionine by 1-aminocyclopropane 1-carboxylate ( ACC ) synthase , an enzyme activated by IAA under biotic and abiotic stress conditions 55 ., ACC is converted to ethylene by ACC oxidase ., A gene coding ACC deaminase is present in the H . seropedicae genome and is known to compete with ACC oxidase , modulating the levels of ethylene in plants , thus decreasing the stress response promoted by ethylene and allowing plant growth under stress conditions 56 ., The coordinated production of IAA and ACC deaminase by H . seropedicae is a likely mechanism for plant growth promotion by this microorganism as shown for the Herbaspirillum-related endophytic , nitrogen-fixing , plant growth-promoting Betaproteobacterium , Burkholderia phytofirmans PsJN 57 ., H . seropedicae genome contains genes coding for degradation of benzoate , benzamide , benzonitrile , hydroxy-benzoate , and vanillate ( Figure 3 ) ., In separate clusters , genes coding for a nicotinic acid degradation pathway similar to that of Pseudomonas putida 58 and a meta pathway of an as yet unknown phenolic compound were found ., These pathways may be important to allow H . seropedicae to thrive on plant tissues by conferring both metabolic flexibility and defense against plant-derived toxic chemicals ., Hemagglutins/hemolysins are cytotoxic proteins implicated in animal pathogenesis , but a large number of genes coding for such proteins have been found in plant pathogens and plant-interacting bacteria 59 ., Twenty genes related to hemagglutinin/hemolysin are present in the genome of H . seropedicae SmR1 , and 9 additional genes code for hemagglutinin/hemolysin accessory proteins such as transporters/activators ., Three genes code for hemagglutinins with adhesin-like domains , two of which are homologous to fhaB of Xanthomonas axonopodis pv citri 59 and are associated with genes coding for the accessory FhaC protein ., The products of these genes may be required for surface attachment and biofilm formation during plant tissue colonization 60 ., The genome of H . seropedicae revealed a metabolically versatile bacterium , with the ability to thrive on a range of plant metabolites from sugars to phenolic compounds ( Figure 4 ) ., It is capable of synthesizing plant-growth modulators such as auxins and gibberellins , although only potential pathways for IAA synthesis were found in the genome; the cryptic genes for gibberellins and citokinins syntheses remain to be identified ., It is surprising that an aggressive plant colonizer such as H . seropedicae 35 is devoid of glycohydrolases involved in plant cell wall degradation ., However , H . seropedicae displays an impressive variety of protein secretion systems and hemagglutinins/hemolysins/adhesins that may facilitate plant invasion , colonization and an endophytic life , following penetration through natural epidermal wounds ., Additional contributors to the plant-growth-promoting capacity of H . seropedicae may depend on the many genes involved in nitrogen fixation , NO3− and NO2− assimilation , NO oxidation , and ACC deamination ( Figure 4 ) ., The presence of ACC deaminase may modulate ethylene production stimulated by IAA from bacterial origin , thus allowing plant resistance to biotic and abiotic stress conditions ., These non-specific plant-interaction systems may endow H . seropedicae with the ability to establish an endophytic life-style in a large number of plant species ., H . seropedicae strain SmR1 was grown in liquid NFbHP medium containing 20 mM NH4Cl and 0 . 5% potassium malate , as described by Klassen et al . 26 ., DNA was purified using phenol-chloroform extraction of cells lysed with lysozyme and SDS ., E . coli strain hosts XL1-Blue and DH10B were grown in LB or Terrific broth 61 ., The genome sequence of H . seropedicae strain SmR1 total DNA was determined by the whole genome sequencing strategy 62 using short fragment ( 1 . 5–3 . 0 kb ) libraries in pUC18 and pUC19 ( Amersham Biosciences ) and cosmid libraries in Supercos ( Promega ) ., DNA inserts were sequenced using the DYEnamic ET kit ( GE HealthCare ) and MegaBace 1000 automatic sequencers ., Plasmid and cosmid DNA template preparation was performed by alkaline lysis and sequenced in 96-well plates according to standard procedures ., A full DNA sequence run was performed in a Roche 454 GS-FLX Titanium by Creative Genomics , USA ., The genome was assembled using the Phred/Phrap/Consed package ( www . phrap . org ) and the Roche NewBler asse | Introduction, Results/Discussion, Materials and Methods | The molecular mechanisms of plant recognition , colonization , and nutrient exchange between diazotrophic endophytes and plants are scarcely known ., Herbaspirillum seropedicae is an endophytic bacterium capable of colonizing intercellular spaces of grasses such as rice and sugar cane ., The genome of H . seropedicae strain SmR1 was sequenced and annotated by The Paraná State Genome Programme—GENOPAR ., The genome is composed of a circular chromosome of 5 , 513 , 887 bp and contains a total of 4 , 804 genes ., The genome sequence revealed that H . seropedicae is a highly versatile microorganism with capacity to metabolize a wide range of carbon and nitrogen sources and with possession of four distinct terminal oxidases ., The genome contains a multitude of protein secretion systems , including type I , type II , type III , type V , and type VI secretion systems , and type IV pili , suggesting a high potential to interact with host plants ., H . seropedicae is able to synthesize indole acetic acid as reflected by the four IAA biosynthetic pathways present ., A gene coding for ACC deaminase , which may be involved in modulating the associated plant ethylene-signaling pathway , is also present ., Genes for hemagglutinins/hemolysins/adhesins were found and may play a role in plant cell surface adhesion ., These features may endow H . seropedicae with the ability to establish an endophytic life-style in a large number of plant species . | In this work we describe the genome of H . seropedicae SmR1 , a bacterium capable of fixing nitrogen and promoting the growth of important plant crops such as maize , rice , and sugar cane ., Several investigations have shown that H . seropedicae supplies fixed nitrogen to the associated plant and increases grain productivity up to 50% ., In the genome of H . seropedicae , we identified all the genes involved in the nitrogen fixation process and its regulation and , in addition , genes potentially involved in the establishment of efficient interaction with the host plant ., Our analyses also revealed that this bacterium has a highly versatile metabolism capable of synthesizing and degrading a large number of organic and inorganic compounds ., We believe that the knowledge of the genome of this bacterium will direct research to a better understanding of this important endophytic organism and allow the construction of new strains with enhanced agronomic efficiency . | bacteriology, genome sequencing, bacterial physiology, microbial metabolism, microbial physiology, biology, genomics, microbiology, bacterial biochemistry, genetics and genomics | null |
journal.pgen.1004290 | 2,014 | Copy Number Variation Is a Fundamental Aspect of the Placental Genome | While the accumulation of somatic copy number variations ( CNVs ) has been proposed to be a result of the aging process , predisposing cell types to cancer progression and neurological diseases , an alternate hypothesis is that they are a normal—or even essential—part of cell biology 1 , 2 ., In support of the latter , lymphocyte-specific CNVs in immunologically important genes generate the genetic diversity of receptor molecules critical to their function 3 ., Although V ( D ) J recombination is found only in the immune system , recent reports hint that lineage-specific somatic CNVs may be essential for healthy cellular differentiation and function in a number of organs such as the liver , pancreas and skin 4 , 5 ., It is unknown how these lineage-specific mammalian CNVs are formed—whether by a process similar to V ( D ) J recombination or by an alternative mechanism ., Although the role of many cell-type specific CNVs in mammals is unclear , lineage-specific CNVs are a normal aspect of cellular development in the fruit fly Drosophila melanogaster 6 ., Lineage-specific CNVs form during Drosophila egg and larval development in polyploid cells via cycles involving DNA replication in the absence of cell division ( endoreplication ) 6 ., In egg formation , somatic CNVs form by selective amplification of genomic regions containing chorion ( eggshell ) genes , which facilitates secretion of chorion proteins by the ovarian follicle cells 7 , 8 ., Drosophila somatic CNVs can also arise due to underreplication of certain genomic regions in the salivary glands , fat body and midgut of the larva 9–13 ., While CNVs in Drosophila polyploid cells have been observed for more than 70 years 14 , it is not known whether a similar mechanism is present in mammalian cells ., However , the recent observation of human tissue-specific CNVs 1–5 suggests that somatic CNVs are as essential in mammalian cells as they are in Drosophila ., Mammals absolutely require polyploid placental cells , corollaries to Drosophila follicle cells , for pregnancy maintenance 15 ., In the placenta , polyploidy is restricted to specialized trophoblast cells that invade and remodel the uterus to promote vascularization and other maternal adaptations to pregnancy 15 ., In rodents , these cells—termed trophoblast giant cells ( TGCs ) , have 50–1 , 000 copies of the genome per cell ., While proper TGC function depends on their polyploidy content 16 , 17 , it is not known what aspect of polyploidy is necessary for fetal survival ., As TGCs are a class of critical polyploid support cells analogous to Drosophila follicle cells , they may similarly use differential replication of the genome to achieve highly specialized function ., Previous studies have addressed possible CNVs in rodent TGCs ., Ohgane et al . 18 used restriction landmark genomic scanning ( RLGS ) to analyze CpG islands in rat junctional zone TGCs during late gestation ( days 18 and 20 ) ., They reported that ≥97% of the spots detected by RLGS were similar to diploid controls and therefore concluded that there are no TGC CNVs ., Sher et al . 19 also argued against the existence of CNVs based on array Comparative Genomics Hybridization ( aCGH ) and quantitative real-time PCR experiments on mouse e9 . 5 implantation site TGCs ., However , as there are several subtypes of TGCs which all have varying ploidy and functional significance during gestation 15 , 20 , CNVs could be present in a subset of cell types or only at certain developmental time points ., Of particular interest are parietal TGCs , which have the highest degree of polyploidy 15 , and are therefore an excellent candidate for differential replication of the polyploid genome ., Genetic mouse mutants affecting the parietal TGCs predominantly die before e12 . 5 15– , suggesting that this is when developmentally important CNV would be required ., Here we report that somatic CNVs are a normal part of placental cell biology ., We utilized whole genome sequencing ( WGS ) and aCGH to identify 47 reproducibly underrepresented ( UR ) domains in mouse e9 . 5 parietal TGCs , totaling 6% of the genome ., Employing a variety of genomic techniques , we demonstrate that UR domains are marked in chromatin prior to endoreplication in TGC progenitor cells and gradually form during the first half of gestation ., UR domains are highly enriched for genes involved in cell adhesion and neurogenesis , as well as for gene deserts ., Furthermore , we specifically show that UR domains are due to underreplication rather than somatic deletions ., Together , these data reveal that lineage-specific CNVs are inherent features of the TGC genome , which are established and regulated throughout placental development ., To investigate whether the 50–1 , 000 genomic copies in polyploid TGCs are uniformly replicated or contain CNVs , we used aCGH to compare genomic regions of mouse parietal TGCs ( TGCs ) and 2N embryos at e9 . 5 ( Figure 1A , Figure S1A ) ., We dissected four embryos and associated TGCs from one litter , representing pairs of genetically identical tissues , performed aCGH using the Agilent SurePrint G3 Mouse CGH Microarray Kit ( two embryos/TGCs pooled per biological replicate ) , and analyzed the data using the R/Bioconductor package cghFLasso 21 ., We identified 45 regions , reproducible between biological replicates , that were underrepresented within the TGC genome compared to the embryonic genome at a false discovery rate ( FDR ) of 0 . 0001 , which we termed underrepresented ( UR ) domains ( Figure 1B , Table S1 ) ., UR domains range in size from 1 , 037 kb to 9 , 429 kb ( Table S1 ) ., In addition to the 45 UR domains common to both replicates , we found 30 domains specific to only one replicate ( Figure 1B ) ., However , when we reduced the FDR ( to 0 . 01 ) , 19/30 of these domains are found in both replicates , suggesting that while the degree of underrepresentation varies , UR domains form in specific regions of the genome ., Importantly , we did not observe any overrepresented regions in TGCs ( FDR\u200a=\u200a0 . 0001 ) ., We next asked whether UR domains were specific to TGCs , or whether they existed in diploid trophoblast cells or other endocycling polyploid cells ., We used aCGH to compare the DNA of megakaryocytes ( up to 64N ) to embryos , placental disk cells ( mostly 2N ) to embryos , and cultured trophoblast stem cells ( TS cells; 2N ) to embryonic stem cells ( ES cells; Figure 1C , Figure S1B , Figure S2 ) ., Megakaryocytes have no detectable underrepresented regions and display one region of overrepresentation common to both replicates , indicating that TGC UR domains are not simply explained by endocycling ( FDR\u200a=\u200a0 . 0001; Table S2 ) ., Placental disk cells lack any over or underrepresentation ( FDR\u200a=\u200a0 . 0001; Table S3 ) , although greatly reducing the FDR ( to ≥0 . 05 ) revealed a weak trend towards UR domains in the same locations as in TGCs , likely explained by the normal presence of a small number of TGCs within this population ( Figure 1C , Figure S2 ) ., Finally , we identified several TS and ES specific CNVs , but these were different from the TGC UR domains and presumably represent adaptations to cell culture ( Tables S2 & S3 ) 22 ., These data suggest that UR domains are important genomic features unique to TGCs ., As Sher et al . 19 have argued against the existence of CNVs in e9 . 5 TGCs , we compared our aCGH data to theirs ., Consistent with Sher et al . , we did not find any CNVs in their data using the R/Bioconductor package cghFLasso and an FDR of 0 . 0001 21 ., However , greatly reducing the FDR ( to >0 . 05 ) revealed a trend towards UR domains in the same locations as in our TGC data ( Figure S3 ) , similar to the report by Sher et al . of finding reduced copy number using a smaller threshold ., Moreover , the Sher et al . data bears a striking resemblance to our placental disk data ( Figure S3 ) , suggesting that their study , on implantation site TGCs , is on a population of trophoblast cells more akin to the placental disk than to the parietal TGCs of the mural trophectoderm described in our study ., In support of this , while parietal TGCs surround the entire conceptus , TGCs over the central region of the placental disk are smaller and less polyploid than those at the periphery 20 ., Together , these data suggest that the parietal TGCs of the mural trophectoderm not only have a higher degree of ploidy , but also have specific CNVs compared to the rest of the placenta ., To quantitatively examine the extent of underrepresentation in TGCs , we performed paired-end WGS 23 ., We sequenced ( at 10× coverage ) six individual e9 . 5 TGCs and their genetically matched embryos from three separate litters ( 2 individuals per litter; Table S4 ) ., To identify CNVs , we used a custom R/Bioconductor program based on CNVnator 24 , which identifies CNVs at a p-value of 0 . 01 ., We found 47 reproducible UR domains on the autosomes in e9 . 5 TGCs in all samples ( Table S5 ) ., UR domains range from 75 kb to 8 , 965 kb and cover 6% of the genome ( 138 Mbs of 2 , 717 total Mbs; Table 1 ) ., We next calculated the fold depletion of each UR domain from the normalized log 2 ratio of sequence coverage of TGC/embryo 25 and found an average reduction between 27% and 51% , with a median between 28% and 54% ( Table 1 ) ., Further , the size and degree of depletion of UR domains correlate such that the larger the size of the domain , the greater the degree of underrepresentation ( Figure 2A ) ., Next , we examined how much variation existed between individuals ., First , we compared aCGH and WGS data , and found 43 UR domains common to both platforms ( Figure 2B , Table 1 , Figure S4 , Table S1 ) ., Of the domains that differ , five additional domains in the WGS data are likely due to the greater sensitivity of WGS , as these domains can also be found in the aCGH data if the FDR is lowered ( to 0 . 01 ) ., Three additional domains in the aCGH data are found in a majority of the WGS samples ( present in four to five out of the six samples ) , suggesting a small amount of variability in UR domain formation ( Tables S1 & S5 ) ., To examine this variability in more depth , we examined the six individual WGS samples ., Besides the 47 UR domains common to all six samples , we also found underrepresented regions present in only a subset ( Figure 2C , Figure S5 , Table S5 ) ., In general , samples with the least number of UR domains have a subset of the domains found in the samples with the most ( Figure 2C , Figure S5 , Table S5 ) ., In addition , the size of a particular UR domain is generally smaller in samples with fewer UR domains ( Figure 2D , Table S5 ) ., As the samples vary slightly in age , this suggests that UR domains amass over time , such that slightly younger placentas have fewer and smaller UR domains ., To test our hypothesis that UR domains develop over time , we performed WGS on e8 . 0 TGCs/embryos ( one litter per replicate ) and compared these results to e9 . 5 ., We found 24 domains common to both biological replicates at e8 . 0 , versus 47 domains common to all samples at e9 . 5 ( Figure 3A & 3B , Figure S6 ) ., All e9 . 5 individuals have 23 of these domains with 5/6 individuals containing the remaining domain ( Figure 3B ) ., We also found 10 domains unique to one of the two biological replicates at e8 . 0; 10/10 of these domains are contained in all e9 . 5 individuals ( Figure 3B ) ., Finally , we found that both size and degree of depletion of UR domains significantly increase between e8 . 0 and e9 . 5 ( Figure 3C ) ., Overall , as all UR domains at e9 . 5 are also present at e8 . 0 , and UR domains at e9 . 5 are also more numerous , larger and more depleted , we propose that they are gradually established during early gestation ., We next asked whether the number and degree of depletion of UR domains continues to increases throughout development ., We performed aCGH on TGCs/embryos collected from the second half of gestation—e11 . 5 , e13 . 5 , e16 . 5—and compared them to e9 . 5 ., Out of 45 UR domains present in both biological replicates at e9 . 5 ( FDR\u200a=\u200a0 . 0001 ) , 22 of these are present in all biological replicates at e11 . 5 , e13 . 5 and e16 . 5 , and an additional 10 ( 32/45 ) are present in all samples except for one of the e16 . 5 replicates ( Figure 3D & 3E , Figure S7 ) ., We next examined size , and found that the 32 common domains are significantly larger than UR domains that arise later in development ( the 147 not present at e9 . 5; Figure 3D & 3E , Figure S7 ) ., However , unlike between e8 . 0 and e9 . 5 , where the degree of depletion expanded , we found no significant change from e9 . 5 to e16 . 5 ( Figure 3F ) ., Although , UR domains slightly trend towards becoming less depleted over time ( Figure 3D & 3F , Figure S7 ) ., There is also more intrinsic variability later in gestation , as the median degree of depletion between biological replicates at both e13 . 5 and e16 . 5 is significantly different ( Figure 3F ) ., The differences between UR domains in early ( e8 . 0–e9 . 0 ) and later ( e11 . 5–e16 . 5 ) gestation correlate with previous data showing that TGC polyploidy drastically increases until e10 . 5 , and endocycling ends by e13 . 5 20 ., These data suggest that the increase in UR domain size and degree of underrepresentation from e8 . 0 to e9 . 5 is linked to the robust endocycles of early gestation ., Furthermore , the termination of endocycles in later development may free cellular machinery to increase representation levels in UR domains ., We also found 33 overrepresented regions at e11 . 5–e16 . 5 that are not present at e9 . 5 ( Figure 3D & 3E , Figure S7 ) ., We examined gene content of overrepresented regions common to at least two staged biological replicates ( 10/33 ) , but did not find any annotated genes ., Thus , while new CNV regions form during late gestation , they are more stochastic , less reproducible , and significantly smaller than those conserved between all stages ., We next examined whether UR domains are also generated in vitro when differentiating TS cells into TGCs ., To this end , we performed aCGH on purified TGCs harvested at 3 , 5 and 7 days after differentiation 26–28 ( Figure S8 ) ., Similar to in vivo , in vitro cells generate the same UR domains and also develop these over time ( FDR\u200a=\u200a0 . 0001 , Figure 4A & 4B , Figure S8 ) ., At day 3 , only one biological replicate has any of the UR domains found in vivo at e9 . 5 ( 3/45 ) ., At day 5 , both replicates contain 1/45 domains , and one replicate contains 21/45 domains ., At day 7 , both replicates contain 34/45 UR domains , and one replicate contains 43/45 domains ., Remarkably , in vitro cells generate the same UR domains as their in vivo counterparts ( Figure 4A & 4B , Figure S8 ) , strongly suggesting that the formation of these UR domains is a fundamental feature of TGC development ., Next , we asked whether genes contained within e9 . 5 TGC UR domains were enriched for certain biological functions ., We found that UR domains are significantly depleted of both protein-coding and non-coding genes as expected by chance ( 386 observed vs . 617 expected , 0 . 63× enrichment , p<0 . 001 ) and when compared to the rest of the genome ( Figure 4C ) ., Further , these domains are significantly enriched for 1 Mb gene deserts ( regions without any Ensembl annotations; 47 observed vs . 9 expected , 4 . 96× enrichment , p<0 . 001 ) ., In total , 386 genes are present within UR domains , 106 of which are functionally annotated ., When we examined these 106 genes for function using GOTERMFINDER 29 , the top enrichment categories are biological adhesion ( p\u200a=\u200a2 . 31×10−9 ) and related categories , followed by neuron projection development ( p\u200a=\u200a4 . 23×10−8 ) , and related neurogenesis categories ., These categories were not enriched when we performed the same analyses on a list of genes found in a random set of regions that have the same length and chromosome distribution ., Finally , using 3′ RNA-Seq ( 3SEQ ) 30 from both in vivo and in vitro TGCs , we compared expression of the genes to the degree of representation and found that genes in UR domains are either not expressed or have much lower levels of transcription than genes in regularly represented regions ( Figure 4D & 4E ) ., Overall , our data show that there are specific classes of genes enriched within the UR domains and these genes are generally not expressed , raising the possibility that UR domains function to limit the expression of a particular subset of genes in TGCs ., To test whether UR domains are characterized by a specific chromatin state , we performed ChIP-Seq using anti-H3K27ac , anti-H3K4me1 , anti-H3K4me3 , anti-H3K9me3 , and anti-H3K27me3 in both in vitro TS cells and derived TGCs 31 ., We used MACS2 to determine the normalized fold change for histone occupancy 32 and then used the Pearson correlation ( R ) to determine how the degree of representation ( normalized log 2 of e9 . 5 WGS ) correlates with signals from histone marks ., In both TGCs and TS cells , we find that UR domains tend to co-localize with the repressive marks H3K9me3 and H3K27me3 ( Figure 5 ) ., Conversely , UR domains have underrepresentation of the active chromatin marks H3K4me3 , H3K4me1 and H3K27ac ( Figure 5 ) ., These results demonstrate that UR domains do not occur in active regions of the genome and that they are marked in the 2N progenitor cells ( TS cells ) ., Interestingly , UR domains are only a fraction of genomic heterochromatin ( Figure 5B & 5C ) ., All UR domains have increased signals for repressive histone marks and only weak signals for active histone marks ., However , not all regions of the genome having repressive marks but not active marks are associated with a UR domain ., Overall , this demonstrates that UR domains have a heterochromatic signature , but represent only a subset of heterochromatin ., We further examined the relationship between UR domains and heterochromatin using an alternative statistical method ., We asked whether the histone marks are significantly enriched or depleted in our defined list of UR domains compared to what would be expected by chance 31 ., Similar to our correlation analysis , marks associated with transcriptional activation ( H3K4me3 , H3K4me1 and H3K27ac ) are significantly depleted in UR domains ( p<0 . 001; Table 2 ) ., Conversely , the repressive mark H3K9me3 is enriched within UR domains ( p<0 . 001; Table 2 ) ., Interestingly , while the repressive mark H3K27me3 is also enriched within UR domains in TS cells , it is depleted within UR domains in TGCs ( p<0 . 001; Table 2 ) ., This observation agrees with previous data where extraembryonic cells have lower levels of H3K27me3 methylation than embryonic cells 33 , and suggests that H3k27me3 is not critical for UR domain maintenance ., Together , our data show that UR domains have a heterochromatic signature , both in TGCs and in their 2N progenitors ., To examine whether UR domains are caused by genomic deletions , we carried out somatic structural variant analysis using paired-end sequencing data from the six TGC and matched embryo samples with the program SMASH 34 ., If UR domains are caused by acquired genomic deletions , we would expect to find multiple library inserts that fully span the deleted regions ( “discordant” paired-end reads; Figure S9 ) ., While we did detect sample-specific CNVs , we did not detect somatic deletions common to all of the six TGCs , but not the embryos ., Moreover , the probability of not detecting a given deletion in each of the six samples is extremely low ( p\u200a=\u200a2•×10−5 ) ., These data show that UR domains are not a result of somatic chromosomal deletions ., Since our WGS data does not support genomic deletions as the source of UR domains , we investigated whether they may be due to underreplication ( Figure S9B ) ., In 2N cells , replication timing is precisely regulated such that specific regions of the genome are replicated early in S phase while others are replicated late in S phase 35 ., To test whether UR domain formation is caused by incomplete replication of regions that are normally replicated late in 2N TS cells , we first generated a replication timing profile of TS cells ., To this end , we captured early- and late-replicating regions in TS cells by pulsing an asynchronous cell culture with BrdU to label replicating DNA followed by FACS , and then used aCGH to compare early and late BrdU-containing DNA 36 ., Next , we compared late-replicating regions in TS cells to UR domains ., Using the Pearson correlation ( R ) , we found that UR domains correlate with late replication ( Figure 6A ) ., Also , 47/47 TGC UR domains reside within late-replicating regions in TS cells ( Figure 6B , Table S6 ) ., UR domains are significantly smaller than the late-replicating regions that they are nested in ( Figure 6C; Table S6 ) , suggesting that they are a subset of these larger regions ., Finally , as only 45 of the 211 late-replicating regions contain a UR domain ( Figure 6D , Table S6 ) , we asked what distinguishes the late-replicating regions that form UR domains from those that do not ., While there is no significant difference in the degree of late replication between these classes , late-replicating regions that contain UR domains are significantly larger ( Figure 6E ) ., However , size is not the sole characteristic determining where UR domains form , as not all regions greater than a certain size contain a UR domain ., We next investigated gene content and found that late-replicating regions that contain UR domains also contain significantly fewer genes than those that do not ( Figure 6F ) ., These regions are also preferentially enriched for 1 Mb gene deserts ( 58 observed vs . 18 expected , 3 . 16× enrichment , p<0 . 001 ) ., Together , our data show that UR domains form from a specific class of late-replicating , heterochromatic regions with low gene content , suggesting that UR domains are not simply a byproduct of late-replicating heterochromatin , but are a precisely regulated subset ., Only a subset of heterochromatic , late-replicating regions form UR domains , suggesting that UR domains are not simply a byproduct of late-replicating heterochromatin , but are precisely regulated ., We propose that either this is dictated by genomic structure or that there are specific DNA binding proteins that define UR domains ., We favor the latter model based on parallels found in Drosophila , whereby mutants for Suppressor of Underreplication ( SuUR ) have underreplicated domains that become replicated to normal levels 12 , 13 , 37 ., However , SUUR protein does not appear to be present in species outside the Drosophilids , and we have not found any SuUR homologs in mice via BLAST , raising the possibility that presently unknown proteins in mammals may be regulating this process ., Lineage-specific CNVs are an overlooked aspect of the mammalian genome ., Although recent data suggests that they are widespread 1–5 , their identification and functional study has not been carried out systematically ., Identification of CNVs may be particularly difficult to define in primary tissues , due to high background of cells lacking CNVs ., In support of this , Abyzov et al . 4 found a low frequency of somatic CNV in human fibroblasts ., Further , even in more homogenous populations , relatively small degrees of CNV may mask their presence ., Van Heesch et al . 38 found tissue-specific CNVs in rat blood , brain , liver and testis , where the degree of underrepresentation does not exceed 50% ., While Van Heesch et al . conclude that their findings were the result of systematic bias in DNA isolation procedures , they could never get rid of these CNVs using any analytical or experimental approach ., Moreover , Manukjan et al . 39 suggest that Van Heesch et al . are identifying the signature of replication timing in their CNV analyses due to the use of proliferating cells ., Intriguingly , this suggests that , analogous to polyploid TGCs in the placenta , underreplication may be crucial in organs containing a highly proliferative population of 2N cells ., While CNVs in Drosophila polyploid cells have been characterized for more than 70 years 14 , our work demonstrates for the first time that CNVs are a normal aspect of mammalian development ., The rarity of endoreplicating polyploid cells in animals suggests that CNVs in mouse and Drosophila arose independently 6 , and therefore may have species-specific differences ., While Drosophila CNVs are typically 90% underrepresented , mouse CNVs are never more than 50% ., We strongly suggest that there are UR domains in both mouse and Drosophila polyploid cells , and that the presence of these domains in both taxa is an example of convergent evolution due to similar selective pressures , indicative of functional importance ., As both mice and flies have a fast rate of early development compared to related species , formation of UR domains could be an integral part of accelerating the cell cycle , and therefore be a key mechanism behind their rapid life cycles ., UR domains are a unique feature of the TGC genome , suggesting that they play a central role in placental function and pregnancy ., Consistent with this , UR domains are enriched for specific classes of genes involved in cell adhesion and neurogenesis ., Intriguingly , there is evidence that downregulation of both classes of proteins is crucial for placental function ., Downregulation of cell adhesion genes is necessary for trophoblast invasion in both mice and humans 40 , 41 ., Further—and quite remarkably—Liao et al . 42 found that upregulation of genes in the SLIT/ROBO neuronal guidance system in the human placenta is associated with the pregnancy disease pre-eclampsia ., UR domain formation could also enable TGCs to simply save materials and time , a hypothesis that has been proposed for polyploidy in general 43 ., TGCs are essential during the first half of gestation , when it is absolutely critical for the rapidly growing embryo to establish a connection with the mother 15 , 44 ., Formation of UR domains could allow for more rapid maturation of TGCs by allowing replication initiation to proceed without waiting for replication of nonessential regions of the genome ., In support of this , UR domains represent a significant part of the genome , 6% ( 138 Mbs of 2 , 717 total Mbs ) , and therefore the cell would require considerable resources to fully replicate these regions ., Together , functional evidence and convergent evolution suggest that UR domains are a critical element during pregnancy ., Regardless , placental UR domains are the first mammalian example , outside of the immune system , of lineage-specific CNVs being an integral part of normal cell biology and development ., All animal work has been conducted according to relevant U . S . and international guidelines ., Specifically , all experimental procedures were carried out in accordance with the Administrative Panel on Laboratory Animal Care ( APLAC ) protocol and the institutional guidelines set by the Veterinary Service Center at Stanford University ( Animal Welfare Assurance A3213-01 and USDA License 93-R-0004 ) ., Stanford APLAC and institutional guidelines are in compliance with the U . S . Public Health Service Policy on Humane Care and Use of Laboratory Animals ., The Stanford APLAC approved the animal protocol associated with the work described in this publication ., 129-Elite , C57BL/6 and pregnant C57BL/6 mice were obtained from Charles River ., Copulation was determined by the presence of a vaginal plug the morning after mating , and embryonic day 0 . 5 ( e0 . 5 ) was defined as noon of that day ., TGCs and embryos were dissected in 1× PBS ( 1∶10 10× PBS , pH\u200a=\u200a7 . 4; Gibco ) and stored on ice until further processing ., After removal of the decidua , parietal TGCs of the mural trophectoderm 15 were dissected away from the placental disk , and , when possible , Reicherts membrane ( Figure S1A ) ., TGCs were identified by their extremely large cell size ( Figure 1A ) ., Using single-nucleotide polymorphism data from F1 crosses , TGCs were predicted to have , at the most , approximately 5% contamination by maternal cells ( Hannibal & Baker , unpublished data ) ., Placental disk tissue was gathered from e13 . 5 placental disks after the removal of the decidua and obvious parietal TGCs ., For gathering 2N genomic DNA , at e8 . 0 , the entire embryo was collected; at e9 . 5 , the embryo body , after removal of obvious organs and head ( removed at otic vesicle ) , was collected; and at later stages , limbs , or a mixture of limbs and the tail , were collected ( Figure S1A ) ., For confocal imaging , TGCs/embryos were fixed in 4% paraformaldehyde at 4°C overnight ., Samples were stained with 0 . 5 µg/mL DAPI ( Life Technologies ) in 1× PBS overnight , washed in 50% glycerol/1× PBS and stored in 70% glycerol/1× PBS ., Confocal images were taken on a Leica DM IRE2 inverted microscope using the Leica SP2 software package , located in the Stanford Cell Sciences Imaging Facility ., Trophoblast stem cells were cultured as described in Chuong et al . 31 following 27 ., TS cells were differentiated into parietal TGCs by replacing the FGF , Activin and Heparin in the media with retinoic acid 27 , 28 ., Mature TGCs are seen after 4–6 days of differentiation 26 and were collected on days 3 , 5 and 7 ., TGCs/TS cells were further isolated for aCGH by placing cultured cells over a two-step density gradient ( 1 . 5% BSA over 3% BSA in a 15 mL tube; Figure S1B ) ., TGCs sank to the bottom of the tube while the smaller TS cells stayed in the upper fraction ., The embryonic stem cell line CGR8 is a germ-line competent cell line established from the inner cell mass of a 129 e3 . 5 male pre-implantation embryo 45 ., ES cells were cultured feeder-free on 0 . 1% gelatin coated plates ., The ES cell medium was prepared by supplementing knockout DMEM ( Invitrogen ) with 15% FBS , 1 mM glutamax , 0 . 1 mM nonessential amino acids , 1 mM sodium pyruvate , 0 . 1 mM 2-mercaptoethanol , penicillin/streptomycin , and 1000 units of leukemia inhibitory factor ( LIF; Millipore ) ., Cell culture was maintained at 37°C with 5% CO2 ., Megakaryocytes were derived and cultured as described in 46 ., Briefly , fetal livers were dissected from e13 . 5 C57BL/6 embryos in Hanks Balanced Salt Solution and placed in DMEM with 10% FBS supplemented with 100 ug/mL penicillin-streptomycin ( Invitrogen ) ., Livers were pooled based on sex of the embryo ( males pooled and females pooled separately ) ., To make a single cell solution , livers were aspirated through a progression of 18G , 21G and 23G needles ., To promote differentiation into megakaryocytes , cells were cultured for five days in media containing thrombopoietin ( TPO; R&D Systems ) at 37°C with 5% CO2 ., Successful differentiation was identified by, 1 ) the presence of large cells ( megakaryocytes ) and by, 2 ) FACS to confirm up to 32N ploidy ., For FACS , propidium iodide stained samples were run on a Cytek DxP10 modified Facscan ( Cytek Technologies , BD Biosciences ) using the blue laser ., Approximately 10 , 000 events per sample were collected ., Data was analyzed using FlowJo ( Treestar , Inc . ) ., Megakaryocytes were isolated for aCGH by placing cultured cells over a two-step density gradient ( 1 . 5% BSA over 3% BSA in a 15 mL tube; Figure S1B ) ., Megakaryocytes sank to the bottom of the tube while smaller , undifferentiated , cells stayed in the upper fraction ., Genomic DNA was extracted from fresh tissue and cultured cells using the DNeasy Blood & Tissue Kit ( Qiagen ) ., Before column purification , in vivo and in vitro samples were digested with proteinase-K ( 600 mAU/ml solution or 40 mAU/mg protein ) overnight and for 10 minutes , respectively , at 56°C , followed by a 4 minute incubation with RNase A ( 100 mg/mL; Qiagen DNeasy Blood & Tissue | Introduction, Results, Discussion, Materials and Methods | Discovery of lineage-specific somatic copy number variation ( CNV ) in mammals has led to debate over whether CNVs are mutations that propagate disease or whether they are a normal , and even essential , aspect of cell biology ., We show that 1 , 000N polyploid trophoblast giant cells ( TGCs ) of the mouse placenta contain 47 regions , totaling 138 Megabases , where genomic copies are underrepresented ( UR ) ., UR domains originate from a subset of late-replicating heterochromatic regions containing gene deserts and genes involved in cell adhesion and neurogenesis ., While lineage-specific CNVs have been identified in mammalian cells , classically in the immune system where V ( D ) J recombination occurs , we demonstrate that CNVs form during gestation in the placenta by an underreplication mechanism , not by recombination nor deletion ., Our results reveal that large scale CNVs are a normal feature of the mammalian placental genome , which are regulated systematically during embryogenesis and are propagated by a mechanism of underreplication . | Generally , every mammalian cell has the same complement of each part of its genome ., However , copy number variation ( CNV ) can occur , where , compared to the rest of its genome , a cell has either more or less of a specific genomic region ., It is unknown whether CNVs cause disease , or whether they are a normal aspect of cell biology ., We investigated CNVs in polyploid trophoblast giant cells ( TGCs ) of the mouse placenta , which have up to 1 , 000 copies of the genome in each cell ., We found that there are 47 regions with decreased copy number in TGCs , which we call underrepresented ( UR ) domains ., These domains are marked in the TGC progenitor cells and we suggest that they gradually form during gestation due to slow replication versus fast replication of the rest of the genome ., While UR domains contain cell adhesion and neuronal genes , they also contain significantly fewer genes than other genomic regions ., Our results demonstrate that CNVs are a normal feature of the mammalian placental genome , which are regulated systematically during pregnancy . | developmental biology, structural genomics, cell biology, chromosome biology, chromosome structure and function, gene expression, genetics, biology and life sciences, epigenetics, genomics, chromatin, cell differentiation, chromosomes, histone modification | null |
journal.pbio.1002290 | 2,015 | The Extracellular Domains of IgG1 and T Cell-Derived IL-4/IL-13 Are Critical for the Polyclonal Memory IgE Response In Vivo | IgE probably emerged during mammalian evolution to defend hosts against parasites , since a correlation between high IgE levels and protection against helminths has been recognized 1 , 2 ., Surprisingly , IgE was also found to mediate protection against bee venom in murine models 3 , 4 ., However , IgE can also mediate adverse effects during allergic inflammation , leading in the most extreme case to death by anaphylaxis ., Free IgE antibodies have a short half-life of only 12 h in the serum of healthy individuals 5 ., IgE is by far the least abundant immunoglobulin isotype with about 10 , 000-fold lower serum concentrations as compared to IgM , IgG , or IgA isotypes ., Class switch recombination ( CSR ) to IgE is induced by IL-4 or IL-13-mediated activation of STAT6 ., Activated STAT6 translocates to the nucleus , binds to the switch promoters in the Cε and Cγ1 genes , and in addition regulates expression of about 100 genes in B cells 6–8 ., We recently discovered that STAT6 expression in B cells was required for germinal center ( GC ) formation in response to helminth infection and during allergic inflammation 9 ., Infection of mice with the gastrointestinal helminth Nippostrongylus brasiliensis is a well-established model to study general mechanisms of IgE production in vivo ., Serum IgE levels of N . brasiliensis-infected BALB/c mice increase up to 1 , 000-fold by day 14 after N . brasiliensis infection 10 ., The IgE response to N . brasiliensis is abolished in IL-4-depleted 10 or IL-4-deficient 11 mice , indicating that IL-4 is the main cytokine that promotes IgE-CSR ., However , IL-13 can also induce IgE-CSR under certain conditions , including the immune response to Schistosoma mansoni eggs 12 , 13 ., IL-4 and/or IL-13 can be produced by many cell types of the adaptive and innate immune system such as Th2 cells , follicular T helper ( TFH ) cells , natural killer T ( NKT ) cells , basophils , eosinophils , mast cells , and type 2 innate lymphoid cells ( ILC2 ) ., Although Th2 cells and TFH cells are generally considered to be the most relevant cell types for induction of IgE-CSR in B cells , it remains unclear to what extent innate IL-4/IL-13-expressing cell types contribute to this process , especially during secondary infection when basophils and mast cells can be rapidly activated to release large amounts of IL-4/IL-13 ., Immunohistological stainings indicated that IgE-CSR occurs outside GCs 14 , while other studies identified IgE+ B cells inside GCs by using fluorescent IgE reporter mice 15–17 ., Reporter mice are valuable tools , but they also bear certain caveats ., In two different IgE reporter mice , expression of membrane IgE is marked by green fluorescent protein ( GFP ) that is translated from a bicistronic IgE-IRES-GFP mRNA 16–18 ., In these mice , GFP also reports germline , immature , and nonproductive transcripts ., Therefore , a substantial fraction of GFP+ GC B cells actually express IgG1 and not IgE on the cell surface ., One of these mouse strains also contains an insertion of a 52 amino acid region that is normally present in the extracellular part of human but not mouse IgE 18 ., Another strain , the Verigem mouse 15 , was constructed to express a membrane IgE-2A-Venus fusion protein that is cleaved into IgE and Venus , a brightly fluorescent protein ., In this mouse strain , only mature transcripts are reported , but a 2–3-fold increase of membrane IgE and a reduction of secreted IgE has been noted ., On the molecular level , IgE-CSR was found to either occur directly from IgM to IgE or sequentially from IgM to IgG1 followed by a second switch reaction to IgE 19 ., The relevance of the sequential switching pathway was questioned by experiments demonstrating that genetically modified mice that cannot switch to IgG1 show the same serum IgE levels after primary N . brasiliensis infection as compared to control mice , but the memory IgE response was not investigated 20 ., Furthermore , sequential switching was reported to be important for generation of high affinity IgE antibodies under rather nonphysiological conditions by either repeated immunizations of BALB/c mice with the hapten antigen NP-KLH or after OVA-PEP1 immunization of T/B monoclonal mice where all B cells are specific for influenza hemagglutinin and all T cells are specific for chicken ovalbumin 14 , 21 , 22 ., Sequentially switched B cells can be identified with a quantitative polymerase chain reaction ( PCR ) assay that detects remnants of the Sγ1 region in the recombined Sμ-Sε allele 14 ., However , switch remnants can also be present in the nonproductively rearranged allele , and only a fraction of the recombined switch regions retains Sγ1 DNA 21 ., This PCR assay cannot provide information about the total frequency and relatedness of the original IgG1 repertoire and the sequentially switched IgE repertoire ., However , this information can be obtained , as we show here , by next generation sequencing ( NGS ) of RT-PCR products that cover the recombined variable , diversity , and joining ( VDJ ) regions and the 5’ part of the Cγ1 or Cε genes , respectively ., The question whether bona fide IgE+ memory B cells do exist or not continues to be controversially discussed 23 ., Mice with deletion of the transmembrane and cytoplasmic tail of IgE ( ΔM1M2 mice ) show a poor memory IgE response indicating that bona fide IgE+ memory B cells exist and directly respond to antigen challenge 24 ., However , the IgE response was also reduced 10-fold after primary infection of ΔM1M2 mice , suggesting that these mice have a general defect to mount IgE responses 24 ., Others have shown that transfer of GFP+-sorted memory B cells from N . brasiliensis-infected IgE-GFP reporter mice into B cell-deficient hosts gave rise to serum IgE levels after N . brasiliensis infection 16 ., However , in these reporter mice , GFP appears to be expressed also by some IgG1+ B cells , which may have contaminated the population of transferred B cells , and these mice express an engineered membrane IgE molecule that contains 52 amino acids of human IgE , which may alter the behavior of IgE+ B cells 25 ., Studies with other IgE reporter mice or transfer of purified IgG+ memory B cells from T/B monoclonal mice indicated that the memory IgE response does not develop from bona fide IgE+ memory B cells but rather depends on an IgG1+ precursor population 14 , 15 , 17 ., Using NGS analyses , we observed a striking overlap between the IgG1 and IgE repertoires in N . brasiliensis-infected or OVA/alum-immunized wild-type BALB/c mice ., Competitive adoptive transfers further revealed that T cell-derived IL-4/IL-13 was required for the memory IgE response but not for expansion of memory B cells ., IgG1+ B cells were required and sufficient to establish the memory IgE response after transfer in IgH allogeneic recipients ., Interestingly , the memory IgE response was also impaired in mice where the extracellular parts of IgG1 had been replaced by IgE domains ., Collectively , our results demonstrate that the memory IgE response is largely dependent on clonal expansion and affinity maturation of IgG1-expressing B cells that require a second IL-4/IL-13 signal from T cells to subsequently switch to IgE and differentiate into IgE-secreting plasma cells ., Mice with selective deletion of IL-4/IL-13 in T cells ( 4-13Tko mice ) are unable to mount a serum IgE response and show impaired GC formation after helminth infection 9 , 26 ., To further address how T cell-derived IL-4/IL-13 promotes development of IgE+ B cells and plasma cells ( PCs ) in vivo , we analyzed wild-type , IL-4/IL-13-deficient ( 4-13ko ) , and 4-13Tko mice after N . brasiliensis infection by flow cytometry ., We first treated cells isolated from the draining lymph nodes ( LN ) with an acidic wash buffer to remove cytophilic IgE bound to the low affinity IgE receptor FcεRII ( CD23 ) and then stained for surface and intracellular IgE and IgG1 ., In our hands , this procedure is comparable to an alternative IgE staining protocol where extracellular IgE is first blocked by anti-IgE antibodies followed by intracellular staining for IgE 15 ( S1 Fig ) ., We observed that IgE+ B cells and PCs were missing in 4-13ko and 4-13Tko mice compared to wild-type ( WT ) controls , and IgG1+ B cells were reduced 20-fold ( Fig 1A and 1B ) ., It is important to note that the acidic wash buffer efficiently removed cytophilic CD23-bound IgE from B cells and PCs , as the vast majority of IgE+ GC B cells and PCs was restricted to the IgG1-negative population and was not found in IgE-deficient mice ( Fig 1C and 1D ) ., The acidic wash buffer did not affect IgE bound to the high affinity IgE receptor FcεRI , which is mainly expressed on basophils and mast cells ( Fig 1C ) ., We observed that the B220−IgE+ cells found in the LN of infected WT mice are composed mostly of basophils ( 80%; CD49b+CD138− ) and by a smaller part of PCs ( 20%; CD49b−CD138+ ) ., Class switch recombination to IgE can occur directly from an IgM+ B cell or sequentially via an IgG1+ B cell intermediary 27 ., We reasoned that sequential switching should be reflected by overlapping repertoires of IgE and IgG1 sequences ., To address this experimentally , we analyzed thousands of VDJ regions from the heavy chains of IgE , IgG1 , and IgM by NGS using a similar approach as we previously described for analysis of the intestinal IgA repertoire 28 ., Reverse transcription PCRs ( RT-PCRs ) were performed with RNA samples from total mediastinal LN cells on day 15 after N . brasiliensis infection using a promiscuous 5’ primer that binds to VH1 , 2 , 3 , 5 , and 14 family sequences and thereby picks up most of the expressed VH genes 29 in combination with 3’ primers that bind in the Cε , Cγ1 and Cμ genes ., The amplified VDJ sequences were then subjected to NGS analysis ., We analyzed several thousand sequences per isotype and determined the richness as a measure of repertoire diversity ., We observed a 3-fold higher richness in the IgM repertoire as compared to the IgE and IgG1 repertoires reflecting clonal expansion of isotype-switched B cells ( Fig 2A ) ., The usage of different VH , DH , and JH segments was very similar among the three isotypes and the majority of sequences used in the VH1/J558 family 30 in combination with DH1 or DH2 families ( Fig 2B ) ., We further determined the relative abundance of individual CDR3 sequences within the total IgE , IgG1 , and IgM sequences from each mouse ., A striking overlap was found between CDR3 regions of IgE and IgG1 sequences within but not between individual mice ( Fig 2C and 2D ) ., In contrast , little overlap of CDR3 sequences existed between the IgE and IgM repertoires ( Fig 2C and 2D ) ., We obtained similar results with samples from mediastinal LN of OVA/alum-immunized mice ( S2 Fig ) and samples from mesenteric LN of five independently N . brasiliensis-infected mice ( S3 Fig ) ., In contrast to the prominent IgE-IgG1 overlap , we found only little overlap between IgA and IgG1 or IgM repertoires or between IgE and IgA repertoires ( S3D and S3E Fig ) ., Comparison of somatic hypermutations ( SHMs ) between the IgE and IgG1 repertoire revealed a similar average number of SHMs in different parts of the VH regions of IgE and IgG1 sequences ( Fig 2E and S2 Fig ) ., The average number of SHMs in VH regions of corresponding CDR3 pools of the IgE and IgG1 repertoire were basically identical ( Fig 2F ) ., Alignments of the patterns of SHMs from three different corresponding CDR3 pools of IgE and IgG1 sequences showed that the core pattern of SHMs was very similar for each CDR3 pool ., However , sequences with additional mutations could be observed for both isotypes ( Fig 2G ) ., To figure out whether the repertoire of IgE+ PCs was closer related to IgE+ or IgG1+ GCs we sorted these populations from LN of infected mice and determined whether the CDR3 sequences that constituted the first 50 most abundant CDR3 pools in the IgE+ PC population also show up in the IgE+ GC or IgG1+ GC population ., This analysis revealed that the IgE+ PC repertoire is more closely related to the IgG1+ GC as compared to the IgE+ GC repertoire ( Fig 3A ) ., IgE+ GC B cells contained relatively few nonsilent SHMs , indicating that they did not undergo affinity maturation ( Fig 3B and 3C ) ., Taken together , these findings clearly demonstrate that the sequential pathway for IgE-CSR dominates the in vivo IgE response and points to an important role of the GC as the site where affinity maturation probably occurs at the level of IgG1-expressing B cells that have the capacity to further switch and differentiate to IgE-producing PCs ., This raises the question whether memory IgE responses are driven by IgE+ memory B cells or rather depend on memory IgG1+ B cells that switch to IgE after secondary antigen encounter ., Consistent with previous reports , we observed that the serum IgE concentration reaches about 10-fold higher levels after secondary as compared to primary infection with N . brasiliensis , and this effect was dependent on the presence of CD4+ T cells 10 , 31 ( Fig 4A ) ., We further found that this increase in serum IgE was accompanied by a 6-fold increase of PCs ( B220−CD138+FSChiSSChi ) when compared to the primary response ( Fig 4B ) ., Interestingly , no expansion of GC B cells ( B220+CD38loGL-7hi ) was observed after secondary N . brasiliensis infection , indicating that the new antibody-secreting cells originate mostly from pre-established memory B cells ( Fig 4C and 4D ) ., Eosinophils and basophils were reported to contribute to the memory response by promoting plasma cell survival in the bone marrow and spleen , respectively 32 , 33 ., However , we observed unimpaired memory IgE responses in eosinophil-deficient ΔdblGata 34 and basophil-deficient Mcpt8Cre mice 35 ( Fig 4E and 4F ) ., Next , we investigated the distribution of IgE- and IgG1-secreting PCs in different tissues after the first and second N . brasiliensis infection ., At day 13 after primary infection , the peak of the humoral response , PCs could be found mainly in the draining LN and spleen of infected mice ( Fig 5A ) ., After secondary infection , the total number of PCs mainly increased in the LN and to a lesser extent in lung , spleen , and bone marrow ( Fig 5A ) ., IgE+ and IgG1+ PCs were found in the spleen and LN of primary infected mice , and in the memory response they became much more abundant in these organs , whereas IgE+ and IgG1+ PCs in the bone marrow and lung remained relatively scarce , although their numbers increased during the secondary response ( Fig 5B–5E ) ., We further analyzed the overlap of the IgE repertoires in bone marrow , lung , spleen , and LN after primary and secondary infection by NGS analysis to determine the clonal dissemination of IgE+ PCs in these tissues ., During primary infection , the IgE repertoires in lung , LN , and spleen showed pronounced overlaps , while the IgE repertoire in the bone marrow was rather unique ., However , an increased overlap between the IgE repertoire in the bone marrow and the other organs was observed after secondary infection ( Fig 6A and 6B ) ., The IgE repertoires had a high diversity with 300–400 different sequences among 1 , 000 analyzed sequences in lung , spleen , and LN during primary or secondary infection , whereas the diversity was 2–3-fold lower in the bone marrow ( Fig 6C ) ., Similar to the primary infection , the majority of sequences used the VH1/J558 family in combination with DH1 or DH2 segments ( S4 and S5 Fig ) ., By analyzing the number of SHMs in the VH region of productive sequences , we observed that during primary infection about 50% of all IgE sequences in all organs contained 0–3 SHMs ., During secondary infection , the repertoires were dominated by sequences with 3–20 SHMs reflecting further selection and affinity maturation ( Fig 6D ) ., Since IgE+ PCs with overlapping repertoires could be found in spleen , mesenteric LN , and bone marrow after secondary N . brasiliensis infection , we sought to evaluate how well memory IgE precursor cells from these organs perform in a competitive transfer experiment with naïve cells ., For this purpose , we used congenic Ly5 mice expressing different immunoglobulin heavy chain allotypes ., First , we infected Ly5 . 2 mice that carried the Ig heavy chain of the “a” allotype ( Ly5 . 2/IgHa mice ) with N . brasiliensis ., Five to six weeks later , we isolated total lymphocytes from LN , spleen , and bone marrow of these N . brasiliensis memory mice and the corresponding cell populations from naïve Ly5 . 1 mice that were of the Ig heavy chain “b” allotype ( Ly5 . 1/IgHb mice ) ., The samples from naïve and memory mice were adjusted to equal numbers of B cells , mixed , and transferred to Rag1–/–mice , which were infected with N . brasiliensis 1 d later ( Fig 7A ) ., For the bone marrow samples , we reasoned that the memory response might be poor because of the relatively low number of CD4+ T cells in these organs ., Therefore , we also included one group of mice that received purified CD4+ T cells from the spleen of naïve WT mice in addition to bone marrow cells from naïve and N . brasiliensis memory mice ., The B cells from LN and spleen of memory mice expanded 4–5 times better as compared to cotransferred B cells from naïve mice , while both populations expanded with the same efficiency when they were derived from the bone marrow , even in the group that received additional purified CD4+ T cells ( Fig 7B and 7C ) ., The dominance of B cells derived from spleen or LN of memory mice was confirmed in the reverse experiment with memory Ly5 . 1/IgHb and naïve Ly5 . 2/IgHa donors ( S6 Fig ) ., Furthermore , B cells derived from memory mice mainly outcompeted B cells derived from naïve mice in the CD38+IgD− B cell population ( largely reflecting memory B cells ) as compared to the CD38+IgD+ population ( largely reflecting naïve B cells ) ( S6 Fig ) ., Most IgE antibodies detected in the serum were secreted by cells that originated from LN or spleen of memory mice ( Fig 7D ) ., In contrast , a memory IgE response from bone marrow samples was only observed when purified CD4+ T cells had been cotransferred ( Fig 7D ) ., This indicates that IgE memory precursor cells are present in the bone marrow , but there is not enough help provided by the few CD4+ T cells in this organ ., Although addition of exogenous T cells did not improve the expansion of B cells derived from bone marrow of memory mice , it induced the production of IgE from memory-mice-derived B cells , suggesting that memory B cells require T cell signals to further differentiate into IgE-producing plasma cells ., To further elucidate whether T cell-derived IL-4 was required for the IgE memory response , we transferred 2 x 105 purified CD4+ T cells from WT or 4-13Tko N . brasiliensis memory mice together with 2 x 105 purified and equally mixed B cells from memory IgHa and naïve IgHb mice into Rag1−/− recipients ( Fig 8A ) ., As additional control , one group of mice only received mixed B cells but not T cells ., B cells derived from memory mice expanded four times better than B cells from naïve mice independently of cotransferred CD4+ T cells ( Fig 8B ) ., In addition , B cells from memory mice appeared more activated based on CD38 down-regulation as compared to B cells from naïve mice independently of T cells ( Fig 8C ) ., However , IgE was secreted in the serum only by cells originating from the memory mice and only if IL-4/IL-13 competent T cells were cotransferred along with the B cells ( Fig 8D ) ., This indicates that T cell-derived IL-4/IL-13 promotes the secondary switch and differentiation of IgG1+ memory B cells into IgE-secreting PCs ., We performed further experiments to clarify whether the memory IgE response is dependent on an IgG1+ memory B cell ., Here , we used the recently described IgE knock-in mouse ( IgEki/ki ) in which the extracellular part of the IgG1 heavy chain had been replaced by the extracellular part of the IgE heavy chain 36 ., When mesenteric LN were analyzed on day 14 after N . brasiliensis infection , wild-type mice had about 43% IgG1+ and 1 . 6% IgE+ GC B cells , while IgEki/ki mice had no IgG1+ and about 27% IgE+ GC B cells ( Fig 9A ) ., However , this 20-fold increased population of IgE+ GC B cells in IgEki/ki mice did not result in the same increase of IgE+ PCs ( Fig 9B ) , and the serum IgE levels were only about 2-fold higher in IgEki/ki mice as compared to control mice at the peak of the primary response to N . brasiliensis ( Fig 9C ) ., Interestingly , serum IgE levels during the recall response in IgEki/ki mice were comparable to the IgE levels after primary infection and much lower compared to IgE levels in control mice ( Fig 9C ) ., We further analyzed the IgE response in F1 mice generated by crossing IgEki/ki mice ( IgHa ) or normal IgHa mice to C57BL/6 mice ( IgHb ) ., Due to allelic exclusion , about 50% of B cells in these mice express the IgHa allele while the other 50% express the IgHb allele ., We found that IgEb dominated the memory IgE response to N . brasiliensis in IgEki, ( a ) /wt, ( b ) mice illustrating the competitive advantage of the wild-type allele encoding the extracellular domains of IgG1 ( Fig 9D ) ., Next , we sorted different B cell subsets and PCs from N . brasiliensis-infected IgHa memory mice and transferred them separately into naïve nonirradiated IgHb recipient mice ., After N . brasiliensis infection of recipient mice , we observed a prominent IgE response in recipients of B220+IgG1+ and B220+IgM−IgD−IgE− cells but not in recipients of B220+IgM−IgD−IgG1− or IgG1− PCs ( Fig 9E and S7 Fig ) ., Taken together , this set of experiments indicates that the memory IgE response to N . brasiliensis unfolds from IgG1+ memory B cells and requires the extracellular part of IgG1 ., A detailed understanding of the mechanisms that regulate memory IgE responses is critical to develop efficient therapeutic strategies against chronic allergic inflammation ., Here , we used mice with a normal T and B cell repertoire and without modification of the IgE locus in combination with the well-established N . brasiliensis infection model or OVA/alum immunization to uncover important new insights of the primary and memory IgE response ., It has been shown that B cells primed with a high concentration of KLH/alum differentiate mainly into IgG1+ B cells and transfer of 107 total B cells from KLH/alum immunized mice into IL-4-deficient recipients results in a poor IgE memory response 37 ., We extend these findings by showing that IL-4/IL-13 from T cells played a critical role for the enhanced IgE response but not for expansion of memory B cells during secondary N . brasiliensis infection , suggesting that memory B cells require T cell-derived IL-4/IL-13 to further differentiate into IgE-producing PCs , but not for proliferation or survival ., Interestingly , the number of GC B cells did not increase during secondary infection , while PCs showed massive expansion ., Neither eosinophils nor basophils were required for secondary PC expansion or for the memory IgE response , although both cell populations are sources of IL-4 and IL-13 and despite previous reports that described critical roles of eosinophils and basophils for PC survival in bone marrow and spleen , respectively 32 , 33 ., The role of GCs for the IgE response is not well understood ., A previous study reported that germline and postswitch IgE transcripts could be detected in GC B cells but most IgE+ cells were found outside the GC and these cells displayed a PC phenotype 14 ., Principally , the IgE response can unfold in the absence of GCs as increased IgE levels can be observed upon immunization of mice that make a poor GC response like Bcl6-deficient mice 38 ., Furthermore , spontaneously increased serum IgE levels are detected in MHC-II-deficient and T-lymphopenic mice 39 or in Omenn syndrome patients with reduced Rag1 or Rag2 activity 40 ., These “natural” IgE antibodies have almost no SHMs and do not require GCs for their generation 39 ., However , GCs probably play an important role for T cell-dependent IgE responses to allergens and helminths , especially for high-affinity IgE antibodies that are most likely generated by sequential CSR from IgG1+ B cells 14 , 21 ., The decision to undergo direct or sequential switching might be regulated in part by the amount of antigen 41 ., IgE+ GC B cells from CεGFP reporter mice showed lower B cell receptor ( BCR ) expression levels , a poor BCR signaling response , decreased expression of costimulatory molecules , and enhanced apoptosis suggesting that IgE+ GC B cells are prone to die rather than giving rise to memory B cells and PCs 17 ., However , no information was given in this study regarding IgE BCR expression levels in CεGFP versus WT mice , which would be an important piece of information to exclude a possible negative effect of the reporter construct ., We further demonstrate that a prominent IgE+ GC B cell population is present in IgEki/ki mice indicating that surface IgE+ B cells can participate in the GC reaction and are not immediately prone to die ., However , we also observed that IgE+ GC B cells contain fewer SHMs as compared to IgG1+ GC B cells indicating that affinity maturation is impaired in IgE+ GC B cells ., Interestingly , IgEki/ki mice failed to mount a normal IgE memory response suggesting that the extracellular part of the chimeric IgE-IgG1 receptor does not allow the formation of memory B cells ., One possible explanation for this finding would be that IgE+ B cells are actively removed or silenced in vivo , although the mechanisms behind such a scenario remain unclear ., An alternative explanation would be that antigen-independent binding of the extracellular part of IgG1 to a yet-to-be-identified target structure induces a prosurvival signal in memory IgG1+ B cells , which then give rise to the memory IgE response ., Achatz et al . demonstrated that the memory IgE response is blunted when B cells express transmembrane IgE with a truncated cytoplasmic tail favoring the concept that the memory IgE response develops directly from IgE+ B cells 24 ., However , the primary IgE response is also affected in these mice , and this finding is not necessarily in conflict with the observation that the memory IgE response develops from IgG1+ precursors ., For instance , it could well be that IgE-secreting PCs develop first from IgG1+ memory B cells and the cytoplasmic tail of IgE is then required for survival of IgE+ PCs since transmembrane IgE is clearly expressed on these cells 15 ., We show here , to our knowledge , for the first time an extensive NGS analysis of the IgE and IgG1 repertoires in normal BALB/c mice after helminth infection or during allergic inflammation ., We found largely overlapping IgE and IgG1 repertoires based on the comparison of CDR3 regions from several thousand IgE and IgG1 sequences ., The analysis of SHMs in selected pools of IgE and IgG1 sequences with the same CDR3 region revealed that the majority of SHMs was identical in both isotypes ., Furthermore , we found that the Ig repertoire of IgE-producing PCs was more closely related to IgG1+ GC B cells as compared to IgE+ GC B cells ., Early IgE+ PCs show very few SHMs and are often generated in extrafollicular foci , while PCs with SHMs are thought to be derived from GC B cells that arise later during an immune response 42 ., The large number of SHMs in IgE+ PCs of N . brasiliensis-infected mice indicates that the repertoire of these PCs was generated during the GC response ., Taken together , this set of experiments provides strong evidence that the majority of the IgE response to primary N . brasiliensis infection in mice with a normal polyclonal B and T cell repertoire is constituted by PCs originating from sequentially switched B cells ., The IgE repertoires between lung , spleen , and LN were very similar after primary and secondary infection , while an IgE repertoire overlap between bone marrow and the peripheral organs could only be observed after secondary infection ., We unexpectedly observed a high IgE repertoire diversity with 300–400 different sequences among 1 , 000 analyzed sequences in lung , spleen , and LN after primary and secondary infection ., This shows that many different clones of IgE-expressing cells disseminate into different tissues and the diversity was maintained after secondary infection , excluding the possibility that only few memory B cells participate in the memory response ., The average number of SHMs in IgE increased after secondary infection and was remarkably constant in all four tissues ., The pool of IgE memory precursor cells ( most likely a population of IgG1+ memory B cells ) is probably established after the first infection and does not require a second GC phase , since we observed no GC response upon secondary infection ., It presently remains unclear whether the mechanisms that we describe here for IgE responses of helminth-infected mice can be translated to human allergic IgE responses ., Most studies with allergic patients used samples from peripheral blood and reported a diverse and somatically mutated IgE repertoire similar to our murine data 43 ., However , the local IgE response in mucosal tissues might be different to what can be detected in the peripheral blood ., In-depth analysis of the IgE repertoire in different tissues and the overlap between the repertoire of IgE and other isotypes has not been performed ., The NGS analysis we described here for the mouse can be adapted to the human immune system so that we expect to see a tremendous gain of information regarding development , distribution , and persistence of the allergic IgE response in the near future ., In conclusion , we directly demonstrate by using NGS analysis that sequential IgE switching via IgG1 dominates the primary and secondary IgE response to the helminth N . brasiliensis ., The main population of memory IgE precursor cells with proliferative capacity appeared to be located in the LN and spleen rather than in the bone marrow ., Furthermore , we found that the memory IgE response is affected by selective deletion of IL-4/IL-13 from T cells or deletion of IgG1+ B cells , while purified IgG1+ B cells gave rise to IgE-producing PCs upon transfer and N . brasiliensis infection of unmanipulated WT mice ., These results strongly suggest that the memory IgE response which accounts for relapsing allergic disorders is driven by IgG1+ precursor cells ., If the human allergic IgE response is subject to the same mechanisms we describe here , then therapeutic strategies should be developed to target or prevent development of allergen-specific IgG1+ memory B cells ., The animal experiments were performed in accordance with the German animal protection law and the EU guidelines 86/809 ., The experiments were approved by the Department of Animal Protection of the Government of Lower Franconia , Germany ( license numbers 54–2532 . 1-23/14 and 54–2532 . 1-26/10 ) ., BALB/c , C57BL/6_Ly5 . 1 ( B6 . SJL-Ptprca Pepcb/BoyJ ) , and Rosa26-YFP reporter mice ( B6 . 129X1-Gt ( ROSA ) 26Sortm1 ( EYFP ) Cos/J ) were originally obtained from The Jackson Laboratory ., Cγ1Cre/Cre mice 44 were crossed to Rosa26-YFP mice to generate Cγ1Cre/+Rosa26loxP-STOP-loxP-eYFP/+ mice ., We further used IgE–/–_BALB/c mice 45 , IL-4/IL-13−/−_BALB/c ( 4-13ko ) mice 13 , CD4Cre mice 46 , conditional IL-4/IL-13-deficient mice 47 , Mcpt8Cre_C57BL/6 mice 35 , and ΔdblGata_BALB/c mice 34 ., IgEki/ki_C57BL/6 mice have been described 36 ., In these mice , the first three exons of the Cγ1 gene were replaced by the first four exons of the Cε gene ., All mice had been backcrossed at least 9 generations to BALB/c or C57BL/6 background and used between 6 and 14 wk of age ., Mice were infected subcutaneously at the base of the tail with 500 L3 stage larvae of N . brasiliensis as described 35 ., Cytophilic IgE was efficiently removed by short treatment with acetate buffer as described 48 ., Single cell suspensions were washed with FACS buffer ( P | Introduction, Results, Discussion, Methods | IgE-mediated activation of mast cells and basophils contributes to protective immunity against helminths but also causes allergic responses ., The development and persistence of IgE responses are poorly understood , which is in part due to the low number of IgE-producing cells ., Here , we used next generation sequencing to uncover a striking overlap between the IgE and IgG1 repertoires in helminth-infected or OVA/alum-immunized wild-type BALB/c mice ., The memory IgE response after secondary infection induced a strong increase of IgE+ plasma cells in spleen and lymph nodes ., In contrast , germinal center B cells did not increase during secondary infection ., Unexpectedly , the memory IgE response was lost in mice where the extracellular part of IgG1 had been replaced with IgE sequences ., Adoptive transfer studies revealed that IgG1+ B cells were required and sufficient to constitute the memory IgE response in recipient mice ., T cell-derived IL-4/IL-13 was required for the memory IgE response but not for expansion of B cells from memory mice ., Together , our results reveal a close relationship between the IgE and IgG1 repertoires in vivo and demonstrate that the memory IgE response is mainly conserved at the level of memory IgG1+ B cells ., Therefore , targeting the generation and survival of allergen-specific IgG1+ B cells could lead to development of new therapeutic strategies to treat chronic allergic disorders . | Allergic inflammation is initiated when IgE antibodies bind to high-affinity receptors on the cell surface of mast cells and basophils , thereby triggering the release of proinflammatory mediators ., The development and persistence of IgE responses in vivo is poorly characterized because of the low number of IgE-producing B cells and plasma cells ., Naïve mature B cells produce IgM antibodies ., Upon activation , they “switch” class to produce IgG , IgA , or IgE antibodies ., It is currently highly debated whether IgE-expressing B cells are generated by direct switching from IgM-expressing B cells or by sequential switching via IgG1-expressing B cells ., Using next generation sequencing , we compared thousands of IgE , IgG1 , and IgM sequences after immunization of mice with parasitic worms and found a striking overlap between the IgE and IgG1 repertoires ., We further show that the memory IgE response to a secondary encounter with the same parasitic worms was dependent on T cell-derived cytokines ., Genetically modified mice and adoptive transfers of B cells revealed that the memory IgE response is conserved at the level of IgG1-expressing B cells ., These results favor the concept that bona fide IgE-expressing B cells do not exist , and memory IgE responses unfold from IgG1-expressing B cells , which undergo a secondary switch reaction and differentiation to plasma cells . | null | This study reveals that repertoires of IgE—the class of antibody that mediates allergic reactions—closely resemble those of IgG1, suggesting that the memory IgE response unfolds from IgG1-switched B cells (and not from IgM-expressing B cells) in response to T cell-derived cytokines. |
journal.pbio.1000234 | 2,009 | Species-Specific Heterochromatin Prevents Mitotic Chromosome Segregation to Cause Hybrid Lethality in Drosophila | A critical stage of speciation is the development of reproductive isolating mechanisms that prevent gene exchange between diverging populations ., Hybrid sterility and lethality are major components of reproductive isolation ., A key to understanding how these hybrid incompatibilities ( HIs ) evolve is discovering the causal genes and determining how they inhibit or perturb normal development ., A number of HI genes have been identified , all of which are protein-coding ., These genes are characterized by two distinct modes of evolution: either high rates of coding-sequence divergence that are consistent with adaptive evolution in many 1–3 but not all 4 cases , or structural changes such as in gene location 5 or gene silencing and loss following duplication 6 , 7 ., These cases suggest that rapid evolution of either protein-coding gene sequence or structure is a general principle underlying the evolution of HIs ., Are rapidly evolving protein-coding genes the only cause of HI ?, Noncoding repetitive sequences , including transposable elements ( TEs ) and satellite repeats , are major contributors to genome evolution in higher eukaryotes ., These sequences comprise heterochromatin , chromosomal regions found primarily around the centromeres and telomeres that remain more condensed than gene-containing euchromatin through the cell cycle ., Pericentric heterochromatin is known to play important roles in mitotic and meiotic chromosome segregation 8–10 ., Heterochromatin may also be important for the transcriptional regulation of flanking sequences such as ribosomal DNA ( rDNA ) loci , since rDNA genes are often found in heterochromatic regions 11 , 12 ., Paradoxically , however , despite these apparently conserved functions in higher eukaryotes , heterochromatin can vary greatly in abundance and sequence composition even between closely related species 13–16 ., These observations have led to speculation that divergence of repetitive noncoding sequences may also directly cause reproductive isolation between nascent species 13 , 17 ., However , to our knowledge no examples have been clearly demonstrated ., One hint that heterochromatin divergence may contribute to HI came from the discovery that the protein encoded by the Drosophila hybrid lethality gene Lhr localizes to pericentric heterochromatin ., Lhr itself shows strong evidence of having diverged under the force of adaptive evolution , leading to the hypothesis that it may be co-evolving with heterochromatic sequences 18 ., An additional possible link between HI and heterochromatin comes from the identification of the gene Prdm9 as causing hybrid male sterility between subspecies of mice , because the heterochromatic meiotic sex body is defective in both sterile hybrids and in Prdm9-mutant pure-species mice 19 ., The sibling species D . melanogaster and D . simulans exhibit large differences in heterochromatin content 15 and strong reproductive isolation 20 ., F1 hybrid females produced from D . simulans mothers and D . melanogaster fathers die as embryos 21 ., This female-specific lethality is intriguing for several reasons ., First , this lethality appears to have a different genetic basis than the F1 male lethality that occurs in the reciprocal cross 22 ., While two major-effect genes causing this male lethality have been cloned 18 , 23 , nothing is known about the molecular basis of the female lethality ., Second , this female-specific lethality is an exception to Haldanes rule , the observation that unisexual hybrid sterility or lethality typically affects the heterogametic ( XY or ZW ) sex rather than the homogametic sex ( XX or ZZ ) 24 ., Third , a link between hybrid female lethality and heterochromatin was strongly suggested by studies of Sawamura and colleagues of the D . melanogaster Zygotic hybrid rescue ( Zhr1 ) mutation , which suppresses lethality of these otherwise lethal hybrid females ., Zhr1 was discovered on an X-Y translocation chromosome that is deleted for much of the X chromosome pericentric heterochromatin 25 ., The deleted region is thought to consist primarily of satellite DNA composed of a tandemly repeated 359-bp long monomer 25 ., We refer henceforth to the monomer unit as the 359-bp repeat , and the heterochromatic region of the D . melanogaster X chromosome as the 359-bp satellite block , and revisit in the Discussion the question of what specific DNA sequences within this block cause hybrid lethality ., In the wild type this satellite DNA ( also known as the 1 . 688 g/cm3 satellite ) is estimated to form a multi-mega-bp block of heterochromatin 26 ., Experiments showing that hybrid viability is sensitive to the dosage of a mini-chromosome containing part of the 359-bp satellite block led to the suggestion that repetitive sequences within the 359-bp satellite block are responsible for the hybrid lethal effect 27 ., However , the mapping studies are consistent with the alternative possibility that the Zhr locus is a protein-coding gene embedded within this heterochromatic region ., This is a plausible alternative , as an unexpected number of protein-coding genes have recently been found on Drosophila Y chromosomes , which otherwise contain mega-bp amounts of heterochromatic repeats 28 ., If Zhr is not a protein-coding locus , then the possibility that an HI locus consists of noncoding , repetitive DNA raises important questions regarding how such sequences could kill hybrids ., One possibility is that heterochromatic sequences such as those comprising the X-linked Zhr locus cause hybrid lethality by inducing in trans a global effect on chromatin structure or gene expression ., Alternatively , the Zhr locus might operate in cis by affecting other adjacent , X-linked sequences such as the rDNA genes or the centromere ., A third alternative is that the lethal effects are confined to this heterochromatic locus itself , such that an aberration in its structure somehow directly disrupts embryonic development ., Given the difficulties involved in the genetic manipulation of heterochromatic sequences , we addressed these questions by combining genetic and cytological approaches to determine when hybrid females die during development , to identify the cellular basis of the lethality , to investigate whether possible heterochromatic defects occur genome-wide or are confined to the Zhr locus , and to test whether such defects are suppressed in hybrid females carrying the Zhr1 rescue mutation and induced in hybrid males carrying a Zhr duplication ., Our results strongly suggest that the Zhr locus directly causes hybrid lethality by inducing mitotic failure in early precellularized embryos , and that the underlying defect is a failure of the 359-bp satellite block to form or maintain a proper heterochromatic state ., These results provide compelling evidence that noncoding heterochromatic DNA can directly cause HI and thus contribute to speciation ., To address the timing and nature of hybrid female lethality , we examined young ( 0–3 h ) hybrid embryos produced from several different wild-type parental strains ( Table 1 ) ., Normal embryonic development in Drosophila begins with a single diploid nucleus that gives rise to several thousand nuclei through 14 synchronous mitotic divisions in the large , single-celled blastula ., During the first nine divisions , the nuclei migrate from the interior of the embryo to the cortex as they expand in number ., Four additional nuclear divisions occur at the cortex before the formation of membrane furrows that transform the syncytial blastoderm into the cellular blastoderm ., This process , termed cellularization , is followed by gastrulation ( for a detailed review of early Drosophila embryogenesis see 29 ) ., As expected , hybrid male embryos , which survive to adulthood 20 , underwent normal nuclear divisions during the blastula stage and progressed into the gastrula stage ( Figure 1A ) ., Hybrid female embryos also had normal nuclear spacing and synchrony during the first nine mitotic divisions ( Figure 1A ) ., However , between mitotic divisions 10–13 , hybrid female embryos exhibited large areas near the cortex devoid of nuclei and abnormal amounts of nuclei remained deep within the cytoplasm , indicating a high level of failed nuclear divisions ( Figure 1A ) ., The nuclei at the cortex were irregularly shaped and spaced ( Figure 1A ) and stained unevenly for the mitotic marker phospho-Histone-3 ( PH3 ) ( Figure 1B ) , demonstrating that these nuclei have asynchronous cell cycles ., We also observed lagging chromatin between the dividing chromosome sets during anaphase and telophase in hybrid female embryos ( Figure 1C and 1D ) ., Lagging chromatin was observed in all analyzed hybrid female embryos ( n\u200a=\u200a16 ) , ranging from 40% ( 13/32 ) to 100% ( 11/11 ) aberrant anaphase spindles per embryo , which is consistent with the high hybrid female lethality ( ∼87%–100% ) produced from these crosses ( Table 1 ) ., It is likely that the lagging chromatin is the direct cause of the mitotic asynchrony and other nuclear defects in hybrid female embryos , an idea supported by studies showing that mutations causing chromosome bridges lead to similar mitotic defects in D . melanogaster embryos 30–32 ., To determine whether the lagging chromatin in hybrid female embryos results from a general defect in chromosome segregation or is instead chromosome-specific , we performed fluorescent in situ hybridization ( FISH ) with probes that recognize distinct satellite sequences in the pericentric regions of different D . melanogaster and D . simulans chromosomes ( Figure 2A ) ., Probe signals for sequences on D . melanogaster Chromosomes 2 and 3 and the D . simulans X chromosome were found in condensed regions near the spindle poles and never within the lagging chromatin ( n\u200a=\u200a75/75 spindles from 13 embryos; Figure 2B ) , indicating normal segregation of these chromosomes ., We also analyzed the segregation of the D . melanogaster X chromosome in hybrid female embryos by using a probe for the 359-bp repeat ., The 359-bp repeat probe labeled two abnormally stretched strands leading outward from the lagging chromatin toward opposite spindle poles ( n\u200a=\u200a56/100 spindles from nine embryos; Figure 2B ) ., Stretched 359-bp repeat DNA was also observed in anaphase spindles in which there was no lagging chromatin ( n\u200a=\u200a37/100 spindles; Figure S1 ) ., Our mapping of the 359-bp repeat probe on chromosome spreads from larval brain tissue confirmed the presence of the major block of 359-bp satellite located on the D . melanogaster X , as well as several minor blocks of related satellites ( 353-bp , 356-bp , and 361-bp repeats ) on the left arm of D . melanogaster Chromosome 3 ( also see Figure S2 ) 33 ., These smaller regions appeared unstretched and segregated normally in hybrid female embryos ( Figure S3 ) ., A variant of the 359-bp repeat is also present in a small satellite block in the pericentric region of the D . simulans X chromosome ( Figure S2 ) but does not cross-hybridize with the 359-bp repeat probe under our experimental conditions ( Figures 2B and S2 ) , presumably because of its high level of sequence divergence from the D . melanogaster repeats 34 ., The lagging chromatin in hybrid female embryos , therefore , is derived solely from the D . melanogaster X chromosome ., Moreover , this stretching effect likely results from partial or complete failure of the sister D . melanogaster X chromatids to separate during anaphase rather than from defective X chromatin condensation because the 359-bp satellite block appeared properly condensed during metaphase ( Figure 2C ) ., We used FISH with additional probes to determine whether separation failure of the D . melanogaster X chromatids is confined to the 359-bp satellite block or occurs in other regions of this chromosome ., Probe signals from a euchromatic region located at the distal end ( cytogenetic location 1C3-4 ) of the major left arm and from the tandemly repeated rDNA genes ( bobbed+ locus ) in the distal pericentric heterochromatin appeared as unstretched and condensed foci ( for euchromatic region , n\u200a=\u200a28/28 spindles from six embryos; for rDNA locus , n\u200a=\u200a13/13 spindles from eight embryos; Figure 3A ) ., A discrete signal of the simple-repeat satellite AATAT , which spans a portion of the minor right arm and part of the centromere immediately adjacent to the large 359-bp satellite block 35 , was present at each end of the stretched 359-bp satellite block near the spindle poles in a pattern similar to the centromeric regions of the other chromosomes ( Figure 3A ) ., Therefore , the centromeres of the sister D . melanogaster X chromatids are active and separate at anaphase ., However , we also observed small amounts of AATAT DNA stretched across the spindle and in the lagging chromatin , similar to the 359-bp satellite block ( n\u200a=\u200a20/34 spindles from three embryos; Figures 3A and S4 ) ., These results demonstrate that the stretched DNA is confined to the proximal X pericentric heterochromatin containing 359-bp and AATAT satellites , suggesting that sequences in this region are responsible for separation failure of the D . melanogaster X chromatids ., To determine the particular causal region of the pericentric heterochromatin , we examined the segregation of the Zhr1 compound-XY chromosome ( Figure 2A ) in hybrid female embryos ., Consistent with previous results 25 , crosses between wild-type D . simulans females and D . melanogaster Zhr1 males resulted in full viability of F1 hybrid female adults ( Table 1 ) ., Our analysis of larval brain chromosome spreads from the Zhr1 strain revealed that the compound-XY chromosome is completely devoid of the 359-bp satellite block but contains Y-derived AATAT repeats ( Figure S2 ) ., In hybrid female embryos the Zhr1 compound-XY chromosome segregated normally , as indicated by the complete absence of lagging chromatin during anaphase ( n\u200a=\u200a68/68 spindles from six embryos; Figure 3B ) ., Furthermore , these embryos advanced properly through subsequent developmental stages into adulthood ., We also analyzed hybrid male embryos whose Y chromosome carries a translocation of approximately half of the X-linked 359-bp satellite block to the Y long arm ( see Figure 2A ) 36 ., This Zhr+ chromosome resulted in hybrid male lethality that was less severe than hybrid female lethality induced by the wild-type X chromosome ( Table 1 ) ., A subset of these hybrid male embryos ( 4/14 ) exhibited mitotic asynchrony and lagging chromatin during anaphase and telophase ( n\u200a=\u200a34/45 spindles; Figure 3C ) , similar to but not as common as the defects described above in hybrid females ., Moreover , FISH analysis showed that the chromatin bridges were comprised of Y-derived 359-bp repeat DNA in these hybrid males ( Figure 3C ) ., These results , together with our analyses of the Zhr1 chromosome , strongly suggest that sequences contained specifically within the 359-bp satellite block induce chromosomal segregation failure in hybrid embryos ., Segregation failure of the D . melanogaster X chromatids in hybrid females occurs between nuclear cycles 10–13 , a period when embryonic development is primarily under control of maternally contributed RNA and proteins 37 ., Our findings , therefore , suggest that the D . simulans maternal cytoplasm lacks factors that are compatible with and necessary for proper segregation of the D . melanogaster X-linked 359-bp satellite block ., This hypothesis is consistent with the fact that hybrid females produced from the reciprocal cross , carrying the 359-bp satellite block and the D . melanogaster maternal cytotype , are fully viable 20 ., We therefore investigated the localization patterns of D1 and Topoisomerase II ( TopoII ) , two proteins known to associate with the 359-bp satellite block in D . melanogaster 38–40 ., Previous studies showed that the protein D1 localizes to AT-rich heterochromatin , including the 359-bp and AATAT satellites , in larval mitotic tissues 39 , 40 ., Additionally , D1 was found to influence the localization of heterochromatin protein 1 ( HP1 ) to the 359-bp satellite block 40 ., On the basis of these results , it was suggested that D1 may be a structural heterochromatin component of these satellites ., To determine if D1 plays a role in the defective structure of the 359-bp satellite block in hybrids , we analyzed the localization of D1 in wild-type D . melanogaster and hybrid embryos with an antibody raised against D . melanogaster D1 39 ., In Western blots , this antibody recognized a single band of approximately 60 kDa , the predicted size of D1 in both D . melanogaster and D . simulans ( Figure S5 ) ., In D . melanogaster and hybrid embryos , D1 was present during anaphase at numerous sites near the spindle poles , which are likely the AT-rich satellites in the centric and pericentric regions ( Figure 4A ) ., However , in hybrid female embryos , we observed no D1 localized to the lagging chromatin containing the 359-bp DNA ( Figure 4A ) ., These observations suggested the possibility that D . simulans D1 fails to bind these sequences in hybrids ., To test this hypothesis , we expressed D . melanogaster and D . simulans D1 in D . melanogaster embryos using the GAL4-UAS system ( see Materials and Methods ) ., Transgenic D1 localized to pericentric regions that completely overlapped with endogenous D1 ( Figure 4B ) ., We performed immuno-FISH experiments to simultaneously visualize D . melanogaster or D . simulans D1 with several satellite sequences ., Both orthologs exhibited identical binding patterns in young embryos ( Figure 4C–4F ) ., Contrary to the prominent localization of D1 to 359-bp DNA in larval mitotic cells ( also see Figure S2 ) 40 , we observed barely detectable levels of D1 on this satellite block ( Figure 4C–4F ) ., Instead , D1 localized primarily to AATAT satellite DNA ( Figure 4G ) ., We propose that the major foci of D1 detected in embryos in earlier studies 39 and presumed to correspond with the 359-bp satellite block actually represent the large regions of AATAT on Chromosome 4 ., Our results demonstrate that unlike in larval brain cells , D1 is not a major component of the 359-bp satellite block during early embryogenesis , and likely does not play a role in the 359-bp structural defects observed in hybrid female embryos ., We also analyzed the localization pattern of TopoII in hybrid female embryos ., TopoII is the primary enzyme in Drosophila that decatenates newly replicated DNA strands and is also believed to be a structural component of condensed chromatin 41 , 42 ., In control D . melanogaster embryos , TopoII localized to 359-bp DNA during interphase and became more evenly distributed across the chromosomes through mitosis , with an occasional , slight enrichment on the 359-bp block at anaphase ( Figures 5 and S6 ) ., However , in hybrid female embryos TopoII localized to the 359-bp satellite block during interphase but remained highly and consistently localized to this DNA through mitosis ( Figures 5 and S6 ) ., We observed no TopoII foci during anaphase in hybrid male or D . simulans male or female embryos ( Figure S7 ) , in which the 359-bp satellite block is absent , further supporting the conclusion that abnormal TopoII persistence in hybrid female embryos occurs specifically on the 359-bp satellite block ., This finding and the observed stretched and lagging 359-bp DNA together indicate the presence of a structural defect in this heterochromatin block that prevents chromatid separation ., We have shown that hybrid females produced from D . simulans mothers and D . melanogaster fathers die during early embryogenesis because of widespread mitotic defects induced by separation failure of the 359-bp satellite block on the paternal X chromatids ., Elegant genetic experiments by Sawamura and colleagues first suggested that hybrid female lethality is caused by a D . melanogaster heterochromatic locus Zhr 25 , 36 ., Genetic mapping localized Zhr to a pericentric region of the X chromosome containing the 359-bp satellite block ., Because it is otherwise unprecedented for a heterochromatic locus to cause HI , this finding raised the key question of how Zhr kills wild-type female hybrids ., We suggest that our results strongly support the conclusion that the 359-bp satellite block directly and specifically causes hybrid lethality , as opposed to alternative possibilities outlined in the Introduction , including indirect effects on other genomic regions ., First , we found that hybrid female embryos exhibit large chromatin bridges during anaphase and telophase of mitotic cycles 10–13 that are almost exclusively comprised of DNA from the 359-bp satellite block on the D . melanogaster X chromosome ., While these bridges also included some flanking AATAT satellite , a large amount of this satellite is present on the Zhr1 chromosome , which segregates normally , arguing against the AATAT satellite being causal for lethality ., The small amount of lagging AATAT DNA detected in hybrid female embryos may result from over-catenation and tangling of AATAT DNA with the 359-bp DNA due to mis-localized TopoII ( see below ) when the chromatin is uncondensed , and is thus likely a secondary effect ., Second , the entire multi-mega-bp satellite block appears to be stretched across the metaphase plate , suggesting that hybrids suffer from a structural defect in this block ., Third , concomitant with these chromatin bridges we observed mitotic asynchrony and other aberrations that have been found in D . melanogaster mutants that have chromatin bridges 30–32 ., In these cases , the lagging chromatin prevents complete separation of the daughter chromosome sets , thus inhibiting further mitotic divisions ., Fourth , we found that all of these mitotic defects are suppressed in the Zhr1 mutant , which lacks the 359-bp satellite block , and are induced on a Y chromosome that contains a translocation of the 359-bp satellite block and causes hybrid lethality in males , albeit with incomplete penetrance ., An important clue comes from our finding that TopoII localizes abnormally to the 359-bp satellite block during mitosis in hybrid female embryos ., Both the DNA-decatenating and structural roles of TopoII are believed to be essential for normal chromatid separation 42 ., These observations suggest several possible explanations for the hybrid phenotype ., One possibility is that X chromatid separation failure results directly from incompatibility between D . simulans TopoII and the D . melanogaster 359-bp satellite block ., TopoII is well conserved in the melanogaster subgroup ( D . melanogaster and D . sechellia TopoII proteins are 95 . 6% identical based on analysis of the full-length D . melanogaster TopoII and the ∼98% of TopoII sequence available for D . sechellia; only ∼78% of D . simulans TopoII sequence has been assembled ) , arguing that TopoII is not a primary incompatibility factor ., Nevertheless , future transgenic experiments will be important for testing this idea ., Alternatively , the persistence of TopoII may reflect a response to incomplete replication of the 359-bp satellite block as a result of incompatibilities with the D . simulans replication machinery ., Extensive and unresolved tangling of daughter DNA strands would prevent separation of the D . melanogaster X chromatids at anaphase ., Our observations suggest that the centromeres of the X chromatids are active and pulled toward the spindle poles , thus creating tension that results in stretching of the 359-bp satellite block ., However , it is unlikely that an incompatibility with the D . simulans replication machinery is the primary cause because the first nine mitotic divisions occur normally , suggesting that replication during these divisions is normal ., A third possibility is that abnormal TopoII persistence may result from improper heterochromatin formation of the 359-bp satellite block ., Chromatid separation failure in hybrid females occurs during mitotic cycles 10–13 when heterochromatin initially forms ., This process involves visible changes in chromatin condensation and localization of HP1 to pericentric and telomeric regions , and precedes the major transition from maternal to zygotic gene expression 43 , 44 ., Our data thus argue that chromatin bridges and lethality result from a failure of heterochromatin formation at the 359-bp satellite block ., Defective heterochromatin formation may lead to other effects such as improper replication and tangling of daughter DNA strands , ultimately causing failure of chromatid separation ., What DNA sequences are responsible for these Zhr lethal effects ?, Our data argue strongly against the possibility that Zhr corresponds to an unknown protein-coding gene embedded within the 359-bp satellite block ., Such a hypothetical gene would have to have the highly unusual property of causing mis-segregation of the entire satellite block in which it happens to be located ., Furthermore , there is unlikely to be sufficient time to transcribe such a gene to cause lethality since the mitotic defects occur during the early stages of embryogenesis when zygotic transcription is minimal 45 ., Previous genetic studies by Sawamura and colleagues led them to propose that the Zhr hybrid lethal effect is caused by repetitive elements in the pericentric region of the D . melanogaster X 27 , 36 , 46 ., By assaying a series of X pericentric deletions and duplications of different sizes they further concluded that the lethality is quantitative , and correlates with the amount of pericentric heterochromatin present ., Several Zhr− stocks contained less 359-bp repeat DNA than a wild-type Zhr+ stock 46 , a finding consistent with the possibility that a dosage threshold of the 359-bp repeat causes hybrid lethality ., However , they excluded the 359-bp repeat ( referred to as the 1 . 688 g/cm3 satellite ) as causing hybrid lethality because two copies of two different mini-chromosomes containing 359-bp repeats did not induce hybrid lethality 46 ., The authors inferred that the double dosage of these mini-chromosomes would contain more 359-bp repeats than a single dose of another mini-chromosome that did reduce viability , thus concluding that dosage of the 359-bp repeat does not correlate with hybrid lethality ., We suggest two caveats to this conclusion ., First , while Southern blots suggested that differences in the abundance of 359-bp repeats are present in the mini-chromosome stocks , quantitative methods were not used to estimate the abundance of 359-bp repeats that are present specifically on the mini-chromosomes ., Second , increased dosage of 359-bp repeats may induce lethality only when present as a single block on a single chromosome , and not when dispersed over multiple chromosomes ., Subsequent experiments , however , showed that a different mini-chromosome can induce lethality when in two doses 27 ., Although the cause of the discrepancy between the two studies remains unclear , they were later interpreted to indicate that either the 359-bp repeat or other repetitive elements are causing hybrid lethality 47 ., Our experiments do not allow us to rule out the possibility that other repetitive elements present in the 359-bp satellite block and also unique to the D . melanogaster X chromosome contribute to hybrid lethality ., Various TEs are known to be interspersed within the 359-bp satellite block 48–50 , however none are specific to the X chromosome and thus cannot account for the X chromosome-specific segregation defects we observed ., In contrast , several lines of evidence argue that the 359-bp repeat is the primary contributor to the Zhr hybrid lethal effect ., First , the 359-bp repeat is among the most highly abundant satellite repeats in the D . melanogaster genome 15 ., And while there are scattered repeats along the D . melanogaster X chromosome 51 , the vast majority are found in the proximal pericentric heterochromatin where Zhr maps ., Second , the 359-bp satellite is essentially species-specific , being ∼50-fold more abundant in D . melanogaster than in D . simulans and highly diverged in primary sequence of its monomers between these species 15 , 34 ., This species-specificity makes it an attractive candidate in evolutionary models that can account for the nonreciprocal nature of the F1 female lethality in D . melanogaster/D ., simulans hybrids ( see below ) ., Third , the entire 359-bp satellite block becomes stretched during mitosis in hybrids ., If another unidentified repetitive element is causing this effect , it must be distributed evenly across the entire 359-bp satellite block and not on other chromosomes ., Our experiments are consistent with the idea that large amounts of the 359-bp repeat present in one block are required to induce chromosome segregation defects ., First , the related 353-bp , 356-bp , and 361-bp repeats , located in much smaller amounts on D . melanogaster Chromosome 3 , do not induce any observable mis-segregation in hybrids ., This observation could mean that only the 359-bp monomer is capable of disrupting chromosome segregation , or , alternatively , that large amounts of this satellite class are required to cause lethality ., Second , translocation of approximately half of the X-linked 359-bp satellite block to the Y chromosome resulted in lagging Y chromatin and hybrid male lethality that are proportionally less penetrant than the effects induced by the full-length 359-bp satellite block in hybrid females ., The multi-mega-bp size of the 359-bp satellite block precludes definitive genetic tests using transgenic methods ., We suggest , however , that the available evidence strongly supports the hypothesis that the 359-bp repeat is the sequence element within the 359-bp satellite block that is the cause of the Zhr hybrid lethal effect ., The fact that hybrid females are lethal when produced from D . simulans mothers and D . melanogaster fathers but viable when produced from the reciprocal cross clearly demonstrates the involvement of a maternal effect in this incompatibility ., Our results can explain this maternal effect as follows ., First , we suggest that the 359-bp satellite block requires maternal factor ( s ) in order to be packaged as heterochromatin during normal embryonic development in D . melanogaster ., Second , D . simulans does not require such factors because it does not contain the 359-bp satellite block ., These factors are therefore diverged in or absent from D . simulans ., Third , in F1 hybrids from D . simulans mothers , the paternally inherited D . melanogaster 359-bp block fails to be packaged properly as heterochromatin because the requisite maternal factors are missing or functionally diverged ., Our proposal that the heterochromatin structure of the 359-bp satellite block is defective in hybrid females provides several promising hypotheses to explain the molecular nature of this incompatibility and the underlying maternal component ( s ) ., Satellites and other repetitive DNA elements are normally packaged into heterochromatin with general heterochromatin factors such as HP1 52 , 53 , and , in some cases , with repeat-class-specific proteins like D1 40 , GAGA 54 , and Prod 55 ., These findings suggest a model in which high divergence in both the primary sequence and the abundance of repeat elements leads to incompatibilities with DNA-binding proteins expressed in the hetero-specific maternal cytoplasm ., We tested D1 as a candidate maternal incompatibility factor because of its specific association in larval tissues with AT-rich satellite DNA , including the 359-bp repeat , but found that D1 does not localize to the 359-bp satellite block during early embryogenesis ., Additional studies will be required to identify new candidate proteins that associate with the 359-bp satellite block in embryos in order to further test this model ., Alternatively , hybrid female lethality may be due to a mechanism involving small RNAs ., In the yeast Schizos | Introduction, Results, Discussion, Materials and Methods | Postzygotic reproductive barriers such as sterility and lethality of hybrids are important for establishing and maintaining reproductive isolation between species ., Identifying the causal loci and discerning how they interfere with the development of hybrids is essential for understanding how hybrid incompatibilities ( HIs ) evolve , but little is known about the mechanisms of how HI genes cause hybrid dysfunctions ., A previously discovered Drosophila melanogaster locus called Zhr causes lethality in F1 daughters from crosses between Drosophila simulans females and D . melanogaster males ., Zhr maps to a heterochromatic region of the D . melanogaster X that contains 359-bp satellite repeats , suggesting either that Zhr is a rare protein-coding gene embedded within heterochromatin , or is a locus consisting of the noncoding repetitive DNA that forms heterochromatin ., The latter possibility raises the question of how heterochromatic DNA can induce lethality in hybrids ., Here we show that hybrid females die because of widespread mitotic defects induced by lagging chromatin at the time during early embryogenesis when heterochromatin is first established ., The lagging chromatin is confined solely to the paternally inherited D . melanogaster X chromatids , and consists predominantly of DNA from the 359-bp satellite block ., We further found that a rearranged X chromosome carrying a deletion of the entire 359-bp satellite block segregated normally , while a translocation of the 359-bp satellite block to the Y chromosome resulted in defective Y segregation in males , strongly suggesting that the 359-bp satellite block specifically and directly inhibits chromatid separation ., In hybrids produced from wild-type parents , the 359-bp satellite block was highly stretched and abnormally enriched with Topoisomerase II throughout mitosis ., The 359-bp satellite block is not present in D . simulans , suggesting that lethality is caused by the absence or divergence of factors in the D . simulans maternal cytoplasm that are required for heterochromatin formation of this species-specific satellite block ., These findings demonstrate how divergence of noncoding repetitive sequences between species can directly cause reproductive isolation by altering chromosome segregation . | Speciation is most commonly understood to occur when two species can no longer reproduce with each other , and sterility and lethality of hybrids formed between different species are widely observed causes of such reproductive isolation ., Several protein-coding genes have been previously discovered to cause hybrid sterility and lethality ., We show here that first generation hybrid females in Drosophila die during early embryogenesis because of a failure in mitosis ., However , we have discovered that this is not a general failure in mitosis , because only the paternally inherited X chromosome fails to segregate properly ., Our analyses further demonstrate that this mitotic failure is caused by a large heterochromatic region of DNA ( millions of base pairs ) that contains many repetitive copies of short noncoding sequences that are normally transcriptionally quiescent ., Interestingly , this block of heterochromatin is only found in the paternal species ., We suggest that a failure of the maternal species to package this paternally inherited DNA region into heterochromatin leads to mitotic failure and hybrid lethality ., If this is a general phenomenon it may explain other examples of hybrid lethality in which F1 females die but F1 males survive . | evolutionary biology/developmental molecular mechanisms, developmental biology/developmental evolution, evolutionary biology/nuclear structure and function | Early embryonic lethality of interspecies hybrids in Drosophila can be caused by defects in mitotic segregation of paternal X chromatids carrying a critical domain of heterochromatic DNA. |
journal.pntd.0000836 | 2,010 | Chagas Disease Risk in Texas | Chagas disease , a result of infection by the hemoflagellate kinetoplastid protozoan , Trypanosoma cruzi , remains an important public health threat in Latin America 1 with an estimated 16–18 million human incidences and deaths annually 2 ., While the Southern Cone Initiative 3–6 has interrupted the transmission of Chagas disease in several South American countries , and similar efforts are being attempted for other countries of Latin America 5–7 , the disease is also endemic in the southern United States , especially in Texas where it is yet to be designated as reportable 8–13 ., Moreover , patterns of human migration into Texas from endemic regions of Latin America may contribute to an increase in the risk of Chagas disease 11 , 14 , 15 ., Because the disease has a chronic phase that may last for decades , during which parasitaemia falls to undetectable levels 7 , the extent of human infection in the southern United States is at present unknown ., Based entirely on demographics , Hanford et al . 10 provided an extreme estimate of more than 1 million infections for the United States with of them being in Texas ., However , Bern and Montgomery 11 have criticized that estimate for using the highest possible values for all contributory factors; they provide a more credible lower estimate of for the entire United States ., Infections of zoonotic origin only add to the number of infections of demographic origin and the risk of disease ., So far infected vectors or hosts have been found in 82 of the 254 counties of Texas ( see Table S1 ) though only four vector–borne human autochthonous cases have been confirmed 16 ., The parasite incidence rate in vectors in Texas has been reported as being 12 , 16 , 17 which is higher than the reported from Phoenix , Arizona 13 , but lower than the reported from Guaymas in northwestern México 18 ., In contrast to Texas , the disease is reportable in Arizona and Massachusetts even though there has not been an autochthonous human case in either state , compared to the four in Texas ., The other autochthonous human cases confirmed for the United States are from California 19 , Tennessee 20 , and Louisiana 9 ., The main human Chagas disease cycle consists of the parasite , T . cruzi , being transferred from a mammalian reservoir to a human host through a vector ., However , infection through blood transfusion , organ transplants , and the ingestion of infected food are also recognized mechanisms of concern; infections may also occur through congenital transmission 7 , 21 , 22 ., A large variety of mammal species can serve as reservoirs for T . cruzi including humans and dogs 7 , which means that a focus on reservoirs would not be effective for disease control ., Given that no vaccine exists 23 , efforts to control the disease must focus on vector control 7 ., Consequently , risk assessment for Chagas disease must focus primarily on the ecology and biogeography of vector species and the incidence of the parasite , besides human social and epidemiological factors 5 ., This analysis consists of a five–stage risk assessment for Chagas disease in Texas:, ( i ) an ecological risk analysis using predicted vector distributions;, ( ii ) an incidence–based risk analysis based on parasite occurrence;, ( iii ) a joint analysis of ecology and incidence using formal multi–criteria analysis;, ( iv ) such a joint analysis using a composite risk model; and, ( v ) a computation of the relative expected exposure rate taking into account human population ., The purpose of the complete analysis is to argue that there is sufficient widespread risk for Chagas disease in Texas to warrant it to be declared reportable and other measures be taken ., The analysis focuses primarily on the vector distributions but also uses available information on parasite incidence ., If the number of human infections in Texas is as high as in the estimates noted earlier 10 , 11 , then humans alone would constitute sufficient reservoirs in disease foci ., Moreover , even if the number of human infections is much lower , there is compelling evidence that the disease has established itself in Texas in domestic and peridomestic cycles with canine reservoirs 16 , 17 ., Thus , also given the abundance of wild zoonotic reservoirs in most of the state , including armadillos , coyotes , raccoons , opossums , and rodents of the genus Neotoma , the distribution of reservoirs is not likely to limit the occurrence or spread of the disease in Texas ., This analysis assumes that competent reservoirs are present everywhere in Texas in sufficient densities to perpetuate or establish the disease cycle ., Moreover , the peridomestic cycle makes human exposure to the parasite more likely than what would have been the case with only a sylvatic transmission cycle ., The vectors of Chagas disease are insects from the family Reduviidae , sub–family Triatominae , and in northern México and the United States , restricted to the genus Triatoma ., Seven Triatoma species have been routinely collected in Texas: Triatoma gerstaeckeri , T . sanguisuga , T . lecticularia , T . protracta , T . indictiva , T . rubida , and T . neotomae 12 ., ( One specimen of T . recurva was reported from Brewster county in far southwestern Texas on the Mexican border in 1984 24 but no further specimen has since been found in Texas; available records are restricted to Arizona and northwestern México . ), Using data from new field collections as well as museum records , this analysis begins by constructing species distribution models for the three most widely distributed Triatoma species in Texas: T . gerstaeckeri , T . sanguisuga , and T . lecticularia ., All three species have been shown to be carriers of T . cruzi 12 , 25 ., The other four Triatoma species were so rare ( collected less than 10 times in total by any researcher in Texas since 2000 ) that they are presumed not to be important for establishing Chagas disease transmission cycles in the state ., The species distribution models were constructed using a maximum entropy algorithm which relies on species occurrence ( presence–only ) records and environmental layers 26 ., Such a modeling strategy , though using a genetic algorithm , has been previously used to model the distribution of T . gerstaeckeri in Texas 16 , and a variety of triatomine species complexes for North America 27 though at a much coarser spatial resolution than this analysis which used cells with 1 arc-minute edges ., The output from these models directly quantify habitat suitability for a species by computing the relative probability of its presence in each cell of the study area ., These probabilities establish the potential distribution of a species ( and are sometimes interpreted as providing an approximate ecological niche model 28 , 29 ) ., The predicted distribution is obtained using biological information such as dispersal behavior and other constraints that limit the potential distribution ., These three species distributions were used to generate a map of the probability of the occurrence of at least one triatomine vector species in each cell ., This is the most basic ecological risk map: when these probabilities are low , there is little risk of Chagas disease occurrence through the major vectorial mode of transmission though disease may still occur through contaminated blood transfusion and , less likely , through parasite ingestion ., ( By “risk , ” throughout this paper , we will mean relative risk , that is , the risk in one cell compared to others throughout the area of interest . ), When the ecological ( relative ) risk is high , other risk factors determine the likelihood of disease , including the abundance of vectors , the incidence of parasites , and anthropogenic features of the habitat , for instance , human behavioral patterns ( including habitation structure ) 30 , 31 ., Ecological risk maps of this kind have previously been used for this region to estimate the risk of the spread of leishmaniasis due to climate change 31 ., The relevance of that work to the present analysis is that the disease agents for leishmaniasis are also kinetoplastid protozoans which share reservoirs with T . cruzi 32–36 ., Independently , at the county level ( which was the finest resolution at which data were available ) , a ( relative ) risk map based on parasite incidence in vectors , canine reservoirs , or humans was constructed using the Bayesian Besag-York-Mollié ( BYM ) model which is widely used in epidemiology 37 ., This map was based on a spatial interpolation of risk from the number of parasite records from each county: it captures the idea that there is spatial correlation between disease incidences ., The implications of the incidence–based risk map were combined with those of the basic ecological risk map in two ways:, ( i ) a simple multi-criteria analysis ( MCA ) 38 was used to find the counties that were most at risk from both suitability for vector species and proximity to locations of parasite incidence;, ( ii ) a multiplicative risk model was used to obtain a composite risk map for Chagas disease in Texas ., Both sets of results were used to prioritize counties for increased surveillance for the occurrence of T . cruzi ., Finally , the composite risk map was combined with the relative human population densities of the counties to produce a “relative expected exposure rate” risk map which provides a rough relative measure of potential extent of human exposure to Chagas disease ., The entire risk analysis was used to recommend that Chagas disease be made reportable in Texas , that the blood supply be screened in south Texas , and that human and canine serological profiles be investigated in the same region ., The study area was delimited at the south by the N line of latitude along the México-Guatemala border , by the coast of continental México to the east and west , continued by the lines W and W within the United States and the line N at the north , thus enclosing all the species occurrence points ( see Figure 1 ) ., It was divided into cells at a resolution of 1 arc–minute ., The average cell area was ., Species distribution models were constructed for the three most important triatomine vector species in Texas 12: T . gerstaeckeri , T . lecticularia , and T . sanguisuga ., At the county level , our data collection and collation extended the known distribution of the seven triatomine species in Texas 12 in six cases: T . gerstaeckeri to Castro , Galveston , Gonzales , Lubbock , Parker , Victoria , Wilson , and Zapata counties , T . indictiva to Hays and Kinney counties , T . lecticularia to Bastrop , Blanco , Burleson , Lubbock , and Parker counties , T . protracta to Andrews , Bexar , and Terry counties , T . rubida to Crane and Upton counties , and T . sanguisuga to Bastrop and Kaufman counties ., For T . gerstaeckeri and T . lecticularia , these results extend their ranges to northwest Texas for the first time ., Over all , triatomines have now been recorded for more counties ( Andrews , Burleson , Castro , Crane , Galveston , Kaufman , Parker , Terry , Upton , and Wilson ) than what was previously established ., ( Relevant maps are provided in the supplementary materials . ), Model performance was judged using the test AUC , that is , the area under the receiver operating characteristic ( ROC ) curve and a set of internal binomial tests in the Maxent software package 26 ., All three species produced test AUC values above the threshold of 0 . 9: averaged over the 100 replicate models , 0 . 979 for T . gerstaeckeri , 0 . 924 for T . sanguisuga , and 0 . 959 for T . lecticularia ., On the average , all binomial tests were significant ( ) ., Because the models for T . lecticularia were constructed using only 11 presence records , the fact that its average AUC , besides being high , was greater than that of T . sanguisuga , suggests that model predictions are reliable ., Moreover , a recent study indicates that models constructed using the Maxent algorithm are reliable so long as there are more than 10 presence records 56 ., Figures 1 , 2 , and 3 show the three species distribution models , respectively ., For T . gerstaeckeri , four out of 74 occurrence records fell in cells with habitat suitability , for the other species , there was in each case one such record ., The presence of a limited number of anomalous points is expected because species are often found in sub-optimal habitats , especially at the geographical margins of their ranges 54 , 57 , as was the case with our points ., The model for T . gerstaeckeri conforms with what is known about the distribution of the species from field records though it differs from the older model of Beard et al . 16 ( see Discussion ) ., There is a high probability of occurrence in the southern United States , especially in and around Texas , as well as in northeast México ., For T . sanguisuga , the two occurrence points from the west ( obtained from museum collections ) have the effect of predicting suitable habitat in the western United States and México where the species has been collected in Arizona , California , and México 8 , 39 , 58 ., T . lecticularia has a widespread predicted distribution along both coasts of North America but remains rare in collections along the western coast where all of our records came from México ., Lent and Wygodzinsky 39 included New Mexico in the distribution of T . lecticularia but the provenance of those data remains unknown ., There appears to be no recent record of the species in New Mexico and predicted highest habitat suitability is only 0 . 16 ., Figure 4 shows the ( relative ) ecological risk map for the region including Texas ., Figure 5 shows the incidence–based risk map for Texas , and Figure 6 the composite risk map ., Table 2 shows the counties with the highest risk in each of these categories ., Compared to the incidence-based risk map , the composite risk map lowers the relative risk of counties to the far west and north of Texas because , even though T . cruzi has been reported in these areas , the habitat suitability for the triatomines remains low ., When we consider ecological risk and incidence–based risk separately in the multi–criteria dominance analysis , instead of compounding them to compute the composite risk , three counties are in the non–dominated set: Cameron , Jim Wells , and Nueces ., All of these counties have incidences of T . cruzi ., When this analysis is restricted to counties with no report as yet of T . cruzi , the non-dominated set consists of Goliad , Kenedy , and Wilson counties ., This means that these three counties have high suitability for the presence of vector species as well as spatial contiguity to T . cruzi occurrences and are foci of special concern for Chagas disease ., When we consider together both non–dominated sets and the top five counties according to the ecological , incidence–based , and composite risk maps , eleven counties are selected ( Bee , Bexar , Brooks , Cameron , DeWitt , Goliad , Hidalgo , Jim Wells , Kenedy , Kleberg , and Nueces ) and all are in south Texas in an almost contiguous cluster starting at the Mexican border ., When we include the top ten counties , an additional nine counties ( Bandera , Dimmit , Frio , Guadalupe , Karnes , Live Oak , Medina , San Patricio , and Willacy ) are selected; once again , all of these counties are from south Texas ., Figure 7 shows the relative expected exposure rate at the county level ., If the top five counties are added to the list of high risk counties , three counties outside south Texas are added: Dallas ( north Texas ) , Harris ( east Texas ) , and Travis ( central Texas ) , because of the high human populations ., If ten such counties are used , three additional counties outside south Texas are included ( Collin and Tarrant in north Texas and Williamson in central Texas ) ., Thus , consideration of human population density in a multiplicative model leads to a slightly more widespread attribution of risk than ecological and incidence–based risk ., Nevertheless , the focus on south Texas remains strong ., Moreover , only two of the high risk counties were ranked very low by median income using 2006 data from the United States Census Bureau 41—Cameron and Hidalgo , which ranked 228 and 234 , respectively , out of 254 counties ., Both of these are in south Texas ., Low median income is likely to be indicative of relatively poorer living conditions and possible lack of concrete housing ., Thus housing and living conditions , which were not quantitatively modeled , also implicate south Texas as the area of highest risk ., On the basis of this analysis , we make the following five recommendations: Finally , beyond those discussed in the Materials and Methods section , eight other limitations of this analysis should be explicitly noted: Finally , one methodological innovation of this analysis should be noted since it is likely to be relevant to other contexts ., This is the use of multi–criteria dominance analysis to identify high risk areas ., In general , formal decision analysis has been surprisingly sparingly used in epidemiological contexts ., However , techniques developed in that field can provide comprehensive decision support whenever complex decisions have to be analyzed ., Here , we used one of the simpler multi–criteria techniques , the computation of non–dominated alternatives , to identify counties which are at high risk from Chagas disease even though the parasite has not yet been reported from them ., Other , model–based techniques , selected the same region as areas of concern in south Texas ., When used together to produce identical or similar results , these strategies lead to a more robust estimation of relative risk than otherwise possible ., The strategy is fully general and can be exported to other contexts in which computing and mapping disease relative risk is of interest . | Introduction, Materials and Methods, Results, Discussion | Chagas disease , caused by Trypanosoma cruzi , remains a serious public health concern in many areas of Latin America , including México ., It is also endemic in Texas with an autochthonous canine cycle , abundant vectors ( Triatoma species ) in many counties , and established domestic and peridomestic cycles which make competent reservoirs available throughout the state ., Yet , Chagas disease is not reportable in Texas , blood donor screening is not mandatory , and the serological profiles of human and canine populations remain unknown ., The purpose of this analysis was to provide a formal risk assessment , including risk maps , which recommends the removal of these lacunae ., The spatial relative risk of the establishment of autochthonous Chagas disease cycles in Texas was assessed using a five–stage analysis ., 1 . Ecological risk for Chagas disease was established at a fine spatial resolution using a maximum entropy algorithm that takes as input occurrence points of vectors and environmental layers ., The analysis was restricted to triatomine vector species for which new data were generated through field collection and through collation of post–1960 museum records in both México and the United States with sufficiently low georeferenced error to be admissible given the spatial resolution of the analysis ( 1 arc–minute ) ., The new data extended the distribution of vector species to 10 new Texas counties ., The models predicted that Triatoma gerstaeckeri has a large region of contiguous suitable habitat in the southern United States and México , T . lecticularia has a diffuse suitable habitat distribution along both coasts of the same region , and T . sanguisuga has a disjoint suitable habitat distribution along the coasts of the United States ., The ecological risk is highest in south Texas ., 2 . Incidence–based relative risk was computed at the county level using the Bayesian Besag–York–Mollié model and post–1960 T . cruzi incidence data ., This risk is concentrated in south Texas ., 3 . The ecological and incidence–based risks were analyzed together in a multi–criteria dominance analysis of all counties and those counties in which there were as yet no reports of parasite incidence ., Both analyses picked out counties in south Texas as those at highest risk ., 4 . As an alternative to the multi–criteria analysis , the ecological and incidence–based risks were compounded in a multiplicative composite risk model ., Counties in south Texas emerged as those with the highest risk ., 5 . Risk as the relative expected exposure rate was computed using a multiplicative model for the composite risk and a scaled population county map for Texas ., Counties with highest risk were those in south Texas and a few counties with high human populations in north , east , and central Texas showing that , though Chagas disease risk is concentrated in south Texas , it is not restricted to it ., For all of Texas , Chagas disease should be designated as reportable , as it is in Arizona and Massachusetts ., At least for south Texas , lower than N , blood donor screening should be mandatory , and the serological profiles of human and canine populations should be established ., It is also recommended that a joint initiative be undertaken by the United States and México to combat Chagas disease in the trans–border region ., The methodology developed for this analysis can be easily exported to other geographical and disease contexts in which risk assessment is of potential value . | Chagas disease is endemic in Texas and spread through triatomine insect vectors known as kissing bugs , assassin bugs , or cone–nosed bugs , which transmit the protozoan parasite , Trypanosoma cruzi ., We examined the threat of Chagas disease due to the three most prevalent vector species and from human case occurrences and human population data at the county level ., We modeled the distribution of each vector species using occurrence data from México and the United States and environmental variables ., We then computed the ecological risk from the distribution models and combined it with disease incidence data to produce a composite risk map which was subsequently used to calculate the populations expected to be at risk for the disease ., South Texas had the highest relative risk ., We recommend mandatory reporting of Chagas disease in Texas , testing of blood donations in high risk counties , human and canine testing for Chagas disease antibodies in high risk counties , and that a joint initiative be developed between the United States and México to combat Chagas disease . | public health and epidemiology, infectious diseases/neglected tropical diseases, ecology/spatial and landscape ecology, infectious diseases/protozoal infections, computational biology/ecosystem modeling | null |
journal.pntd.0002528 | 2,013 | Regulatory T Cells in Peripheral Blood and Cerebrospinal Fluid of Syphilis Patients with and without Neurological Involvement | China has experienced an expanding epidemic of syphilis infection in the last 10 years 1 , 2 ., In 2011 , the national incidence rate was 32 . 04 per 100 , 000 population and 429 , 677 new cases were reported 3 ., This sexually transmitted disease has reemerged as a significant public health issue in China due to its serious , irreversible sequelae 4 and its strong association with HIV infection 5 ., The rapid rise in syphilis rates in China highlight the importance of understanding of the pathogenesis of syphilis and its complications ., The spirochetal bacterium , Treponema pallidum ( T . pallidum ) , is the etiologic agent of syphilis 6 , 7 ., After T . pallidum infection , mammalian hosts mount robust humoral and cellular immune responses aimed at spirochetal clearance 8 , 9 , 10 , 11 ., However , T . pallidum has the ability to escape the host immune response and establish persistent infection ., There are several strategies used by the spirochete to resist host immune effector mechanisms including poor antigenicity 12 , 13 , antigenic variation of membrane proteins 14 , 15 , 16 , and impaired antibody-mediated opsonization 17 ., Interestingly , several studies have demonstrated that T . pallidum may also actively harness host immune suppression mechanisms to facilitate persistence and dissemination 18 , 19 ., A recent study has demonstrated that T . pallidum antigen TpF1 could promote development of regulatory T cells ( Tregs ) in the patients with secondary syphilis 18 ., Tregs represent a unique population of CD4+ T cells with potent immune suppressive activity 20 , 21 ., This regulatory CD4+ T cell population is classically defined by high expression of CD25 ( IL-2 receptor α-chain ) 22 ., The forkhead family transcription factor Foxp3 , the most definitive signature , is critical for Treg development and function 23 ., Emerging evidence from human patients and animal models has demonstrated that Tregs contribute to impaired immune responses and chronic infection with diverse organisms 24 , including mycobacterium tuberculosis 25 , helicobacter pylori 26 , hepatitis B virus 27 , 28 , HIV 29 , and plasmodium falciparum 30 ., The enhanced Treg response in early syphilis patients may down-regulate immune effector function to allow survival of T . pallidum within the host ., T . pallidum infection can infect many organs , including central nervous system ( CNS ) ., This form of syphilis is termed neurosyphilis ., Neurosyphilis may affect the meninges or brain or spinal cord parenchyma and may be asymptomatic or symptomatic 4 , 31 ., Meningeal neurosyphilis usually appears during the first few years of T . pallidum infection ., Patients with meningeal neurosyphilis may be manifested by meningitis ( headache , stiff neck , and cranial nerve abnormalities ) or meningovasculitis ( focal CNS ischemia or stroke ) ., Parenchymal neurosyphilis , presenting as general paresis and tabes dorsalis , occur in the later course of the disease , often decades after the primary infection 4 , 32 ., The mechanisms underlying the development of symptomatic neurosyphilis in some patients are largely unknown ., Previous studies have extensively characterized immune cell infiltrates of early syphilis lesions 8 , 9 , 10 and indicated that the clinical manifestations of early syphilis result from collateral tissue damage caused by host immunity to T . pallidum 6 , 33 ., However , little is known about the immune response in neurosyphilis patients ., In the present study , we performed a comparative analysis of Tregs in peripheral blood and cerebrospinal fluid ( CSF ) from neurosyphilis patients and syphilis patients without neurological involvement ., We found that symptomatic neurosyphilis patients had lower Treg frequencies and numbers in CSF compared to asymptomatic neurosyphilis patients , indicating that an immunopathological mechanism might be present in the onset of neurological symptoms ., This study was performed at the Shanghai Skin Disease Hospital between June 2009 and Jan 2012 ., The hospital is located in central Shanghai , where the syphilis prevalence is highest in China 1 ., The Sexually Transmitted Diseases ( STD ) center in this hospital is the major STD clinic in Shanghai , which provides screening , diagnosis and treatment for most sexually transmitted diseases , including syphilis ., As one of the biggest STD centers in China , more than 300 patients are served in this clinic per day ., Although most of our patients are walk-in , some are referred to our clinic by their doctors at other hospitals across the country ., This study was approved by the Ethics Committee of the Shanghai Skin Disease Hospital ., Written informed consent was obtained from all participants ., Syphilis was determined based on medical history , physical , neurological and psychiatric symptoms and signs , and the results of nontreponemal and treponemal serological tests ., The excluded criteria include HIV; prior syphilis or syphilis treatment ( except in the serofast syphilis group ) ; history of systemic inflammatory , autoimmune disease , other underlying acute or chronic disease , were receiving anti-inflammatory medications , were immunocompromised , or use of antibiotics or immunosuppressive medications in the last four weeks ., Peripheral blood was collected from all healthy donors and syphilis patients ., Lumbar punctures were encouraged to be performed if, i ) patients had neurological or psychiatric signs or symptoms ,, ii ) patients whose serum RPR≥1∶32 , regardless of stage or presentation ,, iii ) patients whose serofast state was more than 2 years and who are anxious regarding their serofast state ., 100 healthy donors , who visited Shanghai Skin Disease Hospital voluntarily for STD prevention and a medical check-up , were recruited to the study ., All healthy control subjects were negative for HIV and serological tests for syphilis ., Primary syphilis:, i ) Chancres or ulcers; and/or, ii ) detection of spirochetes in a dark-field microscopy examination; and, iii ) positive RPR confirmed by Treponema pallidum particle agglutination assay ( TPPA ) ; and, iv ) absence of other causes of genital ulcers , including herpes simplex virus ( HSV ) infections ., Secondary syphilis:, i ) positive RPR confirmed by TPPA; and, ii ) skin or mucocutaneous lesions; Latent syphilis:, i ) positive RPR confirmed by TPPA; and, ii ) without skin or mucocutaneous lesions or any symptoms of syphilis; Serofast syphilis:, i ) previously treated syphilis of any stage;, ii ) an appropriate 4-fold decline in serum RPR titer at 6 months after treatment ( Benzathine penicillin 2 . 4 MU/qw im for 2 or 3 weeks or procaine penicillin 0 . 8 MU/day im for 15 days in most cases , if patient allergic to penicillin ceftriaxone 250 mg/day im for 10 days would be as an alternative ) ;, iii ) persistently reactive serum RPR two or more years after treatment;, iv ) no evidence of reinfection ., The clinical and laboratory characteristics of 71 patients with primary syphilis , 136 patients with secondary syphilis , 127 patients with latent syphilis , and 97 patients with serofast syphilis were shown in Table 1 . All neurosyphilis patients have positive serum RPR and TPPA tests ., The diagnosis of confirmed neurosyphilis also includes reactive CSF-VDRL ( Venereal Disease Research Laboratory ) and CSF-TPPA tests in the absence of substantial contamination of CSF with blood ., Presumptive neurosyphilis was defined as nonreactive CSF-VDRL but reactive CSF-TPPA with either or both of the following:, i ) CSF protein concentration >45 mg/dL or CSF white blood cell ( WBC ) count ≥8/µL in the absence of other known causes for these abnormalities;, ii ) neurological or psychiatric manifestations consistent with neurosyphilis without other known causes for these abnormalities ., Fourteen patients with presumptive neurosyphilis were also included in the study and the data of these patients were combined with those of confirmed neurosyphilis patients for analysis ., In the case of presumptive neurosyphilis , the patient has a nonreactive CSF-VDRL test plus a reactive CSF-TPPA along with either or both of the following:, ( i ) elevated CSF proteins ( normal: 15–45 mg/dL ) or elevated CSF white blood cell ( WBC ) count ( normal: <8/µL ) in the absence of other known causes of the abnormalities;, ( ii ) clinical neurological or psychiatric manifestations without other known causes of these clinical abnormalities ., Neurosyphilis is categorized as asymptomatic , meningeal ( meningitis and meningovasculitis ) and parenchymal ( general paresis and tabes dorsalis ) ., Asymptomatic neurosyphilis is defined by the presence of CSF abnormalities consistent with neurosyphilis and the absence of neurological and psychiatric signs or symptoms ., Meningitis is diagnosed by CSF abnormalities and headache , stiff neck , nausea , or cranial neuropathies ., Meningovasculitis is defined by clinical features of meningitis and stoke with or without neuroradiological confirmation ., General paresis is characterized by personality changes , dementia and psychiatric symptoms including mania or psychosis ., Tabes dorsalis is characterized by sensory loss , ataxia , lancinating pains , and bowel and bladder dysfunction ., All patients diagnosed with neurosyphilis should have no other known causes for these clinical abnormalities ., The features of 100 neurosyphilis patients are shown in Table 2 . These patients are mutually exclusive of those in Table 1 . Peripheral blood mononuclear cells ( PBMC ) were isolated from whole blood from syphilis , neurosyphilis patients and healthy donors via density centrifugation over Lymphoprep ( Axis-Shield ) ., CSF was centrifuged and stained immediately at 4°C after spinal tap ., The volume was 5 mL ., Multicolor fluorescence activated cell sorting ( FACS ) analysis was performed using the following antibodies: PE- , FITC- , PerCP , or PE-Cy5-conjugated antibodies against human CD45 ( Biolegend ) , CD3 ( Biolegend ) , CD4 ( Biolegend ) , CD25 ( Biolegend ) ., For Foxp3 staining , cells were stained using One Step Staining Human Treg Flow Kit ( Biolegend ) according to the manufacturers protocols ., Cells were assessed with FACScalibur ( Becton Dickinson ) or Epics XL ( Beckman Coulter ) cytometers as previously described 34 ., For CSF samples , acquisition of ≥5 , 000 events for gated CD45+ cells was performed ., The CSF Treg number was defined as the total number of CSF cells multiplied by the percentage of Tregs identified by flow cytometry ., Data were analyzed using FlowJo software ( Tree Star ) ., Treg suppression assay was performed as described 35 , 36 ., Briefly , PBMC were used for CD4+ CD25+ and CD4+ CD25− T cell isolation using a Regulatory T Cell Isolation Kit according to the manufacturers instruction ( Miltenyi Biotec ) ., Purity of the cell fractions as determined by flow cytometry was >90% ., Purified CD4+ CD25− T responder cells ( 5×104 cells/well ) were incubated in RPMI 1640 medium with 10% FBS in 96-well U-bottom plates precoated with anti-CD3 antibody ( 1 µg/mL; eBioscience ) ., To assess suppressive ability , purified autologous CD4+ CD25+ T cells were added , at a CD25+/CD25− ratio of 1∶1 , 1∶2 , 1∶4 , or 1∶8 ., All cells were cultured in a final volume of 200 µl in the presence of 2×104 irradiated allogeneic PBMC/well ., After 4 days of culture , 3H thymidine ( Amersham ) was added for an additional 18 h to each well ., 3H thymidine incorporation was measured using a liquid scintillation counter ., Percent inhibition of proliferation was determined as ( 1- 3H thymidine incorporation of CD25+ and CD25− T cells coculture/3H thymidine incorporation of CD25− T cells alone ) ×100 ., Serum and CSF TGF-β1 levels were determined using Human TGF-β1 ELISA kit from eBioscience ., We performed statistical analysis using GraphPad Prism version 5 . 01 ( GraphPad Software ) ., All datasets were first assessed for deviation from a normal distribution using the DAgostino-Pearson omnibus normality test ., Non-normally distributed variables were compared between groups using the nonparametric Kruskal–Wallis test followed by Dunns multiple comparison tests ., If the variables were approximately normally distributed , differences between experimental groups were analyzed using one-way ANOVA followed by Bonferroni test for the selected pairs ., Pearson correlation analysis was used to determine the relationship between the frequency of CD4+ CD25high Treg and other parameters ., A value of P<0 . 05 was considered significant ., Human Tregs were identified as CD4+CD25high or CD4+Foxp3+ T cells 20 , 21 ., The representative gating strategy for CD4+ CD25high and CD4+ Foxp3+ T cells are depicted in Figure 1A ., The majority of Foxp3+ T cells co-expressed high levels of CD25 ( Figure 1A ) ., The baseline frequency of CD25high Tregs among CD4+ T cells in PBMCs from healthy individuals was 2 . 7%±0 . 1% ( Figure 1B ) ., A comparison between syphilis patients and healthy individuals revealed a 1 . 3-fold increase in mean frequency of CD4+ CD25high T cells in primary syphilis patients ( 3 . 6%±0 . 2% , p<0 . 01 ) , 1 . 7-fold increase in secondary syphilis patients ( 4 . 5%±0 . 2% , p<0 . 001 ) , 1 . 5-fold increase in early latent syphilis patients ( 4 . 1%±0 . 2% , p<0 . 001 ) , and 1 . 7-fold increase in serofast syphilis patients ( 4 . 7%±0 . 3% , p<0 . 001 ) ( Figure 1B ) ., Consistently with CD25 expression , the highest percentage of Foxp3+ Tregs among CD4+ T cells were observed in patients with secondary syphilis ( 4 . 3%±0 . 4% , p<0 . 001 ) and serofast syphilis ( 4 . 3%±0 . 3% , p<0 . 001 ) patients , followed by latent syphilis ( 3 . 9%±0 . 4% , p<0 . 01 ) and primary syphilis patients ( 3 . 6%±0 . 4% , p<0 . 05 ) , which were all significantly higher than healthy donors ( 2 . 3%±0 . 1% ) ( Figure 1B ) ., We next investigate the suppressive function of Tregs from syphilis patients on T cell proliferation ., CD4+ CD25+ suppressor T cells were cocultured with autologous CD4+ CD25− T responder cells at different ratios ( suppressor/responder ratios: 1∶1 , 1∶2 , 1∶4 , and 1∶8 ) ., We found that blood CD4+ CD25+ Tregs isolated from secondary syphilis ( 84 . 0%±1 . 4% , P<0 . 05 ) and serofast syphilis ( 84 . 3%±3 . 0% , P<0 . 01 ) but not primary syphilis ( 74 . 5%±1 . 1% , P>0 . 05 ) and latent syphilis ( 73 . 8%±1 . 1% , P>0 . 05 ) patients exhibited significantly higher suppressive activity than healthy controls ( 66 . 3%±1 . 1% ) at a 1∶1 ( suppressor: responder ) ratio ( Figure 1C ) ., Significant increases in suppressive effect of CD4+ CD25+ Tregs were also observed at ratios of 1∶2 and 1∶4 in secondary and serofast syphilis patients compared with healthy donors ( Figure 1C ) ., These data indicated that CD4+ CD25+ Tregs derived from secondary and serofast syphilis patients display enhanced suppressive function ., Since Transforming Growth Factor-β ( TGF-β ) is critical to Treg differentiation and suppressive function 37 , 38 , 39 , we determined whether higher Treg frequency and function in syphilis patients were associated with serum TGF-β levels ., It was shown that serum concentrations of TGF-β were significantly increased in patients with secondary ( 5 . 4±0 . 7 ng/ml , P<0 . 001 ) and , to a lesser extent , in primary syphilis patients ( 4 . 4±0 . 9 ng/ml , P<0 . 05 ) , latent patients ( 4 . 6±0 . 5 ng/ml , P<0 . 01 ) and serofast patients ( 4 . 4±0 . 6 ng/ml , P<0 . 01 ) compared with healthy controls ( 1 . 1±0 . 2 ng/ml ) ( Figure 1D ) ., There was a positive correlation between the percentage of circulating CD4+ CD25high Tregs and serum TGF-β levels in these syphilis patients ( r\u200a=\u200a0 . 20 , P<0 . 05 , Figure 1E ) ., Nontreponemal test antibody titers usually correlate with disease activity 40 ., We thus assessed whether serum RPR titers were associated with circulating Treg percentage in these syphilis patients ., Pearson correlation analysis showed that there was a positive correlation between the percentage of circulating CD4+ CD25high Tregs and serum RPR titer in secondary syphilis ( r\u200a=\u200a0 . 27 , P<0 . 01 , Figure 2B ) , latent syphilis ( r\u200a=\u200a0 . 27 , P<0 . 05 , Figure 2C ) and serofast ( r\u200a=\u200a0 . 44 , P<0 . 01 , Figure 2D ) syphilis patients , but no correlation in primary syphilis patients ( r\u200a=\u200a0 . 10 , P\u200a=\u200a0 . 44 , Figure 2A ) ., If untreated or treated improperly , some syphilis patients will progress to neurosyphilis ., To investigate whether Tregs are associated with the progression of neurosyphilis , we analyzed Treg numbers in the peripheral blood of 49 asymptomatic and 41 symptomatic neurosyphilis patients ., As shown in Figure 3A and 3B , syphilis patients with neurological involvement ( including both asymptomatic and symptomatic syphilis patients ) had higher percentage of CD4+ CD25high Tregs ( 4 . 7%±0 . 2% , P<0 . 001 ) and CD4+ Foxp3+ Tregs ( 5 . 0%±0 . 4% , P<0 . 001 ) in peripheral blood compared with healthy individuals ( 2 . 7%±0 . 1% and 2 . 4%±0 . 1% , respectively ) ., Compared to syphilis patients without neurological involvement ( including primary , secondary , latent and serofast syphilis patients ) , there was a slight but not significant increase in CD4+ CD25high Treg frequency in peripheral blood of neurosyphilis patients ( P\u200a=\u200a0 . 06 ) ( Figure 3A ) , but the percentage of CD4+ Foxp3+ Treg were significantly higher ( P<0 . 05 ) ( Figure 3B ) ., Among syphilis individuals with neurological involvement , there was no significant difference in CD4+ CD25high Treg frequency ( P>0 . 05 ) ( Figure 3C ) and CD4+ Foxp3+ Treg frequency ( P>0 . 05 ) ( Figure 3D ) in peripheral blood among asymptomatic , meningeal , and parenchymal neurosyphilis patients ., CSF mononuclear pleocytosis is one of diagnostic criteria for neurosyphilis 41 , 42 ., As expected , higher numbers of leukocytes were observed in asymptomatic ( 14±3 cells/µL ) , meningeal ( 35±15 cells/µL ) and parenchymal ( 16±4 cells/µL ) neurosyphilis patients compared to those from syphilis patients without neurological involvement ( 4±1 cells/µL ) ( P<0 . 001 , P<0 . 001 , and P<0 . 001 , respectively ) ( Table 3 ) ., Among the CSF leukocytes , higher percentage of CD4+ T cells were found in patients with asymptomatic ( 41 . 8%±2 . 3% , P<0 . 01 ) and parenchymal ( 46 . 4%±2 . 1% , P<0 . 001 ) neurosyphilis compared with syphilis patients without neurological involvement ( 29 . 7%±2 . 4% ) ( Table 3 ) ., There was no significant difference in CSF CD4+ T cell frequency ( P>0 . 05 ) among different types of neurosyphilis patients ( Table 3 ) ., The average percentage of CD25high Tregs in the CD4 compartment was 22 . 0%±1 . 0% for the patients without neurological involvement and did not differ from those with asymptomatic neurosyphilis ( 20 . 0%±1 . 1% , P>0 . 05 ) ., Both meningeal ( 12 . 5%±1 . 4% ) and parenchymal ( 12 . 0%±1 . 2% ) neurosyphilis patients showed pronounced decreases in CD4+CD25high Treg percentage compared to syphilis patients without neurological involvement ( P<0 . 05 , P<0 . 001 , respectively ) and asymptomatic neurosyphilis patients ( P<0 . 05 , P<0 . 001 , respectively ) ( Table 3 ) ., Due to preferential accumulation of CD4+ T cells in the CSF of neurosyphilis patients , both asymptomatic and symptomatic neurosyphilis patients have higher numbers of CD4+CD25high Tregs than syphilis patients without neurological involvement ., Interestingly , lower number of Tregs was observed in meningeal ( 0 . 9±0 . 3 cells/µL ) and parenchymal ( 0 . 5±0 . 1 cells/µL ) neurosyphilis patients than asymptomatic neurosyphilis patients ( 1 . 2±0 . 2 cells/µL ) ., In addition , meningeal ( 3 . 4±0 . 9 ng/ml ) and parenchymal ( 2 . 8±0 . 5 ng/ml ) neurosyphilis patients had significantly lower CSF TGF-β levels than asymptomatic neurosyphilis ( 10 . 7±2 . 0 ng/ml ) and syphilis patients without neurological involvement ( 8 . 2±1 . 7 ng/ml ) , indicating that decreased CD4+ CD25high Treg frequencies in CSF of symptomatic neurosyphilis patients may be associated with low CSF TGF-β concentration ., Syphilis is a multistage chronic disease , which can cause damage to diverse tissues and organs ., An influx of immune cells to skin lesions of early syphilis patients not only mediates bacterial clearance but also lead to tissue damage and clinical symptoms 9 , 10 , 43 , 44 ., Our prior study has shown that immune cells can also infiltrate into the CSF of syphilis patients 45 ., However , this study was limited because of a small number of patients ( n\u200a=\u200a32 ) , selected patient populations ( latent syphilis and neurosyphilis ) and lack of characterization of neurosyphilis patients 45 ., In the present study , a total of 431 syphilis patients without neurological involvement ( including 20 latent syphilis patients in the previous report ) and 100 neurosyphilis patients ( including 12 patients in the previous report ) were included ., This larger number of syphilis patients enables further stratification according to stage and symptoms ., Interestingly , we observed an accumulation of CD4+ T cells in the CSF of both asymptomatic and symptomatic neurosyphilis patients , which were consistent with several previous reports showing that CD4+ T cells were the primary responders to T . pallidum in syphilis lesions 8 , 9 , 46 ., CD4+ T cells can be divided into a variety of effector subsets , including classical Th1 cells and Th2 cells , the more recently defined Th17 cells , follicular helper T cells , and regulatory T cells 47 ., Though we did not elucidate the precise identity of the CD4+ T cell subset , we observed a decreased frequency of CD4+ CD25high Tregs in the CSF of symptomatic neurosyphilis patients compared with those of non-neurosyphilis and asymptomatic neurosyphilis patients ., Given the important role of Tregs in controlling immune-mediated tissue damage , our results suggest that the CNS damage in neurosyphilis patients may be due to an uncontrolled host immune response ., A local decrease in Tregs may facilitate CNS injury in neurosyphilis patients ., A similar scenario has been observed in other CNS disorders 48 , 49 ., T . pallidum can establish persistent infection by promoting Treg response in early stage of syphilis ., In marked contrast to reduced local Treg response in symptomatic neurosyphilis , we found that Treg numbers in circulation of neurosyphilis patients were even higher than early syphilis patients without neurological involvement ., This finding suggests that suppression of the systemic immune response against T . pallidum may favor neurological progression ., Consistent with this notion , studies have found that HIV-positive people infected with T . pallidum are more likely to develop neurosyphilis , even during the early stages of infection 5 , 50 ., The mechanisms underlying Treg differences among syphilis patients are poorly understood ., Given that TGF-β was implicated in modulating Treg differentiation and activity 37 , 38; we investigated whether the frequency and functional status of Tregs were associated with this cytokine ., We confirmed that serum from the patients with secondary and serofast syphilis did express significantly higher levels of TGF-β than those of healthy control subjects , which may be related to the increased frequency and enhanced function of Tregs in these patients ., Lower TGF-β levels were observed in CSF of symptomatic neurosyphilis patients than asymptomatic neurosyphilis patients , which may be associated with a decrease in CSF Treg numbers ., We propose a model to summarize the role of T cell subsets in the pathogenesis of syphilis in Figure 4 ., T . pallidum penetrates through abraded skin where antigen presenting cells ( APC ) , such as dendritic cells ( DC ) , process the bacteria and then migrate to the subcutaneous lymph nodes ., These activated APC 51 may present T . pallidum-derived antigens to naïve T cells and induce production of Th1 9 and Treg 18 , which enter the peripheral blood and circulate widely throughout the body ., T . pallidum has the ability to preferentially enhance the generation of Tregs through TGF-β 18 , which may impair Th1 function to favor bacterial persistence in the circulation and skin ., Antigenic variation and poor antigenicity also enable T . pallidum to evade cell mediated immune response 13 , 15 , 16 ., However , a defective accumulation of Tregs in the CNS ( Table 3 ) may fail to suppress T cell-mediated inflammation and tissue damage in the meninges and parenchyma of brain and spinal cord , resulting in neurological symptoms and signs ., In our study cohort , there are differences in sex distribution among syphilis patients of different stages: 78 . 0% neurosyphilis patients ( 78/100 ) were male , while only 35 . 1% serofast syphilis patients ( 34/97 ) were male ., However , there was no significant difference in blood and CSF CD4+ CD25high Tregs between males and females in each group ( data not shown ) , which indicating that the Treg differences between stages were not due to gender preference ., Serofast status represents a clinical challenge for treatment of syphilis ., There is no universally accepted definition of “serofast” ., The definition of “serofast” in this manuscript is “having had an appropriate 4-fold titer decline after treatment , but not reverting to seronegative” ., Although these syphilis patients meet criteria for being adequately treated , we and others have shown that such “serofast” patients can progress to neurosyphilis 52 , 53 , suggesting that they still harbor T . pallidum ., The immune status of serofast patients is unclear ., A recent study reported that HIV-infected patients are at increased risk for serofast state after treatment 54 ., Our results showed that these patients had enhanced circulating Treg numbers and suppressive function , also suggesting serofast status may be associated with a systemic immune suppression ., There are several limitations in the analysis of Treg activity in this study ., First , future studies should examine Foxp3 expression and define the functional status of CD4+ CD25high Tregs in CSF in neurosyphilis patients ., We were not able to conduct such studies because of the limited availability of CSF T cells ., In addition , studies of Treg loss-of-function and gain-of-function are needed to further explore their role in syphilis , but these experiments have been hampered by inherent difficulty in conducting immunologic studies of syphilis in experimental animal models 9 ., In conclusion , our findings demonstrate for the first time that neurological progression in syphilis patients is associated with increased circulating Tregs and CSF CD4+ T cells and reduced local Treg response is implicated in the development of symptoms in neurosyphilis patients . | Introduction, Methods, Results, Discussion | Syphilis , a sexually transmitted disease caused by spirochetal bacterium Treponema pallidum , can progress to affect the central nervous system , causing neurosyphilis ., Accumulating evidence suggest that regulatory T cells ( Tregs ) may play an important role in the pathogenesis of syphilis ., However , little is known about Treg response in neurosyphilis ., We analyzed Treg frequencies and Transforming Growth Factor-β ( TGF-β ) levels in the blood and CSF of 431 syphilis patients without neurological involvement , 100 neurosyphilis patients and 100 healthy donors ., Suppressive function of Tregs in peripheral blood was also assessed ., Among syphilis patients without neurological involvement , we found that secondary and serofast patients had increased Treg percentages , suppressive function and TGF-β levels in peripheral blood compared to healthy donors ., Serum Rapid Plasma Reagin ( RPR ) titers were positively correlated with Treg numbers in these patients ., Compared to these syphilis patients without neurological involvement , neurosyphilis patients had higher Treg frequency in peripheral blood ., In the central nervous system , neurosyphilis patients had higher numbers of leukocytes in CSF compared to syphilis patients without neurological involvement ., CD4+ T cells were the predominant cell type in the inflammatory infiltrates in CSF of neurosyphilis patients ., Interestingly , among these neurosyphilis patients , a significant decrease in CSF CD4+ CD25high Treg percentage and number was observed in symptomatic neurosyphilis patients compared to those of asymptomatic neurosyphilis patients , which may be associated with low CSF TGF-β levels ., Our findings suggest that Tregs might play an important role in both bacterial persistence and neurologic compromise in the pathogenesis of syphilis . | Syphilis , caused by the bacterium Treponema pallidum , can progress to affect the central nervous system ( CNS ) and cause damage in the brain and spinal cord , which is called neurosyphilis ., While many affected neurosyphilis patients may not have any symptoms , some of the patients will develop severe symptoms that can be life-threatening ., Regulatory T cells ( Tregs ) are a subpopulation of CD4+ T cells functioning in suppression of immune-mediated bacterial clearance and tissue damage ., In this study , we conduct a comparative analysis of regulatory T cells ( Tregs ) in the blood and cerebrospinal fluid ( CSF ) of syphilis patients without neurological abnormalities , and neurosyphilis patients with or without symptoms ., Our results show that neurosyphilis patients had higher Treg percentage in blood than syphilis patients without neurological involvement , suggesting that neurological progression in syphilis patients is associated with an increase in blood Treg numbers ., Strikingly , a decrease in Treg percentage and numbers in CSF of symptomatic neurosyphilis patients was observed compared to asymptomatic neurosyphilis patients ., These results may implicate reduced CNS Treg response as a factor underlying the development of symptoms in some neurosyphilis patients ., Our findings highlight a discordant Treg response in blood and CSF in symptomatic neurosyphilis patients and further underscore the fascinating complexity of immune response in syphilis . | null | null |
journal.ppat.1003036 | 2,012 | Relatively Low Level of Antigen-specific Monocytes Detected in Blood from Untreated Tuberculosis Patients Using CD4+ T-cell Receptor Tetramers | With approximately one-third of the worlds population infected with Mycobacterium tuberculosis ( MTB ) , tuberculosis ( TB ) continues to persist as a major infectious disease that significantly contributes to global morbidity and mortality 1 ., However , 5–10% of infected individuals will eventually develop an active form of the disease ., During TB infection , cellular immune responses are a critical part of the hosts defense mechanisms 2–3 ., Although the mechanisms of protection against TB are not completely understood , many studies have indicated the predominately protective role of CD4+ T cells 4–6 ., MTB is endocytosed and survives in antigen-presenting cells ( APCs ) , such as macrophages , monocytes , and dendritic cells ., Some APCs present antigens in association with major histocompatibility complex ( MHC ) class II molecules that then stimulate CD4+ T cells ., This process is essential to MTB infection 7 , but the in vivo kinetics of APCs in patients with advanced and convalescent TB is not well characterized ., Many methods are available for studying the interactions between the T-cell receptors ( TCR ) on epitope-specific T cells and the epitopes and MHCs on APCs ., Fluorescence-labeled , tetrameric MHC-peptide complexes have been widely used to detect and quantify antigen-specific T-cell populations via flow cytometry ., Since Altman et al . first described the use of peptide/human leukocyte antigen ( HLA ) tetrameric complexes to directly visualize antigen-specific cytotoxic T lymphocytes ( CTLs ) using flow cytometry in 1996 8 , tetramerized MHC I and II complexes have been extensively used to quantify and characterize antigen-specific T cells 9–11 and probe TCR-MHC interactions ., In 2004 , Subbramanian et al . extended the tetrameric technique to TCR and successfully constructed high-affinity TCR tetramers 12 ., In 2008 , Wei H et al . developed γδ TCR tetramers in order to investigate the molecular mechanisms of the presentation of MTB-phospho-antigen to Vγ2Vδ2 T cells 13–14 ., Soluble TCR tetramers have been utilized in a variety of functional assays , including the specific detection of target cells that have been pulsed with cognate peptide , discrimination between the quantitative changes that occur in antigen display at the cell surface , the identification of virus-infected cells , the inhibition of antigen-specific CTL activation , and the identification of cross-reactive peptides 13–19 ., Until now , no MTB-specific CD4+ TCR tetramers have been reported ., In the present study , we describe how we successfully constructed tetrameric CD4+ TCR complexes ., Their binding specificities to monocytes obtained from peripheral blood ( PBL ) samples and macrophages in lung and lymph node sections from pulmonary TB ( PTB ) or lymph node TB patients and the inhibition of peptide-specific CD4+ T cells in PBL samples from patients with PTB were evaluated; In addition , any changes in tetramer-bound CD14+ monocytes from advanced and convalescent PTB outpatients were also tracked ., MTB-specific TCR tetramers may provide a useful methods for detecting target cells and identifying specific , high-affinity interactions between HLA and peptides ., In our previous studies , MTB peptides E6 and E7 from early secreted antigenic target-6 ( ESAT-6 ) and C14 from culture filtrate protein-10 ( CFP-10 ) were confirmed as HLA-DR-restricted and specific TCR ligands of CD4+ T cells , while C5 from CFP-10 was identified as a specific TCR ligand of both CD4+ T cells that is restricted by HLA-DR and CD8+ T cells by testing PBL and pleural fluid ( PLF ) samples from active TB patients using the IFN-γ-enzyme-linked immunospot ( IFN-γ-ELISPOT ) assay , lymphocyte-proliferation and -blocking tests , and intracellular cytokine staining ( ICS ) ., In this study , human MTB peptide-specific CD4+ T cells were obtained in order to access specific TCR tetramers ., Mononuclear cells in PLF samples from patients with active tuberculous pleuritis were first analyzed using ELISPOT ., Over 96% of the PLF samples reacted with the 4 peptides mentioned above , and 12–20% of enriched peptide-specific T cells were positively stained with the anti-CD4 monoclonal antibody ( MAb ) ., After separation of the CD4+ T cells using magnetic beads , the cells were stained with carboxyfluorescein succinimidyl ester ( CFSE ) , and then allowed to proliferate in vitro by incubating them with the peptide for 9 days ., After staining with anti-CD4-phycoerythrin ( PE ) , >98% of the pure , expanded , peptide-responsive CD4+ T cells were obtained following cell sorting ., The CD4+ TCR α and β chain genes were successfully amplified from expanded peptide-responsive CD4+ T cells , each about 0 . 8 kb and 0 . 9 kb , respectively ., Seventy-nine TCR α and β chain gene clones were isolated from 4 active TB patients ., As shown in Table 1 , 2 CD4+ TCR tetramers , eu and hu , were constructed using 2 different TCR α chains ( e , accession number: HE862272 and h , accession number: HE862271; http://www . ebi . ac . uk/ena/ ) and the same TCR β chain ( u , accession number: HE862270 ) that contained the high-frequency VDJ repertoire ( AV12-3*01-J29*01/BV29-1*01-D2*01-J2-5*01 and AV1-2*01-J33*01/BV29-1*01-D2*01-J2-5*01 ) and complementarity-determining region 3 ( CDR3 ) amino acid sequences ( AMSARSGNTPLV/SLRDAKETQY and AVRDQNYQLI/SLRDAKETQY , respectively ) , which are the wild-type TCR α/β chains that were mainly cloned from subpopulations of C14- and E7-responsive CD4+ T cells that were obtained from an active TB patient ( patient 11 ) with an HLA background of HLA-DRB1*1503/*1504 and HLA-DRB1*08032 ., Three other active TB patients shared the VDJ repertoire , the common CDR3 amino acid motifs of the α/β chains ( AV12-3*01/AMSA of patient 10 with the TCR α chain of the eu-tetramer and AV12-3*01/AVRD of patients 5 and 10 with the TCR α chain of the hu-tetramer , respectively , as well as BV29-1*01/TQY of patient 9 and BV29-1*01/ETQY of patient 10 with the TCR β chains of the eu- and hu-tetramers , respectively ) , and HLA-DRB1 alleles ( DRB1*150101 and DRB1*0818/*0806 , DRB1*1503/*1504 and DRB1*03 , and DRB1*1503/*1504 and DRB1*02023 in patients 5 , 9 and 10 , respectively ) ., The expression of monomeric TCR complexes in the culture supernatant of Drosophila Schneider 2 cells ( S2 cells ) was verified by detecting the corresponding tags ., The target protein in the supernatant was purified by Ni-NTA agarose , and the purified sample was concentrated ., Small aliquots of the purified samples were monitored using SDS-PAGE , dot-blot and Western blot assays ., These assays confirmed that about 60 kDa of the soluble , biotinylated TCR α/β monomer was obtained , similar to our previous study 20 ., A panel of the MTB peptide/HLA-DR molecules that are displayed in the S2 cell lines ( i . e . , previously constructed , artificial APC lines ) were used to determine the affinity of the constructed TCR tetramers for different MTB-peptide/HLA-DR molecules ., After 48 hours of induction using CuSO4 , the cells were incubated with PE-labeled TCR tetramer at 4 °C for 20 minutes and analyzed using flow cytometery ., In order to determine the expression of HLA-DR in each cell lines , limited anti-HLA-DR antibody ( L243-fluorescein isothiocyanate FITC; BD Pharmingen , San Jose , CA , USA ) was co-incubated with the cells ., Because the TCR-MHC-peptide interaction can be competitively blocked by L243 , the percentage of tetramer positivity does not represent the tetramer-positive staining of all HLA-DR-peptide complexes , but instead reflects the affinity of TCR tetramers for different HLA-DR-peptides ., TCR tetramers were able to bind to MTB peptide C14/HLA-DRB1*08032 displayed in S2 cells , while only background staining was accomplished in cells without induction ( Figure 1B ) ., The positive-detection rates ( Figure 1A ) of eu-tetramer staining in the cell lines that expressed peptides C14/HLA-DRB1*08032 , C14/HLA-DRB1*150101 , C5/HLA-DRB1*0404 , E6/HLA-DRB1*090102 , C5/HLA-DRB1*090102 , and C5/HLA-DRB1*150101 on the cell membrane were 18 . 65% , 10 . 90% , 7 . 30% , 6 . 40% , 6 . 32% , and 5 . 71% after induction , respectively ., As for the hu-tetramer , the rates were <2 . 7% , except in C14/HLA-DRB1*08032 ( 3 . 13% ) and E7/HLA-DRB1*160201 ( 3 . 12% ) ., The 2 tetramers did not react with non-induced cells or cell lines that only expressed the HLA-DR molecules ( Figure S1 ) ., CD4+ T cells can be activated when TCRs on the cells were occupied by immunogenic peptide bound to an HLA II molecule , together with a co-stimulatory signal from the APC ., Activation leads to cell proliferation which can be identified using CFSE T cell proliferation assay ., Along with the cells that are labeled with CFSE on day 0 , upon cell division each CFSE-high cell will lose half of its CFSE labeling , so that the populations of CFSE-low daughter cells can be visualized using flow cytometry ., On the other hand , when co-incubated with peptide-specific TCR tetramer , the tetramer competitively inhibits the binding of peptide-HLA to the TCR on CD4+ T cell ., As a result , the proliferation of CD4+ T cells is suppressed ., A single dose of TCR tetramer was added to the peripheral blood mononuclear cells ( PBMCs ) that were co-cultured with peptide on day 0 ., After 10 days of culturing , the divided ( i . e . , low CFSE fluorescent ) CD4+ T cells were quantified ., As shown in Figure 2 , the percentage of low-CFSE CD4+ cells was significantly lower in cells cultured with TCR tetramer and peptide than cells cultured with only peptides E7 , C5 , E6 , and C14 , respectively ., However , there were no significantly differences between the cells incubated with or without TCR tetramer when the cells were stimulated with oncopeptide ., This indicates that the eu- and hu-tetramers inhibit , to various degrees , the proliferation of CD4+ T cells that is induced by peptides E7 , C5 , E6 , and C14 , respectively , but do not inhibit oncopeptide-induced CD4+ T cells proliferation ., Above all , the results show that the 2 pure , MTB-specific CD4+ TCR tetramers could be used as staining reagents to analyze tetramer-bound ( CD14+ ) APCs in clinical samples ., In 76 active PTB inpatients , a median of 0 . 60% ( range: 0 . 26–1 . 44% ) of the CD14+ monocytes was positively stained with the eu-tetramer , while a median of 0 . 45% ( range: 0 . 21–0 . 95% ) was positively stained with the hu-tetramer in 104 active PTB inpatients ., Some positively stained CD14+ monocytes were detected in a few samples from the healthy donor and umbilical cord blood groups , though there were definite and significant differences in the median percentages of tetramer-bound CD14+ monocytes between the PTB patient group and each of the control groups ( p<0 . 01 ) , as determined using the Mann-Whitney U test ( Table 2; Figures 3 and 4A clustered bar graph ) ., The actual distribution of the percentage of tetramer-bound CD14+ monocytes in each sample is shown in Figure 4B ( scatter graphs ) ., A few positive stained samples were found in the healthy donor group , which may have been related to latent TB infection ., Nonetheless , high tetramer-positive samples were only apparent in the PTB patients ., The PBL samples , which either demonstrated a high affinity for TCR tetramers or negative double-labeling staining to both tetramers , were selected for HLA-DR typing ., The results show that the eu-tetramer has a high affinity for HLA-DRB1*13 , *16 , *11 , *07 , *14 , and *15 , while the hu-tetramer has a high affinity for HLA-DRB1*16 , *14 , *09 , *15 , and *04 ( Table 3 ) ., These results indicate that the 2 TCR tetramers interact with multiple HLA-DR molecules ., This is because these 2 tetramers consisted of TCR β chains with the same amino acid sequence and obtained from the same patient with an HLA-DRB1 background who shared some similar VDJ repertoires , common CDR3 amino acid motifs , and overlapping HLA-DRB1 backgrounds with other patients , as well as 2 similar α chains that consisted of the 2 TCR tetramers ., In a follow-up study , continuous time point-tracked PBL samples from 9 active PTB outpatients ( Table 4 and Figure 5 ) were drawn every month during regular , 6-month-long , anti-TB treatments in order to assess the changes in the percentage of TCR tetramer-bound CD14+ monocytes using eu- and hu-tetramer staining and flow cytometric analysis ., Figure 5 shows that the percentage changes in all patients followed roughly the same trends , except patient 7 ., In patients 2 , 3 , 6 , and 9 , along with the amendment of TB symptoms ( although there were some small undulations ) , the median percentages were at first low before treatment , increased to their highest levels during the first month , and then began to decrease during the second month until finally reaching and maintaining a relatively low level after 3–6 months of treatment ., These relatively low and small percentage changes were observed in patients 1 , 4 , 5 , 7 , and 8 and might be related to the different HLA backgrounds or immunity and disease statuses of the individual patients ., Furthermore , group PBL samples from 7 continuous time point-tracked PTB outpatients groups , which included time points recorded before treatment and monthly samples obtained during regular 6-month-long anti-TB treatment periods , were detected using flow cytometry and eu- and hu-tetramer staining ., Figure 6 shows that the median percentages of tetramer-bound CD14+ monocytes were 0 . 43% , 0 . 90% , 0 . 59% , 0 . 70% , 0 . 49% , 0 . 64% , and 0 . 74% at months 0 , 1 , 2 , 3 , 4 , 5 , and 6 , respectively , according to eu-tetramer staining , and were 0 . 31% , 0 . 62% , 0 . 20% , 0 . 39% , 0 . 26% , 0 . 43% , and 0 . 33% according to hu-tetramer staining , respectively ., Similarly , as shown in Figure 5 which depicts the continuous time point-tracking results of 9 active PTB outpatients , the median percentages were at first low before treatment , increased to their highest levels during the first month , and then began to decrease during the second month until finally reaching and maintaining a relatively low level after 3–6 months of treatment ( although there was some undulation ) ; however , all patients demonstrated relatively higher levels than the healthy donor and umbilical cord blood groups ., As shown in Table 2 , 0 . 35% and 0 . 35% of samples demonstrated positive eu-tetramer staining and 0 . 14% and 0 . 16% of samples demonstrated positive hu-tetramer staining , respectively ., However , the Mann-Whitney U test determined that the statistical differences were mainly found between the treatment groups during the first month of treatment and both the healthy donors and umbilical cord blood groups ( 0 . 86% , 0 . 35% and 0 . 14%; p<0 . 001 and p<0 . 009 for eu-tetramer staining , respectively; and 0 . 62% , 0 . 14% and 0 . 16%; p<0 . 000 and p<0 . 000 for hu-tetramer staining , respectively ) ; no significant differences were observed between any PTB groups in terms of the results of the either of the tetramer tests , as determined by the Kruskal-Wallis H test ( p\u200a=\u200a0 . 585 and p\u200a=\u200a0 . 141 , respectively ) ., Because TCR tetramers are HLA II-dependent , only MTB-specific APCs with matching HLA background can be detected by the 2 TCR tetramers ., Therefore , the differences between the positive rates of the samples from different treatment periods would be disguised ., Earlier researches have reported that APC apoptosis is an important process in TB 21 , 22 ., We speculate that the relatively low level of tetramer-positive monocytes in blood from untreated TB patients is probably related to the apoptosis of APCs due to live bacteria and their growth; on the other hand , the increase in the first month after treatment may be related to the decrease in APC apoptosis ., In order to verify these , MTB H37Ra-induced apoptosis of human acute monocytic leukemia cells ( THP-1 cells ) was examined by flow cytometry using Annexin V plus PI staining in vitro ., The results show that the apoptosis of the THP-1 cells decreased following treatment with isoniazid ( INH ) ( Figure 7 ) ., Analysis of CD14+ cells obtained from healthy donors , untreated or continuously treated PTB patients also showed that the percentages of apoptosis dramatically decreased following treatment ( Figure 8 ) ., To determine the distribution and number of tetramer-bound and antigen-specific CD14+ macrophages in local TB lesions , fresh-frozen , 8-µm-thick sections of lung and lymph node from active TB patients were probed using the anti-MTB antibody and TCR tetramer or the anti-CD14 antibody and TCR tetramer , respectively , followed by nuclear staining with 4′ , 6-diamidino-2-phenylindole ( DAPI ) and observation with confocal laser-scanning microscopy ., The results of both staining strategies demonstrated double-positive stainings in the lung and lymph node sections from 2 active TB inpatients with HLA-DRB1*040601/DRB1*110103 and HLA-DRB1*1202/DRB1*1202 backgrounds , respectively , and negative responses to the TCR tetramers and anti-TB antibodies in the lung and lymph node sections of 2 non-TB patients with HLA-DRB1*1504/DRB1*1202 and HLA-DRB1*1202/DRB1*0406 backgrounds , respectively ( Figure 9 ) ., These results suggest that there are TCR tetramer-bound and MTB antigen-positive CD14+ macrophages ( i . e . , APCs ) in the local TB tissues ., In this study , we amplified TCR α and β chains from expanded peptide-responsive CD4+ T cells that were separated from ELISPOT-positive PLF mononuclear cells that were obtained from patients with active tuberculous pleuritis ., High-frequency α and β chain families were analyzed and selected ., The full-length TCRs were expressed and biotinylated using insect cells ., The TCR heterodimers were purified and tetramerized ., The affinities and specificities of the 2 selected TCR tetramers ( eu and hu ) for binding to MTB peptide/HLA-DR molecules were confirmed using a series of artificial APCs that expressed different MTB peptide/HLA-DR molecules in S2 cells ., Although CD4+ TCR chain sequences are highly diverse , we found that MTB peptide-responsive CD4+ T cells derived from different individuals shared the exact same TCR α and β chain sequences , CDR3 sequences , genetic families , or common CDR3 amino acid motifs ., The TCR α/β chain families that were present at a high frequency in the PLF mononuclear cells that responded to the same or different MTB peptides were selected for the construction of TCR tetramers ., As shown in Table 1 , 2 peptide C14-responsive α chains from different CD4+ T-cells clones and 1 C14-responsive β chain from the same CD4+ T-cell clone ( these chains share the same or similar VDJ repertoire and CDR3 amino acid motifs ) were selected for the preparation of the eu- and hu-tetramers ., Our data clearly demonstrate that these 2 tetramers are capable of recognizing MTB peptides in the context of multiple HLA-DR molecules , which is consistent with the results of earlier studies 23–25 ., Meanwhile , both eu- and hu-tetramers could inhibit CD4+ T-cell proliferation at different levels , which is induced by a variety of peptides ( E7 , C5 , E6 , and C14 ) ., These results indicate the different detection efficiencies and specificities of these 2 tetramers ., On the other hand , because of the limited numbers of cases enrolled in this study , we were unable to determine all of the HLA-DR alleles that interacted with the tetramers ., TB is characterized by the formation of local granulomas , caseous necrosis , and cavities where macrophages , their derived cells ( e . g . , Langhans-type multinucleated giant cells ) , and a variety of lymphocytes are recruited ., Macrophages are believed to differentiate from the recruited monocytes in circulation ., Both macrophages and monocytes are crucial cells involved in immune defense , in which bacteria grow and survive 26 , 27 ., To investigate MTB-specific APCs in vivo in patients with advanced and convalescent TB , we evaluated the tetramer-positive monocytes in a series of clinical samples using flow cytometry ., Inpatients with advanced PTB , both those who were untreated and those who had just begun treatment , were recruited to participate in the present study ., In their PBL samples , the percentages of tetramer-bound CD14+ monocytes ranged between 0 . 26–1 . 44% and 0 . 21–0 . 95% by according to the results of eu- and hu-staining respectively ., The percentage of tetramer-positive cells was significantly higher than those measured in samples obtained from non-TB patients , healthy donors , and the umbilical cord groups ., The 2 tetramers could also specifically detect macrophages in situ in lungs and lymph nodes sections from untreated and advanced TB patients using immunofluorescentce staining ., Surprisingly , continuous time-point tracking of the 2 tetramers in the PBL samples obtained from active PTB outpatients undergoing treatment demonstrated that the median percentages were at first low before treatment , increased to their highest levels during the first month , and then began to decrease during the second month until finally reaching and maintaining a relatively low level after 3–6 months of treatment; however , all demonstrated relatively higher levels than the healthy donor or umbilical cord groups ., Higher detection rates were measured in the PBL samples obtained from the PTB inpatients group than samples obtained from the PTB outpatients group before anti-TB treatment ., This may have been due to the fact that the former group contained some patients in the initial treatment stage ( usually within the first 20 days of treatment ) ., Also , the inpatients were sicker than the outpatients and carried more MTB ., These results suggest that there is a relatively low level of MTB-specific monocytes in the circulation of advanced and untreated PTB patients ., The quantity of MTB-specific monocytes is probably related to the local recruitment of APCs , focus formation , and APC apoptosis or necrosis due to live bacteria and their growth ., Earlier studies have reported that APC apoptosis is an important process and major event that is necessary to produce caseous necrosis in granulomas and other lesions during mycobacterial infection 21 , 22 and is associated with live mycobacteria and mycobacterial molecules 28–39 ., On the other hand , MTB can also induce macrophage necrosis by inhibiting the repair of plasma membranes 40 ., Placido et al . found that by using a virulent strain MTB H37Rv , apoptosis was induced in a dose-dependent fashion in macrophages that were obtained by broncho alveolar lavage from patients with TB 41 ., In our research , when THP-1 cells were co-cultured with MTB H37Ra in vitro , apoptosis decreased when INH was added ., In addition , we found that early apoptosis of CD14+ cells dramatically decreased after treatment ., Because APC apoptosis decreased , the quantity of APC increased ., Also , during the early stages of treatment , bacteria are killed or a large amount of MTB peptides are picked up and processed by APCs , which expands the T-cell population 42 ., That , in turn releases IFN-γ , other cytokines and lymphokines then activate macrophages ., Pedroza-Gonzalez et al . found that CD14+ cells were recruited into lungs by day 14 after MTB infection , significantly increased by day 21 ( approximately 16-fold over the control group ) , and elevated during the chronic phase of infection 43 ., So , the decrease in the local recruitment and consumption of APCs and the bactericidal effects of anti-TB drugs would induce a peak in the quantity of monocytes for a short period of time during the first month and , subsequently , a relatively lower level would be reached and maintained due to equilibrium between the various factors involved ., Perhaps in the later periods of treatment the peptide levels would decrease to the background level along with the clearance of bacteria ., In our data , although the frequency of TCR tetramer-positive cells in PBL samples was low , the frequency of positive cells in the artificial APCs was high ( up to 18 . 65% ) ., This implies a high affinity for the tetramers ., Although there are no data on the frequency of TCR tetramer-positive cells in TB patients , low levels of peptide/MHC tetramer-positive cells in PBL samples obtained from TB patients have been reported in many studies 44–46 ., In addition , low numbers of peptide/MHC II tetramer-positive cells have been reported in recent studies on the detection of CD4+ T cells in response to infectious agents , autoantigens , allergens and tumour antigens , with frequencies generally ranging from 0 . 02 to 0 . 6% of the total number of CD4+ T cells 47–54 ., The low frequency of tetramer-positive cells may be due to the fact that most tetramer-staining studies on humans have relied on the enumeration of the T-cell populations that are present in circulating PBL not at the primary site of inflammation ., Higher frequencies probably exist in the compartments that are more directly affected by the immune response of interest ., Meyer et al . found a nearly 33-fold increase in the abundance of outer-surface protein A-specific CD4+ T cells at the primary site of inflammation 55 ., Mice infected with the sendai virus demonstrated a remarkably high frequency ( 13% ) of activated CD4+ T cells in the lung sections using specific MHC-immunoglobulin multimers 56 ., Moreover , in PBMCs obtained from TB patients or MTB-infected animals , the 10-fold expansions of peptide/HLA-DR tetramer-bound epitope-specific CD4+ T cells were seen after specific peptide stimulations in vitro 20 , 44 ., Because the affinities of the CD4+ TCR tetramers are correlated with the patients HLA II background to some degree , negative staining does not necessarily indicate that the sample came from a non-TB patient; however the sample may be from an TB patient with an unmatched HLA II background ., The same level of tetramer staining reflects different amounts of the peptide presenting to the APCs due to the different HLA backgrounds of the patients ., This HLA II restriction may be detrimental to the successful and extensive use CD4+ TCR tetramers , but perhaps we can solve the problem by mixing different MTB peptide-specific tetramers that match different HLA-DR backgrounds in a single reagent or arrange them into a protein array in order to reduce the false-negative rate ., The exquisite binding sensitivity and specificity exhibited by these multimeric TCRs allows us to monitor quantitative modifications in the antigens displayed on the APCs and investigate the binding parameters of TCRs with cross-reactive HLA-DR ., We carried out a small-scale study that consisted of monitoring PLF and cerebrospinal fluid ( CSF ) samples from patients with tuberculous pleuritis and tuberculous meningitis , respectively , using eu- and hu-tetramer stainings and flow cytometry , but were unsuccessful due to very low ratios or low numbers of monocytes and macrophages in these samples ( data not shown ) ., Perhaps this problem can be solved by using a Ficoll-Hypaque density gradient , slide smears , and staining ., Further studies are needed to obtain additional information about that how many HLA-DR-restricted TCR tetramers can bind with peptide/HLA-DR , the affinities between TCR tetramers and the different forms of HLA-DR , how in vivo dynamic changes in APCs are related to TB infection and latent TB infection , and the effects of anti-TB treatment ., Our data are the first description of MTB-specific human CD4+ TCR tetramers ., These soluble CD4+ TCR tetramers demonstrate great diagnostic potential and provide valuable insights into the mechanisms of antigen presentation and its relationship with TB infection and latent TB infection , and can potentially be used to develop revolutionary immunotherapies by enabling the targeted delivery of drugs ., The collection , delivery use of clinical samples obtained from TB patients and other control donors and the experimental procedures were approved by the Medical Ethics Committee of Zhongshan School of Medicine , Sun Yat-sen University , the Biosafety Management Committee of Sun Yat-sen University , and the Medical Ethics Committee of Guangzhou Chest Hospital , respectively ., All of the patients and healthy donors gave written , informed consent before enrollment in this study ., Patients were recruited from the Guangzhou Chest Hospital , Guangzhou , China between October 2009 and February 2011 ., A diagnosis of active TB was made based on the following: ( 1 ) positive sputum smear or culture results for MTB; ( 2 ) the detection of active PTB lesions or extrapulmonary TB lesions by X-ray examination or the detection of active PTB or lymph node TB in tissue sections by MTB antigen-specific immunohistochemistry; and ( 3 ) the presence of typical symptoms such as cough , expectoration , bloody sputum or hemoptysis , chest distress , chest pain , short breath , and lymphadenovarix ., The possibility of malignant lesions in the lung or lymph node sections was ruled out using these criteria ., PBL samples , PLF samples , and frozen 8-µm-thick sections of lung and lymph node granuloma and cavernous tissues were obtained from the inpatients , who were either untreated or in the initial stages of treatment ( within the first 20 days ) , and used in this study , in addition to PBL samples that were collected from PTB outpatients during TB development and treatment ., The PBL samples from the non-PTB patients and tissue sections from non-TB patients with pulmonary or lymph node infections were collected from the Guangzhou Chest Hospital ., PBL samples from healthy donors and umbilical cord blood samples were collected from the Guangzhou Blood Center and Guangzhou Women and Childrens Medical Center , respectively , and used as the study controls ., The IFN-γ-ELISPOT assay was used to screen for CD4+ T cells that were secreting IFN-γ in response to MTB-specific peptides E6 , E7 , C5 , and C14 in PLF samples obtained from patients with tuberculous pleuritis , as described in previous studies 20 , 57 ., Briefly , MultiScreen ELISPOT plates ( Millipore , Bedford , MA , USA ) were coated with 5 µg/mL mouse anti-human IFN-γ capture antibody ( eBioscience , San Diego , CA , USA ) and stored at 4 °C overnight ., After blocking , 2 . 0–5 . 0×105 mononuclear cells from the PLF samples were co-incubated with the peptide at a final concentration of 10 µg/mL in a total volume of 200 µL per well for 16–18 hours in the culture medium ( complete RPMI with 10% fetal calf serum FCS; Hyclone , Logan , UT , USA ) ., After washing , the wells were incubated with 250 µg/mL biotinylated mouse anti-human IFN-γ antibody ( eBioscience ) for 2 hours at room temperature ., After washing again , the plates were incubated with 1∶10000 diluted streptavidin-conjugated alkaline phosphatase ( AP ) ( Pierce , Rockford , IL , USA ) for 2 . 0–2 . 5 hours ., IFN-γ-specific spots were developed by adding BCTP/NBT solution ( Pierce ) into each well after washing ., The reaction was stopped after 15 minutes by rinsing the wells with distilled water ., Spots were counted using an ELISPOT reader ( Cellular Technology Ltd . , Shaker Heights , OH , USA ) ., The CD4+ T cells were separated from the ELISPOT-positive PLF mononuclear cells using immunomagnetic anti-human CD4 particles-DM beads ( BD Biosciences , Franklin Lakes , CA , USA ) , according to the manufacturers protocol , and resuspended in RPMI-1640 medium at a concentration of 1 . 0×107 cells/mL ., CFSE ( Enzo Life Sciences , Lausen , Switzerland ) was added to the cell suspension to reach a final concentration of 5 µM ., The cell suspension was incubated for 10 minutes at room temperature in the dark ., Labeling was terminated by adding the same volume of 100% FCS in order to quench the free CFSE for 10 minutes at room temperature ., The labeled cells were washed 3× with sterile phosphate buffer saline ( PBS ) containing 10% FCS , and then counted ., Approximately 2 . 0–5 . 0×106/mL cells were seeded into 1 . 5 mL of culture media ( complete RPMI with 10% FCS ) that was suppl | Introduction, Results, Discussion, Materials and Methods | The in vivo kinetics of antigen-presenting cells ( APCs ) in patients with advanced and convalescent tuberculosis ( TB ) is not well characterized ., In order to target Mycobacterium tuberculosis ( MTB ) peptides- and HLA-DR-holding monocytes and macrophages , 2 MTB peptide-specific CD4+ T-cell receptor ( TCR ) tetramers eu and hu were successfully constructed ., Peripheral blood ( PBL ) samples from inpatients with advanced pulmonary TB ( PTB ) were analyzed using flow cytometry , and the percentages of tetramer-bound CD14+ monocytes ranged from 0 . 26–1 . 44% and 0 . 21–0 . 95% , respectively; significantly higher than those measured in PBL samples obtained from non-TB patients , healthy donors , and umbilical cords ., These tetramers were also able to specifically detect macrophages in situ via immunofluorescent staining ., The results of the continuous time-point tracking of the tetramer-positive rates in PBL samples from active PTB outpatients undergoing treatment show that the median percentages were at first low before treatment , increased to their highest levels during the first month , and then began to decrease during the second month until finally reaching and maintaining a relatively low level after 3–6 months ., These results suggest that there is a relatively low level of MTB-specific monocytes in advanced and untreated patients ., Further experiments show that MTB induces apoptosis in CD14+ cells , and the percentage of apoptotic monocytes dramatically decreases after treatment ., Therefore , the relatively low level of MTB-specific monocytes is probably related to the apoptosis or necrosis of APCs due to live bacteria and their growth ., The bactericidal effects of anti-TB drugs , as well as other unknown factors , would induce a peak value during the first month of treatment , and a relatively low level would be subsequently reached and maintained until all of the involved factors reached equilibrium ., These tetramers have diagnostic potential and can provide valuable insights into the mechanisms of antigen presentation and its relationship with TB infection and latent TB infection . | Mycobacterium tuberculosis ( MTB ) is one of the most dangerous pathogens in the world ., It is estimated that one-third of the world population contracts the bacteria during their lives ., Approximately 5–10% of infected individuals will eventually develop an active form of the disease ., Cellular immunity plays an important role in immunity against tuberculosis ( TB ) ; however , the hosts defense mechanisms are not completely understood ., Here , we developed a novel tool: MTB antigen-specific tetrameric CD4+ T-cell receptor ( TCR ) complexes that can detect MTB peptide-specific antigen presenting cells ( APCs ) in blood and local tissues ., We found that a relatively low level of antigen-specific monocytes ( i . e . , APCs ) was detected in peripheral blood ( PBL ) samples from untreated TB patients , and then increased to their peak levels during the first month after treatment , which probably had something to do with the decrease in APC apoptosis ., Our research provides a new method for tracking dynamic changes in APCs that are associated with TB infection and latent TB infection , and an additional tool for the studies of TB immunity and its pathogenesis . | medicine, infectious diseases, diagnostic medicine, clinical immunology, immunology, biology, microbiology | null |
journal.pntd.0001409 | 2,011 | A Deep Sequencing Approach to Comparatively Analyze the Transcriptome of Lifecycle Stages of the Filarial Worm, Brugia malayi | Wuchereria bancrofti , Brugia malayi and Brugia timori are mosquito-borne filarial nematode parasites that cause the tropical disease lymphatic filariasis ( LF ) ., The manifestation of the disease ranges from swelling of the lymph nodes to elephantiasis and hydrocele ., LF is a major cause of clinical morbidity and disability , leading to significant psychosocial and psychosexual burden in endemic countries ., B . malayi is the primary organism for the study of LF because it has a tractable lifecycle that can be replicated in a laboratory setting ., Like other filarial nematodes it is a heteroxenous parasite alternating between arthropod vectors and vertebrate hosts ., Filarial nematodes are dioecious and reproduce sexually via copulation ., Inseminated adult female worms are ovoviviparous and release live larvae ( microfilariae ) into the lymph , where they eventually circulate in the bloodstream to be taken up by mosquitoes during blood feeding ., After a microfilaria ( MF ) successfully penetrates the midgut of a susceptible vector , it migrates to the thoracic muscles , and develops intracellularly through two molts to achieve the developmentally arrested third-stage larva ( L3 ) that exits the mosquito proboscis during bloodfeeding and subsequently penetrates the mammalian host ., Once L3s enter the definitive host , they undergo two additional molts and mature to adults in the lymphatics ., Characterization of the transcriptional program over the complete lifecycle is necessary to clearly understand the development of the parasite and could help devise better target strategies for control ., From the standpoint of possibly designing drug-based or vaccine interventions that prevent infection or curtail parasite transmission , there is particular interest in understanding the biology of the L3 to L4 transition in the mammalian host , and the reproductive biology of filarial worms ., The completion of the draft genome of B . malayi 1 has ushered in the possibility to use whole-genome gene expression profiling ., With that goal in mind , we used next-generation sequencing to comparatively analyze the transcriptome of seven B . malayi lifecycle stages: eggs & embryos , immature MF ( of less than 3 days of age ) , mature MF , L3 , L4 , adult male and adult female ., We find that the transcriptional program has a number of stage-specific pathways activated during worm development and that a number of these are potential targets for drugs or vaccines ., All animal work was conducted according to relevant national and international guidelines outlined by the National Institutes of Health Office of Laboratory Animal Welfare , and was approved under UWO Institutional Animal Care and Use Protocol 0-03-0026-000246-4-6-11; and UWM Research Animal Resource Center Protocol V00846-0-10-09 ., Brugia malayi adults and MF were obtained from the peritoneal cavities of patently infected dark-clawed Mongolian gerbils ( Meriones uguiculatus ) by peritoneal flush with prewarmed RPMI media ( Fisher Scientific , Piscataway , NJ ) ., MF were purified by centrifugation through Ficoll-Paque® lymphocyte isolation media ( Amersham Pharmacia Biotech , Piscataway , NJ ) , and washed in PBS three times prior to flash freezing at −80°C ., Adult worms were separated by gender , washed three times in RPMI , and flash frozen ., Egg and embryo preparations were made by repeated cutting of 10 female worms with a scalpel to release eggs and embryos into a small volume of cold RPMI ., The sample was examined microscopically and pieces of uterine tissue were removed using watchmakers forceps ., The sample was washed three times in cold RPMI prior to flash freezing ., Immature MF ( ≤3 days old ) were generated and purified as previously described 2 ., L4s were isolated from gerbils 12–13 days post peritoneal infection and were processed as described for adult worms ., L3s were obtained from the NIAID-NIH Filariasis Research Reagent Resource Center at University of Georgia , Athens , GA ., Total RNA was isolated from the majority of samples using a previously described protocol 2 that combines organic extraction with Trizol LS ( Invitrogen , Carlsbad , CA ) and column purification ( RNAqeous-Micro® , Applied Biosystems , Foster City , CA ) ., Samples were treated with DNase I ( Ambion , Austin , TX , USA ) according to the manufacturers instructions , and the absence of background DNA confirmed by using a portion of each sample in a PCR designed to amplify the B . malayi GPX gene GenBank:X69128 ( data not shown ) ., Isolation of RNA from L3s often produces low yields therefore we used a modified protocol employing homogenization of tissue combined with organic extraction in RNAzol 3 followed by cleaning , concentration and DNase treatment using a Zymo Research RNA column ( Zymo Research Corp , Orange , CA ) ., For all samples RNA integrity was confirmed visually by agarose gel electrophoresis ( data not shown ) and purity and concentration determined spectrophotometrically ( NanoDrop ND-1000 , ThermoFisher Scientific ) ; samples were stored at −80°C ., Total RNA was lyophilized under vacuum for transport on dry ice to the Wellcome Trust Sanger Institute Genome Facility ., Polyadenylated mRNA was purified from total RNA using oligo-dT dynabead selection followed by metal ion hydrolysis fragmentation with the Ambion RNA fragmentation kit ., First strand synthesis , primed using random oligonucleotides , was followed by 2nd strand synthesis with RNaseH and DNApolI to produce double-stranded cDNA using the Illumina mRNA Seq kit ., Template DNA fragments were end-repaired with T4 and Klenow DNA polymerases and blunt-ended with T4 polynucleotide kinase ., A single 3′ adenosine was added to the repaired ends using Klenow exo- and dATP to reduce template concatemerization and adapter dimer formation , and to increase the efficiency of adapter ligation ., Adapters ( containing primer sites for sequencing ) were then ligated and fragments size-selected ( 200–275 bp ) by agarose gel electrophoresis ., DNA was extracted using a Qiagen gel extraction kit protocol but with dissolution of gel slices at room temperature ( rather than 50°C ) to avoid heat induced bias ., Libraries were then amplified by PCR to enrich for properly ligated template strands , to generate enough DNA , and to add primers for flowcell surface annealing ., AMPure SPRI beads were used to purify amplified templates before quantification using an Agilent Bioanalyser chip and Kapa Illumina SYBR Fast qPCR kit ., Libraries were denatured with 0 . 1 M sodium hydroxide and diluted to 6 pM in a hybridization buffer to allow the template strands to hybridize to adapters attached to the flowcell surface ., Cluster amplification was performed on the Illumina cluster station or the Illumina cBOT using the V4 cluster generation kit following the manufacturers protocol ., A SYBRGreen QC was performed to measure cluster density and to determine whether to pass or fail the flowcell for sequencing ., This was followed by linearization , blocking and hybridization of the R1 sequencing primer ., The hybridized flowcells were loaded onto the Illumina Genome Analyser IIx for 54 cycles of sequencing-by-synthesis using Illuminas v4 or v5 SBS sequencing kit then , in situ , the linearization , blocking and hybridization step was repeated to regenerate clusters , release the 2nd strand for sequencing and to hybridize the R2 sequencing primer followed by another 54 cycles of sequencing to produce paired end reads ., These steps were performed using proprietary reagents according to manufacturers recommended protocol ( https://icom . illumina . com/ ) ., Data were analyzed using the RTA1 . 6 or RTA1 . 8 Illumina pipeline and submitted to Array Express ( http://www . ebi . ac . uk/arrayexpress/ ) under the accession number E-MTAB-811 ., Each lane of Illumina sequence was assessed for quality based on %GC content , average base quality and Illumina adapter contamination ., To assess the quality of the lane , the mean base quality at each base position in the read was computed over all reads from the lane ., To assess %GC content of the reads a frequency distribution of values was plotted ., For a single sample in a lane , a GC plot with a normal distribution around the expected GC for the organism would be expected ., Any lanes containing a contamination could therefore be identified by the presence of multiple peaks in the %GC plot ., To screen for adapter contamination , the sequence reads were aligned to the set of Illumina adapter sequences using BLAT v . 34 with default parameters 4 ., Any reads matching these sequences were reported as being contaminated with adapter sequence ., Sequence reads from each lifecycle stage were aligned to the genome assembly GenBank:DS236884–DS264093 using TopHat v1 . 0 . 14 , a splice junction mapper built upon the short read aligner Bowtie 5 , 6 ., The pipeline utilized exon records in the genome annotation 1 to build a set of known splice junctions for each gene model , complementing its de novo junction mapping algorithm ., Default parameters were used except for the following: minimum intron length was set to 50; minimum isoform fraction filter was disabled; closure-search , coverage-search , microexon-search and butterfly-search were enabled for maximum sensitivity ., The resulting alignment files were converted to BAM format and low quality alignments with mapping quality scores less than 5 were removed before downstream analyses 7 , 8 ., No replicate samples were sequenced and all data were combined per lifecycle stage ., Reads aligned to exonic regions were enumerated for each gene model using the HTSeq package ( v0 . 4 . 7 ) in Python ( www-huber . embl . de/users/anders/HTSeq ) ., Reads overlapping more than one gene model were counted as ambiguous with the mode parameter set as “union” ., Following Mortazavi et al . 9 , transcript abundance estimates were computed as RPKMs ( Reads Per Kilobase of exon model per Million mapped reads ) with the following modifications:, ( i ) a set of paired-end reads were counted as one in compiling sequence counts to represent a single sampling event and, ( ii ) TMM ( trimmed mean of M ) -normalized values were used in place of the nominal library size to account for compositional biases 10 ., The correction factors for TMM-normalization ( i . e . , the weighted trimmed mean of M values to the reference ) were calculated using the Bioconductor edgeR package 11 ., The weights were from the delta method on binomial data , and the library whose upper quartile is closest to the mean upper quartile was used as the reference ., Differential expression analysis was performed in edgeR by fitting a negative binomial model to the sequence count data ., Using the quantile-adjusted conditional maximum likelihood method , dispersion parameters were estimated for each gene as a measure of the overall stage-to-stage variability to facilitate between-gene comparisons ., All hypothesis testing was carried out using exact test for the negative binomial distribution with a common dispersion term for all genes ., P-values less than 0 . 01 were considered significant ., Dispersion parameters were estimated directly from the count data for comparisons contrasting a single stage or two related stages relative to all other stages ., For comparisons between pairs of lifecycle stages , a common dispersion value of 0 . 2 was used , which is equivalent to allowing within-stage variations in expression levels of up to 45% ., This value was chosen based on the level of variability observed between the immature and mature MF samples ., Because longer transcripts give more statistical power for detecting differential expression between samples 12 , Gene Ontology ( GO ) analysis was performed using the goseq package that adjusts transcript length bias in deep sequencing data 13 ., GO annotation was retrieved from the UniProtKB-GOA database 14 , and statistically over-represented GO terms in a given gene list were identified using the Wallenius non-central hypergeometric distribution ., Hierarchical clustering analysis was performed using GeneSpring GX ( Agilent Technologies ) ., RKPM values for each gene were baseline transformed to the median of all samples , and hierarchically clustered with centroid linkage using Pearsons uncentered correlation coefficient as distance metric ., In total , 104 million paired-end reads ( 2×54 bp ) were generated from polyA-tailed mRNA using the Illumina Genome Analyser IIx ( Table S1 ) ., Sequence reads were aligned to the genome assembly using TopHat 5 , and the number of reads aligned to each gene model was summed yielding relative transcript levels for individual genes ., Approximately 50% of the sequenced reads were mapped to the reference genome after low quality alignments were removed; 10% of which were aligned to genomic regions outside of the current gene models ., Sequencing depth varied between the lifecycle stage libraries , affecting gene model coverage and the distribution of the read counts per gene model for each library ( Figure 1 and Figure S1 ) ., Overall , in each library , 8 , 000–10 , 000 genes ( equivalent to 70 to 90% of the currently annotated gene models ) had 5 or more mapped reads ., Sequence counts were RPKM ( Reads Per Kilobase of exon model per Million mapped reads ) -transformed and TMM ( trimmed mean of M ) -normalized to assist in the interpretation of transcript abundance comparisons between stages and genes 9 , 10 ., For statistical inferences , however , raw read counts were directly used ., Further analysis of our sequence data from a genomics perspective , covering issues related to missing , incomplete or incorrect gene models of the 2007 assembly 1 will be published elsewhere ( in preparation ) ., Our sequencing libraries contained reads that map to the Wolbachia genome GenBank:AE017321 ., However , the study was not adequately designed such that one could quantitatively analyze these reads in a biologically meaningful way ., Abundance estimates ( inferred from read counts ) of these transcripts most likely deviate substantially from their true in vivo levels ., Poly-A selection directly affects the relative abundance of non-poly-A Wolbachia transcripts with respect to B . malayi transcripts ., Moreover , the nature and extent of the biases introduced by oligo-dT method to the relative abundance levels among the non-poly-A species ( with respect to each other ) is not well understood , and one cannot assume that these biases would remain uniform among different sample preparations ., Another layer of uncertainty stems from the possibility that these “Wolbachia” sequences were transcribed from the B . malayi nuclear genome rather than the endosymbiont as a consequence of the past horizontal gene transfer events , leading to a differential capture of ( presumably ) poly-A tailed “Wolbachia” transcripts of the B . malayi nuclear origin ., However , given the incomplete draft nature of the B . malayi genome assembly and the inherent difficulty in mapping short reads originating from multiple loci that are similar in sequences , it remains challenging to rigorously test this hypothesis in silico ., To investigate the global transcriptional differences between stages and between genes during development , a negative binomial ( NB ) based model 11 was fit to sequence count data ., First , the degree of between-stage differences was assessed globally using a multidimensional scaling ( MDS ) of all-against-all comparisons in the NB model ( Figure 2 ) ., The resulting sample relations appear consistent with the expected biological differences between the samples ., The MDS plot indicates that , in relative terms , the transcriptome profiles of the immature and mature MF are more similar to each other than either is to other stages ., Likewise , the eggs & embryos sample is closely related to the adult female sample , part of which consists of the germ-line cells ., Interestingly , this plot also shows how different the transcriptome profiles of adult male and female worms are to each other ., Next , we made between-gene comparisons in terms of overall transcriptional variability across stages ., It is generally hypothesized that while some genes are expressed constitutively , genes with specific developmental functions are expressed at specific stages ., To quantify the level of transcriptional variation for each gene across the seven lifecycle stages , the NB dispersion parameters were estimated for each gene , and used as a measure of the extra-Poisson , stage-to-stage variability ., Genome-wide distribution of the dispersion parameter estimates suggests that the level of transcriptional variation is not uniform across all genes ( Figure S2 ) ., Although the majority of genes show low to moderate levels of variation , certain groups of genes exhibit a significantly greater level of variation ., Approximately 25% of genes have NB dispersion parameter values larger than 1 ., After ranking by dispersion , genes were partitioned into quarters and designated as Q1 through Q4 in the order of decreasing variability ., To examine genes displaying life stage dependent transcriptional patterns in greater detail , the top 25% most variable genes according to the NB dispersion ( i . e . , Q1 ) were subjected to an unsupervised hierarchical clustering ( Figure 3A ) ., The resulting heatmap and dendrogram suggest that there are four major transcriptional patterns , each of which corresponds to an increased transcript abundance in, ( i ) female and/or eggs & embryos ,, ( ii ) male ,, ( iii ) microfilariae , or, ( iv ) late larval stages ., The transcriptional patterns identified through the clustering analysis largely recapitulate the sample relations revealed in the MDS plot ( Figure 2 ) ., To classify genes into these broad but distinct co-expression groups in a statistically robust manner , we performed a series of exact tests for the NB distribution using raw read counts for all genes ( Figure 3B ) ., Relying solely on the “shape” of expression patterns derived from RPKM values , without considering how many reads contributed to each pattern , may lead to false-positive findings ., We first identified genes preferentially transcribed during single stages by performing exact tests contrasting each individual stage relative to the mean of all other stages ., The resulting gene lists were augmented by additional exact tests to include genes displaying increased transcript abundance in two ( related ) stages with respect to all other stages ., At the level of p-value<0 . 01 , mutually-exclusive , non-redundant gene lists were compiled for each group ., In total , we cataloged 2 , 430 genes into groups with distinct life stage dependent transcriptional patterns ., Comparing the gene lists to the highly variable genes in the Q1 group suggests that members of the four main expression groups account for ∼80% of the top 25% most variable genes ( Figure 3C ) ., Genes that are highly variable in transcript abundance , yet are not assigned to any of the four main groups ( n\u200a=\u200a563 ) likely display complex transcriptional patterns falling outside of the four categories ., In addition , five direct pairwise comparisons were made between relevant stages to gain further insights into the transcriptomic features associated with ( 1 ) sex differences , ( 2 ) intrauterine reproductive processes , ( 3 ) MF maturation , and ( 4 ) late larval development ( Figure 3D ) ., Cross-referencing with the previously defined coexpression groups ( Figure 3B ) indicates that stage specificity is not homogeneous within each group of differentially transcribed genes , highlighting the complexity of the relative transcriptome differences among the lifecycle stages examined in the study ., The results outlined above are described in further detail in the following sections ., We identified and compared statistically overrepresented GO terms in groups of genes that differ in their level of transcriptional variation over the lifecycle ( i . e . , Q1 to Q4 ) to investigate specific gene sets and functional categories distinctly associated with high levels of transcriptional variation ( Table S2 and Figure S2 ) ., This analysis identified ‘structural constituent of cuticle’ ( GO:0042302 ) as the most significantly overrepresented GO category among Q1 genes that exhibit high levels of between-stage transcriptional variation ., Forty-six cuticle collagen genes are annotated with this GO term , and thirty-three of these have distinct lifecycle stage dependent transcriptional patterns ( 18 late larval , 12 female/eggs , 2 male and 1 microfilarial; Dataset S1 ) ., Additional GO terms overrepresented among Q1 genes include those related to serine type endopeptidase inhibitor ( serpin ) , structural molecule , and kinase/phosphatase activity ., By contrast , GO categories associated with protein metabolism , such as translation , protein transport and proteasome complex are significantly overrepresented among genes displaying relatively little transcriptional variation over lifecycle stages ( i . e . , Q2-4 ) ., Although transcript levels of 990 genes are significantly higher during larval stages , 886 and 554 genes display elevated transcript abundance in adult male , and adult female and/or eggs & embryos , respectively ( Figure 3B ) ., A direct pairwise comparison of male versus female transcriptome further indentified 1 , 279 genes with male-biased expression and 651 genes with female-biased expression ( Figure 3D ) ., At the level of GO categories , structural molecular activity and those associated with protein phosphorylation and dephosphorylation are prominent among genes preferentially transcribed in adult male ., A closer look at individual genes with male-biased expression reveals that major sperm proteins are largely responsible for driving the statistical significance of structural molecular activity ( GO:0005198 ) in these comparisons ., By contrast , structural constituents of cuticle ( collagens ) , transcription factor/regulator activity , nuclear receptor activity and serpin activity constitute a main theme of the overrepresented functional categories among genes preferentially transcribed in adult female and/or eggs & embryos ., In an effort to elucidate female germline-enriched transcripts and gain insight into intrauterine reproductive processes , the transcriptome profile of a library enriched for eggs and embryos was compared with that of whole adult female ( Figure 3D ) ., However , because the eggs & embryos transcriptome is inherently a subset of the adult female transcriptome , this pairwise comparison is almost subtractive in nature and is likely biased against identifying transcripts enriched in germline tissues ., On the contrary , detection of female transcripts either not expressed or expressed at lower levels in eggs and embryos likely remains unaffected by this asymmetric sample relation ., For this reason , we used the adult male transcriptome profile as an additional reference point to better identify genes showing a germline-enriched expression pattern ., We performed a Venn diagram analysis with three datasets: ( 1 ) genes with enriched expression in adult female relative to eggs & embryos , ( 2 ) genes with enriched expression in eggs & embryos relative to adult male , and ( 3 ) genes with enriched expression in adult female and/or eggs & embryos relative to all other stages ( Figure S3 ) ., We considered genes belonging to the first set to exhibit somatic tissue-enriched expression pattern , and those belonging to either of the last two sets , but excluded from the first set , to exhibit germline-enriched expression pattern ., Based on these criteria , 788 and 239 genes show enriched expression in female germline and somatic tissues , respectively ., GO term overrepresentation analysis indicates that functional categories , such as transcription factor activity , DNA binding , regulation of transcription and nuclear receptor activity are more frequently found among genes displaying germline-enriched expression ., On the contrary , genes implicated in chloride transport , lipid binding , and proteolysis are overrepresented among those with somatic tissue-enriched expression pattern ( Table S3 ) ., Interestingly , structural constituents of cuticle ( GO:0042302 ) is overrepresented among both genes with germline-enriched and somatic tissue-enriched expression patterns ., A closer look at individual genes reveals that mutually exclusive subsets of collagens are overrepresented in each gene set ., When compared across all stages , transcript levels of 148 genes are distinctly elevated during the MF stage ., Overrepresented GO terms in this group include zinc ion binding , nucleic acid binding , chitinase activity , and proteolysis ( Figure 3B and Table 1 ) ., Most notably , among these are 44 genes that encode proteins with C2H2-type zinc finger domains ., There are 195 zinc finger protein genes annotated in the B . malayi draft genome , some of which have high transcript levels in stages other than MF ( i . e . , 3 late larval , 17 male and 6 female/eggs ) ., In a similarly biased manner , 3 out of 4 endochitinase genes identified in the current B . malayi genome show transcriptional increase during MF stages ., Diverse classes of proteases are also represented in this gene set ( e . g . , cathepsin L-like proteases including Bm-cpl-6 , papain cysteine protease family , metalloprotease I , aspartyl protease and trypsin-like protease ) ., Direct comparison of immature and mature MF ( IM and MM ) indicates that 126 genes show differential transcript abundance between the two samples ( Figure 3D ) ., Many different metabolic genes are found in the IM overexpressed gene set , while the endochitinases are overrepresented in the MM ., We identified 842 genes displaying increased transcript abundance during L3 and/or L4 stages relative to other lifecycle stages ( Figure 3B ) ., Functional categories overrepresented among these genes include structural components of the cuticle , oxidoreductase activity , serpin activity , chloride transport , hedgehog receptor activity , glycogen biosynthetic process , and proteolysis ., As suggested by the last GO category , various proteases ( e . g . , metalloprotease , papain family peptidase , zinc carboxypeptidase family and cathepsin-like cysteine proteases , including Bm-cpl-1 , 4 and 5 ) are prominently represented in this gene set , a pattern similarly found in the MF transcriptome ., A pairwise comparison of the transcriptomes of late larval stages indicates that 342 genes have elevated transcript levels in L3s , and 155 in L4s ., At the level of functional categories , cysteine-type peptidase activity ( e . g . , cathepsin-z and -L like proteases ) and serpin activity are overrepresented among L3-enriched transcripts , whereas structural constituents of the cuticle and cellular component organization are overrepresented among L4-enriched transcripts ( Table S3 ) ., In addition , our data indicate that abundant larval transcripts ( Alt1 . 2 and Alt2 ) show increased abundance in L3s relative to L4s ., Using high-throughput sequencing , we have undertaken a comprehensive genome-wide survey of the developmental transcriptome of the human filarial parasite B . malayi ., Although deep sequencing data are highly informative in identifying novel transcribed elements and splice variants that help improve genome annotation 15 , the present study aims to characterize transcriptome changes along the progression of the parasites lifecycle ., Transcriptome changes mediating cuticular molting likely represent one of the most notable developmental transitions in RNA expression ., Like all nematodes , Brugia spp ., have five lifecycle stages that are punctuated by molting of the collagenous cuticle ., The tightly regulated process of molting involves cell signaling within the hypodermis to cue secretion of the new collagenous cuticle , shedding of the old cuticle and proteolytic remodeling of the new cuticle 16 , 17 ., Analysis of overrepresented GO terms highlights structural cuticle components , extracellular matrix components and cysteine-peptidase inhibitors , among others , in genes with high levels of transcriptional variation over the lifecycle ( Table S2 ) ., In particular , the cuticle collagen gene family displays distinct dynamic transcriptional patterns over the course of the lifecycle , likely reflecting compositional variation in cuticular structure among different life stages ., Besides these structural components , genes displaying the most dramatic transcriptional variation in our data set are likely associated with developmental processes that differ between the larval and the adult stages and/or between the genders ( e . g . , gametogenesis ) ., By contrast , genes constitutively expressed over the developmental period studied frequently have predicted cellular functions related to protein expression , modification and transport , possibly representing core cellular processes that are essential to the survival of cells independent of the lifecycle stage ., The present study indicates that genes exhibiting adult male enriched transcriptional pattern ( relative to adult female and/or other stages ) show strong statistical bias towards GO categories related to cytoskeleton , structural molecule activity , protein phosphorylation and dephosphorylation ( Table 1 ) ., Many of these gene sets and functional categories are highly represented among classes of male-enriched transcripts in parasitic nematodes 18 , 19 , 20 , 21 and have been identified in the Caenorhabditis elegans male and hermaphrodite germline as being involved in spermatogenesis 22 ., Nematode sperm are unique in that they utilize a nematode-specific cytoskeletal element , major sperm protein , for ameboid motility ., It is hypothesized that because mature nematode sperm lack ribosomal elements , the phosphorylation and dephosphorylation of molecules by a host of enzymes within the differentiated cells could promote maturation and pseudopod extension 22 ., Seven of the genes found to be differentially expressed in male worms in our study were also found in a microarray comparison of adult male and female worms 23 , and were shown by in situ localization to be expressed either in sperm or vas deferens tissue of adult male worms and not in gravid adult female worms 24 ., If we compare our RNA-seq data with recent microarray work comparing gene expression in adult male and female B . malayi 19 , 515 of our 1 , 276 ( 40% ) genes with male-biased expression match with male up-regulated genes found in the microarray comparison , and 150 out of the 651 ( 23% ) genes with female-biased expression match the microarray findings ., In filarial nematodes , fertilization is internal and gravid females hold oocytes , sperm , zygotes , developing embryos , and MF in their uteri ., Structural constituents of cuticle , transcription factor activity , DNA binding , and regulation of transcription emerged as notable themes in our analysis of overrepresented functional categories among genes with increased transcript levels in adult female and/or eggs & embryos ( Table 1 ) ., These are likely relevant in the context of embryogenesis ., Pairwise comparison of adult female with adult male presents us with a similar but more expanded view on features of genes displaying female-enriched expression ( Table S3 ) ., Further comparisons with genes displaying germline-enriched expression patterns suggest that many of the female-biased transcripts , and more importantly , the majority of the above mentioned functional categories are attributable to the characteristics of the germline transcriptome ., For instance , 33 out of 34 genes annotated with transcription factor activity ( e . g . , nuclear hormone receptors and homeobox domain containing proteins ) that are enriched in female and/or eggs & embryos , have a distinctly germline-enriched expression pattern ., Bm-fab-1 ( Bm1_33050 ) , an embryonic fatty acid binding protein tra | Introduction, Methods, Results, Discussion | Developing intervention strategies for the control of parasitic nematodes continues to be a significant challenge ., Genomic and post-genomic approaches play an increasingly important role for providing fundamental molecular information about these parasites , thus enhancing basic as well as translational research ., Here we report a comprehensive genome-wide survey of the developmental transcriptome of the human filarial parasite Brugia malayi ., Using deep sequencing , we profiled the transcriptome of eggs and embryos , immature ( ≤3 days of age ) and mature microfilariae ( MF ) , third- and fourth-stage larvae ( L3 and L4 ) , and adult male and female worms ., Comparative analysis across these stages provided a detailed overview of the molecular repertoires that define and differentiate distinct lifecycle stages of the parasite ., Genome-wide assessment of the overall transcriptional variability indicated that the cuticle collagen family and those implicated in molting exhibit noticeably dynamic stage-dependent patterns ., Of particular interest was the identification of genes displaying sex-biased or germline-enriched profiles due to their potential involvement in reproductive processes ., The study also revealed discrete transcriptional changes during larval development , namely those accompanying the maturation of MF and the L3 to L4 transition that are vital in establishing successful infection in mosquito vectors and vertebrate hosts , respectively ., Characterization of the transcriptional program of the parasites lifecycle is an important step toward understanding the developmental processes required for the infectious cycle ., We find that the transcriptional program has a number of stage-specific pathways activated during worm development ., In addition to advancing our understanding of transcriptome dynamics , these data will aid in the study of genome structure and organization by facilitating the identification of novel transcribed elements and splice variants . | Lymphatic filariasis , also known as elephantiasis , is a tropical disease affecting over 120 million people worldwide ., More than 40 million people live with painful , disfiguring symptoms that can cause severe debilitation and social stigma ., The disease is caused by infection with thread-like filarial nematodes ( roundworms ) that have a complex parasitic lifecycle involving both human and mosquito hosts ., In the study , the authors profiled the transcriptome ( the set of genes transcribed into messenger RNA rather than all of those in the genome ) of the human filarial worm Brugia malayi in different lifecyle stages using deep sequencing technology ., The analysis revealed major transitions in RNA expression from eggs through larval stages to adults ., Using statistical approaches , the authors identified groups of genes with distinct life stage dependent transcriptional patterns , with particular emphasis on genes displaying sex-biased or germline-enriched patterns and those displaying significant changes during larval development ., This study presents a first comprehensive analysis of the lifecycle transcriptome of B . malayi , providing fundamental molecular information that should help researchers better understand parasite biology and could provide clues for the development of more effective interventions . | medicine, infectious diseases, global health, biology, genomics, genetics and genomics | null |
journal.ppat.1003475 | 2,013 | Deciphering the Cryptic Genome: Genome-wide Analyses of the Rice Pathogen Fusarium fujikuroi Reveal Complex Regulation of Secondary Metabolism and Novel Metabolites | The genus Fusarium is one of the most important groups of phytopathogenic fungi ., They infect a broad spectrum of crops worldwide and are responsible for huge economic losses due to yield reductions and mycotoxin contamination ., The Gibberella fujikuroi species complex ( GFC ) constitutes a monophyletic but diverse subgroup of over 50 Fusarium species with similar morphological features ., The complex is divided into the African , American and Asian clades , according to DNA-based phylogenetic analyses 1–3 ( Figure 1A ) ., The species Fusarium fujikuroi Nirenberg ( teleomorph Gibberella fujikuroi ( Sawada ) Wollenweber ) was first described more than 100 years ago as the causative agent of the “bakanae” ( “foolish seedling” ) disease of rice in Japan 2–4 ., The most characteristic symptom of this disease is excessively elongated seedlings with chlorotic stems and leaves ( Figure 1B ) ., In addition , affected plants are infertile and therefore do not produce edible grains ., The disease symptoms result from the ability of F . fujikuroi to produce and secrete gibberellic acids ( GAs ) , a family of plant hormones 5 , 6 ., Today , the fungus is used worldwide for the commercial production of GAs , which are applied extensively in horticulture to regulate plant growth and development 7 ., Although GAs control growth in higher plants , they are considered as secondary metabolites ( SMs ) in Fusarium because they are not essential for fungal growth and development but instead are thought to contribute to the virulence of the pathogen ., Many fusaria , including multiple species in the GFC , are noted for their production of other SMs , particularly pigments and mycotoxins ., In fungi , genes responsible for the synthesis of a SM are typically located adjacent to one another in a gene cluster ., Such clusters typically include a gene encoding a polyketide synthase ( PKS ) , non-ribosomal peptide synthetase ( NRPS ) or terpene cyclase ( TC ) that is responsible for conversion of primary metabolite ( s ) to a molecule that serves as a precursor for synthesis of a biologically active SM or family of structurally related SMs ( e . g . GAs ) ., SM biosynthetic gene clusters can also include genes that encode:, 1 ) tailoring enzymes that catalyze modification of the precursor molecule or subsequent intermediates in a SM biosynthetic pathway;, 2 ) proteins that transport SMs or intermediates across cellular membranes; and, 3 ) pathway-specific transcription factors that typically induce expression of all the genes in a cluster ., The best studied SMs in F . fujikuroi are the diterpenoid GAs ., Two major milestones in research on GA biosynthesis in this fungus were the identification of the seven-gene GA biosynthetic gene cluster in F . fujikuroi 8 , 9 and the discovery that these genes are regulated by the global nitrogen regulator AreA which had not previously been linked to secondary metabolism 10 , 11 ., Subsequent work revealed a correlation between nitrogen availability and production of other SMs by F . fujikuroi 12 , including carotenoids 13 , the red PKS-derived pigments bikaverin and fusarubins 14 , 15 , and the mycotoxins fusarins 16–18 ( Figure 1B ) ., The availability of genome sequences has significantly impacted examination of secondary metabolism in fungi 19–30 ., To date , publicly available genome sequences of five Fusarium species ( F . graminearum , F . oxysporum , F . pseudograminearum , F . solani and F . verticillioides ) have aided in silico examination of secondary metabolism in Fusarium ., The sequences have facilitated the establishment of a preliminary catalogue of PKS and NRPS genes in Fusarium 31 , 32 , examinations of their phylogenetic relationships 33 , 34 , and bioinformatic identification of novel SM biosynthetic gene clusters 22 ., However , none of the functionally characterized gene clusters have led to the identification of any new secondary metabolite that has not been found to be produced by Fusarium spp ., before ., Most species in the GFC produce multiple SMs 2 , but only a fraction of them have been linked to specific biosynthetic genes ., Prior to this study , a complete genome sequence has been available for only one member of the GFC , the African clade species F . verticillioides , which causes ear and stalk rot of maize 22 ., The genomes of the American clade species F . circinatum , the cause of pitch canker of pine 35 , and the Asian clade species F . mangiferae , that causes mango malformation 36 , 37 ( recently sequenced by S . Freeman and coworkers ) , are not yet publicly available but were used in the current study for comparison of secondary metabolism both in silico and in laboratory experiments ., The objective of the current study is the comprehensive analysis of secondary metabolism in F . fujikuroi , the cause of “bakanae” disease of rice ., We present a draft genome sequence and de novo assembly of exceptional quality for F . fujikuroi , a member of the Asian clade of the GFC ., We assembled 12 scaffolds that correspond to the 12 previously identified GFC chromosomes 38 , 39 ., In addition to the well-known GA gene cluster , we describe novel genes coding for key SM biosynthetic enzymes , such as PKSs , NRPSs , TCs , dimethylallyl tryptophane synthases ( DMATSs ) , and cytochrome P450 monooxygenases ( P450s ) and thereby decoded the complete potential of this important species to produce SMs ., Our analyses revealed differences and similarities between species of different GFC clades and the more distantly related F . oxysporum ( Figure 1A ) ., By applying a combination of microarrays , ChIP-seq , proteomics , and HPLC-FTMS analyses , we demonstrate that nitrogen availability has an enormous impact on secondary metabolism by affecting gene expression , histone modification patterns , protein composition , and SM product levels ., Two of the gene clusters ( PKS19 and NRPS31 ) are not present in any other sequenced fungal genome and thus unique to F . fujikuroi ., The forced expression of these unique clusters by genetic engineering led to structural characterization of corresponding metabolites by HPLC-FTMS ., In planta expression of the PKS19 gene cluster suggest a specific role for the derived chemical product during rice infection thereby adding a second novel metabolite , in addition to GAs , that may contribute to the ability of F . fujikuroi to uniquely infect rice ., Whole genome shotgun sequencing of F . fujikuroi ( strain IMI58289 ) by 454 pyrosequencing yielded 0 . 94 Gb of raw sequence data that was assembled into only 12 scaffolds ( N50 of 4 . 2 Mb; 73 contigs spanning 43 . 9 Mb with an average read coverage of 19 ) ., A total of 14 , 813 gene models were predicted using a combination of gene prediction tools ., Table 1 summarizes physical genome features which are similar to those of closely related species ., To assess the completeness of the F . fujikuroi genome draft , we did BLAST searches with two separate highly conserved core gene sets from 39 and 21 higher eukaryotes species , respectively 40 , 41 ., None of the expected single-copy core orthologs were missing from the F . fujikuroi gene model set indicating that the core gene space has been completely covered ., In order to predict protein functions and reconstruction of evolutionary genesis , a Similarity Matrix of Proteins ( SIMAP ) 42 was generated for the F . fujikuroi gene model set and then queried against the Swiss-Prot ( UniProt Consortium , 2011 ) database ., This analysis revealed 390 F . fujikuroi proteins with higher than 80% identity to proteins in the database , while 4 , 639 F . fujikuroi proteins had little similarity ( <10% ) , indicating novel , species-specific proteins ., In a bidirectional best hits ( BBH ) analysis of the protein set from F . fujikuroi , 71 , 77 and 90% of the proteins were >50% identical to protein sets from the closely related species F . verticillioides , F . circinatum and F . mangiferae , respectively ., In contrast , only 63% of the F . fujikuroi protein set had >50% identity to a F . graminearum protein set ., In F . fujikuroi , the annotated ORFs account for 49 . 2% of the genome with an average coding length of 1 , 457 nt and 2 . 8 exons per gene; the average exon length is 518 nt ., The overall GC content is 47 . 4% , while the average GC content of ORFs is 51 . 5% ., All of these key genome features are similar to those reported for F . verticillioides ( Table 1 ) ., Previous electrophoretic karyotype analysis of F . fujikuroi IMI58289 by contour-clamped homogeneous electric field ( CHEF ) gel electrophoresis led to the assignment of eleven chromosomes to seven bands ., By Southern blot hybridization of the CHEF gel , eight genes were localized to either one or two of the separated chromosome bands 39 ., Due to the high quality assembly of the F . fujikuroi genome into 12 scaffolds corresponding to the 12 chromosomes , we were able to assign these eight genes to specific chromosomes and to link the hybridization signals with the number of the chromosomes ( Figure S1 ) ., Despite the shared synteny between the F . fujikuroi and F . verticillioides genomes 22 , there are several significant differences worth noting ., First , chromosome XII is present in the F . fujikuroi but absent in the genome sequence of F . verticillioides ( Figure 2 ) ., However , the lack of chromosome XII in F . verticillioides may be strain-specific as it has been detected in other strains of F . verticillioides 38 ., In F . fujikuroi , chromosome XII spans 693 kb and includes 173 predicted genes ., A majority ( 139 ) of the corresponding predicted proteins lack sequence similarity to annotated Swiss-Prot proteins ., Functional enrichment analysis using the MIPS ‘FunCat’ program 43 revealed that the remaining 35 predicted proteins are enriched ( FDR<0 . 01 ) in the FunCat category ‘guidance of longitudinal cell extension and cell migration’ 43 ., A second significant difference between the two genomes is that F . fujikuroi chromosome IV lacks 285 and 820 kb from the left and right arms , respectively , relative to F . verticillioides ., In the latter species , the 285 and 820-kb regions collectively include 408 predicted genes that share no significant similarity to genes located elsewhere in the F . fujikuroi genome and are enriched in the FunCat categories ‘secondary metabolism’ , ‘detoxification’ and ‘metabolism of melanin’ ( FDR<0 . 01 ) ., There are also three smaller genomic regions in F . verticillioides that are absent in F . fujikuroi: a 70 kb segment on chromosome VII with 29 genes , a 22 kb segment on chromosome III with eight genes , and a 12 kb segment on chromosome V with six genes ., The majority of these genes code for proteins of unknown function ., To determine whether the presence of chromosome XII and the significantly shorter chromosome IV are strain-specific features of F . fujikuroi , we analyzed nine additional F . fujikuroi isolates from different geographic regions by PCR ., For analysis of chromosome XII , we employed three primer pairs that amplify fragments from the arms and near the center of chromosome XII ., The results from this PCR analysis indicate that chromosome XII is most likely absent in two F . fujikuroi strains ( m570 and C1995 ) from Japan , while only some regions of the chromosome are present in three strains ( E289 , E292 and E325 ) from Italy ( Figure S2 ) ., The fact that all of the F . fujikuroi strains examined were isolated from infected rice plants and cause “bakanae” disease suggests that chromosome XII is not essential for pathogenicity on rice ., Whether chromosome XII contributes to niche specificity , as has been observed for the supernumerary chromosomes of F . oxysporum and Nectria haematococca ( F . solani MP VI ) 22 , 44 , 45 is not yet known ., Variability in presence and absence of chromosome XII in different F . fujikuroi isolates suggests that it might be an “accessory” supernumerary chromosome consistent with previous analyses of GFC species 38 ., For analysis of chromosome IV , we designed two heterologous primer pairs based on F . verticillioides sequence present in the 285 and 820-kb regions of chromosome IV that were absent in F . fujikuroi IMI58289 ., All nine isolates of F . fujikuroi examined have the characteristic shorter chromosome IV observed in strain IMI58289 ( Figure 2 and S2 ) ., Taken together , these results indicate that within F . fujikuroi , the shortened chromosome IV is species-specific , whereas the presence or absence of chromosome XII is strain-specific ., Overall , the F . fujikuroi genome is low in AT-rich regions , as less than 5% of the genome consists of regions with more than 55% of A+T ( Figure 3A ) ., The main AT-rich region on each chromosome is the centromere ( Table S1 ) ., After Neurospora crassa 46 , F . fujikuroi is the second filamentous fungus with an essentially complete assembly of centromeric sequences ., None of the other currently available Fusarium genomes contains more than short pericentric regions flanking the actual , yet unknown centromere sequences 46 ., The positions of most centromeres are conserved in F . fujikuroi and F . verticillioides , and in many cases there is synteny of genes close to centromeres , even in the more distantly related F . graminearum , in which the genome has been condensed into four chromosomes ( Table S2 ) ., Overall , centromeres of F . fujikuroi are significantly shorter than those of N . crassa , ∼50–80 kb compared to 150–280 kb , but similar in size to those of fission yeast 46 , 47 ., Centromeric DNA of F . fujikuroi resembles that of N . crassa , as it is rich in transposon relics but lacks highly repetitive alpha-satellite DNA that is typical of centromeric DNA of mammals and plants 46 , 47–49 ., Comparisons with other Fusarium species failed to reveal common centromeric or pericentric DNA sequences , suggesting that these regions can serve as lineage or species-specific markers ., In F . fujikuroi , none of the terminal contig sequences are capped by telomere repeats , indicating that sequence data for all chromosome ends remain incomplete as for the other sequenced fungi ., A genome-wide comparison of prominent gene families in Fusarium genomes suggest that some families are expanded while others are underrepresented in F . fujikuroi relative to other species ., The number of predicted transcription factors ( TF ) genes in F . fujikuroi is significantly higher ( 950 genes ) in comparison to F . verticillioides ( 640 ) , F . circinatum ( 841 ) , and F . oxysporum ( 876 ) , but almost identical to F . mangiferae ( 945 ) , the closest relative of F . fujikuroi among the species examined ( Table S3 ) ., The expansion of the total number of TFs in both F . fujikuroi and F . mangiferae is reflected in the Interpro domain group ‘fungal-specific TF/Zn ( 2 ) C6 fungal type DNA binding domain’ ( IPR007219; IPR001138 ) ( Table S3 ) ., This TF gene family is expanded to 235 in F . fujikuroi and 208 in F . mangiferae compared to 90 in F . verticillioides and 144 in F . graminearum ., F . fujikuroi has 53 TFs that do not have a closely related homologue ( less than 60% identity ) in other fusaria ., Interestingly , 33 out of these are predicted to encode Zn ( 2 ) C6 TFs ( Table S3 ) ., To determine the complete set of secreted proteins , including those secreted by both classical and non-classical pathways , we applied a combination of five bioinformatic approaches on the predicted protein sets for F . fujikuroi and four other fusaria ., The number of proteins ( 1 , 336 ) in the predicted F . fujikuroi secretome is similar to those for the closely related GFC species F . circinatum , F . mangiferae and F . verticillioides , while F . oxysporum has 15% more proteins ( 1 , 541 ) in its secretome ( Tables 2 and S3 ) ., Similarly , the number of predicted transporters , including ABC and MFS transporters , and all classes of substrate-specific permeases , are very similar in F . fujikuroi and other fusaria as is the number of histone-modifying enzymes ( Tables 2 and S3 ) ., We also determined the coverage of transposable elements ( TEs ) by scanning for known transposons that have been reported to RepBase 50 ., Additionally , we searched for novel TEs in a de novo approach which revealed two LTR-retrotransposons encoding a conserved reverse transcriptase or integrase and one DNA-transposon containing a predicted transposase ., These three transposon families are not contained in RepBase ( BLAST e value<10×10−10 , bitscore >1 , 000 ) but were present in genome sequences of the closely related F . oxysporum and F . mangiferae ., Overall , TEs constitute 2 . 2% of the F . fujikuroi genome , which is higher than in F . verticillioides ( 0 . 5% ) but lower than in F . oxysporum ( 4 . 8% ) ( Table S3 ) ., To estimate the genetic potential of F . fujikuroi to produce SMs , we identified genes predicted to encode five key classes of SM-associated enzymes: PKSs , NRPSs , TCs , DMATSs and P450s ., The genes were identified by the presence of characteristic domains in predicted proteins and by BLAST analyses ( Tables 2 and S4 ) ., We also examined flanking genes to identify putative gene clusters , which could include genes encoding TFs , transporters and modifying enzymes ( e . g . dehydrogenases and acyl transferases ) in addition to genes encoding the SM-associated enzymes noted above ., These genes and potential gene clusters were then compared to homologous sequences in the genomes of F . verticillioides , F . circinatum , F . mangiferae and F . oxysporum as well as the more distantly related species F . graminearum and F . solani 22 , 31–34 , 45 ., This analysis revealed that the F . fujikuroi genome comprises 17 genes that encode putative type I PKSs with canonical ketosynthase ( KS ) and malonyl-CoA:acyl carrier protein ( ACP ) transacylase ( MAT ) domains ., Based on domain content , 14 of the predicted PKSs are reducing-type PKSs ( R-PKS ) in that they have the keto-reductase ( KR ) , dehydratase ( DH ) and enoyl reductase ( ER ) domains that catalyze complete reduction of β-carbonyl during polyketide synthesis ., Four of the R-PKSs include a NRPS module and are also referred to as PKS/NRPS hybrids ., The remaining three PKSs are nonreducing-type PKSs ( NR-PKSs ) because they lack the KR , DH and ER domains ., The F . fujikuroi genome also includes one type III PKS , an enzyme class typical for plants but that has recently been found in some bacteria and fungi 22 , 51 ., The analysis also revealed the presence of 15 NRPS , 2 DMATS , 10 TC ( 2 diterpene and 8 sesquiterpene cyclases ) genes ., In total , the F . fujikuroi genome has the potential to encode 45 enzymes that could give rise to 45 structurally distinct families of SMs ., SMURF analysis and the manual examination of genes flanking the 45 core SM genes indicate that most are part of a SM gene cluster ., Finally , the analysis revealed that the F . fujikuroi genome encodes 143 putative P450s of which 28 are located in putative SM gene clusters ( Table 2 ) ., Recent studies in several fungi showed that chromatin modifications differ in regions with active ( euchromatin ) and silent ( heterochromatin ) gene transcription 52–54 ., There are several examples demonstrating that SM gene clusters in fungi can be regulated by chromatin-modifying enzymes , a form of gene regulation that represents a general level for coordinated control of larger chromosomal segments 53–57 ., Modifications of histone proteins can thus serve as markers for changes in chromatin structure associated with gene expression and silencing ., For example , gene expression has been associated with acetylation of histone H3 lysine 9 ( H3K9ac ) and dimethylation of histone H3 lysine 4 ( H3K4me2 ) , whereas gene silencing has been associated with trimethylation of histone H3 lysine 9 ( H3K9me3 ) 52–54 ., In the current study , the presence of acetylated and methylated forms of histone H3 were determined across all F . fujikuroi chromosomes by ChIP-seq analysis with H3K9ac , H3K4me2 and H3K9me3-specific antibodies ., As in N . crassa , H3K9me3 is mainly present near F . fujikuroi centromeres ( Figures 3A and S3 ) as pericentric regions have higher levels of H3K9me3 compared to the putative centromeric core regions ., Some of this decrease may be due to replacement of canonical H3 with the centromere-specific H3 , CenH3 , in the centromere cores 46 , 47 ., This pattern sets F . fujikuroi centromeres apart from dispersed large heterochromatic regions that show more uniform enrichment of H3K9me3 ( Figures 3 and S3 ) ., Immunofluorescence microscopy with H3K9ac- and H3K9me3-specific dyes revealed that nuclei of F . fujikuroi are highly enriched for H3K9ac ( e . g . extensive euchromatin ) and with no specific subnuclear distribution ( Figure 3B ) ., In contrast , H3K9me3 was concentrated in discrete areas of nuclei , predominantly at the periphery ., Counterstaining with the DNA-binding fluorescent dye DAPI suggested that these are constitute heterochromatic regions , as previously observed in N . crassa 58 ., H3K9me1 and H3K9me2 appear to be absent , at least under the conditions used in this study ( data not shown ) ., All putative SM biosynthetic genes identified above were mapped to chromosome I to XI ( none were present on chromosome XII ) and most were located within subtelomeric regions , loosely defined as within several hundred kb of the putative end of a chromosome ( Figures 3A and S3 ) ., These chromosomal regions are often subject to regulation by posttranslational modification of histones , including acetylation or methylation of the N-terminal tail of histone H3 52–54 , 59 ., To assess whether the location of many SM gene clusters within subtelomeric regions corresponds to a punctate enrichment of H3K9me3 and are thus heterochromatic , we performed ChIP-seq analysis applying H3K9me3-specific antibody under nitrogen-limiting conditions that are favorable for the production of GAs and several other SM ( Figure 3A ) ., As mentioned above , H3K9me3 occurred largely in the pericentric and centromeric regions but each chromosome had several additional large regions with high levels of this mark for constitutive heterochromatin ., All such regions were associated with AT- and transposon-rich DNA that had few , if any , annotated genes ., None of the 45 putative SM enzyme-encoding genes and potential clusters was found in regions enriched for H3K9me3 suggesting that this mark is not important for the regulation of SM in F . fujikuroi ., In contrast , H3K9me3 has been proposed to be critical for regulation of SM clusters in Aspergillus nidulans 53 , 60 ., When the two marks associated with active gene expression , H3K9ac and H3K4me2 , were observed , no H3K9me3 marks were simultaneously present ., H3K9ac and H3K4me2 occurred primarily at the centers or arms of most chromosomes under nitrogen-limiting conditions ., Chromosomes X , XI and XII are unusual in that they nearly completely lack enrichment for H3K9ac and H3K4me2 , which we attribute to the absence of expressed genes on these chromosomes , at least under the conditions tested ., To visualize the global alterations in gene expression in response to a change in nitrogen availability , we mapped the chromosomal positions of genes that are up- or down-regulated two-fold or more , as determined by microarray analysis , in F . fujikuroi grown in high vs . low glutamine medium ( Figure 3A ) ., Overall , subtelomeric regions exhibited decreased expression in high nitrogen , while genes in regions associated with H3K4me2 and H3K9ac are generally induced in high nitrogen conditions ., SMs produced by fungi often play a role in triggering plant cell death and disease 61 , 62 ., Therefore , identification of the whole set of potential SM gene clusters can lead to the development of tools to investigate the role of toxins in disease development ., The availability of genome sequences of F . fujikuroi , F . circinatum 35 , F . mangiferae ( S . Freeman and co-workers , this work ) , and eleven isolates of F . oxysporum ( Broad Institute ) provides an opportunity for a more comprehensive analysis of Fusarium SM biosynthetic genes than has been possible previously ., Due to the association of GA production with “bakanae” disease of rice , GA biosynthetic genes are among the most extensively studied SM genes in Fusarium fujikuroi ., Southern and PCR-based surveys indicate that the GA cluster , or parts of it , occurs in some species of the GFC ( Figure, 4 ) as well as some closely related species , e . g . F . foetens , F . napiforme , F . miscanthi , and F . nisikadoi 8 , 63 ., However , production of GAs has only been detected in isolates of F . sacchari , F . konzum and F . proliferatum 64 , 65 ., In addition , the cluster is absent in the more distantly related species F . graminearum and F . solani 66 ., Here , analysis of genome sequences revealed that homologues of the entire F . fujikuroi GA cluster are present in F . circinatum , F . mangiferae , and five isolates of F . oxysporum ( Figure 4 ) ., All intact clusters share the same gene order and orientation as the previously described clusters in F . fujikuroi and F . proliferatum 8 , 63 , 64 ., In addition , all of the genes appear to encode functional proteins , with two exceptions: the coding regions of P450-2 and P450-3 in F . oxysporum isolates PHW815 and FOSC 3-a , respectively , are interrupted by premature stop codons ( Figure 4 ) ., The seven other F . oxysporum genome sequences have partial GA clusters consisting of one to three intact and partial ( i . e . pseudo ) GA genes ( Figure 4 ) ., While some GA cluster flanking regions share considerable synteny , there are marked differences in content and arrangement of flanking genes among other species ( Figure 4 ) ., Although some of the flanking genes in F . circinatum are homologues of those in F . fujikuroi , the F . circinatum flanking region has a 38-kb insert and the GA cluster itself is inverted relative to F . fujikuroi ( Figure 4 ) ., Despite some differences in gene content and arrangement in the cluster-flanking regions , the presence of four conserved genes ( FFUJ_14327 , 14328 , 14338 , and 14340 ) in all species/isolates with an intact cluster suggests that significant synteny is conserved in the GFC and F . oxysporum ., Furthermore , an ancient GA cluster was present in the ancestral Fusarium genome before divergence of GFC and F . oxysporum ., This conclusion is supported by phylogenetic analysis that resolved Fusarium GA genes into a single clade that is distinct from GA genes in other fungi ., This result indicates that the GA cluster in all Fusarium species examined likely evolved by vertical inheritance from a common ancestor ( Figure S4 ) ., In addition , previously reported PCR and Southern data as well as genome sequence data analyzed here indicate that partial GA clusters are derived via similar patterns of gene loss in three phylogenetically distinct lineages of Fusarium: GFC , F . oxysporum and F . minscanthi/F ., nisikadoi 8 , 63 ., In all three lineages , partial GA clusters always lack P450-2 , GGS2 , CPK/KS and P450-3 ( Figure 4 ) ., The similar patterns of gene loss in different Fusarium lineages indicate that degeneration of the cluster is not random ., It is not clear what selective pressure ( s ) would drive non-random degeneration of the GA cluster , however , one possibility is that populations of each Fusarium lineage were introduced into an environment ( s ) or specified to a new host where GA production was disadvantageous ., Interestingly , functional GA clusters are also present in Sphaceloma ( Dothideomycetes ) and Phaeospheria ( Dothideomycetes ) 67 , 68 , both being only distantly related to Fusarium ( Sordariomycetes ) ., However , the evolutionary mechanisms by which these fungi acquired GA biosynthetic gene clusters are not yet clear ., There is an increasing number of indications that the presence of homologous SM biosynthetic gene clusters in distantly related fungi can result from horizontal gene transfer ( HGT ) 69 ., One example of this is evidence for HGT of the bikaverin biosynthetic gene cluster from Fusarium to the distantly related fungus Botrytis cinerea 70 , 71 ., Phylogenetic studies showed a broad distribution of the GA gene cluster among the genus Fusarium ., However , nothing is known about the ability of F . circinatum , F . mangiferae , and F . oxysporum to produce GAs ., Therefore , we studied the production of GAs by the different Fusarium strains under four culture conditions that varied in nitrogen availability and pH:, 1 ) nitrogen deficient and acidic ( 6 mM glutamine ) ;, 2 ) nitrogen deficient and alkaline ( 6 mM nitrate ) ;, 3 ) nitrogen sufficient and acidic ( 60 mM glutamine ) ; and, 4 ) nitrogen sufficient and alkaline ( 60 mM nitrate ) ., We also examined five recently sequenced strains of F . oxysporum which have an intact GA biosynthetic gene cluster in contrast to F . oxysporum 4287 ( Figure 4 ) ., Despite the presence of the cluster , GA production was detected only in F . fujikuroi ., Lack of GA production in other species could be caused by a number of factors , including mutations that leave ORFs intact but render enzymes nonfunctional , reduced transcription of GA genes , improper GA transcript processing and/or altered translation ( Tables 3; S5A and S5B ) ., Although GA production was not detected in F . circinatum , production of ent-kaurene , the first committed intermediate in the GA pathway , was detected ( Table 3 ) ., The presence of this metabolite is consistent with the detection of transcripts for CPS/KS , which encodes ent-copalyl diphosphate/ent-kaurene synthase in F . circinatum ( Figure S5 ) ., To determine whether fusaria with a remnant of the GA gene cluster have retained the regulatory mechanisms required for GA production , we transformed F . oxysporum 4287 with a cosmid clone carrying a wild-type copy of the F . fujikuroi GA gene cluster ., As in previous experiments with F . verticillioides 66 , transformants of F . oxysporum 4287 with the F . fujikuroi GA cluster produced GAs at levels similar to those produced by F . fujikuroi IMI58289 ( Table S5B ) ., To explore if plant signals can induce GA gene transcription , we examined the expression of GA genes of Fusarium with an intact GA gene cluster ( e . g . F . mangiferae , F . circinatum and some F . oxysporum ) during infection of maize by qPCR ., No GA gene expression was observed in these fusaria , except for low CPS/KS expression levels in F . mangiferae ( data not shown ) ., We also analyzed the expression of CPS/KS by F . fujikuroi during growth on the preferred host plant rice as compared to the non-preferred host maize ., As expected , significantly higher expression for CPS/KS was observed in rice than in maize ( Figure 5 ) ., These differences in GA gene expression suggest a dependency on specific rice signals as expected for the bakanae fungus ., Although “bakanae” disease was described more than 100 years ago , the role of GAs in pathogenesis of F . fujikuroi on rice is not well understood ., To determine whether GA production is essential for pathogenesis , we compared the ability to infect and invade rice roots between the GA-producing wild type strain ( F . fujikuroi IMI58289 ) and the nonproducing mutant SG139 that is missing the entire GA gene cluster ., Microscopic analysis of infected rice roots revealed that the two strains can equally penetrate the rice root epidermis ., Both strains also show the same apoplastic growth behavior within the parenchyma cells of the epidermis and the cortex ( Figure 6B ) ., However , the total number of successfully invaded symplasts per rice root differed significantly ( Figure 6A , C ) ., While we found 103 events of invasive fungal growth of the wild type inside the symplasts of rice root cells , only seven comparable events by the GA-deficient strain in seven independently analyzed roots were observed ., Based on these results | Introduction, Results/Discussion, Material and Methods | The fungus Fusarium fujikuroi causes “bakanae” disease of rice due to its ability to produce gibberellins ( GAs ) , but it is also known for producing harmful mycotoxins ., However , the genetic capacity for the whole arsenal of natural compounds and their role in the fungus interaction with rice remained unknown ., Here , we present a high-quality genome sequence of F . fujikuroi that was assembled into 12 scaffolds corresponding to the 12 chromosomes described for the fungus ., We used the genome sequence along with ChIP-seq , transcriptome , proteome , and HPLC-FTMS-based metabolome analyses to identify the potential secondary metabolite biosynthetic gene clusters and to examine their regulation in response to nitrogen availability and plant signals ., The results indicate that expression of most but not all gene clusters correlate with proteome and ChIP-seq data ., Comparison of the F . fujikuroi genome to those of six other fusaria revealed that only a small number of gene clusters are conserved among these species , thus providing new insights into the divergence of secondary metabolism in the genus Fusarium ., Noteworthy , GA biosynthetic genes are present in some related species , but GA biosynthesis is limited to F . fujikuroi , suggesting that this provides a selective advantage during infection of the preferred host plant rice ., Among the genome sequences analyzed , one cluster that includes a polyketide synthase gene ( PKS19 ) and another that includes a non-ribosomal peptide synthetase gene ( NRPS31 ) are unique to F . fujikuroi ., The metabolites derived from these clusters were identified by HPLC-FTMS-based analyses of engineered F . fujikuroi strains overexpressing cluster genes ., In planta expression studies suggest a specific role for the PKS19-derived product during rice infection ., Thus , our results indicate that combined comparative genomics and genome-wide experimental analyses identified novel genes and secondary metabolites that contribute to the evolutionary success of F . fujikuroi as a rice pathogen . | Fungi produce numerous “secondary metabolites” ( SMs ) that are not essential for life but can provide an advantage under natural conditions , e . g . in fungal-host interactions ., Here , we conducted the most comprehensive analysis to date of secondary metabolism in fungi using Fusarium fujikuroi ., This fungus causes “bakanae” disease of rice and is best known for its ability to produce gibberellins ( GAs ) ., We show that GA production is limited to F . fujikuroi and provides a selective advantage during infection of the preferred host plant rice ., Generation and analysis of a high-quality de novo F . fujikuroi genome sequence combined with comparisons to six other Fusarium genomes revealed the presence of 45 mostly unknown SM gene clusters ., We provide a broad spectrum of experimental data including epigenetic , transcriptional , proteomic and chemical product analyses under different nitrogen and pH conditions ., Two of the SM clusters ( PKS19 and NRPS31 ) are not present in any other sequenced fungal genome ., In planta expression studies revealed that the otherwise silent PKS19 cluster is induced on rice , but not on maize , suggesting a specific role for the PKS19-derived product during rice infection ., Together , our results demonstrate the tremendous potential of a single fungal species to produce a diversity of SMs that likely contributes to adaptation to environmental changes . | genome-wide association studies, genome expression analysis, microbial metabolism, spectrometric identification of proteins, functional genomics, host-pathogen interaction, microbiology, protein abundance, fungal physiology, gene function, genome sequencing, genome analysis tools, genome databases, fungi, fungal evolution, epigenetics, molecular genetics, sequence analysis, mycology, gene expression, microbial pathogens, comparative genomics, biology, proteomics, pathogenesis, proteomic databases, genetics, genomics | null |
journal.pbio.0060149 | 2,008 | Crystal Structure of the FeS Cluster–Containing Nucleotide Excision Repair Helicase XPD | Nucleotide excision repair ( NER ) is the most versatile DNA repair pathway ., 1–5 ., NER is well known for its ability to remove bulky DNA lesions and is unique in its ability to repair structurally and chemically different substrates , including benzoapyrene-guanine adducts caused by smoking , as well as guanine-cisplatin adducts formed during chemotherapy 6 ., NER is the only repair mechanism in humans that is able to remove photoproducts induced by ultraviolet light ., The phenotypic consequences of defective genes involved in NER are apparent in three severe diseases: xeroderma pigmentosum , Cockayne syndrome , and trichothiodystrophy 1 , 7–10 ., The mechanism of the human NER system , while analogous to the well-characterized bacterial system , is less well understood ., Over 30 proteins have been identified in humans that are critical for mediating the individual steps leading from damage recognition to incision and repair ., However , due to the paucity of specific structural intermediates , the precise role for each protein has not been fully delineated ., NER has been proposed to proceed through either a “bipartite substrate discrimination” or a “multi-partite damage recognition” model 11 , 12 ., It is generally believed that NER is initiated by the combined action of XPC and RAD23B , which recognize a general disruption of Watson-Crick base-pairing created in the vicinity of the damaged nucleotide ., Both proteins are required to recruit the ten-subunit transcription factor TFIIH to this site ., The XPD and XPB proteins are two helicases that are present in TFIIH , and which open the DNA around the lesion in an ATP-dependent fashion ., This is the first catalytic step in this reaction pathway , leading to a conformational change that allows the recruitment of additional NER factors 5 , 13 , 14 ., A second , more important function of the two helicases is damage verification ., Recent data suggest very different roles for XPB and XPD 15 ., The helicase activity of the XPB protein seems to be dispensable; however , its ATPase activity is essential for NER ., This has been interpreted to suggest a wrapping of the DNA around XPB , which leads to an opening of the double-stranded DNA ( dsDNA ) close to the lesion ., This opening allows the correct binding of XPD , which then utilizes its helicase activity to verify the damage and ensures that the backbone distortion is not the result of an unusual DNA sequence ., This process was termed “enzymatic proofreading” and supports the bipartite damage recognition model in which the function of XPC-RAD23B is limited to the observation of a backbone distortion , and XPD is required to verify the damage through its helicase activity 16 , 17 ., Very recently , it has been shown that the XPD protein contains an FeS cluster , which is essential for its function 18 ., However , it is not clear whether the cluster has a structural role or is actively involved in the damage recognition process 19 ., We solved the crystal structure of the XPD protein from Thermoplasma acidophilum , which shares high sequence identity to its eukaryotic homologs , and show that it contains two RecA-like helicase domains ., The XPD protein displays high structural similarity to the bacterial UvrB protein , which is also required for enzymatic proofreading in NER ., Two additional domains emerge from the first helicase domain and form a hole that is sufficient to allow passage of ssDNA ., Furthermore , the structure delineates how different mutations in the protein lead to the human genetic disorders xeroderma pigmentosum , Cockayne syndrome , and trichothiodystrophy ., Two different XPD-related protein sequences from T . acidophilum have been deposited in the National Center for Biotechnology Information ( NCBI ) and the Swiss-Prot databases , respectively ., They differ only with respect to their N-terminus , with one of them containing 19 additional amino acids ., We cloned both constructs and obtained crystals of the shorter protein , which was also active with respect to both its helicase and its ATPase activity ( Figure S1 ) ., The protein crystallized in space group P65 and the asymmetric unit contains one XPD molecule , indicating no higher oligomeric states , which is consistent with size-exclusion chromatography results and an analysis of the model using the PISA server 20 ., The structure was solved by multiwavelength anomalous diffraction ( MAD ) using the anomalous Fe signal of the endogenous FeS cluster in the protein and was refined at 2 . 9 Å resolution to an R-factor of 0 . 209 and Rfree of 0 . 287 ( Table 1 ) ., The current model contains residues 23–507 and 515–615 ( 586 out of 602 residues ) of the XPD construct with residues 20 to 22 , 508 to 514 , and 616 to 620 presumably being disordered ., The structure of the protein can be divided into four distinct domains ., Domain 1 is formed by residues 23–87 , 178–225 , and 366–407 , domain 2 by residues 88–177 , domain 3 by residues 226–365 , and domain 4 by residues 408–615 ( Figure 1A and 1B ) ., The first three domains together with α-helix 22 from domain 4 form a donut-shaped structure containing a hole with a diameter of approximately 13 Å ( Figure 1A ) ., The remainder of domain 4 is positioned in front of the ring without obstructing the hole of the donut ., The overall dimensions of the protein can therefore be divided into the donut with a width and height of 65 Å and 75 Å and a thickness of 29 Å ., At the location of domain 4 , the width of the ring is increased to 45 Å ( Figure 1A and 1B ) ., Domains 1 and 4 represent the “classical” RecA-like fold that is present in all helicases of superfamilies 1 and 2 ( SF1 and SF2 ) 21 ., Both domains share approximately 9% sequence identity and can be superimposed with a root mean square ( rms ) deviation of 2 . 4 Å using 101 Cα-atoms out of 153 from domain 1 , and 201 from domain 4 , respectively ., Both domains display a similar α/β/α sandwich architecture with a central parallel seven-stranded β-sheet surrounded by seven α-helices in domain 1 and a six-stranded β-sheet surrounded by seven α-helices and two 310 helices in domain, 4 . The interface between domains 1 and 4 forms the composite ATP binding site ., Domain 1 contains helicase motifs I , Ia , II , and III , whereas domain 4 harbors helicase motifs IV , V , and VI 22 ( Figure 2 ) ., In the context of the overall XPD structure , domain 1 can be viewed as the core domain surrounded by the other three domains ., Domains 2 and 3 are insertions , which emerge from domain 1 ., Domain 2 is inserted between β-strands β3 and β4 , while domain 3 is inserted between α-helices α11 and α17 ., Domain 4 is situated adjacent to domain 1 within the linear protein sequence ( Figures 1 and 2 ) ., Notably , the closest related homolog of the full-length XPD structure as revealed by similarity searches 23 was UvrB 24 , which has been proposed to be the prokaryotic equivalent to XPD and utilizes its helicase activity for damage verification ., XPD and UvrB can be superimposed with an rms deviation of 2 . 6 Å using 254 aligned Cα atoms out of 588 and 505 residues , respectively ., The match is mostly mediated via the two helicase domains , whereas the other domains have no significant structural similarity to each other ( Figure S2 ) ., In addition , we compared XPD to Hel308 and NS3 , two SF2 helicases ( Figure S3 ) ., The superposition shows that structural similarities are again mainly confined to the RecA domains , whereas the auxiliary domains are highly variable ., Hel308 and NS3 have been structurally characterized with DNA substrates , and both represent a closed state of the helicase framework 25 , 26 ., No adenosine nucleotide is bound in these structures , but they are presumed to be in a preprocessive state that only requires ATP binding to reach the processive state 25 ., Using the first RecA domain ( domain 1 ) as a reference point for superposition with either Hel308 or NS3 , XPD assumes a more open state that is mainly mediated via a rotation of the second RecA domain ( domain 4 ) of about 30° or 16° , respectively , relative to domain 1 ( Figure S3C ) ., The composite ATP binding site is located near the hinge region when compared to the closed state of the other two helicases ., Our structure may therefore reflect a ground state of XPD prior to nucleotide and/or DNA binding that underlines the conformational flexibility necessary to translate chemical energy into motion ., The first insertion into helicase domain 1 is of particular interest since it contains an FeS cluster , a unique feature among the XPD-like SF2 helicases 18 ., Domain 2 displays an exclusively α-helical architecture consisting of six α-helices and one 310 helix that surround the central 4Fe4S cluster ( Figure 1A , 1C , and 1D ) ., The FeS cluster is coordinated by four cysteines , consistent with the coordination typically observed in 4Fe4S clusters , and all four cysteines display continuous connectivity in the electron density maps ( Figure 1C ) ., A comparison of the B-factors between the 4Fe4S cluster and the surrounding protein residues reveals similar values , indicating full occupancy of the cluster ., Three of the coordinating cysteines ( Cys92 , Cys128 , and Cys164 ) are located in loops , whereas the fourth cysteine , Cys113 , is located in a central position within α-helix 5 ( Figures 1 and 2 ) ., Surprisingly , it was shown that the helicase activity is not affected when Cys102 or Cys105 in Sulfolobus acidocaldarius or Ferroplasma acidarmanus XPD , respectively , were mutated to serine 18 , 19 ., These two residues correspond to Cys113 in our structure ., Pugh et al . 19 suggested that the aerobically purified protein most likely contained a degraded 3Fe4S cluster , which is still functional , but presumably a 4Fe4S cluster is present in vivo ., When any of the remaining cysteines is mutated to serine , however , the helicase activity of the enzyme is abrogated 18 , 19 ., The cluster is further stabilized predominantly by hydrophobic interactions ., Residues Arg88 and Tyr166 , which shield the cluster from solvent exposure , are strictly conserved and face towards a pronounced solvent-exposed groove that is formed by α-helices 5 and 8 from domain 2 and α-helix 10 from domain 1 at the back of the protein ( Figures 1D and 3 ) ., The closest structural homolog for this domain identified by a secondary structure matching search 23 revealed c-myb , a transcription factor that does not contain an FeS cluster 27 ., Although c-myb superimposes with a relatively low Q-score of 0 . 15 ( Figure S2B ) , it is notable that the structural similarity is restricted to the DNA binding interface of c-myb ., c-Myb superimposes well with α-helices 5 , 6 , 7 , and 8 of domain 2 , of which helices 5 and 8 coincide with the DNA binding interface of c-myb ( Figure S2B ) ., In the XPD structure , these helices form part of the groove mentioned above , thus indicating a possible DNA binding site ., This is further emphasized by the basic nature of this groove ( Figure 3 ) , which is composed of several highly conserved , positively charged residues ., However , no significant sequence conservation can be identified between c-myb and XPD in the structurally homologous regions ., Domain 3 consists mostly of extended α-helices ( α-helices 12 , 13 , 14 , 15 , and 16 ) and four additional antiparallel β-strands ( β6 , β7 , β8 , β9 ) building a “β-bridge” to domain 1 ., The β-bridge is further stabilized by α22 , an α-helical extension located between β15 and α23 of domain, 4 . The helices can be grouped into two α-helical hairpins that stack with each other , with one hairpin containing α12 and α13 , and the second containing α15 and α16 , which is slightly distorted by the insertion of a loop ., The two helical hairpins intersect at an angle of approximately 60° and create an extensive hydrophobic core between them ., Helix α14 is situated in the V-shaped opening that is formed by the tilt between the two α-helical bundles ( Figure 1A and 1B ) ., Similarity searches revealed no significant hit , indicating that this fold has not been encountered previously ., The ring of the donut is closed at its thinnest side via an interface between domains 2 and 3 that has a buried surface area of approximately 620 Å2 ., The interface is formed by 17 residues from each domain , which display little sequence conservation apart from Phe326 , which is always an aromatic residue ( Figure 2 ) ., Most of the interactions are hydrophobic in character , additionally four salt bridges can be observed between Lys323/Asp99 , Arg335/Glu103 , Arg235/Glu103 , and Glu315/Lys111 ., Since the presence of the FeS cluster is essential for helicase activity on dsDNA 18 , 19 , it prompted us to investigate the only other structurally characterized DNA-binding proteins with such a feature , the base excision repair proteins , MutY and Endo III 28 , 29 , with a focus on the first because a structure of a MutY-DNA complex has been described 28 ., For MutY , it was shown that its FeS cluster is required for enzymatic activity and DNA binding 30 ., The XPD protein contains a loop motif in the FeS cluster domain with a high density of positively charged residues similar to the FeS cluster loop motif ( FCL ) in MutY 31 ., The superposition of the XPD and MutY FeS cluster domains ( Figure 4 ) reveals a similar orientation of two conserved arginines ( Arg88 in XPD and Arg153 in MutY ) ., In MutY , it was shown that a neighboring conserved arginine , Arg149 , is perfectly positioned for an interaction with the DNA backbone , and bridges the distance between Arg153 and the DNA 32 ., Based on the similarity to MutY , Arg88 in XPD may fulfill a similar function ., Furthermore , the position of Arg88 at the surface of a pocket where DNA recognition could take place supports the idea proposed by Lukianova et al . that the FeS cluster plays an important role in arranging the residues of the FCL motif for DNA binding 31 ., For MutY , it was shown that the redox properties of the 4Fe-4S2+ cluster are modulated by the presence of DNA 33 ., DNA-binding activates the cluster and facilitates oxidation 34 ., Boal et al . proposed a model for DNA-mediated charge transfer ( CT ) in DNA repair in which one electron is transferred from the cluster to the DNA ., In this model , the CT acts as an initial sorting mechanism , enabling a rapid scanning of undamaged regions by several glycosylase molecules , so that they are able to relocate themselves onto sites near the damage 34 ., In NER , an analogous scanning mechanism seems unlikely , but a change in oxidation state of the 4Fe4S cluster upon DNA binding and as part of the damage verification step may be required , thus suggesting a functional role for the 4Fe4S cluster and not just a structural role ., This hypothesis is further supported by site-directed mutagenesis studies that demonstrate that single mutations of three of the four 4Fe4S cluster coordinating cysteines to serine lead to a loss of the 4Fe4S cluster , and abrogate helicase activity , but retain a correctly folded protein that is still able to translate along ssDNA 18 , 19 ., The XPD protein is a member of the SF2 helicases ., To obtain insight into the DNA binding mode of XPD , we calculated the electrostatic surface potential of the protein and searched for conserved solvent-exposed amino acids ( Figures 2 , 3 , 5 , and 6 ) ., The surface potential indicates a positively charged path for dsDNA along domain 4 , leading towards a highly conserved groove along domain 4 and domain 1 , which provides sufficient space for ssDNA and directs the DNA towards the hole formed by domains 1–3 ., The dsDNA requires separation into ssDNA prior to entering the groove ., Recently , the structure of the SF2 helicase Hel308 was determined in complex with DNA , and a prominent β-hairpin in the second helicase domain was identified that is responsible for initial strand separation 25 ., It was proposed that this β-hairpin could be a general feature of SF2 helicases ., In XPD however , this “wedge” is formed more likely by an α-helical extension in domain 4 ( Figure 5 ) ., Despite the difference in secondary structure , it is located between helicase motifs V and VI as demonstrated for Hel308 and proposed for NS3 25 ( Figure S3 ) ., Two α-helices in XPD , α22 and α23 , form two walls of the wedge and extend farther out towards the solvent compared to other helicases such as UvrD and PcrA 35 , 36 ., We propose that the tip of the wedge composed of residues in the loop between α22 and α23 separates the two DNA strands ., The last two turns of α22 and the first two helical turns of α23 contain several aromatic amino acids , which could stabilize the separated DNA strands in a fashion similar to that observed for Hel308 ., On one side of this wedge , the highly conserved residues Tyr540 and Tyr545 are oriented with their side chains pointing towards the solvent where they could easily form stacking interactions with the exposed bases of ssDNA ., These stacking interactions can then be continued by additional solvent-exposed aromatic residues , such as Tyr23 , leading the ssDNA along the back of the protein to a position where the two strands meet again to reform dsDNA ., Although exposed aromatic residues are also present on the other side of the wedge , their degree of conservation is relatively low ., In our structure , Phe538 and Tyr425 could both stack against the bases in ssDNA ., However , only Tyr425 is conserved , whereas Phe538 is replaced by a leucine in eukaryotic XPDs ., This substitution appears to be compensated by the occurrence of Phe651 in human XPD , which substitutes for Ser552 in T . acidophilum XPD; and due to the close spatial proximity of the two side chains , they would assume similar positions ( Figure 5 ) ., Consequently , there is one phenylalanine available that would represent the required stacking partner ., In addition , several highly conserved , positively charged residues , such as Lys583 and Lys424 , apparently define the path for the second strand leading into the groove described above and from there continues through the central hole ( Figures 5 and 6 ) ., Despite the fact that we crystallized the protein in the absence of DNA and phosphate buffer , we identified significant peaks with heights of more than 2 . 5 times the rms deviation in difference electron density maps ( Figure, 6 ) that are spaced by approximately 6 . 5 Å , as well as slightly longer distances and cannot be explained by the protein model ., Since the distance between phosphates in ssDNA is approximately 6 . 4 Å , it is therefore very tempting to speculate that some DNA remains bound to the protein during purification and gives rise to these residual electron density features ., Further support for this hypothesis is provided by the superposition of our structure with NS3 helicase in complex with ssDNA 26 ( Figure S3 ) ., Based on this superposition , we have built a model for a ssDNA binding mode ( Figure, 6 ) in which the extension of the ssDNA towards the hole positions three of the phosphates into the residual electron density peaks ., The postulated DNA route passes by another highly conserved surface feature in XPD , a narrow pocket that is formed by the strictly conserved Arg88 and Tyr166 on one side and Tyr185 on the other side , and is located in the wall of the central hole , directly adjacent to the 4Fe4S cluster ( Figure 6 ) ., The dimensions and shape of this pocket are ideally suited to accommodate a nonmodified purine or pyrimidine base , which would be held in place through van der Waals interactions with the residues mentioned above ., Due to its location , this surface feature would allow a direct coupling between the FeS cluster and a readout of the DNA ., This pocket is reminiscent of the pocket for the flipped-out base that was observed in the UvrB-DNA structure 24 ., Initial DNA distortion recognition in eukaryotes is achieved through the combined action of XPC and RAD23B 37 ., It was shown that with the recruitment of TFIIH to the site of damage , the helicase XPD is required for proofreading , whereas XPB fulfills a structural role 15 ., In the absence of an XPD-DNA complex containing a lesion , the process of proofreading remains highly speculative ., The structure of XPD clearly reveals structural homology to its prokaryotic homolog UvrB ., In UvrB , it was shown that a β-hairpin , which emerges from the first helicase domain , is critical for damage recognition 38–40 ., However , despite the structural similarity between the two proteins , XPD does not contain a corresponding feature ., In our model of the XPD-DNA complex ( Figure 6 ) , we propose that one of the DNA strands passes through the central hole , which is formed by domains 1–3 ., According to studies by Naegeli et al . 41 , this would be the translocating strand , which contains the lesion , and leads to a stalled protein-DNA complex ., The dimension of this hole , with a diameter of 13 Å , however , is most likely too big to provide a trap for damaged DNA ., One possible candidate for the “analysis” of each base with respect to their correct structure would be the narrow pocket in the wall of the central hole described above ., The size of this pocket suggests that only nondamaged bases could be accommodated , whereas a bulky DNA substrate would be excluded ., This pocket is also an attractive candidate for the damage recognition process due to its close proximity to the 4Fe4S cluster and the involvement of Arg112 of human XPD ( Arg88 in our structure ) , which has been shown to cause trichothiodystrophy when mutated to histidine ., TFIIH in humans is not only required for DNA repair , but is also essential for transcription 42 ., XPD represents one of the ten protein subunits of TFIIH and interacts tightly with the N-terminal 236 amino acids of p44 ., This interaction results in a 10-fold increase in its helicase activity 43 ., It has been shown that the helicase function of XPD is not required for transcription , but is essential for NER 44 ., On the other hand , XPD is required to stabilize the interaction between the core TFIIH complex , which contains seven subunits , and the cdk-activating kinase ( CAK ) subcomplex , consisting of the remaining three subunits 45 , 46 ., Mutations in XPD ( Figure 2 and, 7 ) can therefore lead to three different effects ., The first class of mutations affects the activity of the protein directly , whereas the second group can lead to impaired interactions with p44 , thus affecting its own activity in an indirect way ., The third group of mutations may lead to a destabilization of TFIIH , thereby reducing overall transcriptional activity ., Based on our structure the effects of several point mutations leading to xeroderma pigmentosum , Cockayne syndrome , or trichothiodystrophy can be explained ( Figure 7 ) ., Point mutations associated with xeroderma pigmentosum , such as G47R , D234N , and R666W , are located in helicase motifs I , II , and VI , respectively , and impair the ability to bind and hydrolyze ATP , thus inactivating the enzyme; however , point mutations within other regions have quite distinct effects ., Arg112 ( Arg88 in T . acidophilum ) is located in the FeS cluster domain and is in direct contact with the cluster ., A mutation of this residue to histidine has been identified in several TTD patients 47 ., Analysis of the equivalent residue in S . acidocaldarius XPD abolished its helicase activity 18 ., Arg88 is located in close vicinity to Cys113 one of the Fe-ligands , and shields the cluster , with its long side chain , from solvent ., It is the first residue in a short α-helix , α 3 , which together with the opposite side of the helix forms one wall of the hole where ssDNA most likely passes through ( Figure 6 ) ., The proposed role for Arg88 in analogy to MutY as described above may be accomplished by Arg112 in the human XPD protein and a mutation to histidine , as observed in trichothiodystrophy patients , could prevent this interaction , thus reducing the affinity of the protein to the DNA ., However , the exact role of the 4Fe4S cluster , whether it is involved directly in the recognition process or the translocation along the DNA , remains speculative at this point ., It is interesting to note that Egly and coworkers have shown this variant in human XPD to be completely devoid of helicase activity 48 ., The effects of the C259Y variant can also be readily explained ., This cysteine is replaced in T . acidophilum by another small hydrophobic residue , Ala236 , in α-helix 12 , which points into the hydrophobic core within domain, 3 . This core stabilizes the relative position of the four α-helices within this domain as outlined above ., Replacing this small hydrophobic residue with a tyrosine leads to severe steric clashes within this core and thereby destabilizes the entire domain ., The two mutants Y542C and G602D are very close to each other in the structure ., Tyr458 ( Tyr542 in human XPD ) is located at the beginning of α-helix 20 in domain 4 and forms hydrophobic interactions with another strictly conserved residue , Val501 ( Val599 in human XPD ) , in a neighboring β-strand ., Replacing the tyrosine with a cysteine would weaken the interactions between this helix-strand pair ., Gly504 ( Gly602 in the human enzyme ) is positioned between β-strands 14 and 15 in domain, 4 . If this residue were to be replaced by a larger residue , it would point towards Tyr458 ( Tyr542 in human XPD ) and would thereby interfere with this side chain ., The remaining four mutations D673G , G675R , D681N , and R683W/Q , although causing different diseases , are all clustered closely together towards the C-terminal end of the human XPD protein and correspond to residues Asp574 , Gly576 , Asp582 , and Arg584 in T . acidophilum XPD , respectively ., It has been speculated that residues at the C-terminal end of human XPD interfere with p44 binding , thus leading to an inability to stimulate the helicase activity of XPD 43 ., Of these four mutations , only G675R was analyzed with respect to its ability to interact with p44 , and it was shown that the interaction was severely diminished 43 ., All other analyzed disease mutants are located further towards the C-terminal end of human XPD where our archaeal XPD contains no corresponding residues , which is not unexpected since T . acidophilum does not contain a p44 homolog ., T . acidophilum Asp574 , Gly576 , Asp582 , and Arg584 are located in domain 4 and fulfill important structural roles ., Asp574 forms interactions with the strictly conserved Arg570 ( Arg669 in the human enzyme ) , which is located at the end of helix 24 , and thereby stabilizes the transition from the helix to the following β-strand 16 ., Gly576 is positioned in this β-strand and points towards two hydrophobic residues , Leu568 and Ile569 ( Ala667 and Ile668 in human XPD ) in α24 ., A mutation of Gly675 to an arginine would push the entire helix away from the β-strand and thereby destabilize the integrity of the domain ., Asp582 is located directly behind β16 and forms tight interactions with the strictly conserved Arg584 ( Arg683 in human XPD ) , and the latter forms additional interactions with Asp426 and Phe527 ( Glu509 and Tyr625 in human XPD ) , two highly conserved residues ., The point mutations at the C-terminal end of XPD thus clearly play important structural roles , and any of the four mutations would interfere with the fold of domain 4 , which could also diminish the interactions with p44 ., According to our protein–DNA model , however , T . acidophilum Arg584 ( Arg683 in human XPD ) also plays an important role in DNA binding and is one of the residues that may bind to the DNA close to the double-strand/single-strand junction ., Replacing this positively charged residue with either a glutamine or tryptophan may severely interfere with DNA binding and thereby lead to the disease phenotype ., The crystal structure of XPD from T . acidophilum revealed that the protein contains two RecA-like helicase domains and two additional domains that emerge from the first helicase domain ., Surprisingly , the first three domains form a donut-shaped structure and a protein–DNA model is proposed in which one of the ssDNA strands passes through this central hole in close spatial proximity to the 4Fe4S cluster in the second domain ., The high sequence homology to eukaryotic XPDs allowed the analysis of mutations leading to one of the three severe diseases xeroderma pigmentosum , Cockayne syndrome , or trichothiodystrophy and provides the basis for a more detailed analysis to understand the combined action of the helicase and the 4Fe4S cluster to achieve damage verification within the NER repair cascade ., The genes encoding two XPDs from T . acidophilum with variable N-termini ( residues 1–622 and 23–622 ) were cloned into the pET16b vector ( Novagen ) using the NdeI and XhoI restriction sites ., XPD was expressed as an N-terminally His-tagged protein in Escherichia coli BL21-CodonPlus ( DE3 ) -RIL cells ( Stratagene ) by induction with 0 . 1 mM isopropyl-β-thiogalactoside at 14 °C for 18 h ., The protein was purified by metal affinity chromatography ( Ni-NTA; Invitrogen ) followed by size-exclusion chromatography ( HiLoad 26/60 Superdex 200 prep grade; GE Healthcare ) in 20 mM Tris ( pH 8 ) and 500 mM NaCl ., The protein was concentrated to 5 mg/ml based on a molar absorption coefficient of 65 , 140 M−1 cm−1 ., For construction of the 5′ overhang DNA substrate , a 25-mer oligonucleotide ( MDJ1 , 5′-GACTACGTACTGTTACGGCTCCATC-3′ ) was 5 end labeled and annealed to the 3 end of a 50-mer oligonucleotide ( NDB , GCAGATCTGGCCTGATTGCGGTAGAGATGGAGCCGTAACAGTACGTAGTC ) ., The helicase assay was carried out as described by 18 with slight modifications ., Briefly , the reactions ( 10 μl ) were incubated at room temperature in 20 mM MES ( pH 6 . 5 ) , 1 mM DTT , 0 . 1 mg/ml BSA , 5 mM MgCl2 , 10 nM 32P-labeled DNA substrate , and 500 nM XPD for 10 min ., The reactions were started by the addition of 3 mM ATP and transferred to a 45 °C water bath ., After the specified time , 20 μl of stop solution ( 10 mM Tris-HCl pH8 . 5 mM EDTA , 5 μM cold competitor MDJ1 , 0 . 5% SDS , and 1 mg/ml proteinase K ) was added and incubated for 15 min at 37 °C to allow proteinase K digestion ., Samples were separated on a native 10% acrylamide:bis TBE gel for 1 h at 100 V . XPD crystals were grown by vapor diffusion in hanging drops containing equal volumes of protein in 20 mM Tris/HCl ( pH 8 . 0 ) and 500 mM NaCl at a concentration of 5 mg/ml , and a reservoir solution consisting of 200 mM MgCl2 , 100 mM Hepes ( pH 8 ) , and 5%–10% PEG 400 equilibrated against the reservoir solution ., Crystals grew within 7 d at 20 °C to a maximum size of 100 × 50 × 50 μm3 ., Prior to data collection , the crystals were cryocooled by sequential transfer into mother liquor containing increasing amounts of glycerol in 5% steps to a final concentration of 30% ., The crystals were flash cooled in liquid nitrogen , and data collection was performed at 100 K . Data sets were collected at beamline BM14 ( European Synchrotron Radiation Facility ESRF ) at wavelengths of 1 . 0 Å , 1 . 7 Å , 1 . 7367 Å , and 1 . 7419 Å ., All data were indexed and processed using Moslfm and Scala 49 , 50 ., The crystals belong to space group P65 with unit cell dimensions of a = b = 78 . 9 Å , c = 174 . 0 Å ., Structure solution was achieved utilizing the anomalous signal of the endogenous Fe belonging to the 4Fe4S cluster by MAD data collection at the Fe edge ., The peak and inflection datasets were obtained from one crystal and were merged with a highly isomorphous dataset collected at the remote wavelength ., The Fe sites were located using ShelxD 51 , and phase improvement was achieved with Sharp 52 ., Substructure solution and refinement was carried out at 4 Å resolution , and the 4Fe4S cluster was treated as a “super” atom for phasing ., The initial maps were subj | Introduction, Results/Discussion, Materials and Methods, Supporting Information | DNA damage recognition by the nucleotide excision repair pathway requires an initial step identifying helical distortions in the DNA and a proofreading step verifying the presence of a lesion ., This proofreading step is accomplished in eukaryotes by the TFIIH complex ., The critical damage recognition component of TFIIH is the XPD protein , a DNA helicase that unwinds DNA and identifies the damage ., Here , we describe the crystal structure of an archaeal XPD protein with high sequence identity to the human XPD protein that reveals how the structural helicase framework is combined with additional elements for strand separation and DNA scanning ., Two RecA-like helicase domains are complemented by a 4Fe4S cluster domain , which has been implicated in damage recognition , and an α-helical domain ., The first helicase domain together with the helical and 4Fe4S-cluster–containing domains form a central hole with a diameter sufficient in size to allow passage of a single stranded DNA ., Based on our results , we suggest a model of how DNA is bound to the XPD protein , and can rationalize several of the mutations in the human XPD gene that lead to one of three severe diseases , xeroderma pigmentosum , Cockayne syndrome , and trichothiodystrophy . | Preserving the structural integrity of DNA , and hence the genetic information stored in this molecule , is essential for cellular survival ., It is estimated that the DNA in each human cell acquires about 104 lesions per day ., Consequently , efficient DNA repair mechanisms have evolved to protect the genome ., One of these DNA repair mechanisms , nucleotide excision repair ( NER ) , is present in all organisms and is unique in its ability to repair a broad range of damage ., In humans , NER is the major repair mechanism protecting DNA from damage induced by ultraviolet light ., Defects in the genes and proteins responsible for NER can lead to one of three severe diseases: xeroderma pigmentosum , Cockayne syndrome , and trichothiodystrophy ., The XPD protein is one of the key components of a ten-protein complex and is essential to initiate NER ., In particular , the XPD protein verifies the presence of damage to the DNA and thereby allows DNA repair to proceed ., We have solved the 3-dimensional structure of the XPD protein , and show how XPD has assembled several domains to form a donut-shaped molecule , which is able to separate two DNA strands and scan the DNA for damage ., The structure also helps to explain why some of the mutations that have been identified in humans are associated with disease . | biochemistry | The structure of the DNA repair protein XPD provides insights into how the protein binds and recognizes damaged DNA and how mutations inXPD disrupt its function and lead to disease. |
journal.pgen.1007098 | 2,017 | Selection for long and short sleep duration in Drosophila melanogaster reveals the complex genetic network underlying natural variation in sleep | Sleep remains a classic enigma in biology ., Intense study has only begun to reveal the physiological needs that sleep might satisfy 1 , 2 ., One potential function of sleep is to conserve resources 3 , 4 ., Sleep may increase protein synthesis 5 or downscale wake-active synapses 6 ., Physical remodeling in the brain during sleep may alter brain plasticity and enable the consolidation of memories 6 , 7 ., Metabolic wastes that accumulate during waking may also be eliminated during sleep 8 ., If some or all of these activities are accomplished during sleep , it would ensure the balance of proper energetic resources and avoid the accumulation of waste products ., Another possibility is that sleep is crucial for proper development ., Babies and young infants spend far more time asleep than their adult counterparts 9 , and interfering with sleep in young animals disrupts critical fitness and cognitive behaviors in adults 10 , 11 ., These observations suggest that sleep might be a fundamental property of local neuronal physiology 12 , and the need to sleep is established during development with the formation of neuronal structures ., Sleep-like behaviors are widely conserved among species 1 , 2 , 13–15 , but the type and amount of sleep differs across species 14–16 ., Sleep duration is also variable within a species 17 , 18 , with related individuals having more similar sleep than unrelated individuals 19–27 ., This indicates that sleep duration is under at least partial genetic control 19–28 ., Yet sleep parameters can also vary considerably within an individual at different times 29–31 ., This variation may be driven by ecological demands ., For example , during periods of migration over the ocean , frigate birds sleep less often and less intensely 32 ., Male pectoral sandpipers sleep little during the competitive mating season 33 , and those that sleep less sleep more intensely 34 ., Cetacean mothers and their newborn calves may also have reduced sleep with no apparent effects of sleep loss 35 , 36 , though these findings are controversial due to the challenges of observing sleep accurately in these mammals 37 , 38 ., In addition , night sleep and sunrise anticipation vary with latitude in flies , suggesting that they are modified by the environment 39 ., Taken together , these observations suggest that sleep exhibits environmental plasticity 40; that is , sleep can be modified when ecological demands require it ., Yet environmental plasticity has an underlying genetic component 41; thus , genotype influences the amount of adaptation that is possible ., Here we wanted to determine how far night sleep duration could be driven up or down in constant environmental conditions using a combination of naturally occurring alleles , and to identify allelic variants responsible for the changes ., Previous work has demonstrated the value and utility of D . melanogaster as a model for mammalian sleep 42 , 43 ., The use of flies as a model has made several large-scale mutagenesis and gene knockdown screens 44–51 , genome-wide association mapping 18 , quantitative trait transcript mapping 52 , and gene expression profiling 53–55 possible , identifying unprecedented numbers of novel candidate genes putatively involved in sleep ., The potential to perturb sleep duration to high or low extremes may be greater in D . melanogaster , which lacks the genomic redundancy seen in mammals ., Indeed , extremely low sleep duration has been observed in flies with single mutations in Shaker ( Sh ) , sleepless ( sss ) , insomniac ( inc ) , and nicotinic Acetylcholine Receptor α4 ( nAChRα4 ) 44 , 45 , 50 , 51 ., The short generation time of flies makes it possible to combine artificial selection or laboratory evolution with whole-genome sequencing to understand the genetic basis of complex traits ., Such a strategy has been used to explore adaptation to different environments 56–60 , male courtship song 61 , body size variation 62 , accelerated development 63 , viral adaptation 64 , and food consumption 65 ., Artificial selection for a combination of reduced sleep duration , increased sleep latency , and increased activity produced flies with behavioral phenotypes mimicking insomnia 66 ., We have applied this strategy to drive night sleep duration to long and short extremes and to examine the underlying genomic changes ., In the final generation of selection , there was a pronounced difference in night sleep duration , 9 . 97 hours on average , between the longest-sleeping and shortest-sleeping populations ., There was some indication that flies selected for short sleep had perturbed circadian rhythms as well , while flies selected for long sleep remained diurnal ., Selection for long or short night sleep altered several other sleep traits such as day sleep duration and night average bout length ., Both long- and short-sleepers did not respond to a mild sleep deprivation stimulus with increased recovery sleep , suggesting that either the sleep homeostat was altered , or that increased sleep intensity compensated for the loss of sleep ., Long and short night sleepers had normal lifespan and egg-to-adult viability , suggesting that there is little physiological consequence to being a long or short sleeper ., Yet fewer animals from each population ( including the control populations ) survived sleep monitoring with each successive generation of selection , indicating that either inbreeding depression or stress sensitivity might limit how far up or down sleep can be driven ., DNA sequence from each selection population over seven generations of selection revealed genome-wide changes the underlying allele frequencies , with thousands of polymorphisms significantly changing in frequency between any two generations of selection ., However , regressing allele frequency changes across generations and accounting for potential effects of random genetic drift reduced the number of candidate polymorphisms to 126 ., These polymorphisms are located within ± 1 kb of 80 candidate genes , two of which overlapped with candidate genes for sleep duration in a previous genome-wide study of sleep 18 ., Candidate genes mapped to classic developmental and signaling pathways , and we verified candidate genes and regions by testing mutations and chromosomal deficiencies ., Connecting these genes to known genetic interactions suggest that they may impact a larger network of genes controlling sleep duration ., To determine how far night sleep duration can be driven up or down , we applied an artificial selection protocol to flies from the Sleep Advanced Intercross Panel ( SAIP ) constructed from the most extreme long- and short-sleeping lines of the Drosophila Genetic Reference Panel ( DGRP ) ., Sleep characteristics were uniform in the outbred population ( S1 Fig ) ., We selected two replicate populations for long night sleep , two for short night sleep , and two populations were maintained as unselected controls ., Sleep was measured in 100 virgin males and 100 virgin females of each population each generation ., The 25% most extreme long ( short ) sleepers were chosen as parents for the next generation of the long ( short ) sleeping populations ., Control populations were maintained by choosing 25% of the males and females at random to be parents for the next generation ., Night sleep duration , defined as sleep during the lights-off period , ranges from 0 to 12 hours ( 720 minutes ) ., Flies responded rapidly and dramatically to 13 generations of artificial selection ( Fig 1A; P = 0 . 0002; S1 and S2 Tables ) ., Night sleep in the short-sleeping populations was reduced to 111 . 9 ± 10 . 74 minutes ( replicate, 1 ) and 54 . 8 ± 5 . 66 minutes ( replicate, 2 ) by generation 13 ., In contrast , night sleep in the long-sleeping populations was increased to 685 . 0 ± 3 . 35 ( replicate, 1 ) and 678 . 5 ± 3 . 46 minutes ( replicate, 2 ) in the same generation ., Night sleep differed by 598 . 4 minutes ( 9 . 97 hours ) on average between long sleepers and short sleepers ., The phenotypic response was moderately asymmetrical in the direction of decreased night sleep ( P = 0 . 0344; Fig 1A ) ., Unselected control populations averaged 495 . 9 ± 11 . 71 ( replicate, 1 ) and 364 . 9 ± 11 . 99 ( replicate, 2 ) minutes of night sleep at generation 13 and were not significantly different from night sleep in the outbred population prior to selection ( Fig 1A; S3 Table ) ., The artificial selection procedure was equally effective in both males and females across all generations ( S1 Table ) , though some sex-specific differences were observed for separate generations ( S2 Table ) ., Females were more responsive than males to selection in generations 1 , 3 , 4 , and 6 , increasing the differential between long and short night sleep by as much as 72 minutes more than males ., We modeled the change in variability in sleep among individuals over time as the coefficient of environmental variation ( CVE ) because artificial selection , which uses only a subset of each population as parents for the next generation , tends to reduce phenotypic variance 67 ., Interestingly , night sleep CVE had a significant correlated response to selection for night sleep ( Fig 1B , P <0 . 0001 ) , increasing in flies selected for short night sleep and decreasing in flies selected for long night sleep ., The estimated realized heritabilities h2 , which indicate the degree to which the animals responded to the selection procedure , were relatively high for long-sleepers 65 , 68–70; h2 = 0 . 310 ± 0 . 022 and h2 = 0 . 238 ± 0 . 032 ( all P <0 . 0001 ) for replicates 1 and 2 , respectively ( Fig 1C ) ., For short sleepers , the realized heritabilities were h2 = 0 . 179 ± 0 . 026 and h2 = 0 . 215 ± 0 . 017 ( all P <0 . 0001 ) ( Fig 1D ) ., In addition , the regression of the control after thirteen generations of breeding with random parents was not significant , {-0 . 108 ± 0 . 312 ( P = 0 . 7368 ) and -0 . 271 ± 0 . 206 ( P = 0 . 2161 ) for replicates 1 and 2 ( Fig 1E ) } , suggesting that inbreeding depression did not impact these populations 67 ., Thus , the outbred population , which was derived from DGRP lines with the largest mean differences in night sleep duration responded rapidly to artificial selection for long or short night sleep ., This heritable response indicates that these populations will be informative for identifying genes and pathways involved in night sleep duration ., Many different characteristics of sleep are at least partially genetically correlated with night sleep duration 18 , 52 , 71 , suggesting that they share some of the same genetic architecture ., If selection acts on genes common to these traits , then these traits might also react to artificial selection for long or short night sleep , which is known as a correlated response to selection 67 ., We found that day sleep duration ( P = 0 . 0248 ) , night average bout length ( P = 0 . 0358 ) , day bout number ( P = 0 . 0121 ) , and sleep latency ( P = 0 . 0005 ) responded to selection for long or short night sleep , even though we did not select for these changes ( Fig 2A , 2C , 2E and 2G; S1 Table ) ., Day sleep , night average bout length , and day bout number responded in the same direction as selection for night sleep: for instance , selection for longer night sleep resulted in longer day sleep , longer night average bout length , and greater numbers of day bouts ., Sleep latency , the amount of time to the first sleep bout after lights are turned off , responded in the opposite direction to selection ., Specifically , selection for short night sleep resulted in very long sleep latencies , which might be anticipated if a shift in circadian behavior was also present ( see below ) ., Night bout number , day average bout length , and waking activity did not exhibit a significant correlated response to selection for night sleep , suggesting that the genes underlying these traits were unaffected by selection ( S2 Fig ) ., These trends were generally true for each generation of selection when considered separately , though we observed transient and sometimes sex-specific correlated responses of day average bout length ( over 4 generations ) and waking activity ( in one generation ) with selection ( S2 Table ) ., In addition , day sleep CVE ( P < 0 . 0001 ) , night average bout length CVE ( P < 0 . 0001 ) , day bout number CVE ( P <0 . 0001 ) , and sleep latency CVE ( P = 0 . 0401 ) had a significant correlated response to selection for night sleep ( Fig 2B , 2D , 2F and 2H; S4 Table ) , while night bout number CVE , day average bout length CVE , and waking activity CVE did not ( S2B , S2D and S2F Fig; S4 Table ) ., Thus , many parameters describing sleep architecture responded to selection for long or short night sleep duration ., Notably , night bout number was previously shown to be negatively correlated with night sleep 18 , 71 , but it did not respond to the selection procedure in this experiment ., Overall , many sleep traits were altered by artificial selection for night sleep , suggesting a shared genetic architecture ., We examined the selection populations to determine whether changes in night sleep duration extended to sleep architecture , i . e . , the overall distribution of sleep bouts ., We analyzed sleep architecture for each sex separately as characteristic sex-specific differences are well-known in flies 18 , 72–75 ., Males tend to have pronounced periods of high activity during light-dark transitions and a long siesta during the day , while females have a more uniform distribution of sleep and activity during the day 76 ., We plotted the percentages of flies that were sleeping , awake , or in a transient pause ( 1 to 4 minutes of immobility ) during each minute of the day ( Fig 3 and S3 Fig ) ., Control flies behaved as expected: both males and females were more likely to be asleep during the first third of the night 42 , and males were more likely to be asleep during the day than females ( Fig 3A and 3B ) ., The same pattern is evident in the long-sleeper populations , except that a much greater percentage of flies were likely to be sleeping during the night; most males and females slept right up to the lights-on period ( Fig 3C and 3D ) ., Sleep in female short sleepers , however , did not exhibit these patterns; instead , the trend was flat , with 7% of the flies asleep at any given time on average ( Fig 3E ) ., Male short-sleepers had an almost bimodal distribution of sleep during the night , with the highest propensity for sleep during the first third of the night ( Fig 3F ) ., However , more males were likely to be asleep at midday than the first third of the night , suggesting that the males were somewhat nocturnal in their activity patterns ., Short sleepers did not have more fragmented sleep than long sleepers , however ., Both short and long sleepers had reduced night bout number relative to the control population ( S2A Fig ) , suggesting that the patterns of sleep bouts were more consolidated in both groups of flies ., Night average bout length was reduced in short sleepers and increased in long sleepers ( Fig 2C ) ., Thus , while sleep architecture in long sleepers was similar to that of the unselected control , some disruption of the circadian clock was evident in the short sleepers ., The differences in sleep architecture suggest that the artificial selection protocol affected both sleep and circadian behavior ., Sleep is crucial for life , yet its relationship to important life history and fitness traits is not well understood ., Several previous mutagenesis screens have noted reduced lifespan in mutants with short sleep duration 44 , 45 , 49 , 51 , 77 , 78 , though there are exceptions 51 , 79 ., We measured lifespan in all six selection populations; in contrast to the reduced lifespan seen in short-sleeping mutants , we found no significant differences in lifespan for either sex in any of the selection populations ( Fig 4A; S5 Table ) ., If we assume that sleep is associated with fitness , an asymmetrical response to selection would indicate reduced fitness in the direction of the greater response to selection 67 ., Thus , we would predict that short-sleeping flies would be less fit than long-sleeping ones ., To investigate this possibility , we measured egg-to-adult viability as a proxy for fitness ., We found no differences among selection populations ( Fig 4B; S5 Table ) ., However , we noted a propensity for flies to die during sleep monitoring in the latter generations of the experiment ( Fig 4C ) ., Over the course of the entire experiment there were no significant differences among populations in the numbers of flies surviving , but there were significant differences in survival at generations 3 ( P = 0 . 0429 ) , 9 ( P = 0 . 0352 ) and 10 ( P = 0 . 0455 ) ., Short-sleeping females were the most vulnerable , though flies of all populations were less likely to survive the sleep monitoring ., Thus , any physiological consequences of being an extreme long or short sleeper did not manifest themselves in either lifespan or egg-to-adult viability , but the reduced survival of short sleepers during the later generations of selection suggests that they might be more susceptible to stress ., One of the hallmarks of sleep need is an increase in sleep , or sleep rebound , when normal sleep has been disrupted 42 , 43 ., To assess sleep need , we used a mechanical shaker to gently perturb sleep during the 12-hour night cycle in the long- and short-sleeping populations ., We monitored sleep for two days prior to the mechanical stimulus ( days 1 , 2 , and the day cycle of day 3 ) , during the stimulus ( the night of day 3 ) , and for two days after the stimulus ( days 4 and 5 ) ., We expected to observe a decrease in sleep during the application of the mechanical stimulus , and an increase in sleep during the day after sleep deprivation in addition to normal , unperturbed sleep over the 24-hour period following the stimulus ., We conducted this experiment after the artificial selection procedure had been relaxed for 47 generations , i . e . , at Generation 60 ., We noted that after relaxation of selection , the short sleeper lines had an unexpected increase in night sleep as compared to Generation 13 flies: night sleep was 412 . 8 ± 16 . 23 minutes for replicate 1 , and 186 . 0 ± 17 . 47 minutes for replicate 2 ., This increase in sleep has important implications for the relationship between sleep and fitness ( see Discussion ) ., All four populations responded to the shaking stimulus , as indicated by the decrease in night sleep on day 3 ( Figs 5 and 6A ) , and controls were relatively unperturbed by comparison ( PTreatment and PTreatment×Day < 0 . 0001 for night sleep in all populations ) ( S4 Fig ) ., Long-sleeper flies of replicate 1 had a pronounced response to the sleep deprivation as their night sleep was reduced by 86% to 84 . 7 ± 18 . 49 minutes during the night ., Although sleep increased during the day period after the mechanical stimulus was applied relative to baseline days 1 and 2 ( Fig 6B ) , their sleep in the subsequent 24-hour period was actually reduced compared to baseline sleep ( Fig 6A ) ., Short sleepers of replicate 1 were less perturbed by the shaking procedure; their sleep dropped to 215 . 01 ± 27 . 70 minutes , which was only a 48% decrease in night sleep ( Fig 5B ) ., They did not respond with a significant increase in sleep over the subsequent 24-hour period ( Fig 6A and 6B ) ., The response of long-sleeper flies of replicate 2 to sleep disruption was similar to that of short-sleeper flies of replicate 1: a mild sleep loss of 25 . 7% on day 3 ( Fig 5C ) , with a return to their normal sleep without a rebound ( Fig 6A and 6B ) ., Short sleepers of replicate 2 had almost no sleep during the shaking stimulus ( Fig 5D ) ., Their sleep was reduced to 33 . 7 ± 13 . 12 minutes of sleep , an 81 . 8% loss ., Day sleep increased significantly after sleep duration , though the 24-hour sleep on the recovery day remained unchanged as compared to baseline ( Fig 6A and 6B ) ., Interestingly , both long sleepers of replicate 1 and short sleepers of replicate 2 had greatly disrupted sleep patterns , with longer periods of day sleep and shorter periods of night sleep ( Fig 5A and 5D ) ., Thus , all four populations lost sleep after mechanical perturbation , but their 24-hour sleep did not increase on the recovery day as was expected ., As variability in night sleep duration has a genetic component , we expected that changes in night sleep duration over time would result from changes in allele frequencies in the selected populations ., We therefore extracted and sequenced DNA from pools of flies sampled from seven generations: 0 , 1 , 2 , 5 , 8 , 10 , and 12 ., The starting inbred lines we used to construct the outbred population are sequenced; thus , the number of polymorphisms segregating within the population was known ., 2 , 222 , 264 polymorphisms were expected to segregate among the 10 DGRP lines that we used 80 , 81 ., In addition , we used LoFreq 82 to identify additional potential rare or de novo polymorphisms that might be present in our selected populations; LoFreq detected an additional 258 , 268 potential polymorphisms ( Materials and Methods ) ., We defined major and minor alleles in our population by summing the allele counts for all polymorphisms across populations for generation zero , the generation prior to the start of selection for night sleep ., Minor allele frequency distributions were fairly flat across all five chromosome arms ( S5 Fig ) ; 151 , 694 DGRP polymorphic sites were fixed for the major allele at generation zero of the outbred population ., Thus , 2 , 328 , 838 segregating sites could potentially change allele frequency across generations ., We used the Cochran-Mantel-Haenszel ( CMH ) test to detect significant changes in allele frequency between any two generations for each replicate population 83; forms of this test have been applied previously in artificial selection and laboratory evolution experiments 56 , 59 , 63 , 64 ., Large numbers of polymorphisms across the entire genome were statistically significant for the CMH test at a Bonferroni-corrected threshold P value ( 2 . 3 × 10−8 ) ( Table 1 ) ., We therefore used the following strategies to identify the polymorphisms that were most likely to be the targets of selection ., As a first step , we identified significant polymorphisms in the long- and short-sleeper populations that were also significant in the control populations ., The allele frequency changes in these overlapping polymorphisms cannot be distinguished from random genetic drift or environmentally-mediated adaptation , so we eliminated them from consideration ., Second , we examined the manner in which allele frequencies from long- and short- sleeper populations changed across generations ., We observed many different types of trajectories for polymorphisms having significant allele frequency changes ., In some cases , allele frequency changes sometimes diverged between the long- and short-sleeper populations over time ., Fig 7A shows this divergence; the average minor allele frequency increased over time in the long-sleeper populations to reach fixation for the minor allele at generation 12 , while the average minor allele frequency decreased over time in the short-sleeper populations to reach fixation for the major allele at generation 12 ., We also observed trajectories in which the minor allele frequencies for both types of selection exhibited the same trend over time , such as that depicted in Fig 7B ., We hypothesized that divergent allele frequency patterns such the one depicted in Fig 7A were more likely to be targets of selection ., We therefore combined the data into a logistic regression analysis that incorporated minor allele frequency changes over time for 68 , 971 polymorphisms and 217 indels in the long and short selection populations that had significant changes across any two generations by the CMH test and did not change allele frequency in the control populations ., 126 polymorphisms were significant for the logistic regression ( S6 Table ) ., Interestingly , there was a strong propensity for selection against the minor allele in the long-sleeper populations , and selection for the minor allele in the short-sleeper populations ( Fig 7C ) ; 111 of the 126 polymorphisms had higher minor allele frequencies in short sleepers than in long sleepers ., In addition , we conducted simulations to assess the impact of random genetic drift on these polymorphisms ( Methods ) ., We determined the magnitude of allele frequency change that could occur due to drift , given the starting allele frequency at generation 0 ., These simulations suggested that 59 of the 126 polymorphisms exceeded the upper limit of allele frequency changes that would be expected from drift alone ( S7 Table ) ., Combining the allele frequency data across selection populations and across generations therefore enabled us to pinpoint a small number of likely selection targets , and considering the upper bound of random drift narrowed the number of candidate variants even further , to 59 ., It is quite likely that linkage disequilibrium ( LD ) , the non-random segregation of allelic variants at two loci , affects our results ., Estimating LD using pooled sequence data is a difficult challenge as the haplotypes of each fly are not known 84 ., Although local LD decays within 10 to 30 bp on average in the DGRP 80 , thousands of polymorphisms were in long-range ( i . e . , greater than 1kb ) LD with a given variant , particularly if that variant was at low frequency 81 ., To create the SAIP , flies were randomly mated for 21 generations , so there would be considerable heterozygosity in the genomes of these flies ., Variants unique to a single fly would be in de facto LD with the remainder of the population 85 ., Moreover , recombination rates vary with genotype 86 , 87 , and high recombination can be localized to different positions in the genome based on genotype 86 , suggesting that rates of recombination could change across any two generations of selection ., However , polymorphisms in high LD tend to have allele frequencies that are similar to one another 88–90 ., This observation makes it possible to make a rough estimate of the upper and lower bounds for the minor allele frequency at variant B that would be required to observe LD at variant A , provided that the r2 and minor allele frequency at variant A are specified 89 ., We therefore applied the following strategy to estimate the impact of LD on our results ., First , we calculated LD among 18 , 000 randomly chosen variants in the 10 DGRP lines used to construct the SAIP; we were able to calculate LD directly in this case as the gametic phase of lines of the DGRP is known ., There were 2 , 670 , 445 polymorphic pairs in high LD ( r2 ≥ 0 . 8 ) ; this is 1 . 65% of the possible 161 , 991 , 000 pairwise combinations , indicating that LD among the ten DGRP lines is quite low ., Next , we estimated the change in LD among these 2 , 670 , 445 polymorphic pairs after the 21 generations of random mating used to construct the SAIP ., We binned the minor allele frequencies of the SAIP into 0 . 01-increments from 0 to 0 . 5 ., For each of the polymorphic pairs , we calculated the range of allele frequencies pb at variant B that would result in high LD ( i . e . , an r2 of 0 . 8 or greater ) given a binned allele frequency pa at variant A ( Methods ) 89 ., At generation 0 , the number of variant pairs that were still in LD had decreased to 246 , 779 on average in the control populations , 247 , 483 in the long-sleeper populations , and 232 , 998 in the short-sleeper populations ., These estimates suggest that much of the LD in the 10 DGRP lines decreased during the 21 generations of random mating used to construct the SAIP ., Based on this random sampling we expected the polymorphisms in the starting outbred populations to be relatively independent ., The polymorphisms implicated in long and short sleep duration would be expected to have increased LD with selection , however , as nearby variants ‘hitchhike’ along with the focal variant ., We therefore repeated the above procedure for the polymorphisms that were significant in the logistic regression and examined the changes across generations ., 111 pairs out of a possible 7875 were in high LD in the 10 DGRP lines ., We assessed the LD in each variant for each generation of selection as outlined above ., We found that long-range LD ( i . e . , LD over distances greater than 1kb ) did not decrease in the short- or long-sleeper populations; instead , the average distance between SNPs in high LD tended to remain relatively constant with the exception of long-sleeper variants on chromosome 2R ( Fig 8 ) ., Given these results and the relatively low number of generations of selection , we expected that some of the polymorphisms we identified were still in LD at generation 12 ., These putative LD blocks are indicated in S7 Table ., The presence of these blocks suggested that only a subset of the identified variants were causal for changes in sleep duration ., The outbred population that we constructed used the 5 longest and 5 shortest sleeping lines of the DGRP , and represents the greatest amount of phenotypic variation in night sleep duration in the DGRP ., We wondered whether artificial selection using this subset of lines would produce the same results as the previous genome-wide association study ( GWAS ) of sleep using 167 lines of the DGRP 18 ., That study identified 160 polymorphisms associated with night sleep and 1 , 552 polymorphisms associated with night sleep CVE with an FDR of 0 . 01 or less ., In that study , night sleep duration and night sleep CVE were highly genetically correlated , and 95 . 6% of the polymorphisms identified for night sleep overlapped with night sleep CVE 18 ., We found that none of the polymorphisms that we identified using logistic regression overlapped with those for night sleep duration in the GWAS 18 , consistent with previous studies that have compared the results of advanced intercross population studies 91–93 or artificial selection 65 with GWAS ., Greater overlap between the two studies occurred at the gene level , particularly if pleiotropic effects on other sleep traits were considered ., Two genes , Myb-interacting protein 120 ( mip120 ) and scribbled ( scrib ) , were implicated in night sleep duration in both studies , though only allele frequency changes in an intron of scrib exceeded the simulated drift threshold ., Interestingly , 16 genes implicated in night sleep CVE in the GWAS overlapped the present study and included 5-hydroxytryptamine ( serotonin ) receptor 1A ( 5-HT1A ) , CG33158 , CG34353 , Dpr-interacting protein γ , faint sausage , Fish-lips , frizzled ( fz ) , Guanine nucleotide exchange factor in mesoderm , kin of irre ( kirre ) , mip120 , NK7 . 1 , plum , scrib , still life , Synaptosomal-associated protein 25kDa , and Tie-like receptor tyrosine kinase ., Of these genes , only CG34353 , faint sausage , Fish-lips , fz , kirre , plum , and scrib had allele frequency changes that exceeded the simulated drift threshold ., If all sleep traits measured in the GWAS were considered , 34 genes overlapped with the logistic regression—15 if the drift threshold was considered ( S8 Table ) ., Alternatively , the lack of overlap among polymorphisms for mean night sleep between studies suggests a level of context specificity and complexity that might only be resolved in an analysis that combines the effects of polymorphisms across all loci simultaneously ., Of the 121 single nucleotide polymorphisms ( SNPs ) and 5 indels , 9 were in the 3’-UTR region of 8 genes , 2 were in the 5’-UTR of 2 genes , 11 were in the exon of 8 genes , 55 were in the intron of 46 genes , 17 were within 1 kb of 16 genes , and 32 were intergenic ., Selection polymorphisms fell within the gene regions ( ± 1 kb of the coding region ) of 80 candidate genes ( S7 Table ) ., We | Introduction, Results, Discussion, Materials and methods | Why do some individuals need more sleep than others ?, Forward mutagenesis screens in flies using engineered mutations have established a clear genetic component to sleep duration , revealing mutants that convey very long or short sleep ., Whether such extreme long or short sleep could exist in natural populations was unknown ., We applied artificial selection for high and low night sleep duration to an outbred population of Drosophila melanogaster for 13 generations ., At the end of the selection procedure , night sleep duration diverged by 9 . 97 hours in the long and short sleeper populations , and 24-hour sleep was reduced to 3 . 3 hours in the short sleepers ., Neither long nor short sleeper lifespan differed appreciably from controls , suggesting little physiological consequences to being an extreme long or short sleeper ., Whole genome sequence data from seven generations of selection revealed several hundred thousand changes in allele frequencies at polymorphic loci across the genome ., Combining the data from long and short sleeper populations across generations in a logistic regression implicated 126 polymorphisms in 80 candidate genes , and we confirmed three of these genes and a larger genomic region with mutant and chromosomal deficiency tests , respectively ., Many of these genes could be connected in a single network based on previously known physical and genetic interactions ., Candidate genes have known roles in several classic , highly conserved developmental and signaling pathways—EGFR , Wnt , Hippo , and MAPK ., The involvement of highly pleiotropic pathway genes suggests that sleep duration in natural populations can be influenced by a wide variety of biological processes , which may be why the purpose of sleep has been so elusive . | One of the biggest mysteries in biology is the need to sleep ., Sleep duration has an underlying genetic basis , suggesting that very long and short sleep times could be bred for experimentally ., How far can sleep duration be driven up or down ?, Here we achieved extremely long and short night sleep duration by subjecting a wild-derived population of Drosophila melanogaster to an experimental breeding program ., At the end of the breeding program , long sleepers averaged 9 . 97 hours more nightly sleep than short sleepers ., We analyzed whole-genome sequences from seven generations of the experimental breeding to identify allele frequencies that diverged between long and short sleepers , and verified genes and genomic regions with mutation and deficiency testing ., These alleles map to classic developmental and signaling pathways , implicating many diverse processes that potentially affect sleep duration . | genome-wide association studies, medicine and health sciences, sleep deprivation, sleep, artificial selection, computational biology, alleles, physiological processes, genome analysis, chronobiology, mapk signaling cascades, genetic loci, circadian rhythms, signal transduction, cell biology, natural selection, physiology, neurology, genetics, biology and life sciences, genomics, evolutionary biology, cell signaling, evolutionary processes, signaling cascades, human genetics | null |
journal.pcbi.1005980 | 2,018 | Simulation enabled search for explanatory mechanisms of the fracture healing process | Annually , there are approximately 15 million fractures in the United States , and a significant portion ( 10–15% ) fail to heal properly 1 ., Both numbers and costs are predicted to increase as the population ages and as the number of osteoporosis-related fractures increases 2 ., Therefore , developing intervention strategies to stimulate fracture healing is expected to positively impact health ., Many of the advances made in fracture management in recent years were in mechanical stabilization and biologic bone augmentation materials such as autogenous bone graft , synthetic bone ceramics , or demineralized bone matrix 3 ., The clinical impact of biological therapeutic agents , such as bone morphogenetic proteins , has fallen short of expectations for largely unknown reasons 4 ., It is noteworthy that the gold standard , and most commonly used strategy for fracture nonunion treatment , autogenous bone graft , has not changed in the last 100 years 3 , 5 ., Introductions of new therapeutics have slowed despite expanded research 6 ., Such ineffectiveness reflects significant translation barriers ., The problem is not unique to fracture-healing research; it is encountered within many research domains 7 , 8 ., A translation barrier exists when mechanistic understanding of a particular medical process , such as fracture healing , is insufficient to posit a reliable , efficacious intervention strategy ., A goal of the research described herein is to develop and demonstrate feasibility for a simulation-based approach , facilitating incremental improvement to a plausible mechanism-based understanding of fracture healing processes ., We are not yet aspiring to utilize simulation methods to discover new mechanistic insights; knowledge is currently too sparse to support doing so ., However , the approach that we employ does provide a novel means to explore and think more deeply about plausible virtual ( implemented in software ) mechanism-based fracture healing processes ., Our approach is intended to be extensible to other processes that , like fracture healing , benefit from histologic analyses ., We aim for our model mechanisms to follow a design such that it is straightforward to make them incrementally more biomimetic and fine-grained as new wet-lab knowledge becomes available ., Before proceeding , we need a concise definition of a mechanism ., In S1 Text , we provide several definitions of a mechanism which are drawn from literature sources ., In support of achieving the above research goals , we are using the more detailed definition developed by Darden 9 ., Paraphrasing , a biological mechanism is concrete and can be defined as a real system of entities and activities orchestrated so that it produces the phenomenon of interest , which for this work can be a feature of the fracture healing process ., Thus , a model mechanism is a system of biomimetic software entities and activities organized such that , during execution , the process produces a phenomenon that is analogous to one or more features of the fracture healing process in particular ways ., A model mechanism capability essential to achieving our research goal is that it facilitates hypotheses about corresponding plausible underlying features of the biological mechanism , which produces the fracture callus attribute being simulated ., Fracture healing is described as comprising two phases and three stages that overlap temporally over several weeks: anabolic and catabolic phases; and inflammatory , endochondral , and coupled remodeling stages ., The dominant cell types and subprocesses 10 change as healing progresses ., Recent analyses of transcriptomes present during fracture healing have shown that most of the genes and signaling pathways that are involved in skeletal development in embryos are also expressed in cells of the fracture callus 11 ., Consequently , some pathway components have become the focus of empirical research efforts to develop therapeutic interventions 10 , despite the fact that there is no model of explanation—even at a coarse-grain—for stages in the fracture healing process ., Core phenomena of embryogenesis and some types of tissue regeneration include the evolving small- and large-scale patterns that are readily apparent in recorded images ., There has been considerable progress in developing mechanism-oriented explanations for those phenomena 12 ., However , stained tissue sections of mouse tibia fractures obtained at intervals of several days lack the hallmarks of orderly , organized evolving phenomena exhibited by embryogenesis ., The strikingly less organized callus tissue obscures the ongoing order of the various subprocesses and their mechanisms ., Part of the problem traces to limitations of experimentation ., Healing of mouse tibia fractures typically spans four-to-five weeks ., A major complication is that , within the same experiment , no two fractures are the same ., Although the healing phenomenon is the same , the unfolding healing subprocesses within each callus are unique ., Large observational gaps coupled with the necessary limitations of standard histological techniques means that informative subprocesses or phenomena may be missed ., It is also plausible that informative phenomena—patterns and features—are being observed and recorded , but are not yet recognized as such ., Analogous circumstances have existed in non-biological domains , and significant progress has been achieved using computational and grid-based simulation methods to provide plausible model representations of the missing processes and phenomena ., For example , looking for improved insight into processes occurring at the interface of ecology and geomorphology , Fonstad opined , “we have thousands of such images , but no theories in geomorphology nor ecology can fully explain the patterns in any of them” 13 ., The fact that callus mechanisms have been successfully healing bone fractures for more than 150 million years 14 implies the existence of a well-orchestrated , robust process ., Similarities of callus and embryonic transcriptomes support that inference 2 ., If we accept the premise that fracture healing is a well-orchestrated , robust process , then we need to answer this question: how can we begin developing a theory about the healing process—even if initially coarse and somewhat abstract—so that we can begin theorizing about its orchestration ?, A clearly described phenomenon is a precondition for developing a theory intended to explain that phenomenon ( S1 Text ) ., However , we do not yet have a clear temporal description of the fracture healing process , or even for portions of the process ., We do , however , have detailed descriptions of features of the process at different stages ., With current technology , it is not feasible to measure a callus continuously ., Likewise , it is infeasible to track the changing variety of local structures and cell types ., Must we plead for more data , and then come back to the problem in another decade or two ?, Absent a plausible explanation and theory to test , more data may not be the answer ., In discussing comparable issues at the ecology-geomorphology interface , Fonstad observed that , “both of these disciplines are data-rich … it is immediately apparent that both of these disciplines are far more theory-poor” 13 ., Fracture-healing research is handicapped because it is relatively data-poor and theory-poor ., So , although we can draw inspiration from the explanatory , pattern-oriented simulation methods used by Fonstad and others , their models and those pattern-oriented techniques are not yet applicable in advancing fracture-healing research ., Given the growing interest in increasing the clinical relevance of modeling and simulation research , it is not surprising that the number of such reports in which authors utilize histology images to support face validation and/or guide calibrations is also increasing ., The following are three recent examples ., Marino et al . 15 utilized their model of lung granuloma formation to compare in silico granulomas to those of the nonhuman primate Macaca fascicularis ., Gardiner et al . 16 utilized an agent-based particle system at various granularities to simulate mechanical behaviors of cells and tissues ., Simulations using selected parameterizations bore a close resemblance to histological observations of an epithelial layer , cell clusters , and single cells ., Ziraldo et al . described an agent-based model of ischemia/reperfusion-induced inflammation coupled with pressure ulcer formation and progression in humans with a spinal cord injuries 17 ., Serial photographic images spanning several clinical stages were used to calibrate progression and healing of virtual pressure ulcers ., Virtual pressure ulcers were interrogated to explore how and when a irritation might resolve or become chronic ., The prospect of pulling together a start-to-finish tissue-level mechanism-oriented description of a fracture healing process , even one that is initially coarse-grain , seems distant ., Why ?, It is a consequence of four interrelated obstacles arising from fracture-healing research using rodent models ., Given those obstacles , current knowledge and methods are insufficient to describe , much less begin building a conventional molecular and cellular biology-based model of fracture healing ., The most pressing current need is to develop strategies and methods to circumvent and eventually overcome each of the above four obstacles ., We conjectured that the software-based model mechanism methods , which we have used successfully in other contexts ( e . g . , see 22–27 ) , could provide the foundation for such strategies , even though , for those earlier applications , considerably more mechanism related fine-grain knowledge was available ., Briefly stated , the cited software-based model mechanism approach begins with a target phenomenon ., We build an extant ( actually existing , observable ) , working mechanism in software that is parsimonious and , based on similarity criteria , exhibits essentially the same phenomenon ., Doing so requires making no assumptions about the biology ., However , even when the mechanism is kept coarse-grain , the space of possible software mechanisms capable of generating essentially the same phenomenon can be huge ., So , biologically inspired requirements and constraints along with mechanism granularity limits are imposed incrementally to shrink and constrain possible model mechanism space ., That process shrinks a large set of possible coarse-grain mechanisms into a much smaller set of plausible , incrementally more likely and increasingly biomimetic , model mechanisms ., For fracture healing , we envision simulations generating plausible scenarios for how discretized features of a callus tissue section on one day might transform progressively into the tissue section features—target features—observed several days later ., Wet-lab experiments can target differences in two model mechanisms , where the resulting new evidence is expected to support one mechanism and falsify the other ( as in 28 ) , further shrinking plausible mechanism space ., At that stage , the surviving software mechanism can stand as a coarse-grain theory for how a portion of mouse tibia fracture healing occurs ., Eroding the four obstacles in meaningful ways requires coupling the preceding methods with an important new capability: use of image interpolation strategies to build plausible sequential image models of the same fracture at different stages of the healing process ., A prerequisite for an interpolation strategy is having and aligning discretized coarse-grain models of tissue section images of tibia fractures from different mice at different times ., We report results of a focused demonstration that meets the above requirements ., We present results of workflows that support the feasibility of the approach , while also bringing its weaknesses into focus ., For this demonstration , we limited attention to the critical interval from day-7 to day-10 during healing of a mouse tibia fracture and focused on discretized models of specific tissue sections on both days ., From the latter , we obtained the initial state and final target state for our simulations ., Biomimetic software mechanisms involving actions of quasi-autonomous tissue units spanning , typically , 5 , 000–6 , 000 time steps are responsible for simulated healing ., Similarities ( defined in Methods ) between simulated and referent final states ranged from > 73% to > 93% , depending on the nature and stringency of the Similarity criterion ., Despite the narrow focus , it is clear that a major benefit of the approach demonstrates that simulation experiments can enable discovering , challenging , and improving theories of healing subprocesses ., Because our approach and methods are unconventional , somewhat new , and still evolving , we present that information next under Methods to provide the context needed to present and discuss results ., There are weaknesses and limitations associated with every aspect of our approach ., Some are identified in Methods , and others are addressed under Discussion ., We undertook this demonstration with the expectation that the more successful methods could be repurposed to begin lowering similar barriers faced within some domains of disease progression research ., We begin with a synopsis of our approach from a workflow perspective , as diagrammed in Fig 1 ., We then provide details on methods used during each of the six stages ., In several places , we also provide essential background information that influenced decisions for a particular stage ., Words , such as tissue , mechanism , healing , and process , are used in discussing actual mouse tibia fracture healing and corresponding simulations ., To reduce confusion , we capitalize those words hereafter when discussing Callus Analogs ., We focus on the day-7 to day-10 interval of mouse tibia fracture healing because histomorphological evidence indicates that the relative contributions of chondrogenesis and osteogenesis may undergo important changes during that interval ., The goal is to develop a concrete , quantitative ( and thus challengeable ) but partially coarse-grain theory that may explain how characteristic tissue level features on day-7 are being transformed into corresponding features observed on day-10 ., The discovery effort would be greatly simplified if we could obtain collocated day-7 and day-10 tissue sections from the same callus ( mouse 1 ) , but that is infeasible ., Instead , we used an evidence-based illustration ( created by coauthor M . M . ) of an envisioned tissue section of the mouse 1 callus on day-10 at the same callus location as the day-7 tissue section ., Three domain experts ( see Acknowledgments ) judged it plausible and acceptable ., Hereafter , we refer to the illustration as the day-10i tissue section ., A square grid was used to discretize the day-7 and day-10i images ., The area of tissue at each grid location was labeled as one of nine tissue types , based on staining and preponderance of cell types within that space ., The result ( stage 2 ) was a discretized coarse-grain model of the day-7 and day-10i tissue sections ., Because we are at the beginning of this explanatory discovery process , we needed to select a target region on which to focus ( discussed further under Target Region ) ., From a simulation perspective , the target region has an initial state , which maps to the day-7 tissue section , and a corresponding final state , which maps to day-10i tissue section ., During stage 4 we used the MASON simulation toolkit 29 to create a 2D 25×25 Target Region initial state , in which objects representing tissue units are assigned to each grid space ., We start with a 2D Target Region to limit uncertainty in tissue type identification and to adhere to our parsimony guideline ., Stage 5 efforts focused on answering the following question: how do we enable the Target Region initial state to transform itself so that the arrangement of tissue types mimics the Target Region final state ?, The steps followed to answer that question involved iterative refinements ( discussed below ) and had two objectives ., 1 ) Explore logic to be used by simulated tissue units that enable them to successfully transition into biomimetic final states ., 2 ) In doing so , keep the logic simple and avoid process features that may appear non-biomimetic ., Once we had evidence that reasonably biomimetic final states were achievable , we shifted attention to improving the simulated healing process sufficiently to achieve the following quantitative target Similarity value ( stage 6 ) : compositional and organizational similarity between simulated Target Region and day-10i final state is ≥ 70% ., So doing would support the feasibility of achieving the long-term Fig 1 goals ., A simulation that uses concrete objects ( simulated tissue units ) to generate a process that is analogous to callus healing in several ways is a software analog of the healing process ., We call the parameterized software a Callus Subregion Analog ., Hereafter , for convenience , we refer to the software as Callus Analog and , in some places , simply Analog ., Histologic slides of sagittal sections through mouse tibia calluses at various stages of healing were available from a previous study ., The sections were stained using Hall-Brunt Quadruple to highlight tissue , bone , and cartilage ., Shown in Fig 2 are the tissue sections from mouse 1 on day-7 and from mouse 2 on day-10 ., Coauthor R . M . selected them because they have similar fracture features and exhibit all characteristic callus features ., The following nine distinct microscopic tissue types are common to all normal mouse tibia calluses , beginning before day-7 and extending beyond day-10 ., We assigned a different color to each tissue type , which was used to colorize a discretized version of Fig 2A ., A microscopic area of callus containing about 20 or more cells can be distinguished as being either new marrow ( 4 ) , new bone ( 5 ) , hypertrophic cartilage ( 6 ) , mature cartilage ( 7 ) , or young cartilage ( 8 ) based on the characteristic heterogeneous mix of cell types , the dominant cell type , and extracellular matrix ., As healing progresses the mix of cell types within a microscopic area changes ., Some areas may undergo multiple tissue type transitions ., A working hypothesis is that each of the microscopic tissue types is engaged in somewhat different activities , which are integral to the overall healing process ., The first stage 2 task was to select a square grid mesh size and overlay it on Fig 2A ., Choice of mesh size was somewhat arbitrary ., If it is too fine , there are fewer cells within the microscopic area and so the uncertainty in specifying the dominant cell type increases ., If too coarse , the fraction of microscopic areas containing clearly distinguishable tissue types 4–8 decreases , rendering a single tissue assignment inadequate ( and actions of the analog counterpart would likely require unique logic ) ., A guideline for selecting grid size was that the cellular heterogeneity observed within the larger local callus area be reasonably preserved in the discretized image ., For example , for a macroscopic region characterized by a heterogeneous mix of predominately ~ 60% new marrow ( gray ) and ~ 40% osteoblasts ( burgundy; new bone ) , the discretized image counterpart should be a mix of ~ 60% gray and ~ 40% burgundy tissue units ., We selected a mesh size that corresponds to an 80×80 μm area in Fig 2A , which typically contained roughly 40 cells , and overlaid that grid on the day-7 and day-10i tissue sections ., We then designated each microscopic area as being one of nine concrete , quasi-autonomous , Tissue Unit types , where the behavior of each Tissue Unit type was controlled by a software agent ., Fig 3 contains the resulting discretized , colorized images ., Although physically correct image interpolation ( e . g . , between day-7 and day-10i ) is infeasible , sophisticated image interpolation methods , as demonstrated by Stich et al . 30 , are available to create high-quality , convincing model images that represent unobserved transitions between recorded images of the same object ., The criterion for an acceptable interpolation used by Stich et al . , is qualitative: the interpolated images are perceived as visually correct by human observers ., During stage 1 , we faced the more daunting problem identified in Fig 4: we needed an image that plausibly anticipates the appearance of the mouse 1 fracture if it had been sectioned on day-10 rather than day-7 ., Starting with the features evident in Fig 2A , and drawing on the tissue features in Fig 2B , coauthor M . M . created an illustration of the envisioned mouse 1 , day-10i section ., It was judged plausible and acceptable by coauthor R . M . and , separately , by three independent domain experts ( see Acknowledgments ) , thus concluding stage 1 ., Clearly , a different medical illustrator , one knowledgeable about callus progression , would create a somewhat different illustration ., However , we suggest that variability introduced by such illustrations will not add measurably to the considerable variability and uncertainties already present , as illustrated by Fig 4 ., To demonstrate feasibility , we needed to designate a Target Region , but first , we needed to select a portion of Fig 2A in which to locate the Target Region ., For the latter , we selected the yellow-boxed area in Fig 2A ., It is bordered on one side by bone and marrow cavity , which means that transitions in that area will be focused rightward , rather than occurring in two or more directions ., Because that area , and the corresponding region in Fig 2B , exhibit similarities , we conjectured that the variety of feature changes occurring during transition from day-7 to day-10i might be representative of key healing features occurring elsewhere in the callus during that 4-day interval ., There is no indication that unique healing features may be occurring within this area but not elsewhere during that 4-day interval ., Specifying the size of the target region is subject to opposing constraints ., If the region is too large , with a large variety of tissue transition types , we run the risk that the process of discovering plausible and parsimonious logic to direct transitions will become unwieldy , possibly even problematic ., If the region is too small , the variety of transition types may be too few to enable adequately simulating Target Region final state ., We selected the 25×25 grid region designated by the white box in Fig 3A ., Fig 3B shows the corresponding Target Region final state ., Limiting attention to just one target region can be viewed as a weakness ., On the contrary , it is an essential part of a recognized , long-term mechanism-discovery strategy that can build on methodological lessons learned while using the Iterative Refinement Protocol in other contexts 23 , 25 , 27 , 28 , 31 ., That strategy employes variations of the forward/backward chaining ( described in S1 Text ) and requires selecting a Target Region ( stage 3 , Fig 1 ) ., After we achieve stage 6 for the day-10i Target Region ( described below ) , we envision expanding the temporal reach of Callus Analog Mechanisms along the dotted line illustrated in Fig 4 to include an earlier stage within that same Target Region , such as day-4i , and a later stage , such as day-14i , and doing so all while continuing to simulate the original day-10i Target Region ., Those objectives are illustrated by two unshaded bars labeled a and b in Fig 4 ., Further , the histological evidence suggests that , on the same day , different subregions within a callus can be at somewhat different stages of repair and may progress at different rates ., Given that , a parsimonious strategy is to select separated target regions within the same callus and develop simulations for each in sequence ., They could be treated as independent modules ., Future work based on simulations of independent target regions will help bring regional issues into focus prior to engineering their merger ., The process of merging initially independent modules into a unified model of a tissue healing process would occur further downstream ., Given that this work strives to establish the feasibility of the Fig 1 approach , it is efficient to focus first on one Target Region ., Simulation requirements—and thus software requirements—flow directly from desired use cases 32 ., In the Introduction , we stated that a primary use case is exploratory simulations capable of the following: aiding image interpolation and providing plausible explanations for how callus features are progressively transformed , all while shrinking the space of possible explanatory transformation scenarios ., The last two requirements involve generation of plausible mechanism-oriented explanations , illustrated in Fig 1 ., To realize use cases , we employ the virtual experiment approach described by Kirschner et al . 33 , along with enhancements drawn from Smith et al . 28 and Petersen et al . 31 ., In doing so , the methods employed must meet the following three requirements , which are based on broader sets of requirements discussed by Hunt et al . 32 ., To achieve requirement 2 , Callus Analogs are written in Java , utilizing the MASON multi-agent simulation toolkit 29 ., The data presented herein along with Callus Analog code are available 36 ., We customized the established Iterative Refinement ( IR ) Protocol 22 , 27 , 28 , 31 , 32 to meet the challenges evident in Fig 4 ., Given a software Mechanism that may explain a specified attribute and a virtual experiment design , the goal of an IR Protocol cycle is to test this hypothesis: upon execution , simulation features will mimic the target attribute within a prespecified tolerance ., A concrete software mechanism can be falsified—shown to be inadequate—when , during the course of many Monte Carlo trials , it too often fails quantitatively to achieve its objective and/or exhibits non-biomimetic features ., It is from encountering and overcoming such failures that explanatory insight improves ., Each falsification improves credibility incrementally and shrinks plausible Mechanism space ., Our customized IR Protocol follows: Well-organized processes are responsible for the callus remodeling that occurs between day-7 and day-10 ., Our operating hypothesis is that information available in day-7 and day-10 tissue section images can be used to draw simplified inferences about unobserved transitions that occur during intervening days , analogous to the approach used by Stich et al . in simulating unobserved transitions between recorded images of the same object 30 ., A Callus Analog Mechanism is a system of biomimetic software entities and activities organized such that , during execution , it produces a representation that is measurably similar to the day-10i Target Region ., Feature changes within the Target Region explain how the Phenomenon is generated ., Stained tissue sections provide snapshots of the healing process ., To be explanatory , a biological mechanism will exhibit the fourteen features identified in S1 Text ., Because Callus Analog Mechanisms exhibit those same features ( also identified in S1 Text ) , the two processes may be analogous ., Simulations are discrete time; time advances in steps ., Fig 5 shows the Target Region initial and final state ., The Process responsible for transitioning from initial to final state is the top-level Mechanism ., Changes within local subregions from one time step to the next are lower level Phenomena ., The lower level Mechanisms responsible for those changes are characterized by individual TU changes , which are controlled by the logic that governs TU agent actions during each time step , discussed below ., During each time step , each TU agent , selected randomly , has one opportunity to update and act , based on changes that have occurred within its Moore neighborhood since it last updated ., An action may change a TU type or one of its Moore neighbors ., Coauthor R . M . identified the following as allowed but not required biomimetic transitions ., At each time step , the current simulated Target Region is compared to the Target Region final state and percent Similarity is calculated as follows:, %Similarity=100 ( ∑ ( i=2−8 ) ( CSi/CFi ) n ( CSi/428 ) ) t ,, where t designates the time step , and i specifies the TU type , 2–8 ., CSi is the count of TU type i in the simulated Target Region; CFi is the count of TU type i in Target Region final state; 428 is the number of active TUs in Target Region; n = 1 if CFi > CSi , and n = –1 if CSi > CFi ., A case can be made that , if there is strong similarity between gray and burgundy TUs in the simulated and actual Target Region final states , then the similarity score should not be penalized because there are too many simulated gray TUs and too few simulated burgundy TUs , or vice versa ., New marrow ( gray ) and new bone ( burgundy ) are always formed together ., Thus , in some cases , the decision to designate an 80×80 μm area of stained tissue section as either gray or burgundy can be arbitrary; two experts may make different assignments ., There are several ways to address that issue but they involve adding at least one new TU type ., Given our strong parsimony guideline and the fact that we are at a very early stage in developing the Fig 1 approach , we elected to also calculate a Similarity value when gray are burgundy treated as the same during the calculation ., The resulting value is designated Upper-Limit Similarity , simply UL-Similarity hereafter ., A more realistic value may be between Similarity and UL-Similarity ., When working to discover plausible model mechanisms , there is a risk that the modeler , subconsciously or otherwise , will favor Mechanism features and logic that ensure that outcomes of generated behaviors are as the modeler thinks that they should be ., We strove to eliminate that risk by adhering to the guidelines in steps 4 and 6 of the IR Protocol ., In developing model mechanisms , we did not aim to include established biological features ., Instead , we worked to develop model mechanisms that did not contradict known biology ., Along the same lines , our model mechanisms were developed not to specifically describe characteristics of the fracture healing process , but instead to allow for a new , coarse-grained manner in which to think about the process ., Additional information about the disadvantages of absolute grounding can be found in 40 ., By adhering to a strong parsimony guideline ( IR Protocol step 2 ) , we avoided adding unnecessary details; doing so enabled us to avoid inscription error ., More details on overfitting and analog-to-referent mappings can be found in the Discussion section of Kim et al . 26 ., In the subsections that follow , we describe four workflow stages , designated Mechanisms 1–4 ., What further improvements in similarity values , as calculated above , might reasonably be achieved ?, We answered that question with Mechanism 4 internal control calculations that draw on the fact that the closest Similarities that can be achieved for a simulated day-10i Target Region will be those achieved by independent executions of the Mechanism that generated it ., The analog Healing Process from day-7 initial state to a simulated day-10 final state is unique for each Monte Carlo execution of Mechanism 4 ., We selected one Mechanism 4 Monte Carlo execution from 25 and recorded its Target Region configuration at the time step for which simulated Target Region final state maximum Similarity was achieved ., We designated it to be the internal control simulated day-10i target state ., We then measured maximum Similarity of each of the other 24 Monte Carlo Healing Processes to that simulated day-10i target state ., Data are available and labeled as | Introduction, Methods, Results, Discussion | A significant portion of bone fractures fail to heal properly , increasing healthcare costs ., Advances in fracture management have slowed because translation barriers have limited generation of mechanism-based explanations for the healing process ., When uncertainties are numerous , analogical modeling can be an effective strategy for developing plausible explanations of complex phenomena ., We demonstrate the feasibility of engineering analogical models in software to facilitate discovery of biomimetic explanations for how fracture healing may progress ., Concrete analogical models—Callus Analogs—were created using the MASON simulation toolkit ., We designated a Target Region initial state within a characteristic tissue section of mouse tibia fracture at day-7 and posited a corresponding day-10 Target Region final state ., The goal was to discover a coarse-grain analog mechanism that would enable the discretized initial state to transform itself into the corresponding Target Region final state , thereby providing an alternative way to study the healing process ., One of nine quasi-autonomous Tissue Unit types is assigned to each grid space , which maps to an 80×80 μm region of the tissue section ., All Tissue Units have an opportunity each time step to act based on individualized logic , probabilities , and information about adjacent neighbors ., Action causes transition from one Tissue Unit type to another , and simulation through several thousand time steps generates a coarse-grain analog—a theory—of the healing process ., We prespecified a minimum measure of success: simulated and actual Target Region states achieve ≥ 70% Similarity ., We used an iterative refinement protocol to explore many combinations of Tissue Unit logic and action constraints ., Workflows progressed through four stages of analog mechanisms ., Similarities of 73–90% were achieved for Mechanisms 2–4 ., The range of Upper-Level similarities increased to 83–94% when we allowed for uncertainty about two Tissue Unit designations ., We have demonstrated how Callus Analog experiments provide domain experts with a fresh medium and tools for thinking about and understanding the fracture healing process . | Translation barriers have limited the generation of mechanism-based explanations of fracture healing processes ., Those barriers help explain why , to date , biological therapeutics have had only a minor impact on fracture management ., Alternative approaches are needed , and we present one that is intended to help develop incrementally better mechanism-based explanations of fracture healing phenomena ., We created virtual Callus Analogs to simulate how the histologic appearance of a mouse fracture callus may transition from day-7 to day-10 ., Callus Analogs use software-based model mechanisms , and simulation experiments enable challenging and improving those model mechanisms ., During execution , model mechanism operation provides a coarse-grain explanation ( a theory ) of a four-day portion of the healing process ., Simulated day-10 callus histologic images achieved 73–94% Similarity to a corresponding day-10 fracture callus image , thus demonstrating feasibility ., Simulated healing provides an alternative perspective on the actual healing process and an alternative way of thinking about plausible fracture healing mechanisms ., Our working hypothesis is that the approach can be extended to cover more of the healing process while making features of simulated and actual fracture healing increasingly analogous ., The methods presented are intended to be extensible to other research areas that use histologic analysis to investigate and explain tissue level phenomena . | biotechnology, traumatic injury, medicine and health sciences, engineering and technology, biomimetics, simulation and modeling, physiological processes, histology, mathematics, statistics (mathematics), cartilage, bone fracture, research and analysis methods, bioengineering, tissue repair, mathematical and statistical techniques, connective tissue, biological tissue, monte carlo method, biophysics, critical care and emergency medicine, trauma medicine, physics, anatomy, physiology, biology and life sciences, physical sciences, computational biology, statistical methods, biophysical simulations | null |
journal.pcbi.1003103 | 2,013 | Gag-Pol Processing during HIV-1 Virion Maturation: A Systems Biology Approach | The morphological maturation of human immunodeficiency virus type 1 ( HIV-1 ) depends on the proteolytic processing of the Gag and Gag-Pol polyproteins by the virus encoded PR that occurs concomitant with or shortly after virus release 1 ., PR inhibitors ( PIs ) that interfere with this process result in the production of immature , noninfectious virus particles , and constitute an important drug class in anti-HIV-1 therapy 2 , 3 ., While currently approved drugs act by competitive binding to the PR active site , thereby affecting all cleavage events , individual steps of the maturation process are also potential targets for future drug development 4 , 5 ., The development and therapeutic application of HIV-1 maturation inhibitors requires a detailed understanding of the cleavage process , which has multiple layers of complexity ., First , PR itself is embedded in the Gag-Pol polyprotein , and Gag-Pol auto-processing is required to initiate the maturation process 6–9 ., Liberated PR molecules then catalyze further cleavage events , which might result in accelerated PR release by a positive feedback loop ., Second , due to its relatively broad substrate specificity 10 , HIV PR targets 11 canonical cleavage sites in the Gag and Gag-Pol polyproteins ( Figure 1 ) , generating 66 distinct molecular species ( substrates , intermediates and products ) , and a large number of competing reactions occur simultaneously within the confined space of the virion ., Cleavage at the individual sites occurs with different rates; a 400-fold difference in rate between the fastest ( SP1-NC ) and slowest ( CA-SP1 ) cleavage site in Gag has been determined in vitro 11 ., Third , several intermediates include an active protease domain , but also one or more uncleaved cleavage sites: these molecular species have dual roles as both substrates and enzymes in the reaction network ., The complexity of the reaction system ( number of reactions/reactants ) is comparable to that of the cell cycle , which has been among the most important targets of systems modelling in biology so far 12 ., Understanding and predicting the behaviour of this complex system requires systems modelling and extensive empirical data to parameterize the model ., Accumulating data on the kinetic parameters of the cleavage reactions 6–9 , 13–20 and on the biology of HIV-1 virion assembly and maturation 21–26 now allow us to tackle this problem , and we here present the first full reaction kinetics model of proteolytic processing by HIV-1 PR ., We use the model to characterize the general time course of the process , to identify the parameters with the strongest effect on the maturation process , to assess interactions between the parameters and the potential of individual parameters to compensate for drug effects or changes in other parameters , and to explain the steep dose response curves associated with PIs 27 ., While subject to inevitable limitations arising from the simplifying assumptions used and from uncertainty in the parameter estimates , our model results provide a level of resolution exceeding that of currently available experimental data ., The time course of proteolytic processing has been characterized quantitatively for purified Gag in vitro 11 , but was determined only qualitatively for Gag-Pol 28 , and experiments are typically limited to tracking a small number of molecular species in simplified in vitro systems ., In contrast , the model presented here tracks all intermediates and products of the complex reaction system ., Furthermore , the model can be used to predict the effect of quantitatively characterized mutations or drugs alone or in combination ., It can also be applied to predict the effect of potential perturbations induced by compounds in drug development ., Systems modelling has been used to identify and characterize synergistic drug interactions that can enhance the effect of drugs 29 , 30 ., The model presented here opens the possibility to apply this approach to HIV-1 PR inhibition ., We built a full model of PR catalyzed Gag- and Gag-Pol processing based on mass action Michaelis-Menten reaction kinetics ( Materials and Methods ) ., Based on published data on the architecture of the immature HIV-1 particle 22 , we set initial concentrations to correspond to 2 , 280 molecules of Gag ( corresponding to 3 . 619 mM ) and 120 molecules of Gag-Pol ( corresponding to 0 . 195 mM ) within the confined space of a spherical virus particle with a radius of 63 nm 22 as the starting point for our simulations ., By parameterizing all cleavage reactions according to in vitro empirical estimates ( Table 1 ) , our model generated a detailed predicted time course for the cleavage process ( Figure 2; major intermediates are shown in Figure S1 ) ., The timing of virion maturation ( virion maturation time , VMT; dashed red line in all panels of Figure 2 ) was estimated based on two criteria for maturation:, i ) the presence of a sufficient number of liberated CA molecules to form a mature conical capsid ( one “capsid unit” corresponding to 1 , 500 CA monomers 31 ) , and, ii ) the concentration of the late processing intermediate CA . SP1 falling below a critical level ., Processing at this site is required for mature capsid formation 26 , 32–34 ., A CA . SP1 concentration below 5% of the total initial Gag content is needed to fully alleviate the trans-dominant inhibition effect of this fragment on HIV-1 infectivity 35 , and we used this threshold as a criterion for attaining VMT ., The time needed for the assembly of the mature cone shaped capsid was not considered in our definition of the VMT , which implies that our estimates for VMT can be regarded as lower bounds; however , assembly is likely to be fast compared with the preceding steps of proteolytic processing ( see Discussion ) ., Using the default parameters , our model predicted morphological maturation to occur ∼30 min after the start of the process , which is thought to be initiated at the formation of the virion ., While there are no reliable data on the timing of virion maturation in vivo , the fact that morphological maturation intermediates have not been detected by electron microscopy indicates that the process is comparatively fast ., Our result is roughly consistent with the current assumption that maturation occurs during or shortly after budding 36 , taken together with fluorescence imaging results indicating that most HIV-1 virions are released from the cell within 30 min after formation 37 , apparently with a ∼15 min delay after the assembly of the Gag shell beneath the cell membrane 23 ., Total proteolytic activity ( Figure 2E , green line ) peaks around 39 min , and declines afterwards due to the internal cleavage of PR monomers ., Remarkably , the combined catalytic activity of all intermediate enzyme forms exceeds that of the fully cleaved PR homodimer until ∼35 min after the start of the process ( Figure 2E , blue line indicates the relative contribution of mature PR dimers to the proteolytic activity ) ., Up to the time of VMT ( dashed red line ) , intermediate enzyme forms are predicted to have catalyzed ∼80% of all cleavage reactions ., Functional p66/p51 heterodimers of reverse transcriptase ( RT ) also decline after a peak due to the cleavage of the p66 subunit into p51 and p15 fragments; however , this decay is arrested as PR activity is lost ( this might occur even faster in vivo: see Discussion ) ., Finally , we have verified that the total concentration of uncleaved cleavage sites greatly exceeds the total concentration of active enzyme forms throughout the simulated cleavage process ( Figure 2E ) , which justifies the use of Michaelis-Menten kinetics ( assuming quasi steady state for the enzyme–substrate complexes ) ., We also plot the time course of the overall processing of individual cleavage sites in Figure 3A: the figure shows what fraction of a given cleavage site is yet uncleaved ( the total concentration of all molecular species that contain the uncleaved site , divided by the initial concentration ) ., The order of cleavage can be defined for fixed thresholds of processing: Figure 3B depicts the order obtained for 50% and 95% processing; Figure 3C presents a schematic representation of the order of cleavage events based on 50% processivity ., The order of events in our simulations is roughly consistent with the order of events observed in vitro 11 , 38 , with two exceptions: the removal of the spacer peptide from CA and the cleavage at the N-terminus of PR ( p6pol/PR ) occur much faster in the simulations than in vitro ., These discrepancies arise from the relatively faster rates of cleavage observed during the processing of oligopeptides , which were used to parameterize the model ., However , slowing down the processing of the CA/SP1 site to reproduce the results of in vitro processing of full-length Gag ( as in 39 ) results in VMT>2 hours ( see Discussion ) ; we therefore used the parameter set derived from oligopeptide cleavage ( Table 1 ) in the subsequent analyses ., We thus conclude that our model is able to capture most known characteristics of the cleavage process , and proceed to analyze further properties of the system , for which little or no empirical data exist yet ., We next investigated the sensitivity of the maturation time to the parameters of the model ., These analyses provide insight into the sensitivity of the results to the uncertainty of the parameters , and also predict the response of the system to possible interventions that affect individual steps of the process ., We first varied one parameter at a time ( see Table 1 for the list of all 33 parameters ) in the range of 0 . 1 to 10 times its default value , while fixing all other parameters at their default values ( Figure 4A ) ., Within the studied range , varying most parameters had hardly any effect on the virus maturation time , with the exception of two critical parameters , which emerged as dominant factors: the rate constant of auto-cleavage by the full length Gag-Pol dimers , and the catalytic rate constant of heteromolecular cleavage at the CA/SP1 cleavage site ., The dependence of VMT on both dominant parameters was very similar: at the lower ( slower ) end of the studied range , VMT is very sensitive to small changes in these parameters , while at the higher ( faster ) end , further increase in either rate constant yields diminishing reductions in VMT ., We also tested the effect of initial Gag content of the virus ., Since HIV-1 particles are not homogeneous , but have been shown to vary with respect to diameter 40 and completeness of the spherical Gag shell 22 , 25 , this parameter will vary among individual virions 41 ., Varying initial conditions from 1 , 600 to 3 , 500 molecules of total Gag content ( while keeping the Gag∶Pol ratio of 20∶1 constant ) had negligible effect on the time course of virion maturation ( variation in VMT was ≤1 second ) ., We next performed a multivariate exploration of the parameter space ., Parameters were drawn randomly from lognormal distributions parameterized such that 95% of the values fell in the range of 0 . 1 to 10 times the default value of the parameters; for the few parameters with no direct empirical estimates ( the association and dissociation rate constants of full-length Gag-Pol and partially cleaved PR enzyme forms , and the KM values for the NC/TFP and TFP/p6pol cleavage sites ) , we allowed a range with plus/minus two orders of magnitude around the default value ., We performed 10 , 000 simulation runs with independently generated random parameter sets , of which 8 , 937 achieved virion maturation by 120 min ., Median VMT ( when censoring uncompleted runs at VMT\u200a=\u200a121 min ) was 38 . 5 min ( IQR: 22 . 4–66 . 6 min ) ; the distribution of VMT was non-normal ( Kolmogorov-Smirnov test , p<10−10; Figure S2 ) ., Maturation was triggered by the loss of CA . SP1 inhibition in 7 , 141 ( 80% ) of the cases where maturation occurred , and we verified that the criterion for the Michaelis-Menten approximation ( Stot>Etot ) was fulfilled for nearly all ( >99% ) parameter sets ., The dominance of the catalytic rate constants of initial auto-cleavage and CA/SP1 cleavage was confirmed in this analysis ., Of the 33 parameters , only four had a significant effect on VMT ( Spearman rank correlation test; p<0 . 0015 after Bonferroni correction ) : this included both dominant rate constants , which also displayed considerable correlation strength ( Spearmans Rho of −0 . 58 and −0 . 45 for the CA/SP1 catalytic rate constant and for the rate constant of Gag-Pol auto-cleavage , respectively ) ., The catalytic rate constant of the NC/SP2 cleavage and the association rate constant of active ( N-terminally free ) partially cleaved PR forms also affected the VMT according to this analysis , but displayed only very weak correlation ( Spearmans Rho around −0 . 03 ) ., Only the two dominant rate constants had discernible impact on the distribution of plotted VMT values ( Figures 4B and C show the influence of a dominant rate constant and of a representative “neutral” rate constant , respectively ) ., We thus conclude that the time-limiting steps in virion maturation ( with current maturation criteria ) are the initial auto-cleavage of full-length Gag-Pol and the processing of the CA/SP1 cleavage site ., Given the comparable magnitude of the impact of both dominant parameters on VMT , any effect ( mutation or drug ) involving one of the rate constants might be compensated by a change in the other ., We investigated the potential for such compensation and for interactions ( synergy 29 , 30 or antagonism ) between the rate constants ., Figure 5 shows isoclines of VMT ( isoboles 30 ) with the rate constant of Gag-Pol auto-cleavage plotted against the CA/SP1 catalytic rate constant , with all other parameters fixed at their defaults ., All points of an isocline yielded a fixed VMT ( analogous to isoboles of combined drug doses of equal activity 30 ) ., The isoclines are hyperbola-like functions with both vertical and horizontal asymptotes ., This shape of the functions implies that for any given VMT , there is a minimum value for both parameters needed to achieve maturation within that given time; the vertical asymptotes indicate the minimum rates for CA/SP1 cleavage , the horizontal asymptotes indicate the minimal rate constants for Gag-Pol auto-cleavage ., Close to the asymptotes , the corresponding slow rate becomes rate limiting , and very small changes in the limiting rate constant can only be compensated by large changes in the other parameter to maintain VMT ., The default ( empirical ) parameter setting happens to fall in the regime where both parameters have comparable effect ., This result indicates that small decreases in either rate constant ( by drug or mutational effect ) can be compensated by increases in the other parameter; however , compensation becomes increasingly difficult and eventually impossible as the affected rate parameter approaches its critical ( asymptotic ) value ., Even where compensation is possible in terms of VMT , the time course of Gag-Pol processing cannot be forced to return to the original behaviour ., When a change in one of the dominant parameters is compensated by a change in the other to yield the same VMT , the time course of the process remains different from that obtained with the default parameters ( Figure S3 ) ., “Complete” compensation of the time course would be possible only between parameters that have very similar local sensitivity functions 42; however , the local sensitivity functions of the two dominant rate constants have different shapes ( Figure S4 ) ., While some of the other parameters have similar sensitivity functions ( for example , the sensitivity function of the Gag-Pol dissociation rate constant has similar shape to that of the catalytic rate constant of Gag-Pol auto-cleavage ) , the small magnitude of the effect of these on VMT precludes any meaningful compensation of changes in either of the dominant rate constants ., We next investigated the potential interactions between the effects of the two dominant parameters when both are changed ., In particular , we tested whether combined changes are characterized by either of two simple types of interaction: additive or multiplicative effects ., We used the following simple definitions for the two types of interaction: using the notations VMTdef , VMTA , VMTB and VMTAB to denote VMT obtained with the default parameters , with one , the other , or both of the parameters changed to a defined extent , we denote the absolute changes in VMT due to changes in each parameter with d1\u200a=\u200aVMTA-VMTdef and d2\u200a=\u200aVMTB-VMTdef , and the fold changes with f1\u200a=\u200aVMTA/VMTdef and f2\u200a=\u200aVMTB/VMTdef ., The expected VMT when both parameters are changed is then VMTAB\u200a=\u200aVMTdef+d1+d2 under the additive model , and VMTAB\u200a=\u200aVMTdef*f1*f2 under the multiplicative model ., We use the comparison with these two simple reference cases to illustrate the nature of the interaction depending on the direction of intervention and possible compensatory effects ., We varied both parameters along a geometric series ranging from 0 . 16 to 6 . 25 times the default value , both separately and in all possible combinations ., We used the results from the univariate series to predict the effect of combined changes assuming both additive and multiplicative effects , and tested the deviation of the simulations with combined changes from both predictions ( Figure 6 ) ., We found that the additive model fits qualitatively better when both parameters are changed in the same direction ( both increased or both decreased; Figure 6A ) , while the multiplicative model fits better when one parameter is increased and the other decreased ( Figure 6B ) ., Two scenarios might be most relevant biologically ., First , compensatory mutations in one parameter might restore VMT in the presence of drugs or mutations that decrease the other parameter ., In this case , one parameter is decreased and the other increased , which results in multiplicative interactions , consistent with the shape of the VMT isoclines ( Figure 5 ) ., This implies that a given fold increase in one of the parameters can be compensated by a similar factor of decrease in the other parameter to restore the default VMT ., Second , combinations of drugs might target both rates in concert , which corresponds to a decrease in both parameters ., For this scenario , our results predict additive effects: the increase in VMT induced by such a combination can be approximated by the sum of the increases induced by monotherapy with the individual drugs ., Synergistic drug effects are not expected ., The modelling framework also allowed us to characterize the effect of PIs ., As a test case , we selected darunavir , which is a potent inhibitor of HIV-1 PR 43 , 44 ., Figure 7A depicts the dependence of virion maturation time on the concentration of darunavir ( red symbols ) in the model ., The response is very steep: VMT rises from the default value to infinity within about an order of magnitude range ( ∼0 . 01–0 . 1 mM ) of the drug concentration , which is consistent with the steep dose response curves observed for PIs 27 ., However , the PI concentration , where maturation is lost in the model is several orders of magnitude higher than the IC50 estimated for darunavir in infected cells in vitro 44 , which calls for an explanation ( see below ) ., The vertical asymptote where maturation fails to occur is very close ( at ∼0 . 12 mM ) to the possible maximal concentration of PR dimers ( at ∼0 . 095 mM; half of the initial Gag-Pol content ) , which implies that the majority of the enzyme needs to be blocked by the highly efficient inhibitor , if slower maturation still produces viable virions ., This situation corresponds to the “critical subset” model of drug action 45 , 46 , which applies when enzyme function is insensitive to the drug concentration as long as a critical subset of enzyme molecules is unbound , but is lost quickly in the regime where the increasing drug concentration saturates the critical subset ., Approximating the critical subset with the concentration of PR dimers that remain free in the presence of varied concentrations of the drug , the size of the subset is predicted to be around 30 PR dimers , if VMT\u200a=\u200a60 min is required for viability , or around 15 dimers , if VMT>100 min is still tolerated ( Figure S5 ) ., This result also predicts that the critical drug concentration needed to block virion maturation depends approximately linearly on the initial Gag-Pol content , and mutations affecting Gag-Pol frameshift will therefore have limited potential to compensate for the effect of PR inhibitors 47 ., Figure 7B confirms this prediction ., While the shape of the dose response curve was consistent with the observations , there was a strong quantitative discrepancy between the model predictions and the empirical dose response observed in vitro ., In the simulations , inhibition occurs where drug concentration is in the range of the maximal PR concentration ( corresponding to half of the initial Pol content ) , which is in the ∼0 . 1 mM range ., In contrast , in vitro experiments estimated an IC50 ( half maximal inhibitory concentration ) for darunavir in the nanomolar range 44 , which implies a four to five orders of magnitude discrepancy between the estimates ., The critical drug concentration in the model depends only on the assumption that a single drug molecule binds to and blocks a single PR dimer , and on the estimated Pol content ( ∼120 molecules ) of a single virion ., For a nanomolar drug concentration to take effect , a single molecule of drug should be able to block 104–105 PR dimers; in fact , a nanomolar concentration would imply that the average drug content of individual virions would be well below a single drug molecule per virion ( which would correspond to a “concentration” of ∼1590 nM ) ., That is , most virions would contain no drug molecules , unless there is drug enrichment ., This result is independent of the details of the model , and the discrepancy highlights an important additional process , which has been overlooked previously ., We propose that at low ( nanomolar ) drug concentrations in the medium , the critical drug concentration within the virion can be generated by diffusion and ( near ) irreversible binding to PR , which together result in the accumulation of drug from the surrounding medium to a form bound to PR in the virion ., Darunavir has relatively high membrane permeability 48 and can even accumulate within cells 49 , 50 ., Assuming a drug concentration of 5 nM in the medium , and free diffusion of darunavir to the nascent virion , we calculate that the critical concentration ( ∼0 . 1 mM; a 2×104 fold enrichment ) required to block maturation can accumulate and bind PR in as little as a few minutes ( Materials and methods ) ., The rate limiting step is the association of the drug to PR , rather than diffusion to the virion , and the concentration of unbound PR is approximately halved per minute ., Assuming that the critical subset comprises 1/2 , 1/4 , 1/8 of the total PR pool , it would thus take about 1 , 2 or 3 minutes to accumulate the critical drug concentration needed to inhibit maturation ., This simplistic calculation provides a lower boundary for the length of time when Gag-Pol processing is susceptible to the drug effect 51 ., Note , however , that the beginning of the susceptible period might precede the budding of the virions , if the PR embedded in Gag-Pol can already be targeted by the PI within the cell ., More realistic estimates for diffusion ( that take into account possible barriers or the extensive binding of darunavir to proteins 48 ) might in the future provide additional insight on the time window of susceptibility to PIs during the viral life cycle ., The model can also be used to predict the dependence of the drug effect on the binding affinity ( parameterized by the dissociation rate constant ) of the drug ( Figure 7C ) ., We found that the response to changes in the dissociation rate constant is similarly critical ( steep ) as to the concentration of the drug ., Furthermore , the critical binding affinity required for the inhibition of maturation is several orders of magnitude lower than the estimated binding affinity of darunavir ( and other potent drugs ) , which indicates that potent PIs operate with a broad “safety margin” ., This is consistent with the observation that for darunavir a nearly 1000-fold decrease in binding affinity did not translate into a weaker antiviral activity 43 , and might contribute to the relatively high genetic barrier of the drug ., Implementing a hypothetical inhibitor that binds to full-length Gag-Pol to block the initial auto-cleavage produced dose response curves of very similar shapes; however , such inhibitors require much stronger binding affinity ( close to that of darunavir ) to take effect on VMT ( Figure 7C: blue symbols ) , have weaker effect at the same fixed affinity and concentration ( Figure 7A: blue symbols ) , and imply a smaller critical subset of unbound target molecules ( Figure S5 ) ., This difference probably arises because unbound Gag-Pol molecules that undergo auto-cleavage generate active PR forms that can no longer be targeted by an ( exclusive ) inhibitor of Gag-Pol; in contrast , unbound PR remains a target for PIs until the cleavage of its internal cleavage site , upon which protease activity is lost ., We also tested the potential of the catalytic rate constants to compensate the effect of PIs ( for example , by compensatory mutations in the cleavage sites 52 , 53 ) ., Figure 7D shows the compensation plots ( isoclines of VMT\u200a=\u200a30 min ) of both dominant catalytic rates against the concentration of darunavir , demonstrating a limited potential for compensation ., The vertical asymptote of the isocline for the CA/SP1 catalytic rate constant ( at 10−1 . 36≈0 . 0436 mM ) indicates that even an “infinite” catalytic rate could only compensate a drug dose of about 36% of the critical concentration ( 0 . 12 mM ) that inhibits maturation completely , and in vivo drug levels with current dosing are likely to exceed the critical concentration considerably ., The prediction of limited compensatory potential is in apparent contradiction with some empirical data that show clear compensation by substrate mutations in tissue culture and selection of such mutations in vivo 52 , 53 ) ; see the Discussion for a possible explanation ., Finally , we investigated whether a small initial inoculum of mature PR would be able to accelerate virion maturation ., Such an inoculum could potentially be derived either from the infecting virion or from Gag-Pol processing within the cell before virion assembly and budding ., Figure S6 illustrates that a small initial inoculum has only a modest effect on the time to virion maturation; for example the addition of PR corresponding to 10% of Gag-Pol content reduces VMT from 30 min to about 25 min ., A greater initial concentration of PR is unlikely at the beginning of the maturation process , given that premature proteolysis prior to confining the components in an assembling virion abolishes particle formation 54 , which suggests that the bulk of proteolysis of virion associated proteins only occurs in the assembled virion ( at or shortly after the time of budding ) ., We therefore conclude that an initial inoculum of PR is unlikely to contribute substantially to proteolytic activity during maturation ., The time scale of virion maturation therefore depends on Gag-Pol auto-cleavage within the virion , as has been assumed in our model ., This result is also consistent with the observation that N-terminal cleavage follows first-order kinetics in protein concentration 8 , 9 , which implies that the dominant mechanism is intramolecular , rather than heteromolecular cleavage ., Our simulations of Gag-Pol processing are consistent with most of the known features of Gag-Pol processing ( approximate time scale , order of release of final products ) , and can offer important insights into further details of the process that are not amenable to empirical study ., In particular , we predicted the rate limiting steps in the maturation process , and our results suggest that the auto-cleavage of Gag-Pol dimers and the PR catalyzed cleavage at the CA/SP1 site are the most promising candidates for future drugs that would target individual steps of the proteolytic process ., Importantly , bevirimat , the first clinically tested HIV-1 inhibitor that targets an individual cleavage site , as well as the chemically unrelated inhibitory compound PF-46396 , affect cleavage at the CA/SP1 boundary 4 , 5 ., Unfortunately , drug combinations ( or mutations ) that inhibit or impair both dominant steps are not predicted to have a synergistic effect ., Our model also provides a simple mechanistic explanation for the steep dose response curves associated with PIs 27 , and highlights the importance of diffusion mediated drug accumulation in the virions ( or in the infected cells before virion budding ) , which calls for further analyses ., We demonstrated that the maturation process is robust with respect to variation in Gag content , and therefore also to stochastic biological variations in virion assembly , and showed that a small initial inoculum of mature PR is unable to “kick-start” the process ., The model predicted that intermediate PR forms ( with uncleaved C termini ) may contribute substantially to proteolytic processing ( this result clearly depends on the assumption that such intermediate forms have efficient catalytic activity 8 , 55 ) ., We also found that the self-cleavage of PR results in a loss of PR activity after the completion of maturation , which might be an evolutionary adaptation to avoid the loss of RT activity due to the cleavage of all p66 monomers and the possible loss of CA monomers due to cleavage of CA at non-canonical internal cleavage sites 17 ., Finally , we were also able to predict the compensatory potential between drugs or mutations that affect the rate limiting steps or block PR activity ., The compensatory potential of mutations affecting Gag-Pol frameshift 47 could also be investigated with the models ., While our “full model” of Gag-Pol processing provides valuable insights , this simplistic modelling approach clearly has a number of limitations ., The use of mass action reaction kinetics assumes a well-mixed homogeneous system with concentrations described on a continuous scale , while both immature and mature virions have organized spatial structure 21 , 40 , 56 , 57 and the number of enzyme and substrate molecules within a virion has a limited discrete scale of the order of hundreds and thousands , respectively 22 , 31 ., These constraints are likely to affect HIV-1 proteolytic processing and limit the validity of our model predictions 58 ., To mitigate these limitations , the low number of interacting molecules could be addressed relatively simply by discrete stochastic modelling 59 , while the introduction of explicit space would require a major re-structuring of the model , and might be a promising direction for further study ., Furthermore , the criteria that we used for maturation may have been incomplete ., Our criteria for VMT involved the steps of proteolytic processing required for the morphological maturation of the capsid 25 , 31 , 35 , but not the time needed for the assembly of the mature capsid ., However , two observations indicate that assembly of the mature cone probably does not take very long: EM analyses have never revealed distinct maturation intermediates and in vitro assembly of CA seems to be very rapid following induction by high salt 60 ( although assembly must be induced by a different trigger in vivo ) ., Given these caveats , our definition of VMT based on processing criteria can be regarded as a lower bound but is likely to b | Introduction, Results, Discussion, Materials and Methods | Proteolytic processing of Gag and Gag-Pol polyproteins by the viral protease ( PR ) is crucial for the production of infectious HIV-1 , and inhibitors of the viral PR are an integral part of current antiretroviral therapy ., The process has several layers of complexity ( multiple cleavage sites and substrates; multiple enzyme forms; PR auto-processing ) , which calls for a systems level approach to identify key vulnerabilities and optimal treatment strategies ., Here we present the first full reaction kinetics model of proteolytic processing by HIV-1 PR , taking into account all canonical cleavage sites within Gag and Gag-Pol , intermediate products and enzyme forms , enzyme dimerization , the initial auto-cleavage of full-length Gag-Pol as well as self-cleavage of PR ., The model allows us to identify the rate limiting step of virion maturation and the parameters with the strongest effect on maturation kinetics ., Using the modelling framework , we predict interactions and compensatory potential between individual cleavage rates and drugs , characterize the time course of the process , explain the steep dose response curves associated with PR inhibitors and gain new insights into drug action ., While the results of the model are subject to limitations arising from the simplifying assumptions used and from the uncertainties in the parameter estimates , the developed framework provides an extendable open-access platform to incorporate new data and hypotheses in the future . | Human Immunodeficiency Virus ( HIV ) produces its structural proteins and key enzymes in the form of polyproteins , from which the individual proteins need to be released in a complex and tightly regulated series of cleavage reactions to give rise to a morphologically mature , infectious virus particle ., This process is catalyzed by a viral protease ( PR ) , which is itself embedded in one of the polyproteins , and is one of the main targets of antiretroviral drugs ., We have developed the first full reaction kinetics model that addresses the several layers of complexity ( multiple cleavage sites and substrates; multiple enzyme forms; PR auto-processing ) associated with the proteolytic processing of HIV polyproteins ., The model allows us to identify the rate limiting step of virion maturation and the parameters with the strongest effect on maturation kinetics ., We predict how changes in the individual cleavage rates and the effects of drugs might interact and possibly compensate each other , characterize the detailed time course of the process , and explain why the effectiveness of PR inhibitors rises very steeply at a critical threshold concentration of the drugs ., These new insights promote our understanding of the viral life cycle and may guide the future development of antiviral drugs . | systems biology, biochemical simulations, immunodeficiency viruses, virology, microbiology, biology, computational biology | null |
journal.pcbi.1004752 | 2,016 | Simulation and Theory of Antibody Binding to Crowded Antigen-Covered Surfaces | Because of their prominent role in the human immune system , antibodies are among the most important biomolecules ., Like other large complex proteins , they are increasingly being exploited in modern nanobiotechnology 1 and biomedical 2 applications ., Antibodies are large molecules , whose flexibility is deeply related to their function , granting them enhanced potency 3–6 and astonishing abilities , from binding an extremely diverse palette of antigens 7 to walking on antigen-covered surfaces 8 ., In general , understanding the details of antibody flexibility and the associated limitations can inform the design of antiviral vaccines and therapies 3 ., Unfortunately , simulating many large molecules interacting with one another is a challenging task at present , because even single , medium-size proteins can be simulated at atomistic resolution only for time scales that are several orders of magnitude shorter than the processes they are involved in 9 ., Any description of more articulated systems , composed by several different proteins in mutual interaction goes beyond the possibilities of any detailed simulations ., As a consequence , novel approaches are necessary that allow spanning longer timescales and accounting for more complex settings ., Coarse-graining ( CG ) has come to the fore in recent times as a promising strategy for the simulation of large proteins and of protein complexes 10–19 ., A coarse-grained model is built by neglecting all details below a selected length scale ., Residue based CG 20 , 21 , for example , describes amino-acids as simple beads of a radius that reproduces that of the original residues and positioned at the coordinates of the Cα atoms or of the amino-acid center of mass ., Because of the massive reduction of degrees of freedom and the simplification of the corresponding force-fields , CG schemes can access much longer timescales , at the obvious price of a loss of detail ., Yet , this is not necessarily a limitation , as long as such approaches aim at addressing phenomena whose length scale is consistent with the CG simplification of the system ., Extreme applications of CG have , for example , made possible the simulation of a crowded cellular cytoplasm with the aim of estimating the diffusion constant of proteins 22 , 23 ., In this work we introduce a novel CG model of IgG antibodies , which are large molecules composed of three domains 24–27: two identical Fab arms , that bind antigens , connected to the Fc stem by a hinge region ( Fig 1A ) ., Our CG model is based on the results of recent cryo-electron tomography experiments 28 , 29 , and show that a careful reduction of the system complexity brings within reach a problem that would otherwise be intractable , namely the collective binding of antibodies to antigens distributed on a surface , with account of both the internal dynamics of IgGs and their mutual excluded volume on the surface ., We use our results to validate an analytical model of the reaction kinetics that goes beyond the ones that have been proposed to date , and to provide a more rigorous interpretation of experiments from the literature ., A suitable modeling of IgGs has to take necessarily into account their great flexibility , which has been highlighted in several cryo-electron tomography ( cryo-ET ) 28 , 29 and AFM 30 experiments ., In particular , Cryo-ET reconstructions have provided access to the probability distributions of the angle formed by the two Fab domains ( ψ ) and the one between Fab and Fc domains ( ϕ , see Fig 2 ) ., Here we devise a coarse-grained model where each domain is described as a rigid hard body ., More specifically , the three domains are modeled by revolution ellipsoids ( Fig 1B ) , two prolate ( Fabs ) and one oblate ( Fc ) , whose dimensions have been tuned so as to fit the hydrodynamic sizes measured in sedimentation experiments 31 ( see Methods for more details ) ., In close analogy with real antibodies , the three domains are joined by a flexible hinge designed so as to keep the three domains at distances compatible with the steric constraints that can be evinced from X-ray 27 and Cryo-ET experiments 28 , 29 ., The interaction site for antigen binding is described as a spherical surface ( spot ) on the tip of the Fab domains ( see Fig 1B ) , which corresponds to a radial piece-wise constant attractive potential of finite range ( see Methods for more details on the CG architecture , and Fig 1 for a comparison with the atomistic crystallographic structure , PDB 1IGY ) ., In our model , excluded volume is the only interaction between the three domains within a single IgG molecule ., Nonetheless , Event-Driven Brownian dynamics ( EDBD ) simulations of a single antibody showed clearly that our flexible model is able to reproduce the experimental statistics of ψ and ϕ angles from Cryo-ET measurements ( Fig 2 ) ., It is worth stressing that utterly reasonable angular distributions can be obtained solely by enforcing inter-domain steric effects that take into account, ( i ) the correct shape of the three domains and, ( ii ) the appropriate size of the hinge region ., The weaker contributions from inter-domain potential energy terms highlighted in Ref ., 28 thus only bring about minor modifications that can be safely ignored in the context of this work ., It has now been established experimentally that the binding of antibodies to antigens adsorbed on a surface is a complex phenomenon , with contributions from monovalent IgGs binding , i . e . by means of a single Fab arm , as well as from bivalent IgGs binding , where both Fab arms are bound , each to a different antigen 32 ., The relative equilibrium weight of the two binding arrangements depends on the surface density of antigens and simplified kinetic models have been proposed in the literature 32 , 33 ., Our CG IgG model makes it possible to investigate in silico the behavior of ensembles of antibodies binding to a surface with surface-adsorbed antigens , reproducing exactly the experimental conditions found , e . g . , in Ref 32 ., Accordingly , we simulated N0 = 250 IgGs that freely diffuse in a box of side L and that can bind to antigens randomly distributed on the bottom surface at an assigned surface density σ ., Assuming that the size of Fabs’ semi-axes is 1 nm , the concentration of IgG in our simulations box is 0 . 4 mM ., Surface density of antigens typically ranges from 10−9 mol/m2 to 10−7 mol/m2 , which are comparable with common values on cell surfaces ( around 1 . 5 × 10−9 mol/m234 ) and virus capsids ( up to 10−7 mol/m235 ) ., For influenza A virus , for example , the density is around 10−8 mol/m2 , while for HIV is around 10−10 mol/m23 , while it is between 10−9 and 10−7 mol/m2 on chips used in Surface plasmon resonance ( SPR ) experiments 32 , 36 ., The antigens interact with the Fab tips of incoming antibodies through square-well potentials of prescribed depth , which fixes the kon and koff rates of antigen-antibody binding kinetics ., The molecular-level description of the system allows us to measure the precise number of monovalent ( N1 ) and bivalent ( N2 ) bound antibodies at equilibrium for each value of σ ., Furthermore , we can gauge how the ability of antibodies to bind to the antigens is modulated by, ( i ) intra-IgG flexibility and, ( ii ) inter-IgG excluded volume ., Further details are provided in the Methods section ., Binding of antibodies to surface-adsorbed antigens can be pictured as a two-step process , as sketched in Fig 3 ., First , antibodies diffusing in the bulk can encounter an epitope on the surface and bind to it through one of their Fab domains ., As long as they remain bound , the second Fab domain has an opportunity to bind to any other epitope that lies within reach ., As a consequence , the equilibrium surface concentration of bound IgG molecules , which increases with the surface concentration of antigens , is the sum of two contributions ., This is illustrated in Fig 4, ( a ) ., Antibodies bound through a single Fab dominate at low surface concentrations because there are few reachable epitopes for the second Fab domain ., As σ increases , the number of double-Fab bound antibodies increases ., At surface concentrations greater than ≈ 2 × 10−8 mol/m2 , bivalent binding dominates because there are ample opportunities for the second Fab to bind through the fast exploration of a reduced volume ., Concomitantly , the number of monovalent bound antibodies decreases as σ increases ., Within our scheme , the role of intramolecular flexibility can be easily addressed ., We found that the ability of the three domains to change their relative angles strongly enhances their ability to bind antigens ., Rigid antibodies with Fab-Fab and Fab-Fc angles restrained at 120° ( see also Fig 2 ) exhibit a greatly reduced ability to bind antigens ( Fig 4, ( b ) ) ., In particular , the population of bivalently bound IgGs is significantly depressed with respect to flexible antibodies , with the majority of antibodies being bound by a single Fab arm ., Even at large surface concentrations σ , the equilibrium populations of single and double-bound rigid IgGs remain of comparable magnitude ., We conclude that double binding , which relies on the ability of single-bound antibodies to scan the surface in the proximity of the bound antigen , is greatly favored by the flexibility of the hinge connecting the Fab and Fc domains ., This is an important conclusion , as it holds for any sort of multi-valent surface , such as the surface of large viruses 37 ., In the following we describe a theoretical model that captures all the salient features of the IgG binding kinetics and reproduces perfectly our simulations ., This model is a working tool that can be adapted to make quantitative predictions in many situations involving multivalent binding surfaces ., It is possible to shed further light on the observed binding equilibrium and to obtain more quantitative information by means of a simple analytical model ., As illustrated in Fig 3 , the surface concentration of single-Fab bound antibodies , σ1 ( t ) , evolves in time through exchange with antibodies in the bulk ( volume concentration ρB ( t ) ) , increasing upon IgGs binding at rate k 1 on and decreasing at rate k 1 off due to IgGs detaching from the surface back to the bulk ., As well , σ1 ( t ) varies in time through exchange with bivalent bound antibodies ( surface concentration σ2 ( t ) ) , decreasing because of the binding of the second Fab arm at rate k 2 on and increasing because of its unbinding , at rate k 2 off ., The corresponding rate equations read, { d σ 1 d t = 2 k 1 on ρ B σ av − k 1 off σ 1 + 2 k 2 off σ 2 − k 2 on σ σ 1 ( 1 ) d σ 2 d t = k 2 on σ σ 1 − 2 k 2 off σ 2 ( 2 ) d ρ B d t = k 1 off σ 1 − 2 k 1 on ρ B σ av ( 3 ), where σav ( t ) is the surface concentration of available antigens, σ av ( t ) = σ - σ 1 + 2 σ 2 - σ π ℓ 2 σ 1 + γ σ 2 ( 4 ), The second term in the r . h . s . of Eq ( 4 ) takes into account the antigens that are bound to monovalently and bivalently attached IgGs ., The third term accounts for antigens that are unbound but also unavailable , for screened by ( hidden below ) other antibodies fastened to neighbouring antigens ., In order to describe such screening , we assume that a single-Fab bound IgG screens a circular patch of radius ℓ and that a double-Fab bound IgG screens a disk of radius ℓ γ ., For what concerns the second binding , we assume that , once the first Fab is attached , the second Fab always sees the face-value surface concentration of antigens ., This is due to the effective steric repulsion acting among IgGs bound on the surface , which makes the fraction of bound epitopes in an area within reach of the second Fab negligible ., The rate equations ( 1 ) , ( 2 ) and ( 3 ) are obviously not linearly independent because the total number of antibodies in the simulation box is constant , i . e . L3 ρB ( t ) +L2σ1 ( t ) +σ2 ( t ) = N0 ., The stationary rate equations have to be solved with the above constraint on the total number of particles ., Setting σ0 = N0/L2 , we obtain, { ( σ 0 − σ 1 − σ 2 ) 1 − σ 1 + 2 σ 2 σ − π ℓ 2 ( σ 1 + γ σ 2 ) = ( K 1 L 2 σ ) σ 1 ( 5 ) σ 2 = ( σ 2 K 2 ) σ 1 ( 6 ), where we have introduced the two dissociation constants, K i = def k i off k i on i = 1 , 2 ( 7 ), As a consequence of our assumption about the role of steric repulsion on the second binding , we see that our model predicts that the ratio σ2/σ1 should be linear with the antigen surface concentration σ with slope 1 / 2 K 2 ( see eq ., ( 6 ) ) ., In Fig 5 we plot the results of EDBD simulations , which show excellent agreement with our model and thus confirm the soundness of our hypothesis ., A straightforward calculation shows that, σ 1 σ 0 = 2 K 2 2 K 2 + σ G ( σ ) σ 2 σ 0 = σ 2 K 2 + σ G ( σ ) ( 8 ), where, G ( σ ) = P ( σ ) + Q ( σ ) - P ( σ ) + Q ( σ ) 2 - 4 Q ( σ ) 2 Q ( σ ) ( 9 ), with, P ( σ ) = 1 + K 1 K 2 L σ ( σ + 2 K 2 ) Q ( σ ) = 2 σ 0 ( K 2 + σ ) + σ 0 π ℓ 2 ( 2 K 2 + γ σ ) σ σ ( 2 K 2 + σ ) ( 10 ) Fig 4 shows that the model embodied by Eq ( 8 ) provides an excellent interpolation of the EDBD simulations ., Best-fit values of the floating parameters are reported in Table 1 ( see Methods for details on the fitting protocol ) ., In our model , Fab arms are represented by prolate ellipsoids of aspect ratio 0 . 5 , whose minor semi-axis is our unit of length ., Therefore , in order to express our parameters in physical units , we have to estimate the length of a Fab arm , which is reasonably located between 6 and 7 nm ., We note that the free energy changes associated with monovalent Fab ( ΔG1 ) and bivalent Fab ( ΔG2 ) binding equilibria estimated from our model compare rather well with the experimental values reported in 32 , namely ΔG1 = −26 . 0 ± 0 . 4 kJ/mol and ΔG1 = −44 . 4 ± 0 . 7 kJ/mol ., Our model predicts a decrease in free energy of the second binding event about twice greater than the first one ( see last column in Table 1 ) , in good agreement with the experiments ( ΔG1/ΔG2 = 0 . 58 ) ., It is then apparent that our model correctly captures the physics of the double-step kinetics uncovered in the experiments reported in 32 ., Incidentally , we note that the potential well describing the binding of a Fab tip with a surface-adsorbed hapten in our model could be easily tuned so as to obtain exactly the observed free energy changes as those measured in 32 ., As already observed , and in agreement with physical intuition , for rigid IgGs the second step in the association kinetics ( binding of the second Fab ) becomes strongly inhibited ., This is now confirmed quantitatively by fitting the analytical model onto the simulations , which yields a five-fold increase of the dissociation constant K 2 ( see Table 1 ) ., Furthermore , the fit highlights that the radius of the screening patch ℓ and γ depend as expected on the degree of flexibility of the antibodies ., Monovalently bound rigid IgG molecules conceal a larger surface than flexible ones , ℓ being just about the length of one Fab arm ., However , when the molecules establish a second binding on the surface , flexible IgGs screen a greater portion of the surface to other IgGs in the bulk than flexible single-Fab bound molecules do ( about 1 . 6 greater ) ., Rigid IgGs are seen to screen just about the same fraction of the active surface irrespective of their binding configuration ., We observe that for vanishing antigen concentration , our model predicts Nk ∝ σk , k = 1 , 2 ( see insets in Fig 4 ) ., More precisely , a Taylor expansion for small values of σ shows that , for σ → 0, σ 1 ≃ 2 ρ B K 1 σ σ 2 ≃ ρ B K 1 σ 2 K 2 ., ( 11 ), The above expressions can be used to compute a simple approximation for the fraction of bivalent bound IgG molecules , f 2 m ,, f 2 m ≡ σ 2 σ 1 + σ 2 = σ 2 K 2 + σ ( 12 ), The fraction of bivalently bound sites on the surface , as measured e . g . in Ref ., 32 , f 2 s = 2 f 2 m / ( 1 + f 2 m ) , can be computed in a similar fashion , yielding, f 2 s ≡ 2 σ 2 σ 1 + 2 σ 2 = σ K 2 + σ ( 13 ) Remarkably , a comparison of our results with the experimental data reported in the paper by Yang et al . 32 , provides a further validation of our theoretical and numerical schemes ., Fig 6, ( a ) shows that , in order to capture the experimentally measured fraction of bivalently bound sites ( or , equivalently , molecules ) , flexibility of IgGs is the primary requirement ., Interestingly , Fig 6, ( b ) shows that when the surface density of haptens is normalized to the dissociation constant K 2 , all different models and the experiments collapse on a single curve ., This seems to suggest that K 2 contains all the relevant physics underlying the dynamics of the second binding ., However , a moment’s thought is enough to realize that this conclusion is wrong ., On the one hand , we have already seen that the separate binding profiles of single-arm and double-arm bound antibodies are profoundly influenced by the inherent flexibility of the molecules at high values of σ ( see again Fig 4 ) ., On the other hand , rather surprisingly , it is evident from Fig 6, ( b ) that the master curve is extremely well approximated by the low-σ prediction , Eq ( 12 ) , over the whole concentration range ., This indicates a compensation which is inherent to the specific normalization of the measures Eqs ( 12 ) and ( 13 ) , that , being the latter relative indicators , conceal in the normalization the geometrical constraints that govern the large-σ regime ., The important and non-obvious conclusion is that observables such as f 2 m and f 2 s cannot be used to disentangle the specific contributions of single and double binding ., Summarizing so far , we have introduced a kinetic scheme that describes both the kinetics of flexible and rigid IgGs ., The crucial difference between the two models is that the dissociation constant for the second binding of rigid IgGs ( K 2 = 5 . 45 × 10 - 8 mol/m2 ) is about five times greater than the one for flexible IgGs ( K 2 = 1 . 06 × 10 - 8 mol/m2 ) , which agrees well with the experimental value reported in 32 ,, K 2 = 0 ., 69 × 10 - 8 mol/m2 ., Moreover , we have seen that looking at relative , normalized indicators , such as the fraction of bivalently bound antibodies , can be misleading , as relevant geometrical information is likely to be lost in the normalization ., The previous results clearly highlight the role of excluded volume on the antigen-antibody binding dynamics ., To fully appreciate the impact of the mutual steric hindrance of antibodies on binding , we performed simulations with ghost flexible IgGs ., In this scheme , the two Fabs and the Fc belonging to a given molecule interact with each other normally , so as to ensure the correct internal dynamics , but are transparent to domains belonging to other molecules ., Therefore , a ghost IgG diffusing from the bulk will not see any of the available sites screened ., Based on our theoretical arguments , one would expect that the binding equilibrium of ghost antibodies should be described by the solution of Eqs ., ( 5 ) and ( 6 ) with ℓ = 0 ( no steric obstruction ) ., However , the rate equation for the second binding should also be modified due to the absence of steric repulsion on the surface ., In fact , ghost IgGs trying to bind their second Fab are insensitive to the presence of neighboring bound IgGs ., Thus , they are expected to probe the available density of binding sites and not the nominal one ., In this case , the equilibrium surface densities should ensue from the following stationary equations ( compare to Eqs ., ( 5 ) and ( 6 ) ), ( σ 0 - σ 1 - σ 2 ) 1 - σ 1 + 2 σ 2 σ = K 1 L 2 σ σ 1 σ 2 = σ 2 K 2 1 - σ 1 + 2 σ 2 σ σ 1 ( 14 ), The results of the simulations are reported in Fig 7 ., It is manifest that the solutions of Eq ( 14 ) provide a perfect fit to the simulations , confirming our physical intuition ., At low hapten concentrations , there is virtually no difference between the binding of ghost and hard-core IgG molecules , because bound antibodies are , on average , far from each other ., Hence σk ∝ σk at low values of σ ( see also the inset in panel, ( b ) ) ., At larger hapten densities , instead , ghost molecules can bind significantly more than non-ghost ones ( compare with Fig 4 ) ., This also appears as a marked deviation from the low-σ regime of linearity in the plot of σ2/σ1 vs σ ( see panel, ( a ) ) ., Interestingly , the equilibrium constants K 1 and K 2 do not change appreciably by eliminating excluded-volume interactions at the surface ( see Table 1 ) ., As it is evident from the predictions Eq ( 11 ) , the two dissociation constants are essentially fixed by the low-σ regime ., Therefore , we draw the important conclusion that it is flexibility that gauges the magnitude of the dissociation constants ( especially so concerning K 2 ) ., However , these do not tell the whole story , as the screening effect regulates the binding equilibrium at higher values of σ ., This confirms that excluded-volume screening of otherwise free antigens on the surface is a contribution that is not negligible and that should mandatorily be taken into account in any modeling of the IgG binding dynamics on crowded surfaces ., In this paper we have introduced a coarse-grained model of immunoglobulin G ( IgG ) molecules , realized by fastening three ellipsoids with the proper aspect ratio together around a common hinge ., The purpose of our study is to shed light on the role of the IgG flexibility and large size on its ability to bind to surface-absorbed antigens ., Our coarse-grained ( CG ) model is conceived explicitly so as to reproduce the distributions of inter-domain angles measured by cryo-ET ., In our simulations , a large number of IgGs diffuse in a given volume and a binding equilibrium is reached with antigens adsorbed at a given density on the bottom surface ., The equilibrium profiles of the surface concentrations of IgGs bound with one Fab and with both Fabs to the antigen-covered surface highlight the crucial role played by flexibility ., When compared with antibodies frozen in an equilateral triangular configuration , fully flexible molecules demonstrate a much higher ability to bind with both Fabs ., This is a direct result of a dynamic search process performed by the dangling second Fab of IgGs already bound with one arm ., This capability of adapting to irregular antigen configurations is quenched in the rigid molecules , which are only able to bind bivalently when they happen to find two antigens lying at the appropriate distance matching their fixed Fab-Fab angular aperture ., In order to shed light on the observed binding equilibrium , we formulate a two-step kinetic model , where IgGs first bind from the bulk to the surface with one Fab ( equilibrium dissociation constant K 1 ) and then double with the second Fab ( equilibrium dissociation constant K 2 ) ., Importantly , our model not only includes the information on the on and off rates , but also accounts explicitly for an important geometrical constraint , namely surface screening ., IgGs are large molecules and excluded-volume interactions , especially in the proximity of the antigen-covered surface , prove extremely important ., This effect is two-fold ., On the one side ,, ( i ) IgGs diffusing in the bulk see a number of available epitopes on the surface which is reduced with respect to the bare number of sites that are already occupied ., In fact , a substantial number of non-bound antigens are nonetheless de facto unavailable as a result of the large size of IgGs bound in their proximity , which make them invisible to other antibodies in the bulk ., Moreover ,, ( ii ) as a result of the IgG-IgG excluded-volume interactions at the surface , it turns out that the second Fab of a single-arm bound IgG always sees the face-value antigen concentration around , as it never gets to probe already occupied sites ., These are too far away on average as a result of the effective repulsion among bound IgG molecules on the surface ., We conclude that the large and extremely flexible three-lobe conformation of IgGs is accurately designed to afford bivalent binding and at the same time take advantage of excluded-volume interactions to a maximum ., This is probably the result of concurrent evolutive pressures towards smart antigen chasers capable of, ( i ) bind strongly , i . e . with two binding sites, ( ii ) bind differentially , i . e . bind to antigens of widely different sizes and, ( iii ) bind optimally , i . e . maximize the number of bound molecules at a given concentration of target density ., Our model is in excellent agreement with surface plasmon resonance experiments of IgGs binding to surface-adsorbed haptens ., We establish very clearly that flexibility is essential to reproduce the experiments ( see Fig 6, ( a ) ) ., Furthermore , our theory allows us to isolate the second binding as the key factor that makes flexible IgGs much more powerful antigen binders ., In fact , the dissociation constant measured from our simulations for rigid IgGs is five times larger than for the flexible ones ., A striking confirmation that the equilibrium constant for the second binding is the key factor can be gained by studying the equilibrium fraction of bivalently bound molecules ( or , equivalently , sites ) ., Once plotted against the antigen surface concentration rescaled by the dissociation constant K 2 ( see Fig 6, ( b ) ) , the three models , flexible , rigid and ghost fall on the same curve as the experimental data ( where we have used the experimental dissociation constant ) ., This remarkable fact proves very neatly that such relative measure conceals a great deal of the physics underlying the binding process ., The concealed information is instead conspicuous when one looks separately at the binding profiles , i . e . the average number of single-Fab ( N1 ) and double-Fab bound ( N2 ) IgGs against antigen concentration σ ., More precisely , the low-concentration regime turns out to be the same in the three models , namely N1 ∝ σ , N2 ∝ σ2 ., At low antigen coverage , it makes no difference at all whether IgGs are able to stretch their second arm to get hold of neighboring haptens or whether they repel each other on the surface ., Importantly , the low-σ regime fixes the two equilibrium constants K 1 and K 2 , but obviously bears no sign of the screening effect ., At increasing values of σ , the telltale signs of surface screening emerge clearly ., Fully flexible molecules take advantage of the combined effects of their flexibility and mutual repulsion , which essentially makes haptens within reach of the second Fab always unoccupied on average ., On the contrary , rigid IgGs remain largely unable to bind with both arms , despite their mutual exclusion , while ghost molecules take full advantage of their invisibility to bind with two Fabs , unphysically outperforming fully flexible antibodies at high densities ., We stress that our model includes in a natural way the kinetic parameters ( on and off rates for the two binding events ) and the key geometrical parameters ., This is at variance with an existing model of IgGs binding to antigen-covered surfaces 33 , which conceals the thermodynamic and geometrical information in one and the same parameter , namely an effective screening area ., As such , our model provides a more accurate and valuable theoretical framework to interpret experimental profiles of antibodies binding to multi-valent surfaces in different contexts ., Each domain of the IgG is modeled as a rigid hard body ., More specifically , the Fab fragments are modeled as two prolate ellipsoids while the Fc stem is modeled as an oblate ellipsoid ., All the three fragments are hard ellipsoids of revolution , characterized by an aspect ratio X0 = a/b , where a is the length of the revolution axis and b is the length of the two other axes ., For the Fab fragment we set X0 = 2 , while for the Fc lobe we used the values X0 = 1/2 , in agreement with the measured hydrodynamic radii 31 ., All lengths are measured in units of the minor semi-axis of one IgG Fab , which is our non-dimensional unit ( ndu ) ., As in the real antibody the three domains are joined by a flexible hinge ., This is realized by a spherical pivot , which is a particle with no steric hindrance ( i . e . a ghost particle ) and an attractive site of diameter 0 . 5 ndu , which forms an irreversible bond with the attractive sites placed on the three IgG fragments facing the hinge at a distance of 0 . 725 ndu from their surface ( Fig 1 ) ., In general , in our EDBD simulations two attractive sites of diameter δ1 and δ2 , which decorate two distinct particles , form a bond when their distance is less than ( δ1 + δ2 ) /2 ., The bond between the pivot and the sites located on the fragments is irreversible , i . e . it cannot be broken ., The size of the pivot and of the attractive patches on the fragments have been chosen in such a way that an offset between the principal axes of the ellipsoids is accounted for , as deduced from the cryo-ET experiments of Ref ., 28 ., Each Fab’s tip is decorated with one sticky patch , whose center lies on its surface along the symmetry axis ., Such patch may form a reversible bond with antigens placed on the bottom plane in the simulation box ( cube of side L = 101 . 235 ndu ) ., Antigens are modeled as attractive immobile sites ., The diameter δa of the antigens is assumed to be 0 . 8 ndu and the diameter δab of the patches on the Fab fragments is set to 0 . 6 ndu ., The energy associated with the formation of a antibody-antigen bond is set to 10 kBT ., We performed event-driven Brownian dynamics ( EDBD ) simulations ( see below ) with three different variants of the IgG model illustrated above , which we refer to as fully flexible ., In one variant , the ellipsoidal fragments belonging to different antibodies have no steric repulsion and can overlap ., This model is referred to as ghost ., In the second variant , the three fragments are fixed in a planar configuration where the three angles formed by the symmetry axes of the Fab and an axis perpendicular to the symmetry axis of the Fc fragment are all equal to 120° ., This is achieved by three additional attractive sites located on the fragments , which form irreversible bonds ., This model is referred to as the rigid model ., The IgG model we discussed above comprises excluded volume interactions , permanent bonds between attractive sites with infinite potential wells and reversible bonds implying finite-well potentials ., A valuable tool to simulate such particles is offered by event-driven molecular dynamics ( EDMD ) , especially in view of recent computational developments that allow one to simulate hard rigid objects ( HRB ) of generic shape decorated with attractive sites interacting with stepwise potentials 38–40 ., In 41 an algorithm to perform Brownian dynamics of hard spheres is discussed ., This algorithm has also been extended to anisotropic particles in 42 ., Here we use this algorithm to perform event-driven brownian dynamics ( EDBD ) of the ellipsoidal particles ( i . e . the Fab and Fc fragments ) which form the IgG ., The infinite-well sticky patches on the surface of an IgG keep it together , while the patches with finite-well potentials at the Fab tips bind to the surface-absorbed antigens ., In our EDBD simulations we set a scaling time for the translational and angular velocities of the ellipsoidal fragments ( see 42 for more details ) , which ensures that the typical displacement of their reversible sticky sites δs ( i . e . those that give rise to antibody-antigen bonds ) is smaller than the interaction range , i . e . δs ≪ ( δab + δa ) /2 ., The mass m of the three ellipsoids is equal and their moments of inertia are assumed diagonal and equal for all fragments ., The latter choice is justified by the fact that the equilibrium properties of the antigen-antibody system do not depend on the dynamics used to evolve the IgGs in time ., We co | Introduction, Results, Discussion, Methods | In this paper we introduce a fully flexible coarse-grained model of immunoglobulin G ( IgG ) antibodies parametrized directly on cryo-EM data and simulate the binding dynamics of many IgGs to antigens adsorbed on a surface at increasing densities ., Moreover , we work out a theoretical model that allows to explain all the features observed in the simulations ., Our combined computational and theoretical framework is in excellent agreement with surface-plasmon resonance data and allows us to establish a number of important results ., ( i ) Internal flexibility is key to maximize bivalent binding , flexible IgGs being able to explore the surface with their second arm in search for an available hapten ., This is made clear by the strongly reduced ability to bind with both arms displayed by artificial IgGs designed to rigidly keep a prescribed shape ., ( ii ) The large size of IgGs is instrumental to keep neighboring molecules at a certain distance ( surface repulsion ) , which essentially makes antigens within reach of the second Fab always unoccupied on average ., ( iii ) One needs to account independently for the thermodynamic and geometric factors that regulate the binding equilibrium ., The key geometrical parameters , besides excluded-volume repulsion , describe the screening of free haptens by neighboring bound antibodies ., We prove that the thermodynamic parameters govern the low-antigen-concentration regime , while the surface screening and repulsion only affect the binding at high hapten densities ., Importantly , we prove that screening effects are concealed in relative measures , such as the fraction of bivalently bound antibodies ., Overall , our model provides a valuable , accurate theoretical paradigm beyond existing frameworks to interpret experimental profiles of antibodies binding to multi-valent surfaces of different sorts in many contexts . | Antibodies are the main working horses of the human immune system ., Remarkably , no matter the size or the shape of the pathological intruders , these extremely flexible three-lobe molecules are able to form a complex , thus eliciting an immune response ., What makes antibodies so effective ?, To answer this and other questions , we have developed a simplified computational scheme to simulate the dynamics of many antibodies interacting with each other and with antigens ., Coarse-grained models are a great opportunity , as they give access to a true multi-scale approach to biologically relevant problems ., In this work , our innovative method allowed us to simulate the binding process of many antibodies to surface-adsorbed antigens ., This led us to elucidate and quantify many important physical aspects of their biological function in agreement with experiments , such as the role of their flexibility and crowding effects at the hapten-covered surface , which were shown to finely regulate the avidity . | biotechnology, medicine and health sciences, immune physiology, chemical compounds, small molecules, immunology, radii, geometry, organic compounds, ellipsoids, simulation and modeling, mathematics, antibodies, thermodynamics, research and analysis methods, immune system proteins, proteins, haptens, chemistry, free energy, physics, biochemistry, biochemical simulations, organic chemistry, physiology, biology and life sciences, physical sciences, computational biology | null |
journal.pgen.1003868 | 2,013 | Dominant Role of Nucleotide Substitution in the Diversification of Serotype 3 Pneumococci over Decades and during a Single Infection | Streptococcus pneumoniae is a human nasopharyngeal commensal and respiratory pathogen responsible for a high burden of morbidity and mortality worldwide ., Serotype 3 was one of the earliest pneumococcal capsule types to be identified 1 ., For some time , there was dispute over whether these bacteria should be considered a separate species , named Streptococcus or Pneumococcus mucosus 2 , and whilst such a separation cannot be justified on the basis of genetic divergence it does have distinctive morphological and epidemiological traits ., Bacterial colonies of this serotype have a characteristic mucoid phenotype when grown on agar , as the cellobiuronic acid polymeric chains of the capsule are not covalently attached to the cell wall 3 , 4 ., Unusually for S . pneumoniae , the risk of serotype 3 disease increases with age 5–8 , which may relate to the high immunogenicity of the capsule antigen in young children 9 ., Importantly , disease caused by this serotype has been consistently associated with a high relative risk of mortality in humans 5 , 6 , 10–13 , and correspondingly strains of this serotype are amongst the quickest to cause death in a mouse model of bacteraemia 14 ., The high level of mortality may stem from the high frequency with which serotype 3 isolates cause extrapulmonary manifestations of pneumococcal infection 15 , with evidence that the serotype is associated with causing brain abscesses 16–18 ., Whether these characteristics are the consequence of the capsule or the genetic background itself is difficult to study , because few genotypes are stably associated with the type 3 capsule ., The most common of these in the multilocus sequence type database 19 is represented by isolates of , or closely related to , sequence type 180 ( ST180 ) ; this lineage is therefore termed clonal complex 180 ( CC180 ) , or the Netherlands 3–31 ( PMEN31 ) clone 20 ., This lineage is geographically highly widespread , having been found across Europe 21 , Japan 22 and North and South America 21 , 23–25 ., Although it is not associated with penicillin resistance , macrolide resistant representatives of the genotype have been identified 22 ., Since 2001 , this lineage has been observed to increase in prevalence among invasive disease isolates from the USA following the introduction of the heptavalent conjugate vaccine , which does not protect against serotype 3 pneumococci 23 ., Higher valency anti-pneumococcal conjugate polysaccharide vaccine formulations targeting this capsule , such as the recently introduced 13-valent vaccine , appear to trigger only weak immune reactions to their serotype 3 components , hence it remains unclear how effective they will be against such pneumococci 26 , 27 ., Therefore to better characterise this unusual and important lineage , a complete reference genome was generated and compared to sequence data from 81 other representatives ., The complete genome of S . pneumoniae OXC141 , a serotype 3 ST180 carriage isolate from a child in Oxford , was generated using a combination of 454 and capillary sequence data ., The chromosome was found to be 2 , 036 , 867 bp long and contained 1 , 986 coding sequences ( CDSs; including 153 pseudogenes ) , alongside many small interspersed repeat elements: 122 BOX elements , 106 RUP elements and 29 SPRITE repeats 28 ., Two putative mobile genetic elements could be identified: the 34 kb prophage φOXC141 29 and a 6 . 3 kb island likely to be related to , or derived from , an integrative and conjugative element ( ICE ) 30 ., Two further large , distinctive gene clusters were also evident: a ∼22 kb region directly upstream of pspA appearing to encode multiple bacteriocin production systems , and the variable region of Pneumococcal Pathogenicity Island 1 ( PPI-1 ) containing a ∼25 kb long set of miscellaneous metabolic genes 31 ., In order to ascertain the level of variation in gene content across CC180 , comparative genomic hybridisation was used to select six further representatives to be sequenced using a combination of 454 and capillary technologies ., These were complemented by an international sample of 75 isolates from Europe and North America sequenced as multiplexed libraries using the Illumina GAII platform ( Table S1 ) ., A phylogenetic analysis of this collection was performed as described previously 32 ., A total of 12 , 605 substitutions were reconstructed as occurring over the history of the lineage , of which 77% were introduced by 82 recombinations ( two acquisitions of prophage , one recombination affecting the ICE-related sequence and 79 putative homologous recombinations; Figure 1 ) ., The lengths of the homologous recombinations were exponentially distributed with a mean length of 11 . 5 kb ( Figure S1 ) , each introducing a mean of 116 substitutions ., However , a highly irregular pattern of sequence imports is clear across the phylogeny , with the majority of the variation arising on a small number of long branches separating three clades ( labelled I , II and III in Figure 1 ) ., Single nucleotide polymorphisms ( SNPs ) were detected at just 1 , 925 sites in clade I , which contains all but six isolates ., Only one prophage integration , and eleven putative homologous recombinations of a mean length of 20 . 4 bp , are detected in this clade , resulting in an overall per site r/m ( the ratio of substitutions accumulating through recombination relative to those occurring through point mutation ) of 0 . 07 , approximately two orders of magnitude below the equivalent value of 7 . 2 calculated for the PMEN1 lineage using the same method 32 ., This absence of any signs of extensive sequence import into clade I through homologous recombination was confirmed by analysing the whole genome alignment with BRATnextgen ( Figure S2 ) 33 ., The tree structure suggests clade I is an expansion emerging from a more diverse background of isolates , although the geographic bias of the sample makes it difficult to draw general demographic conclusions ., Hence the apparent absence of widespread recombination may reflect a short evolutionary history of clade I , in which there has been little time to horizontally acquire novel sequence ., However , a coalescent analysis of the phylogeny indicated this clade is likely to have last shared a common ancestor in about 1947 ( 95% credibility interval: 1907–1970 ) ., The same analysis predicted an overall age of around 330 years ( 95% credibility interval: 592-177 years ) for CC180 ., The implied mean substitution rate of 3 . 65×10−7 substitutions per site per year ( 95% credibility interval 1 . 77×10−7–5 . 58×10−7 substitutions per site per year ) is slower than that of PMEN1 ( 1 . 57×10−6 substitutions per site per year ) , which may be the consequence of purifying selection having more time to remove deleterious substitutions in this older lineage 34 ., Nevertheless , it appears clade I is of at least an equivalent age , and very likely older , than the multidrug-resistant lineages in which extensive horizontal sequence transfer has been observed , leading to the impression that the genotype has been effectively ‘frozen’ over decades ., Identification of variable loci through comparison of de novo assemblies revealed extensive overall variation contrasting with stability within clade I , in agreement with the phylogeny ( Figure 2 ) ., For instance , the widespread presence of prophage φOXC141 indicates it was acquired by an ancestor of clade I and subsequently deleted on at least seven independent occasions based on this sample ( Figure S3 ) ., This virus , observed to form intact virions following induction with mitomycin C 29 , appears to be active during in vitro culture based on the sequence read coverage compared to the rest of the genome ( Figure S4 ) ., One of the few genotypes to have lost φOXC141 , BHN167 , is the only isolate in clade I showing evidence of having acquired a novel prophage ( φBHN167 ) , which is highly divergent from the others in the collection ., Nevertheless , the level of flux of such elements appears much slower than observed in PMEN1 32 ., Also stable within clade I are the proteinaceous antigens PspA and PspC ( Figure 2 ) , which are highly variable across the species 35 , 36; however , divergent alleles are evident in the other CC180 clades ., Similarly , loci encoding putative bacteriocin synthesis gene clusters differ between the annotated clades , with the large island upstream of pspA varying even within clade I: one deletion of approximately 10 kb , seemingly driven by a recombination between the very similar regulatory genes SPNOXC01360 and SPNOXC01480 , is homoplasic within the sample ., Metabolic operons , by contrast , only exhibited considerable between-clade variation ., This suggests accessory loci are gained over the long periods of divergence separating clades , with subsequent occasional deletion observed over shorter timescales , although only clade I is sampled with sufficient density to study this pattern in detail ., Just one instance of antibiotic gene acquisition is evident in the collection ., This is an insertion of a Tn916-type element 37 , carrying the tetM tetracycline resistance gene , into the Bolivian strain S . pneumoniae 07-2838 ., Furthermore , no fluoroquinolone resistance polymorphisms within the topoisomerase genes gyrA , gyrB , parC and parE could be found , despite being homoplasic in the phylogenies of such disparate genotypes as S . pneumoniae PMEN1 32 , Staphylococcus aureus ST239 38 and Salmonella Typhi 39 ., These can arise spontaneously through point mutation , and therefore seemed likely to be observed even in cases where recombination is not common ., However , there is one SNP in the phylogeny associated with antibiotic resistance: a polymorphism 46 bp upstream of the start codon of the genes encoding the ABC-type efflux pump PatAB 40 distinguishing the closely-related clade I isolates S . pneumoniae 99-4038 and 99-4039 ., These were cultured from a single patient with meningitis: 4038 was taken from the bloodstream , and 4039 from the cerebrospinal fluid ( CSF ) ., They were selected for sequencing from a screen of isolate pairs , each obtained from the same patient , owing to them exhibiting the most pronounced difference in transcriptional profiles ., Improved high quality draft genomes of both isolates revealed a small number of polymorphisms largely concentrated in repetitive or hypervariable regions of the chromosome that likely represent difficulties in assembly or mutation during in vitro culture ( Table S2 ) ., Nevertheless , three high-confidence mutations can be identified as distinguishing the pair in addition to the base substitution upstream of patAB: a synonymous change in the putative regulatory protein SP4038_08190 , a non-synonymous L227M substitution in the putative hydrolase SP4038_11450 , and a frameshift mutation truncating the putative phosphohydrolase SP4038_15170 in 4038 ., The first test of whether these two isolates from distinct anatomical sites differed in their transcriptional profiles had involved hybridising RNA samples extracted from 4038 and 4039 during in vitro growth to a microarray based on the genomes of S . pneumoniae TIGR4 and R6 ., This revealed the significant differences in their patterns of gene expression detailed in Table S3 ., The patAB genes were found to be approximately five-fold upregulated in 4039 , while the RNA polymerase gene rpoE and translation machinery genes rplS , rpsB and tsf were each expressed at a lower level ., RNA sequencing ( RNA-seq ) of three further independent paired samples from the two isolates grown to an OD600 of 0 . 6 in Brain-Heart infusion were then used to characterise these differences more precisely ., This experiment confirmed the results for the previously mentioned genes , with patAB this time exhibiting approximately four-fold greater transcription in 4039 ( Table S4 ) ., The change in expression appeared to coincide with the SNP distinguishing the isolates ( the allele of this locus found in 4039 is henceforth referred to as the patAB upstream SNP , PUS; Figure 3 ) , with both genes co-transcribed as an operon despite the intervening degenerate transposase sequence being encoded on the complementary strand of the genome ., Overall , RNA-seq found 54 CDSs differed significantly in their level of expression between the two isolates , grouped into 11 gene clusters and 18 singleton CDSs based on the genome sequences ., The chaperone genes dnaK , grpE and clpL were found to be transcribed at a lower level in 4039 ., By contrast , many genes involved in nucleotide biosynthesis and acquisition exhibited higher levels of expression in 4039 ., These include many genes in the pur operon , the adenylate kinase adk , the guanine monophosphate synthase guaA and the putative xanthine or uracil transporter SP4038_03000 ., The RNA-seq data also provided information on the expression of some of the distinctive genetic loci associated with CC180 genome sequences ., Congruent with the DNA sequence coverage mapping , active transcription of the φOXC141 prophages lytic cycle genes was observed ( Figure S4 ) , although transcription of the lysogeny module was also apparent , indicating a mixed population with active phage replication in some cells ., Overall , there was no evidence of discrete states of transcription ( Figure S5 ) , consistent with studies in other species 41 ., When categorised according to gene function , it is clear the most highly expressed genes , in a sense direction , are those involved in glycolysis , central metabolism , transcription and translation ( Figure S6 ) ., By contrast , those CDSs associated with the highest levels of antisense transcription relative to sense activity were pseudogenes ( Figure S7 ) , with a significantly higher proportion of these gene fragments transcribed in a predominately antisense direction in comparison to intact CDSs ( Fishers exact test , p value\u200a=\u200a6 . 14×10−11 ) ., This indicates a level of decay of transcriptional regulation of such loci in the chromosome , with the degenerate IS element in the patAB operon a clear example ( Figure 3 ) ., There are also two notable examples of functional operons being highly expressed in an antisense manner ., One is a gene cluster encoding a series of restriction endonuclease system genes ( SP4038_07830-07890 ) ., Another is the comCDE operon , encoding the competence stimulating peptide precursor and its cognate receptor ., Antisense transcription of these genes , crucial for activating the competence system for DNA uptake , contrasts with their predominately sense transcription in a sample extracted from the PMEN1 isolate S . pneumoniae ATCC 700669 under similar conditions 42 ., Such regulation may indicate one reason that so few transformation events are observed in clade I of CC180 ., Few other reasons are obvious from the genome sequences alone , with the major elements of the competence machinery appearing intact in all isolates with the exception of the isolates BHN644 , BHN587 and BHN605 , with premature stop codons detected in the comD sensor kinase , comFA helicase and comEA transport system , respectively ., The PUS is the polymorphism likely to be making the biggest contribution to the observed changes in transcription , as it is closely linked to the genes undergoing the greatest change in expression , patAB ., This locus has previously found to vary in activity between clinical isolates 43 both in the presence and absence of inducing chemicals such as fluoroquinolones and mitomycin C 44 ., Therefore it seems likely these genes are likely to be subject to altered selective pressures during the progression of disease ., The impact of the other polymorphisms differentiating 4038 and 4039 is more difficult to understand ., The substitution in the putative regulatory gene is synonymous , and therefore unlikely to contribute to alterations in expression ., Ascertaining the effect of the polymorphisms in phosphohydrolases is difficult given the unknown impact of the changes on gene products with functions that have yet to be thoroughly characterised ., Isolates with elevated levels of patAB expression are observed to have reduced susceptibility to fluoroquinolones and other antimicrobials , including linezolid 40 , reserpine 45 , acriflavine , berberine , ethidium bromide 46 and the dye Hoescht 33352 43 ., The sensitivity of isolates 4038 and 4039 to a wide range of antimicrobial compounds and osmolytes at a range of concentrations was therefore tested using phenotype microarrays 47 ., A significant difference between the isolates could be detected in 25 cases; all resulted from S . pneumoniae 4039 exhibiting greater resistance to antimicrobial agents ( Table S5 ) , with no evidence that increased expression of patAB affected the bacteriums membrane integrity based on the sensitivity to concentration gradients of osmolytes ., Isolate 4039 appeared to exhibit higher levels of tolerance of the toxic anions boric acid , sodium metaborate and sodium bromide , which could indicate elevated efflux pump activity ., Other antimicrobials that resulted in 4039 having a significantly elevated respiratory rate relative to 4038 were , like fluoroquinolones , nucleic acid intercalators: proflavine and the related compound acriflavine , along with 5 , 7-dichloro-8-hydroxyquinoline and 6-mercaptopurine , for which a significant difference was observed at three of the four tested concentrations ., Some of the other compounds identified as distinguishing the isolates , such as pentachlorophenol , crystal violet and 2 , 4-dinitrophenol , are known to act as uncouplers of proton gradients ., This could be the consequence of such compounds being removed from the cell by the PatAB efflux pump or , as PatAB is an ABC transporter and therefore not dependent on electrochemical gradients as some other pumps are , this difference in respiration rate may represent a more general change in the overall pattern of molecule efflux ., In conjunction with the previously reported elevated resistance to a variety of compounds , these data indicate elevated PatAB activity is likely to increase tolerance to a broad spectrum of antimicrobials ., To test whether these phenotypic alterations impacted on the virulence of the two isolates , both were assayed in the mouse model of invasive pneumococcal disease ( Figure 4 ) ., When 102 colony forming units ( CFU ) were introduced through intraperitoneal inoculation there was no significant difference in the survival time in animals infected with either isolate ., However , animals infected with 4038 had 1010 CFU/mL in the blood at the time of death whereas animals infected with 4039 had very low bacterial counts in the bloodstream ., When the intraperitoneal challenge dose was increased to 104 CFU all animals infected with 4039 reached the end point by 24 h , whereas those infected with 4038 lasted until 42 h ( Wilcoxon test , n\u200a=\u200a4 , p\u200a=\u200a0 . 0082 ) , although in this experiment there were no significant differences in the organ distribution of the isolates ., When a model of pneumonia was induced by intranasally inoculating animals with 106 CFU there was no difference in survival time in animals infected with either isolate ., However mice infected with strain 4039 developed higher levels of bacteraemia earlier in the infection with significantly higher levels in the blood after 24 and 48 h post infection; 4039 was also present in significantly higher numbers in the nasopharynx and brain relative to 4038 ., Hence the observed significant differences in tissue distribution of each genotype were heavily dependent upon the route of inoculation ., Analysis of the region upstream of the patAB operon revealed promoter motifs close to the consensus sequences appearing to initiate transcription 67 nt upstream of the patA start codon ., The consequent 5′ untranslated region is predicted to fold into a bulged hairpin loop followed by a run of uridine residues , indicating it could function as a terminator ( Figure 5 ) ., This suggests a simple transcriptional attenuation regulatory mechanism: any signal that destabilizes this hairpin seems likely to increase the transcription of the downstream CDSs ., The transcription of these genes is known to be increased by compounds that can intercalate nucleic acids , which have been found to induce conformational changes in bulged RNA hairpin loops 48 ., Whether regulation is via such a direct interaction , or involves other factors , it would seem appropriate that patAB would be induced through a relatively non-specific signal given its apparent ability to remove a range of intercalating compounds from the cell ., The PUS distinguishing 4038 and 4039 is predicted to destabilize this hairpin loop ( Figure 5 ) , likely reducing any transcriptional attenuation and providing a potential explanation for the observed difference in expression levels ., To study the consequences of this SNP in isolation , the genetically tractable S . pneumoniae TIGR4 strain 49 , which has the same sequence upstream of patAB as 4038 , was transformed with the region upstream of patA from isolates 4038 and 4039 ., Isolation of fluoroquinolone resistant colonies that had acquired the PUS from 4039 ( Figure S8 ) , followed by characterisation with E tests , indicated the PUS increased the ciprofloxacin MIC from 1 mg L−1 to 4 mg L−1 , while the MICs of 4038 and 4039 were 0 . 75 mg L−1 and 2 mg L−1 , respectively ( Table S6 ) ., Hence the PUS elevates resistance in both backgrounds , with the MIC also being determined by the rest of the isolates genotype ., Comparing the expression profile of S . pneumoniae TIGR4 and TIGR4PUS using a microarray revealed the patAB operon was upregulated by a relatively small degree , ( ∼2 . 5 fold ) with no evidence of other significant differences , such as those observed between 4038 and 4039 ( Table S7 ) ., Furthermore , comparison of TIGR4 and TIGR4PUS using the mouse model of disease found no significant difference in the rate at which the mice reached the end point of infection , nor the final tissue distribution of bacteria ( Figure S9 ) ., The patterns of evolution observed across this collection of CC180 isolates can be seen as reflecting the impact of selection on accumulated variation over different timescales ., Polymorphisms are acquired horizontally at a heterogeneous rate across the tree , with deep branching clades distinguished by extensive sequence diversity arising through point mutation , transformation and the movement of mobile genetic elements ., Within clade I , however , horizontal sequence transfer makes almost no contribution to the evolution of the genotype , which is ‘frozen’ in a stable form ., Whether this sample , with its geographical bias towards Europe , reflects the overall population structure of CC180 is unclear; denser sampling of clades II and III samples may indicate the recombinations occurred gradually in these lineages , with clade I being atypically stable ., Nevertheless , as the number of point mutations in clade I indicates it has been diverging over decades , it remains clear that transformation events are accumulating at a very low rate in this lineage ., Contrasting with this slow overall net rate of diversification , four high-confidence polymorphisms could be identified distinguishing the isolates 4038 and 4039 from a single patient ., These mutations ( the PUS , a synonymous change in a DNA-binding protein , a non-synonymous substitution in one putative hydrolase and a truncation of another ) may well represent normal neutral diversification that is purged by purifying selection , or lost by drift , over longer timescales ., Alternatively , they may be the product of adaption to the change in environment encountered during the course of invasive pneumococcal disease ., The latter scenario seems most likely in the case of the PUS , based on the diversity in patAB expression observed in surveys of clinical isolates , and a previous observation of resistance emerging during serotype 3 pneumococcal disease ( although the underlying mechanism of resistance , and genetic background of the strain , are not known 50 ) ., However , only with more detailed characterisation of within-host evolution in different anatomical niches will be possible to systematically answer this question ., Such studies are beginning to be performed: for instance , mutations have been observed to accumulate over short timescales during carriage and disease caused by Staphylococcus aureus 51 , 52 , and Escherichia coli 53 , 54 , with some of the observed changes associated with differences in mouse models of virulence ., The alternative explanation to adaptation , that these SNPs represent neutral mutation that does not persist over longer timescales , would suggest that purifying selection might contribute to maintaining the antibiotic sensitive phenotype ., However , lack of resistance to other antibiotics may also be attributable to the inability to acquire the requisite sequences ., For instance , the Tn916-type tetracycline resistance element observed in one isolate indicates it is possible for the genotype to acquire ICEs; the rarity of this resistance in CC180 may just represent such transfers being infrequent , or it may be that selection is the more important factor in eliminating isolates with such transposons ., Perhaps the most interesting case is the absence of any large recombination events in clade I having precluded the development of β-lactam resistance through modification of penicillin binding proteins ., Given the hypothesised ‘purging’ of point mutations through selection suggested by the relative rates of base substitution during disease and over the phylogeny , the ‘frozen’ genotype could be the consequence of recombination events being similarly removed by selection in CC180 ., Alternatively , it may simply be that CC180 imports DNA at a low rate relative to other pneumococci , resulting in the stalling of clade Is diversification ., Reasons behind CC180s lack of transformation could be the consequence of inhibition by the mucoid capsule; this has been observed to slow , but not entirely inhibit , transformation in non-CC180 serotype 3 pneumococci 55 ., Another physiological explanation for a slowed rate of DNA uptake could be the antisense transcription of the competence-inducing comCDE genes , if this were also observed to occur in vivo ., Alternatively , the explanation could reflect the lineages epidemiology ., Serotype 3s increased prevalence in adults ( inferred from disease frequency; e . g . 5 ) may mean it has relatively little opportunity to import sequence diversity owing to the reduced chance of co-colonising with most other genotypes , which are found more frequently in children ., Nevertheless , diversification through point mutations alone can still result in large phenotypic differences , as demonstrated by the behaviour of isolates 4038 and 4039 in the mouse model of disease ., Following intraperitoneal inoculation , 4038 caused high-level bacteraemia , whereas 4039 was cleared from the bloodstream; this is interesting given the isolation of 4038 from the blood of the patient ., By contrast , 4039 rises faster in the blood following intranasal inoculation , and is found at higher levels in the brain , perhaps suggesting an enhanced ability to traverse anatomical barriers could explain its presence in the CSF of the original patient ., Hence polymorphisms distinguishing the pair may represent adaptation to different anatomical niches over a short timescale ., One potential explanation of this multiplicity of phenotypic differences between 4038 and 4039 is that they all represent consequences of the PUS ., This would suggest the PUS is likely to be atypical in having such a strong impact on phenotype , owing to its effects on the regulation of a broad-spectrum pump induced by a wide range of compounds ., However , such a hypothesis must also account for the failure of the SNP to cause the same changes in the TIGR4 strain , perhaps owing to genetic interactions with other loci modulate the impact of the mutation ., The baseline differences in fluoroquinolone susceptibility between TIGR4 and 4038 provide some evidence for this ., Hence further work investigating the activity of the PatAB pump in different backgrounds seems likely to be informative ., Recent characterisation of a patAB knockout mutant of the S . pneumoniae R6 strain using a phenotype microarray found the mutant to have increased susceptibility to fluoroquinolones and acriflavine , consistent with the phenotype microarray work in this study , as well as tetracyclines , consistent with the results of the Vitek 2 analysis ( Table S6 ) ., Although overlapping , the results were not entirely consistent 56 , which could represent the consequences of the contrasting genetic manipulations of overexpression versus removal of the encoding genes , differences in analytic approaches , or the influence of genetic background upon the consequences of a polymorphism ., Combining the data in this study suggests that the PUS has an unusually strong impact on phenotype in the CC180 background in particular ., It is notable that several of the transcriptional changes observed in CC180 , but not TIGR4 , imply the PUS causes dysregulation of purine metabolism ., This could be a consequence of metabolites involved in this pathway being removed from the cell by the pump ., Evidence for this hypothesis is provided by the differences in respiration between 4038 and 4039 in the presence of the antimicrobial close purine analogue 6-mercaptopurine ., One putative reason why the CC180 genotype might be unusually sensitive to such a perturbation of this aspect of metabolism is that expression of the lysogeny module of φOXC141 drives antisense transcription of the adjacent purA gene , crucial for purine generation , in 4038 and 4039 ( Figure S4 ) ., Alternatively , one or more of the other polymorphisms distinguishing 4038 and 4039 may cause the additional effects the phenotypically differentiate this pair , but not TIGR4 and TIGR4PUS ., While the synonymous change in a regulatory protein seems unlikely to have a large effect on measureable traits , the observation of two mutations in phosphohydrolases could potentially indicate selection for a particular alteration in cellular biochemistry ., Further investigation of cases of within-patient evolution will be invaluable in highlighting whether any of these SNPs are commonly identified occurring during the progression of pneumococcal disease ., Under such circumstances where more than one of four observed polymorphisms were to measurably affect the bacterial phenotype , it becomes difficult to make the general assumption that the majority of observed polymorphisms in the pneumococcal chromosome are selectively neutral ., This could be interpreted as supporting a role for purifying selection in removing a high proportion of point mutations from the genotype , congruent with the relative rates of mutation observed within the patient and over the history of clade I . Ultimately , either explanation – that the background into which the SNP is introduced is important , or a high proportion of SNPs significantly impact on the overall phenotype - indicate , even with whole genome sequences , inferring the phenotypic consequences of even small differences between strains will be a complex task ., All animal work was approved by University of Glasgow ethics committee and was conducted under National Guidelines under Home Office Project Licence number 60/3703 ., Seven isolates were selected for sequencing from an international collection on the basis of the diversity of their accessory genomes , as assayed through comparative genome hybridisation ., The reference genome of S . pneumoniae OXC141 was sequenced to completion ., For the assembly of the S . pneumoniae OXC141 genome , two shotgun libraries using the pSMART vector ( insert sizes 4–6 kb and 6–94 kb ) and five shotgun libraries using the pUC vector ( insert sizes 0 . 8–1 . 2 kb , 1 . 2–2 kb , | Introduction, Results, Discussion, Materials and Methods | Streptococcus pneumoniae of serotype 3 possess a mucoid capsule and cause disease associated with high mortality rates relative to other pneumococci ., Phylogenetic analysis of a complete reference genome and 81 draft sequences from clonal complex 180 , the predominant serotype 3 clone in much of the world , found most sampled isolates belonged to a clade affected by few diversifying recombinations ., However , other isolates indicate significant genetic variation has accumulated over the clonal complexs entire history ., Two closely related genomes , one from the blood and another from the cerebrospinal fluid , were obtained from a patient with meningitis ., The pair differed in their behaviour in a mouse model of disease and in their susceptibility to antimicrobials , with at least some of these changes attributable to a mutation that up-regulated the patAB efflux pump ., This indicates clinically important phenotypic variation can accumulate rapidly through small alterations to the genotype . | Streptococcus pneumoniae ( ‘the pneumococcus’ ) is a bacterium commonly found asymptomatically in the human nasopharynx that represents a common cause of diseases such as pneumonia , bacteraemia and meningitis ., Some strains have been found to exchange DNA with other bacteria at a high rate ., However , serotype 3 pneumococci are unusual both in not exhibiting much genetic variation and causing disease with a comparatively high relative rate of mortality ., Here we used whole genome sequencing to characterise 82 serotype 3 pneumococci , finding that the majority of the population accumulate variation very slowly ., However , comparing two isolates from a single case of disease revealed a small number of mutations had occurred over a short period of time ., These resulted in differences in the activity of several genes , including two encoding a drug efflux pump ., The pair of isolates was found to differ in their tolerance of different antimicrobial compounds and their behaviour in a mouse model of disease ., However , moving the mutation that caused the change in resistance into a distantly-related pneumococcus failed to fully replicate the other changes in behaviour , which indicates that interpretation of the impact of mutations in different strains of diverse bacterial species will be difficult . | null | null |
journal.pgen.1001024 | 2,010 | Genome-Wide Screen in Saccharomyces cerevisiae Identifies Vacuolar Protein Sorting, Autophagy, Biosynthetic, and tRNA Methylation Genes Involved in Life Span Regulation | Yeast , worms , and flies have been studied extensively to identify the genetic determinants of aging ., Studies conducted in these model organisms have demonstrated a partially conserved life span regulatory role for the nutrient-sensing/insulin/IGF-I-like pathways , which are found in species ranging from yeast to mice 1 , 2 ., Two different paradigms have been established to study the life span of yeast ., Chronological life span ( CLS ) measures the mean and maximum survival time of populations of non-dividing yeast 3 , while replicative life span ( RLS ) refers to the number of daughter cells generated by an individual mother cell before it ceases to divide 4 , 5 ., Several genes similarly affect both CLS and RLS , while others have opposite effects on the two aging paradigms , suggesting that the mechanisms underlying the CLS and RLS are only partially overlapping 6 , 7 ., By screening transposon-mutagenized yeast populations ( previously selected for their ability to withstand either oxidative or heat stress ) for mutants with an extended CLS , the serine-threonine protein kinase Sch9 and adenylate cyclase ( Cyr1 ) were identified as negative regulators of longevity 8 ., The effect of the Ras/Cyr1/PKA pathway on aging had been previously described based on its role in glucose signaling 9 , 10 ., Reducing the activity of Sch9 or Cyr1 and consequently that of the nutrient-sensing pathways they participate in ( TOR/Sch9 and Ras/Cyr1/PKA ) , CLS is extended by up to 3-fold , with a concomitant increase in resistance to cellular stress 8 ., Consistent with this observation , inactivation of the G-protein Ras2 , which promotes Cyr1 function , also extends CLS 11 ., The two closest metazoan homologues of Sch9 , Akt and S6K , have been implicated in the insulin/IGF-I-like signaling and life span regulation in all the major model organisms 1 , 12 , 13 , 14 ., Conversely , the role of the Ras/Cyr1/PKA signaling in aging of higher eukaryotes has been more elusive 15 ., However , recently , mice lacking adenylate cyclase 5 ( AC5 ) have been reported to be long-lived and fibroblasts derived from these mice have been shown to be resistant to oxidative stress , consistently with previous observations in yeast cyr1 mutants 16 ., Moreover , the disruption of RIIβ , which codes one of the mammalian PKA regulatory subunits , has been shown to promote median and maximum life span extension in male mice 17 ., In the last few years several laboratories have turned to the yeast CLS to elucidate how post-mitotic and reversibly arrested cells age in higher eukaryotes ., However , some concern over the extensibility of this model has been raised in light of recent observations that acetic acid , which accumulates extracellularly in the culture medium , is a key cause of chronological aging in yeast 18 ., The question is if acetic acid-dependent cell death is relevant to aging in metazoans ., Previously , we found that ethanol accumulates during chronological aging and promotes death , and that its removal extends CLS 7 ., We also found that glycerol replaces ethanol in cultures of long-lived yeast and its synthesis is crucial for longevity extension 19 ., Burtner et al . have proposed that ethanol is metabolized to produce acetic acid , to which long-lived mutants are more resistant than wild type yeast 18 ., Others have suggested that ethanol removal via the activation of gluconeogenesis mediates longevity extension 20 ., Although ethanol and acetic acid at high concentrations may in fact be directly toxic to the cell , for S . cerevisiae they are commonly encountered carbon sources and thus , their removal may extend life span in part by promoting calorie restriction , a non-genetic intervention known to extend the life span of a broad range of species 21 ., Further studies are needed to clarify the range of metabolic changes that occur during chronological aging to understand how acetic acid or other acids , ethanol , or glycerol might be relevant to aging of multicellular eukaryotes ., While it is plausible that , by analogy with yeast , the composition of the extracellular milieu of multicellular organisms contributes to aging 22 , different metabolites might be implicated in aging of multicellular species ., Notably , mutations in the Sch9 and Ras/Cyr1/PKA pathways in yeast extend CLS even after removal of extracellular carbon sources indicating that the release of ethanol and acetic acid into the medium is not a requirement for these genes to exert their effect on longevity 23 ., Previously , Powers and coworkers used the yeast diploid homozygous deletion collection , which covers 96% of the yeast genome 24 , 25 , to develop an assay to monitor the CLS of all individual deletion mutants ., The principal finding of their screen was the identification of the TOR pathway as a pro-chronological aging pathway ., In fact , deletion of either TOR1 or of several other genes controlled by the TOR cascade , e . g . GLN3 ( encoding a transcription factor induced by the amino acid starvation response ) , prolongs CLS 26 ., A pro-chronological aging role for the serine/threonine kinase Tor1 has recently been confirmed by others 27 and the down-regulation of the TOR signaling cascade has also been implicated in the CLS extension induced by calorie restriction 23 ., In yeast , Sch9 is a direct target of the Tor-containing complex 1 ( TORC1 ) and its inactivation mediates the CLS extension observed in a tor1Δ context 23 , 28 , 29 ., A role for TOR in longevity regulation has been confirmed in worms and flies 12 , 30 , 31 and recently , by analogy with yeast 26 , mice and flies treated with rapamycin , an inhibitor of TORC1 , have been reported to live longer than untreated controls 32 , 33 ., The conservation of the TOR kinases and of their role in aging across species suggests that rapamycin may represent the first drug that functions to prolong life span of multiple species including mammals ., High rates of false positives and negatives are common in genomic screens 34 , accordingly , we decided to use a different methodological approach to screen for gene deletions that affect CLS ., We relied on competitive screening of pools of the ∼4800 non- essential deletion mutants in the haploid wild type BY4741 genetic background 35 ., Notably , the deletion strategy designed to construct the yeast knock-out collection generates two unique 20bp DNA tags on each deletion mutant ( uptag and downtag ) ., These tags allow the monitoring of the changes in representation of each deletion mutant in a pool using a barcode microarray that carries the complement of the tag sequences ., Thus , our method differs from that of Powers et al . in that:, 1 ) it measures the CLS of pooled , competitive cultures of standard size ( 50 mL ) instead of that of individual micro-cultures ( 0 . 2 mL ) of each deletion mutant , and, 2 ) it employs a DNA microarray-based technique to quantify the age-dependent individual strain abundance rather than absorbance measurement of individual cultures ., In order to identify novel genes implicated in life span regulation we measured the CLS of two independent yeast populations obtained by diluting two identical pools of 4×106 frozen cells into 50 mL of synthetic complete medium containing 2% glucose ( SDC ) ., After 3 days , the two yeast cultures reached a densitiy of 1 . 5×108/mL ., Because in a standard CLS experiment , no further increase of cell density is usually observed after 3 days , the number of colony forming units ( CFUs ) measured at day 3 was defined as 100% survival 3 ., The survival curves for each pooled culture are shown in Figure 1A , the actual CFUs data are reported on Table S1 ., Interestingly , both mean and maximum survival times were significantly shorter as compared to those of the wild type BY4741 ( Figure 2A ) 7 ., This may be due to the fact that numerous deletions reduce survival 36 and/or the survival defects of the corresponding mutants are exacerbated when they grow in the presence of 4800 other deletion strains ., Consistent with this hypothesis , we observed a high number of budded cells in pooled cultures ( data not shown ) suggesting that several deletions may cause an increase of the non-quiescent fraction of cells 37 ., Notably , post-diauxic and stationary phase cultures of yeast aging chronologically are composed of both quiescent and non-quiescent cells , although cell division within the population grown in SDC medium appears to be minimal and to not affect the measurement of CLS 38 , 39 ., Non-quiescent cells differ from quiescent cells in that they do not arrest in G0 properly , are more susceptible to reactive oxygen species and apoptosis , and lose viability more rapidly than G0-arrested quiescent cells 38 , 40 ., The survival curves of both pooled cultures showed an increase of CFUs at day 12 and 15 ( Figure 1A ) ., This may be caused by specific mutants that can utilize the low nutrient medium for growth ( see next section , paragraph on adaptive regrowth ) 41 ., To measure the viable cells corresponding to each individual mutant , aliquots containing 6 . 25×105 cells of each culture were diluted in fresh medium and grown until they reached a cell density of 107/mL ., Samples corresponding to approximately 2×107 cells were frozen at day 3 , 9 , 11 , 15 , and 20 ., Genomic DNA was extracted from cell pellets as described by Pierce et al . 35 ., Aging cultures were not used directly for DNA extraction to avoid any noise that might be contributed by unlysed dead cells ., Both uptags and downtags were PCR-amplified and hybridized to Affymetrix TAG4 arrays , which were processed as previously described 35 ., For each time point , the log2 intensity ratio was calculated with respect to day 3 ( 100% survival ) and the aging profiles for each individual mutant were extracted ( Table S2 ) ., The root squared mean errors ( RSME ) between the two replicates were calculated and mutants with high RSME ( 90th percentile ) were excluded from the analysis ( Table S2 ) ., The microarray results were used to approximate a survival curve for each individual deletion strain by multiplying the fold ratio change in the microarray results by the CFUs relative to the pools ( Table S3 ) ., K-means clustering analysis ( K\u200a=\u200a10 ) was performed on the averaged log2 ratios between the two pools and five clusters corresponding to mutants whose life span trajectories differed from that of the mean of the pool were identified by manual inspection ( Figure 1B ) ., While mutants belonging to four clusters , 1-3-6-7 , were classified as short-lived , cluster 2 included long-lived mutants ( Figure 1B , Tables S5 , S6 ) ., In parallel , we also used a significance analysis of time course microarray experiments developed for identifying differentially expressed genes in a time course to test for consistency between our replicates ( EDGE analysis , see Materials and Methods , Tables S4 , S5 , S6 ) 42 , 43 ., K-means clustering indicated that 594 genes are required for normal life span ( Table S5 ) ., Among these , we observed an enrichment of genes belonging to the “mitochondrion” gene ontology group ( GO: 0005739 , 24 . 3% vs 15 . 4% , relative vs background frequency ) , with 6 . 1% and 3 . 4% being part of the “mitochondrial inner membrane” ( GO:0005743 , background frequency 2 . 4% ) and “mitochondrion degradation” ( GO: 0000422 , background frequency 0 . 5% ) GO categories , respectively ( Table S7 ) ., Many mitochondrial genes among those whose deletion shortens the CLS were expected because functional mitochondria are important for survival after the diauxic shift when glucose is depleted and yeast switch from fermentative to respiratory metabolism 44 , 45 ., The list of genes whose deletion is associated with reduced life span is also enriched in members of the “autophagy” , “macroautophagy” , and “microautophagy” GO biological process categories ( GO: 0006914 , GO:0016236 , GO: 0016237 , respectively ) ( Table S7 ) ., This suggests that protein and organelle turnover by vacuolar digestion is required for normal survival and may contribute to prolong yeast life span , consistently with proposals for C . elegans and Drosophila 46 , 47 , 48 ., Among the shortest-lived mutants , we identified several mutants carrying deletions of genes implicated in protein targeting to the vacuole ( VPS genes ) ., To validate our screening results we measured the life span of mutants lacking individual Vps proteins , namely Vps25 , Vps27 , Vps21 , Vps36 , and Vps8 ( q<0 . 1 , EDGE analysis , Table S5 ) ., Four of the five mutants were short-lived ( Figure 2B , see below ) ., All the experiments described hereafter in the BY4741 background were performed by switching the cells to water at day 3 after the yeast populations had reached saturation rather than by leaving them in medium ., Incubation in water represents a form of starvation/extreme calorie restriction ( CR ) , which , similarly to the reduction of glucose content in the growth medium , promotes life span extension 7 , 39 , 44 , 49 ., Previously , we have shown that similar pathways are implicated in both starvation ( water ) - and CR ( 0 . 5% glucose ) -dependent CLS extension 23 ., We have also shown that virtually all the mutants that show longevity extension in SDC are long-lived also when different media are used for the survival studies ( e . g . synthetic complete + 0 . 5% glucose , water , or SDC without tryptophan on plates ) ( 7 , 19 , 23 and M . Wei , unpublished results ) ., The monitoring of longevity in water is also a useful means to rule out any occurrence of adaptive regrowth , which can confound the interpretation of our survival data ., Adaptive regrowth occurs when aging cells acquire mutations that allow them to reenter the cell cycle in conditions than normally do not promote growth 41 ., It is usually observed in wild type yeast after a large fraction of the yeast population is inviable , because it depends upon the nutrients released by the dead cells to occur and can be prevented by switching the cells to water and washing them periodically 41 , 44 ., The frequency of adaptive regrowth is increased in mutants that are more sensitive to DNA damage , e . g . sod1Δ or sgs1Δ 39 , 41 ., Since BY4741 shows a modest response to starvation/extreme CR in comparison with other genetic backgrounds ( Figure 2A ) ( 7 , 39 and P . Fabrizio unpublished results ) , we hypothesized that this may depend in part on a tendency of BY4741 , in contrast with other strains , to resume cell division when a large fraction of cells is still alive ., Thus , to obtain more conclusive data relative to the nature of our putative BY4741 short- and long-lived mutants , we performed our survival assays in water ., These experiments test the role of the putative life span regulatory genes in starvation/extreme CR-dependent life span extension and do not represent a direct validation of our screen , which did not assay for survival in water ., Nevertheless , the individual strain survival assays in water allow us to identify mutations that diminish or prolong life span in the BY4741 background and to avoid mistaking deletions that promote adaptive regrowth for those that extend life span ., Deletion of VPS25 and VPS27 causes a dramatic reduction of life span ( average of three independent experiments ) to a level below that of wild type cells in SDC ( Figure 2A and 2B ) ( p<0 . 001 ) ., Lack of Vps21 and Vps8 reduced life span under starvation conditions to a level similar to that of wild type cells incubated in SDC ( p<0 . 01 and 0 . 05 , respectively ) ( Figure 2A and 2B ) ., In contrast , the vps36 deletion mutant lived as long as the wild type BY4741 ., Thus , Vps36 is not required for the starvation/extreme CR-dependent life span extension ( Figure 2B ) ., Since the Vps proteins are important for protein degradation , they may contribute to the removal of oxidized/damaged proteins known to accumulate during aging 50 , 51 ., Consequently , in their absence yeast might be more sensitive to oxidants ., To test this hypothesis , we monitored the resistance to hydrogen peroxide ( 100–200 mM for 30 minutes ) of different vps mutants during chronological aging at day 1 and 3 and found an association between life span and resistance to oxidative stress , with vps25Δ and vps27Δ being the shortest-lived and also the most stress sensitive and vps36Δ having a normal life span and also unaltered stress resistance ( Figure 2C ) ., The vps25Δ and vps27Δ mutants were also tested for their resistance to acetic acid by exposing day 3 cultures to 300 mM acetic acid for 3 hours ., Both mutants showed an increased sensitivity to acetic acid in comparison with the wild type ( Figure 2D ) ., Combined with the increased sensitivity to hydrogen peroxide , this appears to reflect a general susceptibility of these mutants to stress and not the mechanism leading to early cell death , since 300 mM acetic acid is much higher than the level normally encountered/generated by cells ( Figure 2D ) 18 ., Together , these results indicate that functional Vps-dependent protein degradation systems are essential for starvation-dependent life span extension ., While mutations that shorten life span may not be directly associated with aging but rather may simply cause reduced cellular fitness , mutations that extend life span are , in most cases , indicative of an involvement of the corresponding genes in the aging process ., K-means clustering analysis allowed us to identify 42 putative long-lived mutants ( Table S6 ) ., To select the strains to be retested for longevity under starvation/extreme CR , after excluding the mutants carrying deletions of dubious ORFs not overlapping any ORF/gene on the complementary strand ( YOR012W , YDR102C ) and the ydr442wΔ and sfl1Δ mutants , which showed a marked flocculation phenotype in synthetic medium , we randomly chose 14/42 mutants ( Table 1 ) ., Five of them , acb1Δ , cka2Δ , trm9Δ , ydr417cΔ , and aro7Δ were confirmed as long-lived in the BY4741 genetic background ( Figure 3A–3E , p<0 . 01–0 . 05 ) ., The life span of mutants lacking either Cup9 , Apd1 , Zta1 , or Ssn2 , a transcriptional repressor , a protein required for normal localization of actin patches , a quinone reductase , and a subunit of the RNA polymerase II mediator complex , respectively , was not significantly different from that of the wild type ( Figure 3F , Figure S1 , and data not shown ) ., While the mutants living significantly longer than the wild type ( acb1Δ , cka2Δ , trm9Δ , ydr417cΔ , and aro7Δ ) were heat resistant ( Figure 4A and 4B ) ( see below ) no major changes in heat-shock resistance were observed in the mutants ( cup9Δ , apd1Δ , zta1Δ , ssn2Δ ) whose life span extension was not significant ( Figure 4A and data not shown ) ., The deletion of ACB1 , which encodes a highly conserved acyl-CoA-binding protein implicated in acyl-CoA-ester transport , sphingolipid synthesis , and fatty acid chain elongation 52 , caused a 2 . 2-fold mean life span extension in the genetic background BY4741 ( Figure 3A ) ., Lack of Acb1 also increased heat-shock resistance in chronologically aging cells ( Figure 4A ) , a phenotype observed in the great majority of long-lived mutants so far identified 53 ., Similarly , resistance to a very high concentration of acetic acid was enhanced by the deletion of ACB1 ( Figure 4D ) ., However , in contrast with other long-lived yeast , the acb1Δ mutants did not exhibit any resistance to oxidative stress measured as the ability to maintain viability after 30 minute-treatment with 200–300 mM H2O2 ( data not shown ) ., To test the role of Acb1 in life span regulation in non-CR conditions ( incubation in SDC medium ) and in different genetic backgrounds , we deleted ACB1 in W303-1A and DBY746 , which usually undergo adaptive regrowth only in the late phases of chronological survival ( 23 , 44 and P . Fabrizio unpublished results ) ., In these backgrounds the acb1Δ mutants showed severe growth defects , were slightly short-lived and heat-shock sensitive ( data not shown ) ., Since a yet uncharacterized adaptation that leads to faster growth has been reported to occur at high frequency in acb1Δ cultures 54 , we verified the linkage between ACB1 and our phenotypes of interest in the BY4741 acb1Δ mutant from the deletion collection , which displays only a modest growth defect and might carry suppressor mutations ., To do this , the mutant was transformed with a centromeric plasmid containing the ACB1 gene under its own promoter and both heat-shock resistance and CLS were monitored ., ACB1 expression complemented both heat-shock resistance and life span extension of the acb1Δ mutant ( Figure 4F and Figure S2 ) indicating that both phenotypes are caused by the deletion of ACB1 ., The deletion of CKA2 , which encodes one of the two catalytic subunits of the serine-threonine kinase CK2 , approximately doubled the mean life span of BY4741 under starvation/extreme CR ( Figure 3B ) ., CK2 is a tetramer comprised of two catalytic and two regulatory subunits , which regulates cell growth/division ( among other functions ) in all eukaryotes so far investigated 55 , 56 ., Analogous to the acb1Δ mutant , yeast lacking Cka2 are heat-shock and acetic acid resistant but not resistant to H2O2 ( Figure 4A and 4D , and data not shown ) ., The deletion mutants corresponding to either one of the two regulatory subunits ( Ckb1 and Ckb2 ) were also resistant to heat ., Conversely , yeast lacking the catalytic subunit Cka1 were approximately as resistant as wild type cells ( Figure 4C ) ., These results suggest that the activity of the holoenzyme and not only of the free catalytic subunits , which are known to have functions independent of the regulatory subunits 57 , are responsible for the phenotypes observed ., Furthermore , the involvement of both Ckb1 and Ckb2 in the regulation of stress resistance is in agreement with the requirement of both regulatory subunits for the full CK2 activation 56 ., The role of CK2 in life span regulation and heat-shock resistance was confirmed in SDC medium in the W303-1A and DBY746 genetic backgrounds ( Figure 5A and 5B , Figure 6A ) ., To support the hypothesis that the holoenzyme activity promotes aging , we deleted CKB2 in DBY746 and monitored life span and stress resistance of the corresponding mutant ., Lack of Ckb2 promoted a modest but significant ( p<0 . 05 ) longevity extension and a marked increase of heat resistance in comparison with the wild type ( Figure 5B , Figure 6B ) ., Two highly specific CK2 inhibitors , 4 , 5 , 6 , 7-tetrabromo-benzotriazole ( TBBt ) and 4 , 5 , 6 , 7-tetrabromo-benzimidazole ( TBBz ) , have been identified and shown to inhibit the activity of the holoenzyme 57 ., More specifically , in yeast TBBz inhibits the CK2 complex selectively and not the free Cka2 catalytic subunits 57 ., We tested both inhibitors in our system and found that TBBz ( 10–200 µM ) but not TBBt ( 5–15 µM ) increased substantially the heat resistance of day 3 DBY746 cultures ( Figure 6C , Figure S3A ) ., Furthermore , TBBz but not TBBt improved survival at day 5 ( Figure 5C , Figure S3B ) ., Together , these results confirm that the activity of the holoenzyme is responsible for the pro-aging effect of Cka2 ., TRM9 codes a tRNA methyltransferase that methylates uridine residues at the wobble position in tRNA ( Glu ) and tRNA ( Arg3 ) 58 ., Its deletion in BY4741 almost tripled yeast mean CLS under starvation/extreme CR ( Figure 3C ) , increased heat resistance ( Figure 4B ) , but reduced resistance to acetic acid ( Figure 4E ) ., Similar results were obtained by testing a trm9Δ mutant generated in the DBY746 background in SDC medium ( Figure 7A and 7D ) ., In this background , lack of Trm9 exacerbated the mild growth defect observed in BY4741 as estimated by colony size ( Figure 4B , Figure 7D ) ., The deletion of YDR417C also promoted longevity extension and heat-shock resistance but reduced acetic acid resistance ( Figure 3D , Figure 4A and 4D ) ., This dubious ORF overlaps widely with the gene coding the ribosomal protein Rpl12b ., The life span and resistance to heat of yeast lacking Rpl12b were similar to that of the ydr417cΔ mutant ( Figure 7C and 7E , Figure 4A , Figure 3D ) ., Notably , no protein expression corresponding to YDR417C was detected by analyzing strains carrying either the GFP- or TAP-tagged version of this ORF ., By contrast , Rpl12b was detected using both tagging systems 59 , 60 ., In DBY746 the deletion of YDR417C caused a marked reduction of colony size , almost doubled the mean life span in SDC medium ( p<0 . 005 ) , and increased heat-shock resistance of chronologically aging yeast ( Figure 7B and 7D ) ., ARO7 encodes for chorismate mutase , which is required for the biosynthesis of the aromatic amino acids tyrosine and phenylalanine from chorismate ., Its deletion lowered fermentative growth rates ( data not shown ) and caused a ∼60% reduction of the total number of cell counted at day 3 ., Approximately 5×107cells/mL were alive at day 3 and ∼70% survived in water until day 37 ( Figure 3E ) ., Chronologically aging aro7Δ mutants were more resistant to heat-shock but more sensitive to acetic acid than wild type yeast ( Figure 4B and 4E ) ., In the W303-1A background , the deletion of ARO7 caused an even more severe growth defect and the mutants were short-lived ( data not shown ) ., This may depend on a different response to lack of Aro7 in different genetic backgrounds ., Notably , extreme growth defects might reflect the inability of old G0-arrested cells to reenter the cell cycle to form colonies , simulating a short-lived phenotype ., Of the remaining putative long-lived mutants whose longevity was tested under starvation/extreme CR , far3Δ , far11Δ , ppg1Δ , and bul1Δ lived shorter than wild type ( the reduction of life span was significant for all the mutants except bul1Δ ) while pan2Δ lived approximately as the wild type ( Figure S4A , S4B ) ., Far3 and Far11 are part of a complex that plays a role in promoting G1-arrest in response to pheromone signaling 61 ., Notably , Far7 , Far8 , and Far10 are found in the same protein complex and the corresponding deletion mutants were all identified as putative long-lived strains ( Table S6 ) ., Furthermore , two of the dubious ORFs whose deletion is associated with longevity extension , YDR199W and YMR052C-A , overlap with FAR9 , coding an additional component of the Far complex , and FAR3 , respectively ( Table S6 ) ., It is plausible that lack of these proteins may inhibit the G1-arrest triggered by further stimuli , i . e . nutrient shortage , and cause adaptive regrowth ., Mutants displaying the adaptive regrowth phenotype may therefore be mistaken for long-lived due to an enrichment of their representation in a pool caused by cell division ., To further characterize the long-lived mutants identified in this study , we measured the budding index of each of them in exponential phase and during chronological aging ( Figure 8 ) ., Notably , a more complete G1/G0-arrest , measured as a decrease of budding index , has been observed in chronologically aging long-lived mutants and is believed to contribute to longevity extension via the reduction of replicative stress 40 ., Our analysis revealed a lower ratio between budded and unbudded cells in all mutants in comparison with the wild type during the exponential phase ., The reduction of budding index was statistically significant for all the mutants except cka2Δ in agreement with the mild growth defects observed in the mutants ( data not shown ) ., On day 1 the budding index of both acb1Δ and trm9Δ was significantly higher than that of wild type cells ( p<0 . 01 ) and in the acb1Δ mutant it remained higher until day 7 ( p<0 . 01 ) ( Figure 8 ) ., By contrast , the budding index of the aro7Δ mutant was significantly lower than that of the wild type on day 3 and 7 ( p<0 . 01 ) ( Figure 8 ) ., The use of the yeast deletion collection combined with a tag microarray detection method has found a wide range of applications , many of which involve drug screening to define their mechanisms of action 62 , 63 ., Here we adapted this methodology to investigate how different genes affect the chronological life span ., By performing a competitive survival assay on a pool of approximately 4800 haploid deletion strains , we identified several novel life span determinants ., Analogously to Powers et al . , we obtained data supporting the importance of functional mitochondria in long-term survival and identified several autophagy-related genes that are required for normal life span 26 ( Figure 2 and Table S7 ) ., The autophagic process is down-regulated by the principal pro-aging pathways and work done in yeast , worms , and flies suggests that it is required for longevity extension 46 , 48 , 64 ., Interestingly , we identified a significant number of genes whose deletion is associated with short life span , which are included in the “mitophagy” GO group ( GO:0000422 ) ( Table S7 ) ., Since in non-dividing cells autophagic breakdown is the only mechanism to remove damaged organelles , we speculate that this is a key element in long-term survival and longevity extension ., Furthermore , autophagy plays an important role in the removal of damaged proteins , which are known to accumulate during aging 65 ., Because our studies suggest that adaptive regrowth is common in the BY4741 background and also to test the mechanisms of starvation-dependent CLS extension , the longevity tests performed on the individual BY4741 mutants were performed under starvation conditions , whereas the original screen was carried out on cells incubated in medium throughout the experiment ., Notably , the great majority of mutations that cause life span extension in medium does so in water 7 , 44 ., Yeast cultures were transferred to water at day 3 , a condition that leads to the activation of an anti-aging response analogous to that promoted by reducing the glucose content of the growth medium and controlled by the same mediators 7 , 23 , 44 ., Thus , our tests on the individual BY4741 mutants studied the effect of individual genes on the starvation/extreme CR-induced longevity extension ., In this context , the results shown in Figure 2B indicate that the protein transport to the vacuole is required for the extended life span associated with starvation/extreme CR ., However , the dramatic shortening of longevity observed in the vps27Δ and vps25Δ mutants and their sensitivity to oxidative stress ( Figure 2B and 2C ) strongly suggest that in chronologically aging yeast protein turnover by autophagy is a crucial function for survival in both regular medium and under starvation/extreme CR ., Interestingly , Vps27 and Vps25 are components of the Vps27-Hse1 and ESCRTII complexes , respectively ., Both complexes are part of ESCRT system and are involved in the degradation of ubiquitylated transmembrane proteins via the formation of multivesicular bodies ( MVBs ) 66 ., Their key role in survival underlines the importance of plasma membrane and Golgi protein breakdown in preserving cellular function over time ., Of the 14 putative long-lived BY4741 mutants retested , 9 lived longer in water , with 5 of them reaching a significantly extended life span ( Figure 3 ) ., Three of the latter ( cka2Δ , trm9Δ , and ydr417cΔ ) were assayed in different genetic backgrounds and their longevity extension phenotype was confirmed in SDC medium ., Overall , half of the mutations retested either under starvation/extreme CR or both in medium and under starvation/extreme CR was confirmed to be implicated in life span regulation , underscoring the effectiveness of our experimental approach but also the importance of using the water paradigm to filter out false positives ., Interestingly , none of the long-lived mutations identified here has been identified by the high-throughput analysis performed previously 26 ., Similarly , we did not identify any mutants | Introduction, Results, Discussion, Materials and Methods | The study of the chronological life span of Saccharomyces cerevisiae , which measures the survival of populations of non-dividing yeast , has resulted in the identification of homologous genes and pathways that promote aging in organisms ranging from yeast to mammals ., Using a competitive genome-wide approach , we performed a screen of a complete set of approximately 4 , 800 viable deletion mutants to identify genes that either increase or decrease chronological life span ., Half of the putative short-/long-lived mutants retested from the primary screen were confirmed , demonstrating the utility of our approach ., Deletion of genes involved in vacuolar protein sorting , autophagy , and mitochondrial function shortened life span , confirming that respiration and degradation processes are essential for long-term survival ., Among the genes whose deletion significantly extended life span are ACB1 , CKA2 , and TRM9 , implicated in fatty acid transport and biosynthesis , cell signaling , and tRNA methylation , respectively ., Deletion of these genes conferred heat-shock resistance , supporting the link between life span extension and cellular protection observed in several model organisms ., The high degree of conservation of these novel yeast longevity determinants in other species raises the possibility that their role in senescence might be conserved . | Model organisms have been instrumental in uncovering genes that function to control life span and to identify the molecular pathways whose role in aging is conserved between the evolutionarily distant unicellular yeast and mice ., Because yeast are particularly amenable to genetics and genomics studies , they have been used widely as model system for aging research ., Here we have exploited a powerful genomic tool , the yeast deletion collection , to screen a pool of non-essential deletion mutants ( ∼4 , 800 ) to identify novel genes involved in the regulation of yeast chronological life span ., Our results show that normal life span depends on functional mitochondria and on the cells ability to degrade cellular components and proteins by autophagy ., Our data indicate that a cell signaling protein , CK2 , and diverse cellular processes such as fatty acid metabolism , amino acid biosynthesis , and tRNA modification modulate yeast chronological aging ., The high level of conservation of the novel life span regulatory genes uncovered in this study suggests that their role in longevity regulation might be conserved in higher eukaryotes . | genetics and genomics | null |
journal.pgen.1002666 | 2,012 | The Mechanism for RNA Recognition by ANTAR Regulators of Gene Expression | When producing a functional protein from a gene sequence , regulation can occur by a multitude of mechanisms at all levels of the process ., The events surrounding control of transcription initiation are arguably the best studied; however , many genes are also controlled by post-initiation regulatory mechanisms ., For example , transcription elongation is oftentimes subjected to post-initiation control by the presence of intrinsic terminator hairpins in the nascent transcript ., Typically , intrinsic terminator sites occur at ends of operons in order to promote site-specific dissociation of RNA polymerase ., However , they are also frequently arranged upstream of open reading frames ( ORFs ) , typically within 5′ leader regions , where they participate in signal-responsive regulatory mechanisms ., In certain instances , proteins with RNA-binding domains interact with these 5′ leader regions to influence terminator formation and thereby control downstream gene expression 1 ., One mechanism by which this can occur is through the formation of an antiterminator , which is a structural element that is mutually exclusive with respect to formation of the terminator hairpin ., For a few well-studied systems , association of the appropriate RNA-binding protein influences which of these RNA structural elements are formed ., For example , certain members of the BglG/SacY protein family contain the PTS regulation domain , which is an RNA-binding domain that associates with a characteristic antiterminator element overlapping a mutually exclusive , adjacent terminator site ., Phosphorylation of the PTS domain by the appropriate carbohydrate transport system controls the RNA-binding activity , thereby coupling signal-responsiveness to direct stabilization of the antiterminator structure ., In contrast , the trp RNA-binding attenuation protein ( TRAP ) associates with a tandem series of triplet sequences in order to prevent formation of a default antiterminator element , thereby permitting formation of an alternate intrinsic terminator structure 1–3 ., Another important family of proteins with putative RNA-binding activity contains the AmiR and NasR Transcriptional Antiterminator Regulator domain ( ANTAR ) 4 ., The ANTAR domain is composed of three helices with five strictly conserved residues ( three alanines , one alanine/serine and one aromatic residue ) that are exposed in the three-helical structure 5 ., Sequence homology based searches have predicted more than 1100 occurrences of the ANTAR domain , widely distributed across at least 644 bacterial species ( Figure 1; http://pfam . sanger . ac . uk/; Pfam: PF03861 ) ., ANTAR-containing proteins typically occur as multi-domain proteins ., A significant class of ANTAR proteins appear to possess an N-terminal domain that resembles a pseudo-receiver domain capable of protein-protein interactions 5 ., This class of proteins may therefore regulate gene-expression via interactions with a modulator protein , which itself may possess signal-sensing function ., For example , the ANTAR protein AmiR from Pseudomonas aeruginosa dimerizes upon binding two molecules of its negative regulator AmiC ., Under inducing conditions AmiC binds a small amide compound , allowing association of AmiR with the 5′ leader of the appropriate target mRNA ., This has been hypothesized to prevent formation of an intrinsic terminator ., However , the molecular mechanism of antitermination , including the AmiR RNA recognition determinants , has yet to be revealed 6–7 ., The ANTAR domain also occurs in combination with a diverse set of signal-sensing domains ( Figure 1 ) ., For example , NasR , a protein with a nitrate and nitrite sensing NIT domain fused to an ANTAR domain , regulates the nasFEDCBA operon in Klebsiella species , which is required for nitrogen assimilation 8 ., In the presence of nitrate , NasR is activated and binds to the 5′ leader region of the nascent nasF transcript ., Association of NasR inhibits formation of a transcription terminator within the 5′ leader region , thereby allowing synthesis of the downstream nas operon ., Like the AmiR system , the molecular mechanism of antitermination , including the NasR RNA determinants , have not been identified ., In fact , it has been speculated that the mechanism might not even involve formation of a specific antiterminator structure , in contrast to the BglG/SacY family of antiterminators 9 ., ANTAR also occurs in combination with the ubiquitous PAS ( found in Period clock protein , Aryl hydrocarbon receptor and Single minded protein ) and GAF ( in cGMP phosphodiesterases , Adenylate cyclases and FhlA proteins ) domains ( Figure 1 ) ., While no specific examples of ANTARs in combination with PAS or GAF domains have been characterized , these respective sensory domains are generally known to be responsive to a diverse array of cellular responses including changes in redox , light intensity , and aerobiosis ., They have also been shown to respond to co-factors such as flavin , heme , or second messenger molecules , among many other molecular ligands 10–11 ., Therefore , the domain organization of ANTAR-containing proteins raises the intriguing possibility that the ANTAR domain may function as a global regulatory module , partnering directly or indirectly with a diverse set of signal-sensing domains to respond to a broad range of cellular signals ., The largest individual class ( nearly 50% ) of ANTAR-containing proteins is comprised of response regulators that are part of bacterial two-component regulatory systems ( TCS ) ., TCS typically consist of a sensor histidine kinase that undergoes autophosphorylation upon sensing its signal and in turn transfers the phosphoryl group to the receiver domain of a cognate response regulator 12–13 ., The phosphoryl transfer reaction subsequently activates the effector domain of the protein ., These effector domains control signaling pathways through a variety of mechanisms , such as promoting DNA-binding activity , altering protein-protein interactions or affecting enzymatic activity 14 ., In contrast , ANTAR-containing response regulator proteins would be postulated to regulate gene expression via RNA-binding mechanisms ., This class of response regulators is the least understood , despite the fact that their widespread occurrence in bacterial genomes suggests they are broadly important in gene regulation ., EutV , a representative of ANTAR-containing response regulators , was discovered to regulate the ethanolamine utilization operon ( eut ) in Enterococcus faecalis and this mode of regulation appears to be conserved in many Firmicutes that contain eut operons 15–17 ., For E . faecalis , the corresponding sensor kinase , EutW , undergoes autophosphorylation in response to ethanolamine whereupon the phosphoryl group is transferred to EutV 15 , 17 ., Phosphorylated EutV is postulated to disrupt terminator sites located just upstream of each of the genes eutP , eutG , eutS and eutA ( Figure 2A ) ; its association is therefore predicted to activate downstream gene expression 15–17 ., These locations within the eut operon were found to share a common 13-nucleotide sequence ( AGCAANGRRGCUY ) overlapping the 5′-proximal portion of their corresponding intrinsic terminator elements ., We previously proposed that these sites could serve as part of the recognition sequence for ANTAR-based regulators in order to promote antitermination and allow production of the downstream transcript 17 ., Recent work investigating the regulation of eutG in E . faecalis supports the model that antitermination occurs at this consensus sequence 18 ., However , no functional studies have yet identified the sequence or structural features that are specifically important for antitermination in the eut system or any other system that utilizes ANTAR-based regulatory proteins ., Using the eut operon from E . faecalis as a model system , we present evidence that a novel RNA motif comprises a specific antiterminator structure containing the full determinants for recognition by the EutV ANTAR domain ., Importantly , the same RNA motif could be identified for the other ANTAR-based regulatory systems that have been studied ( AmiR and NasR ) , suggesting that it is likely to constitute the general recognition element of ANTAR-based regulatory proteins ., This structure consists of a pair of small stem-loops , one of which contains the previously identified 13-nucleotide sequence described above ., Recognition of RNA by EutV relies on a combination of structure and primary sequence determinants ., Specifically , certain residues within the hexanucleotide terminal loops share primary sequence conservation , particularly at the first and fourth positions , and are important for binding ., We also discovered that the minimum RNA-binding module of EutV is composed of a dimer of the ANTAR domain , and that dimerization is stimulated in a signal-responsive manner ., Moreover , conditions that mimic phosphorylation improved RNA-binding activity of EutV , suggesting that signal-induced dimerization is likely to stimulate RNA-binding activity ., Therefore , in aggregate , these data suggest that RNA-binding response regulator proteins are likely to generally rely upon protein dimerization and recognition of tandem nucleic acid substrates , which are mechanistic features that conceptually resemble regulation by many DNA-binding factors ., Finally , to assess whether the dual hairpin RNA structure might be present in other bacteria , we employed a bioinformatics-based search for this element across many bacterial genomes ., These searches led to the discovery of many new regulons that are likely to be coordinated by ANTAR recognition elements ., These searches also revealed that the ANTAR recognition elements described herein are generally involved in coordinating expression of nitrogen metabolism genes ., In aggregate , these data reveal that ANTAR-based genetic circuits are widespread in bacteria and broadly share certain conserved molecular features ., In our previous work using a lacZ reporter translationally fused to the 5′ leader region of the first gene of the eut operon ( eutP ) , we observed that rich medium containing serum modestly induced expression and this induction did not occur in a eutVW in-frame deletion mutant 17 ., Another group found that E . faecalis could grow anaerobically in minimal medium with ethanolamine as the sole source of carbon , as long as AdoCbl was also provided; however , a eutVW mutant was unable to grow under these conditions 15 ., For this study we modified the minimal medium by adding ribose , a carbon source unlikely to cause catabolic repression but allowing for the growth of a eutVW mutant ., As shown in Figure 2B , the medium worked well , and we observed a large induction of eutP-lacZ that was dependent on ethanolamine , AdoCbl , EutV and EutW ., Importantly , all strains , including the eutVW deletion , grew equally well ( data not shown ) ., We constructed and tested individual mutants of eutV and eutW ( in-frame deletions ) and observed the same lack of induction in medium containing ethanolamine and AdoCbl ( data not shown ) ., We additionally constructed a eutS-lacZ reporter and also observed induced expression dependent on ethanolamine , AdoCbl and EutVW ( Figure 2C ) ., In total , the regions immediately upstream of eutP , eutG , eutS , and eutA are predicted to contain intrinsic terminators consisting of a stem-loop followed by a run of uridines ( Figure 2A , Figure 3B ) ., Deletion of these terminator elements from the eutP-lacZ and eutS-lacZ constructs resulted in high levels of unregulated expression ( Figure 2B and 2C ) ., These data , along with a recent investigation of the transcriptional terminator in the region upstream of eutG 18 , validate a model in which the eut locus is regulated in part by a series of intrinsic transcriptional terminators interspersed throughout the operon ., These terminator elements are postulated to keep gene expression off under non-inducing conditions ., To increase downstream gene expression under inducing conditions , the model predicts that activated EutV prevents formation of these terminators ., EutV consists of two domains , a phospho-accepting receiver domain and an RNA-binding ANTAR domain ( PF03861 ) ; however at the onset of our studies , the RNA determinants for protein recognition had not been identified ., To identify features that dictate recognition by EutV , we analyzed the sequences directly upstream of eutP , eutG , eutS and eutA using RNA-fold prediction programs , M-fold ( http://mfold . bioinfo . rpi . edu/ ) and RNAfold ( http://rna . tbi . univie . ac . at/cgi-bin/RNAfold . cgi ) ., A mini-hairpin with a short paired stem and a hexanucleotide loop was predicted upstream of the terminator in each of the four candidates ( Figure 3A , 3B; Figure S1 ) ., Interestingly , we found that the RNA substrates of the two previously characterized ANTARs , NasR and AmiR , also have a short paired stem and a hexanucleotide loop upstream of the intrinsic terminator site ( Figure S1 ) ., Sequences in the hexanucleotide terminal loop were previously shown to be important for NasR binding ., Specifically , an A at the first position and a G at the fourth position in the loop were found to be important for transcription attenuation in vivo 9 ., The mini-hairpins of the eut sequences also have an A and a G at these positions ( Figure 3B and Figure S1 ) ., However , upon further inspection of these respective RNA sequences , we realized that the conserved ANTAR recognition sequences at the 5′ base of the terminators can form a second mini-hairpin , also having a hexamer loop with an A and a G at the first and fourth positions ., This is true of the eut sequences ( Figure 3B ) and also for the AmiR substrate , ( the leader region of the amiE gene ) and NasR RNA substrate ( the leader region of nasF ) ( Figure S1 ) ., These observations provide the basis for a general model of antitermination by members of the ANTAR family ., We predict that in the unbound state , the leader RNAs of the ANTAR substrates fold to form a terminator structure ., Upon activation , ANTAR-containing proteins bind the RNA , specifically interacting with the two terminal loops to stabilize an antiterminator structure and exclude terminator formation ( Figure 3A ) ., To test the predicted importance of these structural features , site-directed mutations were introduced in the eutP-lacZ construct ( Figure 3C ) ., Deletion of the first stem loop ( P1 ) ablated induction of eutP by EutV ., Similarly , a single nucleotide change ( G51C ) within the base-paired region of the second hairpin ( P2 ) , which is predicted to prevent its formation , also reduced the efficacy of in vivo activation by EutV ., Furthermore , we tested constructs containing mutations in the two residues that appeared to be most conserved - positions 1 and 4 within the terminal loops; alteration of any one of these four residues ( A29U or G32A in L1 and A53U or G56A in L2 ) also resulted in reduced induction of the eutP-lacZ constructs ( Figure 3B–3C ) ., To test the contribution of the stem of the putative P1 hairpin , two bases on the left side of the P1 stem were altered to disrupt base pairing ( A25U and C26G ) ., This mutant construct was also no longer inducible ., However , when the corresponding residues on the right side of the stem were additionally mutated to restore pairing ( A25U , C26G , G37C and U38A ) , induction was restored ., These data suggest that the contribution of these stem residues to recognition by EutV is likely to be structural rather than sequence specific ., In contrast , mutation of the two closing base pairs of the stem in a manner that changed the sequence but retained the ability to form base pairs ( A27U , C28G , G35C and U36A ) disrupted induction ., Interestingly , manual inspection of the eut genes from E . faecalis , Listeria and Clostridium species revealed primary sequence conservation of these closing base pairs ( Figure S1 ) ., Based on these results we speculate that EutV is likely to associate with the terminal loops as well as the top base pair of the associated helical structures ., We also assessed the general requirement for the linker region that separates the two stems ., Among the E . faecalis eut genes , the linker varies between 5 and 12 nt ., The linker in the eutP-lacZ construct was either reduced to 3 bases ( short linker ) or elongated to 14 bases ( long linker ) ., Both of these changes caused drastic reductions in induction , suggesting that an optimal proximity of the two stem-loops is required for successful interaction with EutV ., The capability of EutV to bind to the wild-type and mutant versions of the eutP 5′ leader region was then tested directly in vitro via an electrophoretic mobility-shift assay ( EMSA ) using 5′ radiolabeled RNA and purified protein ( Figure 3D ) ., These data demonstrate that full-length EutV associates with the wild-type dual stem RNA substrate with an apparent KD of 10 µM ., Replacement of the hexanucleotide terminal loops with an oligouridine tract greatly reduced EutV binding as did individual mutations in the conserved 1 and 4 positions within each loop ( A29 and G32 of P1 or A53 and G56 of P2 ) ., Therefore , together , these data demonstrate that the tandem hairpins are important recognition elements for regulation by EutV ., E . faecalis EutV is predicted to possess two domains - an N-terminal phospho-accepting receiver domain and a C-terminal ANTAR domain ( Figure 4A ) ., A region separating the two domains forms a coiled-coil as suggested by the COILS 19 server and by structural studies on AmiR as well as Rv1626 , orthologs of EutV from Pseudomonas aeruginosa and Mycobacterium tuberculosis 5 , 20 ., The AmiR structure reveals an intimate dimer with an extended coiled-coil region , although the importance of the coiled coil region for the function of AmiR has not been studied ., ANTAR itself is a poorly understood protein domain and little is known about its RNA-binding properties ., Having identified the RNA target of the EutV ANTAR domain , we then investigated the protein domain requirements for RNA recognition ., We expressed and purified two variants of the EutV ANTAR domain , which both lacked the response regulator receiver domain ., One variant is referred herein as ANTARcc ( which includes the putative coiled coil region ) while the other variant is called ANTAR ( which lacks the coiled coil region ) ( Figure 4A ) ., Via EMSA experimentation using 5′ radioactively labeled RNA substrate and purified protein we determined the binding affinities of different recombinant proteins ( Figure 4B–4C ) ., ANTARcc binds the dual hairpin RNA substrate with an apparent affinity of ∼700 nM , a value that is 100-fold tighter as compared to ANTAR alone ., This data suggests that the coiled-coil region plays an important structural role in EutV-RNA interactions ., Also , as described earlier , full-length EutV in its unphosphorylated state binds RNA with an affinity of 10 µM ( Figure 3D , Figure 4C ) , 10-fold weaker than ANTARcc ., This suggests that in the unphosphorylated state , the receiver domain of EutV may damper RNA-binding activity of the ANTARcc domain ., Phosphorylation of the receiver domain is likely to be accompanied by structural reorganization , perhaps allowing the ANTARcc domain to adopt a conformation better suited for RNA-binding ., Having determined that the ANTARcc protein binds the RNA target with the highest affinity , we tested its ability to discriminate between different variants of the eutP 5′ leader region ., The presence of both of the stem-loops exhibited a significantly better affinity as compared to a single stem-loop element ( Figure 4D ) , suggesting that both stem-loops are required for full association of the ANTARcc protein ., Similarly , as observed for full-length EutV ( Figure 3D ) , mutagenesis of the terminal loop residues deleteriously affected association with ANTARcc ., In total , these data further support our premise that ANTAR domains , potentially including the coiled coil region , promote antitermination by recognizing and binding to the terminal loop residues of a dual hairpin motif ., Bacterial response regulators often display the ability to form dimers or higher oligomers 12 ., We speculated that in order to bind an RNA target that presents two similar surfaces for interaction , the protein component is also likely to form a dimer or higher ordered oligomeric state to recognize the RNA substrate ., To test this , we first investigated the oligomeric state of the EutV ANTAR and ANTARcc domains using size-exclusion chromatography ( SEC ) ( Figure 5A ) ., While SEC is limited in the precise calculation of molar masses , the low extinction coefficients of these domains at 280 nm prevented the use of preferred techniques such as equilibrium analytical ultracentrifugation ., From SEC , we inferred that both the ANTAR and ANTARcc domains formed dimers when compared to the elution profiles of the standard protein markers ., Therefore , dimer formation appears to be an inherent characteristic of this domain , however , as discussed above and shown in Figure 4C , the presence of the coiled coil significantly improved the affinity for RNA-binding ., This suggests that although both versions of the ANTAR domain are able to form dimers , there are likely to be differences between their dimeric conformations , that are crucial for RNA recognition ., After determining the oligomeric state of the isolated ANTAR domains , we investigated the oligomeric state of full length EutV ., Since we were unable to quantitatively resolve EutV oligomeric states by SEC alone , we employed a sensitive method where SEC is coupled in tandem with Multi-Angle Laser Light Scattering ( MALLS ) ., As fractions elute from a gel filtration column , which separates proteins based on size and shape , they are passed through a light scattering device ., The latter conducts measurements of the differential refractive index of the various macromolecules as they elute from the column ., MALLS is independent of the shape of the molecule , thereby allowing precise calculation of the molar mass for all the fractionated species ., Analysis using MALLS after fractionation on a Superdex-200 column is shown in Figure 5B and 5C ., These data revealed that EutV in its native state forms a monomer of approximately 22 . 9 KDa ., We then added the cognate sensor kinase , EutW , which had previously been shown to phosphorylate EutV in the presence of ethanolamine and ATP 15 , 17 ., The presence of EutW alone did not induce dimerization ., However , when ethanolamine , ATP and magnesium were supplied in order to induce phosphorylation , EutVs molar mass approximately doubled , indicating that dimerization was induced by phosphorylation ( Figure 5B ) ., Many response regulators are capable of autophosphorylation in the presence of small molecule phospho-donors such as acetyl phosphate , carbamoyl phosphate , or phosphoramidate ., We tested the two most common small-molecule phosphodonors ( acetyl phosphate and carbamoyl phosphate ) for their ability to induce dimer formation ., Although EutV did not form dimers in response to addition of these small molecules , they appeared to provoke a moderate conformational change in EutV , visualized as a delay in the elution volume ., However , further tests revealed that magnesium alone was responsible for promoting the moderate conformational change in EutV as it had also been included with the small molecule phosphodonor solutions ( Figure 5C ) ., Since the SEC-MALLS experiments suggested that small molecule phosphodonors were unable to promote dimerization we reasoned that they could not be used as tools for probing the effects of phosphorylation-induced dimerization on RNA-binding activity ., For many response regulators , the half-lives of the phosphorylated receiver domains can be very short due to the intrinsically labile aspartyl phosphate bond 21–22 ., As an alternative , we added beryllium fluoride as a nonhydrolyzable mimic of phospho-aspartate 23–24 and measured EutV RNA-binding activity ., Preliminary experiments with addition of beryllium fluoride to EutV revealed that the beryllofluoride addition negatively affected resolution of the EutV-RNA complexes in the EMSA assay format ( data not shown ) ., Therefore , a recently developed non-electrophoresis method called differential radial capillary action of ligand assay ( DRaCALA ) was instead employed for these purposes 25–26 ., DRaCALA is a rapid and quantitative assay for protein-ligand interactions that is based on the ability of nitrocellulose membranes to preferentially sequester proteins over small molecule or nucleic acid ligands ., Specifically , proteins and their radiolabeled ligands are immobilized together when spotted onto nitrocellulose membranes , while unbound radiolabeled ligands freely diffuse by capillary action away from the protein spot ., The fraction of the targeted protein bound with its mobile ligand can be easily calculated using this assay , which has been validated in recent publications for proteins that bind small molecules 25 and nucleic acids 26 ., Using DRaCALA , we radiolabeled the two hairpin RNA motif and quantified binding to EutV in the presence or absence of beryllium fluoride ( Figure 6A–6B ) ., The binding affinity of unphoshorylated EutV for the two hairpin RNA motif as measured by DRaCALA was similar to that seen previously by EMSA , further validating the use of this method ., Addition of beryllium fluoride provoked a significant increase in RNA-binding activity for wild-type RNA but not for a negative control RNA containing mutations in the terminal loops ., Moreover , addition of cold competitor RNA restored the apparent fraction bound to background levels ., From these combined results we propose a general model for EutV regulation ( Figure 6C ) ., Protein variants consisting only of the ANTAR and coiled-coil region can form dimers alone ( Figure 5A ) , whereas full-length unphosphorylated EutV protein remains a monomer ( Figure 5B ) ., Therefore , the unphosphorylated receiver domain is likely to prevent EutV dimerization , possibly by steric hindrance , and only upon signal-induced phosphorylation does the full-length EutV protein dimerize ( Figure 5B ) and bind with highest affinity to the target RNA ( Figure 6A–6B ) ., Indeed , ANTARcc , which forms stable homodimers , exhibits an RNA-binding affinity similar to that of phosphorylated EutV and is significantly improved relative to unphosphorylated EutV ( Figure 4C ) ., Therefore , signal-induced dimerization of ANTAR proteins is likely to be essential for recognition of symmetric nucleic acid ligands , which is conceptually similar to the molecular mechanism exhibited by many DNA-binding response regulator proteins 27 ., Given the close sequence and structural similarity between the dual hairpin RNA motif in the three different characterized ANTAR systems ( AmiR , NasR , EutV ) , we hypothesized that the RNA motif as identified herein might be generally representative of ANTAR substrates in other organisms ., Also , the three previously characterized ANTAR regulatory systems each affected a single locus in their respective host organisms , and we reasoned that a subset of bacteria might instead incorporate multiple ANTAR-responsive RNA elements at disparate genomic locations for coordination of ANTAR-based regulons ., To this end , we searched for additional occurrences of the putative ANTAR RNA substrate using a bioinformatics-based approach ., Specifically , we used a covariance model-based approach 28 wherein a basic sequence alignment of a target RNA element , including certain secondary and primary sequence determinants , is used as input criteria for discovery of additional representatives from fully sequenced bacterial genomes ., This method has been successfully employed for larger , structured RNAs such as riboswitches , and is also the underlying algorithm currently used by the Rfam database team to curate bacterial noncoding RNAs 29 ., Therefore , a seed alignment was created based on the putative ANTAR RNA substrates ( the dual hairpin element ) from the eut loci of E . faecalis , Clostridium and Listeria species , as well as the corresponding RNA sequences for Klebsiella oxytoca nasF and Pseudomonas aeruginosa amiE , which are the target substrates for NasR and AmiR , respectively ( Figure S1 ) ., This RNA element was defined as a dual hairpin motif with a minimum of three base-pairs in each stem and a variable linker region connecting the two stems ., Sequence conservation in the loops , with an adenine at position 1 and a guanine at position 4 of each loop was maintained ., Given the relatively small size of the motif and the small number of residues conserved at the primary sequence level , the first search was targeted against a narrowly defined genomic subset ., We reasoned that this would allow us to fully examine the quality of our individual RNA hits ., For this target analysis we searched against 83 bacterial genomes that were previously predicted 30 to specifically encode for a putative eut locus ., Some eut loci are regulated by a DNA-binding regulator called EutR ( e . g . , Salmonella , Escherichia ) whereas others , especially the Firmicutes , are regulated by a RNA-binding , ANTAR-containing homolog of EutV , as in E . faecalis 30 ., Therefore , a subset of these genomes contains putative eut pathway homologues but lack any ANTAR-encoding genes , while other genomes contain both ., As predicted , we recovered less RNA hits in genomes that lack ANTAR-encoding genes ( Figure 7A ) ., Another strength of the subset of genomes chosen for the initial analysis is that they include phylogenetically diverse species representative of many different evolutionary lineages ., This covariance-based search revealed the presence of many putative ANTAR RNA targets ( Figure S2; Table S1 ) ., Our approach was validated in part by the identification of all 17 input sequences that were used to derive the seed alignment ., Most hits ( >83% ) originated from bacteria that encoded for at least one ANTAR-encoding gene ( Figure 7A; Table S1 ) ., Moreover , the average “hit score” was higher for RNA hits from organisms that encoded for at least one ANTAR gene ( Figure 7A ) , suggesting that the RNA element is at least partially correlative with the presence of ANTAR-containing genes ., These newly identified putative ANTAR substrates originated from diverse bacteria , including Gram-positive bacteria ( e . g . , Mycobacterium , Streptococcus , Fusobacterium , Alkaliphilus , etc . ) and Gram-negative bacteria ( e . g . , Pseudomonas , Burkholderia , etc . ) , and resulted in a consensus pattern that resembled the input consensus pattern ( Figure 7B ) ., The ANTAR systems that have been previously characterized are each used to regulate transcription attenuation ., To examine whether some or all of the hits acquired in this analysis are also likely to mediate transcription attenuation we screened them using TransTerm for candidate intrinsic transcription terminator hairpins that overlapped with the P2 helix ., Approximately 30% of the hits satisfied this criterion for organisms that encoded for ANTAR genes , whereas none of the RNA hits satisfied this criterion from organisms lacking an ANTAR gene ( Figure 7A ) ., Moreover , the average hit score increased further for the hits that contained terminator hairpins ., Therefore , these putative hits represent the best possible candidates for new ANTAR-based regulatory systems ., However , it is important to note that many of the remaining hits ( lacking terminator hairpins ) may still function as actual ANTAR regulatory elements , but via regulatory strategies other than transcription attenuation , such as control of translation initiation ., Indeed , manual inspection of some of these hits revealed instances where they were arranged near to , or overlapping with the | Introduction, Results, Discussion, Methods | ANTAR proteins are widespread bacterial regulatory proteins that have RNA–binding output domains and utilize antitermination to control gene expression at the post-initiation level ., An ANTAR protein , EutV , regulates the ethanolamine-utilization genes ( eut ) in Enterococcus faecalis ., Using this system , we present genetic and biochemical evidence of a general mechanism of antitermination used by ANTARs , including details of the antiterminator structure ., The novel antiterminator structure consists of two small hairpins with highly conserved terminal loop residues , both features being essential for successful antitermination ., The ANTAR protein dimerizes and associates with its substrate RNA in response to signal-induced phosphorylation ., Furthermore , bioinformatic searches using this conserved antiterminator motif identified many new ANTAR target RNAs in phylogenetically diverse bacterial species , some comprising complex regulons ., Despite the unrelatedness of the species in which they are found , the majority of the ANTAR–associated genes are thematically related to nitrogen management ., These data suggest that the central tenets for gene regulation by ANTAR antitermination occur widely in nature to specifically control nitrogen metabolism . | In bacteria , two-component regulatory systems comprise the primary mechanisms for how microorganisms respond to changes in their environment ., These signal transduction systems rely upon phosphotransfer between two conserved proteins , a histidine kinase and a response regulator , to propagate the signal and affect cellular biology ., Phosphorylation of the response regulator has been shown in many systems to control DNA–binding activity , protein–protein interactions , or enzymatic activity ., However , in this study , we discover a general RNA substrate for a large family of putative RNA–binding response regulator proteins called ANTAR proteins ., By identifying the general architecture of this RNA recognition element , our bioinformatic searches were then able to discover many more examples of these RNA motifs in bacteria ., Indeed , our data together revealed that the regulatory relationship between ANTAR proteins and the RNA motif identified in this study is widespread among phylogenetically diverse bacteria for control of numerous nitrogen metabolism genes . | biomacromolecule-ligand interactions, microbial metabolism, gene regulation, regulatory proteins, microbiology, molecular genetics, proteins, biology, biochemistry, rna, nitrogen metabolism, nucleic acids, genetics, metabolism, genetics and genomics | null |
journal.pgen.1003523 | 2,013 | Network Topologies and Convergent Aetiologies Arising from Deletions and Duplications Observed in Individuals with Autism | Autism Spectrum Disorders ( ASD ) form a group of complex disorders affecting ∼1% of individuals 1 ., ASD are characterised by impairments in social interaction , communication , and restricted and repetitive interests and behaviours 2 , although other symptoms such as intellectual disability , seizures or auditory abnormalities frequently co-occur 3 ., Despite the high estimates of heritability for ASD found from monozygotic twin studies ( ∼90% ) 4 , the genetic cause is recognized in only ∼20% of cases suggesting that there are many causal variants yet to be identified 5 , 6 ., ASD-causative alleles are likely to be rare as, ( i ) they are under strong purifying selection from the population due to the low fertility ( ∼5% ) of individuals with ASD 7 , and, ( ii ) there is a strong positive correlation between paternal age and ASD risk which suggests that ASD-contributing mutations frequently may be arising de novo in the continuously-replicating paternal germ line 8 ., Thus , in this study we examine de novo variants , specifically de novo copy number variants ( CNVs ) , found in individuals with ASD as a set of variants likely enriched in causal mutations 6 ., By contrast to methods that require either recurrent or common genetic variation to identify disease-associated loci , functional enrichment analysis ( FEA ) approaches gain considerable power by simultaneously examining the contributions of many disparate variants across many individuals genomes and thus may be particularly appropriate for investigating the many rare and distributed variants underlying autism 9 , 10 ., FEA approaches hypothesise that dispersed variants observed in patients with shared symptoms may be affecting genes that participate in a common biological process and it is the disruption of the same process within each of these patients that underlies their common symptoms 11 , 12 ., Thus , FEA considers whether there is a functional category that is exceptionally common for genes overlapped by dispersed CNVs identified in the genomes of patients that present the same disorder ., It thus associates function with the disorder and nominates those copy number variable genes that participate in that function as candidate disease genes 9 ., The functional category types used in FEA approaches are key to the biological insights that they can provide ., Different approaches have been applied to investigate the genetics underlying autism , including literature annotations 6 , 13 , protein-protein interactions 14 , 15 , mouse model phenotypes 13 , gene co-expression 16 and functional linkage networks 6 , 17 ., As the application of these different approaches in autism studies often accompanies the publication of a novel genetic dataset , each method has highlighted many , usually novel , candidate genes that add to a rapidly growing list 18 and replication of significant functional enrichments has only rarely been attempted , let alone achieved 13 , 19 ., Synaptic functioning has been recurrently associated with ASD by many of the recent studies 13 , 17 but the small proportions of genes that form these associations along with the functional diversity broadly exhibited by genes implicated in ASD has led authors to question specific associations 20 ., However , given that it appears likely that the variants of several hundred genes contribute to autism 6 , identifying those biological process ( es ) that are commonly disrupted may provide a more explanatory approach than to collate individual causative genes ., In particular , FEA , when applied to ASD CNVs , should not just aim to identify unifying functional themes but should also provide a framework for interpreting how these variants exert their proposed phenotypic effects ., In this study , we examined the genes affected by four previously-published sets of rare , de novo CNVs identified in autistic patients ., Given that ASD is a behavioural disorder , we initially considered the phenotype-level gene associations provided by mouse gene models before moving on to consider more molecular gene descriptions ., We identified a significant enrichment of genes whose orthologues disruption in mouse yields an abnormal synaptic transmission phenotype in 3 of 4 sets ., We show that the protein products of the genes contributing to these enrichments form an extensive physical interaction network with genes previously implicated in autism and that extends to many other genes located in CNVs ( herein termed CNV genes ) ., We show that many of the autistic individuals considered here possess multiple CNV genes that reside within the network , suggesting extensive epistasis , and provide evidence that the number of interactions a gene has within the network is related to the propensity of its copy change to cause autism ., Finally , within this network we find that whereas genes deleted in ASD are significantly enriched in those that positively regulate biological processes , the converse is also true: genes that are duplicated are enriched in negative regulators of biological processes ., We provide several examples of how the direction of copy number change reinforces the biological interpretation of the ASD-associated physical interaction network ., The Mammalian Phenotype Ontology , the set of terms by which the MGI annotates the phenotypes of mouse models , is organised at its highest level into 30 over-arching phenotypes 21 ., Of these , three categories ( Behavior/Neurological , Hearing/Vestibular/Ear and Lethality-Postnatal ) were significantly over-represented by AGP ASD dn CNV genes compared to expectation by random chance , thus associated with AGP ASD dn CNV genes ( BH-adjusted one-sided Fishers test p<5%; Table S3 ) ., Importantly , these significant associations are all specific to Gain CNVs ( 2 . 0–2 . 7-fold increases ) whilst observed counts for Loss CNVs differ little from expected values ( data not shown ) ., The significant enrichments of these three overarching categories with the AGP ASD dn CNVs then provided the rationale necessary for testing of all their finer-scale phenotypic terms for association ( 162 , 218 and 2 terms , for Behaviour/Neurological , Hearing/Vestibular/Ear and Lethality-Postnatal categories , respectively; see Materials and Methods ) ., Although the Nervous System phenotypic category was not significantly over-represented among ASD dn CNV genes ( All AGP ASD dn CNVs: 1 . 3-fold increase , p\u200a=\u200a0 . 03 , BH-adjusted p>5% ) , the behavioural presentations of ASD are likely to be manifestations of nervous system abnormalities ., Consequently , we also tested for significant enrichments of finer-scale phenotypes within this category ( 282 terms ) ., Subsequently , 23 behavioural , 21 nervous system , 27 hearing and 1 postnatal-lethality phenotypes were identified as being significantly enriched among the AGP ASD dn CNV genes ( BH-adjusted p<5%; Figure 1 and Table S3 ) ., For 3 CNV sets , namely AGP , Marshall et al . and Levy et al . , we also identified a significant excess of genes associated with abnormal CNS synaptic transmission phenotypes in mice , thereby triplicating this association ( AGP 3 . 0-fold enrichment , p\u200a=\u200a7×10−5 , BH-adjusted p<5%; Marshall et al . 2 . 1-fold enrichment , p\u200a=\u200a1×10−4 , BH-adjusted p<5%; Levy et al . AGP 2 . 2-fold enrichment , p\u200a=\u200a5×10−5 , BH-adjusted p<5%; Sanders et al . 1 . 6-fold enrichment , p\u200a=\u200a0 . 008 , BH-adjusted p>5%; Table 1 , Table S3 ) ., We next sought to determine whether model phenotypes enriched among the mouse orthologues of genes previously implicated in ASD are equivalent to those we now associate with ASD dn CNVs ., Of 36 genes that had been causally-implicated in ASD by previous studies , as defined previously 6 , phenotypic annotations were available for the unique mouse orthologues of 26 ( see Materials and Methods ) ., We removed 4 genes that were also overlapped by an ASD dn CNV to form a wholly independent set of 22 genes herein termed ASD-Implicated genes ( Table S4 ) ., We observed a striking concordance between the model phenotypes associated with the ASD-Implicated genes and those associated with the ASD dn CNV genes despite these sets complete independence: the two abnormal synaptic phenotypes with triplicated associations to ASD dn CNVs ranked 1st and 3rd among those Nervous System-category phenotypes that were most significantly associated with ASD-Implicated genes , while 15 of the top 18 behavioural-category phenotypic associations among ASD-Implicated genes were among those independently associated with the AGP dn CNVs ( Figure 1 , Table 1 , Table S4 ) ., Given the repeated enrichment within independent CNV sets of genes whose mouse orthologues are associated with abnormal synaptic transmission phenotypes , we asked whether the protein products of the 59 synaptic phenotype CNV genes taken from across all sets might interact within common processes or pathways ., Even after correcting for the increased likelihood that the products of genes with behavioural or neurological associations interact , our analysis showed that the number of these proteins interactions is unexpectedly high ( 3 . 75-fold over-representation , p\u200a=\u200a0 . 006; Figure 2 , Table 2 and Table S5; see Materials and Methods ) ., When we then added the set of 36 ASD-Implicated genes , the number of direct protein interactions increased yet further ( 3 . 2-fold over-representation , p<0 . 002 ) ., Cumulatively , our results show that many of the 59 synaptic phenotype CNV genes and 36 ASD-Implicated genes function in concert and yield similar consequences when disrupted ( Figure 2 , Table 2 ) ., Mouse model phenotypic information is available only for the orthologues of fewer than a quarter of human genes ( see Materials and Methods ) ., It is thus expected that not all genes within CNVs that are causally associated with synaptic abnormalities can be identified using this resource alone ., To identify additional ASD candidate genes , we sought all those ASD dn CNV genes whose protein products were known to directly interact with the products of any of the 59 synaptic phenotype CNV genes or 36 ASD-Implicated genes ., This identified an additional 174 CNV genes that form an expanded network with a 5 . 4-fold interaction over-representation ( p<0 . 002; herein termed the ASD-associated network; Figure 2 , Table S5 ) ., Of these 174 additional interacting proteins , the mouse orthologues of 74 ( 43% ) do not yet have phenotypic information ., Of the 100 additional interacting genes with mouse model phenotypes , 44 are known to exhibit behavioural phenotypes , and of these 35 exhibit one or more of the significantly associated behavioural phenotypes identified above ( Figure 1 and Table S5 ) ., Examining the more general functional annotations of genes within the ASD-associated network using Gene Ontology ( GO ) identifies convergent functional themes that are consistent with broad synaptic functioning , organisation and maintenance ( Table S6; Summarised using REVIGO in Figures S1 , S2 and S3 22 ) ., This functional coherence is supported by the observation that 192 of the 210 ( 91% ) proteins within the ASD-associated network reside in a single inter-connected cluster , thereby also providing known interactions that provide pathways through which effects originating from distinct mutations can aetiologically converge ( Figure 2 ) ., Despite their known functional interconnections , the vast majority of these ASD candidate genes are novel ( Table S5 ) ., The 203 CNV genes singled-out through the synaptic mouse phenotype associations and the ASD-associated network provide a causal hypothesis for 81 ( 45% ) of the patients considered ., The median number of candidate genes per patient is 3 ( mean 3 . 8 , s . d . 3 . 2 ) suggesting a substantial role for epistasis in ASD ., The network identified here provides not only the means for mediating epistatic interactions but is also indicative of the deleteriousness of copy change: Among the 22 patients that have only a single copy-changed candidate gene , that candidate gene has on average 3 times the number of interaction partners in the network as compared to the candidate genes from patients with multiple candidate genes ( medians 3 vs . 1 , respectively , Mann-Whitney U test p\u200a=\u200a0 . 002 ) ., Thus , given the known deleteriousness of disrupting highly interacting “hub” genes within biological networks 23 , we propose that the disruption of multiple non-hub genes within the autism network may be required to elicit an autistic phenotype comparable to the singular disruption of a hub gene within the same network ., Of the 203 CNV genes identified through the synaptic mouse phenotype associations and the ASD-associated network , 110 ( 54% ) are found only in duplications while 91 ( 45% ) are only in deletions ., We next investigated how the two directions – duplications or deletions – of copy number change might reflect common or divergent aetiologies ., To achieve this we analysed the GO biological process annotations assigned to duplicated and , separately , to deleted genes for significantly over-represented terms ( Table S6 ) ., While many of the over-represented annotation terms are shared between the deleted and duplicated gene sets , we noted a striking difference: The deleted candidate genes are significantly enriched only in genes that are positive regulators of biological processes ( GO:0048518 , 35/82 annotated genes , 2 . 4-fold enrichment , BH-adjusted p\u200a=\u200a3×10−4 ) while , conversely , an enrichment of genes that are negative regulators of biological processes is only observed amongst the duplicated candidate gene set ( GO:0048519 , 34/105 annotated genes , 2-fold enrichment , BH-adjusted p\u200a=\u200a0 . 006; Table 3 ) ., Each of the 4 CNV sets candidate genes contribute to each of these enrichments with many sets nominally significant individually ( Table S7 ) ., Furthermore , reclassifying the partially duplicated , and therefore likely-disrupted , genes as deletions enhances these enrichments further ( Table S8 ) ., These enrichments are complementary and thus immediately suggest a convergent model of action in which the duplication of negative regulator genes or the deletion of positive regulator genes both act to perturb a common target process and affect the same outcome ., The unusually frequent deletions of positive regulators and duplications of negative regulators enable specific and biologically-meaningful interpretations of the ASD-associated network ( see Figure 3 and Discussion ) ., This study has, ( i ) identified among 3 independent sets of ASD dn CNVs , and therefore triplicated , an enrichment of genes whose mouse orthologues , when disrupted , yield an abnormal synaptic transmission phenotype;, ( ii ) shown that these genes protein products exhibit a significantly high number of interactions between themselves and to the products of genes previously implicated in ASD;, ( iii ) that this interaction network extends directly to include many more proteins of genes affected by the ASD dn CNVs of almost half of the cohort;, ( iv ) that the gene products in this ASD-associated network possess roles in synaptic function , organisation and maintenance;, ( v ) that many individuals with ASD possess multiple copy changed genes from the ASD-associated network;, ( vi ) that genes that are highly connected within the network ( “hub genes” ) are significantly enriched among patients that possess only a single ASD-associated network gene; and , finally, ( vii ) that this networks genes that are deleted are significantly enriched in genes that act to positively regulate biological processes while those that are duplicated are significantly enriched in negative regulators ., An association of ASD CNVs with genes that yield synaptic phenotypes when disrupted in mice has been reported before in rare CNVs but replication was not achieved 13 ., Here , despite little overlap between the 3 CNV sets involved ( Table S2E ) , we are able to triplicate this association ., These synaptic associations provide aetiological insight that accords well with the emerging neurophysiological view of a strong role for synaptic dysfunction in autism 24 ., It is also further strengthened by the over-represented functions among genes within the broader ASD-associated network , whose functions include vesicle transport , cell junction organisation and calcium transport ( Figures S1 , S2 and S3 , Table S6 ) ., However , the breadth of dysfunction suggested by the roles of these physically-interacting proteins implicate other , more intracellular processes , such as the cytoskeletal and cellular transport processes , that may affect synapse formation , structure and/or maintenance ( Figure S1 , Table S6 ) ., The known physical interactions between these genes products provide pathways through which separate genetic perturbations can converge functionally ( Figure 2 , Table S5 ) while the importance of a gene within the ASD-associated network , as specified by the degree of connectivity , appears to be an indicator of ASD-relevant deleteriousness ( see Results ) ., Recently , two large-scale studies examining the exomes of autistic patients also identified an excess of protein-protein interactions between genes harbouring suspected causative mutations , reporting smaller networks with 49 15 and 45 14 participating genes of which 3 genes and 2 genes , respectively , are also identified through our network ., After excluding overlapping genes and compared to random gene sets of equivalent size , the number of connections between gene products in each of the ORoak et al . and Neale et al . reported networks to the network we identify here are 12-fold and 38-fold over-represented , respectively ( p<0 . 002 for both ) ., Thus , despite little overlap in genes , the strong interconnectedness between these networks identifies pathways through which cellular perturbations arising from distinct mutations identified in separate studies may converge ., The single nucleotide variants ( SNVs ) detected in these two published exome studies are largely predicted to be harmful to the function of the encoded proteins , and therefore comparable to the copy number deletion events in our study ., Corroborating our finding of an enrichment of genes that positively regulate among deletions , we also observe a highly significant enrichment of positive regulators among the more strongly-interconnected set of genes identified by Neale et al . ( 2 . 7-fold enrichment , p\u200a=\u200a3 . 8×10−6 , BH-adjusted p<0 . 05 ) which , while enriched , is not significant among the less well-connected genes reported by ORoak et al . ( 1 . 4-fold enrichment , p>0 . 05 ) ., Despite chronologically limiting our mouse phenotypic dataset to avoid bias ( see Materials and Methods ) , the similarities between the behavioural mouse phenotypes associated with the AGP dn CNVs and human ASD presentations appear clear , with abnormal social/conspecific interaction , stereotypic behaviour and abnormal memory/learning/conditioning phenotypes all over-represented ( Figure 1 ) ., Many of our studys ASD-associated phenotypes bear a striking resemblance to other frequently co-occurring symptoms , such as impaired coordination 25 , 26 , 27 , anxiety-related phenotypes 28 , and absence and tonic-clonic seizures 29 , 30 , 31 ( Figure 1 ) ., Finally , we observe a strong enrichment of genes whose disruption yields hearing phenotypes in mice ., This observation accords well with estimates in the literature that hearing abnormalities ( including sensorineural hearing disorders ) affect between 33–46% of ASD cases ( Figure 1G ) 32 , 33 ., Many of the associated hearing , and some nervous system , mouse phenotypes are related to peripheral hearing abnormalities , particularly concerning the cochlea and mechanoreception ( Figure S4 and Table S3 ) ., Inner ear mechanoreception abnormalities appear to have received little attention compared to other regions involved in auditory reception and processing 33 ., This is despite improvements in hearing following cochlear implants in individuals with ASD 34 and the knowledge that rare mutations in several genes implicated in ASD ( including CHD7 , NIPBL , PTPN11 and TBX1 ) can cause inner ear abnormalities in humans 35 , 36 , 37 , 38 ., The enrichment of deleted genes in the network that positively regulate biological processes and a complementary enrichment of duplicated genes that negatively regulate biological processes suggest the occurrence of convergent aetiologies whereby both deletions and duplication act to perturb biological processes relevant to autism in the same direction ( Figures 2 and 3 ) ., Indeed , the interactions within the ASD-associated network reveal this proposition to be consistent with the experimental literature ., For example , considering the STX1A/CFTR/SLC6A3 ( aka . DAT ) interactions ( Figure 3 ) , over-expression of STX1A decreases DAT dopamine transport activity 39 , and reduces the CFTR-mediated chloride current by inhibiting trafficking of CFTR to the cell surface 40 ., These findings predict that over-expression of STX1A yields an effect similar to the deletion of DAT or CFTR and , concordantly , whereas STX1A lies within a duplication , CFTR and DAT are each deleted ., Furthermore , STX1A also interacts with SYP ( also duplicated ) , which negatively regulates SNAP proteins ( SNAP29 is deleted ) 41 , 42 ., SNAP proteins are key to presynaptic exocytosis , a process also likely to be disturbed by altered calcium homeostasis resulting from the array of deleted voltage-dependent calcium channels ( CACNA1B deletion , CACNA1C ASD-implicated deletion , and CACNA1H deletion ) 43 ( Figure 3 ) ., Another example of apparent convergence in aetiology and outcome are the copy number changes affecting the PI3K/Wnt pathways ( Figure 3 ) ., Here , many copy number changes are predicted to converge to reduce or disrupt the action of the β-catenin destruction complex in the Wnt/β-catenin signalling pathway; the deletion of AXIN1 , the increased ubiquitination of AXIN1 by duplication of UBC 44 or the disruption of the PI3K pathway due to mutations in PTEN , TSC1 , or TSC2 45 ( Figure 3 ) ., Perturbations affecting AXIN1in ASD include the duplication of DVL2 which inhibits AXIN1 ( deleted ) function 46 ., Furthermore , as NKD family proteins promote the degradation of DVL proteins 47 , 48 , the deletion of NKD2 may increase the activities of DVL2 and thereby also inhibit AXIN1 ., Concordant with a decrease in β-catenin degradation , an increase in β-catenin stabilization could result from LRPAP1 , NKD2 and DVL2 copy number changes ., LRPAP1 is thought to have protective roles in LRP1 trafficking and its duplication may therefore increase LRP1 availability 49 ., The outcome of the copy number change and disruption of each of these genes is likely to up-regulate the Wnt-stimulated TCF/LEF-dependent transcription , a pathway whose down-regulation has been proposed to have therapeutic benefits in ASD models 50 , 51 , 52 ., Given the ever-increasing number of genetic variants that thus far have been implicated in ASD , the focus will inevitably shift from enumeration towards understanding how these variants contribute to the common pathways and processes underlying this complex disease ., Here we have identified a large network of interacting proteins affected by copy number variants identified in patients with ASD , and shown how the network topology and direction of copy number change can be used to interpret these variants pathway perturbations ., Therapeutically targeting molecules at the ends of pathologically-perturbed regulatory cascades may provide more broadly-applicable treatments , while pathological gene duplications may identify attractive targets for knock-down therapeutics as a means of ameliorating perturbed pathways ., Four sets of de novo CNVs were employed in this study ( Table S1 ) , of which two are drawn from the Simons Simplex Collection and thus overlap 53 ., The largest set consists of 73 de novo CNVs identified in 54 ( out of 996 ) individuals with strict autism by the Autism Genome Project ( AGP; Table S1 ) 6 ., Of these , 39 CNVs had been confirmed as de novo by independent methods while 34 were considered likely to be de novo by the CNV calling algorithms ., The second set consisted of 28 de novo CNVs identified in 24 patients reported in a study by Marshall et al . 54; two patients who had been reanalysed by the AGP have been removed; Table S2 ., The third set consisted of 94 de novo CNVs identified in 82 patients reported in a study by Levy et al . 20 and the fourth set consisted of 67 de novo CNVs identified in 63 patients reported in a study by Sanders et al . 55 ., Forty two patients examined by Levy et al . were also present in the study by Sanders et al . but this does not affect our findings; As the synaptic phenotype associations that we report and take forward in the Results were identified amongst both the AGP and the Marshall et al . sets , neither the Levy et al . nor Sanders et al . sets were required for replication and thus these latter sets non-independence from each other does not undermine this association ., For all sets , contributing patients have been evaluated as having ASD according to ADI-R and/or ADOS criteria ., Herein , de novo CNVs identified in patients with ASD is termed “ASD dn CNVs” ., Human genes were assigned to ASD dn CNVs according to Ensembl Ensmart54 56 ., To be confident that the expressed coding sequence of a gene is affected by the copy number change , we conservatively required at least one coding exon of every known transcript of a gene to be overlapped by a CNV for that gene to be deemed overlapped ( Table S2 ) ., Particular consideration was given to showing that our gene assignment procedure and statistical over-representation analyses did not yield any functional bias under the null hypothesis ( see Figure S5 and Methods S1 ) ., Genes observed to be copy number variable in the same direction ( gain/loss ) within a set of CNVs employed by the AGP as a control , i . e . identified from individuals with no obvious psychiatric history in a previous study were removed from the ASD dn CNV gene lists because these are less likely to be associated with ASD 6 ., Although it remains possible that common variants contribute to ASDs , our study focuses on genes affected by rare , de novo variants ( see Introduction ) ., Annotations of phenotypes resulting from disruptions of mouse orthologues of these affected genes were obtained from the Mouse Genome Informatics ( MGI ) resource ( http://www . informatics . jax . org ) and interpolated as described previously 12 , 57 , 58 , 59 ., Using simple , unambiguous , 1∶1 gene orthology relationships from the MGI resource , 5 , 283 distinct MGI phenotypic terms were mapped to 5 , 671 human genes ., Each phenotype belongs to one or more of 33 over-arching categories ., We considered only 4 , 055 reasonably populated phenotypes , defined as those with at least 1% of all genes associated with the relevant phenotypic category , thereby reducing uninformative results and improving methodological power ., As an unreplicated association between genetic variants in autism patients and a mouse model phenotype was reported in April 2010 13 , we employed only those phenotypes reported in the MGI resource prior to this date , thereby reducing any subsequent phenotyping bias or consequential circularity in discovery ., However , our findings remain , or are strengthened by , those more recently reported mouse model phenotypes ( data not shown ) ., We employed DAPPLE: Disease Association Protein-Protein Link Evaluator 60 to identify direct protein-protein interactions among the protein products of the genes contributing to functional enrichments ., A protein-protein interaction networks connectivity was calculated as published previously 60 ., Enrichment analysis was carried out by comparing the number of identified direct protein interactions with the average of those identified from 500 gene sets , in which genes were randomly sampled while matched in set size ., To account for the increased likelihood that genes that share behavioural associations are more likely to interact than randomly selected genes , we randomly selected sets of orthologues from 1 , 766 genes annotated with behaviour and neurological phenotypes in the MGI ., Due to the small numbers of de novo CNVs considered here and a lack of a control set of de novo CNVs , performing a case-control comparison is not possible ., Thus , employing the one-sided Fishers exact test , we tested the null hypothesis that a ( mouse ) phenotype associated with ( human ) Ensembl genes overlapping a set of ASD-associated CNV genomic intervals occurs at a frequency that is no different from that expected from the genome as a whole ., Randomisations confirmed that this approach did not yield artefactual bias ( see Methods S1 and Figure S5 ) ., A multiple testing correction , BH-adjusted p<5% , was applied to account for number of functional terms ( phenotypes or GO terms ) tested when examining a given gene set 61 . | Introduction, Results, Discussion, Materials and Methods | Autism Spectrum Disorders ( ASD ) are highly heritable and characterised by impairments in social interaction and communication , and restricted and repetitive behaviours ., Considering four sets of de novo copy number variants ( CNVs ) identified in 181 individuals with autism and exploiting mouse functional genomics and known protein-protein interactions , we identified a large and significantly interconnected interaction network ., This network contains 187 genes affected by CNVs drawn from 45% of the patients we considered and 22 genes previously implicated in ASD , of which 192 form a single interconnected cluster ., On average , those patients with copy number changed genes from this network possess changes in 3 network genes , suggesting that epistasis mediated through the network is extensive ., Correspondingly , genes that are highly connected within the network , and thus whose copy number change is predicted by the network to be more phenotypically consequential , are significantly enriched among patients that possess only a single ASD-associated network copy number changed gene ( p\u200a=\u200a0 . 002 ) ., Strikingly , deleted or disrupted genes from the network are significantly enriched in GO-annotated positive regulators ( 2 . 3-fold enrichment , corrected p\u200a=\u200a2×10−5 ) , whereas duplicated genes are significantly enriched in GO-annotated negative regulators ( 2 . 2-fold enrichment , corrected p\u200a=\u200a0 . 005 ) ., The direction of copy change is highly informative in the context of the network , providing the means through which perturbations arising from distinct deletions or duplications can yield a common outcome ., These findings reveal an extensive ASD-associated molecular network , whose topology indicates ASD-relevant mutational deleteriousness and that mechanistically details how convergent aetiologies can result extensively from CNVs affecting pathways causally implicated in ASD . | Autism Spectrum Disorders ( ASD ) are characterised by impairments in social interaction and communication , and restricted and repetitive behaviours ., ASD are highly heritable and many different stretches of DNA have been found to be duplicated or deleted in individuals with ASD ., We found that an unusually high number of genes affected by these DNA deletions/duplications are associated with the functioning of synaptic transmission between nerve cells ., The proteins made by many of these genes are known to interact with each other and , together with proteins from other deleted/duplicated genes , form a large interlinked biological network ., This network was affected by almost 50% of the deletions/duplications in the ASD patients considered ., Many individual ASD patients had deletions or duplications of multiple genes within this network , but for those patients with just a single gene from the network changed , that single gene appeared to play an important role ., Furthermore , the network predicts that the effects arising from the genes in the deletions are similar to the effects arising from the genes in the duplications ., Thus , the way that this ASD-associated network is wired together contributes to the understanding of the impact of these DNA deletions and duplications . | genome analysis tools, genetic networks, functional genomics, protein interactions, biology, genomics, proteomics, computational biology | null |
journal.pcbi.1000231 | 2,008 | Structural and Thermodynamic Approach to Peptide Immunogenicity | In the conventional paradigm of humoral immune responses , B cells recognize conformational epitopes of protein antigens through interactions with surface expressed immunoglobulin receptors 1 ., For most antigens , this process requires T cell help that results in sequential steps of class switching , affinity maturation , and epitope spreading 2–5 ., The nature of the antigen itself influences this highly orchestrated process , as glycosylation patterns and other post-translational protein modifications often impact the affinity and specificity of the immunoglobulin binding domain for relevant three-dimensional epitopes 6–9 ., Based on this mechanism of B cell activation and immunoglobulin production , native protein should be highly immunogenic relative to short peptide sequences less than 20 amino acids in length ., While this concept may hold true for many antigens , the existing literature does provide examples of peptides capable of stimulating antibody production not only against the immunizing peptide , but also against corresponding regions of the native protein 10 , 11 ., This apparent contradiction is often resolved by assuming that peptides are capable of adopting stable structures mimicking those found in the native protein 12–15 ., In particular , Gros and collaborators 16 have shown that the stability of synthetic , cyclized peptides mimicking an immunodominant loop of the Neisseria meningitidis protein PorA correlates with immunogenicity ., However , because typical linear peptides are inherently unstable , with stabilities that are virtually impossible to assess due to the lack of a well defined folded ( reference ) state , more complete elucidation of the molecular mechanism ( s ) underlying these empirical observations remains elusive ., Underscoring the complexity of this problem , an analysis involving a helical motif of the enzyme barnase represents the only published measurement of peptide folding free energy ( ΔGf\u200a=\u200a−1 kcal/mol ) 17 ., In the current study , we have reexamined this issue through detailed analysis of serologic profiles generated in mice immunized with overlapping 18 amino acid peptides comprising the amino terminal portion of histidyl-tRNA synthetase ( HRS\u200a=\u200aJo-1 ) , an autoantigen implicated in the pathogenesis of idiopathic inflammatory myopathy and the anti-synthetase syndrome 18 ., Our published murine model of this disease demonstrates that many of these peptides are highly immunogenic , inducing antibodies that cross react with recombinant murine HRS protein in a predictable , species-specific manner 19 ., Beyond the definition of immunodominant peptides dictating B cell recognition of HRS peptide/protein combinations , this analysis has permitted correlation of the humoral immune response with structural and thermodynamic determinants of peptide immunogenicity ., Of note , molecular modeling calculations indicate that although peptides are intrinsically disordered and therefore less stable than full protein , they are capable of adopting relevant structural “mimetopes” with enough stability to trigger humoral responses against corresponding regions of native protein ., Immunization experiments verify that selected peptides predicted to form higher order structures similar to those existing in parent proteins induce significant antibody responses against intact protein ., Moreover , competition experiments show that several of these immunogenic peptides are able to bind to stimulated antibodies with similar affinity to that of the full protein ., Collectively , these studies provide insight pertinent to the structural basis of immunogenicity and , at the same time , validate this form of thermodynamic and molecular modeling as a tool to probe the development/evolution of humoral immune responses ., To establish a thermodynamic basis for previous observations linking peptide immunization with humoral immune responses against native protein structural motifs , we examined the relationship between peptide folding stability and antibody-antigen binding ., Although the capacity of intrinsically disordered peptides to generate and effectively bind antibodies recognizing three-dimensional epitopes appears counterintuitive , the kinetic scheme in Figure 1 ( equations are in Figure S1A ) demonstrate that , under very general conditions , complete peptide stability is not a necessary condition for effective binding ., Indeed , classification of peptides according to the free energy ( ΔGX ) of their protein-like motifs defines three classes of peptides possessing very different immunogenic properties ., These categories include:, ( a ) “stable” peptides ( for which ΔGX<0 kcal/mol ) that can form the same number of peptide-antibody ( XAb ) complexes as stable protein despite a wide range of folding free energy values;, ( b ) “weakly-stable” peptides with ΔGX>0 kcal/mol ( but <8 kcal/mol ) that have a drastic decrease in antibody binding events relative to the full protein; and ,, ( c ) “unstable” or “non-immunogenic” peptides with ΔGX>8 kcal/mol and resulting unfolding rates of 109 s−1 or higher that preclude any effective binding 20 , 21 ., While the precise stability thresholds are somewhat dependent on concentration and binding affinities , the relative stability grouping of each peptide type is independent of folding rates ., As an example of the epitope classification scheme derived from this thermodynamic analysis , we have mapped relevant B cell epitopes of histidyl-tRNA synthetase ( HRS ) through peptide immunization of NOD . Idd3/5 mice ., As shown in Figure 2 , the panel of HRS peptides consists of overlapping 18 amino acid sequences corresponding to the immunodominant amino terminal portion of HRS ., The relationship between these peptides and different structural motifs of intact protein is highlighted by the accompanying model of HRS ., Review of Figure 3A indicates that several peptides comprising the amino terminal 98 amino acids of HRS generate antibody responses against a HRS fusion protein ( MA/MBP\u200a=\u200aamino terminal amino acids 1–151 linked to maltose binding protein ) by two weeks , most notably peptides 1 ( a . a . 1–18 ) , 4 ( a . a . 31–48 ) , 6 ( a . a . 51–68 ) , 7 ( a . a . 61–78 ) , 8 ( a . a . 71–88 ) , and 9 ( a . a . 81–98 ) ., Temporal assessment of anti-HRS protein antibody responses induced by these peptides and comparison to antibody responses against the immunizing peptide ( Figure 3B ) demonstrates several different recognition patterns consistent with the thermodynamically-defined categories in Figure 1 ., In the case of peptides 1 and 9 , for example , titers of anti-HRS protein and anti-peptide antibodies parallel each other by tending to increase over time ., Conversely , peptides 4 , 6 , and 7 produce more variable temporal patterns of anti-HRS protein antibody responses , generally without corresponding anti-peptide responses over the monitored time course ( significant anti-P6 titers develop in only 1/8 P6 immunized-mice at 8 weeks ) ., Finally , peptides 2 and 5 represent sequences that fail to generate anti-protein or anti-peptide antibodies at any time point ., Complementing these results , competition ELISAs provide further insight regarding the relative antigenicity of HRS peptides and protein ., As shown in Figure 3C , pre-incubating sera from peptide-immunized mice with increasing concentrations of MA/MBP effectively reduces residual binding to MA/MBP substrate , confirming the specificity of antibody responses generated by peptides 1 , 7 , 8 , and 9 ., However , when peptides are used in the pre-incubation phase , the effect is more variable ., With peptide 7- and 8-immunized sera , for example , peptide pre-incubation has little or no detectable effect on the ability of antibodies to bind MA/MBP ., On the other hand , molar equivalent amounts of peptide 1 and 9 compete for antibody binding to both MA/MBP and peptide substrate as effectively as protein—consistent with the ability of peptides 1 and 9 to adopt relatively stable structures in solution that resemble corresponding regions of intact protein ., To correlate these peptide immunization studies with the thermodynamically-defined categories of immunogenicity outlined in Figure 1 , we employ MD simulations ., However , the inherent difficulty in directly measuring peptide folding free energy is also present in MD–namely , the “folded” state of interest ( i . e . , the motif that binds the pool of B cell receptors ) is not well defined ., A second drawback is that an absolute thermodynamic estimate of free energy needs to account for the unstructured , unfolded state ., Cutting edge MD techniques can compute free energy differences between well defined states and may be able to account for the configurational entropy of peptides , but currently cannot properly estimate the required entropy of ∼7000 explicit water molecules 22 ., Despite this caveat , the dashed line in Figure 1A indicates that the stability of states other than the protein-like motif ( X ) is irrelevant from the point of view of establishing a correlation between antibody binding of stable protein versus unstable linear peptides ., Other states could , of course , lead to an immune response targeting an unknown structure ., We note , however , that this scenario does not apply here , since ELISAs involving peptide substrates do not seem to yield a signal if there is no response against protein ., The only exceptions are motifs represented by peptides 3 and 8 which , as argued below , are obscured in their protein form ., Hence , MD simulations represent a valuable and insightful alternative method for probing the relative stability of different epitopes in their corresponding protein fold and for better defining the relevant “folded” state ., In particular , because recognition events occur within a nanosecond time scale 21 , 23 , peptides are simulated over a 10 nanosecond period 16 that allows extraction of the most stable backbone protein-like motifs of four consecutive amino acids ( i . e . , a small binding domain ) ., Figure 4 shows optimal backbone structural alignments of MD snapshots superimposed on the three-dimensional model of murine HRS ., The alignment for each peptide is based on the 4 consecutive residues with the smallest cumulative root-mean-square deviation ( RMSD ) over a 10 nanosecond period 16 ( summarized by the bar graph in Figure 5 ) ., Although peptide conformations fluctuate to varying degrees , the composite profiles of the most structurally stable protein-like motifs provide a visual analogue showing relative stability and similarity to defined motifs found in murine HRS ., Interestingly , each of the peptides with a cumulative RMSD value less than 4 Å ( i . e . , peptides 1 , 3 , 7 , 8 , and 9 ) triggers affinity maturation towards MA/MBP and/or peptide , whereas peptides with less stable backbone structures ( peptides 2 , 4 , 5 , and 6 ) typically do not promote this temporal pattern of increasing antibody titer ., Of note , MD simulations indicate that for those peptides capable of adopting higher order structure , the identified motifs can persist for several nanoseconds—a time period sufficient for antibody recognition 21 , 23 ., A more detailed analysis of the hydrogen bond ( HB ) networks 24 sampled during the MD runs yields similar conclusions , with the caveat that proline-stabilized structures such as peptide 9 do not involve HBs ., Stable HBs from motifs both present and missing in the native protein ( Figure, 2 ) are listed in Table 1 ., Consistent with the RMSD results , peptides 1 , 3 , and 8 preserve protein-like motifs that involve several HBs for a significant amount of the simulation time ., Peptide 7 also preserves a HB at the beginning of a helix that , together with Pro7 , contributes to stability of the motif ., Peptide 4 has one stable HB at the end of a helix ( no proline ) , providing a degree of structural stability that is consistent with the ability of this peptide to generate an initial antibody response against protein two weeks following immunization ( Figure 3A ) ., Despite the fact that peptides 2 and 5 have some secondary structure , these peptides do not preserve their corresponding HBs and fail to trigger antibodies against protein or peptide ., Coupled with the thermodynamic modeling of Figure 1 , these findings strongly suggest that the highly immunogenic peptides 1 and 9 fall into the stable category where ΔGX values allow maximal peptide-antibody complex formation ., In contrast , this combined analysis indicates that peptides 4 and 6 are weakly stable , with ΔGX values that favor diminished antibody binding of peptide relative to full protein ., This classification is fully consistent with ELISAs ( Figure, 3 ) showing that antibodies generated by immunization with peptides 4 and 6 generally bind protein , but not peptide , substrate antigens ., Also dovetailing with experimental results , the non-immunogenic peptides 2 and 5 lack any form of structure resembling native HRS ( Table 1 ) , and no new structural motifs are detected within the limited simulation time ., The latter observation also reflects the fact that although MD simulations and resulting RMSD calculations based on backbone stability provide a framework for ranking the likelihood of forming high affinity peptide-antibody complexes , side chains remain a critical determinant influencing the specificity of this interaction 25 ., More specifically , the loop structure of peptide 5 ( shown in Figure, 4 ) is flanked by highly unstable side chains blocking the relatively conserved backbone ., With peptide 3 , on the other hand , intramolecular HBs linking side chains of Ser7 and Gln10 to side chains of the structurally conserved motif E12E13E14 ( 44% and 29% , respectively ) might be responsible for the weak anti-peptide response shown in Figure 3B ., Collectively , these studies show that several peptides corresponding to the amino terminal portion of murine HRS are capable of inducing anti-protein antibodies of varying affinity and temporal persistence ., As shown by molecular dynamics simulations , sequences of the most immunogenic peptides correspond to highly ordered structural motifs in the parent protein ., Competitive ELISAs provide direct evidence that these peptides share structural determinants with native protein by demonstrating the relative equivalence of antibody affinity for HRS protein ( MA/MBP ) and selected peptides ( i . e . , antibodies recognize or identify , rather than actively define , the immunodominant motif ) ., Of greater significance , first principle calculations and molecular dynamics simulations underscore the thermodynamic and structural basis of these experimental observations ., Among the most interesting findings emerging from the experiments summarized in Figure 3 is the diversity of antibody responses engendered by immunization with different peptides ., While peptides 1 and 9 , for example , bind induced antibodies almost as effectively as full protein , peptides 4 and 7 generate strong antibody responses to protein that fail to recognize peptide in the context of ELISA ., In contrast , peptides 2 and 5 do not support antibody production against either protein or peptide ., For those peptides generating strong antibody responses against the HRS fusion protein MA/MBP , structural mapping indicates correspondence to well-defined domains that involve either α-helices ( peptides 1 , 3 , 4 , 7 , 8 ) or linear motifs stabilized by a proline residue ( peptides 6 , 7 , 9 ) ., To some extent , this result is expected because ( in solution ) such motifs should retain some of the stability present in native protein ., The key question , however , is how peptides bearing only partial structural resemblance to native protein can bind antibodies with similar affinity to that of intact protein ., Answering this question relies on the simple observation that although peptides should be destabilized when isolated from protein ( e . g . , due to solvent exposure of normally buried amino acid residues ) , this instability does not translate into an equivalent drop in affinity towards the repertoire of B cells receptors ., Indeed , thermodynamic calculations in Figure 1 reveal a relatively broad range of ΔGf values ( <0 kcal/mol ) in which peptides are capable of triggering an immune response similar to full protein ., Hence , as long as the peptide fold resembles that of the full protein , this class of peptides ( defined as “stable” in Figure 1 ) should have antigenic properties similar to those of full protein ., Beyond those “stable” peptides with ΔGf<0 kcal/mol , Figure 1 identifies a “weakly-stable” regime where peptides are typically 10–100 times less likely than HRS protein to bind peptide-induced antibodies ., In other words , the same antibodies that rarely bind isolated peptides can readily recognize the corresponding motif in the context of stable protein ., Unlike their more stable counterparts , however , such weakly stable motifs typically do not promote affinity maturation against protein ( compare peptides 1 and 9 to peptides 4 and 6 , Figure 3 ) ., From a modeling point of view , molecular dynamics ( MD ) simulation in explicit solvent represents the most accurate approach to assess peptide stability ., Although this technique has time limitations that prevent a full thermodynamic analysis of each peptide , the 10 nanosecond period used here is sufficient to assess the stability of protein-like conformations relevant to the comparison of peptide- versus protein-targeted antibody responses ., Clearly , the MD simulations demonstrate a wide range of structural stabilities over 10 nanosecond runs; in the case of peptides 1 and 9 , however , the composite structural motifs greatly resemble those presented by full protein , confirming that helical as well as some proline-based linear motifs can preserve their structural integrity over a time frame that is fully compatible with molecular recognition 21 , 23 ., Perhaps the differences in stability and antibody binding affinity between overlapping sequences of peptide 9 ( amino acids 81–98 ) and peptide 10 ( amino acids 91–108 ) best illustrate the power as well as predictive potential of MD simulation ., While competition ELISAs demonstrate that sera derived from peptide 9-immunized mice recognize both peptide 10 and peptide 9 ( consistent with the immunodominant proline-containing epitope suggested by MD that encompasses amino acids 93–96 ) , the relative affinity for peptide 9 exceeds that for peptide 10 by a log order of magnitude ( data not shown ) —a result that again correlates with MD simulations showing that the same proline-containing motif is significantly destabilized by surrounding sequence in peptide 10 , but not in peptide 9 ( see RMSD analysis , Figure 5 and Table 1 ) ., Based on the overall molecular dynamics analysis performed in this study , peptides 1 , 3 , 7 , 8 , and 9 best preserve the folded structure found in corresponding regions of native protein ., This finding is consistent with the data in Figure 3 showing that each of these peptides induces some degree of affinity maturation against either peptide or MA/MBP protein ., With some peptides , however , the failure to stimulate antibodies increasingly cross-reactive with their corresponding HRS structural motifs appears to conflict with the MD stability predictions ., For example , peptide 3 shows no anti-MA/MBP response at any time point ., Yet , analysis of the HRS structure in Figure 2 suggests that peptide 3 is sterically hindered by one side of the α-helical motif of peptide 8 , resulting in mutual epitope blockade ., Note that the suggested negatively charged tri-glutamate epitope of peptide 3 is predicted to face at least four positively charged groups from peptide 8 , further promoting such blockade ( see Figure 4 for additional structural detail ) ., Interestingly , the MA/MBP construct ( Figure, 2 ) still leaves one side of the helix of peptide 8 ( i . e . , the hydrophobic side ) exposed , suggesting that the anti-MA/MBP and anti-peptide responses generated by this peptide ( Figure, 3 ) might be against different faces of this structural motif ., Beyond these structural considerations pertinent to peptides 3 and 8 , the relatively indiscriminate 2 week antibody responses shown in Figure 3 support the prevailing view that early humoral activation involves a lower binding specificity threshold 26–29 than that required for affinity maturation ., The more novel thermodynamic counterpart of this observation is shown in Figure 1 , where 100-fold differences in binding affinity have little effect on the formation of antigen-B cell receptor ( BCR ) complexes involving stable peptides ., Even with weakly stable peptides ( e . g . , peptides 4 and 6 ) where the impact of binding affinity is potentially more significant , early antibody responses against protein can occur—often with titers that are indistinguishable from those generated by their more stable counterparts ., In fact , from the standpoint of stability , Figure 1 suggests that peptides need only eclipse the free energy threshold separating unstable from stable/weakly stable peptides to support early antibody formation ., In contrast , the stability threshold differentiating stable and weakly stable peptides appears to play a greater role in determining those peptides capable of generating long term antibody responses , likely reflecting a requirement for sustained antigen-BCR interactions ., Perhaps peptide 4 best illustrates the immunogenic relevance of this interplay between binding specificity and stability thresholds ., A weakly stable peptide ( see Figure 4 ) that is also the most hydrophobic of all the assessed HRS peptides , peptide 4 triggers unusually high antibody titers at week 2; however , none of these initial responses overcomes the higher activation threshold required to induce affinity maturation ., Although additional factors modulate the selection process that leads to progression/maturation of the humoral immune response , the evidence presented here indicates that this more stringent activation threshold is intimately related to peptide structural stability ., Complementing the overall experimental evidence of HRS peptide immunogenicity presented in these studies , the literature is replete with examples of peptide immunization leading to antibody responses against parent protein ( reviewed in references 10 , 11 ) ., While the original studies involving these peptides do not invoke the novel thermodynamic computation and molecular dynamics simulations employed in this work , complementary analysis indicates that several of the reported peptides are capable of forming higher order structures such as α-helices and proline-stabilized domains ., Moreover , preliminary application of our theoretical and quantitative framework to alternative peptide antigens has yielded data ( not shown ) consistent with these findings and again demonstrates the power/versatility of this approach in characterizing epitope recognition ., However , what is most remarkable about the thermodynamic classification scheme outlined in this work is that peptides with an extraordinarily wide range of folding free energies ( but with structurally conserved core motifs ) behave as “stable” peptides capable of triggering an immune response against defined motifs present in full protein ., Given such links to the immunobiology of antibody-antigen recognition , this work suggests a number of important experimental applications involving the described thermodynamic modeling/computational analysis ., First , more precise mapping of B cell responses over time will help define the sequence of molecular recognition events leading to epitope spreading and , in the process , elucidate the structural component of this process that clearly involves additional factors such as side chain conformation , relative hydrophobicity/hydrophilicity , and overall epitope accessibility ( steric freedom ) ., Second , identification of immunodominant peptide epitopes will permit more detailed categorization of disease subsets and correlation with disease activity ., Finally , this computational tool will facilitate the prediction and design of immunodominant peptide epitopes that can be used to define novel autoantibody specificities in patients with underlying autoimmune diseases ., Through such identification of autoantigen panels , this approach may provide insight regarding more general epigenetic shifts that generate multiple autoantigens and ultimately lead to autoimmunity ., Overlapping peptides ( 18–20 mers ) comprising the amino terminal 108 amino acids of murine histidyl-tRNA synthetase ( HRS ) were synthesized and HPLC purified by the University of Pittsburgh Molecular Medicine Institute using Fmoc chemistry ., As previously described , recombinant murine HRS was generated as a maltose binding protein ( MBP ) fusion protein following subcloning of the appropriate sequence ( derived from RT-PCR amplification of C57BL/6 myocyte RNA ) into the bacterial expression vector pMALc2 ( New England Biolabs , Ipswich , MA ) 19 ., In situ mutagenesis ( Stratagene , La Jolla , CA ) with insertion of a stop codon after base pair 453 yielded a construct encoding the amino terminal 151 amino acids of murine HRS fused to MBP ( MA/MBP ) ., Expressed proteins were purified with amylose resin per the manufacturers protocol ( New England Biolabs , Ipswich , MA ) , filter sterilized , and then subjected to additional column purification for endotoxin removal ( Profos AG , Regensburg , Germany ) prior to use in ELISAs ., NOD . Idd3/5 ( C57BL/6 Insulin dependent diabetes Idd3/5 non-MHC loci transgressed onto the NOD background ) mice were bred in our animal facility ., Eight to ten week old mice were used in immunization protocols approved by the University of Pittsburgh IACUC ., PBS containing 90 µg of the indicated peptides was emulsified with CFA in a 1∶1 ratio and then injected at the base of the tail in a total volume of 200 µl ., Pertussis toxin ( Sigma-Aldrich , St . Louis , MO ) was administered intraperitoneally ( 200 ng/mouse in 100 µl PBS ) at the time of immunization and 48 hours later ., Mice were tail-bled 2 and 4 weeks after immunization ., 8 weeks post immunization , mice were sacrificed , and additional blood was collected from the heart ., Standard solid phase ELISAs provided measurements of IgG anti-MA/MBP and anti-HRS peptide antibody levels in the sera of mice immunized with different HRS peptides 19 ., Briefly , appropriately diluted serum samples ( 1∶500 ) from immunized mice were added to wells containing substrate antigens that included MA/MBP ( 2 µg/ml ) , MBP ( 2 µg/ml ) , HRS peptide ( 2 µg/ml ) , or no antigen ., Following a 60 minute incubation with horseradish peroxidase-conjugated goat anti-mouse IgG ( 0 . 04 µg/ml , Santa Cruz Biotechnology , Santa Cruz , CA ) , enzymatic reactions were visualized using 3 , 3 , 5 , 5-Tetramethylbenzidine ( TMB ) ( Sigma-Aldrich ) and subsequently terminated with 1 N H2SO4 ., Color development was measured at 450 nm by a Wallac 1420 multilabel counter ( PerkinElmer , Wellesley , MA ) , and values were plotted as OD450 substrate antigen - OD450 no antigen ., All assays were performed in triplicate wells ., Plates were coated and blocked as described above ., Diluted serum samples ( 1∶250 ) were mixed in a 1∶1 ratio with serially diluted MA/MBP or HRS peptide solutions in microtubes and preincubated for 30 minutes at room temperature ., Preincubated samples ( final serum dilution of 1∶500 ) were then applied to the plates and incubated for another 2 hours at room temperature ., ELISAs were completed using the same protocol as described above ., The structural model of Mus musculus histidyl-tRNA synthetase ( HRS\u200a=\u200aJo-1 ) in Figure 1 concatenates the NMR structure of the Whep-Trs domain ( Protein Data Bank-PDB code 1X59 , unpublished ) of human HRS ( amino acids 1–64 ) and a homology model of residues 60–498 that is based on the crystal structure of Thermoplasma acidophilum HRS ( PDB code 1WU7 , unpublished ) ., With more than 25% sequence identity , including perfect matching of prolines and glycines in the domains of peptides 1 to 9 listed in Figure 1 , the alignment shown in Figure S1B and the corresponding homology model represent a robust working model of the full protein 25 , 26 ., Only the linker region encompassing residues 46 to 68 ( represented by peptide 6 ( amino acids 51–68 ) in Figure 2 ) is not well resolved in either the NMR or the crystal structures ., Figure 1 solves the standard rate equations for folding and binding of protein/peptide based on typical thermodynamic parameters and the assumption that protein ( X ) binds antibody ( Ab ) only when folded in state Xf ., The results in Figure 1B depend only in the folding free energy ( independent of the folding rates ) , which is varied to cover the full range between −8 and 8 kcal/mol ., Under the additional assumptions that appropriately folded peptides fully encompass the corresponding protein binding domain and that antibody-antigen association and dissociation rates are 106 M−1s−1 and 10−1 s−1 ( alternative dissociation rate of 10−3 s−1 is shown as a dotted line ) , respectively 30 , binding affinity depends more directly on the concentration of Xf ( Xf ) than on peptide stability ., For simplicity , we assume a concentration of antibody ( Ab ) and protein ( X ) equal to 1 µM ., However , the overall shape of the curve does not change significantly with a higher or lower Ab ., For Ab>1 µM , the maximum amount of complex XAb remains the same , but the stability thresholds ( dashed lines in Figure, 1 ) move up ., For Ab<1 µM , the amount of complex will be limited by Ab , and the stability threshold will decrease only slightly ., Molecular dynamics simulations were performed using the MD simulation package GROMACS 3 . 3 . 1 31 on individual peptides of HRS ., Each peptide was centered in a rhombic dodecahedron box with a 15 Å minimum distance from the protein surface to the box edges ., The resulting system was solvated with simple point charge water molecules and then minimized by using steepest descent method with the GROMOS96 force field ., Counter ions were added to neutralize the system ., The temperature was coupled to a bath of 300K with a coupling time constant of 0 . 1 ps ., The pressure was coupled to 1 Bar using a 0 . 5 ps time constant and water compressibility of 4 . 5×10−5 Bar−1 ., A cut-off radius of 10 Å was used in the simulations for non-bonded interactions ., Initial velocities were generated randomly from a Maxwell distribution at 300˚K ., Simulations consisted of 10 nanosecond runs using the corresponding protein structure depicted in Figure 2 as a starting conformation for each peptide ., Accuracy/reliability of the simulations was confirmed with duplicate runs for each peptide . | Introduction, Results, Discussion, Materials and Methods | In the conventional paradigm of humoral immunity , B cells recognize their cognate antigen target in its native form ., However , it is well known that relatively unstable peptides bearing only partial structural resemblance to the native protein can trigger antibodies recognizing higher-order structures found in the native protein ., On the basis of sound thermodynamic principles , this work reveals that stability of immunogenic proteinlike motifs is a critical parameter rationalizing the diverse humoral immune responses induced by different linear peptide epitopes ., In this paradigm , peptides with a minimal amount of stability ( ΔGX<0 kcal/mol ) around a proteinlike motif ( X ) are capable of inducing antibodies with similar affinity for both peptide and native protein , more weakly stable peptides ( ΔGX>0 kcal/mol ) trigger antibodies recognizing full protein but not peptide , and unstable peptides ( ΔGX>8 kcal/mol ) fail to generate antibodies against either peptide or protein ., Immunization experiments involving peptides derived from the autoantigen histidyl-tRNA synthetase verify that selected peptides with varying relative stabilities predicted by molecular dynamics simulations induce antibody responses consistent with this theory ., Collectively , these studies provide insight pertinent to the structural basis of immunogenicity and , at the same time , validate this form of thermodynamic and molecular modeling as an approach to probe the development/evolution of humoral immune responses . | In the current paradigm of immune system recognition , T cell receptors bind to relatively short peptide sequences complexed with major histocompatibility complex proteins on the surface of antigen presenting cells , while B cell receptors recognize unprocessed protein structures ., Yet , ample data exist showing that peptide immunization can trigger B cell responses targeting both the immunizing peptide and peptidelike motifs contained within intact protein—despite the fact that the folding stability of such peptides is often quite low ., Using thermodynamic modeling and the technique of molecular dynamics simulations , this work provides a cogent framework for understanding the relative capacity of inherently unstable peptide structures to faithfully trigger B cell antibody production against specific conformational motifs found in native/intact proteins . | computational biology/molecular dynamics, biophysics/theory and simulation, immunology/immune response | null |
journal.pcbi.1004669 | 2,016 | Brain Connectivity Dissociates Responsiveness from Drug Exposure during Propofol-Induced Transitions of Consciousness | Understanding how the human brain reversibly generates and loses consciousness , through complex interactions of neural activity at multiple spatial and temporal scales , is a grand challenge for modern neuroscience ., Recent theoretical advances have argued that consciousness changes when the balance between integrated and differentiated neural activity is affected 1–4 ., However , accurately tracking these changes in brain dynamics remains a key research challenge with potentially wide-ranging applications , and is complicated by the significant individual variability in the trajectory along which consciousness is lost and regained ., The process of reversibly inducing unconsciousness using anaesthetic drugs like propofol is commonplace in clinical medicine 5 ., However , tracking brain activity to accurately assess the depth of anaesthesia in an individual is currently not a universal component of clinical practice ., Indeed , surface electroencephalography ( EEG ) is relatively easy to measure from the scalp and has long been known to index changes in brain dynamics induced by anaesthetic action 6 , but it is still not universally used in the clinical setting ., This is despite the fact that intraoperative awareness during surgery continues to result in pain and distress 7 , highlighting the need for reliable depth of anaesthesia monitoring in the operating room ., The absence of ubiquitous brain monitoring during general anaesthesia is , in part , due to the lack of robust EEG markers derived from current advances in neuroscience 8–12 , which can accurately track the loss and reestablishment of reportable consciousness ., Monitoring of brain states is currently limited to proprietary systems with mixed results 13–15 ., Crucially , one reason for this is the considerable individual variability in susceptibility to anaesthetic dosage 16 , which adversely affects the accuracy of these systems 17 ., To better understand the factors underlying this variability , we combined the measurement of high-density resting state EEG from healthy volunteers sedated with propofol with measurement of drug concentrations in blood , in addition to objective assessment of behavioural responsiveness ., With this aim in mind , we administered propofol at dosages expressly aimed at engendering varying degrees of mild to moderate sedation across our participant group , rather than complete unconsciousness in all of them ., Employing modern functional EEG tools to assess spectral power and connectivity , we identified key changes in brain networks using graph-theoretic tools , and linked these changes to individual variability in drug concentrations and loss of behavioural acuity during sedation ., Drawing upon previous research 18–21 , we hypothesised characteristic impairments in the strength and topography of EEG power and connectivity , especially manifesting in the slow and alpha frequency bands , alongside administration of propofol ., In addition to confirming these hypotheses , our findings highlight valuable EEG-derived signatures that can not only track the actual amount of propofol in blood , but also predict loss of responsiveness even before any drug is administered ., These findings contribute to the current interest in identifying consistent markers of the loss and recovery of consciousness during propofol sedation ., In the clinical context , these findings could lead to more accurate drug titration and brain state monitoring during anaesthesia ., The behavioural changes accompanying the administration of progressively increasing amounts of propofol ( Fig 1A ) are shown in Fig 1B , which plots the hit rate of participants as a function of the level of sedation ., Based on binomial modelling of their hit rates ( see Materials and Methods ) , we identified a subgroup of 7 participants who became behaviourally impaired at this simple task during moderate sedation; 13 others remained responsive throughout , though their reaction times were impaired during sedation ( Fig 1C ) ., We designate these two groups as drowsy ( green triangles ) and responsive ( blue triangles ) in the following descriptions ., As expected , we found a highly significant interaction between group and sedation level in hit rates ( Fig 1B; F ( 3 ) = 38 . 4 , p = 9e-09 ) ., Further , in the responsive group , there was a significant effect of sedation on reaction times ( Fig 1C; F ( 2 ) = 14 . 6 , p = 0 . 0002 ) ., In comparison to the relative distinction between the two groups in their hit rates , there was considerably more overlap in drug concentrations measured in blood plasma ( Fig 1D ) ., We found a relatively weaker interaction between group and level of sedation in drug concentrations: F ( 2 ) = 4 . 7 , p = 0 . 0242 , and the difference between drug concentrations in the two groups reached significance only during moderate sedation ( p = 0 . 0181 ) ., This finding points to the well-studied inter-individual variability in pharmacodynamic impact of propofol 16 , 17 , and motivates the development of more accurate signatures of responsiveness that can be measured passively and non-invasively during propofol sedation ., Connectivity between EEG channels was assessed to directly investigate the impact of propofol on the structure of brain networks of oscillatory neural interactions , using the debiased weighted Phase Lag Index ( dwPLI , see Fig 2 and 22 ) ., Here , we define brain networks as the characteristic patterns of scalp-level connectivity observable in human EEG at different frequencies , generated by underlying cortical networks 23 with firing rates oscillating at their natural frequencies 24 ., We employed the dwPLI connectivity matrices in each band to construct such EEG-derived brain networks , and used graph-theoretic algorithms to quantitatively compare their topological properties ., By representing the EEG channels as nodes of a network and the strength of dwPLI between them as weighted , undirected links between them , we calculated four measures that captured micro-scale ( clustering coefficient ) , meso-scale ( modularity and participation coefficient ) and macro-scale properties ( characteristic path length ) of each participant’s network at each level of sedation ( see bottom right panel of Fig 2 for a visual description of these properties ) ., Importantly , these metrics were chosen a priori to summarise key network properties that we expected to be modulated during propofol sedation ., In the alpha band , median dwPLI across all channel pairs was significantly more reduced in the drowsy group during mild ( p = 0 . 003 ) and moderate sedation ( p = 0 . 01 ) ., Further , the clustering coefficient 25 , 26 , which measures local efficiency , was significantly lower ( Fig 3A ) in the frontal alpha networks of the drowsy group during mild ( p = 0 . 007 ) and moderate sedation ( p = 0 . 04 ) ., Furthermore , within the responsive group , clustering during moderate sedation tended to decrease linearly alongside increasing reaction times ( Fig 3B ) , though this effect only approached significance ., Conversely , characteristic path length ( Fig 3C ) , the inverse of global efficiency , was significantly higher during mild ( p = 0 . 0004 ) and moderate sedation ( p = 0 . 0035 ) , and tended to increase with slower reaction times among responsive participants ( Fig 3D ) ., Taken together , small-worldness , a combined measure of a network’s local and global efficiency ( calculated as the ratio of clustering to path length 26 , 27 ) , was significantly reduced in the drowsy group during mild ( p = 0 . 005 ) and moderate sedation ( p = 0 . 03 ) ., At the meso-scale , these drowsy alpha networks were also more modular at moderate sedation ( Fig 3E , p = 0 . 02 ) , and hence more separable into relatively disconnected topological modules 28 ., Crucially , these modules lacked hub nodes that connected them into an integrated network , as evidenced by statistically lower standard deviation ( p = 0 . 002 ) of participation coefficients 29 in the drowsy group ( Fig 3F ) ., Together , these network differences demonstrated that the frontal alpha connectivity in the drowsy group did not have the network capacity of the occipital alpha network commonly observed in human resting EEG during wakefulness ., These changes in alpha networks can be understood more visually with Fig 4A ., At baseline , both groups had prominent frontocentral and occipital modules of strong connectivity ., While these modules persisted through moderate sedation in the responsive group , the structure of connectivity networks in the drowsy group shifted to qualitatively distinct state comprising of coherent , frontally centered oscillations that manifested as a frontal module ( Fig 4B ) , before reverting back to the typical pattern of baseline connectivity during recovery ., On the whole , this shift in alpha connectivity mirrors the frontal shift in alpha power ( Fig, 5 ) commonly observed during propofol sedation 18 , 19 , 30–32 ., In contrast to these changes in alpha networks , no differences were observed between delta networks in the two groups ( see S1 Fig ) ., Spectral connectivity in the alpha band identified a prospectively valuable determinant of the variability in susceptibility to propofol seen in the behavioural data ., During the baseline period before sedation , though there were no differences in the topography or relative strength of alpha power between the responsive and drowsy groups ( Fig 5A and 5B ) , there were significant differences in median dwPLI ( p = 0 . 0085 ) and key network properties that captured the topological structure of connectivity in the alpha band ., Specifically , alpha networks in the drowsy group were already less clustered ( Fig 3A; p = 0 . 04 ) and less small-worldy ( p = 0 . 0187 ) at baseline ., They were also more modular ( Fig 3E; p = 0 . 04 ) , and had fewer hubs ( Fig 3F; p = 0 . 0018 ) ., Remarkably , these baseline alpha network differences were evident when the two groups of participants were indistinguishable , both in terms of behavioural hit rates ( Fig 1B ) and occipital alpha power ( Fig 5B ) ., Furthermore , this predictive value of brain connectivity was unique and specific to the alpha band , and not evident in other frequency bands ( see S1 Fig ) ., In line with previous findings 31 , 33 , 34 , sedation selectively increased beta/gamma power and connectivity among responsive participants , but baseline power or connectivity in these bands was not significantly different between the two groups ., To explicate this result further , Fig 6A depicts a scatter plot of alpha network small-worldness in each participant measured during pre-drug baseline , against their consequent behavioural hit rates and drug concentrations measured during moderate sedation ., Though there was considerable variability in small-worldness across the responsive group at baseline , the drowsy group already had relatively lower small-worldness in comparison ., To directly test whether participants who already had less robust brain networks at baseline later became drowsy or unresponsive during moderate sedation , Fig 6B plots the individual hit rate trajectories of the participants separated based on whether their baseline small-worldness was above or below the median ., Those in the group with high baseline small-worldness remained responsive , and had significantly higher hit rates during moderate sedation ( Fig 6B , inset; p = 0 . 0093 ) ., This predictive role of alpha brain networks in characterising individual variability in susceptibility to propofol is exemplified in Fig 6C , which depicts their evolution in two ‘drug concentration-matched’ participants ., Despite registering relatively similar drug concentrations at moderate sedation , one of them remained responsive while the other became completely unresponsive ., As is evident , the latter participant already had a comparatively less robust alpha network already at baseline , which then evolved into a frontally alpha module at moderate sedation ., In comparison , the responsive participant had a relatively more small-worldy , less modular network at baseline , which was sustained during moderate sedation ., These differences potentially explain why the drowsy group , whose alpha networks were already compromised to some degree , became behaviourally impaired while the responsive group did not , despite both groups registering overlapping levels of propofol as measured in their blood at moderate sedation ., It is important to note that these differences observed in the baseline alpha networks were abolished at recovery ( see Fig 3A and 3C ) ., This suggested that these differences between the two groups were essentially dependent on the latent alpha network state of the participants at the beginning of the data collection rather than any individual trait , and were ‘reset’ after the washout of the drug ., We found that , at baseline , participants in both responsive and drowsy groups had similar temporal coupling between the phase of slow oscillations and alpha power , with negative values of phase-amplitude coupling ( PAC; Fig 7A ) over occipital channels ( delineated in Fig 5A , top left ) ., This pattern persisted during mild sedation and only changed during moderate sedation within the drowsy group , in whom it shifted toward positive PAC values , before reverting back to negative PAC at recovery ., There was a significant interaction in occipital PAC between level of sedation and group ( F ( 3 ) = 3 . 8 , p = 0 . 021 ) ., Fig 7C provides more detail on this , using angular histograms of alpha power distributed over slow phase , for a pair of representative participants , one in each of the two groups , responsive and drowsy ., At baseline , occipital alpha power was either evenly spread over slow phase , or was greater near the trough of the slow oscillation , resulting in a trough-max distribution and negative PAC ., During moderate sedation , only the drowsy participant’s distribution shifted towards peak-max positive PAC with greater alpha power near slow oscillation peaks ., At recovery , this distribution reverted back to a trough-max pattern with negative PAC ., Further , we also found a highly significant positive correlation between PAC and drug concentrations in blood during moderate sedation ( Fig 7B ) ., This correlation did not manifest during mild sedation or recovery , when drug concentrations were relatively low ., Importantly , there was no significant correlation between PAC and reaction times ., This was in contrast to the correlations between alpha power/connectivity and reaction times ( Figs 3B and 3D and 5C ) , and highlights a novel dissociation between phase-phase and phase-amplitude coupling: while the former correlated with responsiveness as measured by hit rates and reaction times , the latter correlated drug concentrations in blood ., Juxtaposed with previous research , our findings are convergent with existing evidence for characteristic changes in PAC alongside propofol induction ., Trough-max slow-alpha PAC has been shown to accompany transitions to unconsciousness in frontal EEG channels , which then switches to a peak-max pattern in the same channels following loss of consciousness during deep sedation 18 , 35 ., While we have highlighted complementary changes in occipital channels , we also replicated these previous findings ., In frontal channels , slow-alpha PAC values were close to zero at baseline , and progressed to a trough-max pattern during moderate sedation ( see S2 Fig ) ., This resulted in a significant interaction between level of sedation and group in frontal PAC values ( F ( 3 ) = 4 . 1 , p = 0 . 0136 ) , with the drowsy group showing a significantly stronger trough-max pattern than the responsive group during moderate sedation ( p = 0 . 011 ) ., Further , as with occipital PAC , there was a significant correlation between frontal PAC and drug concentrations in blood during moderate sedation ( S2 Fig ) ., Our experimental design used propofol sedation to engender transitional states of responsiveness that varied across participants ., The levels of drug administered produced a variable pattern that spread the participant group along a spectrum of varying behavioural impairment , rather than resulting in complete unconsciousness in all of them ., Using EEG to track brain activity and measuring actual levels of drug in blood alongside this spectrum of impairment has enabled us to identify neural markers that dissociate conscious report from drug exposure 2 , and makes the results presented here distinctive in their contribution to advancing understanding of the neural markers of loss of consciousness due to propofol ., We have built upon previous research that has shown that while occipital alpha power progressively drops as participants become behaviourally compromised as measured by reaction times , the qualitatively dissimilar onset of frontal alpha power is a characteristic marker of the loss of consciousness 18 , 19 , 30 , 32 , 36 ., Confirming our hypotheses , while this frontal alpha generates meso-synchronous modules , brain network connectivity as a whole is nevertheless impaired ., Graph-theoretic measures quantify this loss of the capacity of individual brain networks in the alpha band , linking them to concomitant variability in behavioural impairment across participants ., Small-worldness is commonly seen as a measure of the cost-versus-efficiency optimality of a network configuration , and our findings converge with previous evidence 37 highlighting the reduction in the efficiency of cortical networks during loss of consciousness during propofol sedation , potentially due to dysfunctional modulations in thalamocortical connectivity 8 , 38 , 39 ., It is worth noting that a similar breakdown in the capacity of alpha networks has also been reported with other anaesthetic agents like sevoflurane and ketamine 40–42 ., This is despite the fact that these distinct anaesthetic agents had varying effects on EEG oscillations and , unlike propofol , did not always produce increases in frontal alpha ., Hence the observed changes in alpha networks due to sedation cannot be explained as a shift of alpha power and connectivity from posterior to anterior areas ., Rather , our results , along with these previous findings , point toward a broader understanding of characteristic signatures of connectivity in alpha networks as potentially reliable correlates of reportable consciousness 43 ., Measurement of drug concentrations at each level of sedation dissociated a principal clinical pharmacodynamics target per se ( sedation and consequent behavioural unresponsiveness ) from incidental pharmacodynamic consequences of drug exposure during propofol sedation ., The considerable individual variability in the susceptibility to anaesthesia has been documented 16 , and is evident in the large overlap between blood levels of drug in our responsive and drowsy groups ., While our measurement of modulations in phase-phase coupling in delta and alpha bands during sedation showed clear correlations with behavioural impairment , we have also demonstrated a latent relationship between slow-alpha phase coupling and individual variation in drug concentrations ., It is important to distinguish these dynamic slow oscillations from stable slow cortical potentials observed during propofol anaesthesia 12 , and from delta oscillations during sleep 44 ., This link between PAC and individual levels of drug in blood was not observed in the delta or alpha bands separately , in either power or connectivity ., Analytical approaches used for estimating Bispectral Index ( BIS , see 45 that do not take phase information into account are unlikely to detect this key marker of individual drug concentration 18 ., Hence our findings are relevant to the challenge of engendering an appropriate level of unconsciousness by accurately tailoring drug concentrations to individuals , a key consideration with significant implications for clinical anaesthesia ., Finally , by tracking individual brain networks across levels of sedation , we have shown that the quantifiable robustness of alpha connectivity networks in the awake state before sedation predicts susceptibility to propofol ., Specifically , given two behaviourally indistinguishable individuals undergoing administration of sedative , the one with the more robust , small-worldy alpha network with well-connected hubs is likely to require a greater amount of drug to render them unresponsive to the same degree ., It is important to note that this latent variability in the state of alpha connectivity at baseline could be detected despite the lack of any significant differences in behavioural performance or alpha power at that time ., Orthogonally , slow-alpha PAC complements this predictive capability by tracking the concentration of propofol in blood plasma ., This set of results , if replicated and verified in the clinical context , could contribute to reliable applications of brain monitoring for tracking and accurately modulating consciousness with anaesthetics during routine surgery ., All healthy controls gave written informed consent ., Ethical approval for testing healthy controls was provided by the Cambridgeshire 2 Regional Ethics Committee ., All clinical investigations were conducted in accordance with the Declaration of Helsinki ., A convenience sample of 22 neurologically healthy adults participated in the study ., Data from two participants could not be used due to technical issues , leaving 20 participants ( 9 male; 11 female ) ( mean age = 30 . 85; SD = 10 . 98 ) whose data were analysed ., Each experimental run began with an awake baseline period lasting 25–30 minutes ( Fig 1A ) following which a target-controlled infusion of propofol 46 was commenced via a computerized syringe driver ( Alaris Asena PK , Carefusion , Berkshire , UK ) ., With such a system the anesthesiologist inputs the desired ( “target” ) plasma concentration , and the system then determines the required infusion rates to achieve and maintain the target concentration ( using the patient characteristics which are covariates of the pharmacokinetic model ) ., The Marsh model is routinely used in clinical practice to control propofol infusions for general anesthesia and for sedation ., Three blood plasma levels were targeted– 0 . 6μg/ml ( mild sedation ) , 1 . 2μg/ml ( moderate sedation ) , and recovery from sedation ., The state of mild sedation was aimed to engender a relaxed but still responsive behavioural state ., At each target level , a period of 10 minutes was allowed for equilibration of plasma propofol concentrations to attain a steady state , following which behavioural tests and EEG measurements were commenced ., After cessation of infusion , plasma propofol concentration exponentially declined toward zero ., Computer simulations with the TIVATrainer pharmacokinetic simulation software revealed that plasma concentration of propofol would approach zero in 15 minutes leading to behavioural recovery; hence behavioural assessment was recommenced 20 minutes after cessation of sedation ., Blood samples of 1cc each were taken at the beginning and end of the mild and moderate sedation states , and once at recovery , as indicated in Fig 1A ., In total , 5 blood samples were taken during the study ., These samples were analysed offline for characterising the significant inter-individual variability in actual propofol levels in blood plasma ., We confirmed that the samples taken at the beginning and end of mild and moderate sedation had similar values of propofol concentration ., The average of the two values , along with the value at recovery , were used as distinct covariates for EEG data analysis ., At each of the 4 steady-state levels above , participants were requested to perform a simple behavioural task involving a fast discrimination between two possible auditory stimuli ( Fig 1A ) ., Specifically they were asked to respond with a button press to indicate whether a binaurally presented stimulus was a buzz or a noise ., These stimuli constituted either broadband noise or a harmonic complex with a 150Hz fundamental frequency ( buzz ) ., Forty such stimuli , twenty of each kind , were presented in random order over two blocks , with a mean inter-stimulus interval of 3 seconds ., We calculated a participant’s cognitive processing of these stimuli at each sedation level based on their hit rates , i . e . , percentage of correct responses ., In addition , we measured reaction times based on the delay between auditory tone onset and correct button press ., We employed binomial modelling to distinguish participants who became behaviourally impaired during moderate sedation , from those who remained responsive , albeit with slower reaction times ., Specifically , we fitted a binomial distribution to each participant’s hit rates at baseline and during moderate sedation ., With each fitted model , the distribution parameter p , the probability of a correct response , and its 95% confidence intervals were estimated ., For a given participant , if the confidence interval at moderate sedation was lower than and non-overlapping with that at baseline , they were considered to have become significantly impaired , and we designated them as drowsy ., If the confidence intervals overlapped , we designated them as responsive ., From each participant , approximately 7 minutes of 128-channel high-density EEG data were collected at each level of sedation ., EEG was measured in microvolts ( uV ) , sampled at 250Hz and referenced to the vertex , using the Net Amps 300 amplifier ( Electrical Geodesics Inc . , Eugene , Oregon , USA ) ., Participants had their eyes closed in a resting state during data collection ., Data from 91 channels over the scalp surface ( Fig 2 ) were retained for further analysis ., Channels on the neck , cheeks and forehead , which tended to contribute most of the movement-related noise , were excluded ., Retained channels were filtered between 0 . 5–45Hz , and segmented into 10-second long epochs ., Each epoch thus generated was baseline-corrected relative to the mean voltage over the entire epoch ., Data containing excessive eye movement or muscular artefact were rejected by a quasi-automated procedure: abnormally noisy channels and epochs were identified by calculating their normalised variance and then manually rejected or retained by visual inspection ., After pre-processing , a mean ( SD ) of 38 ( 5 ) , 39 ( 4 ) , 38 ( 4 ) and 40 ( 2 ) epochs were retained for further analysis in the baseline , mild sedation , moderate sedation and recovery conditions , respectively ., An ANOVA revealed no statistically significant difference between the numbers of epochs retained ., Finally , previously rejected channels were interpolated using spherical spline interpolation , and data were re-referenced to the average of all channels ., These processing steps were implemented using custom MATLAB scripts based on EEGLAB 47 ., Fig 2 depicts the data processing pipeline employed to calculate spectral power and connectivity measures from the clean EEG datasets ., Spectral power values within bins of 0 . 25Hz were calculated using Fourier decomposition of data epochs using the pwelch method ., At each channel , power values within canonical frequency bands , namely delta ( 0–4Hz ) , theta ( 4–8Hz ) , alpha ( 8–15Hz ) , beta ( 12-25Hz ) and gamma ( 25–40Hz ) , were converted to relative percentage contributions to the total power over all five bands ., Alongside , cross-spectrum between the time-frequency decompositions ( at frequency bins of 0 . 49Hz and time bins of 0 . 04s ) of every pair of channels was used to calculate debiased weighted Phase Lag Index ( dwPLI , see 22 ) ., For a particular channel pair and frequency band , mean dwPLI across all time at the peak frequency within each band was recorded as the ambient amount of connectivity between those channels ., dwPLI is a sensitive measure of connectivity between cortical regions that has been shown to be robust against the influence of volume conduction , uncorrelated noise , and inter-subject variations in sample size 22 , and has previously be used to characterise connectivity in pathological 48 and pharmacological 49 alterations in consciousness ., However , as pointed out by Vinck , Oostenveld 22 , dwPLI is relatively insensitive to true connectivity at phase differences close to 0 or 180 degrees ., Further , the actual locations of brain sources producing dwPLI connectivity between a pair of sensors might not necessarily be spatially proximal to those sensors ., Nevertheless , for the purposes of this study , it provides a robust measure for estimating how this indirect connectivity is affected by propofol sedation ., Phase-amplitude coupling ( PAC ) , also referred to as cross-frequency coupling 50 , was used to measure the propofol-induced changes in the relationship between the phase of ongoing oscillations in the slow ( 0 . 5–1 . 5Hz ) and alpha ( 8–15Hz ) bands at each channel ., Calculation of PAC was based on the Direct PAC estimator formally defined by Ozkurt and Schnitzler 51 and implemented in the Brainstorm 3 . 2 toolbox 52 ., Purdon , Pierce 18 and Mukamel , Pirondini 35 previously identified changes from trough-max to peak-max PAC during propofol sedation , as determined by whether the slow oscillation is at its trough ( at a phase angle of pi ) or its peak ( phase angle of 0 ) when alpha power is maximal , respectively ., Such variations were measured by assigning a negative or positive sign to the amplitude of the complex-valued Direct PAC estimator depending on whether its phase angle was closer to pi or 0 radians , to indicate trough-max and peak-max coupling respectively ., The 91x91 subject-wise , band-wise dwPLI connectivity matrices were thresholded to retain between 50–10% of the largest dwPLI values ., They were then represented as graphs with the channels as nodes and non-zero values as links between nodes ., The lowest threshold of 10% ensured that the average degree was not smaller than 2 * log ( N ) , where N is the number of nodes in the network ( i . e . , N = 91 ) ., This lower boundary guaranteed that the resulting networks could be estimated 26 ., Similar ranges of graph connection densities have been shown to be the most sensitive to the estimation of ‘true’ topological structure therein 53 , 54: higher levels of connection density result in increasingly random graphs , while lower levels result in increasingly fragmented graphs ., At each step of the connection density between 50% and 10% in steps of 2 . 5% , the thresholded graphs were submitted to graph-theoretical algorithms implemented in the Brain Connectivity Toolbox 55 ., These algorithms were employed to calculate metrics that captured key topological characteristics of the graphs at multiple scales , and avoided the multiple comparisons problem entailed by comparing large numbers of network connections ., These included the micro-scale clustering coefficient and macro-scale characteristic path length 26 , alongside meso-scale measures like modularity and community structure 56 , and participation coefficient 29 ., Here , this functional notion of modularity measures the extent to which the nodes of a graph can be parcellated into topologically distinct modules with more intra-modular links than inter-modular links 28 ., Modularity as calculated by the heuristic Louvain algorithm , and all measures derived therefrom , were averaged over 50 repetitions ., Next , each graph metric thus derived was normalised by the average of 50 null versions of the metric similarly derived , but after repeatedly phase-randomising the original cross-spectra and recalculating dwPLI for each channel pair ., Finally , the small-worldness index of a graph was calculated as the ratio of normalised clustering coefficient to characteristic path length 57 ., Metrics were compared using two-way ANOVAs with one non-repeated ( group ) measure and one repeated ( level of sedation ) measure ., The | Introduction, Results, Discussion, Materials and Methods | Accurately measuring the neural correlates of consciousness is a grand challenge for neuroscience ., Despite theoretical advances , developing reliable brain measures to track the loss of reportable consciousness during sedation is hampered by significant individual variability in susceptibility to anaesthetics ., We addressed this challenge using high-density electroencephalography to characterise changes in brain networks during propofol sedation ., Assessments of spectral connectivity networks before , during and after sedation were combined with measurements of behavioural responsiveness and drug concentrations in blood ., Strikingly , we found that participants who had weaker alpha band networks at baseline were more likely to become unresponsive during sedation , despite registering similar levels of drug in blood ., In contrast , phase-amplitude coupling between slow and alpha oscillations correlated with drug concentrations in blood ., Our findings highlight novel markers that prognosticate individual differences in susceptibility to propofol and track drug exposure ., These advances could inform accurate drug titration and brain state monitoring during anaesthesia . | Though scientific understanding of how brain networks generate consciousness has seen rapid advances in recent years , application of this knowledge to accurately track transitions to unconsciousness during general anaesthesia has proven difficult due to considerable variability in this gradual process across individuals ., Using high-density electroencephalography , we studied changes in these networks as healthy adults were sedated using propofol ., By measuring their behavioural responsiveness and amount of sedative in their blood , we found a striking pattern: the strength of their brain networks before sedation predicted why some participants lost consciousness while others did not , despite registering similar blood levels of drug ., By uncovering underlying signatures of this variability , our findings could enable accurate brain monitoring during anaesthesia and minimise intra-operative awareness . | null | null |
journal.pcbi.1003639 | 2,014 | A New Tool to Quantify Receptor Recruitment to Cell Contact Sites during Host-Pathogen Interaction | C . albicans is a commensal of the human oropharyngeal cavity , gastrointestinal tract and female lower reproductive tract ., It is also a significant opportunistic pathogen 1 ., Infection by Candida species causes illnesses ranging from superficial mucosal infections that markedly diminish quality of life to bloodstream infections associated with high mortality ., Systemic fungal infections by C . albicans have emerged as important causes of sickness and death in immunocompromised patients 2 ., Some major risk factors associated with Candidemia involve neutropenia and prolonged hospitalization ( days ) involving in-dwelling medical devices which can become infected with Candida 3 ., There is mortality rate associated with systemic Candida infection and an increased incidence of these types of infections in cancer patients 4–6 ., For instance , Candida accounts for about one quarter of the fungal infections seen in leukemia patients 7 ., During tissue colonization and invasion , C . albicans can undergo a transition from ellipsoidal yeast to filamentous hyphae , and this dimorphism is thought to be important for the infectious process ., C . parapsilosis is one of the more commonly isolated non- albicans Candida species and is particularly problematic in neonates ., It is clinically identified in 7–21% of systemic Candidiasis cases , where it is associated with 10–28% mortality 8–10 ., C . parapsilosis colonizes human skin and nails , which is significant for its role in nosocomial infection 11 ., C . parapsilosis can also be isolated from non-human animals , soil and physical surfaces 12 ., S . cerevisiae is an environmental yeast most commonly associated with baking and fermentation processes ., It is an exceedingly rare human pathogen , but can infect severely immune compromised patients 13 ., The differing lifestyles of the three species compared may require different adhesive properties and regulation of cell wall structures so these fungi may adapt to and persist within their various niches ., Nevertheless , they all contain grossly similar cell wall polysaccharide components and organization ., Around 85% of the C . albicans cell wall is made up of diverse carbohydrates—primarily mannoproteins , -glucans , and chitin 14–16 ., Chitin is deposited at sites deep within the cell wall and also exhibits some surface-accessibility at yeast bud scars 17–19 ., However , the outermost layer of the Candida cell wall presents an external surface dominated by N-linked glycans which are comprised mostly of mannans 20 with punctate exposure of - and -glucans 17–19 ., The cell wall contains a variety of mannosylated species including protein N- and O-linked -mannosides 16 , β-linked mannosides within N-linked mannan 21 and phospholipomannan 22 , 23 ., Cell wall polysaccharides are essentially immobile on the time scale of host-pathogen interaction ., Candida may modulate the degree of ligand exposure during infection 24 ., Because the fungal cell wall is so complex , leukocytes must use multiple receptors in order to detect , interact with and initiate immune responses to fungal pathogens 20 , 25 , 26 ., Innate immune cells , such as dendritic cells ( DCs ) , rely on pattern recognition receptors ( PRRs ) to identify fungal pathogens ., These PRRs recognize pathogen-associated molecular patterns , which are characteristic molecular signatures of microbial biology 1 , 27 , 28 ., Significant PRRs for fungal mannan recognition include the C-type lectins ( CTLs ) DC-SIGN , CD206 ( Mannose Receptor ) , Dectin-2 and Mincle ( N-linked mannan ) ; the Toll-like receptors TLR4 ( O-linked mannan ) and TLR2 ( phospholipomannan ) ; and Galectin-3 ( -linked mannosides ) 23 , 25 , 26 , 29 , 30 ., -glucans are also immunogenic ligands of Dectin-1 ( a CTL ) and can be recognized by the integrin Mac-1 31 ., These receptors are expected to be relatively mobile in the plasma membrane ., Recent research advances have clarified the identities of many receptors involved in fungal recognition , and increasingly ( i . e . , for DC-SIGN and Dectin-1 ) , signal transduction cascades have been elucidated 32 ., For Candida albicans , there is evidence that receptors can tailor specific downstream signaling and cytokine responses depending on the morphological state of the pathogen ., For example , investigators have reported that CLR-mediated recognition of both C . albicans yeasts and hyphae 33 , 34 and C . parapsilosis 35 results in divergent T helper cell polarization responses ., Nevertheless , the specific contributions of individual receptors and their integration into the larger , multi-receptor system of fungal pattern recognition is not clear ., Despite their ability to bind important pathogenic antigens , genetic ablation of CD206 or a murine homolog of DC-SIGN , SIGNR1 , has been shown to have little impact on host defense in murine models of Candidiasis and S . mansoni infection 36 , 37 ., However , the existence of redundant systems for mannan sensing and species-specific differences in CTL function likely explain these findings ., Furthermore , the interaction of Candida mannan with CD206 and DC-SIGN is well recognized as an important event in the generation of cytokine responses and phagocytosis by leukocytes 25 , 32 , 38–40 ., While the functional consequences of CTL engagement are partially overlapping , evidence suggests that specific CTLs may be important for specific functions such as pathogen binding , phagocytosis and inflammatory cytokine generation 41 and co-engagement can modify CTL function 42 ., Innate immune antigen presenting cells , such as dendritic cells , are some of the first responders to fungal infections and they also activate adaptive immune responses that are critical for clearing Candida infections 43 , 44 ., The earliest event that occurs in response to a Candida infection is the formation of a contact between an innate immune cell and the pathogenic fungal cell , which then determines the course of downstream signaling to activate inflammatory responses ., Understanding the biology of fungal recognition requires elucidation of, 1 ) the transport of C-type Lectins and other pattern recognition receptors to the site of host-microbe interaction ,, 2 ) rearrangement and coalescence of these receptors to achieve lateral segregation or clustering , and, 3 ) the initiation of signaling cascades at the host-microbe contact site ., Despite the identification of various receptors involved in fungal recognition , many questions remain regarding the mechanisms of receptor assembly at host-fungal pathogen contact sites , the role of receptor aggregation at nano- and micrometer length scales 45 , and the spatiotemporal regulation of receptor cross-talk 31 , 46 ., Key to answering these questions are tools that provide rigorous quantification of receptor redistribution and signaling at host pathogen contacts ., The distribution of CTLs can be imaged at high resolution by three-dimensional multicolor confocal laser scanning microscopy ( 3D CLSM ) ., A major difficulty in developing analysis tools is that the imaging data is collected using rectangular voxels while the yeast cell is nearly spherical and rigid , so the contact between the yeast and dendritic cell is part of an essentially spherical surface ( Fig . 1 ) ., To overcome this difficulty , we developed geometric algorithms that construct spherical voxels that contain the yeast cell ., The intensities in the rectangular voxels are transferred to the spherical voxels and then projected onto the surface of a sphere that approximates the surface of the yeast cell using weighted sums along the radial direction ., The approximation is lenient , so a spectrum of geometries of the contact site are tolerable as long as they reside on a roughly spherical surface or within a spherical shell ., We used these tools to quantitatively compare the differences in the contact site organization for the pathogens C . albicans , C . parapsilosis , and the environmental yeast S . cerevisiae ., Some previous studies have used spherical coordinates to analyze biological data in ways that are related to , but significantly extended by , what we do here 47–50 ., For instance , the tool we describe solves the above problems with particular attention to accurate transfer of intensity information to spherical voxels , use of equal area surface pixels for orientation-independence of contact site quantification , and a user-friendly interface that automatically computes a variety of spatial statistical measurements to assist in analysis of cell-cell contacts ., We cultured immature DCs with yeast cells for various times , then fixed the cells and fluorescently labeled the CTLs , DC-SIGN and CD206 , as well as the DC membrane lipids , as described in Materials and Methods ., We used one environmental yeast ( Saccharomyces cerevisiae ) and two pathogenic yeasts ( Candida albicans and Candida parapsilosis ) to form the host-microbe contacts ., We have chosen to focus our attention on these fungi because Saccharomyces and Candida cell wall composition and structure are thought to be mostly similar ( see Discussion ) , yet the innate immune system is often called upon to discriminate between harmless environmental fungi and pathogenic ones ., Furthermore , we have focused on two receptors prominently involved in mannan recognition in order to elucidate how mannan sensing is orchestrated ., Three color 3D fluorescence distributions at cell-pathogen contact sites were measured by 3D CLSM ., Representative examples of the initial data are shown in Fig . 2 ., We compared DC-SIGN and CD206 at fungal contacts formed in response to S . cerevisiae , C . albicans and C . parapsilosis with respect to spatiotemporal patterns of receptor entry at 0 , 1 and 4 hours of exposure to yeasts ., These time points were chosen to focus on stable contact sites ., Previous research has shown that the majority of zymosan particles bound to human DCs exhibit stable extracellular contacts over hours , and CTL signaling can occur from extracellular contacts with fungal ligands and from internal compartments over prolonged periods of time 51–53 ., We observed differential CTL spatiotemporal distribution patterns in contact sites with the three fungal species ., These contact sites contained zones that were colocalized ( on a diffraction limited scale ) or single positive ( schematically represented in Fig . 3A ) ., S . cerevisiae and C . albicans provoked the greatest amount of DC-SIGN and CD206 recruitment respectively , within the first hour , and then both lost receptor intensity in the fourth hour ., In contrast , C . parapsilosis continued to recruit significant amounts of both receptors from the start of the experiment into the fourth hour ( Fig . 3B , C ) ., The slower recruitment of DC-SIGN by C . parapsilosis resulted in contact site accumulations that were times less than S . cerevisiae and times less than C . albicans at the first hour ( Fig . 3D ) ., However , by the fourth hour , C . parapsilosis had recruited times more DC-SIGN than C . albicans and was still significantly less than S . cerevisiae ( Fig . 3E ) ., Similarly , C . parapsilosis recruited CD206 slowly , times less than both the other yeasts ( Fig . 3F ) , but by the fourth hour recruited times more than S . cerevisiae and times more than C . albicans ( Fig . 3G ) ., We observed large increases in DC-SIGN intensity recruited to the contact site in the first hour for S . cerevisiae , C . albicans and C . parapsilosis : 161-fold , 140-fold and 82-fold , respectively ., Likewise , we observed contact site enrichments , albeit lower in magnitude , for CD206 intensity in the first hour for S . cerevisiae , C . albicans and C . parapsilosis : 63-fold , 73-fold and 34-fold , respectively ., This data suggested that DC-SIGN and CD206 recruitment patterns varied in a manner that was quite sensitive to the species of yeast being recognized by the DC—both in terms of the amount and spatiotemporal distribution of receptor recruited ., It was further notable that DC-SIGN , and CD206 to a somewhat lesser extent , was highly enriched in contact sites relative to resting cells and that both CTLs were well recruited to C . albicans contacts , as seen for the other yeasts as well ., Receptor total intensity increase might derive from an increase in contact site area and/or increase of receptor density in contact sites ., We proceeded to examine the contribution of these factors , starting with an assessment of contact site area ., For all cases , we found that augmentation of CTL contact site area occurred most dramatically in the first hour , which is expected based on previously reported findings with macrophages interacting with C . albicans 54 ., We found significant differences in the evolution of contact site area for DC-SIGN and CD206 amongst the three fungal species used to challenge DCs ., S . cerevisiae was notable for the fact that it produced the contacts with largest area occupied by either receptor over the course of the experiment ( Fig . 4A , B ) ., In contrast , both C . albicans and C . parapsilosis contacts were significantly smaller at one hour for both individual CTL contact site areas and total contact area ( Fig . 4A , B ) ., S . cerevisiae contacts contained at least and times larger DC-SIGN and CD206 area than either of the other yeasts at one hour ( Fig . 4C , D ) , and times greater DC-SIGN and CD206 area relative to C . albicans at four hours ( Fig . 4E , F ) ., S . cerevisiae contacts rapidly and effectively expanded , likely indicating a strong cytoskeletal response driving pseudopod extension for engulfment of the yeast ., In contrast , C . albicans failed to produce contact site areas comparable to S . cerevisiae at either time point ( Fig . 4A , B ) ., This may reflect a blunted cytoskeletal response to C . albicans and poorer engulfment , which is addressed further below ., While S . cerevisiae and C . albicans contacts were quantitatively different but followed a similar pattern of CTL spatiotemporal distribution , C . parapsilosis contacts were qualitatively different from the other yeasts contacts in that they exhibited a slow , progressive area increase ( Fig . 4A , B ) ., This progressive area increase for C . parapsilosis mirrored a similar trend seen for receptor recruitment ( Fig . 3B , C ) ., To address the question of whether contact site area correlated with fungal particle size , we measured the major and minor radii of S . cerevisiae , C . albicans and C . parapsilosis yeasts ( each ) from DIC images ( data not shown ) ., From these measurements we also calculated mid-sectional elliptical perimeters ., Upon comparing these results by ANOVA and post-hoc test , we determined that C . albicans and S . cerevisiae yeast sizes were not significantly different for any of these quantities ., C . parapsilosis did exhibit significantly larger major radii ( ) and elliptical perimeters ( ) compared to S . cerevisiae ., S . cerevisiae generated the largest contact sites and C . albicans had the smallest contacts , yet these yeasts were similar in size ., Therefore , we conclude that contact site size is not dictated by particle size but is more likely a reflection of the DCs response to the particle ., Next we wanted to examine what population of the CTLs contributed to the increase in area ., As illustrated in Fig . 3A , the contact can be divided into membrane regions with receptors that are colocalized at the limit of resolution ( , ) and single positive ( , ; or , ) regions ., After analyzing the different populations of CTLs within the contact site , we found that the significant increase in total receptor area was primarily due to an increase in colocalized populations of CTLs in the contact site ( Fig . 4G ) ., On the contrary , both populations of single-positive CTLs ( DC-SIGN and CD206 ) did not change significantly throughout the experiment and comprised a small fraction of the total contact site ( Fig . 4H , I ) ., Taken together , our observations demonstrate that the spatial assembly of the contact site structure is regulated differentially in response to the fungal species presented ., It is also clear that all examined contact sites prominently featured increased predominance of receptor-colocalized membrane areas ., Notably , C . albicans recognition by DCs generated the smallest contact sites despite our finding that this yeast was not deficient in recruiting DC-SIGN or CD206 total intensity ., The clustering of receptors at cell-cell contacts is a common theme in immunoreceptor signaling , and this mechanism drives the formation of membrane regions with increased receptor density ., Receptor density is one factor that can regulate the efficiency of signal transduction and membrane trafficking of the receptor ., Because receptor density in the contact is coordinately defined by the total amount of receptor recruited and the membrane area that it occupies , we created density graphs to display the difference between colocalized and single-positive DC-SIGN and CD206 distributions ( Fig . 5 ) ., Fig . 5A provides a schematic example of contact site density over three time points ( T1-3 ) , and Fig . 5B provides the corresponding density graph analysis ., At T1 , there is a small area with a small amount of intensity within that area that increases in intensity but not area in T2 ( thus , higher density in T2 vs . T1 ) ., At T3 , this region exhibits increases in area and intensity ., The dashed “isodensity” line depicts the set of all combinations of intensity and area with the same density as at T2 ., Thus , because the slope is greater than that of the isodensity line ( i . e . , T3 lies in the green shaded area ) , the transition involves an increase in density at T3 relative to T2 ., This would not be immediately apparent without reference to the isodensity line ., In colocalized regions ( where both DC-SIGN and CD206 are found within the same voxel ) , we found that C . albicans accumulated the highest density for both DC-SIGN and CD206 within the first hour ( Fig . 5C , D ) ., The same trend was also found in S . cerevisiae and C . parapsilosis , but with somewhat lower CTL densities achieved ( Fig . 5E , F , G , H ) ., The development of a pronounced colocalized region with high receptor density could promote receptor cross-talk and strong adhesion ., When we compared fungal species to one another , we found that C . albicans accumulated times more colocalized DC-SIGN density than S . cerevisiae and C . parapsilosis at the first hour ( Fig . 5C , E , G ) , but interestingly C . albicans accumulated times more colocalized CD206 than S . cerevisiae and C . parapsilosis ( Fig . 5D , F , H ) ., Contact sites with S . cerevisiae and C . albicans both reduced their CTL colocalized density between the first hour and fourth hour ( Fig . 5C , D , E , F ) , whereas C . parapsilosis likewise gained density but did not exhibit an area or intensity loss at longer duration ( Fig . 5G , H ) ., We note that all contacts increased their receptor density greatly in the first hour ( slopes well above the stated isodensity line ) , but C . albicans contacts were notable for being dense because they recruited DC-SIGN and CD206 well but remained small in area ., Prior to our detailed analysis of the contact sites , we used the Manders coefficients to estimate the degree of colocalization ., The coefficient M1 ( the proportion of DC-SIGN colocalized with CD206 ) indicated very high degrees of colocalization in 1 and 4 hour contacts for all three yeast species and both CTLs ., As the Manders coefficients are influenced by both degree of overlap and intensity , they are not completely specific for variations in the amount of colocalization ., Our contact site analysis provides more detailed results on colocalization in general ., In this case , the Manders analysis and our contact site analysis of colocalization agreed with one another in finding predominant colocalization in contacts under all tested conditions ., We hypothesized that the differential spatiotemporal patterns of receptor recruitment that we observed for S . cerevisiae , C . albicans , and C . parapsilosis would be correlated with the functional differences in binding and/or phagocytic efficiency during DC-yeast interaction ., In particular , the smaller area contacts observed for C . albicans were suggestive of less actin reorganization and pseudopod extension ., We quantified binding and phagocytic efficiency for DCs treated with yeasts for 1 and 4 hours , as described in the methods section ., Interestingly , there was no significant difference in the median number of yeasts captured per DC between species at 1 or 4 hours ( Fig . 6A , B ) ., We categorized DCs based on their interaction with yeasts as “neither” ( no bound or internalized yeast; excluded from analysis ) , “bound” ( only surface bound yeast ) , “internalized” ( only internalized yeast ) , and “B&I” ( some bound and some internalized yeasts ) ., Despite this equivalent capture of yeasts , we found that DC populations exposed to C . albicans were skewed to distributions that reflected lower levels of internalization ( i . e . , decreased percent of the population in the “B&I” category ) relative to that seen for DCs exposed to S . cerevisiae or C . parapsilosis ( Fig . 6C , D ) ., To understand this phenomenon in more detail , we examined cumulative probability distributions of phagocytic efficiency ( PE ) for DCs exposed to all three yeasts over 1 or 4 hours ., We found that the proportion of DCs that failed to internalize any bound yeast ( ) was higher for C . albicans than the other species for both time points ( Fig . 6E , F ) ., Furthermore , of those DCs that did internalize some yeasts ( ) , these DCs exhibited generally lower phagocytic efficiencies for C . albicans than other species ., These trends represented a significant difference in PE distributions for C . albicans versus S . cerevisiae at 1 and 4 hours , and a significant difference between C . albicans and C . parapsilosis at 4 hours ., The distribution of PE values was not significantly different between S . cerevisiae and C . parapsilosis at either time ., The analysis tool that we developed allows quantification of receptor behavior on an approximately spherical surface extended across multiple -axis confocal sectioning depths ., This capability , coupled with the ability to resolve and quantify receptor structures on this host-pathogen contact site surface , allowed us to discern interspecies differences in CTL mobilization and organization during fungal recognition by dendritic cells ., Despite the presence of abundant -mannoside ligands of DC-SIGN and CD206 in the cell walls of all fungi tested , we observed dissimilar spatiotemporal patterns of receptor recruitment amongst S . cerevisiae , C . albicans and C . parapsilosis ., DCs recruited DC-SIGN and CD206 to contact sites with all three yeast species to achieve tens to over a hundred fold enrichment of receptors ., However , receptor recruitment peaked earlier for C . albicans and S . cerevisiae , while C . parapsilosis contacts developed in a slower , progressive manner ., Also interesting was the observation that S . cerevisiae contacts were quite large while C . albicans contacts were notable for being the smallest at both one and four hours ., Because contact site area is likely to reflect the success of cytoskeletal remodeling in response to fungal recognition , we examined whether receptor recruitment patterns or contact site area characteristics correlated with the functional outcome of phagocytosis ., We found that , despite similar ability to capture all yeasts , DCs exhibited significantly lower phagocytic efficiency when challenged with C . albicans in comparison with S . cerevisiae and C . parapsilosis ., These data suggest that strong contact site recruitment of mannan-binding CTLs is important for capture of fungi by DCs , which is consistent with the fact that mannan is the dominant ligand on the cell wall surface ., However , intensity of DC-SIGN or CD206 recruitment is not a strong predictor of phagocytic outcome ., For instance , S . cerevisiae recruited the most DC-SIGN at one hour , while the intensity of DC-SIGN in C . parapsilosis contacts was much slower to develop to similar levels , but both yeasts were well-phagocytosed with similar efficiencies ., Contact site area was a good predictor of phagocytic efficiency , and it is likely that both readouts reveal a relative paucity of cytoskeletal response to C . albicans yeast relative to S . cerevisiae or C . parapsilosis ., This could reflect the existence of cell wall features possessed by C . albicans that minimize phagocytosis and aid in partial evasion of the innate immune response ., These differences in spatiotemporal distribution patterns may result from subtle differences in the fine structure of mannan ., C . albicans mannans have been shown to contain structural features such as β- ( 1 , 2 ) -linkages and branching α-linked oligomannoside side chains 55 , 56 which are not shared by S . cerevisiae or C . parapsilosis ., Mannan structural differences can influence the antigenicity and surface chemistry of the cell wall 57 , 58 ., In contrast to other cell-cell contact signaling systems with more laterally mobile ligand/receptor pairs ( i . e . , the immunological synapse ) , the ligands presented by the fungal cell wall are part of a dense and highly interconnected network ., Although the cell wall does undergo remodeling , the lateral mobility of polysaccharide ligands in the contact site is quite low ., Interestingly , recent work from Dufrêne and Lipke and colleagues has demonstrated that important mannoproteins of the Als adhesin family can be reorganized into distinct 100–500 nm amyloid domains in the cell wall of C . albicans upon application of force , and changes in Als protein exposure and organization are also seen under conditions such as hyphal germination and treatment with echinocandin drugs 59–62 ., The consequent spatial reorganization of mannan ligands could be important for the nanoscale organization of DC-SIGN and CD206 in contact sites with DCs ., Als adhesins are anchored to fibrillar glucan in the cell wall and above referenced results suggest that their mobility in the cell wall consists of gyration about their anchorage points , not long-range lateral mobility ., However , some mannoproteins are known to be non-covalently associated with the cell wall and these could possess greater lateral mobility ., In our analysis of fungal contact sites , we saw that receptors congregated in specific , micron-scale membrane structures despite presumed low levels of ligand lateral mobility ., This study utilized fixed yeasts to provide more controlled experimental conditions and more straightforward data interpretation ., This simplification precludes mannoprotein mobility during DC-yeast interaction , so future experiments in live cell interaction systems will be necessary to fully elucidate the role of fungal cell wall reorganization in these host-microbe interactions ., The organization of receptors into micron-scale membrane substructures , wherein transmembrane protein populations may mix and achieve altered density , will likely influence the efficiency and maintenance of signal transduction ., A previous report describing the “phagocytic synapse” showed that the lateral reorganization of the CTL Dectin-1 and the phosphatase CD45 influences Dectin-1 signaling 63 ., The mechanisms that drive the formation of specific membrane structures in fungal contacts , such as ligand patterning on cell wall surfaces , observed for patches of -glucan exposure on C . albicans 24 , 64 , are an interesting topic for future research ., CTLs have been described to exist in DC membranes as discrete nanodomains of approximately 80–100 nm diameter by several imaging methods such as transmission electron microscopy , near-field scanning optical microscopy and super resolution fluorescence imaging 45 , 65–68 ., These domains have interesting biophysical properties , such as a lack of exchange of receptor with the surrounding membrane and nearly complete segregation of DC-SIGN and CD206 nanodomains in resting DC membranes 68–70 ., Recently , we have observed that nanoscale organization of CTLs in fungal contacts is altered relative to non-contact membrane in favor of less individual nanodomain structure and more longer-range nanostructure , consistent with close packing of domains ( unpublished data , AKN ) ., The significance of receptor colocalization and changes in receptor density in contact sites is that spatial proximity influences signal transduction by increasing amplitude and persistence of signaling as well as promoting crosstalk between receptors ., Application of our analysis tool to higher resolution imaging modalities , such as Stimulated Emission Depletion microscopy and 3D direct Stochastic Optical Reconstruction Microscopy , may provide insights into critical early receptor rearrangement events in innate immune fungal recognition in future studies ., Cell-cell contacts are a common theme in biology , being integral to such diverse processes as lymphocyte activation , tissue development and neural communication ., Therefore , we anticipate that this tool will have broad utility in other fields where quantification of receptor and/or organelle mobility relative to a cell-cell contact is needed ., Some examples of other potential biomedical applications include other phagocytic synapses ( i . e . , macrophage scavenging of apoptotic bodies ) , the immunological synapse between T cell and antigen presenting cell , receptors within the synapse between neurons , the association between plasma membrane and SNARE complexes on the ER for calcium signaling , between cytotoxic T cells or NK cells and virally infected target cells , and B cell or mast cell activation by particulate antigen ., Much information can be derived from standard confocal optical imaging , as we have demonstrated ., However , promising progress in techniques for 3D super resolution microscopy should provide access to structural detail on at least a log-order higher resolution , and such data could be analyzed by our method to assess changes in biologically significant structures such as receptor microclusters and STIM/Orai mediated signaling microdomains ., C . albicans ( ATCC , Manassas , VA , #MYA-2876 ) , C . parapsilosis ( ATCC , Manassas , VA , #22019 ) , and S . cerevisiae ( ATCC , Manassas , VA , #26108 ) were cultured in YPD broth in an orbital incubator at until exponential phase growth ., Prior to application to dendritic cells , yeasts were fixed with 2 . 5% PFA at room temperature for 20 min followed by extensive PBS washing ., We obtained human peripheral blood leukocytes from discarded leukocyte reduction filters provided by United Blood Services of Albuquerque ., The filters were back-flushed with 300 mL HBSS , and the collected cells were spun over Ficoll-Paque Plus ( GE Healthcare , Sweden , #17-1440-02 ) ., Monocytes were purified by adherence on tissue culture flasks ., Immature dendritic cells were prepared by differentiation of monocytes in RPMI supplemented with 10% FBS , 1% Penicillin/Streptomycin , 10 mM Hepes , and 1 mM sodium pyruvate , 500 IU/mL human IL-4 ( Peprotech , Rocky Hill , NJ , #200-04 ) and 800 IU/mL human GM-CSF ( Sanofi , Bridgewater , NJ , Leukine/sargramostim/ ) at , 5% for 7 days ., Immature DCs existing in 7 day cultures were exposed to the specified yeasts ( per sample ) for the specified times ., These conditions were found to represent a relatively light challenge for DCs with yeast that is unlikely to overwhelm the ability of DCs to bind yeast , recruit receptors to contact sites or engulf particles ., This use of human blood products was reviewed and approved by the University of New Mexico Health Sciences Center Human Research Review Committee ., Fixed specimens were blocked and stained with primary and secon | Introduction, Results, Discussion, Materials and Methods | To understand the process of innate immune fungal recognition , we developed computational tools for the rigorous quantification and comparison of receptor recruitment and distribution at cell-cell contact sites ., We used these tools to quantify pattern recognition receptor spatiotemporal distributions in contacts between primary human dendritic cells and the fungal pathogens C . albicans , C . parapsilosis and the environmental yeast S . cerevisiae , imaged using 3D multichannel laser scanning confocal microscopy ., The detailed quantitative analysis of contact sites shows that , despite considerable biochemical similarity in the composition and structure of these species cell walls , the receptor spatiotemporal distribution in host-microbe contact sites varies significantly between these yeasts ., Our findings suggest a model where innate immune cells discriminate fungal microorganisms based on differential mobilization and coordination of receptor networks ., Our analysis methods are also broadly applicable to a range of cell-cell interactions central to many biological problems . | Specialized cell-cell contacts are a common theme in cell biology ., These structures increase sensitivity and specificity of cellular activation and information flow in contexts ranging from activation of immune responses to transmission of nerve action potentials ., Candida species fungal pathogens are responsible for significant morbidity associated with mucocutaneous infections as well as mortality ( ) caused by bloodstream infections ., The initial contact between innate immune cells and Candida results in a cell-cell contact between host and microbe ., Leukocytes mobilize a network of receptors to these contact sites , and these receptors collaborate to recognize molecular patterns characteristic of microbial surfaces ., Receptor recruitment , activation , and cross-talk are critical determinants of the evolution of signaling that directs the activation of downstream immune responses ., However , host-pathogen contacts with fungi are complex and variable , and accurate quantification of receptor distribution in space and time is difficult with existing image analysis tools ., Therefore , we have developed computational algorithms and a user interface that allows the scientist to both visualize and quantify receptor distribution in and recruitment to cell-cell contacts ., We have used this software to show significant differences in contact site receptor accumulation and organization for three different host-fungal contact sites with environmental and pathogenic fungi ., We also explored the correlation of contact site characteristics with the important functional outcome of phagocytosis . | biomacromolecule-ligand interactions, immunopathology, biochemistry, protein chemistry, immune cells, mathematics, cell biology, animal cells, clinical immunology, antigen processing and recognition, antigen-presenting cells, biology and life sciences, cellular types, immunology, physical sciences, biophysics, immune response | null |
journal.pcbi.1006273 | 2,019 | Quantitative cell-based model predicts mechanical stress response of growing tumor spheroids over various growth conditions and cell lines | Mechanotransduction is the mechanism by which cells transform an external mechanical stimulus into internal signals ., It emerges in many cellular processes , such as embryonic development and tumor growth 1 ., Cell growth in a confined environment such as provided by the stroma and surrounding tissues increases cell density and affects the balance between cell proliferation and death in tissue homeostasis 2 , 3 ., Tumor spheroids have long been considered as appropriate in vitro models for tumors 4 ., While the dynamics of freely growing spheroids has been extensively studied both experimentally 5 and numerically ( e . g . 6 , 7 , 18 ) , more recent experiments have also addressed the growth of spheroids under mechanical stress ., Helmlinger et al . ( 1997 ) and later Cheng et al . ( 2009 ) and Mills et al . ( 2014 ) 8–10 experimentally investigated the growth of spheroids embedded in agarose gel pads at varying agarose concentration as a tunable parameter for the stiffness of the surrounding medium ., Other approaches such as the application of an osmotic pressure determined by a dextran polymer solution have also been developed to investigate the impact of external pressure on spheroid growth 11 ., In all cases mechanical stress was reported to slow down or inhibit spheroid growth ., Delarue et al . 12 suggested that growth stagnation is related to a volume decrease of the cells ., However , a quantitative relation between pressure and cell fate is not reached yet ., The works of Helmlinger et al . 8 and their follow-ups have inspired a number of theoretical papers aiming at explaining the observations , either based on continuum approaches considering locally averaged variables ( e . g . for density and momentum , for overview see 13 ) 3 , 14–17 , or by agent-based models ( ABMs ) representing each individual cell 19 , 20 belonging to the class of models , which are extended and refined in the presented work ., For example , the growth kinetics of multicellular spheroids ( MCS ) embedded in agarose gel as observed by Helmlinger et al . 8 could be largely reproduced , if cell cycle progression was assumed to be inhibited either above a certain threshold pressure or below a certain threshold distance between the cell centers , whereby growth inhibition occurred at different spheroid sizes for different densities of extracellular material 19 ., However , the model developed in that reference has no precise notion of cell shape , hence does not permit definition of cell volume , thus pressure and compression cannot be physically correctly related 21 ., Here , we first establish a computational model to quantitatively explain the growth kinetics and patterns found for CT26 ( mouse colon carcinoma cell line ) multi-cellular spheroids constrained by a spherical elastic capsule , partially based on data previously published 26 and partially based on new data introduced below ., This novel experimental technique , called the “cellular capsule technology” 26 allows to measure the average pressure exerted by the cell aggregate onto the calibrated capsule by monitoring the radial expansion of the shell once confluence is reached ., Pressure can be recorded over periods as long as a week and the histological data collected and analyzed on fixed and sliced spheroids can provide snapshots of the spatial multicellular pattern ., We refer to this experimental technique as “Experiment I” ., The thickness , and thus the stiffness of the capsule , was varied to mimic different mechanical resistance conditions ., Delarue et al . ( 2014 ) 12 investigated the effect of mechanical stress on MCS growth using the same cell line in a different experimental setting ., We exploit these results to challenge our model and determine whether the same computational model designed to match experiment I is capable to quantitatively explain also this experiment ( referred to as “experiment II” ) ., In experiment II , mechanical compression was imposed using the osmotic effects induced by a dextran solution ., The main difference between those two experiments is that whereas the pressure gradually increases with increasing deformation of the elastic capsule in experiment I , in experiment II a constant stress is applied due to osmotic forces in the absence of any obstructing tissue ( see Fig 1A ) ., In this paper , we aim to decipher and quantify certain mechanisms of spheroid growth altered by mechanical stress ., At this stage , we establish a robust computational approach that can be applied to various systems ( cell lines and experimental procedures ) and that allows to recapitulate the growth dynamics and the observed cellular patterns ., We will show that this can be reached with a minimal number of hypotheses without having to explicitly integrate specific molecular pathways ., Gaining insight in the molecular mechanisms would require additional challenging experiments in which the pathways are selectively inhibited or enhanced in a three-dimensional environment , and would add further parameters to the model ., To the best of our knowledge , a specific mechanotransduction molecular pathway has been highlighted once , demonstrating the impact of cell volume change on the expression of the proliferation inhibitor p27Kip1 12 ., As modeling technique we here developed an agent-based model ., Simulations with ABMs provide a computer experiment representing an idealized version of the true wet-lab experiment 77 ., ABMs naturally permit accounting for cell to cell variability and inhomogeneities on small spatial scales as they represent each cell individually ., Center-Based Models ( CBM ) are a prominent representative in the class of ABMs in which forces between cells are calculated as forces between their centers ., Center-based models for multicellular systems were derived from conceptual anologies to collodial particle dynamics by re-interpretation of parameters and addition of growth and division processes 53 , 75 ., The model developed here is fully parameterized in terms of physical parameters , which makes each component possible to validate ., However , it circumvents difficulties that standard center-based models have at large compression ( see 21 ) establishing a hybrid modeling strategy to compute the mechanical interaction forces by so-called three dimensional ( 3D ) Deformable Cell Models ( DCMs ) 70 , 79 ., A DCM displays cell shape explicitly at the expense of high computational cost ( see Fig 2 ) ., In our hybrid strategy the parameters of the CBM that considers the cell shape only in a statistical , “coarse grained” sense thereby permitting simulations of large cell population sizes , are pre-calibrated from a finer scale DCM ., This strategy permits to combine the advantages of the DCM with the short simulation time of the CBM ., Both CBM and DCM are parameterized by measurable quantities to identify the possible parameter range of each model parameter and avoid non-physiological parameter choices ., We studied the series of experimental settings in the works 26 and 12 as both utilize a common cell line , and exert stress on growing MCS of that cell line in different experimental settings ., The model is then further tested with experiments on other cell lines as provided in the second work ., To unravel the dynamics of MCS subject to compression , our modeling strategy is to postulate and implement hypotheses on cell growth , quiescence and death , and iteratively adapt or extend them in case the model simulations are falsified by comparison with the experimental data ., Pursuing a similar strategy enabled us to obtain predictions of subsequently validated mechanisms in liver regeneration 27 , 28 ., Based upon analysis of the relation between pressure , cell density and cell compressibility in the two different experiments , our findings suggest that contact inhibition can be regarded as a robust continuous process imposed by a reduction of cell volume as a consequence of increasing pressure and individual cell compressibility ( see Fig 3 ) ., In addition , the high-resolution model shows that potential effects of micro-mechanics at the interface with the capsule may depend on the mechanical properties of the cells ., For the sake of clarity , we below start to first present the minimal model that was able to explain the data , before discussing in which ways simpler models with other hypotheses failed ., Experiment I: Following microfluidics-assisted encapsulation of CT26 cells into alginate capsules , the growing aggregates of cells were monitored by phase contrast microscopy ( see 26 for details ) ., After the tumor cells reached the inner border of the elastic alginate capsule corresponding to a radius of about 100 μm ( t = 0d in Fig 1B ) , they were observed to further induce a dilatation of the capsule , which is an indicator of the exerted pressure ., The capsule expansion was measured from the point of confluence over several days , while histological data of the spheroids were collected at the stage of confluence and at 48h past confluence ., Capsules have been designed to generate shells with two different thicknesses ., The thin ones ( H/R0 ≈ 0 . 08; H = 8μm ) are the softer while the thick ones ( H/R0 ≈ 0 . 25; H = 30μm ) mimic a larger mechanical resistance against growth ., Besides the data extracted from 26 , we have also exploited and analyzed unpublished data corresponding to new sets of experiments in order to critically test the reliability of the method ( see Fig 4 ) ., We extract four main observations from these experiments ., ( EI . OI ), In the absence of a capsule , an initial exponential growth stage was observed with doubling time Tcyc = 17h 26 ., The growth kinetics however starts to deviate from exponential growth for spheroid size R ≈ 175 μm , ( see Fig 1B ) ., ( EI . OII ), In the presence of a capsule , the exponential growth is maintained until confluence , i . e . R = R0 ≈ 100 μm , which shows that the capsule is permeable to nutrients and allows normal growth ., Once confluence is passed , the time evolution of the capsule radius exhibits two regimes:, i ) an initial “fast” growth stage T1 ( t < 1day ) , crossing over to, ii ) a “slow” quasi-linear residual growth stage T2 ( t > 1 day ) that at least persists as long as the capsules are monitored , i . e . up to one week ., The transition happens roughly at a pressure of ∼ 1 . 5 kPa , see Fig 4C ., The observed long-time growth velocities were ∼ 2 μm/day for the thin capsules ( Fig 4A ) and 0 . 7 μm/d for the thick capsules ( see Fig 5 ) ., ( EI . OIII ), The nuclei density , obtained from cryosections , increases from ∼ 1 nucleus / 100 μm2 before confinement , to roughly 2 nuclei / 100 μm2 after confluence , with a relatively higher number near the center of the spheroid ( 1 . 2 times more compared to the outer regions ) , and a local increase at the border of the capsule ., The distribution and shape of cell nuclei reported in 26 suggests that cells near the capsule border are deformed with a flatened shape , while those in the interior look compact shaped ., ( EI . OIV ), Most of the cells in the core of the spheroid are necrotic after 48h of confinement , while the cells located in a peripheral viable rim of roughly two cell layers thickness ( λI ≈ 20 μm ) , show viability and proliferative activity during the whole time course of the experiment , including period T2 ., ( EI . OV ), Fibronectin staining indicates there is ECM present during free growth; staining after 48h indicates more ECM regions near the capsule border and a weak signal inside the spheroid ., Experiment II: in the work of Delarue et al . ( 2014 ) 12 , CT26 spheroids ( initial radius ∼ 100 μm ) were grown in a dextran polymer solution ., To recover osmotic balance , water expulsion out of the spheroid generates osmotic forces exerted to the outer cells that are transferred as compressive stresses to the interior ( bulk ) cells ., The concentration of dextran regulates the applied pressure ., ( EII . OI ), The growth speed at p = 5 kPa is significantly lower than in control spheroids where no pressure is exerted ., ( EII . OII ), The spheroid free growth data does not show an initial exponential phase found in ( EI . OI ) ( Fig 1B ) ., This surprising discrepancy might result from the different culture conditions between both experiments ., In experiment I , the medium has repeatedly been refreshed 26 , while in experiment II this has not been done so often ( private communication ) , leading to lower concentrations of nutrients and other molecular factors in experiment II ., During the whole course of osmotic stress application , an over-expression of the kinase inhibitor p27Kip1 together with an increased number of cells arrested in the G1 phase was observed , but no significant change in apoptosis rates after 3 days was reported ., ( EII . OIII ), Delarue et al . ( 2014 ) also considered the stress response for other cell lines ( AB6 , HT29 , BC52 , FHI ) performing steps EII . OI and EII . OII for each cell line ., These data will be used to validate our model despite less information concerning cell size and cycling times is available for these cell lines ., As a first step we proposed a number of hypotheses for the growth dynamics common to experiments I and II ., ( H . I ), In both experiments a linear growth phase was observed after exposing the MCS to external stress ., The growth of the cell population that is not constrained by either mechanically-induced growth inhibition , nutrient , oxygen or growth factor limitations is exponential 4 ., We assumed that deviation of growth from an exponential indicates restriction of proliferation to a rim ., This may have different reasons , for example necrosis that has been only reported for experiment I ( EI . OIV ) , or of cells being quiescent ., Both necrosis and quiescence can result from a lack of nutrients or other factors 6 , 29 , that may indirectly be promoted by pressure , e . g . in case the compression of the cell layer squeezed between the capsule shell and the inner cell layers leads to the formation of an obstructive barrier for some nutrients ( as glucose ) to the cells located more deeply in the interior of the tumor ., However , cell quiescence ( or cell death ) may also be a direct consequence of mechanical pressure , e . g . if cells subject to compression cannot advance in cell cycle for too long and then undergo apoptosis 6 , 29 ., We do not specify the origin of the rim here , we take it into account through the definition of a thickness λk ( k = I , II is the experiment index ) ., In Exp ., I , λI distinguishes the necrotic cells from viable ones ., In Exp . II , λII separates the quiescent cells from the ones that can still proliferate ., Necrotic cells as observed in experiment I can undergo lysis , in which they steadily lose a part of their fluid mass ., The decrease of mass is limited to about 70%–90% of the total initial mass of the cell 30 , 31 ., ( H . II ), Cell growth rate may be declined or inhibited by pressure 8 ., The authors of a recent study 12 hypothesized that the growth rate may be down-regulated if the cell volume is reduced as a consequence of pressure ., We here test the hypothesis that growth rate is dependent on the volumetric strain ( “true strain” , commonly used in case of large strains ) ,, ϵ V = - log ( V / V r e f ) , ( 1 ), where V is the actual compressed volume and Vref is the volume of the cell in free suspension ., The volumetric strain can be related with the pressure by integration of the relation dp = −KdϵV ., K is the compression modulus of the cell and depends on the actual volume fraction of water , and the elastic response of the cytoskeleton 42 ., It may also be influenced by the permeability of the plasma membrane for water , the presence of caveolae , and active cellular responses 32 , 78 ., As such , the timescale at which K is measured is important ., In our final model ( presented here first ) we further assume that the cell exhibits strain hardening effects , and hence K depends on the volumetric compression of the cell ( see Section Models ) ., In our simulations , we regarded K as the long timescale modulus of cell , as growth and divisions are slow processes ., We studied constant and a volume-dependent compression moduli ( the calculation of growth , volume and pressure for each cell in the model is explained in Section Cell growth , mitosis , and lysis , Eq 8 ) ., On the molecular level , volume reduction correlates with over-expression of p27Kip1 which progressively decreases the proliferating potential ., Other molecular players such as the transcriptional regulators YAP/TAZ were also reported to be mechano-sensitive 33 ., In the scope of the present work , these reports suggest that quiescence , and perhaps also apoptosis , may be controlled by either pressure or cell volume ., Experimental studies 34–37 mainly measured the growth rate of dry mass or size ., These indicate that the growth rate α varies within the cell-cycle , yet a unique relationship is difficult to infer ., We propose as general form for growth rate α a Hill-type formula defined as ( 1—Hill function ) :, α = α 0 ϵ V t r n ϵ V n + ϵ V t r n , ( 2 ), where α0 is the growth rate of the unconstrained cell , ϵ V t r is a threshold value1 , and n is an integer ., The parameter ϵ V t r is the value where the cells have lost 50% of their initial growth rate ., Note that for ϵ V t r → ∞ we retrieve a constant growth scenario , whereas increasing n from 1 to ∞ modifies the curve from a smooth decrease to a sharp pressure threshold ( see Fig 3A ) ., The use of a Hill-type function thus makes a variety of growth scenarios possible ., Hill formulas have been used in the past to simulate contact inhibition in epithelial tissue and tumors 17 , 38 , 39 ., We discuss the generality of this approach in the Discussion section ., ( H . III ), It is generally accepted that cells that have passed the G1 checkpoint ( also known as restriction point ) are committed to divide , else they go into quiescence ( G0 ) ., In our model we assume this checkpoint is situated after 1/4 of the total cell cycle time 40 ., The transition criterion to the quiescence state can be defined as the one at which the growth rate “stalls” , i . e . α/α0 < αqui ( see Fig 3A ) ., “Sizer versus Timer”: According to hypothesis H . II growth rate depends on the compression of the cells , hence the volume doubling time can locally vary and is larger than for uncompressed cells ., Limiting cases would be that division occurred after volume doubling at a variable time 6 ( “sizer” ) , or after a pre-defined time ( “timer” ) often mentioned in developmental biology 41 ., We therefore also compared the effect of constant time vs . doubling of volume criterion in cell division on the cell population behavior ., Also mentioned in H . II , the unconstrained growth rate α0 itself may vary during the cell cycle ., To study the potential effect of these variations we performed comparative runs considering constant growth rate as well as exponential growth rate during the cell cycle ( details in Cell growth , mitosis , and lysis ) ., For the model development and parameterization we pursued a multi-step strategy sketched in Fig 2 ( see also Tables 1 and 2 ) ., The model parameters for the “model I” to mimic experiment I , { P M 1 } , and “model II” to mimic experiment II , { P M 2 } , were step-wise calibrated from experiments I and II , and in each case first for growth in absence of external mechanical stress on the growing population , then in presence of stress ., They can be categorized by separating between cell line-specific parameters { P C = j } , where j ∈ {CT26 , AB6 , HT29 , BC52 , FHI} , determines the cell line , and experiment-specific parameters { P E x p = k } with k = I , II characterizing the experimental setting ., The simulations were performed with a center-based model ( CBM ) ., As the model is parameterized by measurable physical and bio-kinetic parameters , parameter ranges could readily be determined within narrow limits ( Table 2 , 27 ) ., First { P M 1 } was identified in three steps ( 1 ) - ( 3 ) ( Table 1 ) ., ( 1 ) As the “standard” CBMs are inaccurate in case of high compression 21 , the cell-cell interaction force in the CBM in this work was calibrated using computer simulations with a deformable cell model ( DCM ) , resulting in an effective stiffness E ˜ for every cell in the CBM for every cell at high compression , that increases with increasing compression , see Calibration of the CBM contact forces using DCM ., E ˜ belongs to { P C = C T 26 } of the CBM ., The DCM could not be directly used for the growth simulations , as it is computationally too expensive to run simulations up to the experimentally observed cell population sizes of ∼ 104 cells ., Next , the experimental information was taken into account ( Fig 2 ) ., ( 2 ) Comparing simulations of the CBM with the data from the stress-free growth control experiment of multicellular CT26 spheroids ( MCS ) in experiment I permits determining those parameters of { P C = C T 26 } that were are unaffected by the presence of the elastic capsule ( Table 2 ) , see Model setup and parameter determination ., ( 3 ) Adding a thin elastic capsule specifies the set of experimental parameters { P E x p = 1 } ( Young modulus , Poisson ratio and thickness of the capsule , etc . ) , and permits identifying those cell line specific parameters that respond on the presence of the capsule ., In experiment I these are the parameters characterizing cell cycle entrance and cell growth ( 2 ) ., Finally , model I is characterized by the conjunction of the cell-specific and the experiment-specific parameter sets { P M 1 } = { P C = C T 26 } ∪ { P E x p = 1 } ., Replacing the thin by a thick capsule in the simulations by changing the experimentally determined thickness parameter for the thin capsule in { P E x p = 1 } by that for the thick capsule leads to a predicted simulated growth dynamics that matches well with the one experimental data without any additional fit parameters ( Fig 5B ) ., Experiment II has been performed with CT26 , AB6 , HT29 , BC52 , FHI cells ., For CT26 cells , the cell-line specific parameter set remains the same in experiment II as in experiment I . Differently from experiment I , stress-free growth in experiment II is not exponential but linear , reflecting different growth conditions that limit cell proliferation to a “proliferating” rim ., This determines the proliferating rim size λII as the experimental parameter of set { P E x p = 2 } that summarizes the impact of growth medium under the conditions of experiment II in stress-free growth ., In presence of dextran , { P E x p = 2 } is expanded by only the measured pressure exerted by dextran , which as it is experimentally determined , is no fit parameter ( λII remains unchanged ) ., With the parameter set { P M 2 } = { P C = C T 26 } ∪ { P E x p = 2 } , the simulation model predicts a growth dynamics that quantitatively agrees with the one experimentally found indicating that the growth response only depends on the exerted pressure , not on any other parameter ( Fig 1B ) ., In a last step , the stress responses of the other cell lines , j = {AB6 , HT29 , BC52 , FHI} have been modeled for the experimental setting of experiment II , again in two steps ( Fig 1D–1G ) ., The first step was to adjust the cell cycle time Tcyc of the cell line to fit the stress-free growth leading to replacement of that one parameter in passing from { P C = C T 26 } to { P C = j } , the second was predicting the growth subject to dextran-mediated stress without any parameter fitting i . e . , using { P E x p = 2 } for the experimental parameters ., Summarizing , almost the entire parameter determination is done by adjusting the model parameters to experiment I for a thin capsule ., After this step there is only one fit parameter for each cell line , summarizing the cell-line specific effect of growth conditions of experiment II for the stress-free growth ( i . e . , the control experiment ) ., The step to simulate population growth subject to external stress , both in the thick capsule for CT26 as well as in experiment II with dextran for the cell lines CT26 , AB6 , HT29 , BC52 and FHI is performed without parameter fitting ., By establishing a quantitative model of growing multicellular spheroids ( MCS ) subject to compressive stress calibrated with data on growth in an elastic capsule we were able to demonstrate that the stress response of a growing tumor is quantitatively robust and reproducible even if cells grow under different conditions and if the pressure is exerted by different experimental methods ., Given the enormous complexity of intracellular processes involved in the control of MCS growth this is fascinating as it might open the possibility that largely separated robust functional modules may be identified and studied in separation without the need to analyze all interactions of the components of one module with the components of other modules , and without incorporating all interactions at the molecular level ., In particular , we first developed a model to study CT26 cells grown in an elastic thin and thick capsule , and then modified this model in a minimal way by taking into account the remarkably different growth behavior of freely growing tumor spheroids ( i . e . not subject to compressive stress ) to simulate the tumor growth response of CT26 and other cell lines in a dextran solution ., We show that the mechanical stress response is quantitatively the same despite significantly different culture and protocol conditions ., Without the model , it would have been very difficult to identify this equivalence ., The key results of our analysis are: ( R . I ) With increasing compression the cell growth rate decreases ., This relation could be well captured by a Hill-type function for the growth rate α that depends on the volumetric strain ( Eq 2 ) , and a transition into quiescence if the growth rate dropped below a threshold value ., A sharp volume or pressure threshold below which no cell cycle entrance would occur anymore , is not compatible with the data ., Together with the strain hardening assumption of cells during compression , this overall points to a nonlinear increasing growth resistance of the cells upon mechanical stress ., ( R . II ), Cells divide when their dry mass has doubled during the cycle ., A “timer” as a decision mechanism for dividing could not explain the data ., A particular point of concern in many studies of spheroids is the appearance of cell death ., Our work is based on the observations of Alessandri et al . ( 2013 ) , who observed necrosis ( CT26 cells , using FM4-64 ) in capsule confined cells , while their free growing spheroids exhibited the normal exponential growth for R < 150 μm ., Helmlinger et al . ( 1996 ) 8 observed a decrease in apoptotic ( LS174T cells , using TUNEL ) events during compression , and reported little necrosis ( not quantified ) for spheroids with R < 150 μm ., They concluded that the haltered growth of the spheroids is mainly due to the increasing compressed state , which can be partially confirmed by our simulations ., In the work of Delarue et al . ( 2014 ) 12 , no increase of apoptosis ( HT29 cells , using cleaved-caspase 3 ) was observed after 3 days for spheroids with R ∼ 100 μm ., Contrary , earlier Montel et al . ( 2012 ) 11 did report increased apoptosis using cleaved-caspase 3 for CT26 cells , while Cheng et al . ( 2009 ) 9 did observe an increase of necrosis ( 67NR cells , using propidium iodide ) even in very small spheroids R ∼ 50 μm , yet mainly for the interior cells ., At the periphery , cells were still dividing ., Whether necrosis and apoptosis occurs may well be dependent on the cell type and experiment , but overall it seems that the peripheral cells are unaffected ., Another issue that deserves attention is that despite recent significant advances in exploring the relations between the cell mechanical parameters and cell responses during an externally applied mechanical stress , a coherent consensus has not been reached ., One issue in this discussion is the cell compression ( bulk ) modulus ., For instance , in Delarue et al . ( 2014 ) 12 , one concludes that cells are compressible reporting a rapid cell volume reduction at the level of the MCS ( Multicellular Spheroids ) under compressive stress ., Another work of Delarue et al . ( 2014 ) 43 indicates bulk moduli of the order of 10 kPa ., Both works consider the long-term effects ( > 1h ) of compression on spheroids ., The work of Lin et al . ( 2008 ) 44 seems to concur with this as they measure cell bulk moduli of about 10 kPa with measurements on a timescale of minutes ., On the other hand , the Monnier et al . ( 2016 ) 78 report individual cell compression moduli of several orders of magnitude higher ( 1 MPa ) than the ones reported above , also on short time periods of minutes ., Yet they state in their paper that on longer timescales , the cell response may become more complex due to intracellular adaptations ., We emphasize that in our paper we are considering timescales of larger than one hour as cells are doubling their volume in about a day so that the rate of percentage of the volume increase is about 0 . 07%/min ., As such , the compression moduli of the cells that we find should be regarded as long-term values , where the cell can respond differently as compared to short timescales ., For instance , the cell may respond by expelling fluid through aquaporins ., In the work Tinevez et al . ( 2009 ) 42 , the cytoplasm bulk modulus is estimated as ±2500 Pa ., Despite not being the modulus of the whole cell , it indicates that if cells are able to expel water through the aquaporins on longer timescales , their resulting bulk moduli agree with our values ., Our modeling strategy is based on in silico experiments i . e . , abstracted experiments on the computer , where each individual cell was represented as modeling unit with those properties , actions and interactions that were considered as necessary to quantitatively explain the cellular growth response on mechanical compression ., The implementation of cell-cell and cell-environment interaction directly accounts for physical laws with ( in principle ) measurable physical parameters that permit straightforward limitation of parameter ranges to those physiologically relevant ., This made it possible for us to largely confine the parameter values to published or directly observed relatively narrow ranges , and introduce free fit parameters only for the cell cycle progression ., A particular challenge was to construct an individual agent-based model that permits stable and robust simulations up to several tens of thousands cells under high compression ., Under these conditions cell displacements may have to be minimal , which rules out models operating on lattices unless the lattice size would be chosen a very small fraction of the cell diameter ( in which case they would lose their computational advantage ) ., Thus , the requirements of constraining the parameters , and providing realistic simulation trajectories in time favored models operating in lattice-free space implementing a dynamics simulated by equations of motion ( as opposed to a Monte Carlo dynamics , which under some condition mimics a master equation ) ., The prototype of lattice free models are center-based models that calculate the forces between cells as forces between cell centers ., However , as mentioned above and explained in more detail elsewhere 21 this model type has significant problems in dealing with cell populations under large compressive stress i . e . , with exactly the situation we are faced with in this work ., To solve this issue , we developed a deformable cell model , which represents each individual cell in much greater detail as in center-based models but at the expense of much longer simulation times ., As simulations with that mode | Introduction, Results, Discussion, Models | Model simulations indicate that the response of growing cell populations on mechanical stress follows the same functional relationship and is predictable over different cell lines and growth conditions despite experimental response curves look largely different ., We develop a hybrid model strategy in which cells are represented by coarse-grained individual units calibrated with a high resolution cell model and parameterized by measurable biophysical and cell-biological parameters ., Cell cycle progression in our model is controlled by volumetric strain , the latter being derived from a bio-mechanical relation between applied pressure and cell compressibility ., After parameter calibration from experiments with mouse colon carcinoma cells growing against the resistance of an elastic alginate capsule , the model adequately predicts the growth curve in, i ) soft and rigid capsules ,, ii ) in different experimental conditions where the mechanical stress is generated by osmosis via a high molecular weight dextran solution , and, iii ) for other cell types with different growth kinetics from the growth kinetics in absence of external stress ., Our model simulation results suggest a generic , even quantitatively same , growth response of cell populations upon externally applied mechanical stress , as it can be quantitatively predicted using the same growth progression function . | The effect of mechanical resistance on the growth of tumor cells remains today largely unquantified ., We studied data from two different experimental setups that monitor the growth of tumor cells under mechanical compression ., The existing data in the first experiment examined growing CT26 cells in an elastic permeable capsule ., In the second experiment , growth of tumor cells under osmotic stress of the same cell line as well as other cell lines were studied ., We have developed an agent-based model with measurable biophysical and cell-biological parameters that can simulate both experiments ., Cell cycle progression in our model is a Hill-type function of cell volumetric strain , derived from a bio-mechanical relation between applied pressure and cell compressibility ., After calibration of the model parameters within the data of the first experiment , we are able predict the growth rates in the second experiment ., We show that that the growth response of cell populations upon externally applied mechanical stress in the two different experiments and over different cell lines can be predicted using the same growth progression function once the growth kinetics of the cell lines in abscence of mechanical stress is known . | cellular stress responses, classical mechanics, cell cycle and cell division, cell processes, biological cultures, radii, mechanical stress, geometry, simulation and modeling, mathematics, research and analysis methods, compression, cell lines, glucans, physics, biochemistry, polysaccharides, ht29 cells, dextran, cell biology, biology and life sciences, physical sciences, glycobiology | null |
journal.ppat.1000548 | 2,009 | Two HIV-1 Variants Resistant to Small Molecule CCR5 Inhibitors Differ in How They Use CCR5 for Entry | Small molecule drugs or drug candidates bind to the cell surface CCR5 protein and prevent human immunodeficiency virus type 1 ( HIV-1 ) from using it as a coreceptor for entry into CD4-positive target cells 1 , 2 ., These compounds , which include the licensed drug maraviroc ( MVC ) and the clinical candidate vicriviroc ( VVC , also known as SCH-D ) , bind within the transmembrane helices of CCR5 and stabilize the protein in a conformation that cannot be recognized efficiently by the HIV-1 gp120 surface glycoprotein 3–7 ., The interaction between gp120 and CCR5 is considered to involve two structural elements ., The CCR5 N-terminus ( NT ) interacts with a site on gp120 that involves the 4-stranded bridging sheet region and the base of V3 , which assembles upon CD4 binding , while the second extracellular loop ( ECL-2 ) of CCR5 interacts with a second region of V3 located near its tip 8–12 ., Viruses resistant to the small molecule CCR5 inhibitors can be generated in vitro and in vivo 13– ., The dominant route to resistance involves the acquisition of sequence changes that render gp120 capable of recognizing the inhibitor-CCR5 complex , without losing its ability to also interact with the free coreceptor 16 , 18 ., Hence the escape mutants become inhibitor-tolerant , but not inhibitor-dependent ., The most common genetic route to resistance is the acquisition of multiple sequence changes in V3 13 , 16 , 19–21 ., This pathway was followed when the primary R5 isolate CC1/85 was cultured with the AD101 inhibitor in vitro , creating the CC101 . 19 resistant variant ., However , we have described a V3-independent route to the same phenotype that was taken by the same input virus under the selection pressure of a similar compound , VVC , to yield the D1/86 . 16 escape mutant 14 , 22 ., We have recently shown that this alternative pathway involves three sequence changes in the fusion peptide ( FP ) region of the gp41 transmembrane glycoprotein ., These changes exert broadly similar effects to the more conventional V3 changes , in that the resistant virus was able to use the inhibitor-CCR5 complex for entry 22 ., By using CCR5 point-mutants and gp120-targeting agents , we now seek to learn more about how the parental and both resistant viruses interact with the coreceptor ., A small molecule that interacts with gp120 at the V3 region , IC9564 , had differential activities against the various viruses , as did monoclonal antibodies ( MAbs ) and polyclonal Abs directed against the V3 region and MAbs to the CD4-induced epitopes associated with CCR5 binding ., We conclude that the V3 sequence changes in CC101 . 19 create a variant that is more dependent than its parent on interactions with the CCR5 NT ., Elements of the CCR5 binding site associated with the V3 region and the CD4i epitope cluster in the bridging sheet have become more exposed on the native Env complex of this virus , and hence accessible to neutralizing antibodies ( NAbs ) ., However , the D1/86 . 16 variant with changes in the gp41 FP has followed a different pathway to resistance that does not involve an increased dependency on the CCR5 NT , and in which the CCR5 binding site has not become more exposed ., How this virus interacts with the inhibitor-CCR5 complex therefore remains to be determined ., Isolates CC101 . 19 and D1/85 . 16 are resistant variants derived from the primary R5 isolate CC1/85 after selection with the small molecule CCR5 inhibitors AD101 and VVC , respectively 14 , 20 ., As the emphasis of the present study was to gain a better understanding of how resistant variants interact with CCR5 , we used infectious , Env-chimeric clonal viruses CC101 . 19 cl . 7 and D1/85 . 16 cl . 23 , derived from the above resistant isolates , and compared their properties with inhibitor-sensitive clones of the parental isolate , CC1/85 ., A multiple sequence alignment based on the Env amino-acid sequences of seven parental clones derived from the CC1/85 isolate shows that CC1/85 cl . 7 and CC1/85 cl . 6 were the most similar to CC101 . 19 cl . 7 and D1/85 . 16 cl . 23 , respectively ( data not shown ) ., For simplicity , we have summarized these results in a tree based on the percent similarity between the four clones ( Fig . 1A , B ) ., The majority of the amino-acid differences between the two pairs of viral clones are in the V4 and V5 regions of gp120 ., Taking into account also the replication properties of the various parental clones , we chose to use CC1/85 cl . 7 for comparisons with CC101 . 19 cl . 7 , and CC1/85 cl . 6 as a comparator for D1/85 . 16 cl . 23 ., Clones CC101 . 19 cl . 7 and D1/85 . 16 cl . 23 contain amino acid changes that have been shown to be necessary and sufficient to confer resistance to small molecule CCR5 inhibitors 20 , 22 ., Thus , CC101 . 19 cl . 7 has four substitutions in the V3 region of gp120 ( K305R , H308P , A316V and G321E ) , while D1/85 . 16 cl . 23 contains three changes in the gp41 FP ( G516V , M518V and F519I ) ( Fig . 1C ) ., The phenotypic properties of these four clones that were derived from the studies outlined below are summarized in Table S1 ., The four clones used in this study recapitulate the phenotypes of the corresponding isolates in respect of VVC sensitivity ., Thus , in an assay using PBMCs , the parental isolate , CC1/85 , was completely inhibited by VVC concentrations ≥100 nM , whereas replication of the two resistant isolates was not affected by the presence of VVC ( Fig . 2A ) ., Similarly , the parental clones CC1/85 cl . 7 and CC1/86 cl . 6 were each completely inhibited by VVC concentrations ≥10 nM ( 10-fold lower then that needed for complete inhibition of the isolate ) ., In contrast , replication of the resistant clones in PBMCs was either modestly enhanced ( for CC101 . 19 cl . 7 ) or unaffected ( for D1/85 . 16 cl . 23 ) by VVC ( Fig . 2B ) ., Env-pseudotyped viruses derived from the above clones behaved similarly to the infectious , chimeric clonal viruses in U87-CD4-CCR5 assays ( data not shown ) ., Similar data were obtained using other small molecule CCR5 inhibitors such as AD101 , maraviroc and aplaviroc ( data not shown ) ., To determine whether the resistant clones differ from each other , and from the corresponding parental clone , in how they interact with CCR5 , we first used a panel of point-mutated coreceptors ( Fig . 3 , Table 1 ) ., The composition of the test panel was biased towards mutants of the NT and ECL2 , since these CCR5 domains have the greatest influence on HIV-1 entry 10 , 23–25 ., The various CCR5 mutants were transiently expressed in U87-CD4 cells for 48 h before incubation for an additional 72 h with luciferase-expressing , Env-pseudotyped clonal viruses derived from the parental and resistant isolates ., The CCR5 mutants were all expressed at comparable levels on the cell surface as determined by FACS ( data not shown ) ., The relative level of entry via each mutant , compared to wild-type CCR5 , was calculated for each test virus , to identify coreceptor variants that were used with different efficiency under the conditions of this single-cycle assay ( Table 1 ) ., As expected , none of the Env-pseudotyped viruses could use the Δ18 mutant that lacked the first 18 residues of the CCR5 NT 26 , 27 ., The tyrosine residues at NT positions 10 and 14 are sulfated , a modification known to be important for HIV-1 entry 28 , 29 ., Accordingly , none of the viruses utilised the Y10A/Y14A double mutant efficiently , although D1/85 . 16 cl . 23 was able to use it for low-level entry ( Table 1 ) ., Three other mutations adversely affected entry of all four viruses to a meaningful extent ( <50% entry compared to wild-type ) : D11A in the NT , C178A and F189A in ECL2 ( Table 1 ) ., This outcome is consistent with previous studies on the same mutants with different test viruses , and arises because these residues ( particularly D11 and C178 ) are important for maintaining the appropriate CCR5 conformation 25 , 30 ., The entry of various viruses via certain other mutants was reduced to a lesser extent ( 25–50% ) ., Such reductions may be biologically relevant but can be difficult to distinguish from background variation with confidence ., Several mutations differentially affected entry of the four Env-pseudotyped viruses ., Thus , CC101 . 19 cl . 7 was particularly affected by NT mutations Y10A , Y14A , Y14F , Y14Q and C20A ( Table 1 ) ; depending on the mutation , the entry of this virus was reduced to ≤26% of the extent conferred by wild-type CCR5 ., The identity of the substituted residue at position 14 was an additional variable; more specifically , CC101 . 19 cl . 7 could use the Y14Q mutant with low efficiency ( ∼20% ) , but not Y14A or Y14F ( <1% entry ) ., In contrast , D1/85 . 16 cl . 23 could enter via all three of the residue-14 mutants at >70% of the level mediated by wild-type CCR5; indeed , this escape mutant and its parent , CC1/85 cl . 6 , were little affected by the identity of the residue at position 14 ( Table 1 ) ., These observations , taken together , suggest that the tyrosine residues at positions 10 and 14 were both required for efficient entry of CC101 . 19 cl . 7 , whereas the presence of either sulfated-tyrosine was sufficient to mediate entry of the other three viruses to at least some extent ., Conversely , the ECL2 mutations F182A and P183A impaired entry of both resistant viruses a little more than they did the two parental clones , while the Y187A and F193A changes selectively , although modestly , affected entry of D1/85 . 16 cl . 23 ( Table 1 ) ., The triple Ala mutants with changes at residues 184–186 and 187–189 were , however , used by all four viruses ( Table 1 ) ., Overall , the pattern of entry via the various CCR5 mutants suggests that the two resistant viruses differ markedly in how they interact with the coreceptor ., Thus , CC101 . 19 cl . 7 is particularly reliant on the sulfated tyrosine residues at positions 10 and 14 in the NT , but this is not the case for D1/85 . 16 cl . 23 ., The latter virus is somewhat more affected by some substitutions within ECL2 , but not dramatically so ., Their differential sensitivity to CCR5 mutations suggests that the two escape mutants differ in how they interact with the coreceptor ., A corollary of the increased dependence of CC101 . 19 cl . 7 on the CCR5 NT might be that the region near the tip of V3 is now less involved in gp120-CCR5 binding , compared to both of the parental clones and D1/85 . 16 cl . 23 ., If so , the 4 amino acid changes in the V3 region of CC101 . 19 cl . 7 might be acting to change the orientation of V3 with respect to the rest of gp120 , disrupting its ability to interact with ECL2 while increasing the accessibility of the bridging sheet to the NT ., This argument would not apply to D1/85 . 16 cl . 23 , which has followed a different route to resistance that is less apparent from the studies using the CCR5 mutants ., To gain information on what changes in Env conformation took place as resistance developed , we measured the responses of the two resistant viruses to compounds that interact with different regions of gp120 ., We first used various inhibitors of the gp120-CD4 interaction to assess whether there are differences in the CD4-binding events of the VVC-sensitive and -resistant clones that could influence the subsequent conformational changes in gp120 involved in creation of the CCR5 binding site ., When the four clones were incubated with a range of sCD4 concentrations before infection of PBMCs , CC1/85 cl . 7 and CC101 . 19 cl . 7 were both highly sensitive , with IC50 values ∼0 . 1 µg/ml ( Fig . 4A , Table 2 ) ., In contrast , D1/85 . 16 cl . 23 was ∼100-fold less sensitive to sCD4 and CC1/85 cl . 6 was almost completely resistant ( Fig . 4A , Table 2 ) ., Of note is that CC1/85 cl . 7 and CC101 . 19 cl . 7 are unusually sensitive to sCD4 , compared to the corresponding isolates ( IC50 values ∼5 µg/ml ) and to typical primary isolates , which typically have IC50 values >10 µg/ml 31–34 ., The same data pattern was observed with CD4-IgG2 ( PRO542 ) ; again CC1/85 cl . 7 and CC101 . 19 cl . 7 were much more sensitive than D1/85 . 16 cl . 23 and all three of the isolates ( Table 2 ) ., Hence the sCD4 and CCR5 inhibitor sensitivity profiles of these four clones are not correlated; one parental and one VVC-resistant clone are sCD4-sensitive , the other two are sCD4-resistant ., In contrast to what was observed using sCD4 and CD4-IgG2 , the four clones ( and the corresponding isolates ) did not differ markedly in their sensitivities to MAb b12 against the CD4-binding site on gp120 or to the anti-CD4 MAb RPA-T4 that inhibits gp120-CD4 binding ( Fig . 4B , C and data not shown ) ., The binding of MAbs and other ligands to gp120 monomers is usually not predictive of how the same agents interact with the native Env trimer and neutralize the corresponding virus 35–37 ., However , because of the unusual characteristics of the CCR5 inhibitor resistant viruses , we considered it worth assessing whether the differential inhibition patterns described above might be manifested at the level of the gp120 monomer ., In a gp120-capture ELISA , CD4-IgG2 bound with equivalent affinity to gp120 proteins derived from all four parental clones and resistant clones ( Fig . 5A ) ., Hence the differential sensitivity of the corresponding viruses to CD4-based inhibitors ( Table 2 ) is not manifested at the level of the gp120 monomer , consistent with previous findings 35–37 ., The small molecule HIV-1 gp120 ligand , BMS-806 , was initially classified as an inhibitor of gp120-CD4 binding 38 ., However , it also inhibits subsequent conformational changes in the gp120-gp41 complex 39 ., It is not a direct competitor with gp120 for CD4 binding but instead reduces the affinity of CD4 for gp120 allosterically , without inducing the CD4i epitope 40 ., The BMS-806 infectivity-inhibition pattern for the four clones was the converse of that seen with sCD4 ( Fig . 4D , Table 2 ) ., Thus , D1/85 . 16 cl . 23 and CC1/85 cl . 6 were markedly more sensitive to BMS-806 than the other two clones ( IC50 values ∼20-fold lower ) ., D1/85 . 16 was also the most sensitive of the three isolates to BMS-806 , by ∼7 to 10-fold ( Table 2 ) ., We then tested whether the differential sensitivities of the viral clones to BMS-806 were also reflected at the gp120 monomer level ., In an ELISA , BMS-806 inhibited the binding of CD4-IgG2 to gp120s from D1/85 . 16 cl . 23 and CC1/85 cl . 6 more efficiently than to gp120s from CC1/85 cl . 7 and CC101 . 19 cl . 7 ( Fig . 5B ) ., Hence the increased BMS-806 sensitivity of clones D1/85 . 16 cl . 23 and CC1/85 cl . 6 probably arises at the gp120 monomer level ., Given the similarities at the amino acid sequence level between CC1/85 cl . 7 and CC101 . 19 cl . 7 ( Fig . 1A , B ) , it appears likely that CC101 . 19 cl . 7 evolved from a sCD4-sensitive , minor variant present in the uncloned isolate that is related to CC1/85 cl . 7 ., Conversely , D1/85 . 16 cl . 23 presumably evolved from one of the more prevalent sCD4-resistant viruses in the CC1/85 isolate ., These assumed relationships should be noted when interpreting later experiments ., MAbs in the CD4i family bind to a CD4-induced epitope on gp120 that substantially overlaps the element of the CCR5 binding site that is located within the bridging sheet and the base of V3 ., Their interaction with gp120 mimics that of the critical sulfated tyrosine residues in the CCR5 NT 10 , 41 ., CC101 . 19 cl . 7 was markedly more sensitive than CC1/85 cl . 7 , CC1/85 cl . 6 and D1/85 . 16 cl . 23 to neutralization by CD4i MAbs 48d and 17b ( Fig . 6A , B ) , and also by MAbs ED10 , 2 . 1C and 3 . 1H against the same epitope cluster ( data not shown ) ., Among those five CD4i MAbs , only 48d had even limited neutralizing activity against the two parental clones , and none of them had any detectable activity against D1/85 . 16 cl . 23 ( Fig . 6 , and data not shown ) ., None of the CD4i MAbs had detectable neutralizing activity against any of the uncloned parental or VVC-resistant isolates ( IC50 values >100 µg/ml ) ( data not shown ) ., In a gp120-capture ELISA , D1/85 . 16 cl . 23 and CC1/85 cl . 6 gp120s were the most reactive with MAb 17b ( Fig . 5C ) , which is in marked contrast to the infection-inhibition experiments where D1/85 . 16 cl . 23 and CC1/85 cl . 6 were the clones least sensitive to 17b and the related 48d MAb ( Fig . 6A , B ) ., Hence although the 17b epitope is well exposed on the gp120 monomer from these VVC-resistant viruses , that exposure is not relevant to what happens with the infectious virus ., In the presence of sCD4 , 17b bound almost equally well to all four gp120 monomers ( Fig . 5C ) ., sCD4 therefore has only a small inductive effect on the 17b epitope on the D1/85 . 16 cl . 23 and CC1/85 cl . 6 gp120s , but a much more marked action on gp120s from CC1/85 cl . 7 and CC101 . 19 cl . 7 ( Fig . 5C ) ., BMS-806 partially inhibited 17b binding to all four gp120s , but its blocking activity was less efficient with CC101 . 19 cl . 7 gp120 than with the other three ( Fig . 5D ) ., The significantly greater sensitivity to CD4i MAbs of CC101 . 19 cl . 7 compared to CC1/85 cl . 7 stands in marked contrast to the similar sCD4 sensitivities of these two clones ( compare Fig . 4A and Fig . 6 ) ., The increased sensitivity of CC101 . 19 cl . 7 to CD4i MAbs may , therefore , be informative about how this clone is VVC-resistant ., The simplest explanation is that at least one major element of its CCR5 binding site has become accessible or has been formed constitutively on the native Env complex , and not just after CD4 has bound ., IC9564 is a small molecule that binds to positively charged residues on the N-terminal side of the V3 stem and/or tip 42 , 43 ., It does not inhibit CD4 binding or CD4-induced conformational changes , but impedes further structural changes in gp120 that are necessary for fusion , perhaps by locking gp120 in a CD4-induced conformation 43 , 44 ., CC101 . 19 cl . 7 was the most sensitive of the four clones to IC9564 , with an IC50 value 11-fold lower than CC1/85 cl . 7 ( 0 . 62 nM compared to 7 . 2 nM , respectively ) ., D1/85 . 16 cl . 23 and CC1/85 cl . 6 were both much less sensitive to IC9564 , with IC50 values of 690 and 150 nM , respectively ( Fig . 7 , Table 2 ) ., The relative resistance ( ∼1100-fold ) of D1/85 . 16 cl . 23 compared to CC101 . 19 cl . 7 was only partially recapitulated by the corresponding isolates , for which there was a 10-fold differential in IC50 values ( Table 2 ) ., These observations suggest that the V3 binding site for IC9564 is significantly more accessible on CC101 . 19 cl . 7 , or the corresponding interaction with CCR5 more easily disrupted , than it is on the related parental clone CC1/85 cl . 7 ., However , the IC9564 binding sites on the Env complexes of D1/85 . 16 cl . 23 and its related parental clone CC1/85 cl . 6 are much less exposed , or are less relevant to entry ., As the V3 sequences of D1/85 . 16 cl . 23 and CC1/85 cl . 6 are identical to that of CC1/85 cl . 7 , sequence differences elsewhere in Env must be responsible for the ∼100 fold differences in IC9564 sensitivities between the first two and the last ( Table 2 ) ., The increased sensitivity of CC101 . 19 cl . 7 to IC9564 suggests that its V3 region may also have become more accessible to MAbs ., We have shown that several V3 MAbs ( 447-52D , F425-B4e8 and 39F ) lacked significant neutralizing activity against the CC1/85 parental isolate and both VVC-resistant isolates 31 ., Since D1/85 . 16 has the same consensus V3 sequence as CC1/85 , this finding suggested that the V3 region of D1/85 . 16 remained shielded from NAbs , just as it is on most primary isolates ., However , the V3 region of CC101 . 19 contains four sequence changes compared to CC1/85 , specifically K305R , H308P , A316V and G321E ( Fig . 1C ) ., Variation of this magnitude could directly affect the binding sites for MAbs , limiting their value as probes for V3 accessibility ., Indeed , we showed that the four sequence changes destroyed the epitope for V3 MAb 39F on gp120 derived from CC101 . 19 , as assessed by a gp120-capture ELISA 31 ., Using the same assay , we found that the V3 epitopes for MAbs F2A3 and C011 were also lost from CC101 . 19 gp120 compared to CC1/85 cl . 7 gp120 , and from the corresponding V3 peptide ( data not shown ) ., The epitopes for V3 MAbs 19b , 2 . 1e , 447-52D and F425-B4e8 were , however , still present on CC101 . 19 cl . 7 gp120 ( Fig . 8 ) ., Indeed , the binding of MAb 19b to gp120 from CC101 . 19 cl . 7 was markedly greater than to the other three gp120s ( Fig . 8A ) ., In contrast , although the V3 MAbs 2 . 1e , 447-52D and F425-B4e8 did bind detectably to CC101 . 19 cl . 7 gp120 , they did so to greatly reduced extents compared to the gp120 from the other three viruses ( Fig . 8B , C , D ) ., Note that each MAb bound equally well to the gp120s from the two parental clones and D1/85 . 16 cl . 23 ( Fig . 8 ) ., This observation is consistent with these three gp120s having isogenic V3 sequences ( Fig . 1C ) ., We also tested the binding of the MAbs to peptides based on the V3 sequences of CC1/85 cl . 7 and CC101 . 19 cl . 7 ( Fig . 8 , panel insets ) ., For 19b , the peptide-binding and gp120-binding data were consistent , in that the MAb recognized the CC101 . 19 cl . 7 sequences better than CC1/85 cl . 7 ( Fig . 8A ) ., This was not the case , however , with MAbs 2 . 1e , 447-52D and F425-B4e8; compared to the CC1/85 cl . 7 ligands , 2 . 1e and 447-52D bound more strongly to the CC101 . 19 cl . 7 peptide but less well to the corresponding gp120 , whereas F425-B4e8 bound equally well to both peptides but poorly to CC101 . 19 cl . 7 gp120 ., Hence the four sequence differences between CC1/85 cl . 7 and CC101 . 19 cl . 7 affect the epitopes for different V3 MAbs to different extents when the these epitopes are presented in different contexts ( i . e . , peptide vs . gp120 ) ., We therefore tested the neutralization activity of V3 MAbs 19b , 2 . 1e , 447-52D and F425-B4e8 against the four clonal infectious viruses ., The resulting data pattern for three of the MAbs was similar to that observed using IC9564 ., Thus , CC101 . 19 cl . 7 was markedly the most sensitive of the four clones to MAbs 19b , 2 . 1e and 447-52D ( Fig . 9A , B , C; Table 3 ) ., Compared to the related parental clone CC1/85 cl . 7 , the IC50 differentials ranged from ∼6-fold for 447-52D to ∼30-fold for 19b and 40-fold for 2 . 1e ( Table 3 ) ., However , CC101 . 19 cl . 7 was no more sensitive than CC1/85 cl . 7 to MAb F425-B4e8 , with an IC50 differential of <2-fold ( Fig . 9D , Table 3 ) ., The increased neutralization sensitivity of CC101 . 19 cl . 7 to 2 . 1e and , to a lesser extent , 447-52D , was particularly striking given the reduced binding of these MAbs to the corresponding gp120s ( compare Figs . 8 and 9 ) ., Presumably , the four sequence changes must increase the exposure of the V3 region at the quaternary structural level ( i . e . , on the CC101 . 19 cl . 7 virus ) to an extent that is more than sufficient to overcome any locally adverse impact they may have on the epitope itself ( i . e . , on gp120 ) ., Of note is that the V3 peptide-binding profiles were a better neutralization predictor than the gp120-binding profiles for MAbs 2 . 1e , 447-52D and F425-B4e8 ., In contrast to CC101 . 19 cl . 7 , both D1/85 . 16 cl . 23 and the related parental clone CC1/85 cl . 6 were highly resistant to V3 MAbs 19b , 2 . 1e and 447-52D ( Fig . 9A , B , C; Table 3 ) ., CC1/85 cl . 6 was also much more resistant than the other parental clone , CC1/85 cl . 7 , to neutralization by MAb F425-B4e8 , which was the only V3 MAb able to neutralize D1/85 . 16 cl . 23 ( Fig . 9D ) ., Given that the V3 sequences of these three clones are identical ( Fig . 1C ) , and that F425-B4e8 binds comparably to all three gp120s ( Fig . 8D ) , quaternary structural differences in the native Env complexes must again be responsible for the neutralization sensitivity differences ., Taken together , the inference of the above experiments is that the V3 region of CC101 . 19 cl . 7 has become unusually accessible to antibodies and a small molecule ligand , even compared to CC1/85 cl . 7 ., In contrast , the V3 region is poorly exposed on CC1/85 cl . 6 and on D1/85 . 16 cl . 23 , with the exception that the F425-B4e8 epitope is accessible on the latter virus ., The two VVC-resistant viruses have therefore taken routes to resistance that not only differ at the genetic level , but also at the phenotypic ., To create additional antibody probes for studying CC101 . 19 cl . 7 , we immunized rabbits ( two per group ) with 34-residue V3 peptides derived from this virus and also from CC1/85 cl . 7 , which has the same V3 sequence as CC1/85 cl . 6 and D1/85 . 16 cl . 23 ( Supporting Information , Text S1 ) ., Both V3 peptides were immunogenic in rabbits , inducing antibodies that bound to the cognate and , to a lesser extent , non-cognate , peptide and gp120 in ELISA ( Supporting Information; Figs . S1 and S2 ) ., The rabbit anti-V3 sera were then tested for neutralizing activity against the Env-pseudotyped clonal viruses in U87-CD4-CCR5 cells ., None of the four antisera neutralized CC1/85 cl . 7 , CC1/85 cl . 6 or D1/85 . 16 cl . 23 ( Fig . 10 ) ., However , CC101 . 19 cl . 7 was specifically neutralized by the two antisera raised against the autologous V3 peptide ( Fig . 10C ) ., Hence the V3 sequence changes that drive VVC resistance have not only caused the V3 region of the CC101 . 19 Env complex to become more accessible to neutralizing antibodies , they have also created a neo-epitope for the induction of such antibodies ., To assess how the V3 sequence differences between CC1/85 cl . 7 and CC101 . 19 cl . 7 may affect the tertiary structure of V3 in the context of gp120 , we introduced the two gp120 sequences into two different X-ray crystal structures of a V3-containing gp120 core 9 , 10 , 43 , and then superimposed the resulting models ( colored red and yellow , respectively in Fig . 11A , B ) ., In the first template , gp120 is bound to sCD4 and MAb X5 that , like 17b and 48d , binds to the CD4i epitope cluster overlapping the CCR5 binding site 9 ., The second template was based on a gp120 core bound to both sCD4 and the tyrosine-sulfated 412 MAb that mimics the CCR5 NT 10 ., We elected to use both templates , because unlike template 1 , template 2 may mimic the interaction with the tyrosine-sulfated CCR5 NT ., The comparison might be informative for understanding why CC101 . 19 cl . 7 has become more dependent on the latter interaction ., Although the gp120 structures align well , the V3 domains assume different structures in the two templates ( Fig . 11A , B ) ., In template 1 , V3 protrudes from the gp120 core and has three distinct structural regions:, ( i ) a conserved base connected by a disulfide bridge ., This β-sheet is part of a 6-stranded β-barrel that forms the core of the gp120 outer domain 45;, ( ii ) a flexible stem that extends away from the core; and, ( iii ) a β-turn tip ( Fig . 11B ) ., In template 2 , the tyrosine-sulfated residues bind to the bridging sheet-V3 interface and induce a structural rearrangement in V3 ( Fig . 11B ) ., As a result , the N- and C-terminal constituents of the V3 stem are brought into proximity to form a 2-stranded β-sheet that replaces the unstructured V3 stem from the first template ., In addition , the V3 tip is displaced by 16 Å ( Fig . 11B ) ., We then inspected where the V3 amino acid changes between CC1/85 cl . 7 and CC101 . 19 cl . 7 were located on the two templates ( Fig . 11D–F ) ., Two of the substitutions in CC101 . 19 cl . 7 , K305R and G321E , are on opposite strands of the β-sheet that is present in the gp120 complex with the tyrosine-sulfated 412d MAb ( template 2 ) but not in the X5 complex ( template 1 ) ., Both these changes increase the local propensity for forming a β-sheet ( Gly , in particular , is accommodated poorly in β-sheets ) ., Moreover , the E321 and R305 side chains in the CC101 . 19 cl . 7, V3 protrude from the same lateral side of the β-sheet and are positioned close enough to form a salt-bridge that could contribute to inter-strand stability ., Although the model was based on a V3 conformation derived from a CD4-bound gp120 model , it is possible that a salt bridge could form between E321 and R305 , prior to gp120 engagement with CD4 or CCR5 ., Circular dichroism experiments suggest that a CC101 . 19 cl . 7, V3 peptide has more secondary structure than the corresponding peptide from CC1/85 cl . 7 ( our unpublished observations ) ., Moreover , and as noted previously , the H308P may facilitate a bend in the V3 structure of CC101 . 19 cl . 7 20 , which may contribute to a relocation of the V3 tip and affect its ability to interact with CCR5 ., Thus , the characteristics of the amino acid changes and the available structural data are consistent with a model , based on template 2 , in which the CC101 . 19 cl . 7, V3 region constitutively assumes a stabilized conformation that is compatible with binding to the Tyr-sulfated CCR5 NT ., In this conformation , the V3 region is more structured , accommodating the binding of the tyrosine-sulfate moieties while at the same time displacing its V3 tip away from the CCR5 ECL2 ., Whether this model is valid is the subject of ongoing experimental and structural studies ., To assist the interpretation of the V3 MAb binding experiments , we mapped the epitopes for MAbs 447-52D , 2 . 1e , F425-B4e8 and 19b on the V3 crystal structures represented by templates 1 and 2 ( Fig . 11G ) ., Note that the available crystal structures for 447-52D and F425-B4e8 with their peptide V3 epitopes reveal more contact residues than are indicated here 46 , 47 ., However , we choose to focus on the more essential residues revealed by phenotypic analyses 48–50 ., MAbs 447-52D and 2 . 1e bind primarily to the V3 tip , although their requirements are subtly different ., The essential residues for F425-B4e8 are immediately adjacent to the tip , while 19b also requires residues in the stem ., If our interpretation of Fig . 11 is correct , MAbs 19b , 447-52D and 2 . 1e , but not F425-B4e8 , may preferentially recognize the V3 configuration represented by the right-hand panels in Fig . 11G ., Our goal in this study was to learn more about how HIV-1 Env interacts with the CCR5 co-receptor ., We used two different but genetically related viruses , CC101 . 19 and D1/85 . 16 , which have become resistant to small molecule CCR5 inhibitors such as VCV , AD101 and maraviroc ., Both resistant variants still use CCR5 for entry , but they have acquired the ability to recognize the inhibitor-CCR5 complex as well as the free co-receptor; their parental strain , CC1/85 , can only use free CCR5 for entry and is , therefore , sensitive to small molecule CCR5 inhibitors 18 ., Of note is that although both variants share the resistance phenotype , they have taken different genetic routes to it; thus CC101 . 19 has four amino acid changes in the V3 region of gp120 whereas D1/85 . 16 has three substitutions in the gp41 fusion peptide 20 , 22 ., Additional changes elsewhere in Env may contribute to the replication capacity of each virus , but they are neither necessary nor sufficient for resistance 20 , 22 ., We are now assessing whether these other sequence changes have any influence on any of the phenotypes described here ., By studying how these two viruses accomplished the same task in such radically different ways , we reasoned that we might learn something useful about inter-domain interactions within the HIV-1 Env complex ., For example , do changes in the fusion peptide have the same effect on Env topology as ones in the V3 region of an entirely different subunit ?, We used infectious chimeric viruses and Env-pseudotyped viruses based on clones from the parental and each resistant isolate ., The CC1/85 parental isolate was derived from an HIV-1 infected individual who had been infected for at least five years 51 , | Introduction, Results, Discussion, Materials and Methods | HIV-1 variants resistant to small molecule CCR5 inhibitors recognize the inhibitor-CCR5 complex , while also interacting with free CCR5 ., The most common genetic route to resistance involves sequence changes in the gp120 V3 region , a pathway followed when the primary isolate CC1/85 was cultured with the AD101 inhibitor in vitro , creating the CC101 . 19 resistant variant ., However , the D1/86 . 16 escape mutant contains no V3 changes but has three substitutions in the gp41 fusion peptide ., By using CCR5 point-mutants and gp120-targeting agents , we have investigated how infectious clonal viruses derived from the parental and both resistant isolates interact with CCR5 ., We conclude that the V3 sequence changes in CC101 . 19 cl . 7 create a virus with an increased dependency on interactions with the CCR5 N-terminus ., Elements of the CCR5 binding site associated with the V3 region and the CD4-induced ( CD4i ) epitope cluster in the gp120 bridging sheet are more exposed on the native Env complex of CC101 . 19 cl . 7 , which is sensitive to neutralization via these epitopes ., However , D1/86 . 16 cl . 23 does not have an increased dependency on the CCR5 N-terminus , and its CCR5 binding site has not become more exposed ., How this virus interacts with the inhibitor-CCR5 complex remains to be understood . | Human immunodeficiency virus type 1 ( HIV-1 ) is the causative agent of AIDS ., HIV-1 entry into target cells is triggered by the interaction of the viral envelope glycoproteins with a cell-surface receptor ( CD4 ) and a co-receptor ( CCR5 ) , and culminates in fusion of the viral and cell membranes ., Small molecule inhibitors that bind to CCR5 are a new class of drug for treating HIV-1-infected people ., However , HIV-1 can evolve ways to become resistant to these compounds , by acquiring mutations that alter how its envelope glycoproteins ( gp120-gp41 ) interact with CCR5 ., In this study , we investigated how two resistant viruses gained the ability to use the inhibitor-bound form of CCR5 through two different mechanisms ., In the first virus , four amino acid substitutions in the V3 region of gp120 created an increased dependency on interactions with the CCR5 N-terminus ., These changes altered the configuration of gp120 , increasing the exposure of antibody epitopes in the V3 region and the CD4i epitope cluster associated with the CCR5 binding site ., In contrast , the second virus , which became resistant via three sequence changes in the gp41 subunit , did not become more dependent on the CCR5 N-terminus and remained resistant to neutralization by antibodies against elements of the CCR5 binding site . | virology/immunodeficiency viruses, virology/antivirals, including modes of action and resistance, infectious diseases/hiv infection and aids, microbiology/medical microbiology, virology/host invasion and cell entry | null |
journal.pntd.0001832 | 2,012 | Whole Genome Sequence of Treponema pallidum ssp. pallidum, Strain Mexico A, Suggests Recombination between Yaws and Syphilis Strains | Treponema pallidum ssp ., pallidum ( TPA ) and Treponema pallidum ssp ., pertenue ( TPE ) strains , the causative agents of syphilis 1 and yaws 2 , infect more than 12 and 2 million people annually , respectively 3 ., Whereas syphilis is a sexually transmitted and congenital disease affecting adults and newborns worldwide , yaws is transmitted predominantly through direct skin contact and affects preferably children in warm , humid , rural areas ., During the last several years , a number of treponemal genomes have been completely sequenced including TPA Nichols ( GenBank acc . no . AE000520 . 1 4 ) , TPA SS14 ( CP000805 . 1 5 ) , TPA Chicago ( CP001752 . 1 6 ) , TPE Samoa D ( CP002374 . 1 ) , TPE CDC-2 ( CP002375 . 1 ) , TPE Gauthier ( CP002376 . 1 ) 7 and T . paraluiscuniculi strain Cuniculi A ( CP002103 . 1 8 ) ., In general , when compared to TPE strains , TPA strains differ by less than 1 , 200 nucleotide positions 7 , 9 ., Phylogenetic trees constructed from whole genome binary restriction target site data 9 , from multilocus sequencing 10 and whole genome sequence alignments 10 showed a distinct clustering of TPA and TPE strains ., As shown by Centurion-Lara et al . 11 and Gray et al . 12 , the unusual clustering of the Mexico A TP0131 gene with several TPE strains is the result of intra-chromosomal gene conversion events ., Three different alleles of the tprD ( TP0131 ) gene ( D , D2 , and D3 ) have been identified among TPA and TPE strains 11 and the presence of individual gene alleles determines the cluster patterns 12 ., The TPA Mexico A strain was isolated in 1953 from an 18-year-old male , with primary syphilis , living in Mexico 13 ., Attempts to cultivate TPA Mexico A strain under in vitro conditions revealed a lower growth rate ( compared to other tested TPA strains ) and also a decreased percentage of motile treponemes compared to TPA strain Nichols 14 ., The lower growth potential of Mexico A is likely to result from genetic differences between this strain and other TPA strains ., Our previous study 9 revealed that the Mexico A strain contained the largest genome of all investigated TPA strains ., In this study , we compared the complete genome sequence of TPA Mexico A to complete TPA and TPE genome sequences and found a mosaic character of the Mexico A TPAMA_0326 ( tp92 ) and TPAMA_0488 ( mcp2-1 ) loci , i . e . having both TPA and TPE specific nucleotide sequences ., The TPA Mexico A strain used in this study was kindly provided by David L . Cox , CDC , Atlanta , GA , USA ., The DNA was amplified directly from 1 µl of cells ( 105 cells per µl ) frozen in glycerol using a QIAGEN Whole Genome Amplification REPLI-g Kit ( QIAGEN , Valencia , CA , USA ) ., To separate treponemal cells from rabbit testicular cells , the samples were first centrifuged at 100×g for 5 min ., Supernatant containing treponemal cells was carefully extracted and centrifuged at 14 , 100×g for 3 min ., The resulting pellet containing treponemal cells was washed 2× in PBS buffer and centrifuged at 14 , 100×g for 3 min ., The supernatant was removed for a final volume of 3 µl and the procedure continued according to the manufacturers instructions ., Amplified DNA was purified using a QIAEX II kit ( QIAGEN , Valencia , CA , USA ) ., The resulting DNA concentration was 602 ng/µl in a 30 µl volume ., The chromosomal DNA was sequenced using the Illumina ( Illumina , San Diego , CA , USA ) technique ., Several chromosomal regions of the Mexico A strain , representing sequentially related and repetitive components of treponemal genome , were amplified using a GeneAmp XL PCR kit ( Applied Biosystems , Foster City , CA , USA ) using the previously described TPI amplicons 9 , 15 ., These regions comprised the following TPI amplicons ( genes ) : TPI-11 ( tprC ) , TPI-12 ( tprD ) , TPI-13B ( TP0136 ) , TPI-17A ( 5S , 16S and 23S rRNA rRNA operon 1 ) , TPI-21B ( 5S , 16S and 23S rRNA rRNA operon 2 ) , TPI-25A ( tprE ) , TPI-25B-A ( tprF ) , TPI-25B-B ( tprG ) , TPI-26 ( TP0326 ) , TPI-32B ( arp ) , TPI-34 ( TP0470 ) , TPI-38 ( TP0488 ) , TPI-42A ( TP0548 ) , TPI-48 ( tprI , tprJ ) , TPI-66A ( TP0868 ) and TPI-67 ( tprK ) ., From these XL-PCR amplicons , small insert libraries were prepared and the resulting clones were sequenced as previously described 5 ., Alternatively , amplified DNA was Sanger sequenced directly using specific primers ., A set of 639 Illumina contigs ( 100–69 , 908 bp in length ) and 16 Sanger contigs , resulting from sequencing of XL-PCR products , were assembled using the TPA SS14 reference genome 5 ., This assembly contained 122 gaps ( 8 . 9 kb in length ) in the TPA Mexico A sequence ., Altogether , 117 DNA regions ( containing all 122 gaps ) were additionally PCR amplified and sequenced using the Sanger method ., The TP0326 ( tp92 ) and TP0488 ( mcp2-1 ) loci of Treponema pallidum subsp ., endemicum ( TEN ) , strain Bosnia A , were amplified using GeneAmp XL PCR kit ( Applied Biosystems , Foster City , CA , USA ) and Sanger sequenced using specific primers ., The resulting genome assembly was verified using the previously described fingerprinting technique 15 , 16 ., The experimentally identified DNA fragments ( resulting from DNA digestion at 1774 restriction target sites; 7 ) were compared to the corresponding in silico restriction fragment lengths ., The 1774 restriction target sites corresponded to a total sequence length of 10 . 6 kb ., The average error rate of WGF was calculated previously 8 and corresponded to 27 . 9 bp ( 1 . 6% of the average fragment length ) with a variation range between 0 and 132 bp ., Considering the close relatedness of the Mexico A and SS14 genomes ( 99 . 99% identity at the nucleotide level ) , the Mexico A genome was annotated according to the SS14 genome 5 with minimal gene length of 150 bp ., Genes identified in the Mexico A genome were denoted with the prefix TPAMA followed by four numbers to indicate gene number ., Putative virulence factors were defined as those previously described by Čejková et al . 7 and comprised 31 genes ( including tpr , arp , and TPAMA0136 genes ) ., All of these genes are listed in Table S1 ., The G+C content was calculated in 501 bp windows using CLC Bio software ( CLC Bio Katrinebjerg , Denmark ) ., The whole genome sequence of TPA strain Mexico A was placed in the GenBank under accession number CP003064 . 1 ., Sequences of TP0326 ( tp92 ) and TP0488 ( mcp2-1 ) of TEN strain Bosnia A were deposited in the GenBank under accession numbers JX392330 . 1 and JX392331 . 1 , respectively ., The genome of the Mexico A strain was determined to be 1 , 140 , 038 bp with 1 , 035 predicted ORFs ., The final assembled genome sequence was verified using a fingerprinting technique 15 , 16 where 1774 experimentally identified DNA fragments were compared to in silico restriction fragment lengths ., No differences in fragment lengths were identified indicating correct overall assembly of the Mexico A genome ., The 1774 restriction target sites corresponded to a total sequence length of 10 . 6 kb ., Since no discrepancies between the in silico and the experimental restriction analysis were found ( i . e . in 10 . 6 kb of the genome sequence out of 1 , 140 kb ) , the sequencing error rate was estimated to 10−4 or less ., From all annotated ORFs , 161 ( 15 . 6% ) are involved in general metabolism , 125 ( 12 . 1% ) in cell structure and cell processes , 51 ( 4 . 9% ) in DNA replication , repair and recombination , 173 ( 16 . 7% ) in regulation , transcription and translation , 113 ( 10 . 9% ) in transport , and 31 ( 3% ) in virulence ., 327 ORFs ( 31 . 6% ) had unknown function ., In addition , 54 ( 5 . 2% ) genes encoded RNAs ., Coding regions represented 93 . 5% of the Mexico A genome ., As in the SS14 ( CP000805 . 1 5 ) and Chicago ( CP001752 . 1 6 ) genomes , the tprK gene ( TPAMA_0897 ) is represented by a number of variable sequences and the consensus sequence , therefore , contains unidentified nucleotides in these regions ., Altogether , six genes ( pseudogenes ) were annotated to contain authentic frameshifts ( AF ) in the Mexico A genome ( TPAMA_0009 , TPAMA_0146 , TPAMA_0316 , TPAMA_0520 , TPAMA_0532 and TPAMA_0812 ) compared to 9 genes with AF annotated in the Nichols and SS14 genomes , where 3 additional genes with AF were described ( TP0217 , TP0575 and TP0866 ) ., In an additional 21 cases , frameshift mutations identified in the Mexico A genome resulted in gene fusions ( Table S2 ) ., Whole genome sequence of the TPA strain Mexico A has been compared with other sequenced genomes of TPA strains including the Nichols strain ( AE000520 . 1 4 ) , SS14 ( CP000805 . 1 5 ) , and Chicago ( CP001752 . 1 6 ) using the Lasergene software package ( DNASTAR , Madison , WI , USA ) and Crossmatch ( P . Green , unpublished ) ., Because of high sequence diversity , TP0131 ( tprD ) and TP0897 ( tprK ) were excluded from our calculations ., The Mexico A genome differed from the SS14 genome in 175 substitutions , 85 insertions and 28 deletions , from the Chicago genome in 419 substitutions , 18 insertions and 20 deletions , and from the Nichols genome in 438 substitutions , 94 insertions and 38 deletions ( ambiguously identified bases present in the Nichols genome were not counted ) ., Changes differentiating Mexico A and Nichols genomes were found in 206 ORFs listed in Table S3 ., Since it is known that the Nichols and SS14 genomes contain about 200 nt errors ( 10 , Pospíšilová , unpublished results ) , we also compared the Mexico A genome with the improved version of the Nichols genome ( Pospíšilová , unpublished results ) ., From 206 ORFs originally identified as sequentially different , 138 ORFs ( 67% ) also showed differences when compared to the improved Nichols genomic sequence ., The originally identified nucleotide changes in the remaining 68 ORFs ( 33% ) were considered to be Nichols sequencing errors ., However , in the case of 14 Nichols ORFs ( 1 . 3% of the total Nichols ORFs ) , only partial or no sequencing data were available ., In general , the identified changes were more frequently found among genes encoding putative virulence factors and among genes involved in cell structure and processes and in genes coding for DNA replication , repair and recombination ., In contrast , genes encoding components associated with general metabolism , transcription , translation , gene regulation and transport contained nucleotide changes less frequently ( Table S4 ) ., In addition to TPA strains , the Mexico A genome sequence was also compared with whole genome sequences of three TPE strains including Samoa D ( GenBank acc . no . CP002374 . 1 ) , CDC-2 ( CP002375 . 1 ) and Gauthier ( CP002376 . 1 ) 7 ., Of all the annotated genes , two ( TPAMA_0326 ( tp92 ) and TPAMA_0488 ( mcp2-1 ) ) showed a mosaic character , which combined sequences from both TPA and TPE strains ( Fig . 1 ) ., The complete set of nucleotide changes found in the TPA and TPE regions for TP0326 and TP0488 loci are shown in Table 1 and Table 2 , respectively ., In the TP0326 locus , there were 8 single nucleotide positions and one 15 bp deletion that differentiated TPE strains ( Samoa D , CDC-2 and Gauthier ) from TPA strains ( Nichols , SS14 , Chicago ) ., Out of these 9 positions , the TPAMA_0326 locus contained 5 nucleotide positions with an identical sequence to the TPA strains and 4 regions that were identical to the TPE regions , including 3 nucleotide positions and the 15 bp deletion ( Fig . 1 , Table 1 ) ., Similarly , the TP0488 locus contained 30 nucleotide positions that were found to be different for all analyzed TPA and TPE strains ., In addition , two nucleotide positions ( 584 , 1655 ) differentiated the Nichols and Chicago strains from TPE strains and from the SS14 strain ., In TPAMA_0488 , 12 of these 30 positions contained sequences identical to TPA strains , whereas 18 positions corresponded to sequences of TPE strains ( Fig . 1 , Table 2 ) ., In the remaining part of the Mexico A genome , similarities to the TPE sequences were only found in the tprC sequence and at two additional nucleotide positions ( present in TP0314 locus and TPAMA_0319 , respectively ) ., The average G+C content of the Mexico A genome was found the same as for other treponemal species , 52 . 8% ., Based on an analysis of G+C content , codon and amino acid usage , and gene positions , 77 ( 8 . 32% ) of the TPA genes were predicted to be horizontally transferred 17 ., To identify chromosomal regions with horizontal transfer potential , G+C content was calculated in 501 bp windows in TPA Mexico A , TPA SS14 5 , TPE Samoa D 7 and Treponema paraluiscuniculi strain Cuniculi A ( CP002103 . 1 8 ) ( Fig . 2 ) ., The chromosomal regions showing different G+C content ( defined as G+C content higher than 63% or lower than 41% ) showed a similar pattern in all four tested genomes ., We compared regions with higher/lower G+C content with 5 kb-long chromosomal regions containing 40 or more nucleotide changes differentiating TPA and TPE strains which were previously identified by Čejková et al . 7 ., From 11 such regions 7 ( Fig . 2 ) , only 3 showed significant differences in G+C content ., Similarly , no clear association was found in regions with different G+C content and tpr-containing DNA regions ., Complete genome sequences of the TPA Mexico A strain was revealed ., The genome size , G+C content and gene order was identical with other already sequenced TPA genomes 4–6 ., The Mexico A genome was most closely related to SS14 genome and differed in less than 300 hundred substitutions and indels ., Since it has been published that the Nichols and the SS14 genomes contain about 200 nt errors 10 a lower number of nucleotide changes differentiating the Mexico A and SS14 genome can be expected ., In fact , the number of nucleotide differences between Mexico A and SS14 genomes ( except of differences present in the tprD and tprK genes ) is probably lower than one hundred ( Pětrošová , unpublished results ) ., In any of these comparisons , the identified differences were more frequently present in, ( i ) genes encoding putative virulence factors ,, ( ii ) genes involved in cell structure and processes and, ( iii ) genes coding for DNA replication , repair and recombination ., In contrast , genes encoding components of general metabolism , transcription , translation , gene regulation and transport appear to be conserved ., The observed mosaic character of the Mexico A TPAMA_0326 ( tp92 ) and TPAMA_0488 ( mcp2-1 ) loci , combining both TPA- and TPE-specific nucleotide sequences , can be , in principle , explained by six independent mechanisms including, i ) an ancestral position of the Mexico A strain with respect to both TPA and TPE strains ,, ii ) rapid accumulation of nucleotide changes during evolution of TPA strains from TPE strains with the Mexico A as an intermediate ,, iii ) intra-strain recombination between paralogous sequences ,, iv ) artifacts during PCR amplification ( as a result of contamination with TPE genomic DNA ) and/or contamination with TPE-amplified DNA ,, v ) convergent evolution and, vi ) inter-strain recombination between TPA and TPE strains during simultaneous infection of one host ., i ) The first explanation can be ruled out because only two chromosomal loci ( TPAMA_0326 and TPAMA_0488 ) showed demonstrable similarity to TPE strains ., Moreover , the number of Mexico A-specific mutations ( i . e . , mutations that are only present in the Mexico A genome and not in other sequenced TPA genomes ) is not significantly different from the number of specific mutations in other TPA genomes ( data not shown ) ., In a predicted common ancestor , one would expect a considerably higher number of ancestor-specific mutations in comparison to progenies ., ii ) The second hypothesis is illustrated in Fig . 3B ., The hypothetical evolution scheme comprises TPA , TPE and TEN strains arranged according to their relatedness to other TP strains 18 ( see also Fig . 3A ) ., We sequenced TP0326 ( tp92 ) and TP0488 ( mcp2-1 ) loci in TEN strain Bosnia A ( GenBank acc . no . JX392330 . 1 and JX392331 . 1 , respectively; our TP0326 sequence is identical to partial tp92 sequence of Bosnia A published by Harper et al . 19 ) ., The sequencing data showed that TEN strain Bosnia A contains the same nucleotide mosaic in the TP0488 ( mcp2-1 ) locus as Mexico A ( with the exception of 2 single nucleotide substitutions ) and similarly , some TPA isolates belonging to the SS14-like group of TPA strains show a TEN-specific pattern in the TP0326 ( tp92 ) locus ., It was impossible to propose an evolutionary model based only on accumulation or loss of nucleotide changes ( see Fig . 3B ) , and this fact supports recombination hypothesis ., iii ) The third hypothesis was rejected when we failed to identify potential recombinant ( donor ) sites for the TPAMA_0326 and TPAMA_0488 genes in the Mexico A genome , despite several attempts to identify such regions using several computer programs and algorithms ( RDP3 , EditSeq ( DNASTAR ) , BLAST ) ., iv ) While it is known that PCR amplification of two sequentially related templates can result in the production of chimeric DNA amplicons 20 , contamination of the Mexico A genomic DNA with TPE genomic DNA can be ruled out because recombinant genes were only found for two genes of the genome ., Contamination with TPE-amplified DNA ( corresponding to TPAMA_0326 and TPAMA_0488 genes ) was excluded based on careful analysis of Illumina reads , where no TPA- or TPE-specific Illumina reads were found in any of these regions ., In fact , the presence of 15 bp-deletions in the TPAMA_0326 gene was found in all 169 individual Illumina reads covering this region ., Similar analysis of the TPAMA_0488 region revealed no TPA- or TPE-specific Illumina reads; and all 37 reads , covering regions with both TPA and TPE molecular signatures , revealed the Mexico A consensus sequence ., Since Illumina technology sequences individual DNA molecules , contamination of Mexico A genomic DNA with TPE PCR product can be excluded ., To exclude artifacts during REPLI-g kit amplification of the Mexico A genomic DNA , three different REPLI-g amplifications were used for TPAMA_0326 and TPAMA_0488 sequencing ., No discrepancies were identified during analysis of Sanger reads in these regions ., Moreover , Harper et al . 19 sequenced partial tp92 locus of the Mexico A strain ( obtained directly from CDC , Atlanta ) and the sequenced region ( 960 nt , GenBank acc . no . EU102088 . 1 , containing TPE-like sequence in three nucleotide positions and a 15-bp deletion ) was identical to our sequence ., Sequences of TP0326 ( tp92 ) from various TPE isolates published by Harper et al . 19 contained the 15 bp TPE-like deletion and also corresponded to TPE-like changes in the South Africa treponemal isolate ., All 21 South Africa partial nucleotide sequences available in the GenBank 19 were 100% identical to the corresponding sequences of Mexico A published by Harper et al . 19 ., Therefore , the South Africa strain appears to be another strain that is identical , or very closely related , to the Mexico A strain ., Nevertheless , we found 3 nucleotide changes differentiating South Africa and Mexico A sequences published by Harper et al . 19 from our own sequences of Mexico A . Two of these differences were found in homopolymeric stretches ( in fliG-tp0027 and tp0347 regions ) and one SNP ( C→T ) was found in the rpiA-tp0617 region ., Since both Mexico A strains came from the same laboratory ( D . L . Cox , CDC Atlanta ) , the data suggest that possible sequencing errors in sequences published by Harper et al . 19 may explain these differences ., To further asses the frequency of strains similar to Mexico A/South Africa , we investigated clinical samples published by Flasarová et al . 21 for Mexico A-specific mutations ., No such nucleotide changes were found in 49 genotyped samples , indicating that the Mexico A/South Africa group of strains is not prevalent in central Europe ., v ) Since convergent evolution assumes acquisition of the same biological trait in unrelated lineages ( operating on the level of biological function ) , it is extremely unlikely that it would result in exactly the same amino acid sequence of the relevant proteins ., Due to degeneration of the genetic code , it is even more unlikely that convergent evolution would end up in two identical nucleotide sequences ., vi ) In contrast to previous alternatives , inter-strain recombination cannot be ruled out despite the fact that the probability of such event is relatively low ., Moreover , the mosaic character of the TPAMA_0326 and TPAMA_0488 loci , combining both TPA- and TPE-specific nucleotide sequences , is a typical result of a recombination event after horizontal gene transfer 22–24 ., Also , patterns found in TEN strains indicate that observed mosaics in the Mexico A genome are not artifacts , but rather the results of recombination events in the common ancestor of TPA and TEN strains ( see Fig . 3C ) ., There are several possible molecular mechanisms that could lead to the formation of the mosaic structure seen at the TPAMA_0326 and TPAMA_0488 loci ., We propose two models ( Fig . 4 ) that are based on the incorporation of TPE double stranded DNA ., In the first model , dsDNA was integrated into the chromosome of the Mexico A ancestor through homologous recombination ., The resulting DNA heteroduplex was block-repaired via mismatch repair mechanisms ., Similar reparation patterns have been observed after DNA transformation of Escherichia coli 25 and Helicobacter pylori 24 ., In other bacteria , mismatch repair involves the cleavage of a daughter strand by MutH , which recognizes methylated cytosine in the GATC sequence ., Since TPA does not contain a MutH orthologue and no methyltransferases , the mechanism of DNA cleavage remains unknown ., Both mutS and mutL have been annotated to the TPA genome ., The second mechanism is based on gene conversion events following internalization of dsDNA ., Gene conversion is a common mechanism for producing antigenic variability in TPA 26 ., Since TPA possesses only the RecF recombination pathway , gene conversion in TPA is likely to follow the successive half crossing-over model 27 , as shown in Fig . 4 ., However , the mosaic structure observed at the TPAMA_0326 and TPAMA_0488 loci would require multiple successive gene conversion events in both loci , which is unlikely ., One possible explanation would presume a partial mosaic structure ( Fig . 4 ) in both loci in the TPE donor DNA prior to crossing-over ., Assuming this , the observed mosaic sequence at the TPAMA_0326 and TPAMA_0488 loci could result from a single gene conversion/recombination event ., Alternatively , there is a possibility of active DNA uptake across the cell membrane , which is more efficient , compared to natural competence of bacteria ., Although no gene orthologs involved in natural competence have been identified in the TPA genomes , one cannot exclude this activity in one or more genes with unknown function ., Internalization of TPE ssDNA would follow the model of mismatch repair ., TPAMA_0326 and TPAMA_0488 are mosaics resulting from interchromosomal recombination/gene conversion between TPA and TPE strains , while tprC and tprD alleles are the results of intrachromosomal recombination in tprC and tprD loci 12 ., Therefore , similarities to TPE strains seen in tprC locus and TPAMA_0326 and TPAMA0488 loci arose via different mechanisms ., Except for the TPAMA_0326 and TPAMA_0488 loci , two additional nucleotide positions ( 2 out of 1 , 192 single nucleotide changes differentiating TPA and TPE strains 7; i . e . 0 . 168% ) were found in the TP0314 locus and TPAMA_0319 gene ., In these cases the Mexico A sequence was identical to the TPE sequences ., These two nucleotide differences appear to represent differences that occurred by chance ., For a single nucleotide position , the theoretical probability is 1 , 192/1 , 140 , 038*1/3 ( i . e . 0 . 035% ) , where 1/3 is the probability that a particular nucleotide would be changed into a TPE nucleotide ., Moreover , since the set of 1 , 192 single nucleotide changes that differentiate TPA and TPE strains is only based on comparisons of three TPA and three TPE strains , it is likely that the number of nucleotide positions differentiating all TPA and TPE strains will decrease with the newly reported whole genome sequences from other TPA and TPE strains ., Horizontal gene transfer ( HGT ) is an important process in bacterial evolution and the most frequently transferred genes usually bring selective advantage to the host cell ., The TPA genome contains no prophages or IS-elements 28 or plasmids 29 ., Nevertheless , the absences of modification and restriction systems together with the presence of genes for homologous recombination in TPA strains 4 appear to allow incorporation of foreign DNA molecules with subsequent integration into the chromosomal DNA ., DNA transformation is commonly used in cultivable Treponema denticola 30 and related Borrelia burgdorferi strains 31 ., Moreover , natural gene transfer among Borrelia burgdorferi has been observed 32 ., In fact , 77 ( 8 . 32% ) TPA genes were identified to be horizontally transferred by analysis of G+C contents , codon and amino acid usage , and gene position 17 ., In our analysis , we did not find DNA regions of different G+C content to be associated with regions that differentiate TPA and TPE strains 7 , nor were such associations found in tpr regions , indicating that the genome rearrangements took place before the diversification of these strains ., It is therefore likely that the diversification of TPA and TPE strains was due to an accumulation of more subtle changes ., As shown by Centurion-Lara et al . 11 , recombination mechanisms are more active during treponemal infection and gene conversion events represent important mechanisms for avoiding the host immune response ., Therefore , uptake of TPE DNA by TPA strain , during a simultaneous TPA and TPE infection of a single host , with subsequent integration into TPA chromosome , appears to be a plausible explanation ., Simultaneous infection with TPA and TPE is certainly possible during the early stages of syphilis infection ., It has been shown that experimental infection with either TPA or TPE strains did not result in complete cross-protection , which suggests differences in the pathogenesis of syphilis and yaws 33 , 34 ., Although syphilis is preferentially transmitted sexually among adults , and yaws is preferentially transmitted via direct skin contact among children , simultaneous infection in a single host cannot be ruled out ., The Haiti B strain , originally classified as a TPE strain due to having been isolated from “typical yaws lesions” in an 11-year-old child 13 , has been recently reclassified as a TPA strain 19 , 35 , 36 ., Moreover , Mexico A strain was isolated in a geographic region where both TPA and TPE infections occurred 37–39 ., Nevertheless , recombination could also take place outside Mexico ., The mosaic TPAMA_0326 protein ( Tp92 ) belongs to a relatively small group of treponemal outer membrane proteins 40 and is an ortholog of the BamA protein involved in outer membrane biogenesis 41 ., BamA protein was identified as a TPA antigen exhibiting reactivity with sera from patients with syphilis 42 , 43 , and antibodies against this protein have opsonized living treponemes 44 ., The 15 bp ( TPE-like ) deletion in the TPAMA_0326 influences the polyserine tract in a predicted large extracellular loop of TPAMA_0326 protein , which serves as a potential site for attachment to the host cells 44 ., TPAMA_0488 encodes the methyl-accepting chemotaxis protein ( Mcp2-1 ) 45 ., Mcp2-1 is strongly expressed during experimental rabbit infections 46 and elicits a humoral response 45 ., In the Mcp2-1 protein , there are 18 TPE-like changes , 8 of which are localized in the Cache domain 47 , which binds small molecules during chemotaxis ., All of these TPE-like changes cause amino acid changes , 7 non-conservative and 1 conservative ., Taken together , due to described changes in extracellular/sensoring protein domains , both proteins can exhibit different antigenic epitopes and/or ligand binding activities ., Both TPAMA_0326 and TPAMA_0488 genes are under positive selection within TPA strains , as well as between TPA and TPE strains ( genes were tested using codon-based testing by Čejková et al . 7 ) ., The recombinant TPA strain ( Mexico A ) can thus possess a selective advantage in an infected host and could provide evasion from the hosts immune system ., However , it was recently shown that β-barrel structures , including surface-exposed loops of TPAMA_0326 , where the TPE-like deletion is present , do not induce antibody response in humans 41 , 48 On the other hand , positive selection need not be driven solely by the production of antibodies and may also comprise T-cell mediated cellular response , similar to the case of TprK 49 ., In addition , positive selection operating on the periplasmic Cache domain of TPAMA_0488 , recognizing small molecules , could reflect changed tissue tropism of TPE bacteria in comparison to TPA ., Despite selective advantage in the infected host ( evasion from immune response , changed tissue tropism ) , these changes could result in the observed lower growth ability of the Mexico A strain compared to the Nichols strain under in vitro conditions 14 ., Under positive selection , such a change can still have a growth advantage relative to the selective pressure on the hosts immune system ., In summary , the mosaic character of the TPA Mexico A genome is likely the result of interstrain recombination between TPA and TPE strains during simultaneous infection in one host and similar patterns can be observed among other TP strains ., These findings suggest the importance of horizontal gene transfer in the evolution of pathogenic treponemes . | Introduction, Materials and Methods, Results, Discussion | Treponema pallidum ssp ., pallidum ( TPA ) , the causative agent of syphilis , and Treponema pallidum ssp ., pertenue ( TPE ) , the causative agent of yaws , are closely related spirochetes causing diseases with distinct clinical manifestations ., The TPA Mexico A strain was isolated in 1953 from male , with primary syphilis , living in Mexico ., Attempts to cultivate TPA Mexico A strain under in vitro conditions have revealed lower growth potential compared to other tested TPA strains ., The complete genome sequence of the TPA Mexico A strain was determined using the Illumina sequencing technique ., The genome sequence assembly was verified using the whole genome fingerprinting technique and the final sequence was annotated ., The genome size of the Mexico A strain was determined to be 1 , 140 , 038 bp with 1 , 035 predicted ORFs ., The Mexico A genome sequence was compared to the whole genome sequences of three TPA ( Nichols , SS14 and Chicago ) and three TPE ( CDC-2 , Samoa D and Gauthier ) strains ., No large rearrangements in the Mexico A genome were found and the identified nucleotide changes occurred most frequently in genes encoding putative virulence factors ., Nevertheless , the genome of the Mexico A strain , revealed two genes ( TPAMA_0326 ( tp92 ) and TPAMA_0488 ( mcp2-1 ) ) which combine TPA- and TPE- specific nucleotide sequences ., Both genes were found to be under positive selection within TPA strains and also between TPA and TPE strains ., The observed mosaic character of the TPAMA_0326 and TPAMA_0488 loci is likely a result of inter-strain recombination between TPA and TPE strains during simultaneous infection of a single host suggesting horizontal gene transfer between treponemal subspecies . | Treponema pallidum is a Gram-negative spirochete that causes diseases with distinct clinical manifestations and uses different transmission strategies ., While syphilis ( caused by subspecies pallidum ) is a worldwide venereal and congenital disease , yaws ( caused by subspecies pertenue ) is a tropical disease transmitted by direct skin contact ., Currently the genetic basis and evolution of these diseases remain unknown ., In this study , we describe a high quality whole genome sequence of T . pallidum ssp ., pallidum strain Mexico A , determined using the ?, next generation ?, sequencing technique ( Illumina ) ., Although the genome of this strain contains no large rearrangements in comparison with other treponemal genomes , we found two genes which combined sequences from both subspecies pallidum and pertenue ., The observed mosaic character of these two genes is likely a result of inter-strain recombination between pallidum and pertenue during simultaneous infection of a single host . | medicine, biology | null |
journal.pntd.0003522 | 2,015 | Time since Onset of Disease and Individual Clinical Markers Associate with Transcriptional Changes in Uncomplicated Dengue | Dengue virus ( DENV ) infection is endemic in South-East Asia and has a large impact on society , both in terms of burden of disease as in economic costs 1 ., DENV belongs to the Flaviviridae family and consists of at least four serotypes: DENV-1 , -2 , -3 and -4 ., DENV infection has been described as a triphasic disease in the 2009 WHO dengue case classification 2 ., The disease starts with the febrile phase in which all patients suffer from fever and a flu-like disease with general symptoms , such as fever , myalgia , arthralgia , headaches , and retro-orbital pain ., After 3–5 days , patients enter the critical phase of disease , characterized by resolution of fever ., The majority of patients with non-severe dengue recover in this phase , but some patients develop severe symptoms , such as shock , haemorrhage , or organ impairment , and they are classified as having severe dengue ., Typical for the development of severe disease in the critical phase of dengue is a rapid decrease in platelet count with a concomitant increase in haemo-concentration due to plasma leakage ., The critical phase usually lasts 24–48 hours , after which patients enter the recovery phase ., To investigate the underlying biological processes involved in DENV pathogenesis , several studies have applied transcriptome profiling to cohorts of dengue patients 3–5 ., Some studies report that the acute ( febrile ) phase of dengue is characterized by an increased expression of genes involved in immunity and inflammation 6 , 7 ., In this phase , transcripts involved in the innate immune response , in particular interferon induced genes and complement , are highly upregulated 6 , 7 ., Other studies report that the convalescent ( critical/recovery ) phase is characterized by increased abundance of transcripts involved in cell cycle and cell repair mechanisms 5 , 8 ., For disease severity in the acute phase of dengue , it has been shown that interferon-induced genes have a lower expression in patients with dengue shock syndrome ( DSS ) compared to patients with uncomplicated dengue 4 , 6 ., In contrast , genes induced by the activation of neutrophils showed an increased expression level in patients with DSS 3 , 9 ., This suggests that lower levels of interferon lead to impaired viral clearance and increased activation of neutrophils results in enhanced immune activation , which could both contribute to the development of severe dengue ., In this study , we investigated the biology of dengue pathogenesis over time in a cohort of dengue patients from Jakarta , Indonesia , where dengue is endemic and incidence increases during the rainy season 2 ., Using a transcriptomics approach , we studied gene expression patterns , focusing on the association with dengue-specific clinical markers over time ., We quantify the overlap of our data with results from other studies , focussing in particular on the stage of dengue disease ., Furthermore , we perform a network analysis that relates clinical parameters to gene modules , offering potential markers for disease activity ., The research ethics committee of the Faculty of Medicine , University of Indonesia in Jakarta , Indonesia , approved this study ., Patients were included after written informed consent ., If patients were younger than 18 years written informed consent was obtained from the parent and/or legal guardian ., Data and samples were anonymized with a study number ., Between March and June 2010 all patients ≥ 14 years of age with a fever onset ≤ 48 hours before presentation and a clinical suspicion of dengue were recruited in community health centers ( ‘puskesmas’ ) in Jakarta , Indonesia ., Blood was drawn and a NS1 antigen and IgM/IgG antibody rapid test ( SD Dengue Duo , Standard Diagnostics , inc , Korea ) was performed ., If tested positive for NS1 and/or IgM , patients were admitted to the Cipto Mangunkusomo Hospital in Jakarta for seven days ., The admission was only dependent on a positive outcome from the rapid test and not based on clinical disease severity ., Clinical data were recorded daily with a standard case report form ., Blood samples were collected on every other day ( including the day of admission ) , both for clinical laboratory tests and for transcriptome profiling ., At day 3 , 5 and 7 of admission ultrasound examination was performed to investigate whether patients suffered from ascites and/or pleural effusion ., In this study , day 0 refers to the day of admission unless stated otherwise ., According to the inclusion criteria , onset of disease was less than 48 hours before admission ., Patients were classified according to the 2009 WHO dengue case classification 2 ., Briefly , patients with fever and general symptoms were classified as non-severe dengue without warning signs ( WS- ) ., Patients with one of the following warning signs were classified as non-severe dengue with warning signs ( WS+ ) : abdominal pain , vomiting , minor mucosal bleeding , pleural effusion , ascites and hepatomegaly ., Patients with shock , respiratory distress , severe bleeding and/or organ impairment were classified as severe dengue ., Healthy controls were matched to age , sex and socio-economic status and recruited in the same geographical area as the study subjects ., During the 2010 dengue outbreak in Jakarta , Indonesia , 157 patients were recruited into this study ., Of these patients , 52 were admitted to the hospital with a positive dengue IgM and/or NS1 rapid test ., All four dengue serotypes were circulating during this outbreak ., Out of the 52 admitted patients , 26 patients were selected for blood transcriptome analysis based on the completeness of clinical data ( i . e . symptoms , ultrasound data , laboratory parameters ) , and the availability of samples from day 0 and day 4 of admission and a confirmed laboratory diagnosis of the rapid test for DENV infection ( see Table 1 for clinical characteristics ) ., During hospital admission , seven patients were diagnosed with WS- , eighteen with WS+ and one with severe dengue ., The patient with severe dengue displayed signs of severe haemorrhage , including melaena ., No patients developed shock , although fifteen patients did receive a large amount of IV fluid during their admission ( more than 10 litres in total and one patient even 25 liters ) , indicating that these patients were critically ill ., Interestingly , the leukocyte count was not different between day 0 of admission ( median 4070 , ( IQR: 2575–5380 ) ) and day 4 ( median: 3930 , ( IQR: 2495–5355 ) ) , although at day 0 the leukocyte count was lower in the WS+ group compared to the WS- group , but this difference was not significant ( Table 1 ) ., Fifteen age- and sex-matched healthy controls from the same geographical location in Jakarta and similar socio-economic status were also included in this study ., A maximum of three tempus tubes from each patient was included in this analysis ., We included a total of 61 tempus tubes from 26 patients ., 20 tempus tubes were collected at day 0 of admission ( i . e . , day 1–2 after onset of fever ) and 20 at day 4 ( i . e . , day 4–5 after onset of fever ) ; these time points were therefore analysed in detail ., An overview of the timepoints and disease categories is provided in S1 Table ., To obtain a global overview of the dengue transcriptome profiles , we applied principal component analysis ( PCA ) ( Fig . 1 ) ., This non-supervised analysis method finds the ‘optimal point of view’ for observing differences between the samples and depicts this as a distance in a 2-dimensional plot ., The first principle component ( PC1 ) accounts for 47% of the variance in the dataset and concurs with time since admission ., The second principle component ( PC2 ) accounts for 17% of the variance in gene expression and segregated the dengue samples from the healthy controls; together , PC1 and PC2 account for 64% of gene expression differences in the dataset ., PCA did not show any segregation of patients by disease severity according to the 2009 WHO classification ., Taken together , PCA demonstrates that time since admission has the highest impact on the dengue transcriptome profiles in our cohort ., To obtain insight into the transcriptional changes that are associated with disease severity and time since admission , we performed differential gene expression analysis and gene set analysis in dengue patient and control transcriptomes ( FDR ≤ 0 . 05 and fold change ≥ 2 ) ., We used the 2009 WHO dengue case classification system to group patients and excluded the single case with severe disease from gene expression analysis ., Combining data from all time points revealed that 161 genes were up- and 73 genes were downregulated in WS- patients compared to healthy controls ( Fig . 2A , S1 Information ) ., In WS+ dengue patients , 186 genes were up- and 100 genes were downregulated relative to healthy controls ., There is considerable overlap ( 216 genes ) of differentially expressed genes in both dengue groups , suggesting that similar biological processes are ongoing in both WS- and WS+ dengue patients ., Indeed , no genes were differentially expressed when comparing WS- to WS+ dengue patients directly ., Next , the transcriptome profiles of samples from day 0 and day 4 since admission were compared to identify genes differentially expressed over time , regardless of disease severity ( Fig . 2B , S1 Information ) ., More genes were differentially expressed in time since admission than between WS- and WS+ disease , confirming the PCA results that time since admission has the largest impact on the transcriptome ., To study dengue disease effects independently of time since admission , we restricted our analysis to transcriptome profiles from WS- , WS+ and healthy controls at day 0 and day 4 of admission ., On day 0 , many genes were differentially expressed in each of both dengue groups compared to healthy controls , but no genes were differentially expressed when the severity groups were compared to each other ( Fig . 2C ) ., At day 4 , the number of differentially expressed genes in WS- and WS+ dengue compared to healthy controls was lower than at day 0 ( Fig . 2D , S1 Information ) ., When severity groups were compared at day 4 , again no genes were differentially expressed ., Taken together , in our study , WS- and WS+ blood transcriptional profiles cannot be distinguished from each other ., Over the past few years , several studies have examined the transcriptional profile of dengue infections ., Three cross-sectional studies ( Tolfvenstam et al . , Long et al . and Loke et al . ) 6 , 7 , 18 were similar in the type of sample used ( whole blood ) and the included data on time since onset of symptoms , allowing these studies to be compared to results from our cohort ( Fig . 3A , Table 2 ) ., Tolfvenstam et al . and Long et al . have a fairly large overlap in differentially expressed genes ( Fig . 3D ) , presumably because both studies included patients early ( <72 hours ) after onset of disease ., The signatures published by Loke et al . have little overlap with those of the other studies ( 1 and 5 genes only , Loke et al . DF and DHF signatures combined ) , most likely due to the fact that patients were included at a later time point after the onset of disease ( 3–6 days after onset ) ., To compare our results to these studies , we compared the early and late general dengue signatures to those of the other studies ., 48% of differentially expressed genes in the signature from Tolfvenstam et al . and 63% of Long et al . are also part of our day 0 dengue gene signature ( collected <48 hours after onset of disease ) ( Fig . 3B ) ., On the contrary , only 1% of the genes in the DF and 7% of the genes in the DHF signature in Loke et al . were similar to our day 0 signature ( Fig . 3C ) ., In contrast , our day 4 signature showed the greatest similarity with the signatures in Loke et al . ( 68% DF , 68% DHF; Fig . 3F ) , but much less so with those from Tolfvenstam et al . and Long et al . ( 18% and 35% , respectively; Fig . 3E ) ., Our results therefore concur with all three studies and confirm that these signatures can occur within one cohort , but at different time points after onset of symptoms ., In conclusion , time since the onset of symptoms accounts for most of the transcriptome differences between mRNA profiling studies in dengue patients ., The type I interferon pathway is known to be differentially expressed in DENV infection 19 ., We investigated the interferon response during the course of infection by selecting genes from the interferon pathway ( Reactome curated pathway database , 57 genes ) and plotting their expression over time ( Fig . 4 ) ., The expression of the majority of interferon genes was highly increased on the first day of admission , but decreased rapidly after that day and continued to be low , consistent with the notion that the interferon pathway is active in the early stages of dengue infection 4 ., To functionally annotate the differences in gene expression , we performed gene set analysis using the Reactome curated pathway database 20 ., Applying the Roast algorithm 21 , we found that when compared with healthy controls , both WS- and WS+ have ‘complement’ and ‘interferon signalling’ ranked among the most highly up-regulated pathways ( Fig . 4 , S2 Information ) ., None of the pathways were differentially regulated when comparing the WS- with the WS+ patients ., In addition to severity signatures , we investigated the transcriptional changes related to time since admission in our longitudinal cohort ., By comparing day 0 dengue samples to healthy control samples , we observed an up-regulation of pathways related to innate immunity and cytokine signalling ( Fig . 4 , S2 Information ) ., When we compared Day 4 to healthy controls , a pronounced shift to cell cycle and DNA repair mechanisms was evident ., A direct comparison between day 0 and day 4 samples additionally showed up-regulation of metabolism ( Fig . 4 ) ., Taken together , we see that , initially , innate immunity and interferon is up-regulated , followed by repair mechanisms that mark the beginning of recovery from dengue ., Next , we investigated the association between the identified gene modules and clinical parameters ., To this end , we used weighted gene correlation network analysis ( WGCNA ) that organizes genes into 25 modules that are subsequently correlated to 18 clinical parameters ( Fig . 5 , S3 Information ) ., This analysis confirms that time since admission has a strong effect on the transcriptome of dengue patients and that immune-related genes dominate the early response ., Significant associations between gene modules and the clinical parameters platelet count , fibrinogen level , albumin level and volume of IV fluid per day were found ., Most modules that displayed a positive correlation with time after admission also did so with the quantity of IV fluid and the liver enzyme SGOT ., These same modules displayed a negative association with the platelet count and levels of fibrinogen and albumin ., Platelets , albumin and fibrinogen are all part of the blood compartment in which dengue targets monocytic cells to replicate 22 ., The pro-inflammatory environment due to DENV replication probably affects the expression of these markers ., This may explain the association of these markers with these gene modules ., In contrast to the above-mentioned clinical parameters , the 1997 WHO dengue case classification as well as the 2009 WHO dengue case classification showed no significant association with any of the gene modules , suggesting that these classifications do not reflect the underlying biological processes , or that there are no differences in the underlying biological processes ., The non-specific warning signs ‘abdominal pain’ and ‘vomiting’ showed no statistical significant associations , which is in line with the generic nature of these symptoms ., In contrast , DENV-specific warning signs including ‘epistaxis’ and ‘gum bleeding’ did correlate with the gene modules B-antiquewhite4 , O-brown4 , M-salmon4 and W-sienna3 ., Enriched GO terms for genes in the O-brown4 module are “cell organelles” , such as mitochondrion and cytoplasm ., Module W-sienna3 has wound healing and coagulation as enriched GO terms and module M-salmon4 is related to catabolic processes ., Ascites , which is a typical sign of plasma leakage , is significantly associated with modules G-palevioletred3 and P-yellowgreen ., These two gene modules were not associated with time , suggesting that this is a specific biological pathway ., All in all , network analysis showed that the WHO classifications couldn’t be related to specific gene modules; however , there are many significant correlations between gene modules and dengue-specific clinical parameters ., In order to identify genes that may serve as a marker of immune activation in the early phase of disease , we focused on modules with a strong negative correlation with time after admission ., These modules contain genes that are upregulated specifically in the earliest phase of disease that could represent biomarkers for disease progression , including modules M-salmon4 , R-cyan , T-lightyellow , U-orange , and X-darkorange ., By correlating genes contained in these modules with clinical parameters that mark disease severity , genes associated with can be identified ., Five genes in the module T-lightyellow showed a significant direct association with the platelet count ( Fig . 6 ) , including two that play a role in immunological processes ., The IL-18 receptor accessory protein ( IL-18RAP , Fig . 6A ) forms the receptor complex with IL-18Rα and is needed for IL-18 signalling ., Cytidine deaminase ( CDD , gene CDA ) is highly expressed by activated granulocytes and serves as a negative feedback mechanism of these cells by inhibiting the function of granulocyte-macrophage colony formation in the bone marrow 23 ( Fig . 6B ) ., KCNJ15 and G-protein coupled receptor 27 ( GPR27 ) ( Fig . 6C-D ) are both described to play a role in insulin secretion 24 , 25 ., In the module X-darkorange , the gene Tropomodulin 1 ( TMOD1 ) was directly associated with the platelet count ( Fig . 6E ) and the genes Mical2 and dematin ( gene EBP49 ) with SGOT ( Fig . 6F-G ) all play a role in actin regulation of the cell 26–28 ., Seven other genes with an inverse association with SGOT play a role in metabolic processes , including sestrin 3 ( SESN3 ) , adiponectin receptor 1 ( ADIPOR1 ) and STE20-related kinase adaptor beta ( STRADB ) ( Fig . 6H-J ) ., Sestrin 3 is required for regulation of the blood glucose levels 29 and sestrins can reduce the levels of reactive oxygen and protect cells against cell death 30 ., Adiponectin acts through the adiponectin receptor 1 , which results in increased fatty acid oxidation in the liver 31 ., Adiponectin activates serine/threonine kinase 1 via its receptor ( LKB1 ) 31 ., Interestingly , the gene STE20-related kinase adaptor beta was also significantly associated with the SGOT and is part of a complex involved in the activation of LKB-1 ., LKB-1 is important in maintaining cell polarity of hepatocytes , which is essential in the formation and maintenance of the bile canalicular network 32 ., In summary , we find genes that correlate with clinical parameters and that can be related to either dengue pathogenesis or tissue physiology , suggesting that these genes may be directly associated with the ongoing biological processes in dengue infection ., In this study , we examined DENV infected patients with uncomplicated disease using transcriptome profiling ., By using a variety of analysis techniques , we show that time since admission , which is a proxy for time since onset of disease , is the most important determinant of the blood transcriptome profile changes in DENV infected patients ., Regardless of the analysis techniques used , we did not observe differences in blood transcriptional profiles between WS- and WS+ patients ., Conversely , the clinical parameters platelet count , albumin , fibrinogen , SGOT , SGPT and volume of IV fluid administered showed a highly significant association with specific gene modules ., Gene module expression may serve as a novel marker to monitor the biological processes involved in dengue pathogenesis ., A recurring theme in our results is that time after the onset of disease is the main determinant of transcriptome profile changes in DENV infected patients ., This observation is in line with the results reported by Sun et al . , although PBMCs were used in that study 5 ., Moreover , the comparison of our data to signatures published by Loke et al . , Long et al . and Tolfvenstam et al . show that stratifying the expression data by time after onset of disease results in a large overlap with these gene expression profiles ., These studies performed transcriptome profiling in unrelated cohorts from populations of patients with diverse genetic backgrounds , geographical locations and age distributions , demonstrating that the impact of time since onset of disease on gene expression is a general and robust feature of DENV infection ., In contrast to the strong signal related to time since onset of disease , there was no detectable transcriptional difference between WS- and WS+ patients ., Earlier studies showed that gene expression patterns from patients with DSS did segregate from those of DF and DHF patients 9 , 18 ., This is the first time that the 2009 WHO classification was used in transcriptome analysis , but our results extend other studies showing that no clear distinction can be made between the transcriptome profile of DF and DHF samples 9 , 18 , 33 ., This conclusion is based on differential gene expression and co-expression network analysis of dengue transcriptome data that both take all available genes into account; it is therefore unlikely that this is due to a gene inclusion bias during the analysis phase ., The fact that no differentially expressed genes could be identified in comparisons between WS- and WS+ dengue suggests that the biological processes in these two disease entities are very similar or that the generated blood transcriptome profiles do not accurately reflect disease processes in other parts of the body ., The latter is not expected given that DENV infection is a systemic disease that targets monocytic cells in the blood compartment 22 ., Our network analysis showed that epistaxis , gum bleeding and ascites were associated with gene modules distinct from those that associate with markers reflecting systemic disease , such as platelet count , fibrinogen , albumin and IV-fluid , suggesting that these markers reflect different biological processes ., The network analysis also showed that the parameters platelet count , fibrinogen , albumin and IV fluid reflect the processes of systemic immune activation and subsequent repair mechanisms in the blood ., We conclude that distinct dengue-related signatures can be identified , but that these do not concur with the comprehensive WS- and WS+ categories in dengue diagnosis ., Furthermore , if WS-/WS+ specific biomarkers could be identified , the strong effect of time upon infection on transcriptome dynamics would limit the application of such biomarkers in a clinical setting ., However , the expression level of the identified gene modules specific for biological processes relevant in dengue disease may support earlier detection of progress to severe disease and improve clinical management of dengue ., The clinical parameter platelet count has frequently been associated with DENV infection 34 and was even one of the four criteria for DHF in the 1997 WHO dengue case classification 35 ., It has been shown that children with lower platelet counts in the early phase of disease are more likely to develop DHF later on 36 ., Several hypotheses to link DENV infection with platelet depletion have been postulated , such as DENV induced bone marrow suppression 37 , complement-induced lysis of platelets through the binding of autoantibodies 38 or the binding of platelets to activated endothelial cells 39 ., Our study finds , besides an association between the platelet count and certain gene modules , a strong activation of the innate immunity and complement , which could contribute to all these three mechanisms of platelet depletion ., Furthermore , we found that expression of IL-18RAP , which is involved in the induction of IFN-γ production in NK and Th1 cells 40 , to be directly associated with the platelet count ., Fagundes et al . showed that IL-18 signalling was necessary to inhibit viral replication in DENV infected mice and that IL-18 knock-out mice showed increased virus titres and more severe disease 41 ., The occurrence of plasma leakage in dengue patients has been extensively documented ., We observe that markers of plasma leakage , including the quantity of IV-fluid supplied and the levels of albumin , were both associated with the same gene modules as platelet count ., Plasma leakage tends to correlate inversely with the platelet count 42 and it has been suggested that platelets may directly induce vascular permeability by the release of IL-1β 43 ., Albumin is strongly negatively charged , which prevents leakage from the circulation under normal conditions ., However , decreased levels of albumin have been detected in patients with DSS , suggesting that selective restriction by the endothelial barrier is impaired during DENV infection , resulting in leakage of albumin from the circulation to the tissue 44 ., In different cohorts of patients , we showed that dengue shock syndrome and a pro-inflammatory cytokine profile were strongly associated with microbial translocation and the presence of lipopolysaccharide ( LPS ) in the blood , suggesting that plasma leakage is the result of immune activation during DENV infection 45 ., In this study , we find that the R-cyan module is strongly associated with albumin concentration and plasma leakage; the module’s contents show an association with pro-inflammatory cytokines of the interferon response , as well as upregulation of the TLR-4 pathway that detects LPS ., This result is in line with our previous observations 45 , 46 , that link pro-inflammatory cytokines profiles , microbial translocation and plasma leakage in DENV infection ., In severe dengue , dysregulation of coagulation is frequently observed ., Fibrinogen is consumed after thrombin generation and decreased levels have been detected in severe dengue 47 ., The strong association of fibrinogen with gene modules involved in immunity and inflammation suggests that activation of the coagulation cascade is associated with the strong immune response in the acute phase of DENV infection ., It has been shown that activation of the coagulation cascade can induce the production of cytokines through NF-κB activation 48 , indicating that crosstalk between coagulation and inflammation may contribute significantly to disease severity ., In our study , gene modules were enriched for metabolic and catabolic processes , kinase activity and organelles , such as mitochondria ., Loke et al . , also showed that many upregulated genes in the acute phase were involved in metabolic processes and shared between acute DF , DHF and DSS samples 18 , suggesting that a highly activated metabolic state is part of the general dengue signature ., Acute DENV infection is also characterized by extensive activation of the innate immune response , such as complement and neutrophils 5 , 7 , 49 ., Complement is suggested to be an important inhibitor of replication of Flaviviruses 50 , and was among the most highly overrepresented pathways in our study ., Similarly , studies performed in whole blood have shown that neutrophil-related genes are highly expressed in dengue expression signatures that correlated with disease severity 3 , 9 , 18 ., In our study , the neutrophil derived protein CDD associated with the platelet count , suggesting increased abundance in the acute phase of disease ., This protein could be associated with severe disease , because high levels of CDD have also been shown in patients with meningococcal sepsis 51 ., Our results indicate involvement of the liver in dengue pathogenesis ., Especially the gene modules involved in inflammation and immunity correlated with the liver enzymes SGOT and SGPT , suggesting that dengue induced inflammation affects the liver ., Moreover , ten individual genes showed a significant association with the liver enzyme SGOT ., The liver may be affected directly by viral replication or indirectly by cytokines and immune cells ., One surprising finding from our study is differential regulation of several genes that play a role in diabetes ., The genes adiponectin receptor 1 , sestrin 3 , KCNJ15 and G-protein coupled receptor 27 have all been described to play a role in insulin resistance , decreased insulin secretion and impaired blood glucose homeostasis 24 , 25 , 52 ., It has been shown that inflammation and certain cytokines in particular lead to insulin resistance , which may result in further activation of multiple inflammatory processes 53 , 54 ., In support of this finding , it has been shown that DENV infected patient with diabetes had a higher risk to develop severe disease 55 , 56 ., Altogether , the above suggests involvement of the liver in dengue pathogenesis , in particular related to insulin and blood glucose regulation during DENV infection and pathogenesis ., Future studies should aim to carefully track time since the start of infection , as time is the main source of variance in transcriptomes from dengue patients ., Since the transcriptome effects in time are larger than potential transcriptome effects between severity classes , a synchronous longitudinal cohort is an absolute requirement for any biomarker study ., Furthermore , individual symptoms and markers appear to better reflect the biological processes underlying dengue pathogenesis ., Classification on the basis of gene signatures related to specific symptoms , rather than overall diagnosis , may enable earlier identification of patient subgroups that are at increased risk of developing severe dengue , and to a better understanding of dengue disease pathogenesis . | Introduction, Methods, Results, Discussion | Dengue virus ( DENV ) infection causes viral haemorrhagic fever that is characterized by extensive activation of the immune system ., The aim of this study is to investigate the kinetics of the transcriptome signature changes during the course of disease and the association of genes in these signatures with clinical parameters ., Sequential whole blood samples from DENV infected patients in Jakarta were profiled using affymetrix microarrays , which were analysed using principal component analysis , limma , gene set analysis , and weighted gene co-expression network analysis ., We show that time since onset of disease , but not diagnosis , has a large impact on the blood transcriptome of patients with non-severe dengue ., Clinical diagnosis ( according to the WHO classification ) does not associate with differential gene expression ., Network analysis however , indicated that the clinical markers platelet count , fibrinogen , albumin , IV fluid distributed per day and liver enzymes SGOT and SGPT strongly correlate with gene modules that are enriched for genes involved in the immune response ., Overall , we see a shift in the transcriptome from immunity and inflammation to repair and recovery during the course of a DENV infection ., Time since onset of disease associates with the shift in transcriptome signatures from immunity and inflammation to cell cycle and repair mechanisms in patients with non-severe dengue ., The strong association of time with blood transcriptome changes hampers both the discovery as well as the potential application of biomarkers in dengue ., However , we identified gene expression modules that associate with key clinical parameters of dengue that reflect the systemic activity of disease during the course of infection ., The expression level of these gene modules may support earlier detection of disease progression as well as clinical management of dengue . | An acute dengue virus infection usually starts with a febrile disease phase that can progress to severe disease around the time fever abates ( defervescence ) ., Here we study dengue patients that were included very early after the onset of disease and carefully monitored in a longitudinal cohort study ., Our results show that time after the onset of disease has a major impact on the transcriptome profile of patients with dengue , which is confirmed by comparing our results with three other dengue studies that included patients at different disease stages ., There is a gradual shift in transcriptome profile from an ‘immunity & inflammation’- to a ‘repair & recovery’ phenotype ., Furthermore , the expression of gene network modules could be linked to specific clinical parameters of dengue virus infection ., Platelet counts and the levels of fibrinogen and albumin are shown to be good markers for the activity and timing of the disease , reflecting relevant biological processes in the patient ., In contrast , conventional WHO classification systems did not show any association with any of the 25 gene modules identified and only yielded a few differentially expressed genes ., The expression level of gene modules specific for certain biological processes in dengue support earlier detection of progression to severe disease and may improve clinical management . | null | null |
journal.pgen.1003220 | 2,013 | Susceptibility Loci Associated with Specific and Shared Subtypes of Lymphoid Malignancies | Lymphoid malignancies represent clonal proliferations occurring at various stages of differentiation of B and T cells ., B-cell differentiation is characterized by a canonical set of DNA modifications , including somatic hypermutation , class switching , and VDJ recombination ., If aberrant , these result in lymphoid neoplasms ranging from less differentiated acute leukemia and lymphoma , to well-differentiated plasma cell malignancies 1 ., Some genetic and environmental risk factors for lymphoma have been defined and antecedent autoimmune disorders increase risk for lymphoma several fold 2 ., Familial clustering of lymphomas has been observed and may comprise mixed phenotypes of Hodgkins lymphoma ( HD ) as well as the subsets of non-Hodgkins ( NHL ) including follicular ( FL ) , diffuse large B-cell ( DLBCL ) , and chronic lymphocytic/small lymphocytic ( CLL/SLL ) 3 ., While less common than B cell neoplasms , T cell malignancies are also part of the spectrum of familial lymphoma and may be seen alone or in combination with B cell neoplasms in kindreds with underlying immune deficiency or genomic instability 3 ., The lack of genetic linkage to specific loci in such families has prompted the search for common susceptibility variants in the germline , which may provide evidence as to the etiology of these disorders ., Genome wide association studies ( GWAS ) examining lymphoma susceptibility have focused on identifying risk loci associated with different subtypes of the disease , based on the a priori assumption that each of the subtypes have distinct biology and therefore , distinct pathogenesis ., Thus far , a locus on 6p21 . 33 , near PSOR1 , and another region at 6p21 . 32 , near HLA-DRB1 have been associated with FL 4 , 5 , 6 and Hodgkins disease 7 , 8 ., A smaller study has described CDC42BPB at 14q32 to be associated with diffuse large cell lymphoma 9 ., In order to test the paradigm that there are common and subtype specific germline susceptibility loci for lymphoma , we conducted a two-stage genome-wide association study ( GWAS ) ., Our stage-1 consisted of 944 cases of lymphoma , including 282 familial cases , and 4044 public shared controls ., Stage-2 consisted of 1245 cases and 2596 controls ., We have used a higher ratio of controls to cases to enhance power to detect association , as the use of public shared controls comes at no cost 10 ., We also analyzed published data for overlap of the GWAS hits to expression quantitative trait loci ( eQTL ) in lymphoblastoid cell lines ., Secondary analyses , such as gene set enrichment were carried out to detect enrichment of biologically relevant candidates for further study ., In stage-1 , we analyzed 944 cases of lymphoma , including 275 FL , and 4044 controls and documented strong evidence of association between SNPs on Chr6 , with at least 9 SNPs showing PFL<1×10−7 at the HLA region ( chr6:32 . 17–32 . 89 Mb ) encompassing genes TNXB to HLA-DOB ., The results of the stage-1 analysis for LYM , NHL , FL and DLBCL are shown as Manhattan plots ( Figure 1 ) and quantile-quantile ( QQ ) -plots ( Figure 2 ) ., FL showed the strongest enrichment of association signals; particularly on Chr6 ., We refrained from detailed analysis of smaller subsets , based on the power calculations performed using PGA 11 taking into account sample sizes , detectable relative risk and case to control ratios ( Figure S1 ) ., Analysis of the major classifiers LYM and NHL and only the major subgroups FL , DLBCL were performed ., In addition , a subset designated as NFD comprised any non-Hodgkins lymphoma cases that were neither FL nor DLBCL ., This subgroup was created to test if the associations in the larger LYM and NHL were driven primarily by the pre-dominant subgroups FL and DLBCL ., Among all analyses , the lowest p-values in the FL subset were observed on chromosome 6p ., The smallest p-value was for rs2621416 ( PFL\u200a=\u200a8 . 69×10−9 , OR 1 . 82 ) ( Table S1 ) followed by rs9268853 ( PFL\u200a=\u200a1 . 76×10−8 , OR\u200a=\u200a1 . 74 ) ., Imputation of the stage-1 data revealed strong associations with FL for the 6p21 . 32 SNP rs12194148 ( PFL\u200a=\u200a1 . 18×10−16 , 14 . 5 kb from rs9268853; r2\u200a=\u200a0 . 62 , D′\u200a=\u200a1 . 0 ) , suggesting a subtype specific association with the HLA locus ( Figure 3C ) ., In addition to the SNPs on chromosome 6p HLA region , we also found preliminary evidence of association of several SNPs at chromosome 3q25 . 2 with LYM , NHL and NFD ., Another locus at 11q12 . 1 was defined by two SNPs with suggestive associations ( P<10−5 ) ( Table S1 , Figure 3B ) ., Fifty SNPs were selected from stage 1 for genotyping in a larger set of 1245 lymphomas ( Table S1 ) ., After adjusting for age and Jewish ancestry , nine of 50 SNPs had P-values below the nominal alpha level of 0 . 05 , while showing the same direction of effect as observed in stage 1 ( Table S2 ) ., After adjusting for the 50 SNPs tested , rs4530903 , at the HLA locus , remained significantly associated with NHL , FL , and DLCBCL ., This SNP also appears to be associated with LYM , but the p-value was marginally higher than the Bonferroni corrected threshold ., Two other tests were significant after multiple test correction: rs707824 on chromosome 6p23 with NHL and rs12289961 on chromosome 11q12 . 1 with LYM ., Thus , two novel susceptibility loci replicated in stage 2 ., Notably , the SNPs at 11q12 . 1 also are nominally significant ( P<0 . 05 ) in the NFD subgroup , which is different from the observation for the SNPs at 6p21 . 32 ., Based on this analysis , nine of these SNPs were advanced to a meta-analysis of both stage-1 and stage-2 data ( Table 1 ) ., The major finding of this study is the observation that some regions are most strongly associated with a particular subtype of lymphoma , e . g . 6p21 . 32 in FL , while others are most strongly associated with combined types of lymphoma , e . g . the novel regions on 11q12 . 1 ., Evidence favoring a model of common susceptibility loci includes observations of familial clustering of multiple subtypes of lymphoma ., Several studies have now discovered pre-disposing genetic loci at the HLA region for FL , DLBCL , CLL and HD 4 , 5 , 6 , 7 , 8 and some of these reports highlight the existence of shared susceptibility loci at the individual subtype levels that were studied ., Etiologically , patients with HD have a higher risk of developing NHL as a secondary malignancy 14 ., Similarly , patients with NHL have a higher risk of developing HD at a later stage 15 ., At a molecular level , the model of common susceptibility pathways is supported by recent studies examining the coding sequences and genomes of non-Hodgkins lymphomas , which have demonstrated increased mutation burden in shared genes 16 , 17 ., In addition , recent tumor analysis has demonstrated that DLBCL and FL share somatic mutations in the same chromatin and histone modifying genes , MLL2 and MEF2B , respectively 16 ., Such evidence notwithstanding , a direct test of subtype-specific association would require a very large number of cases per subtype , feasible as part of a combined consortium approach ., However , as a first approximation of shared versus subtype specific susceptibilities to lymphoma , it is possible to determine if a putative locus shows heterogeneity ., For the 11q12 . 1 region shown here to be a pan-lymphoma susceptibility locus , there was no evidence of such heterogeneity within the largest subtypes ., Of the susceptibility markers reported here , the 6p21 . 32 HLA II region has been previously associated with FL and NHL 4 , 5 , 6 ., In our report , the 6p21 . 32 region was implicated by three SNPS; rs4530903 upstream from HLA-DRB1 and HLA-DQA1 , rs2621416 upstream of HLA-DQB2 , and rs9268853 downstream of HLA-DRA , HLA-DRB5 and HLA-DRB1 , but upstream of BTLN2 ., rs2621416 and rs9268853 have also been associated with risk for ulcerative colitis 18 and rheumatoid arthritis 19 respectively , both of which increases risk for certain types of lymphoma ., Allelic heterogeneity at this same locus has also been demonstrated in FL , with both protective and risk alleles described 6 ., rs2647012 , a previously reported SNP 6 is correlated ( r2\u200a=\u200a1 , D′\u200a=\u200a1 ) with rs2647046 in our results ., None of the 6p21 . 32 SNPs are correlated with rs10484561 , the HLA-associated SNP previously described 4 ., Our data support the earlier findings of allelic heterogeneity at this region , with a slightly stronger magnitude of the effect size ., The novel regions reported here include 6p23 and 11q12 . 1 , represented by SNPs mapping near genes with biologically plausible ties to lymphoid development ., The novel SNP at 6p23 , rs707824 , is upstream of JARID2 , encoding Jumonji , which co-localizes with the polycomb repressive complex 2 and H3K27me3 on chromatin and plays a role in self-renewal and differentiation of embryonic stem cells 20 ., JARID2 is regulated by miR-155 where very high levels decrease endogenous JARID2 mRNA levels 21 ., High levels of miR-155 are observed in different types of B-cell lymphomas ( DLBCL , HD and latency type III EBV-positive Burkitt lymphoma ) , and transgenic mice expressing miR155 at the late pro-B-cell stage of differentiation developed B-cell tumors ., JARID2/Jumonji-deficient mice have widespread developmental defects including abnormalities of hematopoiesis 22 ., rs707824 is located downstream of CD83 ., CD83 antigen , also known as B-cell activation protein , is expressed on dendritic cells and is thought to have roles in the modulation of antigen presentation and CD4+ T cell generation 23 ., The 11q12 . 1 region reported here was marked by two SNPs , rs948562 , located within the non-coding gene ZFP91 , and rs12289961 ., rs12289961 at 11q12 . 1 is 230 kb upstream of the LPXN ( leupaxin ) locus , originally identified binding to alpha4 integrins and playing a role in integrin-mediated cell adhesion 24 ., LPXN was found to be a member of a fusion protein with RUNX1 in human acute leukemia where wild-type LPXN was shown to transform NIH 3T3 cells 25 ., Particularly relevant to its putative role suggested here in B-cell lymphomagenesis , LPXN is preferentially expressed in hematopoietic cells and plays an inhibitory role in B-cell antigen receptor signaling and B-cell function 26 ., eQTL analysis showed that there was overlap between the most significant SNPs in the GWAS and lymphoblastoid cell lines cis-eQTL candidate genes , such as HLA-DQA2 and TAP2 ., HLA-DQA2 plays a pivotal role in the immune system by presenting peptides derived from extracellular proteins ., Gene set enrichment analysis showed interesting candidates related to lymphomagenesis and hematopoietic cell development in the top 20 significant genes ., The one variant common in all gene enrichment analyses was RELN , which has been shown to be recurrently mutated in acute lymphocytic leukemia 27 ., Based on patterns of inheritance of multiple subtypes of lymphoid neoplasms in families , as well as from the GWAS data reported here , there is evidence to suggest that multiple phenotypes of lymphoma may be associated with shared common genetic predispositions ., The candidate genes uncovered in this GWAS suggest that in addition to the genes involved in immune regulation , such as HLA and JARID2 , those involved in B-cell development ( e . g . LPXN ) are logical targets for further studies ., It is possible that the GWAS associations with multiple phenotypes reported here have resulted from the ascertainment utilized , since the study was enriched with a familial subset of samples ., However , we included only one individual from each kindred , precluding a spurious association of a single SNP with multiple phenotypes in the same family ., SNPs that show shared susceptibility , including some of those discovered here , may yet have strongest association with specific lymphoma subtypes ., While this study reports associations within combined smaller subtypes , e . g . mantle cell and marginal zone lymphoma , larger sample sizes will be required to delineate whether these and other associations are shared or subtype specific ., Thus , we have described two novel lymphoma-susceptibility regions , one at 11q12 . 1 and another putative susceptibility locus at 6p23 , and further characterized the 6p21 . 32 ( HLA class II ) association signal observed in a prior GWAS of FL ., While genetic susceptibility to lymphoma has been viewed as subtype specific , here we propose an alternate model ., Based on our analysis of the overlap between genotypes and phenotypes ( Figure S5 ) , we predict that the shared loci associated with multiple subtypes of lymphoma will be less frequent than subtype-specific susceptibilities ., Finally , the effect sizes observed in this report ( 0 . 59–1 . 93 ) are somewhat higher than those previously reported , e . g . for breast and colon cancer , but well below thresholds required for clinical utility 28 ., As in other cancer genome-wide association studies , the novel loci reported here harbor interesting genes in pathways that regulate hematopoiesis , offering potential new insights into the pathogenesis of lymphoid neoplasms ., All cases were ascertained through Memorial Sloan-Kettering Cancer Center IRB-approved protocols , or a protocol approved by the IRB at the Dana Farber Cancer Institute or Hadassah Hebrew University ( Table S4 ) ., These protocols either required informed consent for identified use of specimens for research into the genetic basis of lymphoma , or allowed research use of specimens permanently de-identified prior to genotyping ., The stage-1 of our study was comprised of 944 unrelated probands ., This ascertainment was enriched to included 282 cases of familial lymphoproliferative syndrome , defined as two or more lymphoid cancers in the same lineage ., These kindreds were characterized by mixed phenotypes of lymphoid malignancy ( Figure S6 ) , and kindreds contained from 2 to 5 affected relatives ., In addition , stage-1 contained 107 cases of lymphoma with a first degree relative affected by a lymphoid malignancy , and 347 cases of early onset ( age of diagnosis <45 years ) lymphoma ., Stage 2 was comprised of 1245 unrelated lymphoma probands from a prevalent ascertainment at MSKCC and unselected for specific histology or family history of lymphoma ., Lymphomas were categorized according to a modification of the 2008 World Health Organization classification system; primary reports were obtained in all cases and reviewed by two of the authors ( KO and AZ ) ., Because of the presence of multiple subtypes in kindreds with familial lymphoma , all subtypes of B and T cell lymphoma , including Hodgkins disease and plasma cell neoplasms were included in both stage 1 and 2 , although it was recognized that sizes of these subgroups would be too small to allow subset analysis ., The sample distribution of histologic subsets of lymphoma mirrors the prevalence of the disease subtypes in the US population ., Genotyping of the cases was performed utilizing the Affymetrix 6 . 0 SNP array ., For control data , Bipolar and GENEVA Diabetes Study ( NHS/HPFS ) data were downloaded from dbGAP ( accession phs000017 . v3 http://1 . usa . gov/xrXL1D and phs000091 . v2 http://1 . usa . gov/yevUOY ) ., Affymetrix SNP 6 . 0 CEL files were arranged according to the batches in which data were originally genotyped ., Data were initially quality checked for the gender and Mini-DM thresholds ., Only CEL files that passed a Mini-DM >85% were used in the full Birdseed 29 genotyping of the 906 , 000 SNPs ., The mean heterozygosity of each sample was computed ( 26 . 8 ) and samples with low or high heterozygosity were excluded ., Samples that passed >95% Birdseed calls were further processed to generate PLINK 30 formatted files , using only calls that had copy number state two and a confidence score >0 . 9 ., This was performed using the utility Birdsuite to PLINK from Broad Institute ., Hapmap controls were removed ., In addition , any sample that showed abnormal copy number profile states in Birdsuite were excluded ( CN0% , CN1% , CN2% , CN3% and CN4% ) ., Particular attention was paid to any samples that had the CLL/SLL phenotype in the copy number variability screen , to exclude samples with somatic mosaicism caused by circulating tumor cells ., Individuals from dbGaP marked as controls in the data-manifest were retained for further study ., Samples with genetic or cryptic relatedness were excluded by using the relationship score-matrix ( PI_HAT<0 . 1 ) in the entire dataset ., Data was filtered for multi-mapping , mitochondrial and monomorphic SNPs on the Affymetrix 6 . 0 SNP Chip ., Individuals and SNPs were filtered for 95% genotyping rate and departures from Hardy-Weinberg equilibrium 31 ., SNPs were also removed if they failed differential missing or haplotype-based differential missing tests as implemented in PLINK ., Finally , the data was matched against previously called genotyping data from dbGAP for a subset of SNPs and their allele frequencies ., Analyses were carried out on 944 cases and 4044 controls on 530 , 583 SNPs ., Principal component analysis was carried out to test for population match in both cases and controls ( Figure S7 ) ., Association was performed using case-control status with each phenotype specifically defined , along with age and the first four eigenvectors from the output of EIGENSTRAT 32 program using logistic regression ., Controls for the replication were gathered from the New York Cancer Project ( NYCP ) , which is a study of 18 , 000 New York City residents that allows researchers to better understand how factors such as environment , lifestyle , diet , family health history , and genetics affect the development of cancer and an array of other life threatening diseases ., The data include age , gender , history of cancers ( including lymphoma ) and ethnicity 33 ., All subjects consented to use of samples to study the genetics of any disease state ., Only samples with self-declared European ancestry were used for stage-2 ., Since individuals of Ashkenazi Jewish ethnicity formed a subset of both ascertainments , ethnicity was used as one of the covariates in the analysis in stage-2 ., Genotyping for stage-2 was carried out by designing multiplexed PCR using Sequenom iPLEX assays and analyzed using MassARRAY 34 ., Genotypes were called using TYPER 4 . 0 . 2 software ., The dataset ( BED , BIM , FAM ) was split to each chromosome , then subset using gtool 35 to create ., gen and ., sample files ., Imputation was done using pre-phasing and best-guess imputing using IMPUTE2 36 with the references used being 1000 genomes and Hapmap3 populations for genome build v36 ., Best practices for imputation of the data were followed The dataset ( BED , BIM , FAM ) was split to each chromosome , then subset using gtool 35 to create ., gen and ., sample files ., Imputation was done using pre-phasing and best-guess imputing using IMPUTE2 36 with the references used being 1000 genomes and Hapmap3 populations for genome build v36 ., Best practices for imputation of the data were followed 37 ., The dosage output was filtered for confidence scores and analyzed using PLINK , filtered on INFO and plotted using locuszoom 38 ., Haplotypes were viewed in Haploview 39 ., The dosage output was filtered for confidence scores and analyzed using PLINK , filtered on INFO and plotted using locuszoom 38 ., Haplotypes were viewed in Haploview 39 ., SNPs were ranked on p-value in both major types and subtype specific analyses ., Each index-ranked SNP ( within top 100 SNPs ) was graded based on a custom script used to generate scatterplots from Birdsuite , which were inspected and graded on the cluster separation and skew ., In order to prioritize the SNPs that were to be replicated , SNPs were given a negative grade if they were singletons ( i . e . neighboring SNPs not showing low p-values ) ., A positive grade was given if a given SNP showed low p-value ( P<5×10−4 ) in any other type or subtype ., Only SNPs with good scatterplots were selected for the iPLEX design ., Analysis was performed by logistic regression using the same criteria as stage-1 , however , instead of the PCA , self-reported ethnicity information was used ., Only Caucasian samples were used in the replication study ., A meta-analysis of the stage-1 and stage-2 data was performed using the results of the logistic regression ., For test of heterogeneity specifically for the 6p21 . 32 locus , the combined dataset consisting of stage-1 and stage-2 was split into three major groups namely FL , DLBCL and any other NHL subgroup designated as NFD in this report ., Since we have only one control set , the control samples were randomly assigned in a fixed ratio to match the percent cases per subset without replacement ., The three clusters were joined together to perform Breslow-Day test using PLINK ., We performed gene set enrichment analysis using the p-values from each of the subgroup and group analyses ., The program VEGAS 40 was used to compute the gene enrichment analyses ., It annotates SNPs to corresponding genes ( ±50 kb boundaries ) , produces a gene-based test statistic , and then uses simulation to calculate an empirical gene-based p-value ., The Hapmap population was used as a reference ., The top 10 percent of significant SNPs were chosen for the analysis with simulation performed 106 times ., Venn diagram was created using Venny ( http://bioinfogp . cnb . csic . es/tools/venny/index . html ) ., We analyzed available hapmap3 population data from lymphoblastoid cell lines 12 for eQTLs 12 using GENEVAR 13 ., Two types of analyses were performed , ( 1 ) identifying cis-eQTLs in candidate genes discovered from the GWAS and ( 2 ) SNP-gene association analysis ., Adjusted p-values ( Padj ) were derived from 10 , 000 permutations as implemented on the GENEVAR applet . | Introduction, Results, Discussion, Methods | The genetics of lymphoma susceptibility reflect the marked heterogeneity of diseases that comprise this broad phenotype ., However , multiple subtypes of lymphoma are observed in some families , suggesting shared pathways of genetic predisposition to these pathologically distinct entities ., Using a two-stage GWAS , we tested 530 , 583 SNPs in 944 cases of lymphoma , including 282 familial cases , and 4 , 044 public shared controls , followed by genotyping of 50 SNPs in 1 , 245 cases and 2 , 596 controls ., A novel region on 11q12 . 1 showed association with combined lymphoma ( LYM ) subtypes ., SNPs in this region included rs12289961 near LPXN , ( PLYM\u200a=\u200a3 . 89×10−8 , OR\u200a=\u200a1 . 29 ) and rs948562 ( PLYM\u200a=\u200a5 . 85×10−7 , OR\u200a=\u200a1 . 29 ) ., A SNP in a novel non-HLA region on 6p23 ( rs707824 , PNHL\u200a=\u200a5 . 72×10−7 ) was suggestive of an association conferring susceptibility to lymphoma ., Four SNPs , all in a previously reported HLA region , 6p21 . 32 , showed genome-wide significant associations with follicular lymphoma ., The most significant association with follicular lymphoma was for rs4530903 ( PFL\u200a=\u200a2 . 69×10−12 , OR\u200a=\u200a1 . 93 ) ., Three novel SNPs near the HLA locus , rs9268853 , rs2647046 , and rs2621416 , demonstrated additional variation contributing toward genetic susceptibility to FL associated with this region ., Genes implicated by GWAS were also found to be cis-eQTLs in lymphoblastoid cell lines; candidate genes in these regions have been implicated in hematopoiesis and immune function ., These results , showing novel susceptibility regions and allelic heterogeneity , point to the existence of pathways of susceptibility to both shared as well as specific subtypes of lymphoid malignancy . | B-cell lymphomas comprise several diseases representing aberrant proliferations of immune cells at various stages of maturation ., It might be expected that dissimilar subtypes of lymphoma will have different etiologic and pathogenic mechanisms , reflecting the distinct histologic and clinical characteristics of these diseases ., This study aims to define both shared as well as specific genetic risk factors for lymphoma ., Utilizing a genome-wide approach , we discovered novel locations in the genome associated with risk for lymphoid malignancies ., Common variants in these regions , on chromosome 11q12 . 1 and 6p23 , were each associated with a modest modification of risk for lymphoma ., These regions harbor several genes of biological importance in lymphoid maturation and function ., We also further characterized the HLA region at 6p21 . 32 , previously associated with lymphoma risk and thought to be important in immune function ., Some of the associated SNP markers were specific for one common subtype of lymphoma , e . g . follicular lymphoma ., However , others were associated with combined subsets of disease , suggesting that there are both shared and subtype-specific associations between common genetic variants and human lymphoid cancer ., Secondary analyses showed that the two novel regions harbor candidates that are biologically relevant and that regulate cell development and hematopoiesis . | genome-wide association studies, cancer genetics, genome scans, population genetics, genome analysis tools, trait locus analysis, population biology, genetic polymorphism, biology, genetics, genomics, genetics of disease, genetics and genomics, human genetics | null |
journal.pcbi.1005004 | 2,016 | Properties of Neurons in External Globus Pallidus Can Support Optimal Action Selection | A set of subcortical brain nuclei known as the basal ganglia are thought to be involved in action selection 1 ., The external globus pallidus ( GPe; sometimes referred to as simply the ‘globus pallidus’ in rodents ) plays an important role within the basal ganglia , in part because it is an ‘integrative hub’ that is connected to all other nuclei in these circuits 2 , 3 ., The function of the GPe within the basal ganglia has been conceptualized in many computational models 4–8 ., A class of models 9–12 suggests that , during selection of the most appropriate action , cortico-basal ganglia circuits approximate a statistical procedure known as the Multihypothesis Sequential Probability Ratio Test ( MSPRT ) 13 ., These models assume the basal ganglia continuously update the probabilities of different actions being appropriate given sensory signals , and that an action is initiated whenever its corresponding probability exceeds a threshold of confidence ., Such a procedure for making decisions has been shown analytically to yield the fastest possible choices for a given accuracy level , when the accuracy level approaches 100% 14 , and in simulations with lower accuracy , the MSPRT makes faster or equally fast choices compared to other decision algorithms 15 ., For brevity , we will refer to this property as the ‘optimal action selection’ ., The optimal action selection models 9–12 assume that the GPe together with the subthalamic nucleus ( STN ) , another basal ganglia nucleus , compute the normalization term from the equation of Bayes’ theorem ., This normalization ensures that the probabilities represented in the basal ganglia add up to 1 across all actions ., Hence the normalization computed by the STN-GPe network mediates the competition between actions by ensuring that an action is only selected when there is high evidence for it relative to the other options ( this normalization is also critical to implement the MSPRT procedure , where the actual probabilities are compared against the threshold; thus , to perform MSPRT , it is not sufficient to just know the relative probabilities , as proposed in other Bayesian models 16 ) ., The optimal action selection models have predicted that , in order to compute the normalization , the GPe needs to send feedback to the STN that is proportional to a particular function of STN activity ( we review this function in detail below ) ., However , because of the complex form of this function , it is not clear whether GPe neurons could compute it , and thus whether the basal ganglia could approximate the optimal action selection ., The goal of this paper is to refine the mapping of the Bayes’ equation on the basal ganglia anatomy by taking into account new insights into GPe cell types , and investigate whether the GPe could compute this function ., In previous models of action selection in basal ganglia , it has been widely assumed that GPe neurons are homogenous in form and function 4–6 , 9 , 11 , 12 , 17 ., However , recent work shows that the GABAergic projection neurons of the GPe can be divided into two main cell types , namely arkypallidal neurons and prototypic neurons ., These two cell types exhibit largely distinct firing rates and patterns in vivo , including divergent encoding of spontaneous movements 18 as well as selective temporal coupling with different phases of the oscillations present in the cortex of dopamine-intact and Parkinsonian rodents 19–21 ., The physiological dichotomy is mirrored by a molecular dichotomy ., Thus , arkypallidal neurons express the transcription factor forkhead box protein 2 ( FoxP2 ) , whereas prototypic neurons do not 18 , 19 ., Conversely , most prototypic neurons express the calcium-binding protein parvalbumin ( PV ) , whereas arkypallidal neurons do not 18–20 ., Equally important , arkypallidal and prototypic neurons preferentially innervate distinct sets of basal ganglia neurons 19 , 20 , which we review in more detail below ., In summary , there is now compelling support for the idea that a dichotomous functional organization , as actioned by arkypallidal and prototypic neurons with specialized physiological , molecular , and structural properties , is fundamental to the operations of the GPe ., In this paper , we extend this notion by examining how a GPe network composed of these two distinct types of neuron could compute the function required for optimal action selection ., In the next section , we review the optimal action selection model and its predictions concerning computations in GPe ., In the subsequent section , we show that the observed connectivity of arkypallidal and prototypic neurons , as well as the relationships between firing rate and injected current ( f-I curves ) for the two populations of the GPe neurons , fulfil the requirements necessary to approximate optimal action selection ., Finally , we discuss our results and consider future directions ., Let us first introduce a simple choice task , in the context of which we present the model ., Consider a rat that has to press either a lever to the left or to the right on the basis of an auditory stimulus ., On each trial , pressing only one of the levers will lead to the reward ., The auditory stimulus consists of a sequence of short intervals during which a low- or high-pitched tone is presented , which provide probabilistic information on which lever is correct on a given trial ., During trials in which pressing the left lever is rewarded , the low tone has 70% chance of occurring in each interval , while the high tone has only 30% probability of occurring ., Conversely , on trials when pressing the right lever is rewarded , the low and high tones have 30% and 70% probabilities , respectively ., Let us assume that the rat is well trained in this task ., Please note that , in this hypothetical task , in order to maximize its reward , the rat needs to listen to the stimulus , accumulate information from successive beeps , and then only makes a choice ( i . e . selects an action ) once it reaches a certain level of confidence ., Let us denote different actions available in a given context by Ak , thus in the above example , the rat has two potentially rewarded actions , A1 and A2 , corresponding to pressing the left and the right lever , respectively ., The model suggests that , during action selection , the cortico-basal-ganglia circuit is evaluating the probabilities of alternative actions being appropriate in a given context , which we denote by P ( Ak ) ., Whenever any of the probabilities exceeds a threshold of confidence during the internalized process of action selection , the corresponding action is triggered ., In the model , the probabilities of actions P ( Ak ) are updated on the basis of sensory input ., For simplicity , let us assume that the time during the action selection process is divided into discrete intervals , and during each interval a sensory input S in presented ., The sensory input S could be used to update the probabilities of action P ( Ak ) , because from past experience the animal could have learned how often S appeared on trials on which action Ak was rewarded ., Let us denote this rate of occurrence by P ( S|Ak ) ., Thus , for example , in the task described above , if the low tone is presented at the current time step , then P ( S|A1 ) = 0 . 7 and P ( S|A2 ) = 0 . 3 ., Bayes’ theorem ( see Eq 1 ) describes how to update the probabilities of actions according to the sensory input:, P ( Ak|S ) =P ( Ak ) P ( S|Ak ) P ( S ), ( 1 ), Bayes’ theorem says that in order to compute the updated or ‘posterior’ probability of action P ( Ak|S ) , one needs to multiply the previous or prior probability P ( Ak ) by the learned probability of the sensory input S appearing on trials on which action Ak was correct , i . e . P ( S|Ak ) ., For example , when the low tone is presented , then P ( A1 ) is multiplied by 0 . 7 and P ( A2 ) is multiplied by 0 . 3 ., Additionally , to ensure that the posterior probabilities add up to 1 , these products are divided by a normalization term P ( S ) equal to the sum of the products across all actions:, P ( S ) =∑k=1NP ( Ak ) P ( S|Ak ), ( 2 ), In Eq 2 , N denotes the number of available actions ., If for any action the posterior probability computed from Eq 1 exceeds a threshold of confidence , the corresponding action is chosen ., Otherwise the integration of information continues and the posterior probability P ( Ak|S ) from the current time interval becomes the prior P ( Ak ) for the next one ., Eq 1 includes multiplication and division , which are not natural operations for neurons ( as classical neural networks models rather assume that neurons add their inputs and potentially transform them through non-linear functions e . g . 22 ) , but this problem can be solved by taking the logarithm ., Recall that the logarithm has the following properties: log a·b = log a + log b , and log a/b = log a–log b ., Hence taking the logarithm of both sides of Eq 1 we get:, logP ( Ak|S ) =logP ( Ak ) +logP ( S|Ak ) −logP ( S ), ( 3 ), Thus , if in the context of neurons , they have firing rates proportional to the logarithms of probabilities , the update according to Bayes’ theorem can be performed just using addition and subtraction ., The computation of the logarithm of the normalization term becomes only slightly more complex , as it needs to include nonlinear transformations:, logP ( S ) =log∑k=1Nexp ( logP ( Ak ) +logP ( S|Ak ) ), ( 4 ), Fig 1 illustrates how Eq 3 could be mapped onto a subset of cortico-basal-ganglia-thalamic circuits 10 ., The model assumes that within the circuit there exist populations of neurons selective for different actions ( shown in different colours in Fig 1 ) ., The notion that different actions could be subserved by discrete neuronal populations within cortico-basal-ganglia-thalamic circuits is supported by anatomical data demonstrating that these circuits are composed of partially segregated ( and topographically organized ) ‘loops’ 23 ., It has been demonstrated that , for certain assumptions , log P ( S|Ak ) is proportional to the activity of the sensory neurons selective for stimuli associated with action Ak being correct 9 , 24–26 , so the term log P ( S|Ak ) could be encoded directly in the activity of cortical sensory neurons ., In the model framework in Fig 1 , the neurons in the frontal cortex add the input from sensory neurons to the logarithm of the prior probability , which is provided by a feedback from the thalamus , thus they perform the addition in Eq 3 ., The logarithm of the normalization term is computed in the model in a circuit of reciprocally connected STN and GPe neurons , and this computation is described in detail in the next subsection ., The output nuclei of the basal ganglia receive excitation from STN ( which , in the model , is proportional to log P ( S ) ) and inhibition from the cortex via the striatum ( which , in the model , is proportional to log P ( Ak ) + log P ( S|Ak ) ) , and subtract these two inputs; thus , according to Eq 3 , their activity is proportional to –log P ( Ak|S ) ., The output nuclei send inhibitory signals to the thalamus , so the activity in the thalamus is proportional to the logarithm of the posterior probability , i . e . log P ( Ak|S ) ., Finally , the logarithm of the posterior probability is sent back from the thalamus to the frontal cortex as it becomes the basis of the computation ( or prior log P ( Ak ) ) in the next time step ., The model described so far assumes that certain neural populations have activity proportional to the logarithms of probabilities , but these quantities are negative ( as probabilities are smaller than one ) ., This problem can be solved by assuming that the firing rates are proportional to the logarithms of probabilities increased by a constant c ( we discuss the required value of this constant in the Results section ) ., Eqs 5–9 below describe computations performed by each of the nuclei in the model:, SENk=logP ( S|Ak ) +c, ( 5 ), CTXk={logP ( Ak ) +c+SENkat the first intervalTHk ( t−1 ) +SENkat subsequent intervals, ( 6 ), STN=log∑k=1NexpCTXk, ( 7 ), OUTk=–CTXk+STN, ( 8 ), THk=c–OUTk, ( 9 ), In the above equations SENk , CTXk , OUTk and THk respectively denote the firing rates of populations of sensory cortical neurons , frontal cortical neurons , basal ganglia output nuclei neurons , and thalamic neurons , selective for alternative k ., At the start of the trial , the frontal cortical neurons are initialized to the logarithms of initial prior probabilities of actions , and subsequently they receive feedback equal to thalamic activity in the previous time step i . e . THk ( t–1 ) ., The STN term denotes the sum of activities across all STN neurons , while we denote the activity of STN the neurons selective for action Ak as STNk , i . e . Thus , according to Eq 8 , each neural population in the output nuclei in the model receives input from all populations in the STN , in agreement with experimental data suggesting that the axonal projections of STN neurons are relatively diffuse 27 ., We will describe in the next subsection how the activity described by Eq 7 could arise in the STN , but first let us show that the model described in Eqs 5–9 correctly updates probabilities ., At the first time-step , the activity of frontal cortical neurons is equal to ( according to Eqs 5 and 6 ) :, CTXk=logP ( Ak ) +logP ( S|Ak ) +2c, ( 11 ), According to Eqs 7 , 11 and 2 , the activity in the STN is:, STN=log∑k=1Nexp ( logP ( Ak ) +logP ( S|Ak ) +2c ) ==log∑k=1NP ( Ak ) P ( S|Ak ) exp ( 2c ) ==logP ( S ) +2c, ( 12 ), Constants 2c then cancel while computing the activity in the output nuclei ( using Eqs 8 , 11 , 12 and 3 ) :, OUTk=−logP ( Ak ) −logP ( S|Ak ) −2c+logP ( S ) +2c=−logP ( Ak|S ), ( 13 ), We have shown that at the end of the first time interval , the model computes the posterior probabilities of actions ., Since the posterior probabilities are then fed back to frontal cortical neurons as a prior for the next interval , it can be shown using analogous calculations that the network correctly computes the posterior probability in every subsequent interval ., We now describe the conditions under which the activity in STN is proportional to the logarithm of the normalization term ., Bogacz and Gurney 9 have shown that the STN-GPe circuit with the architecture shown in Fig 1 would produce activity of STN that is given in Eq 7 , if and when the neural populations in STN and GPe had the following relationships between their inputs and their firing rates:, STNk=exp ( CTXk−GPk ), ( 14 ), GPk=STN−logSTN, ( 15 ), In the above equations , GPk denotes the firing rates of GPe neurons selective for action Ak ( in the original model 9 the GPe neurons were assumed to belong to a single cell type ) ., The STN neurons receive excitation from cortex and inhibition from GPe , so their total input is CTXk−GPk , thus Eq 14 implies that the STN neurons in the model have exponential input-output relationships ( often termed ‘f-I curves’ in empirical studies ) ., The GPe neurons receive input from STN , but this input is coming in Fig 1 from STN neurons selective for all actions which we denote by STN without a subscript ( see Eq 10 ) ., Eq 15 implies that the GPe provides inhibition proportional to STN–log STN ., Before giving a mathematical proof for how the STN-GPe circuit computes Eq 7 in the model , let us first provide an intuition ., Starting from the right end of Eq 7 , the frontal cortical activity CTXk is provided to the STN in the model by the cortico-subthalamic pathway ( see Fig 1 ) ., The exponentiation is performed by the STN neurons ( cf . Eq 14 ) ., The summation is achieved due to the diffuse projections from the STN: in the model each neural population in the output nuclei receives input from all populations in the STN , hence the neurons in the output nuclei can sum the activity of STN populations ., The only non-intuitive element of the computation of Eq 7 is the logarithm–this comes from the interactions between STN and GPe , as shown below ., We now present a sequence of simple mathematical operations that show that Eqs 14 and 15 imply Eq 7 ., Substituting Eq 15 into 14 gives:, STNk=exp ( CTXk−STN+logSTN ), ( 16 ), Using the property of exponentiation ea+b = eaeb we obtain:, STNk=exp ( CTXk ) exp ( −STN ) STN, ( 17 ), Summing over k and using the definition of STN ( given in Eq 10 ) we obtain:, STN=∑k=1Nexp ( CTXk ) exp ( −STN ) STN, ( 18 ), Taking the logarithm of Eq 18 we get:, logSTN=log∑k=1NexpCTXk−STN+logSTN, ( 19 ), log STN cancel on both sides in Eq 19 , and moving STN to the left side we see that the sum of activities of all STN neural populations is equal to the required value of Eq 7:, STN=log∑k=1NexpCTXk, ( 20 ), Eqs 14 and 15 thus describe the predictions of this model on the response properties of STN and GPe neurons , respectively ., Bogacz and Gurney 9 have shown that the published f-I curves of STN neurons 28 , 29 indeed follow the exponential function precisely up to the firing rate of 135 spikes per second ( STN neurons are unlikely to fire at higher rates in vivo ) ., In the next Section , we investigate whether the properties of GPe neurons match those required to compute Eq 15 ., The model described in the previous section predicts that the GPe neurons send an inhibitory signal to the STN that is proportional to STN–log STN ., The black curve in Fig 2A illustrates the shape of this function ., It is worth clarifying that the axes in Fig 2A are expressed in the units of log of probability 25 , which are related to the units of firing rate and input current through scaling factors ( discussed later ) because the model assumes that the firing rates are proportional to the probabilistic quantities they represent ., As shown in Fig 2A , the function STN–log STN diverges to infinity for very low STN input ., However , such very low values of STN input are not biologically relevant because STN neurons are autonomously active 30 , 31; GPe will thus receive input from STN even when STN itself does not receive any organised excitatory input ., The lower bound on STN is provided by Eq 20 –please note that CTXk cannot be negative thus STN ≥ log N . The lowest possible number of choice alternatives is 2 , thus STN ≥ log 2 ≈ 0 . 7 ., The upper bound on STN is provided by Eq 12; please note that log P ( S ) cannot be positive thus STN ≤ 2c ., Because the upper bound depends on constant c we now consider its value ., Recall that constant c was added in the model to the activity of the neurons representing logarithms of probabilities to ensure their firing rates are not negative ., Nevertheless for any value of c , for sufficiently low probability p , the value of log p + c will be negative , so such low probabilities will not be represented in the model ., Thus the value of constant c determines the lowest probability of action that can be represented in the model ., It has been demonstrated that humans can represent prior probabilities of actions as low as 0 . 05 32 ., If we wish the model to represent the probability of 0 . 05 , then c needs to be around 3 , as log 0 . 05 + 3 ≈ 0 ., If we set c to 3 , then the upper bound on STN considered above becomes STN ≤ 2c = 6 ., In summary , we will consider the relevant range of STN for which GPe needs to compute STN–log STN to be from around 0 . 7 to 6 ., The black curve in Fig 2A is solid in this range , while it is dashed outside it ., In the relevant range STN ϵ ( 0 . 7 , 6 ) , function STN–log STN is non-monotonic , i . e . it initially decreases ( for STN < 1 ) and then increases ( for STN > 1 ) , so it is very unlikely that neurons with such an f-I curve would exist , and thus that this function could be computed by single neurons ., Even ignoring the non-monotonicity , which occurs on only a small part of the relevant range , the function STN–log STN is convex on its whole range , i . e . the larger the input , the larger is its rate of growth ., By contrast , the previously published f-I curves of GPe neurons were only convex in a very narrow range of small input currents , while on the majority of their range they were linear or concave , i . e . they were decreasing their rate of growth for larger inputs 33–35 ., Hence , the published data suggest that it is unlikely that individual GPe neurons , or a single type of GPe neuron , could compute even the monotonic part of function STN–log STN ., Theoretically , however , this function could be computed in a microcircuit composed of two populations of GPe neurons with distinct activities and connections ., Fig 2A shows how the function STN–log STN could be represented by a difference of two functions: a Prototype ( P ) function , shown by a blue line , and an Adjustment ( A ) function , shown by a red striped area ., Therefore , the function STN–log STN could be computed by two neural populations , ‘P’ and ‘A’ , with the connectivity shown in Fig 2B , and with f-I curves corresponding , respectively , to the blue line and the height of red striped area ( i . e . the difference between blue and black lines ) in Fig 2A ( such f-I curves are monotonic and non-convex ) ., In this architecture , both populations P and A receive input from STN ., Population A transforms this input via its non-linear f-I curve and sends inhibition to neurons P thus adjusting their response ., In this architecture only the neurons in population P have activity proportional to STN–log STN , so only they project back to STN ., GABAergic projection neurons in the GPe can be divided into two major cell types , termed prototypic and arkypallidal , on the basis of their distinct firing in vivo , molecular profiles and structure 18–20 ., These sub-populations exhibit clearly distinct connectivity within the STN-GPe network ( Fig 2C ) , as derived from empirical studies in rodents 19 , 20 and previous computational modelling of empirical data 36 ., Interestingly , this pattern of connectivity resembles that of the model computing function STN–log STN ( cf . Fig 2B ) ., In particular , only one of the GPe populations , i . e . the prototypic neurons ( but not arkypallidal neurons ) send projections back to STN 19 , 20 ., Additionally , recent computational modelling of effective connectivity in the STN-GPe network suggest that both prototypic and arkypallidal neurons receive input from STN 36 ., Thus , in summary , the observed pattern of GPe connectivity ( Fig 2C ) includes all the connections present in the model computing function STN–log STN ( Fig 2B ) , but it also includes two additional connections ( i . e . that between prototypic neurons and that from prototypic to arkypallidal neurons ) and we will address their roles below ., We investigated whether the f-I curves of GPe neurons have characteristics required for computation of function STN–log STN ., The first characteristic we expected was that f-I curves of GPe neurons should have linear or logarithmic shape , allowing them to jointly compute the function STN—log STN ., Then we sought to explore if the populations P and A introduced in our theoretical model could correspond to the prototypic and arkypallidal neurons reported experimentally ., Such a correspondence would require further two characteristics in their f-I curves ., For low levels of input from STN , the average firing rate of prototypic neurons should be higher than that of arkypallidal neurons ., This requirement arises because the “adjustment” to the firing rate of the P sub-population ( red area in Fig 2A ) is only necessary for high inputs , so one could expect arkypallidal neurons to have a firing rate closer to zero for low STN ., Furthermore , one would expect that the responses of prototypic neurons to be more linear than those of arkypallidal neurons ( as the two populations in Fig 2B compute the linear and logarithmic functions in Fig 2A ) ., The f-I curves of molecularly-identified prototypic and arkypallidal neurons have been recently measured experimentally by Abdi et al . 19 using perforated patch-clamp recordings in rat brain slices ., Experimental procedures in that study were conducted either in Oxford in accordance with the Animals ( Scientific Procedures ) Act , 1986 ( United Kingdom ) , or in Bordeaux according to institutional guidelines and the European Communities Council Directive 86/609/EEC and its successor 2010/63/EU ., During these recordings , the slices were perfused continuously with oxygenated artificial cerebrospinal fluid at 35°C–37°C ., Firing rates were recorded as a function of injected current for 18 prototypic neurons ( expressed PV but not FoxP2 ) and 18 arkypallidal neurons ( expressed FoxP2 but not PV ) ( Fig 3A ) ., In these measurements , the depolarising current injection was gradually increased in magnitude until a point at which the neuron was unable to follow ( by firing well-defined action potentials ) ., To avoid excluding any neurons from the analysis , the average f-I curve for a given type of neuron was computed for the range in which all the studied neurons of this type were able to respond ., All prototypic and arkypallidal neurons responded to currents up to 150 pA and 225 pA respectively , and hence the average f-I curves were computed up to these values ., For each value of current , the firing rate was measured over 2 s interval during which the current was injected ( Fig 3C ) ., Additionally , the rate of autonomous firing was measured ( Fig 3B ) , that is , the firing present with 0 current injection in the presence of receptor antagonists 19 ., The average f-I curves of all prototypic and all arkypallidal neurons are shown in Fig 3D , and the f-I curves of individual neurons are shown in Fig 4B ( the data is provided in S1 Table ) ., One characteristic we expected was that , for low values of excitatory input ( nominally from STN ) the activity of prototypic neurons should be higher than that of the arkypallidal neurons ., This expectation agrees with the experimental data ( see Fig 3D ) , as the average rate of the autonomous firing of prototypic neurons ( 18 . 0 spikes/s ) was higher than that of arkypallidal neurons ( 5 . 1 spikes/s ) , and this difference was highly significant ( p = 0 . 0001 , unpaired t-test ) ., To test the linear or logarithmic behaviour of GPe neurons , we compared the fitting quality of different functions to the f-I curves of each neuron ., We obtained the best fitting parameters a , b and c for different functions ( linear f = a + bI , logarithmic f = a + blogI , a combination of both functions f = a + bI + clogI , or a power function f = a ( I + b ) c ) by minimizing the root mean squared error ( RMSE ) between the actual and the predicted firing rates over the range of positive input currents for each neuron ., To help visually asses the shape of f-I curves in Fig 4B , we ordered the neurons according to a bias for linear fit , defined as a ratio of RMSE between linear and logarithmic fits ., We found that all f-I curves can be well explained by a combination of linear and logarithmic functions ( shown as solid curves in Fig 4B; average r2 for arkypallidal and prototypic neurons were 0 . 998 and 0 . 994 with standard deviations 0 . 0041 and 0 . 013 ) ., Furthermore , the f-I curves ranged from almost fully linear to clearly logarithmic ( i . e . very low values of linear coefficient b in function f = a + bI + clogI ) ., The power function could describe the f-I curves equally well ( Fig 4A ) as both a power function and a combination of linear and logarithmic functions can take a similar , concave shape ., To verify whether the diversity of the shapes of f-I curves is not an artefact stemming from differences in the recording quality of individual neurons , we computed the correlation between the bias for linear fit and the series resistances during perforated-patch recordings , which was not significant ., Contrary to our expectations , we did not find statistically significant differences in the linearity of the f-I curves of prototypic and arkypallidal neurons ( the linearity was quantified as the ratio of RMSE between linear and logarithmic fits ) ., Indeed , both populations included neurons that lied within a continuum ranging from highly linear to highly logarithmic ( Fig 4B ) ., Even though this third expected characteristic was not present , it is striking that the f-I curves of the GPe neurons range from the linear to the logarithmic , which are the two components of Eq 15 that describe the predicted computation in GPe ., Although not all prototypic neurons had linear response curves , their reciprocal connections may contribute to adjusting the shape of their response profiles ( these connections have been experimentally observed , but were not initially included in our theoretical network shown in Fig 2B ) ., We now show that the mutual inhibitory connections within the population of prototypic neurons linearize their response profile ., The intuition for this effect is provided in Fig 5A , which shows a hypothetical concave ( or saturating ) f-I curve that qualitatively resembles the average empirical response of prototypic neurons to depolarizing current injections ( Fig 3D ) ., Let us consider two cases of excitatory input currents ., A small input current I1 without the mutual inhibition would produce firing f1 ., However , with mutual inhibition , the overall input is reduced proportionally to f1 to a smaller value I1s , which gives a lower firing f1s ., Analogously , for a higher input current I2 the mutual inhibition reduces the firing from f2 to f2s ., Please note that the reduction in firing due to mutual inhibition in the case of small input , i . e . f1 –f1s , is more pronounced than for the case of large input , i . e . f2–f2s ., Since the reduction is more pronounced in the less linear part of the curve than the more linear one , the mutual inhibition linearizes the response profile ., To quantify the effect of mutual inhibition on response properties of prototypic neurons , we modelled the responses of a population of prototypic neurons receiving an external excitatory input I and inhibiting the neurons within the population ( Fig 5B ) ., Throughout the paper , we denote the strength of connections between population X and Y by wXY , where X and Y can be P for prototypic , A for arkypallidal or S for STN ., Thus , the dynamics of this population of prototypic neurons with mutual inhibition are described by the following equation:, PR˙O=fP ( I−wPPPRO ) −PRO, ( 21 ), In the above equation , function fP ( x ) is based on the average f-I curve of prototypic neurons ( see Fig 3D ) , and was defined in the following way ., If x was equal to a current used in the in vitro experiment for all prototypic neurons , fP ( x ) was simply equal to the average firing rate for this current ., If x was between two currents tested in experiment , fP ( x ) was found using linear interpolation ., If x was below the lowest current tested in experiment , fP ( x ) was set to 0 ., Finally , if x was above the range on which the average f-I curves were computed ( see above ) , fP ( x ) was set to the firing at the maximum current used with all prototypic neurons , but in all analyses below , we ensure that we do not present results relying on this value , as in this case the f-I curve is undetermined ., The last term ( –PRO ) in Eq 21 is a decay term ., The value to which the variable PRO converges can be found by setting the left hand side of Eq 21 to 0 , because at convergence , the value of PRO does not change ., Thus we find that at convergence PRO = fP ( I − wPP PRO ) ., So in this model , when wPP = 0 , the activity PRO is equal to the activity determined by the f-I curve for given input I , while when we increase wPP , we can investigate the effect of mutual inhibition on the response profile ., The activity of the prototypic neurons at convergence as a function of external input is shown in Fig 5C for different values of mutual inhibition ., As the strength of the connection between prototypic neurons ( wPP ) increas | Introduction, Model, Results, Discussion | The external globus pallidus ( GPe ) is a key nucleus within basal ganglia circuits that are thought to be involved in action selection ., A class of computational models assumes that , during action selection , the basal ganglia compute for all actions available in a given context the probabilities that they should be selected ., These models suggest that a network of GPe and subthalamic nucleus ( STN ) neurons computes the normalization term in Bayes’ equation ., In order to perform such computation , the GPe needs to send feedback to the STN equal to a particular function of the activity of STN neurons ., However , the complex form of this function makes it unlikely that individual GPe neurons , or even a single GPe cell type , could compute it ., Here , we demonstrate how this function could be computed within a network containing two types of GABAergic GPe projection neuron , so-called ‘prototypic’ and ‘arkypallidal’ neurons , that have different response properties in vivo and distinct connections ., We compare our model predictions with the experimentally-reported connectivity and input-output functions ( f-I curves ) of the two populations of GPe neurons ., We show that , together , these dichotomous cell types fulfil the requirements necessary to compute the function needed for optimal action selection ., We conclude that , by virtue of their distinct response properties and connectivities , a network of arkypallidal and prototypic GPe neurons comprises a neural substrate capable of supporting the computation of the posterior probabilities of actions . | Choosing an appropriate action as quickly and accurately as possible in a given situation is critical for the survival of animals and humans ., One of the brain regions involved in action selection is a set of subcortical nuclei known as the basal ganglia ., The importance of understanding information processing in the basal ganglia is further emphasised by the fact that their disturbed interactions in Parkinson’s disease results in profound difficulties in movement ., Computational models have suggested how the basal ganglia could select actions in the fastest possible way for the required accuracy level ., These models further predict that a part of basal ganglia , called the external globus pallidus ( GPe ) , needs to calculate a particular function of its inputs ., This paper proposes how this function could be computed in a mathematical model of a network within GPe ., Furthermore , it shows that the experimentally observed connectivity and response properties of GPe neurons fulfil the requirements necessary to support optimal action selection ., This suggests the GPe neurons have properties that allow them to contribute to optimal action selection in the whole basal ganglia . | medicine and health sciences, neurochemistry, chemical compounds, neural networks, decision making, neurodegenerative diseases, brain, neuroscience, organic compounds, hormones, cognition, amines, neurotransmitters, catecholamines, dopamine, research and analysis methods, curve fitting, computer and information sciences, mathematical functions, animal cells, basal ganglia, mathematical and statistical techniques, chemistry, neostriatum, movement disorders, biochemistry, cellular neuroscience, cell biology, anatomy, organic chemistry, neurology, neurons, parkinson disease, biogenic amines, biology and life sciences, cellular types, physical sciences, cognitive science | null |
journal.pgen.1004137 | 2,014 | Accurate and Robust Genomic Prediction of Celiac Disease Using Statistical Learning | Improving the diagnosis of celiac disease ( CD ) , a common immune-mediated illness caused by dietary gluten , remains a clinical challenge 1 , 2 ., Despite a prevalence of approximately 1% in most Western countries , lack of awareness and failure to implement appropriate serological , histological and genetic testing means that less than 30–40% of those affected by CD are diagnosed 1 , 3–5 ., Undiagnosed CD is associated with reduced quality of life , substantial morbidity , and increased mortality , however , prompt diagnosis and treatment lowers the burden of disease and may reduce the rate of complications such as osteoporosis , autoimmune disease , and malignancy ., Optimizing the diagnosis of CD is now recognized as an important goal for clinicians 6 ., CD is characterized by a variable combination of gluten-dependent clinical manifestations , CD-specific antibodies and small bowel inflammation ( villous atrophy ) 7 ., Traditional guidelines for the diagnosis of CD rely on demonstrating villous atrophy and improvement of symptoms , laboratory abnormalities , and/or small bowel inflammation upon exclusion of dietary gluten 8 ., Current clinical practice is to screen for CD by detecting CD-specific serum antibodies and then confirm the diagnosis by undertaking small bowel biopsy to demonstrate typical villous atrophy ., Serologic screening for CD with transglutaminase-IgA antibodies is reported to be highly sensitive and specific for CD ( both >90% ) , imparting a high positive predictive value ( PPV ) of over 90% when assessing most populations 9 , 10 , although the PPV can fall to 45–70% in community screening settings 11 , 12 ., In practice , serological and histological assessments have technical limitations that generate both false negative and false positive diagnoses ., A key feature of CD is its strong dependence on the presence of susceptibility genes encoding for HLA DQ2 . 5 , DQ8 , and/or half the HLA DQ2 . 5 heterodimer ( typically DQ2 . 2 ) , seen in approximately 99 . 6% of all patients with CD 13 ., These genes encode immune-recognition molecules which facilitate CD4+ T cell recognition of specific gluten-derived peptides , a critical step in disease pathogenesis 14–18 ., Recognizing the crucial role of these genes , the latest consensus diagnostic guidelines for CD recommend testing for these HLA heterodimers ( HLA typing ) as a first-line investigation for asymptomatic individuals identified at-risk of CD , such as 1st-degree relatives of an affected individual or those with suggestive symptoms 7 ., However , a major flaw of HLA typing as a diagnostic tool is that a substantial proportion of the community , typically reported to be 30–40% , express HLA DQ2 . 5 , DQ8 , and/or DQ2 . 2 , thus making the presence of these HLA types poorly predictive and of low specificity for CD 13 ., Indeed , a recent Australian population study revealed that 56% of the community possessed at least one of these CD susceptibility haplotypes 5 ., Thus , while HLA typing can exclude CD in the community with high confidence when the susceptibility haplotypes are absent , these haplotypes will be present in 30–56% of the population , the majority of whom would not have CD ., Therefore , if assessed as a stand-alone test , HLA typing has exceptionally high sensitivity and negative predictive value ( NPV ) but very poor specificity and low positive predictive value ( PPV ) for CD ., Since a positive result poorly predicts the presence of CD , HLA typing is not useful as a stand-alone diagnostic tool for CD ., While the relative-risk for CD can be stratified based on the HLA subtype ( CD risk DQ2 . 5>DQ8>DQ2 . 2 ) 19 , these categories have low positive predictive value and do not provide clinically-informative attribution of CD risk 20; HLA results are therefore interpreted as a binary outcome: CD susceptibility positive or negative ., Despite these limitations , HLA typing is now widely utilized in clinical practice and typically determined using polymerase chain-reaction sequence specific oligonucleotide ( PCR-SSO ) hybridization , which is time and labor intensive , and costly ( AU $120/sample , Medicare; in the USA cost varies but is typically US $150/sample or greater ) ., It is important to distinguish between three different approaches to analyzing the HLA region for association with CD ., The first approach , currently in clinical practice , is HLA typing , as described above , where the HLA result is considered a binary variable and its utility is to exclude CD ., A second approach , such as that taken by Romanos et al . , utilizes the same HLA-DQ haplotypes , stratifies individuals into several nominal risk levels then fits a statistical model to empirically estimate the true risk in each group 21 , 22 ., While HLA-DQ haplotypes may be inferred from typing several HLA SNPs , importantly the HLA SNPs are only used to assign the HLA type and the SNPs themselves are not directly modeled ., The third approach , such as that used here , is based on direct concurrent modeling of many thousands of individual SNPs for association with CD in order to produce a more fine-grained predictive “genomic risk score” ( GRS ) ., GRSs have been enabled by the advent of genome-wide association studies ( GWAS ) , which perform unbiased testing of many thousands of SNPs for association with CD ., Using GWAS , recent studies have identified multiple non-HLA SNP associations with CD 23 , 24 ., GWAS are primarily concerned with the detection of variants associated with disease in order to gain insight into the disease etiology and genetic architecture ., Due to the high number of significance tests , controlling for false positive associations is a major , valid concern ., Therefore , SNP-based risk scores have tended to be constructed from the SNPs found to be significantly associated with the disease status 22 , 25 ., However , due to the stringent multiple-testing corrections utilized in GWAS there may be other SNPs that fail to achieve genome-wide significance but may be predictive of disease status nonetheless and including them in the model could potentially result in higher predictive ability than achievable by models based solely on genome-wide significant SNPs ., In contrast to the GWAS approach , the main overriding aim of a GRS from a clinical perspective is to achieve maximal predictive capacity , the inference of genetic architecture is secondary ., We have recently designed computational algorithms which efficiently fit L1-penalized multivariable classification models to genome-wide and whole-genome SNP data 26 ., Such models were then shown to be preferable to several other methods such as the standard method of summing the per-SNP log odds ( polygenic score ) 27 , mixed effects linear modeling 28 , 29 , and unpenalized logistic regression , with both better precision for detecting causal SNPs in simulation and better case/control predictive power 30 ., These advantages were consistent across several complex diseases , including two British studies of CD ., However , the diagnostic implications of penalized models have not been previously examined nor has the robustness of such models in other populations or the advantage over HLA-typing approaches ., In contrast to existing studies that examine a small number of genome-wide significant SNPs , we have shown that many more SNPs ( potentially hundreds ) are required to achieve optimal predictive ability for CD ., Further , the standard GWAS approach of considering each SNP separately when estimating its effect size does not consider its correlation with other SNPs ., We have shown that unpenalized predictive models based on these top SNPs suffer from lower predictive ability than L1-penalized models since the pre-screening introduces multiple highly correlated SNPs into the model , of which a substantial proportion may be redundant in terms of contribution to the predictive ability ., Similar L1-penalized approaches have also recently been successfully applied to inflammatory bowel disease case/control Immunochip data , where models based on several hundred SNPs have led to high predictive ability 31 ., Here , we provide a proof-of-concept that the GRS for CD , induced by L1-penalized support vector machine models , are able to achieve a predictive capacity and robustness that provides information not afforded by current diagnostic pathways utilizing HLA typing alone ., This GRS has the potential to provide greater clinical diagnostic utility by enabling each individual to be assigned a more informative risk score beyond the simple designation of “CD susceptible” or “CD non-susceptible” , or “high risk” versus “low risk” ., To enable useful comparisons between diagnostic approaches , we model the GRS as a stand-alone test to “diagnose” CD , while at the same time acknowledging that real world clinical practice will need to draw upon clinical history , CD-specific serology and small bowel histology to confirm the diagnosis of CD ., We assess the predictive power of the GRS both in cross-validation and in external validation , across six different European cohorts , showing that the models strongly replicate ., We test our GRS on three other autoimmune diseases: type 1 diabetes , Crohns disease , and rheumatoid arthritis , finding some predictive ability for T1D status but none for the others , thus largely supporting the specificity of the scores for CD ., To overcome limitations of previous studies utilizing GWAS case/control studies , where ascertainment bias incurs substantially higher rates of false positive results , we undertake genomic prediction of CD in “real world” settings where the prevalence of CD is far lower and evaluate the performance of the GRS using PPV and NPV at several levels of CD prevalence ., Unlike HLA typing , the GRS allows flexibility in determining who is considered at higher risk for CD by selecting a clinically determined user-specified threshold ., We demonstrate how these scores can be practically applied at various prevalence levels to optimize sensitivity and precision ., Finally , we show how the model can be calibrated to produce accurate predicted probabilities of disease ., Within each dataset , we used 10×10 fold cross-validation to estimate the AUC and explained phenotypic variance on the liability scale ., The explained phenotypic variance was derived from the AUC assuming a population prevalence of K\u200a=\u200a1% ., All cohorts showed high AUC in cross-validation ( Figure 2a ) , with the Finnish and Italian cohorts having a maximum of 0 . 89 , followed by the UK1 cohort ( AUC\u200a=\u200a0 . 88 ) , and finally the UK2 and Dutch cohorts with a maximum AUC of 0 . 87 ., Both the UK1 and the Italian cohorts peaked at ∼64 SNPs with non-zero weights , whereas the rest peaked at ∼250 SNPs ., Subsampling of the individuals in the UK2 dataset indicated diminishing returns with 80% of the sample size having the same AUC as 100% ( Figure S1 ) ., Consistent with this , combining the UK1 and UK2 datasets did not increase AUC beyond UK2 alone ( results not shown ) ., It is also important to note that some of the control samples were population-based and were not explicitly screened for celiac disease , thus ∼1% may be cryptic CD cases which potentially underestimates prediction performance in downstream analyses ., These AUCs correspond to explained phenotypic variance of 30–35% ( Figure 2b ) ., Assuming a CD heritability of 80% , this translated to an explained genetic variance of 37–43% ., While cross-validation provides an estimate of the models ability to generalize to unseen datasets , choosing the model with the highest AUC may lead to so-called “optimization bias” ( also called “winners curse” ) 33 , 34 , potentially manifesting as lower performance in independent validation ., Additionally , cross-validation cannot compensate for intra-dataset batch effects , as these would be present in both the training and testing folds , potentially artificially inflating the apparent predictive ability ., To assess whether the models suffered from optimization bias and to control for the possibility of intra-dataset batch effects , we performed external validation ., Based on the results of the cross-validation , we selected the best models trained on the UK2 dataset then , without any further tuning , tested them on the UK1 , Finn , IT and NL datasets and computed the receiver-operating characteristic ( ROC ) curves ( Figure 3a ) ., Overall , the models trained on the UK2 cohort showed high reproducibility on the other cohorts , achieving AUC of 0 . 89–0 . 9 in the Finnish and UK1 datasets , indicating negligible optimization bias from the cross-validation procedure ., We also examined the replication of different SNP sets ( all autosomal , MHC , and non-MHC ) trained on the UK2 dataset and tested on the others ( Figure S2 ) ., The trends observed in cross-validation , namely similar performance for MHC and all autosomal SNPs and lower but still substantial performance of non-MHC SNPs , was observed in all external validation experiments ., Since HLA typing is commonly used for assessing CD risk status , we sought to compare the performance of an approach based on inferred HLA types with the GRS ., We utilized the approach of Romanos et al . 21 on the Immunochip data , which relies on both HLA types and 57 non-HLA Immunochip SNPs ( including one chrX SNP ) ., Since directly measured HLA types were not available for our datasets , we imputed HLA-DQA1 and HLA-DQB1 haplotype alleles using HIBAG 35 and derived the presence of DQ2 . 2/DQ2 . 5-homozygous/DQ2 . 5-heterozygous/DQ8 heterodimer status ., The coefficients for the HLA risk types in the HLA+57 SNP model were not available for the Romanos et al . method , thus we had to estimate these from our data ., For application of the GRS method , we trained models on 18 , 309 autosomal SNPs from UK2 ( the subset shared between the Illumina 670 and Immunochip ) then externally validated these models on the Immunochip data ., We trained three separate models: All autosomal SNPs , MHC SNPs , and autosomal non-MHC SNPs ., As seen in Figure 3b , the GRS trained on either all SNPs or the MHC SNPs yielded higher AUC ( 0 . 87 ) than the Romanos HLA+57 SNPs ( AUC\u200a=\u200a0 . 85 ) or HLA type alone ( AUC\u200a=\u200a0 . 8 ) ., The predictive power of the GRS induced by SNPs outside the MHC was lower but still substantial at AUC\u200a=\u200a0 . 72 ., We also performed similar analyses on the rest of the datasets ( UK2 in cross-validation then externally validated on the rest ) , comparing the GRS with the HLA type and with analysis of HLA tag SNPs 36 commonly used to infer HLA types since the 57 non-HLA SNPs used by Romanos were not available on these platforms ., As shown in Figure S2 , the HLA type approach had consistently lower AUC ( 0 . 795–0 . 86 ) than analysis of the individual HLA tag-SNPs of Monsuur ( AUC of 0 . 85–0 . 876 , directly modeled using logistic regression on the SNPs in the UK2 then tested on the other datasets ) and substantially lower than the GRS ( AUC of 0 . 86–0 . 894 ) ., Overall , our results showed that the L1-penalized SVM approach which modeled the SNPs directly was able to extract more information from the HLA region than the coarse-grained HLA haplotype model , either with or without the addition of the 57 non-HLA SNPs ., This resulted in a gain in explained phenotypic variance of 3 . 5% over the best Romanos et al model in the Immunochip data ., We investigated whether the models of CD were predictive of case/control status in other immune-mediated diseases , specifically type 1 diabetes ( T1D ) , rheumatoid arthritis ( RA ) , and Crohns Disease/Inflammatory Bowel Disease ( Crohns ) from the WTCCC 37 ., We utilized the 76 , 847 post-QC SNPs that appeared on both the UK2 Illumina and WTCCC Affymetrix 500K arrays ., Despite the substantial reduction in the number of SNPs from the original data , we observed only small reductions in AUC in the restricted UK2 dataset in cross-validation , indicating that most of the predictive information was retained in the reduced SNP set ( AUC\u200a=\u200a0 . 85 at ∼200 SNPs ) ., The models trained on the UK2 were subsequently tested on the T1D , RA and Crohns datasets ., We also used the Finnish CD dataset as external validation to ensure that the high predictive performance observed in cross-validation on UK2 was replicated on other CD datasets and not degraded by using fewer SNPs ., Overall , the models showed some predictive ability of T1D ( AUC\u200a=\u200a0 . 69 ) , consistent with previous findings showing shared genetics between T1D and CD 38 , 39 ( see Figure S3 for results for the MHC and non-MHC SNPs in T1D ) but displayed very low performance ( AUC 0 . 51–0 . 54 ) on the RA and Crohns datasets ., In contrast , performance on the Finnish CD cohort was only slightly lower ( AUC\u200a=\u200a0 . 85 ) compared with the full SNP set , again confirming that the CD models replicated across ethnic cohorts despite using a reduced set of SNPs ( Figure 3c ) ., All CD datasets showed consistently high AUC both in cross-validation and in external validation , indicating that the risk of substantial confounding of the case/control status by ethnic cohort ( via population stratification or strong intra-cohort batch effects ) was low ., Therefore , in order to increase statistical power in comparing the performance of the models , we created a combined dataset consisting of the Finnish , Dutch , and Italian cohorts , totaling 5158 samples ( 1943 cases and 3215 controls , 512 , 634 SNPs ) ., This combined dataset is likely more representative of a real screening scenario where individuals of different ethnicities are being screened for CD ., Figure 4a shows kernel density estimates of the predicted risk scores for cases and controls in the combined dataset , where the scores are based on models trained on the UK2 dataset as previously described ., As expected from the high AUC , there is substantial separation between the score distributions for the two classes ., Also shown is the percentage of the combined population corresponding to a range of GRS thresholds ( Figure 4b ) ., The prevalence of CD in the general population , here taken to be 1% , is much lower than the prevalence in the case/control datasets where the cases are substantially over-represented owing to the study design ., Considering the prevalence as the prior probability of a person having the disease ( without knowing their genetic profile ) , then unless the likelihood of disease given the genotype is high as well , the posterior probability of disease will remain low ., To quantify the predictive performance of our models while accounting for the prevalence , we estimated the precision of our models trained on UK2 on the Finn+NL+IT combined dataset ., We down-sampled the cases in the combined dataset to simulate settings with different CD prevalence levels ( 1% , 3% , 10% , and 20% ) and estimated precision and sensitivity in the test data , repeated in 50 independent simulations for each prevalence level ( Figure 5a ) ., The precision here is equivalent to the PPV 40 as the precision is estimated in data with the same prevalence as assumed by the PPV ., The PPV is the posterior probability of having the disease given a positive diagnosis , and the NPV is the posterior probability of not having the disease given a negative diagnosis ( a perfect model offers PPV\u200a=\u200aNPV\u200a=\u200a1 ) ., Note that the lowest NPV achievable is 1−prevalence which translates to seemingly high NPV values in the low-prevalence setting , rendering NPV less useful for assessing classifiers in such settings as even a weak classifier can achieve apparently high NPV ., Population screening for CD is not currently accepted practice ., Most evidence supports an active case-finding strategy where patients with risk-factors for CD , and therefore higher pre-test probability of CD than the population-wide average , are identified by their primary practitioner and screened ., For example , the prevalence of CD in patients with a first-degree relative with CD is 10% or higher 41 , 42 , and the prevalence of CD in patients with T1D ranges from 3–16% 43 ., The increased CD prevalence in these groups of patients improves the apparent ‘diagnostic’ performance of the GRS ., To examine the effect of prevalence on PPV , we first employed the GRS in a population-based setting ( prevalence of 1% ) which resulted in a PPV of ∼18% at a threshold that identified 20% of the CD cases , but this dropped to ∼3% at a threshold identifying 85% of the CD cases ., In contrast , performance in more clinically relevant settings with higher CD prevalence was substantially better ., For instance , the PPV increased from ∼18% at 1% prevalence to ∼40% at 3% prevalence , and to ∼70% at 10% prevalence , with the sensitivity setting at 20% ( Figure 5a ) ., At 10% prevalence , increasing the GRS sensitivity to 60% resulted in a PPV of 40% , and at a sensitivity of 80% the PPV was ∼30% ., There were small differences in the AUC between prevalence levels , on the order of 1–3% , however all settings maintained high AUC at ≥0 . 86 ( Figure 5b ) ., Since sensitivity and specificity are independent of prevalence , these differences are likely due to the small number of cases in the low-prevalence settings and stochastic variations in the data caused by randomly sampling cases from different ethnicities , as each ethnicity showed slightly different predictability of CD in external validation , together with clinical heterogeneity resulting from different numbers of cryptic cases in the controls of each cohort ., Another way to quantify the usefulness of predictive models as diagnostic tools is to evaluate the number of subjects without CD that are incorrectly identified as potential CD cases per each true CD diagnosis , and to do so at different levels of clinical risk ( prevalence ) ., This measure is equivalent to the posterior odds of not having CD given the genotypes ( 1 – PPV ) /PPV , where a lower number is better ( fewer incorrect cases implicated per true CD case ) ., Figure 6 shows that at a sensitivity threshold to detect 20% of CD cases , the odds of incorrectly implicating CD were ∼7∶1 at prevalence of 1% , but this decreased to ∼1∶2 and ∼1∶5 at a prevalence of 10% and 20% , respectively ., Further , at 10% CD prevalence the odds of incorrectly implicating CD were less than 1∶1 while maintaining sensitivity of more than 30% , and for 20% CD prevalence up to 80% of true CD cases could be detected with such odds ., The application of the GRS is straightforward: once the SNPs in the model have been genotyped for a given patient , the GRS can be easily computed as the sum of the SNPs weights times the allele dosages plus an intercept term ( Methods and Table S1 ) ., Our models consist of ∼200 SNPs , hence the score can be easily computed in a spreadsheet or with PLINK ., Whereas the models are fixed in the training phase , the interpretation of the scores depends on the screening setting in which they are used since selection of different risk thresholds leads to different false positive and false negative rates ., In other words , the same numerical risk score may be interpreted differently in each setting , depending on the performance criteria required by the clinician , such as a minimum level of sensitivity or a maximum number of non-CD implicated per true CD implicated ., Figure 7 illustrates how the GRS could be applied in two commonly encountered but different clinical settings to, ( i ) exclude individuals at average ( background ) risk of CD with high confidence , or to, ( ii ) stratify individuals at higher risk of CD for further confirmatory testing ., In the first setting , in order to optimize the NPV , a suitably low GRS threshold is selected , leading to a relatively large proportion of the population being considered as potentially at-risk of CD ., An NPV of 99 . 6% ( comparable to HLA testing ) can be achieved at the population-wide 1% prevalence by setting a threshold corresponding to designating 15% of the population as CD cases ( PPV of 5% ) ., In the second setting , we modeled a scenario where the risk of CD is increased ( for instance in patients with suggestive symptoms or clinical conditions ) and risk stratification is sought to identify the patients most likely to benefit from further definitive investigation for CD ., The prevalence of CD in those with higher-risk symptoms is approximately 3% 3 , 44 and in first-degree relatives of CD patients it is 10% 41 , 42 ., In this second setting , we highlight two extreme choices of threshold as an example of what is achievable using the GRS at each prevalence level ., The first threshold is stringent , predicting only a small number of high-confidence individuals as likely to have CD and subsequently leading to low sensitivity but higher PPV ., The second threshold is low , implicating a larger number of individuals as likely to have CD and leading to higher sensitivity at the expense of reduced PPV ., More detailed results for a range of prevalence levels are shown in Table S2 ., These consider different cutoffs of the risk score expressed as a proportion of the population implicated for CD ., We used the proportion of the population rather than proportion of the cases ( sensitivity ) to select risk thresholds since the true number of cases is unknown and we must select how to classify a given individual based only on their score relative to the population scores estimated in our data ( Figure 4 ) ., As expected , sensitivity and specificity remain unchanged between the prevalence levels using the same risk score cutoff , however PPV , NPV and consequently the number of people incorrectly implicated as CD for each true CD case , depend strongly both on the prevalence and on the cutoff ., Therefore , at a given prevalence level a suitable risk score cutoff can be selected in order to balance the two competing requirements of increasing the number of people correctly identified as having CD per true cases ( PPV ) while maintaining an acceptable level of sensitivity ( coverage of the cases ) ., A major benefit of the GRS is its flexibility in adapting to the appropriate clinical scenario and needs of the clinician ., The PPV of the GRS can be adjusted up or down by varying the GRS cutoff and considering the acceptable level of sensitivity to detect CD ., In practice , the most clinically appropriate cut-off thresholds would ideally be determined in local populations by undertaking prospective validation studies utilizing the GRS ( See Discussion ) ., While the raw GRS cannot strictly be interpreted as the probability of disease given the genotypes , since it hasnt been normalized to be between 0 and 1 , the score can be transformed into a probability using the empirical distribution of scores in the data ( Figure 4 ) ., To assess the agreement between the predicted probability of disease and the observed probability of disease , we used calibration plots 45 that compared the predicted 5% quantiles of the risk scores , derived from models trained on the UK2 dataset and externally applied to the other datasets , with the observed probability of cases in each bin ., For a well-calibrated GRS , the proportion of cases to samples in each bin should be approximately equal to the predicted risk ., To correct for potential lack of calibration , we fitted a LOESS smooth to the calibration curve , which was then used to adjust the raw predictions into calibrated predictions ., To avoid biasing the calibration step and to assess how well it performed in independent data , we randomly split each external dataset ( Finn , IT , NL , and UK1 ) into two halves of approximately equal size ., We assessed calibration in the first half of each dataset and fitted a LOESS smooth to the calibration curve ( Figures S4a and S4c ) ., We then used the LOESS smooth to calibrate the predictions for the other half of each dataset and assessed the calibration there ( Figures S4b and S4d ) ., Since the calibration is affected by prevalence , we assessed this procedure both in the observed data ( prevalence of ∼40% ) and in a subsampled version with prevalence of ∼10% ., Overall , our calibration procedure was able to correct for a substantial amount of mis-calibration in the raw scores , even in the more challenging case of 10% prevalence ., In this study , we have sought to exploit the strong genetic basis for CD and leverage comprehensive genome-wide SNP profiles using statistical learning to improve risk stratification and the diagnosis of CD ., Our models showed excellent performance in cross-validation and were highly replicable in external validation across datasets of different ethnicities , suggesting that the genetic component is shared between these European ethnicities and that our models were able to capture a substantial proportion of it ., Importantly , even without explaining a majority of CD heritability , the models were robust and accurate , showing that it is not necessary to explain most of the heritability in order to produce a useful model ., The most frequently employed tools to diagnose CD are serology and small bowel histology , but both have limitations ., Differences in the sensitivity of antibody recognition of commercially employed CD-specific antigens such as tissue transglutaminase , deamidated gliadin peptides , and endomysial antigen , as well as the human operator performing the assay can all influence findings and affect reproducibility of serological testing 9 , 46–49 ., Serologic testing in children is reported to be less reliable before the age of 4 and up to 50% of children normalize elevated antibodies over time 50 , 51 ., While small bowel histology remains the ‘gold standard’ confirmatory test , it is dependent upon patients willing and available to undergo endoscopy , adequate sampling by the gastroenterologist , and appropriate pathological processing and interpretation 52–54 ., The frequencies of false positives and false negatives in CD serology assays vary widely and also partly depend upon what degree of histologic inflammation is considered compatible with CD 52 , 54–58 ., Notably , the accuracy of both serologic and histologic testing for CD is dependent on the ongoing consumption of gluten ., It is clear that clinically significant variability exists in serologic and histologic work-up for CD and new tools to improve the accuracy of CD diagnosis would be of benefit to clinicians ., Given the strong genetic basis for CD , genomic tools are logical and appealing because they are relatively robust and less subject to the kind of variability seen with serologic and histologic assessment , are independent of age , and do not rely on dietary intake of gluten ., A major shortcoming of clinical HLA typing for risk prediction of CD is its poor specificity ., HLA testing would result in virtually all CD cases detected but at the cost of approximately 30–56 people incorrectly implicated for each true case of CD ., A significant advantage of the GRS approach is that it can be adapted to the clinical scenario in order to maximize PPV and diagnostic accuracy ., By promoting accurate clinical stratification , the GRS could reserve invasive and more expensive confirmatory testing for those who would most likely benefit from further investigation to secure a diagnosis , and it would avoid unnecessary procedures in those who are HLA susceptible but unlikely to have CD ., This provides both clinical and economic benefits ., HLA typing does not provide the flexibility afforded by the GRS and cannot be effectively employed to identify those who would benefit from endoscopy ., For instance , if HLA typing were used as a guide for further investigations , at 10% CD prevalence it would generate over five unnecessary endoscopies per correct endoscopy and at 1% CD prevalence it would generate 30–56 unnecessary endoscopies ., Small bowel endoscopy is not a trivial undertaking – the procedure is costly ( approximately AUD $750–$1000 for the pr | Introduction, Results, Discussion, Methods | Practical application of genomic-based risk stratification to clinical diagnosis is appealing yet performance varies widely depending on the disease and genomic risk score ( GRS ) method ., Celiac disease ( CD ) , a common immune-mediated illness , is strongly genetically determined and requires specific HLA haplotypes ., HLA testing can exclude diagnosis but has low specificity , providing little information suitable for clinical risk stratification ., Using six European cohorts , we provide a proof-of-concept that statistical learning approaches which simultaneously model all SNPs can generate robust and highly accurate predictive models of CD based on genome-wide SNP profiles ., The high predictive capacity replicated both in cross-validation within each cohort ( AUC of 0 . 87–0 . 89 ) and in independent replication across cohorts ( AUC of 0 . 86–0 . 9 ) , despite differences in ethnicity ., The models explained 30–35% of disease variance and up to ∼43% of heritability ., The GRSs utility was assessed in different clinically relevant settings ., Comparable to HLA typing , the GRS can be used to identify individuals without CD with ≥99 . 6% negative predictive value however , unlike HLA typing , fine-scale stratification of individuals into categories of higher-risk for CD can identify those that would benefit from more invasive and costly definitive testing ., The GRS is flexible and its performance can be adapted to the clinical situation by adjusting the threshold cut-off ., Despite explaining a minority of disease heritability , our findings indicate a genomic risk score provides clinically relevant information to improve upon current diagnostic pathways for CD and support further studies evaluating the clinical utility of this approach in CD and other complex diseases . | Celiac disease ( CD ) is a common immune-mediated illness , affecting approximately 1% of the population in Western countries but the diagnostic process remains sub-optimal ., The development of CD is strongly dependent on specific human leukocyte antigen ( HLA ) genes , and HLA testing to identify CD susceptibility is now commonly undertaken in clinical practice ., The clinical utility of HLA typing is to exclude CD when the CD susceptibility HLA types are absent , but notably , most people who possess HLA types imparting susceptibility for CD never develop CD ., Therefore , while genetic testing in CD can overcome several limitations of the current diagnostic tools , the utility of HLA typing to identify those individuals at increased-risk of CD is limited ., Using large datasets assaying single nucleotide polymorphisms ( SNPs ) , we have developed genomic risk scores ( GRS ) based on multiple SNPs that can more accurately predict CD risk across several populations in “real world” clinical settings ., The GRS can generate predictions that optimize CD risk stratification and diagnosis , potentially reducing the number of unnecessary follow-up investigations ., The medical and economic impact of improving CD diagnosis is likely to be significant , and our findings support further studies into the role of personalized GRSs for other strongly heritable human diseases . | medicine, mathematics, gastroenterology and hepatology, personalized medicine, statistics, celiac disease, biology, genomics, biostatistics, genomic medicine, clinical genetics | null |
journal.pntd.0000301 | 2,008 | Needles in the EST Haystack: Large-Scale Identification and Analysis of Excretory-Secretory (ES) Proteins in Parasitic Nematodes Using Expressed Sequence Tags (ESTs) | Molecules secreted by a cell , often referred to excretory/secretory ( ES ) products , play pivotal biological roles across a diverse range of taxa , ranging from bacteria to mammals 1 ., ES proteins can represent 8±20% of the proteome of an organism 1 , 2 ., ES proteins include functionally diverse classes of molecules , such as cytokines , chemokines , hormones , digestive enzymes , antibodies , extracellular proteinases , morphogens , toxins and antimicrobial peptides ., Some of these proteins are known to be involved in vital biological processes , including cell adhesion , cell migration , cell-cell communication , differentiation , proliferation , morphogenesis and the regulation of immune responses 3 ., ES proteins can circulate throughout the body of an organism ( in the extracellular space ) , are localized to or released from the cell surface , making them readily accessible to drugs and/or the immune system ., These characteristics make them attractive as targets for novel therapeutics , which are currently the focus of major drug discovery research programmes 4 ., For example , knowledge of the molecular basis of secretory pathways in bacteria has facilitated the rational design of heterologous protein production pathways in biotechnology and in the development of novel antibiotics ., From a more fundamental perspective , proteins secreted by pathogens are of particular interest in relation to the pathogen-host interactions , because they are present or active at the interface between the parasite and host cells , and can regulate the host response and/or cause disease 5 , 6 ., ES proteins have long been the focus of biochemical and immunological studies of parasitic helminths , as such worms secrete biologically active mediators which can modify or customize their niche within the host , in order to evade immune attack or to regulate or stimulate a particular host response 7 , 8 , 9 , 10 ., Parasitic nematodes are responsible for a range of neglected tropical diseases , such as ancylostomatosis , necatoriasis , lymphatic filariasis , onchocerciasis , ascariasis and strongyloidiasis in humans 11 , 12 , and others can cause massive production or economic losses to farmers as well as to animal and plant industries 13 ., There have been efforts to identify and characterize ES proteins in different parasitic nematodes in various studies ., For instance , Robinson et al . 14 used a proteomic approach to identify ES glycoproteins in Trichinella spiralis , an enoplid nematode ( or trichina ) of musculature ., In another effort , Yatsuda et al . 9 undertook an analysis of ES products from Haemonchus contortus ( barbers pole worm ) , a parasite of small ruminants; these authors identified several novel and known proteins but were only able ( based on comparative analysis ) to investigate known proteins , such as serine , metallo- and aspartyl- proteases and the microsomal peptidase H11 , a vaccine candidate , previously recognised as a “hidden antigen” 15 ., The precise role of ES proteins from parasitic nematodes in mediating cellular processes is largely unknown due to the difficulty in experimentally assigning function to individual proteins 14 ., In this context , computational approaches applied to identify and annotate ES proteins have significantly complemented experimental studies of different cells , tissues , organs and organisms ., For example , in an early study , Grimmond et al . 16 developed a computational strategy to identify and functionally classify secreted proteins in the mouse , based on the presence of a cleavable signal peptide ( required for its entry into the secretory pathway ) , along with the lack of any transmembrane ( TM ) domain or intracellular localization signals , in full-length molecules ., This study was followed by the computational reconstruction of the secretome in human skeletal muscle from protein sequence data by Bortoluzzi et al . 17 ., Also , Martinez et al . 18 identified and annotated the secreted proteins involved in the early development of the kidney in the mouse from microarray ‘expression’ profiling , using computational strategies ., While expressed sequence tag ( EST ) data have been mined for many interesting functional molecules 19 , 20 , predicting ES proteins from ESTs has been relatively uncommon ., For example , Vanholme et al . 21 identified putative secreted proteins from EST data sets for the plant parasitic nematode , Heterodera schachtii ., Harcus et al . 22 investigated the signal sequences inferred from the EST data for the parasitic nematode Nippostrongylus brasiliensis , and related them to “accelerated evolution” of secreted proteins in this parasite , compared with host or non-parasitic organisms ., Ranganathan et al . 23 identified ES proteins from EST data for the bovine lungworm , Dictyocaulus viviparus , whereas Nagaraj et al . 24 identified and classified putative secreted proteins from Trichostrongylus vitrinus , a parasitic nematode of ruminants and suggested some molecules as candidates for developing novel anthelmintics or vaccines ., One of the suggested molecules , Tv-stp1 , was investigated further and functionality established 25 ., While single EST or protein data sets have been examined for the presence of secretory or ES proteins , large-scale analysis has not been conducted to date , due to the lack of effective high-throughput , computational pipelines for analysis 16 ., Recently , we designed a high-throughput EST analysis pipeline , ESTExplorer 26 to provide comprehensive DNA and protein-level annotations ., Based on earlier work 23 , 24 , ESTExplorer has been adapted to predict ES proteins with high confidence , and then provide extensive annotation , including Gene Ontologies ( GO ) , pathway mapping , protein domain identification and predict protein-protein interactions ., Our new pipeline , EST2Secretome , is a freely available web server that can directly process vast amounts of EST data or entire proteomes ., In the present study , approximately 500 , 000 ESTs , representing 39 economically important and disease-causing parasitic nematodes of humans , other animals and plants , were subjected to a comprehensive analysis and detailed annotation of inferred ES proteins using EST2Secretome , with specific reference to candidate molecules already being assessed as intervention targets ., We compared the predicted ES proteins with those inferred from the free-living nematode C . elegans , to establish whether these proteins could be nematode-specific and propose their functionality ., Also , we examined whether the ES proteins had homologues in their respective hosts ( animal , human or plant ) , as such proteins and their genes are less likely to be useful as intervention targets ., Pathway , interactome and literature-based ES protein analyses have assisted in gleaning sets of candidate molecules for future experimental studies ., The present results lay a foundation for understanding the functional complexity of ES proteins from parasitic nematodes and their interactions with other proteins ( within the nematodes ) and/or with host proteomes ., EST2Secretome ( http://EST2secretome . biolinfo . org/ ) is a comprehensive workflow system comprising carefully selected computational tools to identify and annotate ES proteins inferred from ESTs ., EST2Secretome provides a user-friendly interface and detailed online help to assist researchers in the analysis of EST data sets for ES proteins ., The workflow can be divided into three phases , with Phase I dedicated to pre-processing , assembly and conceptual translation , similar to that of ESTExplorer ( details described in Nagaraj et al . 26 ) ., In Phase II , putative ES proteins are identified based on the presence of signal sequences and the absence of transmembrane helices ., Phase III contains a comprehensive annotation layer , comprising a suite of bioinformatic tools to annotate the ES proteins inferred in Phase II ., ESTs can be submitted to Phase I for EST pre-processing , assembly and conceptual translation , followed by the identification of putative ES proteins in Phase II and annotation in Phase III ., Alternatively , instead of EST data , protein sequences may be submitted directly to Phase II to identify putative ES proteins and functionally annotate them in Phase III ., Phase I of EST2Secretome shares SeqClean , RepeatMasker and CAP3 ( contig assembly program ) programs with ESTExplorer 26 , based on the analysis presented elsewhere 20 ., The contig and singleton sequences generated by CAP3 are transferred to the program ESTScan 27 for conceptual translation into proteins , using the genetic code from the nearest organism ., EST2Secretome currently implements the genetic codes for 15 organisms , covering the most studied organisms , including human , mouse , rat , pig , dog , chicken , rice , wheat , thale cress ( Arabidopsis thaliana ) , zebrafish , fly , yeast and a free-living roundworm ( Caenorhabditis elegans ) ( Figure 1 ) ., In Phase II , putative ES proteins are identified from the protein sequences generated in Phase I , using the two programs SignalP 28 and TMHMM 29 ( Figure 1 ) ., SignalP first checks whether a signal sequence 30 is predicted both the artificial neural network and the hidden Markov model probability scores ( SignalPNN and SignalP-HMM ) , using default parameters that can be modified by experienced users ., Subsequently , all proteins with signal sequences are passed on to TMHMM 29 , a hidden Markov model-based transmembrane helix prediction program , to “filter out” of transmembrane proteins ., The subset lacking transmembrane helices is selected as ES proteins for further annotation ., Phase III is the annotation layer , comprising a suite of six computational tools for the functional annotation of ES proteins , of which the first three ( Gene Ontology using BLAST2GO , InterProScan and pathway mapping using KOBAS ) are also implemented in ESTExplorer and described elsewhere 26 ., The other three components are unique to EST2Secretome and incorporate protein BLAST searches against three different data sets derived from Wormpep 31 for locating nematode homologues , IntAct 32 for protein-protein interaction data and a non-redundant known secreted protein database ( SecProtSearch ) derived from the literature , the secreted protein database , SPD 33 and the manually curated signal peptide database , SPdb 34 ., Mapping to Wormpep gives a list of homologous proteins in C . elegans , linked to WormBase 31 ., Homologues from the IntAct database are determined using the concept of interlogs ( evolutionarily conserved interactions identified by conservation among homologous proteins in different species ) and are linked to all molecular interaction partners of homologous proteins ., EST2Secretome provides a link to the relevant interlog page at IntAct , containing all interaction partners ., The interaction data culled from these interlogs can be extrapolated to predict protein interactions of the query sequence , for validation by complementary double-stranded RNA interference ( RNAi ) , gene deletion or fluorescence-based interaction studies ., The final module compares the query sequence to a specialised data set of known secreted proteins ( SecProtSearch ) , in order to identify orthologous secreted proteins , which would provide a second level of validation for the ES protein dataset ., Phase III ( Figure, 1 ) thus allows extensive characterization and validation of ES proteins predicted by EST2Secretome ., Once an EST ( or a protein dataset ) has been submitted to EST2Secretome , a status page is accessible , for the monitoring of the progress of the analysis , at the program level ., As each selected program is completed , the status page is updated and the output from that program becomes available ., The outcome from each run is summarized , with links to output files from each selected program being listed ., When a large dataset is analysed using a workflow , it is challenging to collate the results of the analysis from multiple steps ., To address this issue , EST2Secretome provides a summary file for each ES protein , comprising the assembled contig/singleton sequence , the peptide sequence and all the annotations ( such as homologous proteins , protein domains , pathways and interaction partners ) ., The details of the EST2Secretome workflow , including the software and hardware used , are provided on the website ., A detailed tutorial , frequently asked questions ( FAQ ) and sample EST and protein datasets are available online for the effective use of EST2Secretome ., 452 , 134 ESTs ( as at 18 December 2007 ) from 39 parasitic nematodes ( 7 from human , 18 from other animals and 14 from plants , Table, 1 ) were downloaded from dbEST 19 ., ESTs from each organism were submitted to Phase I of EST2Secretome , where they were pre-processed ( SeqClean and RepeatMasker ) , aligned/clustered using CAP3 35 , with a minimum sequence overlap length “cut-off” of 30 bases and an identity threshold of 90% , for the removal of flanking vector and adapter sequences , followed by assembly ., These high quality contigs and singletons were conceptually translated using ESTScan 27 , based on a “smat” matrix , generated from available mRNA data for each organism ., When the smat file for a specific organism is not available , the nearest well-studied organism has to be selected as a reference , based on taxonomy , and its smat file is used instead ., We used data ( 25 , 481 cDNA sequences ) from C . elegans ( as it is the best studied nematode ) for the generation of the smat file ., The conceptually translated peptide data were transferred to Phase II of EST2Secretome , for the prediction of ES proteins , by sequentially running the SignalP 28 and TMHMM 29 programs ., For SignalP , the threshold values for the D-score and the Signal peptide probability were both set to 0 . 5 , based on a validation carried out for 1946 sequences of experimentally verified signal peptides from the recently updated SPdb 34 , with an accuracy of prediction of 98 . 1% ., Any protein that simultaneously fulfilled the threshold set for both the D-score and the Signal peptide probability score , was classified as a secretory-excretory ( ES ) protein ., Inferred ES proteins were then tested for the presence of transmembrane domains using the transmembrane helix and membrane topology prediction program , TMHMM 29 and sequences containing predicted transmembrane regions were eliminated to yield only those proteins that were predicted as destined for secretion ., Inferred ES proteins were annotated by selecting all of the programs in Phase III of the EST2Secretome ., Gene Ontology ( GO ) 36 terms were assigned using BLAST2GO ( v 1 . 6 . 2 ) 37 ., Sequences were then mapped to biological pathways employing the KEGG Orthology-Based Annotation System ( KOBAS ) 38 , with C . elegans data selected for the construction of background pathway maps ., The query sequences were then compared using BLASTP against Wormpep v183 ( e-value threshold of 1e-05 ) ., For each predicted ES sequence , the protein domain/family/motif was mapped using InterProScan 39 , including 13 member databases , and the results were tabulated in decreasing order of abundance ., Inferred ES protein sequence data were queried against the IntAct database ( version 1 . 7 . 0 ) 32 to retrieve all interaction partners ( e-value threshold of 1e-05 ) ., A comparison of homologues , based on BLAST scores from three different datasets , can be efficiently compared and presented visually using the program SimiTri 40 ., In the case of parasitic nematodes , we generated BLAST-indexed datasets for the host organisms ( human , other mammals or plant ) , C . elegans as the primary reference organism for nematodes and parasitic nematodes , based on NCBI protein datasets ( defined by keyword ) , followed by local processing to add or remove selected organisms ., EST2Secretome made possible the large-scale analysis and annotation of all publicly available EST data for nematodes that are parasitic in humans , other animals and plants ., In total , 452 , 134 ESTs from 39 parasitic nematodes were downloaded from dbEST 19 ., The organisms were broadly categorised on the basis of the host ( s ) they infect ( Table 1 ) with seven , 18 and 14 nematodes parasitic in humans , other animals and plants , respectively , being selected for secretome analysis ., Putative ES proteins were identified in the first two phases of EST2Secretome ( see Figure 2 ) ., Phase I pre-processing and assembly resulted in a total of 152 , 702 representative ESTs ( rESTs ) comprising 53 , 377 contigs and 99 , 326 singletons , with 152 , 702 rESTs being conceptually translated into 101 , 514 peptide sequences ., In Phase II , these conceptually translated peptide sequences were first analysed for the presence of N-terminal signal peptide , followed by the absence of transmembrane helices ., We thus identified a total of 4 , 710 putative soluble ES proteins ( 2 , 632 in animal- , 1 , 292 in plant- and 786 in human-parasitic nematodes ) ( see Table 2 ) , representing 4 . 6% of the total number of putative sequences identified ., This result is comparable with earlier single organism studies of the bovine lungworm , D . viviparus 23 , in which 85 secreted proteins were identified ( representing 5 . 0% of 1685 peptides ) and T . vitirinus 24 , in which 40 secreted proteins were identified ( representing 6 . 2% of 640 proteins ) ., We manually examined the ES protein sequence data and found that 14 of 4710 entries were low quality sequences containing predominantly long stretches of unknown amino acids ( Xs ) , as a result of repeat masking , followed by conceptual translation ., These sequences were from organisms like Meloidogyne chitwoodi and Pratylenchus vulnus which lack repeat libraries ., These 14 sequences were functionally analysed and annotated in the EST2Secretome pipeline but could not be assigned any function ., This step represents one of the challenges involved in the computational analysis of single pass reads from any organism which is not well characterized based on genomic data ., We employed EST2Secretome for the analysis of the entire proteome ( 23 , 624 sequences ) of the model free living nematode , C . elegans , in the Wormpep database ( 18th February 2008 ) ., 2 , 649 ( 11 . 2% ) sequences were predicted to be ES proteins , which is in the range of 8–20% suggested by Grimmond et al . 16 ., These results independently validated the ability of the EST2Secretome pipeline to correctly identify ES proteins , using the Phase II filtering steps ., The lower percentage of 4 . 6% ES proteins from EST data compared to 11 . 2% in C . elegans could be due the low coverage of the entire protein-coding gene set , compared to entire proteome comprising full length protein sequences in C . elegans , or to the low quality of some ESTs in public databases ., We carried out a comprehensive analysis of the 4 , 710 ES proteins predicted , using all relevant components of Phase III in EST2Secretome as well as some additional bioinformatic tools specific to nematodes ( Figure 2 ) ., Functional annotation comprised the assignment of GO terms and pathway associations using KEGG pathways; mapping protein domains/motifs , with a particular focus on nematode-specificity and identifying protein interaction partners ., Subsequently , we used comparative genomics approaches to identify orthologues in the free-living nematode C . elegans , with their associated loss-of-function RNAi phenotypes ., From database comparisons with human , other animal and plant host sequences , we predicted several ES proteins that were either absent from their host or distantly related to host homologues , which might represent potential novel drug or vaccine targets for parasite intervention ., Results of these analyses are described in the following sections ., C . elegans represents the best characterized nematode in many respects , particularly in terms of its genome , genetics , biology , physiology and biochemistry 31 , 54 , 55 ., In addition , C . elegans ( non-wild-type or loss-of-function ) RNAi phenotypes may provide indications of the relevance and function ( s ) of homologous genes in other nematodes ( of animals ) for which the complexity of an obligate parasitic life cycle and the lack of an effective in vitro culture system and/or an RNAi assay make high-throughput screening impractical 56 ., Moreover , the set of genes with RNAi loss-of-function phenotypes constitutes a pool of significant and potentially essential C . elegans genes ., The RNAi phenotype data , comprising , ∼62 , 000 entries ( on 10 January 2008 ) , is available to download through WormBase 31 ., In this study , we compared the 4 , 710 predicted ES proteins to the C . elegans proteome using BLASTP program and predicted 2 , 490 ( 52 . 8% ) homologues in C . elegans ( threshold e-value of 1e-05 ) ., From these 2 , 490 C . elegans homologues , we retrieved exclusively protein entries that had been reported with any one of the following observed strong RNAi phenotypes: Emb ( embryonic lethal , including pleiotropic defects severe early emb ) , Lvl ( larval lethal ) , Lva ( larval arrest ) , Stp ( sterile progeny ) , Ste ( maternal sterile ) and Gro ( slow growth ) ., In the present dataset ( available from Table S5 ) , 267 C . elegans homologues were identified that had one or more observed “strong” loss-of-function phenotype in RNAi; selected examples are listed in Table 6 ., The latter RNAi phenotypes were selected as they have been inferred to be essential for nematode survival or growth 56 , 57 , also representing potential drug and/or vaccine targets ., Sequence-based searches were performed to classify the ES proteins , to identify the presence or absence of putative homologues in C . elegans , and to infer nematode-specific and parasite-specific genes ., For parasitic nematodes , Parkinson et al . 40 , 58 suggested previously that it is beneficial to make simultaneous three-way comparisons ( using SimiTri ) of a specific organism or a group of organisms with homologues in C . elegans ( the ‘model nematode’ ) , other nematode species as well as the host organism ., Such an analysis provides a means for the rapid identification of genes/proteins conserved between any two datasets compared ( e . g . , between parasitic nematodes and free-living ones , or between parasitic nematode and its host ) ., In the present study , we systematically compared inferred ES protein data with those available in three relevant databases ., For the three ES protein datasets from nematodes parasitic in humans ( 786 proteins ) , animals ( 2 , 632 proteins ) or plants ( 1 , 292 proteins ) , we selected C . elegans and parasitic nematode databases as well as databases specific to the host organisms for comparative analysis ., For instance , data for parasitic nematodes of humans were matched with those of the human host , C . elegans and parasitic nematodes from other hosts ., Similarly , ES proteins predicted for nematodes parasitic in animals or plants were compared against host datasets ., Protein sequences available in the following three datasets, ( i ) C . elegans ( from Wormpep 31 ) ,, ( ii ) parasitic nematodes ( constructed locally ) and, ( iii ) respective hosts ( human , other animal and plants sequences from NCBI non-redundant protein database ) were processed ., Three-way comparison of the parasitic nematode database with homologues in C . elegans , their principal definitive host organism ( human , other animal or plant ) and the database of all available parasitic nematodes , have been presented using SimiTri 40 in Figure 4 ., In all three datasets for parasitic nematodes , inferred ES proteins congregated with parasitic nematodes rather than with C . elegans or with the host species ( lower right hand corner of each triangle , coloured in red in Figure 4 ) ., Overall , 320 ( 40 . 7% ) , 789 ( 29 . 7% ) and 581 ( 44 . 9% ) ES proteins inferred from human- , other animal- and plant-parasitic nematodes were associated exclusively with parasitic nematodes and are interpreted to be parasite-specific , based on the data currently available ., Of the homologues predicted to be nematode-specific ( along the side of the triangle connecting C . elegans and parasitic nematodes ) , 585 ( 74 . 4% ) , 1 , 511 ( 57 . 4% ) and 1 , 034 ( 80 . 0% ) of the inferred ES proteins were confined to nematodes ( based on currently available datasets ) ., Based on these comparisons , we illustrate that a significant percentage of these proteins in parasitic nematodes are either parasite- or nematode-specific and are either absent from or very divergent in sequence from molecules in their host ( s ) ., These molecules might represent candidate targets for novel anthelmintics for parasite intervention ., Importantly , their apparent specificity to parasitic nematodes or different groups within the phylum Nematoda renders them as important groups of molecules for future study , particularly in relation to the roles of these molecules in the host-parasite interplay , their involvement in inducing immune responses and disease in the host ., Based on evidence from the literature , we selected candidate molecules from parasitic nematodes which have already proven to be therapeutic or vaccine targets for scrutiny ., Such targets are either in early phases of clinical trials or have been identified as candidates following detailed experimental study ., Firstly , prominent anti-parasite vaccine candidates have been identified through the Human Hookworm Vaccine Initiative and include a family of pathogenesis-related ( PR ) proteins , such as the Ancylostoma-secreted proteins ( ASPs ) 59 ., This initiative has characterized Na-ASP-2 , a PR-1 protein , from Necator americanus 59 which is in Phase II clinical trials 60 and Ac-ASP-1 from Ancylostoma caninum which exhibits 97% identity to Na-ASP-2 61 ., Secondly , cathepsin L and Z-like cysteine proteases ( known to have been implicated in moulting and tissue remodelling in free-living and parasitic nematodes ) represent potential targets for onchocerciasis and have been studied in significant detail in Onchocerca volvulus 62 , 63 , 64 ., Also , astacin-like metalloproteases ( MTP ) was selected , as L3s of parasitic nematodes secrete MTPs that are considered critical to invasion and establishment of the parasite in the host 65 , 66 ., Astacin-like MTPs , such as MTP-1 , have been characterized mainly in Ancylostoma caninum and are secreted by infective hookworm larvae 66 , 67 ., The sequences for four such proteins were retrieved from NCBI and matched to the present ES dataset using BLASTP ., We discovered likely homologues for all of these proteins in parasitic nematodes of humans , other animals and plants ( Table 7 ) ; organisms for which there is published information on these proteins are indicated ( in bold font ) ., Based on the present analysis , we identified 12 homologues of Ancylostoma-secreted proteins ( ASPs ) ( above the threshold e-value of 1e-05 ) in the datasets in following nematodes ( Strongylida ) : Necator americanus , Ancylostoma duodenale , Ancylostoma caninum , Haemonchus contortus and Teladorsagia circumcincta ., Of these , published reports are available for only Necator americanus , Ancylostoma caninum , Haemonchus contortus and Ostertagia ostertagi 7 , 61 , 65 , 66 , while the analysis , based exclusively on available data , showed that this group of proteins ( inferred from ESTs ) occurs in the parasitic nematodes Teladorsagia circumcincta and Meloidogyne chitwoodi ., Moreover , we identified eleven cathepsin L-like cysteine proteases , nine cathepsin Z-like cysteine proteinases and eight astacin-like metalloproteases in ES protein datasets , providing novel , yet unpublished evidence for the presence of these proteins in a number of key parasitic nematodes of socio-economic importance ., In this study , based on a comprehensive , targeted analysis of almost 0 . 5 million publicly available ESTs , we have inferred and functionally annotated 4 , 710 putative ES proteins from 39 parasitic nematodes infecting humans , other animals or plants , using the EST2Secretome , a new workflow developed for the large-scale processing of EST and complete proteome data ., Furthermore , EST2Secretome has been developed as a multi-purpose , high-throughput analysis pipeline for diverse applications ., For instance , it is possible to conduct analyses of all predicted proteins containing only signal sequences by selecting only SignalP and deselecting the TMHMM option , or select only the TMHMM program to investigate transmembrane proteins ., The option to enter protein sequence data alone into the pipeline is also useful following the direct sequencing of proteins in proteomic studies ., Detailed annotations of inferred ES proteins revealed several parasite-specific ( being absent from C . elegans and the host ) and nematode-specific molecules as potential drug or vaccine candidates ., Included in this set of molecules are pathogen-related protein ( PRP ) domains and several novel , nematode-specific protein domains ., Gene Ontology ( GO ) annotations , at the level of molecular function , revealed an overwhelming representation of binding ( 63 . 4% ) and catalytic activity ( 54 . 1% ) , supporting the further biochemical , proteomic and/or functional characterization of the ES proteins inferred herein ., Predicted protein interaction data for each ES protein enables the classification of molecules as essential for parasite existence or survival , with relative potential to serve as target for parasite intervention , based on the number of primary and secondary interaction partners , as well as those interactions that are specific to parasites , rendering such “hub proteins” as potential targets for functional studies ., In order to predict which ES proteins are essential , we also categorised molecules according to “strong” loss-of-function RNAi phenotypes for corresponding homologues in C . elegans ., ES proteins homologous to these “loss-of-function” phenotypes are considered the best candidates for functional characterization , and possibly linked to the survival of the parasites ., Finally , we selected some proteins for further characterization based on their similarity to proteins currently under evaluation as vaccines or drug targets ., The present , systematic approach of inferring ES protein data from EST data sets represents a starting point for understanding the role ES proteins in parasitic nematodes and serves as a useful tool for the future study of essentially any eukaryotic organism . | Introduction, Materials and Methods, Results/Discussion | Parasitic nematodes of humans , other animals and plants continue to impose a significant public health and economic burden worldwide , due to the diseases they cause ., Promising antiparasitic drug and vaccine candidates have been discovered from excreted or secreted ( ES ) proteins released from the parasite and exposed to the immune system of the host ., Mining the entire expressed sequence tag ( EST ) data available from parasitic nematodes represents an approach to discover such ES targets ., In this study , we predicted , using EST2Secretome , a novel , high-throughput , computational workflow system , 4 , 710 ES proteins from 452 , 134 ESTs derived from 39 different species of nematodes , parasitic in animals ( including humans ) or plants ., In total , 2 , 632 , 786 , and 1 , 292 ES proteins were predicted for animal- , human- , and plant-parasitic nematodes ., Subsequently , we systematically analysed ES proteins using computational methods ., Of these 4 , 710 proteins , 2 , 490 ( 52 . 8% ) had orthologues in Caenorhabditis elegans , whereas 621 ( 13 . 8% ) appeared to be novel , currently having no significant match to any molecule available in public databases ., Of the C . elegans homologues , 267 had strong “loss-of-function” phenotypes by RNA interference ( RNAi ) in this nematode ., We could functionally classify 1 , 948 ( 41 . 3% ) sequences using the Gene Ontology ( GO ) terms , establish pathway associations for 573 ( 12 . 2% ) sequences using Kyoto Encyclopaedia of Genes and Genomes ( KEGG ) , and identify protein interaction partners for 1 , 774 ( 37 . 6% ) molecules ., We also mapped 758 ( 16 . 1% ) proteins to protein domains including the nematode-specific protein family “transthyretin-like” and “chromadorea ALT , ” considered as vaccine candidates against filariasis in humans ., We report the large-scale analysis of ES proteins inferred from EST data for a range of parasitic nematodes ., This set of ES proteins provides an inventory of known and novel members of ES proteins as a foundation for studies focused on understanding the biology of parasitic nematodes and their interactions with their hosts , as well as for the development of novel drugs or vaccines for parasite intervention and control . | Excretory-secretory ( ES ) proteins are an important class of proteins in many organisms , spanning from bacteria to human beings , and are potential drug targets for several diseases ., In this study , we first developed a software platform , EST2Secretome , comprised of carefully selected computational tools to identify and analyse ES proteins from expressed sequence tags ( ESTs ) ., By employing EST2Secretome , we analysed 4 , 710 ES proteins derived from 0 . 5 million ESTs for 39 economically important and disease-causing parasites from the phylum Nematoda ., Several known and novel ES proteins that were either parasite- or nematode-specific were discovered , focussing on those that are either absent from or very divergent from similar molecules in their animal or plant hosts ., In addition , we found many nematode-specific protein families of domains “transthyretin-like” and “chromadorea ALT , ” considered vaccine candidates for filariasis in humans ., We report numerous C . elegans homologues with loss-of-function RNAi phenotypes essential for parasite survival and therefore potential targets for parasite intervention ., Overall , by developing freely available software to analyse large-scale EST data , we enabled researchers working on parasites for neglected tropical diseases to select specific genes and/or proteins to carry out directed functional assays for demystifying the molecular complexities of host–parasite interactions in a cell . | infectious diseases/helminth infections, biochemistry/bioinformatics, computational biology/genomics, genetics and genomics, biochemistry/drug discovery | null |
journal.pgen.1006180 | 2,016 | The CaM Kinase CMK-1 Mediates a Negative Feedback Mechanism Coupling the C. elegans Glutamate Receptor GLR-1 with Its Own Transcription | Homeostatic synaptic plasticity alters synaptic strengths in order to compensate for perturbations in neuronal activity ., Homeostasis is thought to stabilize neuronal firing rates to remain within a physiological range in response to developmental changes in connectivity or alterations in synaptic strength during experience-dependent plasticity 1 , 2 ., Synaptic scaling is a form of homeostatic synaptic plasticity that has been widely studied in vitro 2–6 and in vivo after sensory deprivation in the rodent visual cortex 7–9 ., One major mechanism underlying changes in synaptic strength during synaptic scaling is the regulation of AMPA receptor ( AMPAR ) levels at synapses ., During homeostatic scaling , chronic activity-blockade or enhancement of activity results in compensatory increases or decreases , respectively , in AMPAR abundance at synapses ., These changes in synaptic AMPARs are achieved , in part , by altering the rates of receptor exo- or endocytosis 3–6 , 10–14 ., Many molecules have been implicated in regulating synaptic AMPAR levels during homeostasis 11–13 , 15–17 ., In particular , homeostatic synaptic plasticity requires calcium signaling and the CaM kinases CaMKK and CaMKIV 3 , 18–20 ., Inhibition of calcium transients or CaMK signaling phenocopies activity-blockade and leads to increases in synaptic AMPARs 19 ., Similarly , inhibition of voltage-gated calcium channels or CaMK signaling prevents scaling down of synaptic AMPARs 18 ., Homeostatic synaptic plasticity is dependent on transcription , as pharmacological inhibition of transcription prevents bidirectional synaptic scaling 18 , 19 , 21 , 22 ., Interestingly , activity-blockade results in decreased levels of activated CaMKIV in the nucleus in a transcription-independent manner 19 , suggesting that CaMKIV may translocate between the cytoplasm and nucleus during synaptic scaling to regulate transcription ., These studies suggest that nuclear CaMKIV represses synaptic scaling and the associated increase in synaptic AMPARs in response to activity-blockade , but the transcriptional targets of CaMKIV responsible for the increase in synaptic AMPARs have not been defined ., Here we investigate a compensatory feedback pathway in C . elegans where synaptic levels of the AMPAR GLR-1 are negatively coupled to glr-1 transcription via the CMK-1/CaMK signaling pathway ., In C . elegans , CMK-1 is the sole ortholog of mammalian CaMKI and CaMKIV ., As in mammals , CMK-1 is phosphorylated by CKK-1/CaMKK and can regulate CRH-1 , the C . elegans homolog of CREB 23–25 ., Recent studies in C . elegans show that CMK-1 can shuttle between the nucleus and cytoplasm to regulate temperature thresholds and experience-dependent thermotaxis under physiologic temperature and in response to noxious heat 26–28 ., While much progress has been made identifying molecules involved in homeostatic synaptic scaling in neuronal and slice cultures 13 , in vivo studies of mechanisms directly linking chronic changes in activity to regulation of AMPAR expression are lacking ., Here we use a genetic approach to identify in vivo mechanisms involved in a negative feedback pathway in C . elegans that is reminiscent of synaptic homeostasis ., We show that chronic activity-blockade or enhancement of GLR-1 function results in bidirectional changes in glr-1 transcription in vivo ., We find that regulation of glr-1 transcription in response to chronic changes in synaptic activity requires the CMK-1 signaling pathway and redistribution of CMK-1 between the nucleus and cytoplasm ., This study identifies the signaling mechanism underlying a compensatory feedback pathway that couples GLR-1 with its own transcription ., We previously found that trafficking mutants with reduced GLR-1 abundance at synapses in the ventral nerve cord ( VNC ) exhibit reciprocal increases in glr-1 mRNA levels ., Specifically , animals with mutations in the deubiquitinating enzyme USP-46 , which removes ubiquitin from GLR-1 and protects it from degradation , exhibit decreased levels of GLR-1 in the VNC and a compensatory 3 fold increase in glr-1 transcript levels as measured by real-time quantitative PCR ( RT-qPCR ) 29 ., Similarly , mutations in the kinesin motor KLP-4/KIF13 , which positively regulates GLR-1 trafficking to the VNC , result in decreased levels of GLR-1 in the VNC and a compensatory 2–3 fold increase in glr-1 transcript levels 30 ., We hypothesized that GLR-1 levels or function at synapses in the VNC are monitored and coupled via a negative feedback mechanism to glr-1 transcript levels ., To investigate the molecular mechanisms involved in this feedback pathway , we created a series of transgenic animals expressing different combinations of a nuclear-localized GFP reporter ( NLS-tagged GFP fused to LacZ ) under control of the glr-1 promoter ( Pglr-1 ) and/or the glr-1 3’ untranslated region ( UTR ) ., Pglr-1 includes 5 . 3 kilobases of sequence upstream of the transcription start site 31 and allows monitoring of transcriptional activity of the promoter ., The glr-1 3’UTR includes 100 base pairs downstream of the ORF , as predicted by modENCODE 32 , and allows us to monitor the contribution of the 3’UTR to transcript levels ., We first validated this glr-1 reporter under control of both Pglr-1 and the glr-1 3’UTR by testing if GFP fluorescence was altered in klp-4/KIF13 trafficking mutants ., Briefly , we measured the maximum fluorescence intensity of GFP in the nucleus of the GLR-1-expressing interneuron PVC in wild type and klp-4 ( tm2114 ) loss-of-function mutants ( see Materials and Methods ) ., We found that GFP fluorescence increased in klp-4 ( tm2114 ) mutants ( Fig 1A ) , consistent with our previous RT-qPCR results 30 ., Because klp-4 mutants have reduced GLR-1 at synapses in the VNC , this data implies that decreased synaptic GLR-1 may trigger a compensatory feedback pathway resulting in increased glr-1 transcript ., To directly test if loss of GLR-1 itself could trigger the feedback pathway , we measured the GFP reporter under control of Pglr-1 and the glr-1 3’UTR in glr-1 ( n2461 ) null mutants ., We found that GFP fluorescence increased in glr-1 mutants to a similar extent as in klp-4 mutants ( Fig 1A and 1B ) ., These data suggest that decreased GLR-1 protein or function is sufficient to trigger a compensatory feedback mechanism negatively coupling GLR-1 to its own transcript levels ., These data also indicate that the glr-1 promoter together with the glr-1 3’UTR are sufficient to mediate the feedback mechanism ., To determine the respective contributions of Pglr-1 and the glr-1 3’UTR to the feedback mechanism , we generated additional GFP reporter transgenes consisting of NLS-GFP-LacZ under the control of either the glr-1 or nmr-1 promoters combined with either the glr-1 or unc-54 3’UTRs ., The nmr-1 promoter provides an alternative promoter that is expressed in an overlapping set of neurons with GLR-1 , including the interneuron PVC 33 ., The unc-54 3’UTR is widely used for permissive gene expression in C . elegans 34 ., We crossed these GFP reporter transgenes into several genetic backgrounds and measured GFP fluorescence in the nucleus of PVC interneurons as described above ., When fluorescence was measured from a GFP reporter under control of the nmr-1 promoter ( Pnmr-1 ) and the glr-1 3’UTR , we observed no significant change in fluorescence in either klp-4 ( tm2114 ) or glr-1 ( n2461 ) loss-of-function mutants ( Fig 1C and 1D ) ., This result suggests that the glr-1 3’UTR is not sufficient to mediate the feedback mechanism ., On the other hand , when GFP fluorescence was measured from the reporter transgene containing Pglr-1 and the unc-54 3’UTR ( hereafter referred to as the glr-1 transcriptional reporter ) , we observed a small but significant increase in fluorescence in both klp-4 and glr-1 mutants ( Fig 1E and 1F ) ., This glr-1 transcriptional reporter was also increased in usp-46 ( ok2232 ) loss-of-function mutants ( Fig 1G ) , consistent with our previous RT-qPCR results 29 ., Importantly , the nmr-1 promoter and the unc-54 3’UTR are not regulated by the feedback pathway because a GFP reporter containing these elements was unaltered in klp-4 and glr-1 mutants ( S1 Fig ) ., Together , these data indicate that Pglr-1 is sufficient to mediate the feedback mechanism , suggesting that neurons respond to decreased GLR-1 levels or function in the VNC by increasing glr-1 transcription ., We next investigated whether the feedback mechanism was bidirectional by testing if increased GLR-1 in the VNC triggers a decrease in glr-1 transcription ., UNC-11/AP180 is a clathrin adaptin involved in endocytosis of GLR-1 , and the receptor accumulates at the plasma membrane in the VNC of unc-11 mutants 35 ., We found that fluorescence of the GFP reporter under control of Pglr-1 and the glr-1 3’UTR decreased in unc-11 ( e47 ) null mutants ( Fig 1H ) ., We observed a similar reduction of the glr-1 transcriptional reporter in unc-11 mutants ( Fig 1I ) , suggesting that Pglr-1 is sufficient to mediate decreased glr-1 transcription ., Interestingly , genetic double mutant analyses indicate that the effects of unc-11 on glr-1 transcription are not dependent on glr-1 ( S2 Fig ) ., Together , these data suggest that mutation of the clathrin adaptin unc-11/AP180 likely blocks the endocytosis of another membrane protein or ion channel in addition to GLR-1 , whose accumulation results in reduced glr-1 transcription ., We performed several experiments to test if changes in glutamate signaling , rather than levels of synaptic GLR-1 , were sufficient to trigger the transcriptional feedback mechanism ., First , we tested whether reductions in glutamatergic transmission could trigger the feedback mechanism by analyzing glr-1 expression in eat-4 synaptic transmission mutants ., EAT-4 is a vesicular glutamate transporter ( VGLUT ) responsible for loading glutamate into synaptic vesicles 36 , 37 ., Loss of eat-4 results in defects in glutamatergic transmission 37 , 38 and a compensatory increase in synaptic GLR-1 in the VNC 10 ., We found that eat-4 ( n2474 ) loss-of-function mutants exhibit increased endogenous glr-1 mRNA levels compared to wild type controls using RT-qPCR ( Fig 2A ) ., In support of this data , we found that eat-4 ( n2474 ) mutants also exhibit increased GFP fluorescence from the reporter under control of Pglr-1 and the glr-1 3’UTR ( Fig 2B ) ., Furthermore , Pglr-1 was sufficient to mediate this effect because GFP fluorescence still increased in eat-4 ( n2474 ) mutants expressing the glr-1 transcriptional reporter ( Fig 2C ) ., Together , these data suggest that chronic decreases in glutamate signaling ( Fig 2 ) or postsynaptic glutamate receptors ( Fig 1 ) are sufficient to trigger the glr-1 transcriptional feedback pathway ., We next investigated whether direct and more acute suppression of neuronal activity specifically in GLR-1-expressing neurons could trigger the feedback mechanism using a recently developed chemical genetic silencing strategy ., Ectopic expression of a Drosophila histamine-gated chloride channel ( HisCl1 ) in C . elegans neurons enables relatively acute repression of neuronal activity by exogenous histamine 39 ., We generated transgenic animals expressing HisCl1 in GLR-1-expressing neurons ( Pglr-1::HisCl1 ) and verified the efficacy of this silencing approach by measuring GLR-1-dependent locomotion reversal behavior ., The frequency of spontaneous reversals is regulated by glutamatergic signaling , and mutants with reduced glutamatergic signaling ( i . e . , glr-1 or eat-4 mutants ) exhibit decreased reversal frequencies 33 , 35 , 40 ., We found that exposure of animals expressing HisCl1 to exogenous histamine for 10 minutes led to a dramatic decrease in spontaneous reversal frequency compared to wild type controls ( Fig 2D ) ., This data suggests that activation of HisCl1 channels specifically in GLR-1-expressing neurons suppresses their activity and impacts GLR-1-dependent locomotion behavior ., In order to test whether direct inhibition of GLR-1-expressing neurons could increase glr-1 transcription , we exposed HisCl1-expressing animals to histamine for one and four hours and then measured Pglr-1 activity using the glr-1 transcriptional reporter ., Fluorescence at each time point was normalized to HisCl1-expressing animals in the absence of histamine ( see Materials and Methods ) ., We found a small increase in GFP reporter fluorescence after both one and four hours of histamine treatment ( Fig 2E ) ., Although the histamine-induced effect on the glr-1 transcriptional reporter was modest , it was significant ( p<0 . 05 ) and suggests that direct inhibition of GLR-1-expressing neurons can trigger an increase in glr-1 transcription ., In contrast , wild type animals not expressing HisCl1 showed no significant increase in Pglr-1 activity when exposed to histamine ( S3 Fig ) ., We did , however , observe a reduction in Pglr-1 activity in wild type animals after four hours of histamine exposure ( S3 Fig ) ., Unfortunately , this decrease in Pglr-1 activity precluded our ability to test whether long term inhibition by histamine could also induce a late glr-1 transcriptional response ., Nevertheless , these results suggest that decreasing neuronal activity specifically in GLR-1-expressing neurons can trigger the feedback mechanism to increase glr-1 transcription in the mature nervous system ., Finally , we investigated whether directly increasing GLR-1 function could regulate the transcriptional feedback pathway ., We increased GLR-1 activity in a subset of interneurons by expressing a mutant version of GLR-1 ( under control of the nmr-1 promoter ) , that contains an alanine to threonine substitution ( A/T ) in the pore domain resulting in a constitutively active channel with increased conductance 40 ., Animals expressing GLR-1 ( A/T ) exhibit a dramatic increase in spontaneous locomotion reversals consistent with increased glutamatergic signaling 29 , 40 , 41 ., We found that Pglr-1 activity decreased in GLR-1 ( A/T ) -expressing animals compared to wild type controls ( Fig 2F ) ., These data are consistent with the hypothesis that increased GLR-1 function triggers the feedback pathway to reduce glr-1 transcription ., Together , our data show that increasing or decreasing glutamatergic signaling results in compensatory and reciprocal changes in glr-1 transcription ., CaM kinases ( CaMKs ) I and IV are important mediators of calcium-dependent signaling mechanisms involved in neuronal development and function ., In particular , CaMKIV can mediate activity-dependent regulation of gene transcription 42 , and has been shown to mediate AMPAR-dependent homeostatic synaptic scaling in a transcription-dependent manner 18 , 19 ., In C . elegans , CMK-1 , the homolog of CaMKI and CaMKIV 24 , is widely expressed in the nervous system 26 , and has been shown to function in specific sensory neurons to mediate experience-dependent thermotaxis at physiological temperatures and avoidance of noxious heat 26–28 ., However , the downstream transcriptional targets of CMK-1 and CaMKIV that mediate their physiological effects are not clear ., To test whether CMK-1 was involved in regulating glr-1 transcription , we first measured endogenous glr-1 mRNA levels in cmk-1 ( oy21 ) loss-of-function mutants using RT-qPCR ., Intriguingly , we found increased glr-1 mRNA levels relative to two reference genes ( act-1 and ama-1 ) in cmk-1 ( oy21 ) mutants ( Fig 3A ) , suggesting that CMK-1 negatively regulates glr-1 transcript levels ., Consistent with this result , loss-of-function mutations in ckk-1/CaMKK , the upstream activator of CMK-1 , resulted in increased GFP fluorescence from a reporter under control of Pglr-1 and the glr-1 3’UTR ( Fig 3B ) ., We next tested whether Pglr-1 was sufficient to mediate the effects of the CMK-1 pathway using the glr-1 transcriptional reporter ., We found that Pglr-1 activity increased in ckk-1 ( ok1033 ) loss-of-function mutants ( Fig 3C ) and two independent loss-of-function alleles of cmk-1 ( oy21 and ok287 ) ( Fig 3D and 3E ) ., These results indicate that the CMK-1 signaling pathway acts basally to repress glr-1 transcription ., Expression of cmk-1 cDNA specifically in GLR-1-expressing neurons rescues the increase in Pglr-1 activity observed in cmk-1 ( oy21 ) loss-of-function mutants ( Fig 3H ) , whereas expression of a kinase-dead version of CMK-1 ( K52A ) 25 does not rescue ( Fig 3I ) ., The difference in rescue between the wild-type and kinase-dead versions of CMK-1 cannot be explained by different levels of transgene expression , as both Pglr-1::CMK-1 and Pglr-1::CMK-1 ( K52A ) transgenes were expressed at comparable levels as determined by RT-qPCR ( S4 Fig ) ., These results indicate that CMK-1 functions in a kinase-dependent manner specifically in GLR-1-expressing neurons to repress glr-1 transcription ., CaMKI and CaMKIV in mammals , and CMK-1 in C . elegans , have been shown to phosphorylate the transcription factor cyclic AMP response element binding protein ( CREB ) to regulate gene expression 24 , 25 , 43–46 ., Thus , we tested whether mutations in crh-1 , the C . elegans homolog of CREB , affected glr-1 transcription ., We found that fluorescence of the glr-1 transcriptional reporter was increased in crh-1 ( tz2 ) loss-of-function mutants ( Fig 3F ) , consistent with a role for CREB as a downstream target of CMK-1 in regulating glr-1 transcription ., Additionally , since CREB is known to function together with the transcriptional co-activator CREB binding protein ( CBP-1 ) /p300 which can also be phosphorylated by CaMKIV 42 , 47 , we took advantage of a gain-of-function allele in cbp-1 ( ku258 gf ) 48 to test if cbp-1 was involved in regulating glr-1 transcription ., We found that cbp-1 ( ku258 gf ) mutants exhibited decreased fluorescence of the glr-1 transcriptional reporter ( Fig 3G ) ., Together , these results indicate that the CaMK signaling axis , including CKK-1/CaMKK , CMK-1/CaMK , CRH-1/CREB and CBP-1/CBP act to repress glr-1 transcription ( Fig 3J ) ., To test whether the negative feedback pathway triggered by loss of glr-1 was mediated by CMK-1 signaling , we generated a series of genetic double mutants between glr-1 and various CMK-1 pathway mutants ., We hypothesized that if decreased GLR-1 signaling triggers an increase in glr-1 transcription by deactivating the CMK-1 pathway , we would expect glr-1; cmk-1 double mutants to have non-additive effects on the glr-1 transcriptional reporter ., Alternatively , if cmk-1 functions in an independent pathway to regulate glr-1 transcription , we would expect glr-1; cmk-1 double mutants to have an additive effect on the glr-1 transcriptional reporter ., We found that glr-1 ( n2461 ) ; cmk-1 ( oy21 ) double mutants exhibited an increase in the glr-1 transcriptional reporter that is indistinguishable from either single mutant ( Fig 4A ) ., This non-additive effect is consistent with the idea that the glr-1-triggered feedback mechanism and cmk-1 function in the same pathway to increase glr-1 transcription ., In support of this finding , we found that glr-1 ( n2461 ) ; crh-1 ( tz2 ) double mutants also exhibit an increase in the glr-1 transcriptional reporter that was identical to either single mutant ( Fig 4B ) , suggesting that CRH-1/CREB also likely functions in the same pathway to negatively regulate glr-1 transcription ., Although these non-additive effects support the idea that the CMK-1 pathway may mediate the glr-1 transcriptional feedback mechanism , we cannot formally rule out a potential ceiling effect of the reporter ., To provide further genetic evidence for a role for CMK-1 in the glr-1 transcriptional feedback mechanism , we tested whether a recently isolated gain-of-function ( gf ) allele of cmk-1 , pg58 , could suppress the increase in glr-1 transcription observed in glr-1 mutants ., cmk-1 ( pg58 gf ) contains a premature stop codon at W305 resulting in a truncated version of CMK-1 ( 1–304 ) ., CMK-1 ( 1–304 ) is missing most of its regulatory domain and a putative nuclear export sequence ( NES ) , and the altered protein has been shown to accumulate in the nucleus 27 ., Interestingly , we found that although cmk-1 ( pg58 gf ) did not affect basal glr-1 transcription , this gain-of-function allele completely blocked the increase in the glr-1 transcriptional reporter triggered by loss of glr-1 ( Fig 4C ) ., Together , these data are consistent with the model that CMK-1 signaling mediates the glr-1 transcriptional feedback mechanism ., CaM kinases are well-known mediators of activity-dependent gene expression , and specific isoforms have been shown to translocate between the cytoplasm and nucleus 42 , 49 ., For example , in mammalian neuronal cultures , homeostatic increases in synaptic GluRs are correlated with a reduction in activated CaMKIV in the nucleus 19 ., In C . elegans , CMK-1 can shuttle between the cytoplasm and nucleus to regulate thermosensory behaviors 27 , 28 ., Thus , we tested whether alterations in glr-1 transcription were accompanied by changes in the subcellular localization of CMK-1 ., We expressed GFP-tagged CMK-1 ( CMK-1::GFP ) 26 in GLR-1-expressing interneurons and used confocal microscopy to determine the relative subcellular localization of CMK-1::GFP in the cytoplasm versus nucleus of PVC neurons ( see Materials and Methods ) ., The subcellular localization of CMK-1::GFP is regulated by changes in physiological temperature and noxious heat 27 , 28 , and CMK-1::GFP can rescue heat avoidance behavioral defects in cmk-1 mutants , suggesting that the tagged protein is functional 27 ., Since CKK-1 phosphorylation of CMK-1 has been shown to promote the nuclear accumulation of CMK-1::GFP in sensory neurons 27 , 28 , we first analyzed the subcellular localization of CMK-1::GFP ( Fig 5A ) in GLR-1-expressing neurons in ckk-1 ( ok1033 ) loss-of-function mutants ., We found that CMK-1::GFP decreases in the nucleus and increases in the cytoplasm in ckk-1 ( ok1033 ) mutants ( Fig 5B ) ., In other words , the subcellular localization of CMK-1::GFP shifts from the nucleus towards the cytoplasm in ckk-1 mutants , consistent with previous studies 27 , 28 ., To test whether the subcellular localization of CMK-1 is regulated by GLR-1 signaling , we analyzed the distribution of CMK-1::GFP in glr-1 mutants ., Similar to ckk-1 mutants , we found that CMK-1::GFP decreases in the nucleus and increases in the cytoplasm in glr-1 ( n2461 ) mutants ( Fig 5B ) ., These results are consistent with the idea that decreased synaptic GLR-1 results in increased retention of CMK-1 in the cytoplasm and relief of repression of glr-1 transcription ., In contrast , we found that increasing GLR-1 signaling by expression of constitutively active GLR-1 ( A/T ) in interneurons results in increased localization of CMK-1::GFP to the nucleus ( Fig 5C ) ., Together , these data suggest that increased or decreased GLR-1 signaling in interneurons results in increased or decreased accumulation , respectively , of CMK-1 in the nucleus ., To specifically test whether nuclear localization of CMK-1 is sufficient to repress the increase in glr-1 transcription triggered by loss of glutamatergic signaling , we expressed a constitutively nuclear-localized version of CMK-1 containing an exogenous NLS ( Pglr-1::CMK-1::EGL-13-NLS ) in GLR-1-expressing neurons ., CMK-1::EGL-13-NLS was shown to be five-fold enriched in the nucleus where it can rescue cmk-1 null mutants for several thermosensory defects 28 ., We found that expression of constitutively nuclear CMK-1 was sufficient to block the increase in the glr-1 transcriptional reporter observed in glr-1 ( n2461 ) mutants ( Fig 5D ) ., These data suggest that nuclear localization of CMK-1 represses glr-1 transcription and provides further evidence that the CMK-1 signaling pathway mediates the glr-1 transcriptional feedback mechanism ( Fig 5E ) ., We found that GLR-1 trafficking mutants ( i . e . , klp-4/KIF13 or usp-46 mutants ) with decreased GLR-1 in the VNC exhibit compensatory increases in glr-1 expression ( Fig 1 ) ., Analysis of fluorescent reporters containing either Pglr-1 or the glr-1 3’UTR revealed that the glr-1 promoter was sufficient to mediate the feedback mechanism ( Fig 1 ) ., Interestingly , although the glr-1 3’UTR alone did not appear to be sufficient to mediate the feedback pathway ( Fig 1C and 1D ) , we noticed that reporter constructs containing the glr-1 3’UTR together with Pglr-1 ( Figs 1A , 1B , 1H and 3B ) appear to have larger magnitude effects versus the unc-54 3’UTR ( Figs 1E , 1F , 1I and 3C ) hinting at a potential contribution of the glr-1 3’UTR ., Statistical comparison of the relevant data sets revealed significant contributions ( p<0 . 05 , Two-way ANOVA ) of the glr-1 3’UTR ( together with Pglr-1 ) in klp-4 ( p = 0 . 03 ) and ckk-1 ( p = 0 . 03 ) mutant backgrounds ., The contribution of the glr-1 3’UTR versus the unc-54 3’UTR in glr-1 ( p = 0 . 1 ) and unc-11 ( p = 0 . 2 ) mutant backgrounds did not reach statistical significance ., Thus , the glr-1 3’UTR appears to contribute to the regulation of glr-1 expression in the feedback pathway in some genetic backgrounds ., A more detailed analysis of the glr-1 3’UTR together with other endogenous regulatory elements is warranted to fully understand the role of the glr-1 3’UTR in the feedback pathway ., Interestingly , a recent study in rodent hippocampal neurons showed that microRNA miR-92A inhibits translation of GluA1 by binding to its 3’UTR , and that this miRNA-mediated mechanism regulates homeostatic scaling in response to chronic activity-blockade 50 ., However , we did not find any conserved miRNA binding sites in the glr-1 3’UTR using several target site prediction algorithms ., Furthermore , we found that the glr-1 3’UTR alone was not sufficient to mediate the feedback mechanism in C . elegans ( Fig 1C and 1D ) ., Thus , while non-conserved miRNAs may still contribute to the regulation of the glr-1 3’UTR , this regulation does not appear to be sufficient to mediate the feedback pathway ., We also investigated whether changes in glutamate signaling could trigger the feedback mechanism ., We found that glutamatergic transmission mutants lacking glr-1 itself ( Fig, 1 ) or the presynaptic eat-4/VGLUT ( Fig, 2 ) were sufficient to trigger the glr-1 transcriptional feedback mechanism ., Furthermore , expression of a constitutively active GLR-1 , GLR-1 ( A/T ) , resulted in decreased glr-1 transcription ( Fig 2F ) ., These data indicate that bidirectional changes in GLR-1 signaling are negatively coupled to glr-1 transcription ., A previous study showed that chronic activity-blockade in eat-4/VGLUT mutants results in a homeostatic compensatory increase in synaptic GLR-1 levels that is mediated by changes in clathrin-mediated endocytosis 10 ., We found that eat-4 mutants also exhibit increased endogenous glr-1 transcript based on RT-qPCR and increased Pglr-1 activity based on a glr-1 transcriptional reporter expressing nuclear-localized NLS-GFP-LacZ ( Fig 2 ) ., Given the multiple mechanisms that contribute to synaptic scaling in mammalian neurons , we suspect that the homeostatic compensatory increase in GLR-1 observed in eat-4 mutants is likely mediated by several mechanisms including changes in both transcription and trafficking of GLR-1 ., In vitro studies using rodent neuron or slice cultures showed that the CaMKIV signaling pathway regulates bidirectional synaptic scaling 18 , 19 ., In C . elegans , cmk-1 is the only homolog of mammalian CaMKI and CaMKIV and shares features with both kinases ., While the primary sequence of CMK-1 shows more homology to mammalian CaMKI , CMK-1 appears to function more like CaMKIV based on its neuronal expression pattern , its ability to phosphorylate CREB , and its localization to both the cytoplasm and nucleus 23–25 , 51 ., Our data show in vivo that the CMK-1/CaMK signaling pathway mediates the feedback mechanism and acts in the nucleus to repress glr-1 transcription ( Figs 3–5 ) ., We showed that cmk-1 loss-of-function mutants had increased glr-1 transcript levels based on RT-qPCR and fluorescent reporters ( Fig 3 ) ., Analysis of a glr-1 transcriptional reporter in CMK-1 signaling pathway mutants including ckk-/CaMKK1 , cmk-1/CaMK , crh-1/CREB and cbp-1/CBP indicates that the CMK-1 signaling pathway represses glr-1 transcription ( Fig 3 ) ., Furthermore , rescue experiments indicate that CMK-1 functions in GLR-1-expressing neurons to repress glr-1 transcription , and this effect is dependent on its kinase activity ( Fig 3H and 3I ) ., Several pieces of evidence suggest that in addition to repressing basal glr-1 transcription , CMK-1 also mediates the glr-1 transcriptional feedback mechanism ., First , analysis of genetic double mutants between cmk-1 signaling pathway components and glr-1 showed non-additive effects on glr-1 transcription ( Fig 4 ) , consistent with the idea that CMK-1 signaling functions in the same pathway as the feedback mechanism triggered by loss of glr-1 ., Second , the feedback mechanism triggered by loss of glr-1 or by expression of constitutively active GLR-1 ( A/T ) regulated the subcellular distribution of CMK-1 between the cytoplasm and nucleus ( Fig 5 ) ., These bidirectional changes in GLR-1 signaling had opposite effects on CMK-1 localization to the nucleus , consistent with the idea that decreased GLR-1 signaling results in decreased translocation of CMK-1 to the nucleus whereas increased GLR-1 signaling results in increased translocation of CMK-1 into the nucleus ., Third , a gain-of-function allele ( pg58 ) of cmk-1 missing its NES and autoinhibitory domain 27 blocked the glr-1 transcriptional feedback mechanism ( Fig 4C ) ., Furthermore , addition of an exogenous NLS to CMK-1 , which forces CMK-1 into the nucleus 28 , was sufficient to inhibit the glr-1 transcriptional feedback pathway ( Fig 5D ) ., Together , these data are consistent with a model whereby increased synaptic GLR-1 activates the CMK-1 signaling pathway resulting in increased nuclear accumulation of CMK-1 and repression of glr-1 transcription ( see model in Fig 5E ) ., A recent study by Ma et al . , ( 2014 ) using cultured rodent neurons showed that activation of nuclear CaMKIV and phosphorylation of CREB in response to acute stimulation is mediated by the nuclear translocation of γCaMKII 49 ., Interestingly , γCaMKII functions in a kinase-independent manner as a shuttle to transport CaM into the nucleus to activate CaMKK and CaMKIV ., In contrast , and consistent with previous studies in C . elegans reporting nuclear translocation of CMK-1 in sensory neurons 27 , 28 , our results show that CMK-1 translocates into the nucleus ( Fig 5 ) and regulates glr-1 transcription in a kinase-dependent manner ( Fig 3 ) ., Although Ma et al . ( 2014 ) did not investigate the role of γCaMKII in activating CaMKIV in response to chronic changes in activity , our study suggests that mechanisms of activation of nuclear CaMK may differ between mammals and C . elegans ., It will be interesting to test whether chronic changes in activity during synaptic scaling in mammalian neurons also require nucleocytoplasmic shuttling of CaM by γCaMKII ., Our results suggest that CMK-1 regulates glr-1 transcription both basally and in response to changes in activity ., We found that glr-1 transcription increases in cmk-1 signaling pathway mutants ( Fig 3 ) , suggesting that a low level of CMK-1 activity is required to basally repress glr-1 transcription ., However , manipulations that increased CMK-1 activity ( i . e . , cmk-1 ( pg58 gf ) mutants ) were not sufficient to repress basal glr-1 transcription , but interestingly , could completely block the increased glr-1 transcription triggered by loss of glr-1 ( Fig 4C ) ., This effect of cmk-1 ( pg58 gf ) is reminiscent of a previous finding in which the gain-of-function allele had no effect on basal secretion of neuropeptides from FLP thermosensory neurons but completely blocked heat-induced secretion of neuropeptides 27 ., Together , these studies suggest that CMK-1 regulation of basal resp | Introduction, Results, Discussion, Materials and Methods | Regulation of synaptic AMPA receptor levels is a major mechanism underlying homeostatic synaptic scaling ., While in vitro studies have implicated several molecules in synaptic scaling , the in vivo mechanisms linking chronic changes in synaptic activity to alterations in AMPA receptor expression are not well understood ., Here we use a genetic approach in C . elegans to dissect a negative feedback pathway coupling levels of the AMPA receptor GLR-1 with its own transcription ., GLR-1 trafficking mutants with decreased synaptic receptors in the ventral nerve cord ( VNC ) exhibit compensatory increases in glr-1 mRNA , which can be attributed to increased glr-1 transcription ., Glutamatergic transmission mutants lacking presynaptic eat-4/VGLUT or postsynaptic glr-1 , exhibit compensatory increases in glr-1 transcription , suggesting that loss of GLR-1 activity is sufficient to trigger the feedback pathway ., Direct and specific inhibition of GLR-1-expressing neurons using a chemical genetic silencing approach also results in increased glr-1 transcription ., Conversely , expression of a constitutively active version of GLR-1 results in decreased glr-1 transcription , suggesting that bidirectional changes in GLR-1 signaling results in reciprocal alterations in glr-1 transcription ., We identify the CMK-1/CaMK signaling axis as a mediator of the glr-1 transcriptional feedback mechanism ., Loss-of-function mutations in the upstream kinase ckk-1/CaMKK , the CaM kinase cmk-1/CaMK , or a downstream transcription factor crh-1/CREB , result in increased glr-1 transcription , suggesting that the CMK-1 signaling pathway functions to repress glr-1 transcription ., Genetic double mutant analyses suggest that CMK-1 signaling is required for the glr-1 transcriptional feedback pathway ., Furthermore , alterations in GLR-1 signaling that trigger the feedback mechanism also regulate the nucleocytoplasmic distribution of CMK-1 , and activated , nuclear-localized CMK-1 blocks the feedback pathway ., We propose a model in which synaptic activity regulates the nuclear localization of CMK-1 to mediate a negative feedback mechanism coupling GLR-1 activity with its own transcription . | Synaptic homeostasis increases or decreases synaptic strengths in order to stabilize neuronal firing in response to alterations in neuronal activity ., Synaptic homeostasis plays an important role during neuronal development and may be deregulated in several neurological diseases ., Neurons regulate glutamate neurotransmitter receptor levels at synapses to alter the strength of synaptic signaling during a form of homeostasis termed synaptic scaling ., While many molecules have been implicated in synaptic scaling in vitro using cultured rodent neuron or slice preparations , the underlying in vivo mechanisms are not well understood ., Here we use the genetic model organism C . elegans to identify in vivo mechanisms involved in a compensatory feedback pathway reminiscent of synaptic homeostasis that couples activity of the glutamate receptor GLR-1 with its own transcription ., We show that glr-1 transcription is regulated in a compensatory manner by bidirectional changes in synaptic activity ., We find that the CMK-1/CaM kinase signaling pathway represses glr-1 transcription ., Furthermore , the subcellular distribution of CMK-1 between the cytoplasm and nucleus is regulated by GLR-1 and is important for mediating the feedback mechanism ., This study uses genetics to dissect a negative feedback pathway in vivo and identifies the signaling mechanism that links changes in synaptic activity directly to glr-1 transcription . | invertebrates, neurochemistry, chemical compounds, mechanisms of signal transduction, caenorhabditis, gene regulation, neuroscience, animals, organic compounds, dna transcription, animal models, caenorhabditis elegans, model organisms, materials science, neurotransmitters, cellular structures and organelles, macromolecules, materials by structure, research and analysis methods, polymers, polymer chemistry, feedback regulation, animal cells, gene expression, chemistry, cytoplasm, biochemistry, signal transduction, cellular neuroscience, histamine, cell biology, organic chemistry, polyvinyl chloride, neurons, genetics, nematoda, biology and life sciences, cellular types, biogenic amines, physical sciences, organisms | null |
journal.pcbi.1003547 | 2,014 | A Synergism between Adaptive Effects and Evolvability Drives Whole Genome Duplication to Fixation | Eukaryotic genomes differ up to an astonishing 200000 fold in the amount of their DNA , by far the widest range within all domains of life 1 ., In eukaryotic evolution large changes in genome size have heralded major transitions , starting with the radiation from a common ancestor of the eukaryotic supergroups within a short evolutionary timespan 2 , 3 ., Subsequent dramatic radiations of animals in the Cambrian explosion and flowering plants have also been preceded by extensive increases in genome size 4 , 5 ., But even within narrow taxonomic bounds remarkable levels of genome size variability exist , such as the seven fold difference within the Brachionus plicatilis species complex 6 ., What are the evolutionary mechanisms underlying this flexibility in genome size and how does it affect the dynamics of eukaryotic evolutionary history ?, Ever since Ohno first proposed that the genome of the vertebrate ancestor had undergone two rounds ( 2R ) of duplication 7 , evidence of the pervasiveness of WGD in eukaryotic evolution has been mounting ., The 2R hypothesis itself has been strongly backed by recent phylogenetic studies 8 , 9 ., Similarly , species radiations of angiosperms 10 , teleost fish 11 and yeasts 12 have all been associated with rounds of WGD ., Especially in plants , the transition to polyploidy appears to be remarkably frequent ., Therefore , in addition to all flowering plants being of paleopolyploid descent 10 , 13 , it is estimated that up to a third of all extant plant species underwent polyploidization since their most recent speciation 14 ., Recognizing the ubiquity of WGD in eukaryotic evolution , it becomes crucial to understand the mechanisms that lead to their fixation in evolving populations ., Data on plants suggest that changing environmental conditions can give rise to the establishment of polyploid lineages ., For example , polyploid incidence is increased in harsher and newly arisen environments such as the arctic 15 and on islands created by volcanic activity 16 or at the ecological limits of non-polyploid parent species ( reviewed extensively in 17 ) ., An extensively studied case of ancient WGD that happened in the ancestor of S . cerevisiae was shown to potentially yield a direct adaptive benefit when a novel , glucose rich environment arose 18 , 19 ., However , direct adaptive benefits may not play a role in other historic cases of WGD which instead may be better explained by a general increase in evolvability ., This may be the reason why a burst of WGDs in plants appears to coincide with the K-T boundary event , explaining the success of these lineages in overcoming the drastic change in climate conditions 20 , 21 ., Most duplicates that arose from an ancient WGD event will have typically returned to a single copy state , thereby eroding the signal of WGD 12 ., Remaining ohnolog ( duplicates arising from WGD ) fractions , ranging from 16% in yeast 22 to more than 50% in P . tetraurelia 23 , have been studied to gain insights in the potential adaptive benefits of WGD and evolutionary forces that play a role in post WGD genome evolution ., In general , duplicate retention post WGD is not equal for all gene classes ., A pattern found across species is an over-retention of transcription factors ( TFs ) and signaling genes in duplicate 24 , 25 , 26 , 27 , 28 , 23 ., Neutral loss of subfunctions in both copies , for example losing different subset of target genes for TFs could enforce this retention 29 and has indeed been observed for Arabidopsis ohnologs 30 , 31 ., However , a characteristic reciprocal relationship between the retention of duplicates resulting from WGD and small scale duplication ( SSD ) can not be easily explained by subfunctionalization ., For example , TFs have been overretained post WGD , while underretained post SSD 32 , 33 , 34 , 28 , 35 , 26 ., This pattern would , however , be predicted by the gene balance theory , because the two modes of duplication affect the balance between interacting gene products differently ., Whereas a WGD should generally retain the balance between highly interacting genes , SSD most likely disrupts this balance by only increasing the dosage of a few genes 34 , 26 , 36 , 28 ., This suggests that dosage balance selection could drive retention of duplicates post WGD 28 , 37 at least on short evolutionary timescales ., Transient retention due to dosage balance selection can increase the chance that duplicates subfunctionalize or even neofunctionalize , further increasing the likelihood of duplicate retention 27 ., How gene balance constraints affect gene divergence and loss remains , however , poorly understood ., One important reason is that adaptive and neutral genome evolution post WGD can produce mixed conservation patterns 27 ., In short , we lack a comprehensive mechanistic understanding of the causes and consequences of WGD when populations adapt to environmental change as well as its impact on long term genome evolution ., Here we have taken an integrated modeling approach to study conditions for and consequences of fixation of WGD in populations that adapt to an environmental change ., Within our Virtual Cell model , we tracked mutations and patterns of genome conservation along the line of descent ., WGD was modeled as an ongoing mutation , alongside small scale duplications , deletions and rearrangements , as well as point mutations ., Lineages arising from identical ancestral populations alternatively evolved with and without WGD , allowing for a direct comparison of the two modes of evolution ., Our results show that fixation of a WGD increases the likelihood that a population will readapt successfully to a novel environmental condition ., Surprisingly , the ancestral gene content of WGD lineages declines more slowly than that of lineages without WGD , while per gene mutation rates were higher in WGD lineages ., At the same time , we found that ohnologs were over-retained relative to expectations based on random losses ., This effect was strongest for TFs ., In agreement with predictions from the gene balance hypothesis we found that TFs with many outgoing interactions were most likely to remain in duplicate ., Because very little subfunctionalization was detected in these TFs we concluded that selection for dosage balance caused the over-retention pattern ., Hence , a relatively simple , biologically inspired model can explain the association between WGD and environmental change as well as the overarching pattern of biased gene retention that is found in an expanding body of phylogenetic studies of paleopolyploidy ., Initial adaptation times varied widely and those that reached high fitness within 15000 generations almost always involved one or more WGDs ( Fig . 2A ) ., In contrast , re-adaptation times for lineages after environmental change were much shorter ( Fig . 2B , D–E ) ., More than reached high fitness within 1000 generations ., This was surprising , because at the start of re-adaptation , fitnessess dropped on average below the level of randomly initialization starting populations ., In addition , in successfully re-adapting lineages WGD events became fixed in a minority of lineages , being particularly rare in rapidly re-adapting lineages ( Fig . 2B inset , F ) ., The cases with rapid re-adaptations suggest that mutational paths to new phenotypes can be very short , requiring very little change at the genomic level ., Notwithstanding the near absence of WGD in rapidly re-adapting lineages , fixation of WGD in the line of descent improved the overall success rate of re-adaptation from to ( Fig . 2C ) ., Even though WGD-mutants were generated continuously in the population throughout the evolutionary experiments , very few WGDs were ultimately accepted in the line of descent of the final population ., Accepted WGDs occured almost exclusively ( in of cases ) within 500 generations of the environmental change ., The much shorter time scale of genomic expansion relative to the timescale of full re-adaptation is in agreement with our previous work on the Virtual Cell model , showing that early evolution of large genomes generally resulted in better long term evolvability 38 ., To study the adaptation process following WGD in more detail we analyzed the evolution of gene content after the environmental change was applied ., For all populations the environmental change took place 1000 generations after a fitness was first recorded in the population ., At that time the genome was typically several fold larger than the minimum genome size reached towards the end of the simulation as a result of long term streamlining ( Fig . 3 inset ) ., Our previous work on the Virtual Cell model showed that streamlining reduces mutational load by the removal of redundant genes and a focussing cellular function into a small set of essential genes 38 , explaining how a relatively large proportion of ancestral gene content is lost during the re-adaptation to the novel environment ( Fig . 3 ) ., The conservation of gene content was measured as the fraction of genes in the ancestor , alive during the environmental change , that was maintained in subsequent descendants ., Duplicates that arose from WGD and SSD later in evolution were not included in counts of ancestral gene content ., As expected , continued neutral evolution in the control set led to drastic streamlining and turnover of the genome , resulting in the loss of approximately two thirds of the original gene content ( Fig . 3: gray shaded area ) ., Re-adaptation to environmental change led to even larger changes in gene content , as expected ., However , in contrast to our expectation that WGD copies are functionally redundant , a larger fraction of ancestral gene content was conserved in WGD lineages than in non-WGD lineages for more than 5000 generations after environmental change ( Fig . 3 ) ., This was despite the fact that the per gene deletion rate remains constant with differences in genome size ( see Methods ) ., Also , on the long run , the average conserved fraction in WGD lineages , although dropping below that of non WGD lineages , always remained above half the conserved fraction in non-WGD lineages ., This shows that at least some fraction of the ancestral content was selectively retained in duplicate ., To find an explanation for the difference in gene content conservation between WGD and non-WGD lineages we analyzed the fixation of different mutation types ., The frequency of accepted deletions in neutral , WGD and non-WGD lineages were very similar , although slightly lower for WGD lineages over the whole simulation interval , compared to non WGD and neutrally evolving lineages ., However , the fraction deleted per event , for mutations that were accepted , was much smaller for WGD lineages than for neutrally evolving and non-WGD lineages ( Fig . 4A; ) despite this fraction being equal in the background mutations for all three categories ., The result was that smaller fractions of the genome were lost per generation in WGD lineages ( Fig . 4A inset; ) ., In contrast , the rate at which point mutations were accepted was significantly higher in WGD lineages compared to non-WGD lineages ( Fig . 4B; ) , while for the latter , this rate was again very similar to that in the neutral control set ( ) ., This suggested that individual genes diverged much faster in WGD lineages than non-WGD lineages ., In summary , WGD appeared to promote the conservation of duplicated genes , while at the same time enabled genes to diverge more and change their function ., A process that fits with these two characteristics is subfunctionalization ., To investigate whether the contrast between gene content conservation and higher gene function divergence in WGD lineages could be explained by a subfunctionalization process we focused our subsequent analysis on the fates and divergence of the ohnologs ., We performed random deletion simulation to find the expected pair retention fractions for TFs , enzymes and pumps , separately ., For every evolutionary simulation in our test set a random deletion simulation was performed that had the genome configurations of the common ancestor at the time of environmental change in the evolutionary run as its starting point ., In the random deletion run , equal amounts of deletions per gene class were performed to those found in the line of descent in the evolutionary run , but selection was omitted ., The random deletion runs were pooled in the same way as the evolutionary runs to make comparisons ., As shown in Fig . 5 the expected fractions of ohnologs after randomly selecting genes for deletion are much lower than in the evolutionary simulations ., Over-retention is highly significant in the case of TFs ( ) and detectable in enzymes and pumps ( ; ) ., Despite the difference in the strength of the bias between TFs and enzymes , the fraction of these respective gene types that is conserved as ohnologs is very similar toward the end of the simulation ( ) , although the fraction has stabilized for TFs , while it is still declining for enzymes ., This can be understood by the fact that the rate of deletions is much higher for TFs than for enzymes , resulting in a shift towards higher fractions of enzymes and lower fractions of TFs in the late , streamlined descendants ( Fig . S1 ) ., Thus , even though TFs were on the whole more likely to be removed from the network by streamlining , the TFs that were conserved at long evolutionary timescales were much more likely to remain in the genome as ohnologs ., In the next section we will test the hypothesis that TF connectivity is the determining factor for the retention of TF ohnologs ., In the neutral control set continuous streamlining is responsible for the pattern of increasing TF outdegree ( Fig . 6A gray shaded ) ., TFs with a relatively high outdegree remained in the genome at the expense of more sparsely connected TFs , which was also true for WGD and non-WGD simulations ., Despite going through environmental change , the evolutionary pattern of non-WGD ( cyan ) lineages is very similar to the neutrally evolving controls ., For the WGD lineages , the connectivity of retained genes was calculated separately for ohnologs ( red ) and singles ( yellow ) , revealing a marked difference in their evolved connectivity ( ) ., Significantly higher connectivities of ohnologs compared to those of conserved genes in non-WGD ( ) and neutrally evolving lineages ( ) suggests that ancestral connectivity influences duplicate retention post-WGD ., At the same time , singles in WGD lineages had significantly lower connectivities , both compared to the ohnologs and the conserved genes in non-WGD ( ) and neutral lineages ( ) ., These results raised the possibility that the observed biased retention of TF ohnologs was a side effect of the conservation of highly interacting genes ., To test this , we performed additional random deletion experiments ., Now , instead of having an equal probability for each TF to be deleted , deletion probability was made dependent on the ancestral TF connectivity ., The probabilities were determined by looking at the distribution of connectivities in the ancestral network and determining the fractions of conserved genes per connectivity bin , at subsequent points in evolutionary time ( see Methods for details ) ., Performing such a simulation on the ancestral networks produced connectivity changes over time that were highly comparable to the evolution of connectivity in the evolutionary runs ., Importantly , however , adding connectivity bias to the random deletion experiment did not change the result that ohnologs were over-retained in the evolutionary simulations ( Fig . S2 ) ., We therefore concluded that conservation of highly connected TFs alone could not explain the over-retention of TF ohnologs ., Continuing our investigation of the role that subfunctionalization may have in the conservation of gene content and high levels of divergence at the gene level in WGD lineages we investigated the functional divergence of TF ohnologs ., If both ohnologs would diverge in function at the same time , they would no longer be able to fully compensate for each others loss , making the conservation of both more likely ., Functional divergence of a TF could happen if its binding site ( BS ) changes and it starts to regulate a different gene set ., In general , BS divergence of ancestral genes was substantial on a short timescale , even in neutrally evolving lineages ., Later , however the initial divergence was largely undone ( Fig . 6B ) ., This reversal of initial divergence can be attributed to the long term genome streamlining that is expected to remove redundant and non-functional genes 38 , expected to be enriched in highly diverged genes ., Compared to lineages that did not undergo an environmental change , BSs of re-adapting lineages initially diverged much more initially , highlighting the fast pace of evolutionary change immediately after the environmental change ( Fig . 6B ) ., Interestingly , WGD lineages had significantly higher levels of BS divergence compared to non-WGD lineages on the shorter timescale , both in the ohnologs ( ) and singles category ( ) ., Subsequently , the remaining ohnologs showed a drastic reduction of the level of divergence , eventually reaching a BS conservation level above that of conserved genes of non-WGD lineages ( ) ., The sharp reduction in average BS divergence indicated that fast diverging ohnologs were overwhelmingly lost , while ohnologs that , on the other hand , did not mutate away from their ancestral BS were conserved at long evolutionary timescales ., When one of an ohnolog pair is deleted in the course of evolution the remaining gene is subsequently categorized as a single ., It is therefore not surprising that the singles category had a final level of BS divergence that was much higher than that of ohnologs ( ) , receiving an influx of highly diverged genes from the ohnolog category ., However , their long term conservation may in fact be best explained by their diverged role in the network ., It suggests an interesting dual character for the dynamics of post WGD genome evolution ., On the one hand , selection acted to conserve highly interacting genes most strongly , both in their copy number and their interaction partners ., At the same time , genes of lower connectivity may be returned to a single copy status and diverge in their role within the gene network ., The latter process may be particularly important for adaptation in a new environment ., Together , our results show that the conservation duplicate pairs and interaction partners of highly connected genes is compatible with the gene balance hypothesis , while subfunctionalization did not play a significant role in pair retention in our model ., Instead , most functional divergence was observed in genes conserved as singles after WGD ., Apparently these were more free to evolve and adapt to the new environment than ‘singles’ in non-WGD lineages , in congruence with the overall higher rates of divergence in WGD lineages compared to non-WGD lineages ( Fig . 4 ) ., In this study we have taken an open-ended approach to studying the relationship between drastic changes in the environment and the occurrence of WGD in the line of descent ., Evolving lineages could potentially follow many different evolutionary paths to re-adaption as a result of mutations at multiple scales and a complex genotype to phenotype map ., WGD , despite being an ongoing mutation , was observed exclusively in lineages that were still ill-adapted to the prevailing environment , as was the case early during the initial adaptation phase and shortly after an environmental change ( e . g . Fig . 2D ) ., This mirrors phylogenetic studies linking WGD to environmental and other types of drastic intracellular change 16 , 15 , 20 , 19 , 39 ., One or more WGDs occurred during the initial adaptation phase in almost all lineages that would eventually obtain high fitness ., In contrast , a minority of lineages ( ) fixed a WGD following environmental change , while almost no WGDs were observed when re-adaptation was very rapid ., This indicates that some of the imposed environmental changes were more easily met by a relatively minor recalibration of the pre-evolved regulatory circuits , despite causing severe initial drops in fitness ., Nevertheless , successful re-adaptation was more prevalent in lineages with WGD , and consequently larger genomes ., This corroborates our previous research , showing that large genome increases early during adaptation benefit long term adaptation 38 and is in accordance with a similar inference drawn by Van de Peer and co-workers 21 , 20 based on parallel paleopolyploidy events in plants and frequent species radiation in the wake of WGD 40 , 33 , 41 , 5 ., A particular case in point of long term evolvability due to WGD is the evolution of novel signaling and developmental pathways in vertebrates 42 , 43 , 24 ., In addition to the long term benefits , in most lineages immediate positive fitness effects also played a role in establishing WGD ( Fig . S3 ) ., Moreover , WGD was more frequent after particular types of environmental change , most notably when enzyme degradation rates increased ( Fig . S4 ) ., This again parallels observations from the phylogenetic record and experiments ., For example , there is strong evidence that the ancient WGD in yeast had an immediate benefit in the context of newly evolved fruiting plants 18 , 19 ., Moreover , short term fitness advantages appear to play a role in establishing polyploid lineages in founder populations within newly arisen environments 16 , 15 , 44 , 17 ., Strong genome streamlining occurred in all simulations , irrespective of environmental change and the fixation of WGD , indicating that maintenance of large genomes comes at a considerable mutational cost 44 , 38 , 45 ., However , WGDs create “irremediable complexity” 46 , 28 , 47 , enforcing the maintenance of larger genomes , which would put lineages that evolve to equal fitness without WGD at an advantage ., This may explain the relatively low fraction of WGD lineages in our experiments and could be the reason that , although polyploids are widespread among current plant species , their long term survival rate tends to be lower than that of non-polyploids 14 ., Summarizing , our simple Virtual Cell model shows a pattern of occurrence of WGD very similar to that in the expanding record of established WGD events in extant organisms ., We conclude that it is a generic property of the evolutionary process irrespective of particular evolutionary contingencies and most biochemical constraints ., Our results highlight the intricate interplay of short and long term adaptive evolution as well as neutrality and irremediable complexity in shaping the gene content ., This is moreover apparent from the duplicate retention pattern , as discussed below ., The fractions of ancestral genes that remain in WGD pairs were higher in all functional categories compared to a neutral expectation based on random deletions ., Duplicate retention post WGD was strongly biased towards highly interacting genes , a pattern that has been reported for many paleopolyploid species 30 , 48 , 23 , 28 , 43 , 26 , 25 ., However over-retention of pairs , in particular in TFs , was much higher than expected from a biased retention of highly connected genes ., The maintenance of duplicate pairs therefore needs another explanation and suggests a form of irremediable complexity ., The two main explanations being subfunctionalization and , as recognized more recently , dosage balance selection ., We found no evidence that subfunctionalization played a role in WGD pair retention withing our model , as the duplicates remained very similar ., This is in contrast to what has been reported in various cases of duplicate retention 29 , 30 , 31 , 49 , 50 and the hypothesis that it was the main cause of genome complexification in eukaryotes 51 ., There is in fact ample evidence that sub- and neofunctionalization play an important role in cementing the retained duplicates in the genomes of real organisms 30 , 27 and promote innovation 43 , 24 , 52 , although evidence exists that competitive interference between the paralogs may impose a significant obstacle to neutral loss of subfunctions 53 ., The lack of subfunctionalization in our model can be explained as follows ., Subfunctionalization of regulatory interactions would require that TFs can conserve binding interactions with a subset of ancestral sites , while at the same time losing some other sites ., As such fine grained alterations of binding motifs was not possible within the current model due to the discreteness of the binding motifs and hence regulatory interactions , it presented a hard case scenario for subfunctionalization ., The fact that we still observed over-retention , most prominently in TFs , again suggests the relevance of the dosage mediated retention mechanism ., Another indication that dosage effects were important in the evolutionary dynamics was the observation that high protein degradation rates triggered fixation of adaptive WGD ., Dosage balance selection was proposed to account for the inverse relationship between retention of duplicates post WGD and post SSD 37 , 34 , 28 , 54 , 55 , 26 ., Originally , dosage balance selection is expected to affect proteins that are part of larger protein complexes ., For complex assembly it is assumed that the relative dosage of the constituents is required to stay within narrow bounds , to prevent the accumulation of incomplete complexes 32 , 28 , 37 ., Therefore single deletions of a duplicate will mostly not be tolerated after WGD , preventing the return to single copy of subunits of large complexes ., Interestingly , our results show that resistance to the deletion of a member of a WGD pair was high , even in the absence of protein complex assembly or physical protein interactions , but that it was still a function of the number of its interactions ., This indicates that dosage balance drove the retention ., Although weaker than TFs , enzymes pairs were also significantly over-retained post WGD in our simulations ., Biased retention of enzyme duplicates has also been reported for the latest of P . tetraurelias three successive WGDs 23 ., Curiously however , enzymes were significantly under-retained from the earlier WGD events ., Initially , stoichiometric constraints likely impose dosage balance selection on enzymes in metabolic pathways 56 ., However , over longer evolutionary timescales , the enzymatic pathways may acquire compensating expression level changes that free the enzyme duplicates of dosage balance constraints , allowing them to be deleted ., Indeed , looking at the trend within the fraction of enzymes found in pairs in our simulations ( Fig . 5 ) , the decline phase is longer than for TFs and may have continued with longer simulation times , explaining the varying levels of retention at different evolutionary timescales ., Summarizing , in our simulations dosage sometimes played an important role in establishing adaptive WGD as well as driving the retention of duplicate pairs , conserving core regulatory interactions in the absence of subfunctionalization ., This raises the question how novel functions could evolve within our simulations , without significant divergence of conserved ohnologs ., The answer appears to be provided by the behavior of the singles in WGD lineages ., They were changing much faster than duplicates and also notably faster than genes retained in non-WGD lineages ( Fig . 6B ) ., This opens the possibility that the adaptive success of WGD lineages was in part due to more sparsely connected TFs ( Fig . 6A ) that were not essential for fitness and were therefore more free to evolve ., These are expected to be in relative abundance immediately after a WGD ., This scenario can , moreover , explain the result that even though genome conservation was higher in WGD lineages , individual genes appeared to diverge faster from the ancestral state ., Thus , enhanced evolvability of WGD lineages was not primarily a consequence of ‘freeing’ redundant paralogs to adopt new functions , but most importantly due to unhindered evolution of non-paralogous genes to adapt to novel conditions ., An important aspect of polyploidization that was left out of our modeling is the variety of mechanisms that can lead to polyploidization ., WGD in the current model happened exclusively through autopolyploidization , causing a strict duplication of the genetic material ., In contrast , hybridization between individuals from substantially diverged subpopulations can give rise to important phenomena such as biased fractionation patterns 57 , 58 and hybrid fitness 59 , 60 ., We envision that incorporating these mechanisms into the current model could give insight into the adaptive consequences of hybridization events and help recognize the type of ancient polyploidization events by observing characteristic patterns of genome evolution ., Concluding , our model highlights how the interplay between short and long term adaptive and neutral processes underlies the presence of WGD and post-WGD gene retention and its role in genome complexification ., Although we did not set out to model this property explicitly , dosage effects and selection for retaining balanced gene expression readily emerged in the Virtual Cell model , underlining its importance as a generic property of evolution , shaping the content of genomes ., In addition , WGD appears to enable the divergence of singly retained ancestral genes , which may endow WGD lineages with long term adaptive benefits ., From a broader perspective , our results suggest that WGDs had a defining role in enabling the innovations in eukaryotic lineages , while preserving the hallmarks of their ancestors ., The evolutionary simulations were run in two stages ., In the first stage 100 populations were randomly initialized and independently evolved until 1000 generations after they passed the high fitness cutoff , continuing to a maximum of 15000 generations ., All populations in this batch were evolved under the same standard environmental conditions ( the same as those in 38 ) ., From the populations that evolved to a high fitness 10 were randomly selected to go to the next stage ., In this stage all ten populations were initially cloned 80 times and each cloned populations uniquely assigned to 1 out of 80 novel environmental conditions , per seed population ., After applying the environmental change , populations evolved a further 15000 generations ., As a control for the effect of environmental change , evolution was continued without environmental change for all populations from the batch of 100 simulations that evolved to high fitness , including the 10 selected seed populations ., The new environments were made by changing the values of five parameters of the system relative to their standard values ., These parameters separately control the degradation rate of enzymes , permeability of the resource molecule ( A ) , the internal target concentrations for homeostasis in X and A and the metabolic conversion rate of A to X . For all 5 parameters low and high conditions were chosen by making them a factor 2 to 4 different from the standard environment , depending on the severity of the effect on populations in test simulations ( see Table S1 ) ., For some parameters , too large changes resulted in non-viable conditions for most populations , constraining the change we could effectively apply in our simulations ., Finally , a systematic set of 80 environmental changes was constructed by making all combinations where exactly three parameters differ from their value in the standard condition ., Exact counts of mutations can be traced in the line of descent ., To do this , one individual of the final popula | Introduction, Results, Discussion, Materials and Methods | Whole genome duplication has shaped eukaryotic evolutionary history and has been associated with drastic environmental change and species radiation ., While the most common fate of WGD duplicates is a return to single copy , retained duplicates have been found enriched for highly interacting genes ., This pattern has been explained by a neutral process of subfunctionalization and more recently , dosage balance selection ., However , much about the relationship between environmental change , WGD and adaptation remains unknown ., Here , we study the duplicate retention pattern postWGD , by letting virtual cells adapt to environmental changes ., The virtual cells have structured genomes that encode a regulatory network and simple metabolism ., Populations are under selection for homeostasis and evolve by point mutations , small indels and WGD ., After populations had initially adapted fully to fluctuating resource conditions re-adaptation to a broad range of novel environments was studied by tracking mutations in the line of descent ., WGD was established in a minority ( ≈30% ) of lineages , yet , these were significantly more successful at re-adaptation ., Unexpectedly , WGD lineages conserved more seemingly redundant genes , yet had higher per gene mutation rates ., While WGD duplicates of all functional classes were significantly over-retained compared to a model of neutral losses , duplicate retention was clearly biased towards highly connected TFs ., Importantly , no subfunctionalization occurred in conserved pairs , strongly suggesting that dosage balance shaped retention ., Meanwhile , singles diverged significantly ., WGD , therefore , is a powerful mechanism to cope with environmental change , allowing conservation of a core machinery , while adapting the peripheral network to accommodate change . | The evolution of eukaryotes is characterized by drastic changes in their genome content ., Genome expansions have often occurred by duplication of the entire genome ., It is generally not know whether organisms gain any adaptive advantage from these mutations ., However , they appear to become fixed in response to environmental change ., Many interesting whole genome duplications happened long ago in eukaryotic evolutionary history during periods of turbulent genome and species evolution ., Genomic data analysis alone cannot resolve the evolutionary mechanisms and consequences of whole genome duplication ., Here , we modeled evolution with whole genome duplications in a Virtual Cell model ., Simulating populations that undergo a range of different environmental changes we found that next to often increasing fitness directly , whole genome duplications made lineages more evolvable and hence more able to adapt to harsh new environments ., Although most duplicates are deleted in subsequent evolution , genes with many interaction partners were retained preferentially , increasing regulatory complexity ., Interestingly however , we found that innovation happened most likely in the more loosely connected and less essential genes . | genome evolution, evolutionary modeling, biology and life sciences, computational biology, evolutionary biology | null |
journal.pntd.0003178 | 2,014 | NLRC4 and TLR5 Each Contribute to Host Defense in Respiratory Melioidosis | Burkholderia pseudomallei is a tropical soil saprophyte and Tier 1 select agent that causes the infection melioidosis 1 ., The bacterium may be inoculated through the skin , inhaled , or ingested ., Although infection can manifest in myriad ways , pneumonia is identified in 50% of cases ., Mortality from melioidosis ranges from 14–40% despite appropriate antibiotic treatment , and the risk of death is higher with pulmonary involvement 2 , 3 ., This indicates an urgent need for a better understanding of host-pathogen interactions in melioidosis and adjunctive immuno-modulatory therapies ., Innate immune mechanisms of recognition of invading bacteria include membrane-bound Toll-like receptors ( TLRs ) and cytosolic NOD-like receptors ( NLRs ) 4 , 5 ., These pathogen recognition receptors bind conserved pathogen associated molecular patterns and drive the host response ., For example , as a Gram-negative , flagellated bacterium , B . pseudomallei is predicted to activate sensors of LPS ( such as TLR4 ) and flagellin ( such as TLR5 ) ., We have found that B . pseudomallei LPS is a TLR4 ligand that drives much of the innate immune response to B . pseudomallei , and that human genetic variation in TLR4 is associated with susceptibility to melioidosis 6–8 ., We have also shown that B . pseudomallei activates TLR5 , and that polymorphisms in TLR5 are associated with survival from melioidosis 9 , 10 , however TLR5-deficient mice have not been infected with B . pseudomallei to demonstrate the role of TLR5 in an experimental setting ., These findings point to an important role for flagellin in activation of immune responses in melioidosis ., Whereas TLR5 detects flagellin at the cell surface , cytosolic flagellin is detected through NLRC4 , an inflammasome that activates caspase-1 11 ., NLRC4 is one of a number of NLRs that can assemble a canonical caspase-1-dependent inflammasome that in turn cleaves pro-IL-1β and pro-IL-18 to their active forms and induces pyroptosis 5 , 12 ., More recent work identified murine NAIP5 and NAIP6 as direct flagellin sensors that signal through NLRC4 13 , 14 ., NLRC4 also contributes to the sensing of bacterial components other than flagellin: murine NLRC4-NAIP1 and NLRC4-NAIP2 inflammasomes recognize bacterial type three secretion system ( T3SS ) needle and rod proteins , respectively 13–16 ., In contrast to mice , humans have only a single NAIP , and in human U937 monocytes the NLRC4-NAIP inflammasome recognizes a T3SS needle protein but not flagellin 14 ., The functional interpretation of the three NLRC4 agonists is similar – flagellin , rod , and needle protein are all believed to be accidentally injected into the cytosol by bacterial T3SS ., This is in contrast to TLR5 detecting extracellular flagellin , the presence of which will not be strictly linked to a particular virulence trait ., In addition to the canonical caspase-1-dependent inflammasome , a noncanonical inflammasome involving another inflammatory caspase , caspase-11 , has recently been described in mice 17 ., Caspase-11 protects mice from B . pseudomallei infection 18 ., In this study , our primary objective was to determine the relative importance of NLRC4 in murine respiratory melioidosis in comparison to TLR5 , and with respect to canonical and noncanonical inflammasomes ., Our secondary objective was to test whether genetic variation in NLRC4 was associated with outcome in human respiratory melioidosis ., All animal experiments were approved by the University of Washington Institutional Animal Care and Use Committee ( protocol number 2982-03 ) ., The University of Washington complies with all applicable provisions of the federal Animal Welfare Act and with the Public Health Service ( PHS ) Policy on Humane Care and Use of Laboratory Animals ., The University of Washington Human Subjects Division Institutional Review Board; the Ethical Review Committee for Research in Human Subjects , Ministry of Public Health , Thailand; and the Ethics Committee of the Faculty of Tropical Medicine , Mahidol University , Bangkok , Thailand approved the human genetic studies on subjects who had provided or whose next of kin had provided written informed consent for enrollment into clinical studies of melioidosis at the time of recruitment ., B . pseudomallei 1026b was grown in LB broth shaking in air at 37°C , washed twice , resuspended in PBS containing 20% glycerol , and frozen at −80°C ., Immediately before each aerosol infection experiment , the freezer stock was thawed and diluted in PBS to the desired concentration , as previously described 19 ., NLRC4 SNP identification and selection was performed using the Genome Variation Server ( http://gvs . gs . washington . edu/GVS/ ) ., Coding SNPs in the gene and haplotype-tagging SNPs were selected ., Within the region encompassed by 50 , 000 bases upstream and downstream of NLRC4 , SNPs with a minor allele frequency ≥2% in populations identified as Japanese , Chinese and Asian were binned into groups with R2≥0 . 8 to identify haplotype-tagging SNPs ., Genotyping was performed using an allele-specific primer extension method ( Sequenom Inc . , San Diego , CA , USA ) with reads by a MALDI- TOF mass spectrometer 8 ., Comparisons of two and three groups of data expected to follow a normal distribution were made using Students t test and ANOVA with a Bonferroni post-test , respectively ., CFUs were log10 transformed before analysis ., Survival analyses were performed with the log rank test ., SNPs were tested for deviation from Hardy-Weinberg equilibrium using the exact test ., The association of genotype with death was performed using a Chi square test or , for contingency tables with cell counts <10 , the exact test ., For multivariate analysis of genetic associations , logistic regression was performed adjusting for age , gender , diabetes , renal disease , or liver disease ., A conservative Bonferroni correction was not performed as the variants are unlikely to be independent ., Effect modification was assessed by testing the incorporation of an interaction variable into the regression model , using the likelihood ratio test ., Statistics were performed with GraphPad Prism 5 . 0f ( San Diego , CA ) or Stata 11 . 2 ( College Station , TX ) ., A two sided p value of ≤0 . 05 was considered significant ., Given our previous identification of a strong association between a nonsense TLR5 polymorphism that renders TLR5 insensitive to flagellin and survival from melioidosis 9 , we examined whether the presence of Tlr5 in murine melioidosis altered survival ., We infected mice with 361 CFU B . pseudomallei per lung , a dose that approximates the median lethal dose ( Figure 1A ) ., We found that Tlr5−/− mice had significantly poorer survival than wild type mice , a phenotype that contrasts with that observed in Tlr2−/− or Tlr4−/− mice 23 ., We next asked how the absence of Nlrc4 modulated this phenotype ., We infected Tlr5−/− and Tlr5−/−Nlrc4−/− mice with a similar dose ( 400 CFU/lung ) of B . pseudomallei but found no difference in survival between mouse strains ( Figure 1B ) ., This finding suggested that the absence of flagellin sensing at the cell surface sufficiently impaired the host response such that impaired cytosolic detection of the pathogen did not substantially impact survival further ., We then tested whether lack of Nlrc4 alone altered survival in respiratory melioidosis , and how this differed from combined deficiency of Tlr5 and Nlrc4 ., To increase the sensitivity of our model , we chose a lower inoculum that is non-lethal to wild type mice ( 91 CFU/lung ) ., We found that Nlrc4-deficient mice were more susceptible to melioidosis than wild type mice , consistent with results from Ceballos-Olvera 24 , but that there was no difference in survival between Nlrc4−/− mice and Tlr5−/−Nlrc4−/− mice ( Figure 1C ) ., Together , these experiments demonstrate that TLR5 and NLRC4 each contribute to host defense in murine respiratory melioidosis ., Caspase-11 has recently been identified as a component of the noncanonical , caspase-1-independent inflammasome ., We and others have found that Casp1−/−Casp11−/− mice infected with B . pseudomallei by the respiratory route failed to control infection ( unpublished data , 24 , 25 ) ., To examine the effects of NLRC4 relative to other caspase-1- and caspase-11- dependent inflammasomes , we directly compared bacterial burdens in organs of wild type , Nlrc4−/− , or Casp1−/−Casp11−/− mice infected with B . pseudomallei ., Twenty four hours after an inoculum of 314 CFU/lung , bacterial growth in the lungs of both Nlrc4−/− and Casp1−/−Casp11−/− mice was about 0 . 87 log10 CFU greater than in wild type mice , and there was no significant difference between CFU in Nlrc4−/− compared to Casp1−/−Casp11−/− mice ( Figure 2 ) ., Forty eight hours after infection , bacterial growth in the lungs of both Nlrc4−/− and Casp1−/−Casp11−/− mice had increased significantly compared to wild type mice ( by 1 . 87 log10 CFU and 2 . 69 log10 CFU , respectively ) ., Despite a trend towards greater pulmonary bacterial burdens in Casp1−/−Casp11−/− mice than in Nlrc4−/− mice , this did not reach statistical significance ., Bacterial burdens in the spleens are an indication of dissemination beyond the pulmonary compartment ., Although bacteria were detectable 24 hours after infection , there were no significant differences between the three mouse strains ., Forty eight hours after infection , CFU were significantly greater in Casp1−/−Casp11−/− mice compared to Nlrc4−/− mice which in turn had greater bacterial burdens compared to wild type mice ., These data confirm that while deficiency of both caspase-1 and caspase-11 severely impairs control of B . pseudomallei replication in the lung , NLRC4 accounts for much of the inflammasome-dependent phenotype 24 ., We next evaluated selected cytokine and chemokine responses in the lungs of these mice ( Figure 3 ) ., There were no differences in TNF-α or MIP-2 levels between mouse strains at 24 hours ., As expected , IL-1β was very low in Casp1−/−Casp11−/− mice but was not impaired in Nlrc4−/− mice ., Chemokine KC was higher in Nlrc4−/− mice compared to wild type and to Casp1−/−Casp11−/− mice ., By 48 hours after infection , TNF-α levels in Casp1−/−Casp11−/− mice were significantly greater than wild type ., MIP-2 and KC levels in Casp1−/−Casp11−/− and Nlrc4−/− mice were higher than in wild type mice ., IL-1β was elevated in all mice compared to 24 hour levels , but was significantly elevated in Nlrc4−/− mice in comparison to wild type and to Casp1−/−Casp11−/− mice ., In serum 24 hours after infection , TNF-α , MIP-2 , and Il-1β levels were low but KC was readily detectable and higher in Nlrc4−/− mice compared to Casp1−/−Casp11−/− mice ., At 48 hours , despite higher bacterial burdens in the spleens of Nlrc4−/− and Casp1−/−Casp11−/− mice compared to wild type mice , serum TNF-α and IL-1β remained uniformly low ., In contrast , MIP-2 and KC levels increased substantially in both Nlrc4−/− and Casp1−/−Casp11−/− mice ., In line with previously published data 24 , these results point to non-NLRC4-mediated pathways of IL-1β production in the lung , but suggest that systemically , NLRC4 mediates TNF-α and IL-1β but not MIP-2 or KC release ., Inhalation of B . pseudomallei results in scattered , dense cellular pulmonary infiltrates 19 ., Histopathologic examination of the lungs of Nlrc4−/− and Casp1−/−Casp11−/− mice 24 hours after airborne infection with B . pseudomallei showed relatively similar sized neutrophilic infiltrates and percent of lung involved in these mice compared to wild type mice although there was minor variation in morphologic features , such as earlier evidence of nuclear fragmentation in Casp1−/−Casp11−/− mice ( Figure 4 ) ., However , at 48 hours , inflammation was more severe , particularly in Casp1−/−Casp11−/− mice , which displayed larger and necrotic parenchymal lesions that lacked identifiable intact inflammatory cells ., We have found that a human genetic polymorphism in TLR5 is associated with outcome from melioidosis 9 ., Given the clear role for Nlrc4 in murine respiratory melioidosis , we investigated whether human genetic variation in the NLRC4 region was associated with death in human respiratory melioidosis ., We genotyped five NLRC4 region single nucleotide polymorphisms ( SNPs ) ( rs455060 , rs212703 , rs410469 , rs462878 , and rs6757121 ) selected as described in the methods in 173 melioidosis patients with clinical evidence of pulmonary involvement ., The call rate for four SNPs was above 97 . 5%; one ( rs212703 ) was discarded due to a low call rate ., Fifty eight of the 173 subjects ( 34% ) died ., In survivors , no variant deviated from Hardy-Weinberg equilibrium ., rs6757121 was associated with protection against death in a general genetic model , p\u200a=\u200a0 . 012 ( Table 1 ) ., Adjusting for age , sex , and pre-existing conditions , the effect was strongest in a dominant model odds ratio ( OR ) 0 . 35 , 95% CI:0 . 13–0 . 91 , p\u200a=\u200a0 . 03 ., rs6757121 is located about 0 . 3 kb downstream of NLRC4 and occurs with a minor allele frequency of 10% ., We next tested whether our previously reported association between TLR51174C>T – a nonsense polymorphism that truncates the receptor in the extracellular domain rendering it non-responsive to flagellin – and survival in melioidosis 9 is also seen in the subset of melioidosis patients with respiratory disease ., We found that the adjusted OR of death was 0 . 14 , 95% CI 0 . 03–0 . 64 , p\u200a=\u200a0 . 01 ., To determine whether co-inheritance of this TLR5 variant and the NLRC4 region variant rs6757121 alters the risk of death from respiratory melioidosis , we assessed the effect of including both together in the model ., The OR of death for each variant remained unchanged , although the effect of a cross-product interaction term could not be determined due to 100% survival in carriers of both variants ., The estimated OR of death for carriers of both variants was 0 . 04 , 95% CI: 0 . 006–0 . 27 , p\u200a=\u200a0 . 001 ( Table 2 ) ., Together , these data show that the NLRC4 and TLR5 variants are each associated with survival and that co-inheritance of the variants has an additive but not synergistic effect ., The results of our investigations show that NLRC4 and TLR5 , key components of two flagellin sensing pathways , each contributes to host defense in murine respiratory melioidosis ., We did not detect any additional impact of deficiency of both Nlrc4 and Tlr5 on survival ., Furthermore , NLRC4 is responsible for much of the failure of pulmonary bacterial containment seen in caspase-1/-11-deficient mice ., In humans , we show that an NLRC4 genetic variant is associated with survival in respiratory melioidosis , and there is an additive effect of co-inheritance of risk variants in TLR5 and NLRC4 ., Recent investigations have demonstrated that NLRC4 is involved in recognition of several bacterial ligands such as components of the T3SS or flagellin , and this specificity is determined by various NAIPs 13–16 ., In contrast , the only reported ligand of TLR5 is flagellin 4 ., B . pseudomallei activates TLR5 9 and aflagellated B . pseudomallei induces impaired TLR5-dependent NF-κB activation in vitro unpublished data ., Our present results show that Tlr5−/− mice are more susceptible to B . pseudomallei in a model of respiratory infection , in contrast to deficiency in Tlr2 , which actually confers resistance , or Tlr4 , which has no apparent effect on survival 23 ., Although MyD88 is an adapter molecule for all three of these TLRs , mice deficient in Myd88 show a similar phenotype to deficiency in Tlr5 after respiratory infection with B . pseudomallei 26 ., Flagellin-sensing appears to be a crucial element of host defense in murine respiratory melioidosis ., However , we have not observed significant impairment in TNF-α production from Tlr5−/− alveolar macrophages stimulated ex vivo with killed B . pseudomallei unpublished data and it is notable that our studies of murine respiratory infection with B . thailandensis ( a related and flagellated but less virulent organism ) have not shown any Tlr5-dependent phenotype 20 ., Thus , in vitro data , and infections with model organisms may not fully recapitulate the complexity of in vivo infections with fully virulent B . pseudomallei ., Like TLR5 , NLRC4 appears to play a central role in host defense in respiratory murine melioidosis ., Interestingly , while NLRC4 detects flagellin from many bacterial species , it appears to not detect B . thailandensis ( and presumably B . pseudomallei ) flagellin 14 , thus , the effect of NLRC4 in vivo may be attributable to T3SS sensing ., B . pseudomallei expresses several T3SSs 27 and T3SS3 facilitates virulence in a number of ways 28–31 ., The B . pseudomallei T3SS rod and needle proteins BsaK and BsaL , respectively , are detected in an NLRC4-dependent fashion in mice 15 , 32 ., Recent work by Bast et al demonstrates the importance of BsaK for NLRC4-dependent caspase-1 activation in B . pseudomallei-infected macrophages and for virulence in murine melioidosis 33 ., Intriguingly , despite the different sensing functions of TLR5 and NLRC4 , absence of only one sensor imparts significant clinical impairment; there is no additive effect on survival of combined Tlr5 and Nlrc4 deficiency in murine melioidosis , even at doses that are non-lethal to wild type mice ., NLRC4 is just one of many pathogen recognition receptors that activate the caspase-1-dependent inflammasome ., The inflammasome processes pro-IL-1β and pro-IL-18 to their active forms and also induces pyroptosis , a caspase-1-dependent lytic cell death pathway ., In our studies , Nlrc4−/− mice did not show a significant difference compared to Casp1−/−Casp11−/− mice with respect to bacterial replication in the lung following respiratory infection , but did show a difference in disseminated infection to the spleen , consistent with the work of Ceballos-Olvera et al 24 ., This difference in dissemination may be due to caspase-11 , which also has been implicated in defense against B . pseudomallei 18 ., Relative to Casp1−/−Casp11−/− mice , Nlrc4−/− mice showed preserved pulmonary IL-1β production ., These data raise the possibility that much of the early effect of inflammasome-dependent control of bacterial replication in the lung is primarily NLRC4-dependent and the function of NLRC4 may be due to pyroptosis or as-yet-undefined roles of NLRC4 rather than cytokine processing ., It may be that a secondary canonical inflammasome , perhaps NLRP3 , responds to B . pseudomallei infection only once bacterial burdens become extremely high , resulting in the observed IL-1β secretion ., These observations are concordant with the work by Ceballos-Olvera et al , who additionally showed that processing of pro-IL1β to the active form was not impaired in Nlrc4−/− bone marrow-derived macrophages or in the bronchoalveolar lavage fluid of Nlrc4−/− mice infected with B . pseudomallei 24 ., Furthermore , despite differences in experimental methods and timing , the histology of lungs from Nlrc4−/− mice infected with B . pseudomallei in our study appeared comparable to that of wild type mice treated with IL-1β and infected with B . pseudomallei by Ceballos-Olvera et al 24 ., Notably , however , we found that systemic IL-1β and TNF-α levels were almost undetectable in Nlrc4−/− mice , despite high bacterial burdens in the spleen ., This contrasted with high MIP-2 and KC concentrations in the serum , suggesting that there may be distinctly different regulatory effects of NLRC4 in various compartments ., Our data also demonstrate that NLCR4 inflammasome-dependent innate immune signaling is not the same for B . pseudomallei as other Gram-negative pulmonary pathogens ., Respiratory infection with Legionella pneumophila , another Gram-negative , flagellated , intracellular pathogen is also restricted by NLRC4 and this effect is dependent on the presence of flagellin 21 , 34 ., However , following L . pneumophila infection there was no difference in bronchoalveolar lavage fluid cell counts or in lung cytokine levels of Nlrc4−/− mice compared to wild type mice , although there was greater histologic inflammation in the lungs of Nlrc4−/− mice 21 ., As in B . pseudomallei infection , Nlrc4−/− mice are more susceptible to Klebsiella pneumoniae ( a non-flagellated , extracellular pathogen ) infection by the pulmonary route , with greater bacterial replication in the lungs , dissemination to the spleen , and death 35 although this effect was not observed at higher doses 36 ., In contrast to our findings , pulmonary inflammation as assessed by TNF-α , KC , IL-1β , and MIP-2 levels and histologic score is reduced in Nlrc4−/− mice infected with K . pneumoniae 35 ., These differences may be due to the presence of flagellin or the intracellular nature of B . pseudomallei , or to the apparent lack of NLRC4-mediated pyroptosis induced by K . pneumoniae 35 ., Our human genetic study provides adjunctive evidence for the importance of NLRC4 in respiratory melioidosis although it requires validation ., Few clinically associated polymorphisms in NLRC4 have been described thus far and the function of rs6757121 is otherwise unknown ., We have previously reported the association of variation in TLR5 with survival in melioidosis regardless of site of infection and here show that the association holds in respiratory disease 9 , 10 ., Unlike in mice , modeling suggests that co-inheritance of variants in NLRC4 and in TLR5 increases the effect in an additive manner ., Another important difference between mice and humans is that in humans , blunting of TLR5 function – as found in carriers of a nonsense polymorphism – is in fact protective against death from melioidosis 9 , 10 ., This seemingly opposite phenotype from mice underscores the challenges of mimicking human sepsis in mice 37–39 ., In conclusion , we show that NLRC4 and TLR5 are essential elements of host defense in murine respiratory melioidosis , and that genetic variation in these genes is associated with outcome from human respiratory melioidosis . | Introduction, Methods, Results, Discussion | Burkholderia pseudomallei causes the tropical infection melioidosis ., Pneumonia is a common manifestation of melioidosis and is associated with high mortality ., Understanding the key elements of host defense is essential to developing new therapeutics for melioidosis ., As a flagellated bacterium encoding type III secretion systems , B . pseudomallei may trigger numerous host pathogen recognition receptors ., TLR5 is a flagellin sensor located on the plasma membrane ., NLRC4 , along with NAIP proteins , assembles a canonical caspase-1-dependent inflammasome in the cytoplasm that responds to flagellin ( in mice ) and type III secretion system components ( in mice and humans ) ., In a murine model of respiratory melioidosis , Tlr5 and Nlrc4 each contributed to survival ., Mice deficient in both Tlr5 and Nlrc4 were not more susceptible than single knockout animals ., Deficiency of Casp1/Casp11 resulted in impaired bacterial control in the lung and spleen; in the lung much of this effect was attributable to Nlrc4 , despite relative preservation of pulmonary IL-1β production in Nlrc4−/− mice ., Histologically , deficiency of Casp1/Casp11 imparted more severe pulmonary inflammation than deficiency of Nlrc4 ., The human NLRC4 region polymorphism rs6757121 was associated with survival in melioidosis patients with pulmonary involvement ., Co-inheritance of rs6757121 and a functional TLR5 polymorphism had an additive effect on survival ., Our results show that NLRC4 and TLR5 , key components of two flagellin sensing pathways , each contribute to host defense in respiratory melioidosis . | Melioidosis is an infection caused by Burkholderia pseudomallei , a bacterium that is found in tropical soil and water ., Melioidosis can present in a variety of ways , but lung involvement is common and usually severe ., The host response to infection governs outcome ., In this study , we examined the role of two host sensors of bacterial components–TLR5 and NLRC4–to determine their necessity in respiratory melioidosis ., Although both proteins are involved in detection of bacterial flagellin , in mice we defined specific and individual roles for TLR5 and NLRC4 in protecting against death from melioidosis ., In humans with melioidosis involving the lung , genetic variation in these receptors also had independent associations with survival ., These results underscore the importance of these elements of host defense in respiratory melioidosis and support further studies of the underlying mechanisms . | bacterial diseases, infectious diseases, medicine and health sciences, genetics of the immune system, clinical immunology, melioidosis, biology and life sciences, immunology, bacterial pneumonia, immune response | null |
journal.pcbi.1004374 | 2,015 | Increased Aggregation Is More Frequently Associated to Human Disease-Associated Mutations Than to Neutral Polymorphisms | Protein aggregation is found to be associated to an increasing number of human diseases 1 ., In many cases aggregation directly contributes to or modulates the pathology with which it is associated ., The mode of action of these protein aggregates in disease is generally classified into loss-of-function and gain-of-function effects 2 ., Loss-of-function results from the sequestration of misfolded proteins into inactive cellular inclusions and can functionally be equated to a genetic deletion ., In addition , aggregated proteins can also acquire novel aggregation-specific functions that further contribute to the disease ., In this case , the presence of an aggregated protein results in a worse disease outcome than the absence of the native protein ., In Alzheimer disease for example , Aβ peptide aggregation generates synaptotoxic activity leading to neurodegeneration , while absence of the Aβ peptide does not result in neuronal loss ., However , the mechanisms whereby protein aggregates acquire gain-of-function in more than 30 neurodegenerative diseases remain largely unknown ., In vitro evidence showing that small amyloid-like aggregates perforate biological membranes supports the assumption that protein aggregates act as lethal toxins and that these properties emanate from generic structural properties of amyloid aggregates 3 ., Recent evidence however suggests that ( 1 ) gain-of-function is not restricted to amyloid aggregates and ( 2 ) aggregates can acquire alternative gain-of-function activities that are not directly cytocidal but rather modify cell physiology in more subtle ways ., For instance , it was found that non-amyloid aggregation of p53 confers oncogenic gain-of-function activity to tumors resulting in increased cell proliferation rather than apoptosis 4 ., In familial Fabry disease , an archetypical loss-of-function disease resulting from α-galactosidase inactivation , aggregating mutants nevertheless acquire gain-of-function in the form of pharmacological resistance to the chemical chaperone DGJ-15 ., These results suggest that neurodegenerative and other amyloid diseases only form the tip of the iceberg and that protein aggregation might be implicated in far more pathologies than presently suspected , including cancer and metabolic diseases ., In order to probe the potential of protein aggregation as a disease-modifying factor , we here analyze a curated set of polymorphisms and disease-associated mutations from a VariBench subset6 for which structural information is available ( 5480 pathogenic and 1015 neutral mutations ) ., Protein aggregation is determined by short aggregation prone regions ( APRs ) that are generally buried in the hydrophobic core of the protein where they participate in the stabilization of tertiary interactions ., However , when proteins get ( partially ) unfolded , these APRs become solvent exposed and can self-assemble into aggregates by forming intermolecular β-strand interactions ( Fig 1A ) 7–9 ., The aggregation potential of a protein is thus determined by two factors:, 1 ) the tendency of APRs to self-assemble by β-strand aggregation ( i . e . the intrinsic aggregation propensity of the polypeptide sequence ) and, 2 ) the availability of these APRs as determined by the stability of the native protein ., Mutations that increase the intrinsic aggregation of a protein sequence , destabilize its protein structure or both , will increase the potential for aggregation of a given protein ., The effect of these mutations on protein aggregation is evaluated with a set of computational tools calculating the intrinsic aggregation propensity of the unfolded protein chain ( TANGO10 ) as well as the thermodynamic effect of mutations on the stability of the native protein ( FoldX11 ) ., Mapping the conjugated effect of these two aggregation-determining parameters on mutated protein domains rather than on full-length proteins , we here identify a characteristic signature of aggregation enhancing human variations and find that 22 , 5% of disease mutants in the VariBench set result in enhanced aggregation propensity in comparison to 7 , 5% in human polymorphisms ., Our results suggest that aggregation might be a disease modifier in a wide range of human diseases including metabolic diseases , infection and immunity , and especially cancer ., Given the high incidence of aggregation-promoting mutations in cancer we further compared the COSMIC database12 with the 1000 genomes dataset13 ., This confirmed the enrichment of aggregation prone mutants in cancer mutations , as 38% of cancer mutations result in an increased aggregation propensity of the affected protein ., To analyze the effect of disease-associated and neutral mutations on protein aggregation , an unbiased and representative benchmark dataset is required ., VariBench6 overcomes this problem and offers datasets of experimentally verified high-quality data , either from literature or relevant databases ., More specifically , the neutral dataset , comprising 21 , 170 human non synonymous coding SNPs , and the pathogenic dataset , comprising 19 , 335 mutations , were selected ., The intrinsic aggregation propensity of a protein is defined as the propensity of an unfolded protein sequence to aggregate ., Independent grafting experiments have shown that the intrinsic aggregation propensity is related to the presence of short aggregation-prone regions ( APR ) that self-associate to form intermolecular β-structured assemblies ., These APRs are typically short sequence segments ( 5–15 amino acids ) that display high hydrophobicity , low net charge , and a high tendency to form β-structures14 ., A variety of methods have been developed to identify such APRs in amino acid sequences15 , 16 and in this study the TANGO algorithm10 was used ., TANGO is a statistical thermodynamics algorithm that identifies aggregation nucleation sites by not only considering the factors described above , but also the competition between β-sheet formation and other structured states ., A proteome-wide analysis using TANGO has shown that 10 . 6% of the residues in the entire human proteome are part of an APR ( 1168232 APR residues over 11071210 amino acids ) and thus directly contribute to the intrinsic aggregation propensity of the unfolded polypeptide chain ., We find that the frequency of mutations falling within APRs is random and amounts to 11 , 3% in neutral mutations whereas this is enriched to 15 , 4% in disease mutants ( p<0 . 00001 , Chi- square test ) , as such modifying the intrinsic aggregation tendency ., The aggregation propensity of an APR can also be modified by mutations in so-called gatekeeper residues , i . e . residues that directly flank APRs ( positions -3 and +3 ) and the role of which is to slow aggregation kinetics and mediate chaperone interactions 17 , 18 ., Gatekeeper residues generally consist of charged residues ( Arg , Lys , Glu , Asp ) and proline that counteract aggregation by, i ) charge repulsion ( Arg , Lys , Glu , Asp ) ;, ii ) being large and flexible ( Arg and Lys ) ; or, iii ) being incompatible with the beta-structure ( Pro and Gly ) 19 , 20 ., A proteome-wide study has shown that 90% of all APRs are capped with at least one gatekeeper residue20 ., Consistent with their role in controlling protein aggregation , we found in the dataset analyzed here that 12% of the pathogenic mutations affect these gatekeeper residues , versus 8% of the neutral mutations ( p<0 . 00001 , Chi- square test ) ., Filtering out only the mutations that increase the intrinsic aggregation and discarding those that reduce or do not affect the intrinsic aggregation propensity of APRs , 40 . 8% of pathogenic mutations affecting the APR or the surrounding gatekeepers actually increase the intrinsic aggregation tendency and an additional 4 . 3% of the pathogenic mutations increase the intrinsic aggregation propensity by causing de novo creation of an APR that is not present in the wild type sequence ( only 1 . 7% for neutral mutations , p<0 . 00001 , Chi- square test ) ., To summarize , pathogenic mutations seem to increase the intrinsic aggregation propensity more often than neutral mutations , respectively 15 . 5% and 10 . 1% ( p<0 . 00001 , Chi- square test ) ., This can occur either through 1 ) increasing the aggregation propensity of an existing APR , 2 ) removal of a gatekeeper residue , or 3 ) introduction of a new APR in the protein ., In order to estimate the impact of aggregation-promoting variants on unstructured proteins , we identified all unstructured protein segments in the entire VARIBENCH set using the IUPRED algorithm 21 ., This analysis revealed that 12% and 24% of respectively pathogenic and neutral variants are within unstructured protein domains ( 9 . 1% and 18 . 4% of disordered residues in pathogenic and neutral set ) ., Variants within unstructured protein domains only marginally affect the intrinsic aggregation propensity of the amino acid sequence and are not significantly enriched in pathogenic variants ( 1 . 9% and 1 . 4% in pathogenic and neutral mutants respectively increase the intrinsic aggregation propensity of unstructured protein sequences ( p>0 . 05 , Chi- square test ) ) ., This observation is not unexpected: as the sequence composition of unstructured protein sequences are enriched in charged and polar residues and therefore have a lower hydrophobic content , the frequency of APRs in unstructured protein domain sequences is approximately three times lower than in globular domains 22 reducing the probability of mutations that increase the propensity of APRs ., Moreover , the low hydrophobic moment of these sequences also makes de novo creation of APR by a single mutation much more unlikely ., Finally , as these domains are devoid of tertiary structure the increase of aggregation by exposing APRs through structural destabilization are de facto absent ., We therefore conclude that disease mutations are less likely to induce protein aggregation in unstructured protein domains than in globular protein domains ., However , this does not mean that aggregation is irrelevant for unstructured proteins ., Indeed , important proteopathies such as Parkinson disease ( alpha-synuclein ) and amyotrophic lateral sclerosis ( TDP-43 , Fus ) are associated with the aggregation of unstructured proteins ., The previous sections describe the effect of mutation on the intrinsic aggregation propensity , i . e . the aggregation propensity of the unfolded protein ., However , under native condition , the APRs that define the intrinsic aggregation tendency are often ‘protected’ , i . e . they are generally unavailable for aggregation as they participate in the network of contacts that stabilize the native state 23–26 ., However , mutants that thermodynamically destabilize the native state or at least the structural region in which an APR is embedded will result in an increased likelihood that this APR is unfolded and solvent exposed , and thus available for self-assembly into β-structured aggregates ., The role of protein destabilization in aggregation-associated human diseases has been amply documented 1 and this is e . g . the case for familial mutations in transthyretin ( TTR ) 27 and lysozyme 28 ., In these proteins , mutations affecting the protein stability expose an APR that drives aggregation ., To assess the effect of mutation on the thermodynamic stability of APRs in our VARIBENCH dataset , the FoldX forcefield11 was used ., This empiric forcefield allows obtaining a fast and accurate estimation of the free energy change of protein stability upon mutation ( called ΔΔG , expressed in kcal/mol ) , starting from a high-quality crystallographic structure ., Therefore , the VARIBENCH set used above has been filtered for variations in proteins having either an experimentally determined crystal structure in the Protein Data Bank ( PDB ) 29 , or a high-quality homology model ( homology ≥ 90 ) ., This filtering resulted in a final dataset of 5480 pathogenic and 1015 neutral mutations ., On this set , we confirmed the previously known observation that pathogenic mutations are generally more destabilizing than neutral mutations30 , 31 ( p = 1 x 10−66 , Mann-Whitney U test ) ( Fig 2A ) : using a threshold of ΔΔG > = 2 kcal/mol results in an enrichment of 30% of destabilizing variants in disease-associated mutations ( Fig 2B ) ., As severe structural destabilization generally results in loss-of-function by disruption of binding and catalytic sites , this explains why thermodynamic destabilization is more frequent in pathogenic mutations ., However , as discussed above , thermodynamic destabilization will also result in the solvent exposure of APRs in misfolded proteins resulting in an increased aggregation propensity of disease mutants , a factor that can potentially contribute to additional pathophysiological stresses ., The majority of proteins are composed of multiple structural domains that fold more or less independently ., As a result , the structural consequences of a destabilizing mutation will be most severe for the structural domain in which the mutant is located even though its effect will generally not be restricted to it ., Consequently , destabilizing mutations are more likely to expose APRs present in the comprising domain than in neighboring domains ( Fig 1B ) ., To gain further evidence that aggregation plays an important role beyond loss-of-function in shaping the pathogenic nature of disease mutants , we compared the enrichment of destabilizing mutations in whole proteins with the enrichment of destabilizing mutations in the individual protein domains ., If aggregation plays a role in the pathogenic nature of mutations , there should be a stronger enrichment of destabilizing mutations in domains that possess strong APRs ., To analyze mutations in the context of their structural domain , we used the SMART database to identify and annotate protein domains ., Using the available domain boundaries , we mapped in which structural unit of the protein a mutation is located and using TANGO , we identified the APRs inside this protein domain to determine the following characteristics:, i ) the average intrinsic aggregation propensity of the protein domain ( total TANGO score normalized by protein domain length ) ,, ii ) the number of aggregating segments in the protein domain ,, iii ) the aggregation propensity of the strongest aggregating segment in the protein domain , and, iv ) the aggregation propensity of the strongest aggregating segment in the complete protein ( Fig 3 ) ., This revealed that on average “disease proteins” do not display a higher average aggregation tendency ( p = 0 . 02 , Mann-Whitney U test ) ( Fig 3A ) or a higher number of APRs ( p = 0 . 03 , Mann-Whitney U test ) ( Fig 3B ) , but have a higher prevalence of APRs with a strong aggregation propensity in the specific protein domain bearing the mutation ( p = 2 x 10−29 , Mann-Whitney U test ) ( Fig 3C ) , as well as in the complete protein ( p = 2 x 10−22 , Mann-Whitney U test ) ( Fig 3D ) ., However , the enrichment of disease mutations is higher when analyzing the strongest APR present in the structural domain compared to the strongest APR in the whole protein ( Fig 4 ) , confirming that it is more relevant to consider the association of mutations and APRs within the same structural domain rather than considering the entire protein ., The association of domain destabilization with strong APRs therefore further confirms that beyond loss-of-function , aggregation is a factor contributing to the pathogenic nature of human disease ., By combining both stability and intrinsic aggregation propensity of globular proteins , we find that 22 . 5% of the pathogenic mutations significantly increase the aggregation propensity of the affected protein by destabilizing ( ΔΔG > = 2 ) a structural protein domain containing an APR with a strong aggregation propensity ( TANGO > 70 ) ., As only 7 . 5% of neutral mutations display this combination , this suggests that many disease-mutations will result in increased protein aggregation ( p<0 . 00001 , Chi- square test ) through exposure of strong APRs ., This might not only eliminate the function of the affected protein through misfolding , but also change its synthesis , trafficking , and degradation through protein aggregation ., A non-exhaustive search through existing literature confirms the aggregation propensity or association with an aggregation pathology of 38 out of the 80 predicted proteins of our VARIBENCH set ( S1 Table ) ., In order to understand why a minority of neutral mutations seems tolerable ( ΔΔG ≥ 2 & TANGO > 70 ) , we compared the structural properties of these mutants with the pathogenic mutants harboring the same ( ΔΔG ≥ 2 & TANGO > 70 ) threshold ., Both in pathological and neutral variants the APRs of the affected protein domain are buried inside the hydrophobic core ( i . e . high sidechain/mainchain burial , Fig 5A and 5B ) and contribute to the thermodynamic stability of the domain ( i . e . negative dg , Fig 5C ) ., In addition , there is no difference in geometric distance relating site of mutation and APR and both are frequently distant from each other ( Fig 5D ) ., This indicates that under native conditions , these APRs are buried inside the protein core , whereby they generally only become exposed upon significant unfolding of the protein domain ., Intriguingly however , Fig 5A also shows that APRs associated to neutral mutations are , under native conditions , more exposed than APRs associated to pathogenic mutations ( p = 7 . 4 x 10−5 and p = 9 . 1 x 10−6 , resp . sidechain and mainchain burial , Mann-Whitney U test ) ., It is unclear why this is the case , but a plausible explanation could be the participation of these APRs in protein-protein interaction interfaces ., Alternatively , as aggregation is a concentration-dependent event , it is possible that proteins with low expression levels are more tolerant to mutations that increase their aggregation potential ., However , we should also take into account that, a ) some of the mutations can be misclassified and, b ) some of the aggregation-increasing mutations are mispredicted ., Application of the same rule on the SNPeffect 4 . 0 database32 showed that 26 . 9% of all disease-associated mutations ( with structural and domain information ) result in an increased potential for aggregation , compared to 8 . 2% in polymorphisms ., These are associated with very diverse diseases , including metabolic disorders such as Gaucher disease and Phenylketonuria , cancer ( Li-Fraumeni syndrome ) , and others such as Retinitis pigmentosa ., Some of these diseases have already been observed to be associated with the formation of protein inclusions , suggesting our predictions provide a realistic basis to judge the aggregation propensity of disease mutants 33 ., Interestingly , the aggregation propensity of cancer-associated mutations is particularly enriched ( 33 . 2% ) ., This observation is in agreement with more recent studies finding both in vitro and in vivo that misfolded p53 aggregates in tumors4 , 34 ., To analyze this in more detail , the COSMIC database containing somatic mutations in human cancer was investigated and compared to the 1000 genomes dataset , i . e . neutral mutations ., The prevalence of destabilizing mutations occurring in a domain with a strong aggregation tendency was higher in the first set ( 23 . 3% versus 15 . 4% ) ., Although this number was dominated by the presence of mutations in the p53 protein , possible aggregation-inducing mutations also occur in CDKN2A , PTEN , KRAS , NRAS BRAF , HRAS , and FLTR3 ( S2 Table ) ., Our lab already illustrated that both destabilized p53 4 and PTEN ( unpublished results ) are prone to aggregation ., Moreover , a study of Scaini et al . suggests that the Gly23Asp missense mutation in CDKN2A results in protein aggregation35 ., This study used the VariBench 6 dataset to analyze the effect of mutations on protein intrinsic aggregation parameters in order to investigate whether pathological mutations in general are associated to an increased aggregation potential ., Our findings demonstrate that the propensity to aggregate of disease-associated mutations is not restricted to familial cases of bone fide conformational diseases but that more generally protein aggregation is a property that is strongly enriched in pathological mutations across all types of human disease , including cancer , immune disorder , and inflammation ., The likelihood of protein aggregation being a real disease modifier is further corroborated by the fact that protein aggregation is more strongly enriched in pathological variants that are structurally associated to highly aggregation prone APRs ., The overall impact of protein aggregation on human pathology remains of course to be evaluated ., Nevertheless , the ability of protein aggregation to modify cellular physiology in multiple manners , thereby producing diverse phenotypic gain-of-function effects , is now well recognized and extends beyond synaptic loss and cell death in neurodegenerative diseases 36 , to englobe cell proliferation in cancer 4 and pharmacological resistance in metabolic diseases 5 ., Although the molecular mechanisms leading to these various effects are still unclear , there is no doubt that uncontrolled protein misfolding and aggregation impacts normal cell physiology and that the risk of protein aggregation increases with age due to a gradual loss of the capacity of cells to maintain protein homeostasis 37 , 38 ., It is therefore plausible that the impact of aggregation on human disease is much broader than currently expected , especially in conjunction with ageing ., If this is the case , preventive therapeutic strategies aiming at maintaining cellular proteostasis through age might have beneficial effects that extend well beyond the prevention of known age-related aggregation-associated degenerative diseases ., The strong enrichment of aggregation-prone disease mutants in globular proteins can be explained by the fact that protein structure and aggregation-prone protein sequences are evolutionary coupled properties ., Indeed , as tertiary protein structure requires hydrophobic sequence fragments , the corollary is a relatively high occurrence of APRs in globular protein sequences ( about 10% of residues are within an APR ) and less than 10% of globular protein sequences are devoid of APRs 39 ., As a result , mutants that thermodynamically destabilize structure will very often also promote aggregation by deprotection of APRs ., Moreover , mutations within APRs that increase their propensity to self-interact by β-strand interactions or mutations that create new APRs will further exacerbate aggregation by increasing the ‘stickiness’ of the primary sequence ., The same relationship dictating an association between aggregation and protein structure also explains why disordered protein domain sequences have a much lower aggregation propensity and also why pathogenic variants in these proteins generally do not increase their aggregation propensity ., Indeed , disordered protein sequences are enriched in charged and polar amino acid residues and depleted of hydrophobic residues ., As a result , they also have a much lower APR content and more than 40% of IDPs are devoid of APRs ., Mutations in disordered proteins are therefore much less likely to increase the aggregation propensity of the primary sequence , and as they are virtually devoid of tertiary interactions , structural destabilization is expected to only play a marginal role in the associated protein aggregation ., This however does not mean that protein aggregation is irrelevant to disordered proteins ., Indeed , several unstructured protein domains are associated with notorious aggregation-associated diseases , for instance α-synuclein in Parkinson disease ., Interestingly , although this protein is largely disordered , it still contains one strong APR ., It was recently found however that this region forms an α-helix that participates in alpha-synuclein tertramerisation in vivo 40 ., Incidentally , the frequent association of aggregation and RNA binding activity in disordered proteins , such as observed for TDP-43 and Fus in ALS and frontotemporal dementia , suggests the possibility that—just as structure in globular proteins—RNA binding activity and aggregation represent another set of co-evolved biophysical properties ., In conclusion , though much still remains to be explored experimentally , the current study predicts a much larger role for protein aggregation in disease than currently envisioned ., The importance of protein aggregation in disease is largely the consequence of the evolutionary association of protein structure and aggregation , an entanglement that is crucially controlled by the proteostatic machinery which itself erodes with ageing ., We assessed the frequency of aggregating mutations using VARIBENCH , a benchmark database for variations 6 , more specifically the datasets of neutral single nucleotide polymorphisms ( SNPs ) , comprising 21 , 170 human non-synonymous coding SNPs , and the pathogenic dataset , comprising 19 , 335 mutations ., The neutral dataset consists of non-synonymous coding SNPs with allele frequency 40 . 01 and chromosome sample count 449 from the dbSNP database build 131 ., The pathogenic dataset was obtained from the PhenCode database ( June 2009 ) , IDbases and from 18 individual LSDBs ., These are available for download at http://structure . bmc . lu . se/VariBench/download . php ., Selecting only those within a protein with an experimentally-determined crystal structure or a high-quality homology model ( homology > = 90 ) , the dataset is reduced to 5480 pathogenic and 1015 neutral mutations ., For the complete proteome analysis , we made use of the human proteins stored in the UniProt database excluding trans-membrane proteins with TMHM41 ., From the 1000 genomes project13 , release v3 . 20101123 was used and from the COSMIC database12 v65_28052013 ., Only non-synonymous mutations were analyzed ., Determining the protein domains present in a particular protein was possible using the SMART dataset42 ., 4838 pathogenic and 828 neutral mutations were located in a protein domain and further analyzed ., For the statistics , we made use of the Mann-Whitney U test , a nonparametric test for assessing whether 2 samples come from the same underlying population ( H0 ) ., Statistical significance for frequency distribution of disease and neutral mutations among different classes has been estimated using the Chi-squared test ., TANGO10 was used to determine the aggregation-prone regions ( APRs ) in the human proteins ., Aggregation regions were defined as a continuous stretch of at least five residues with a TANGO score higher than 5% ., The three positions before and after aggregation-prone regions are considered ‘gatekeeping flanks’ , with each P , R , K , E or D counting as gatekeepers ., No distinction was made between gatekeepers at the N or C terminus of the aggregating stretch ., APRs were considered to reside in a structural domain when at least one amino acid was present in this unit ., The FoldX3b5 forcefield11 was employed to model the mutations and to calculate the effect of the mutation on protein stability , the so-called ΔΔG ., A difference in stability ( ΔΔG ) higher than 0 . 5 or lower than -0 . 5 , indicates a destabilizing or stabilizing mutation respectively ., To calculate the distance in structural space between an aggregating stretch and a mutation , we made use of YASARA43 ., The minimal distance was selected when calculating the all-atoms distances from the mutation to the aggregation stretch . | Introduction, Results, Discussion, Materials and Methods | Protein aggregation is a hallmark of over 30 human pathologies ., In these diseases , the aggregation of one or a few specific proteins is often toxic , leading to cellular degeneration and/or organ disruption in addition to the loss-of-function resulting from protein misfolding ., Although the pathophysiological consequences of these diseases are overt , the molecular dysregulations leading to aggregate toxicity are still unclear and appear to be diverse and multifactorial ., The molecular mechanisms of protein aggregation and therefore the biophysical parameters favoring protein aggregation are better understood ., Here we perform an in silico survey of the impact of human sequence variation on the aggregation propensity of human proteins ., We find that disease-associated variations are statistically significantly enriched in mutations that increase the aggregation potential of human proteins when compared to neutral sequence variations ., These findings suggest that protein aggregation might have a broader impact on human disease than generally assumed and that beyond loss-of-function , the aggregation of mutant proteins involved in cancer , immune disorders or inflammation could potentially further contribute to disease by additional burden on cellular protein homeostasis . | Protein aggregation has been recognized to contribute to the development of more than 30 human diseases such as Alzheimer and Parkinson disease ., Here we have performed an in silico survey of human sequence variations to evaluate whether protein aggregation might impact human disease beyond the above-mentioned aggregation diseases ., We find that human disease mutations are more likely to increase the aggregation potential of proteins than non-disease associated mutations ., This survey therefore suggests the possibility that protein aggregation is a more widespread disease modifier than previously expected . | null | null |
journal.pcbi.1002747 | 2,012 | Theory on the Coupled Stochastic Dynamics of Transcription and Splice-Site Recognition | Transcription of eukaryotic genes by the RNA polymerase II complex ( RNAPII ) produces a primary mRNA transcript ( pre-mRNA ) that contains both exons and introns ., Introns are removed by splicing 1 , 2 , 3 via the assembly of a spliceosomal complex including small nuclear ribonucleo proteins ( snRNPs ) 4 , 5 , 6 , 7 ., Recent studies show that the majority of genes in higher eukaryotes are alternatively spliced and , therefore , contribute significantly to the structural as well as functional complexity and diversity of organisms 8 , 9 , 10 ., The process of splicing can start as soon as the pre-mRNA begins to emerge from RNAPII ., Cis-acting regulatory elements such as splicing enhancers and silencers generally determine the splicing pattern of a given multi-exonic gene especially when transcription is not kinetically coupled to the splicing 11 , 12 , 13 , 14 ., However , when transcription is coupled to splicing , inclusion or exclusion of an exon in the final transcript will also be strongly influenced by the transcription elongation rate as well as the local concentrations of various factors involved in the spliceosomal assembly and their interactions 15 , 16 , 17 , 18 ., Two basic models have been proposed to explain the various differences in the alternative splicing patterns of a given gene ., According to the kinetic model 19 , inclusion or exclusion of an exon in the final transcript is determined by the transcriptional elongation rate associated with the corresponding pre-mRNA in addition to the cis-acting regulatory elements ., Exons are classified as ‘strong’ or ‘weak’ depending on whether they possess cis-acting regulatory elements associated with them or not ., The inclusion of ‘strong’ exons is favored at higher transcriptional elongation rates whereas ‘weak’ exons may be included in the final transcript only when the transcriptional elongation rate is comparatively slower ., Since the concentration of snRNPs in the vicinity of the transcriptional machinery is fixed under steady state conditions , a strong exon that has emerged recently from the transcriptional assembly will have a better chance of interacting with the snRNPs as compared to a weak exon that emerged earlier ., Therefore , a weak exon will have a better chance to interact with the snRNPs only when there is a decrease in the rate or a pause in the transcriptional elongation process ., According to the recruitment model 20 , inclusion or exclusion of an exon is also decided by the interaction of the C-terminal domain ( CTD ) of RNAPII with a set of gene and exon specific DNA binding proteins and the snRNPs 19 , 20 in addition to cis-acting regulatory elements ., The CTD of the RNAPII interacts directly with the snRNPs and other factors , increasing the local concentrations of these factors in the vicinity of the emergence of a weak exon and thus enhancing the probability of weak exons to interact with the snRNPs ., There are four basic variables involved in the definition of an exon: ( 1 ) cis-acting regulatory elements 11 , 12 , 13 ( 2 ) transcription elongation rate 19 ( 3 ) interactions between the CTD of RNAPII and the snRNPs , hnRNPs and SR proteins 19 , 20 ( often referred to as ‘recruitment’ ) and ( 4 ) the stochastic dynamics involved in the recognition of the 5′ donor splice sites by U1 snRNPs while the pre-mRNA is evolving from the transcription assembly ., Variables 1 and 3 are specific to each exon whereas variables 2 and 4 are generic and affect all the exons across various transcripts of an organism ., Most of the current splice pattern prediction algorithms consider mainly the cis-acting regulatory elements ( variable, 1 ) 21 , 22 , 23 , the kinetic model focuses on variable 2 19 and the recruitment model considers mainly variable 3 19 , 20 ., None of the current algorithms or models considers the stochastic dynamics associated with the snRNP search process ( variable 4 ) ., Here we propose a biophysically plausible theory from first principles to describe the coupled dynamics of transcription and splicing ., This work presents initial steps towards capturing the basic relationship between transcriptional elongation and splicing; the simplified model that we propose does not include multiple critical components that affect the splicing outcome including cis-acting pre-mRNA sequence motifs , trans-acting interactions with different proteins and variable rates of RNAPolII transcription ., We focus on the stochastic dynamics whereby snRNPs locate the 5′ donor sites and how this search influences the outcome of splicing ., We evaluate the theoretical predictions by analyzing expression data at the exon level from exon microarrays and RNAseq experiments across different tissues in mice and humans ., Recent single cell studies have revealed 24 , 25 , 26 that small nuclear ribonucleoproteins ( snRNPs ) and other splicing proteins are freely diffusing inside the entire volume of various nuclear and splicing factor compartments of within the eukaryotic cell nucleus ., Splicing is kinetically coupled to transcription when the time required to generate a complete transcript is longer than the time required for the assembly and catalytic activity of the spliceosomal proteins ., Under such coupled conditions , we must simultaneously consider at least two different types of dynamical processes:, ( i ) transcription elongation by the RNA polymerase II transcription complex ( RNAPII ) and, ( ii ) the search process whereby snRNPs locate the 5′ donor splicing sites ( DSS ) on the emerging pre-mRNA to initiate the spliceosomal assembly ( Figure 1 ) ., The freely diffusing U1 snRNP can locate the donor splicing sites via two different types of mechanisms: a pure three-dimensional diffusion-controlled collision route ( 3D ) and a combination of three-dimensional and one-dimensional diffusion dynamics as in the case of typical site-specific DNA-protein interactions ( 3D+1D ) 27 , 28 , 29 , 30 ., Upon successful binding of the U1snRNP molecule to the 5′ donor site , a cascade of molecular processes involving multiple snRNPs ensues , culminating in the formation of the spliceosomal complex and intron removal 1 , 2 , 3 ., Except for the binding of U1 snRNPs at the 5′ donor site , all the other steps involve the hydrolysis of ATPs ., This means that the binding of U1 is a purely thermally driven process and here we focus on the dynamics involved in this rate-limiting step ., All the other binding events and reactions , including transcription elongation , involve ATP hydrolysis and we therefore assume that the effects of thermal induced fluctuations are minimal in these reaction steps ., We ignore the thermal induced fluctuations over these reaction steps while describing the search dynamics of snRNPs along the pre-mRNA ., The overall probabilities associated with the interaction of snRNPs with various DSSs depend on the type of search mechanism followed by the snRNPs ., We start by considering the model illustrated in Figure 1 where the U1 snRNP has bound the emerging pre-mRNA via non-specific interactions facilitated by 3D diffusion and it scans the concomitantly emerging pre-mRNA for the presence of DSSs via 1D diffusion ., At a given time t , let y ( t ) denote the length of the emerging pre-mRNA and let x ( t ) denote the position of the non-specific bound U1 snRNP on the pre-mRNA chain ., The DSS under consideration is located at position x\u200a=\u200an ( DSSn ) , which has not been transcribed at time t ( or is currently not reachable by the snRNP due to steric hindrance ) ., Such coupled dynamics of snRNPs and RNAPII , represented by the set of dynamic position variables x and y ( ) on the same pre-mRNA , can be described by the following set of Langevin type stochastic differential equations 31: ( 1 ) The transcription elongation rate is denoted as kE ( bases s−1 ) ., xd ( bases2s−1 ) is the 1D diffusion coefficient associated with the searching dynamics of U1 snRNPs towards the DSSn and is the delta-correlated Gaussian white noise with and ., The movement of RNAPII along y is energetically driven via the hydrolysis of ATPs ., As a result , the fluctuations in y are negligible and we use a deterministic description for RNAPII in Eq ., 1 ., Let denote the joint probability of finding the snRNPs at position x and RNAPII at position y at time t given initial conditions x0 , y0 ., The Fokker-Planck equation associated with the temporal evolution of can be written as follows 31: ( 2 ) Here the initial condition is , ensuring that at time t0 , the probability of finding x0\u200a=\u200a0 , y0\u200a=\u200a0 is normalized to one ., The boundary conditions are as follows: ( 2′ ) Here x\u200a=\u200a0 as well as x\u200a=\u200ay ( y<n ) act as reflecting boundary conditions for the dynamics of snRNP ., Whenever the snRNP tries to visit x≤0 or x≥y it is reflected back into x0 , y ., Here acts as absorbing boundary condition whenever ., Let indicate the probability that RNAPII and snRNP are between position 0 and n at time t ( given starting points x0 , y0 ) ., Let denote the mean first passage time ( MFPT ) associated with the binding of snRNP at DSSn starting from initial conditions ( x0 , y0 ) ., From the definition of MFPT , ., Noting that before time n/kE , the DSSn has not emerged yet , we have:and therefore obeys the following backward type Fokker-Planck equation 31: ( 3 ) with the following boundary conditions: ( 3′ ) We assume that the residence time associated with dissociation of the non-specific bound snRNPs from the pre-mRNA is much higher than the time required by the snRNPs to locate the 5′ donor splicing sites ., As a result , we have introduced a reflecting boundary condition at x\u200a=\u200a0 in the first boundary condition ., The other boundary conditions can be directly derived from Eq ., 2′ ., The second boundary condition describes the conditions where RNAPII transcription elongation is the limiting step and the third boundary condition describes the conditions where snRNP diffusion is the limiting step ., The particular solution to Eq ., 3 for the boundary conditions in Eqns 3′ can be written as follows: ( 4 ) Considering x0\u200a=\u200a0 and y0\u200a=\u200a0 ( both RNAPII and snRNP start at the origin ) , we have ., The first term is the time required to generate a pre-mRNA of n bases and the second term is the time required by the snRNPs to completely scan this pre-mRNA length via 1D diffusion ., The validity of this equation for the MFPT under various values of n and kE is illustrated in Figure 2A–B using random walk simulations ., In line with site-specific DNA-protein interactions 27–30 , we assume that snRNP molecules locate their respective DSS binding sites on the growing pre-mRNA via a combination of 1D and 3D diffusion-controlled collision routes ., Under such conditions , from Eq ., 4 we find the average overall search time ( ) required by the snRNPs to locate DSSn ( x0\u200a=\u200a0;y0\u200a=\u200a0 ) : ( 5 ) Here ( units of seconds ) is the 3D diffusion-controlled collision time required for non-specific binding of U1 snRNP with the pre-mRNA of length n ., Eq ., 5 suggests that there exists an optimum position of DSSn on the emerging pre-mRNA such that the search time required by the snRNPs to locate this DSSn will be a minimum ., This optimum value can be obtained by solving for n ., The explicit real solution of the resulting cubic equation is: ( 6 ) where ., Upon substituting nopt in Eq ., 5 we find the minimum search time ., In line with the prediction of the kinetic model , when the snRNPs locate the DSSn via a purely 3D diffusion-controlled collision route , the overall search time is: ( 7 ) In this equation , c ( units of bases ) is the sequence length within which the snRNPs can be captured at the 5′ donor site ., A precise and tight binding would correspond to c\u200a=\u200a1 ., Upon comparing this expression with Eq ., 5 we find that there exists a critical position on the pre-mRNA ( nc ) such that τS , 1D3D\u200a=\u200aτS , 3D ., Solving the cubic equation for n ( Figure 2C ) : ( 8 ) where ., While deriving Eq ., 5 we have assumed that the non-specific bound snRNP does not dissociate from the pre-mRNA chain until it reaches DSSn ., We relax this assumption by modeling the search dynamics of snRNPs as multiple cycles of dissociation-scan-association events ., In this modified version of the model , the non-specific bound snRNP can dissociate after scanning an average pre-mRNA length of L bases and then it re-associates back at the same or different location of the pre-mRNA chain ., In this way , snRNPs are required to undergo at least ( n/L ) such association/dissociation events to scan the entire length of n bases ., Under such conditions , the expression for the overall search time ( ) can be written as follows: ( 9 ) Here L2/6xd is the average time required by the non-specific bound snRNPs to scan an average of L bases of pre-mRNA before the dissociation event ., The scan length L depends on the magnitude of the interaction between the snRNPs and the pre-mRNA ., When L\u200a=\u200an , Eq ., 9 reduces to Eq ., 5 ., When , there exists an optimum value of L in Eq ., 9 at which is a minimum: ., The corresponding minimum achievable search time is: ( 10 ) One should note that the optimum 1D scanning length can be achieved by the diffusing U1 snRNPs only when the inequality condition holds since by definition ., Further analysis shows that will reach a minimum only when ., Upon comparing Eqns 5 , 7 and 9 we find that when n<nc , then both and will be lower than ., In the range L ( 0 , nopt ) the cubic equation has two real solutions for n ( n1∼L and n2 , marked in Figure 2C ) for n ., When n ( L , n2 ) , we find that ., The relationship among these different search times is shown in Figure 2C ., These results suggests that among the three possible modes of searching ( pure 3D , 1D3D with multiple dissociations and 1D3D without dissociation ) , the 1D3D search mode of search without any dissociation event will be the most efficient and preferable one in the range n ( L , n2 ) where L is the possible 1D scanning length associated with diffusion of U1 snRNPs along the emerging pre-mRNAs ., We find from Eqs ., 9–10 that similar to the pure 3D diffusion mediated search time ( ) , is also a monotonically increasing function of n ., On the macroscopic level , the interactions of snRNPs with DSSn can be described by the following chemical reaction scheme I: ( Scheme I ) Here ( bases−1s−1 ) is the bimolecular type forward on-rate constant associated with the site-specific interaction of snRNP with the DSSn and ( s−1 ) is the respective dissociation or off-rate constant ., The sequence of DSSn plays critical role in determining the value of the off-rate ., The number of snRNPs will be higher than the number of DSSs of a particular pre-mRNA transcript ., In this situation , the thermodynamic probability of finding DSSn ( ) to be bound with snRNPs is: ( 11 ) Here N0 is the total number of the freely diffusing snRNPs inside the nucleus ., It follows from Eqns 5–6 that the probability is maximized when n\u200a=\u200anopt irrespective of the value of the intra nuclear concentrations of snRNPs or the amount of time for which the completely transcribed pre-mRNA chain stays inside the nuclear compartment for further post-transcriptional processing ., On the other hand , when the snRNP search mode is purely via 3D routes then the probability ( ) is a monotonically decreasing function of n ( Figure 2D ) : ( 12 ) From Eqs 11–12 , we find ( all DSSn bound by the snRNP given infinite concentration ) ., Those splicing sites located closer to the optimum position ( ) approach this limit faster ., Using Eq 11 we define the overall splicing efficiency of a transcript of length n as follows: ( 13 ) The value of the splicing efficiency ( between 0 and 100% ) indicates how well exons present in a given pre-mRNA transcript of length n interact with the available pool of snRNPs , are subsequently spliced and hence get included in the final transcript ., This means that the overall levels of the final transcript should be directly proportional to this splicing efficiency ., There exists an optimum length of pre-mRNA transcript ( μ ) at which achieves a maximum ., The optimum μ can be obtained by numerical solving for n ., The overall level of the final transcript will be maximum at since the overall average probabilities associated with all those exons of the given pre-mRNA transcript of length μ to interact with the available snRNPs will be a maximum ., We consider a transcript c of length n and its expression in tissue k ., We define the overall signal as where is the signal from the exon located at position i in transcript c in tissue k ., With this definition we find that the maximum gene signal value of n occurs at which means that when the equality holds ., This follows from the fact that ., We compare the theoretical predictions outlined in the previous section with two different types of experimental measurements:, ( i ) experiments based on exon microarray data and, ( ii ) experiments based on high-throughput RNA sequencing data ( RNAseq ) ( “Materials and Methods” ) ., Upon substituting the parameters τt , kE and xd into Eq ., 6 for the optimum position of the DSS on the pre-mRNA we find bases and the minimum achievable overall search time required by the snRNPs ., This search time is significantly higher than physiologically relevant timescales ( for example , the cells generation time ) ., One should note that this higher timescale corresponds to the interaction of a single snRNP molecule with a single splicing site ., The search time will be proportionately scaled up/down depending on the number of freely available snRNPs and nascent splicing sites inside the nucleus as ., There are ∼2×104 genes in the human genome , and there are on average ∼10 exons per gene ., This means that there are d0∼4×103 such splicing sites at any given active region of the chromosome ( corresponding to ∼1% of the total pre-mRNAs being processed ) ., With these values we find ., These results suggest that the appearance of the speckles where snRNPs are concentrated inside the nucleoplasm of higher eukaryotes is mainly to scale down the search time required by snRNPs to locate the splicing-sites on the pre-mRNA ., We conclude from the expression for the probability of finding the snRNP at position n ( , Eq . 11 ) that the DSS located at position of the growing pre-mRNA will have more chances to interact with the available snRNPs ., Here the minimization of the overall search time is achieved mainly via the enhancing effects of the increasing numbers of non-specific interactions of snRNPs with the growing pre-mRNA ., We learn from Eq ., 8 that the inequality condition will hold whenever ., The current parameter settings yield bases ., Various single-cell studies using fluorescence recovery after photo bleaching ( FRAP ) provide an empirical estimate for the dissociation rate of snRNPs from the pre-mRNA chain: 24 , 25 , 26 ., This is an overall off-rate that includes dissociation of snRNPs from both the non-specific and specific binding sites ( the off-rate of snRNPs from the splicing sites will be lower than the off-rate from non-specific binding sites . ) Using this value of , the limiting behavior of pn , 1D3D and pn , 3D as is demonstrated in Figure 2D ., This figure suggests that the optimum position of DSS will spread into a wider range as the total concentration of snRNPs increases inside the nucleoplasm ., Single molecule studies suggest an average 1D scanning length of L∼100 bases for the DNA-binding proteins under in vivo conditions 32 ., With this value , upon solving the cubic equation for n we find that n1\u200a=\u200a100 and n2\u200a=\u200a2×106 bases ., Since within this range , this result suggests that the dominating mode of searching of U1 snRNPs for the 5′ splicing sites is likely to be via the combination of 1D and 3D without dissociation for most of the pre-mRNAs ., We considered microarray data evaluating exon levels in different tissues and species ( Materials and Methods . ) Examples of mouse and human constitutively spliced multi-exonic genes across various tissues are shown in Figure 3A–B ., These examples , identified using the ranking metric defined in Eq ., 14 , suggest that there exists a broad optimum position of splicing sites on the pre-mRNA at which the probability associated with the inclusion of the associated exon is maximized ., This position is approximately independent of the tissue analyzed ., In these particular mouse and human genes ( Dtnb dystrobrevin beta in mouse and VIT vitrin in human ) , this optimum exon number occurs at the pre-mRNA position of n∼5×104 to 105 bases ( arrow in Figure 3A–B ) ., Other examples are included in supplementary materials ( Figure S1 , S2 ) ., The position of the maximum splicing index value , independently of the tissue , occurs around nopt∼7×104 bases as predicted by Eq ., 6 , with an error margin of ∼25% ., Overall analysis of the multi-exonic genes present in both human and mouse genomes revealed an average intron length of ∼4×103 bases with a median of ∼103 bases ., Here the average length of exons is ∼2×102 bases with a median of ∼102 bases ., Results of genome wide analysis of the median of exon positions on pre-mRNAs of human and mouse is shown in Figure 3C–D which reveals the following approximate scaling relationships between the positions ( n ) and the exon numbers ( ε ) :The standard error ( SE ) in such transformation is approximately 5 to 25% of the mean ( n ) for ε in the range 1 to 100 ( Figure 3C–D ) ., This suggests that the optimum positions nopt and minτS , 1D3D may be observed anywhere in the ±25% of the predicted values upon a genome wide averaging across exon numbers ε ., The computed first exon normalized average signal ( FENAS , defined in Eq . 15 ) associated with various mouse tissues ( kidney , brain , liver , muscle and heart ) and human tissues ( cerebellum , kidney , liver , heart , muscle and normal and cancerous colon ) is shown in Figure 4A–B ., This figure indicates a maximum at approximately ., This value corresponds to the optimum position of the Affymetrix annotated exon on the pre-mRNA at bases , which is broadly consistent with our theoretical predictions ., We also compared the theoretical predictions with experimental data obtained from RNAseq experiments ( Materials and Methods ) ., The data from the exon level and transcript level signals obtained from RNASeq data of mouse brain and human T293 cells are shown in Figure 4C–D ., The results from the RNASeq data are comparable to those from the microarray data and also reflect an optimum exon position , approximately around ., Upon substituting molecules , s−1 and the empirical values of τt , kE and xd into Eq ., 13 and numerically solving it for the optimum transcript length n\u200a=\u200aμ we find bases ( Figure 5 ) ., This value corresponds to approximately exons ., From the theoretical analysis , we learn that the overall transcript signal of a given gene is maximized when the number of exons present in that gene is closer to this value ., We find from Figure 5 that the splicing efficiency is >95% whenever the length of the pre-mRNA transcript falls inside the range of ∼ ( 102–107 ) bases ., The distribution of transcript lengths both in humans and mouse is well within this broad range ., Furthermore , we calculated the genome level averaged transcript signal across various mouse and human tissues using Eq ., 16 ., Figure 6 suggests that there is a broad maximum in the transcript signal approximately centered around both based on the microarray data ( Figure 6A–B ) as well as the RNAseq data ( Figure 6C–D ) ., Within the expected error range of ±25% , these distributions and the location of the maxima are consistent with the theoretical predictions ., To further evaluate whether the experimental data are consistent with the existence of optimal exon positions , we computed the distribution of FENAS values for two separate broad ranges: ( 1 ) ( i . e . around the theoretical optimum ) and ( 2 ) or ( i . e . far from the theoretical optimum ) ., The distributions of FENAS signals were significantly different for these two ranges ( t-test , p<0 . 05 , Figure 7 ) ., While the RNA polymerase II complex ( RNAPII ) is producing the pre-mRNA , multiple splicing factors diffuse inside the nucleus and initiate the recognition steps required in the process of splicing ., Therefore , the ultimate mature mRNA product depends on several variables that affect the kinetics of these chemical and diffusion processes ., These variables include RNAPII elongation speed and the presence of pausing events during transcription , the steric availability of splicing signals along the emerging pre-mRNA , exon and intron lengths , the abundance of different splicing factors and the sequence and hence affinity of those sequences for the splicing factors ., Here we develop a simple theoretical framework that aims to capture the key interactions between transcriptional elongation and splicing ., The biophysical model proposed here can explain the effects of the stochastic search dynamics of small nuclear ribonucleo proteins ( snRNPs ) on the splicing pattern of eukaryotic genes ., We considered two different ways to model the dynamics of snRNPs in the process of locating the splicing sites on the concomitantly evolving pre-mRNA: a pure three-dimensional diffusion process and a combination of three- and one-dimensional diffusion along the pre-mRNA ., Our theoretical analysis on the coupled dynamics of transcription elongation and splicing revealed that there exists an optimum position of the splice sites on the growing pre-mRNA at which the time for snRNP binding is minimized ( Figure 2 ) ., The minimization of the overall search-time is achieved mainly via increasing non-specific type interactions between the RNA binding domains of snRNPs and the pre-mRNA ., The theory further revealed that there is an optimum transcript length that maximizes the sum of the probabilities for the exons in the transcript to interact with the snRNPs ., This suggested that the overall transcript signal should be maximized at this transcript length ., We evaluated the theoretical predictions by analyzing exon microarray data from various mouse and human tissues ( Figures 3–6 ) ., The empirical data revealed that the optimum position of the splice sites on the growing pre-mRNA occurs at ∼4 . 5×104 bases and the optimum length of the transcript occurs at ∼7 . 5×104 bases ( corresponding approximately to the ∼11th and ∼20th exon in the genome wide first exon normalized average signal space . ) The empirical data are broadly consistent with the theoretical predictions and the model captures , to a first approximation , some of the variability in exon level signals and splicing patterns ., Several computational algorithms have been developed to attempt to predict splicing patterns from DNA sequence ., Most of the current splicing pattern prediction algorithms are solely based on cis-acting regulatory elements 21 , 22 , 23 ., Typically each exon of a given pre-mRNA transcript is assigned a score depending on the presence or absence of exonic and intronic enhancer or silencer elements and their degree of conservation across different species 31: ., Using these exon level scores , transcript level scores are computed ., Our work points out that , before computing the exonic scores for the presence of cis-acting elements , the ‘backbone’ of the scoring scheme assumes that all the exons are probabilistically equivalent ., This uniform distribution of exon probabilities may hold only when the snRNP search mode is via pure 3D diffusion ( Figure 2D ) or the nuclear concentration of snRNPs is infinite ., In more general scenarios , instead of a uniform distribution , our theoretical model suggests that the backbone of the scoring scheme should be given by the probability functional as defined in Eq ., 12–13 ., In other words , the backbone of the scoring scheme is determined by the generic variables 2 ( transcription elongation rate ) , 3 ( interactions between RNAPII and snRNPs ) and 4 ( stochastic dynamics of snRNP search processes ) as highlighted in the introduction ., The model suggests that a modified scoring scheme would include the background model that accounts for the coupled kinetics of transcription and splicing in addition to the exonic scores for the presence of cis-acting regulatory elements ., The theoretical framework presented here provides initial steps to describe the coupled chemical and diffusion process that underlie transcription and splicing ., While we focused here on generic variables that affect all transcripts and genes , a lot of the transcript-to-transcript and gene-to-gene variability depends on sequence specific factors , gene-specific transcription pausing events , regulation of transcriptional termination and the speed at which the mRNA is transported to the cytoplasm ., The theory proposed here constitutes a starting point to build more sophisticated models that further incorporate important aspects of the biology that were not considered in this initial examination ., To compare our theoretical predictions with experimental observations , we considered two different types of publicly available data:, ( i ) exon microarray data and, ( ii ) RNAseq data ., Experimental artifacts are introduced in the exon microarray data by factors such as cross-hybridizing probes , signal heterogeneity due to variation in the base composition of probes and signal variation due to fluctuations in the spot size of probes during microarray design ., The cross-hybridization problem was solved by removing those probes showing hybridization at more than one location ., Since the variations in probe level signals due to base composition , spot size and RT reaction are approximately random in nature , we assume that these errors are ameliorated by averaging over the scale normalized and background subtracted probe level signals of a probe set id , exon cluster id or transcript cluster id . ., Exon level signals are computed by averaging the probe-set id level signals contained in an exon-cluster id and transcript level signals are computed by averaging the exon level signals contained in a transcript cluster id ., Only the Refseq annotated transcript cluster ids were considered for all the subsequent calculations ., We used the standard Tukey biweight algorithm 39 to remove the outlier probe signals before computing the average ., We considered multiple transcripts ( indexed by c ) and different tissues ( indexed by k ) ., Let sε , c , k denote the log2 of the expression level of the εth exon in transcript number c and tissue number k ., The relative probability associated with the εth exon to get included in the final transcript was defined as where mc is the total number of exons in transcript c ., The probability is directly related to the splicing-index ( ) of the associated exon which is a measure of the extent of alternative splicing in that transcript , defined as where gc , k is the overall level of transcript c in tissue k ., In addition to the stochastic component , other splicing variables such as the presence of cis-acting regulatory elements including splicing enhancers and suppressors can significantly modify the probabilities defined here ., To evaluate the expression derived in Eqns ( 11–12 ) we need a splicing probability profile of a pre-mRNA transcript that contains multiple exons spliced in a ‘constitutive’ manner across various tissues ., Here we use the term ‘constitutive splicing’ to indicate the splicing pattern of a given pre-mRNA that is conserved across various tissues in a given organism ., We use the following variance-based scoring metric to rank and select such constitutive transcripts from the pool of multi-exonic pre-mRNAs of a given genome: ( 14 ) We ranked the transcripts based on and we consi | Introduction, Results, Discussion, Materials and Methods | Eukaryotic genes are typically split into exons that need to be spliced together to form the mature mRNA ., The splicing process depends on the dynamics and interactions among transcription by the RNA polymerase II complex ( RNAPII ) and the spliceosomal complex consisting of multiple small nuclear ribonucleo proteins ( snRNPs ) ., Here we propose a biophysically plausible initial theory of splicing that aims to explain the effects of the stochastic dynamics of snRNPs on the splicing patterns of eukaryotic genes ., We consider two different ways to model the dynamics of snRNPs: pure three-dimensional diffusion and a combination of three- and one-dimensional diffusion along the emerging pre-mRNA ., Our theoretical analysis shows that there exists an optimum position of the splice sites on the growing pre-mRNA at which the time required for snRNPs to find the 5′ donor site is minimized ., The minimization of the overall search time is achieved mainly via the increase in non-specific interactions between the snRNPs and the growing pre-mRNA ., The theory further predicts that there exists an optimum transcript length that maximizes the probabilities for exons to interact with the snRNPs ., We evaluate these theoretical predictions by considering human and mouse exon microarray data as well as RNAseq data from multiple different tissues ., We observe that there is a broad optimum position of splice sites on the growing pre-mRNA and an optimum transcript length , which are roughly consistent with the theoretical predictions ., The theoretical and experimental analyses suggest that there is a strong interaction between the dynamics of RNAPII and the stochastic nature of snRNP search for 5′ donor splicing sites . | The DNA encoding most eukaryotic genes is interrupted by long sequences called introns ., These introns need to be removed through the process of splicing to produce the mature messenger RNA ., The process of splicing plays a critical role in determining the exact aminoacid content of the ensuing protein ., Several molecules denominated small nuclear ribonucleo proteins ( snRNPs ) are involved in finding the appropriate 5′ donor splicing sites for splicing ., Transcription and splicing occur simultaneously and the ultimate product depends on the relative speed of transcription and the stochastic dynamics underlying splicing ., Here we propose a biophysically plausible theory that describes the ongoing interactions between transcription and splicing ., We show that the theoretical predictions are consistent with experimental measurements of the abundance patterns of different exons and transcripts across tissues . | genome expression analysis, rna synthesis, theoretical biology, biophysics theory, biology, biophysics, physics, microarrays, rna, systems biology, rna processing, nucleic acids, genomics, computational biology, genetics and genomics | null |
journal.pntd.0004612 | 2,016 | Leprosy on Reunion Island, 2005-2013: Situation and Perspectives | Leprosy , also known as Hansen’s disease , is a chronic infectious disease caused by Mycobacterium leprae ., Its most likely route of transmission is the upper respiratory tract , and it has a long incubation period 1 ., The disease primarily affects the skin and peripheral nerves , causing sensory loss 2 ., If not treated , it can cause progressive and permanent damage to the skin , nerves , limbs or eyes , and may lead to amputations and disabilities ., Because of these visible symptoms , leprosy has always been strongly stigmatized , preventing patients to seek treatment ., Leprosy can be cured using multidrug therapy ( MDT ) , an association of different antibiotics including rifampin , dapsone and clofazimine 3 ., Human leprosy has been documented for millennia and is probably the oldest human-specific infection 4 ., The disease was distributed worldwide during the Middle Ages , but its prevalence has considerably decreased since MDT became available in the early 1980s 5 and national campaigns and disease surveillance systems were developed in most endemic countries ., At the beginning of 2012 , the registered prevalence of leprosy at global level was around 180 , 000 cases ., The majority of new cases ( 95% ) were reported from 16 countries ., Some areas remain highly endemic , such as the Comoros and Mayotte in the Indian Ocean6 ., Reunion Island is a French overseas territory located in the South West Indian Ocean , 700 km to the East of Madagascar ., Leprosy first arrived on Reunion Island in the early eighteenth century with African slaves and immigrants from Madagascar 7 ., Leprosy continued to be a serious concern on Reunion Island until the 1960s , when about 148 patients were still followed in 1966 8; The disease was still endemic on Reunion Island until 1980 9 ., Improvements in the health care and living conditions of residents of the Island led to a significant decrease in the number of new cases of leprosy ., However , since the prevalence of the illness on Reunion Island has been poorly documented due to the lack of an adequate surveillance system long preventing proper reporting of the illness , which means that it was impossible to know if the World Health Organisation’s goal to eradicate the disease ( i . e . , prevalence rate <1/10 000 ) has been truly achieved on Reunion Island ., In this context , the Regional Office of the French Institute for Public Health Surveillance ( CIRE Indian Ocean ) conducted in 2010 a retrospective study to collect information on all cases diagnosed between January 2005 and December 2010 ., This retrospective study showed that leprosy was still present on Reunion Island ., A prospective surveillance system was then implemented in January 2011 10 ., The aim of the present study was to estimate the number of new cases of leprosy detected annually on Reunion Island between 2005 and 2013 , describe the clinical features of patients and finally to evaluate eradication of leprosy on Reunion Island, This article is based on a descriptive study of new leprosy cases diagnosed between 2005 and 2013 on Reunion Island ., The study was conducted retrospectively between 2005 and 2010 and prospectively between January 2011 and December 2013 ., In 2010 , as the lack of an adequate surveillance system made impossible to know if WHO objective for eradication was achieved , the CIRE Indian Ocean decided to conduct a retrospective study on all cases diagnosed in the last 5 years ( 2005–2010 period ) ., This study involved health professionals who were in a position to collect diagnosed cases of leprosy ., Private and hospital dermatologists , infectiologists and anti-tuberculosis centres were first informed about the study by letter and then contacted by telephone to report all cases of leprosy that occurred during this period by completing a standardized questionnaire ., This retrospective study showed that leprosy was still present on Reunion Island and that the implementation of a prospective surveillance system was needed 10 ., From January 2011 , health professionals reported systematically all newly diagnosed cases using the same standardized questionnaire as in the retrospective study ., In addition to clinicians , pathology laboratories are now requested to report the histologic diagnoses of new leprosy cases in order to ensure the completeness of data collection ., All new leprosy patients must be sent for treatment to a referent physician in the anti-tuberculosis centre of the University Hospital of Reunion Island ., The diagnosis of leprosy is based on the World Health Organization’s criteria: “patient presenting skin lesion consistent with leprosy and with definite sensory loss , with or without thickened nerves and/or positive skin smears” 11 ., In our study , all patients had a skin biopsy and/or a nose and ear smear to confirm the diagnosis , except for 2 patients who had been diagnosed several years earlier and presented clinical features of relapse ., When the bacteriological index was positive on the biopsy or nose and ear smear , the patient was classified as multibacillary ., Patients showing clinical manifestations of leprosy but negative smears were classified as paucibacillary ., The retrospective study was based on data collected at CIRE Indian Ocean ., The prospective study was based on systematic reporting ., All the information was collected using a standardized questionnaire ., The following data were collected for each patient: socio-demographic data ( age , country of birth , country of residence , sex , profession ) , type of leprosy according to WHO classification ( multibacillary for patients with positive smears and paucibacillary for patients with negative smears ) , and clinical data ( method of diagnosis , degree of disability evaluated at the time of reporting , i . e . , before treatment ) ., Disability was classified according to the WHO grading system ( grade 1: decrease or loss of sensibility in the eyes , hands and/or feet; grade 2: Disability or deformity in the eyes , hands and/or feet ) 11 ., All patient data were anonymized ., Quantitative variables were expressed as mean and standard deviation or median and interquartile range ., Qualitative variables were expressed as proportions and 95% confidence interval ., We performed separate analyses for each period of collection ., Registered prevalence was calculated by dividing the number of yearly cases reported by Reunion Island’s population that year , and multiplying that number by 10 , 000 ., Results are summarized in Table 1 ., From January 2005 to December 2013 ( 9 years ) , 25 new cases of leprosy were diagnosed on Reunion Island ., During the first period of our study ( 2005–2010 ) 18 cases were diagnosed and 7 during the second period ( 2011–2013 ) ., The median age of patients at the time of diagnosis was 48 . 2 years in the first period versus 44 . 3 years in the second; moreover , male patients were predominant in the entire period ( 68% , 17/25 ) ., Only 1 child under 15 years of age was diagnosed with leprosy in the first period , and none in the second ., This child was born in the Comoros and had recently migrated to Reunion Island; hence he was probably contaminated in the Comoros ., Among the 25 new cases , 12 are Reunion Island residents who never lived outside the Island , and are therefore considered to be autochthonous patients ( “Confirmed autochthonous cases” ) ., There were 10 new autochthonous cases in the first period ( 6 years: 2005–2010 ) , and only 2 in the second ( 3 years: 2011–2013 ) ., 6 of these autochthonous patients lived in the same area of Saint-Louis , a popular city in the southwest of the island that constituted a focus of transmission ., Among the 13 patients born or having resided outside Reunion Island ( Comoros , Mayotte or Madagascar ) , 10 cases arrived on Reunion Island less than 5 years before the diagnosis ., Considering the mean duration of leprosy incubation ( about 5 years according to the WHO ) , these cases were then considered as imported cases ., 2 other cases arrived on Reunion Island 9 and 12 years before the diagnosis; and for the last one the date of arrival on Reunion Island was not specified ., Those 3 cases were doubtful and were then , taking the most pessimistic option , considered as possible autochthonous cases ., Skin biopsy was largely available on the island; the majority of diagnoses were therefore made with skin biopsy ( 84% , 21/25 ) ., However , some patients had a complementary ear and nose biopsy to classify the case based on WHO criteria ., The multibacillary form was predominant ( 72% , 18/25 ) ., The rate of new cases with grade 2 disability was 24% ( 6/25 ) , and 56% ( 14/25 ) of patients had a grade 1 or 2 disability at the time of detection ., Although leprosy is now diagnosed in less than 1/10 000 inhabitants on Reunion Island , it is important to follow indicators of active transmission in the community ., Our study shows that few autochthonous cases of patients are still present ., These autochthonous cases are proportionally fewer in the second period than in the first with no more focus of transmission which suggests that autochthonous transmission on the Island is disappearing ., Interestingly , no cases of patient under 15 years of age were detected in the second period of the study , indicating that there has been no active transmission for the last 3 years ., These 2 keys indicators supports that autochthonous transmission of leprosy has stopped on Reunion Island ., However , regarding clinical features of patients , a high rate of disability at the time of diagnosis has been reported for 24% of the patients ( grade 2 disability ) , which is indicative of late detection ., This might be explained by general practitioners’ poor knowledge of the disease ., Following the study , communication has been performed to renew GPs awareness of the risk of leprosy on Reunion Island ., Our study presents some limitations ., Indeed , the possibility that some misdiagnosed or undeclared cases cannot be excluded ., Furthermore , the prospective period ( 3 years ) is too short to conclude that the illness has been lastingly eradicated from the island ., This result has to be re-evaluated regularly ., In our opinion , this favourable evolution can be explained by one major historical reason ., Reunion Island was the first overseas territory to become an administrative French department in 1946; as a result , the island has benefitted from the French public health system for more than 50 years ., For example , dermatologic consultation has been available on the entire territory for over 3 decades ., The skin biopsy became the gold standard for diagnosis in routine ., This important access to dermatologists has played a large role in the early detection of new cases , which is the key for preventing aggravation and transmission 12 , 13 ., In addition , improvements in quality of life , better housing conditions and lower promiscuity have played an important role in the reduction of autochthonous transmission ., Indeed household and dwelling contact are among the most important risk factors for transmission 14 ., By contrast , Mayotte Island has just gained the same administrative status as Reunion Island , and the living conditions of the majority of its inhabitants are still rudimentary , with small homes for large families and poor access to medical care ., This situation may well explain partly why leprosy is still endemic in Mayotte 15 ., Moreover , given their long-standing historical ties , Mayotte has seen an important number of imported cases from the Comoros , where the disease is also endemic ( Fig 1 ) ., In fact , Mayotte and the Comoros have the highest prevalence rate of leprosy of the South Indian Ocean area ., Lastly , the implementation of a tuberculosis and leprosy control program—which includes active surveillance , early declaration of new cases prompting the screening of household contacts and rapid access to MDT , —can also explain the progressive eradication of the disease ., However , Reunion Island remains highly exposed to resurgence ., Indeed , the reintroduction of the disease through immigration from endemic neighbouring countries such as the Comoros , Mayotte or Madagascar is a real and continuing risk ., This risk is illustrated by the prevalence rates of Leprosy in Indian Ocean in 2014 ( Fig 1 ) ., Main immigration in Reunion Island comes from Madagascar , Comoros , and Mayotte ., In 2014 , Comoros and Mayotte were still highly endemic with prevalence rates of 3 . 54 and 7 . 25/10 000 inhabitants ., With a rate of 0 . 83/10000 inhabitants , Madagascar is also among the endemic countries ., Constant vigilance should then be maintained to assure that the disease does not reappear in the community ., Leprosy is no longer a major public health problem on Reunion Island , as indicated by the low prevalence rate and the absence of active transmission ., Improvements in living conditions and access to health care meeting French metropolitan standards have put an end to autochthonous transmission ., However , given the significant influx of migrants from leprosy-endemic neighbouring countries , the risk of resurgence of the disease and of renewed autochthonous transmission is real ., In conclusion , our experience shows that “active detection , systematic declaration and rapid treatment” are the 3 key measures to obtain eradication of leprosy in a community ., In our opinion , those measures must be maintained to consolidate eradication . | Introduction, Methods, Results, Discussion | Reunion Island is a French overseas territory located in the south-western of Indian Ocean , 700 km east of Madagascar ., Leprosy first arrived on Reunion Island in the early 1700s with the African slaves and immigration from Madagascar ., The disease was endemic until 1980 but improvement of health care and life conditions of inhabitants in the island have allowed a strong decrease in new cases of leprosy ., However , the reintroduction of the disease by migrants from endemic neighbouring countries like Comoros and Madagascar is a real and continuing risk ., This observational study was then conducted to measure the number of new cases detected annually on Reunion Island between 2005 and 2013 , and to describe the clinical features of these patients ., Data were collected over two distinct periods ., Incident cases between 2005 and 2010 come from a retrospective study conducted in 2010 by the regional Office of French Institute for Public Health Surveillance ( CIRE of Indian Ocean ) , when no surveillance system exist ., Cases between 2011 and 2013 come from a prospective collection of all new cases , following the implementation of systematic notification of all new cases ., All patient data were anonymized ., Among the 25 new cases , 12 are Reunion Island residents who never lived outside Reunion Island , and hence are considered to be confirmed autochthonous patients ., Registered prevalence in 2014 was 0 . 05 /10 000 habitants , less than the WHO’s eradication goal ( 1/10 000 ) ., Leprosy is no longer a major public health problem on Reunion Island , as its low prevalence rate indicates ., However , the risk of recrudescence of the disease and of renewed autochthonous transmission remains real ., In this context , active case detection must be pursued through the active declaration and rapid treatment of all new cases . | Leprosy was still endemic on Reunion Island 30 years ago but improvements in health care and treatments led to a significant decrease in the number of new cases of leprosy ., Nevertheless , the long-standing lack of a surveillance system prevents a real evaluation of endemicity ., This is the first study to evaluate eradication of Leprosy on Reunion Island ., The prevalence rate of less than one case per 10000 inhabitants is necessary , but not sufficient to claim eradication ., Remaining active transmission of the disease is to be explored ., The most widely used indicator of active transmission , the absence of new cases detected in children younger than 15 years of age , and the lack of focus of transmission , confirmed the eradication assumption ., Improvements in quality of life , better housing conditions and lower promiscuity have played a key role in the reduction of autochthonous transmission ., Active detection among relatives , systematic declaration and rapid treatment are the most effective way of preventing disabilities and further transmission of the disease ., However , if elimination of leprosy is no longer a major public health problem on Reunion Island , the risk of reintroduction of the disease through immigration from endemic neighbouring countries is a real and continuing risk ., Preventing resurgence is now the challenge . | medicine and health sciences, disabilities, biopsy, tropical diseases, oceans, geographical locations, surgical and invasive medical procedures, bacterial diseases, research design, comoros, bodies of water, neglected tropical diseases, africa, research and analysis methods, public and occupational health, infectious diseases, marine and aquatic sciences, madagascar, people and places, earth sciences, leprosy, retrospective studies, indian ocean | null |
journal.pntd.0004312 | 2,016 | 3D Architecture of the Trypanosoma brucei Flagella Connector, a Mobile Transmembrane Junction | Cellular junctions are crucial for the formation of tissues , pathogen/host cell interactions and communication between cells , e . g . , the plasmodesmata in plants and the gap junctions in animals ., However , junctions can also exist within a single cell , such as the top connectors between sterocilia and the kinocilium on outer hair cells in the ear 1 ., Trypanosoma brucei are unicellular protozoa able to form multiple kinds of cellular junctions ., These parasites cause the devastating African sleeping sickness that is transmitted to humans and cattle by the bite of an infected tsetse fly ( Glossina spp ) ., The ability to adapt to a changing environment is essential to their complex life cycle 2 ., One such adaptation is the asymmetric intercellular junctions between the T . brucei flagellum and the microvilli in the tsetse fly salivary gland epithelia 3 ., When the parasites are attached like this , the cells divide asymmetrically to generate daughter cells of a different shape ., Similar cellular junctions between the flagellum and the host species tissue are also found in T . congolense , T . vivax and Leishmania mexicana 4–6 , providing not only a physical tethering to the substrate but also a signaling opportunity such the one described between the parasitophorous vacuole and the amastigote L . mexicana flagellum 5 ., Procyclic T . brucei , the form that infects the fly mid-gut , possess a single flagellum that originates in the flagellar pocket and exits the cell body near the posterior end of the cell 7 , 8 ., The extracellular part of the flagellum contains an extra-axonemal structure called the paraflagellar rod ( PFR; 9–12 ) , and is attached to the plasma membrane through a region called the flagellar attachment zone ( FAZ; 2 , 3 , 13 ., Inside the FAZ , a specific complex junctional component , the recently discovered ‘staple’ is found 12 ., These are extracellular plate-like structures with fibrous connections into both the flagellum and cell body ., These are , in contrast to the first example of T . brucei cellular junctions , intracellular connections , connecting one part of the cell to another ., A third cellular junction in T . brucei is the flagella connector ( FC ) ; a specialization that is unique to procyclic cells in division that are assembling a second flagellum that will be inherited by a daughter cell 5 , 14–16 ., The FC is a mobile trans-membrane junction that links the tip of the new flagellum to the side of the old flagellum ( Fig 1A; 14 , 17 ) ., Once the new flagellum tip , and the FC , has reached a point roughly 50% along the length of the old flagellum , it stops migrating ., From then on the tip of the new flagellum is immobile on the surface of the old flagellum , and continued flagellar growth is temporally accompanied by independently separating basal bodies and kinetoplasts 18 ., The physical connection between old and new flagellum probably ensures that the elongating new flagellum copies the left-handed helical path of the old flagellum 19 , facilitates flagellar attachment zone formation and thus imposes a similar cell shape on the ensuing daughter cells after division ., The basic outline of the FC structure has been described using conventional thin-section electron microscopy of chemically fixed material 14 , 17 ., This work showed that the FC consists of a tri-laminar structure composed of three distinct electron dense layers found in the new flagellum , the interstitial space , and the old flagellum ., Each layer is subdivided into three plates ., Interconnecting these layers with the axonemal microtubule doublets are thin intra-flagellar filaments ( Fig 1B; 7 , 8 , 17 ) ., However , many aspects of the FC structure and behaviour have remained elusive ., For example it is not known how it moves along the old flagellum , although we do know this motion is separate from the extension of the new flagellar axoneme 9–12 , 18 , indicating the presence of some sort of molecular motor ., We have now performed ( cryo ) electron microscopy and ( cryo ) electron tomography with the hope of further clarifying the function and mechanism of this junction and its motility ., The combination of techniques used has resulted in our developing a comprehensive 3D architecture , presented here , that provides insight into the physical properties of the FC ., To investigate the FC ultra-structure , we performed transmission electron microscopy on both formaldehyde fixed cells and cells cryoimmobilised by high pressure freezing ., In thin cross-sections of chemically fixed flagella , both the new and old flagella had irregular outlines ( Fig 1C ) ., The tip of the new flagellum lay quite distant from the plasma membrane of the cell , and some of its doublet microtubules were missing15 ., The FC displayed partitions in the electron dense material ( previously named “plates” 17 ) throughout the interstitial zone ., In high pressure frozen cells , on the other hand ( Fig 1D ) , interstitial zone material was visible , but no clear partitions into plates were visible ., In this preparation , both flagella were round in cross-section ., New and old flagella are both in close proximity to the cellular surface ., The new flagellum in Fig 1D had a central pair that was parallel with the underlying sub-pellicular microtubule array , but the old flagellum’s central pair axis was rotated in comparison to the cellular microtubules ( Fig 1D and S1 Fig ) ., The FC was then examined using cryo-electron microscopy of sections cut from high pressure frozen cells embedded in vitreous ice ., Because this sample preparation does not involve dehydration of the cells , nor coating of proteins with heavy metals , it displays cell structure in a close-to-native state 12 , 20 ., This image shows the two flagella , both close to the cell membrane and between them we find the FC ( Fig 1E ) ., Distinct , regular filamentous densities project from both flagella membranes in the FC and a region of darker electron density is found in the middle of the interstitial zone ., This filamentous arrangement is interrupted in three areas by smoother electron densities across the FC ( Fig 1F–1F’; blue boxes ) ., We conclude that studying the FC ultra-structure using various sample preparations and imaging techniques yields new information about its ultrastructure ., Therefore , we progressed to study its 3D structure using electron tomography of the conventional chemically fixed , high pressure frozen and vitrified samples ., A tomographic reconstruction of chemically fixed FCs was performed ., A thin slice of one reconstruction ( Fig 2A; S1 Movie ) shows the tri-laminate structure and filamentous connections from it to both axonemes ( arrows ) ., A 3D model of the FC was produced by drawing around the structural features of interest in the tomogram ., The top view ( Fig 2B; S2 Movie ) displays the flagellar membranes and shows how the tip of the new flagellum is apposed to the old flagellum ., In the en face view ( Fig 2C ) , membranous components were subtracted to visualise the microtubule doublets and the 3D morphology of the tri-laminar complex and its associated filamentous network ( doublet microtubules are colour coded with a gradient from doublet 1 in pale yellow to doublet 9 in dark red ) ., Previously , each layer within the tri-laminar structure has been described as partitioned into three electron dense plates 14–18 , 21 ., However , in the tomographic reconstruction , the 3D structure of complete layers shows that they are subdivided into a range of 2–4 plates , of which 3 plates is the most common ( Fig 2D ) ., The 3D reconstruction also shows a correlation between the angles of partitions and the two axes underlying axonemes ( Fig 2E ) ., The FC tri-laminar structure was less noticeable in the high pressure frozen material ( Fig 2F; S3 Movie ) ., A new flagellum plate was not detectable , probably because of the electron dense cap coating the entire inside of the new flagellum tip ( Fig 2G–2H; S4 Movie ) ., The interstitial layer was thinner than previously seen in the chemically fixed sample ., Also here , the plates within the tri-laminar structure were not detectable ( Fig 2I ) , neither was the filamentous network between the FC and the axonemes ., Thus , the high pressure frozen FC reveals a more compact FC morphology with an electron dense cap instead of the new flagellum plate ., The time of progression of a cell through the cell cycle is directly correlated to the length of the new flagellum ., We therefore measured the length of the new flagellum in those high pressure frozen cells in which the FC had been reconstructed ., When the new flagella were short ( below 2 μm ) , the FC was in the process of being formed inside the flagella pocket ( Fig 3A; S5 Movie ) ., At this stage , the FC was seen as a thin electron density in the interstitial zone extending from the wall of the old flagellum ., We correlated the new flagellum length with the FC interstitial zone thickness ( distance between the flagella ) and found that this parameter decreased considerably as the FC matures ( Fig 3C ) , being 28 ± 6 nm in short flagella ( n = 4 ) and 13 ± 2 nm in longer flagella ( Fig 3B; n = 4 ) ., The structural maturation of the old flagellum FC layer during the cell cycle was , however , less clear ( Fig 3D ) ., The old flagellum layer in two cells with long new flagella had the same thickness ( ~20 nm ) as found in cells earlier in the cell cycle; however two flagella connectors had old flagellum layers almost twice as thick ( ~35 nm ) ., There was no difference in FC length ( 273 ± 61 nm; n = 9; Fig 3E ) or depth ( 116 ± 30 nm; n = 8; Fig 3F ) over the cell cycle ., We conclude that the thickness of the interstitial layer of the FC changes as the cell cycle progresses ., To image the FC protein architecture in a more native state , we made cryo-electron tomograms of vitreous sections ., One electron tomogram of such a section , contained the most distal ~70 nm of the FC between the old and new flagellum , as well as the cell body ( Fig 4A; S6 Movie ) ., To examine the 3D architecture of the region we modelled the FC , membranes and microtubules ( Fig 4B ) ., The generated 3D model shows the complete FC structure , including membranes , as a ~100 nm wide connection between the old and new flagella ( Fig 4C ) ., In views sliced through the tomogram , it is apparent that the distance between the old and new flagella is the greatest ( ~50 nm ) closest to the distal tip of the new flagellum ( Fig 4D ) ; that this distance shrinks to ~20 nm as one moves more proximal in the new flagellum ( Fig 4E–4F ) ., The extracellular density we interpret as the FC lies close to the old flagellum throughout this volume , perhaps suggesting that it originates from that organelle ., A very electron dense structure is also seen in close vicinity to the FC , which relevance we do not know , but a similar electron density was seen in a similar position in another cryo-electron microscopy image ( Fig 4A–4B; S2 Fig ) ., The FC layer in the new flagellum was not distinguishable , nor the axonemal microtubules in the new flagellum in this sample preparation ., A faint density was visible in the old flagellum at the location of the FC ., The dimensions measured in cryo-sectioned specimens have to be carefully interpreted due to compression , a characteristic artefact of vitreous sectioning 14 , 17 , 22 ., The compression factor is estimated here at 50% based on the ovoid shape exhibited by the microtubules and the old flagellar membrane , and should be considered when reading the measurements here and in Table 1 ., The electron density that forms the interstitial FC component had a clear periodicity when seen in cross-section , even though there were tomographic slices where this periodicity was not as strong ( possibly correlating to the areas indicated with blue boxes in Fig 1F’ ) ., When the distance between the flagella was larger ( Fig 4D ) , three stacks of periodical electron densities were present ., As the distance grew closer , this decreased to two ( Fig 4E ) and then one line ( Fig 4F ) of periodical densities ., When the tomogram was rotated , these periodical densities showed as parallel lines ( Fig 4G ) 7 nm apart , and with denser areas spaced 11 nm along them ( arrows; Fig 4G ) ., We conclude that cryo electron tomography of frozen hydrated sections of T . brucei cells has revealed the structural periodicity of the interstitial zone of the FC ., In the old T . brucei flagellum , defined numbers can be assigned to each doublet microtubule , as the central pair does not rotate 18 , 23 and a fixed , external structure , the paraflagellar rod ( PFR ) , exists ., The attachment of the axoneme to the PFR occurs at microtubule doublets 4–7 19 ., As previously described 14 , 17 , the FC complex faces microtubule doublets number 7–9 in the old flagellum , but here we also see a close proximity between the FC and microtubule doublet 1 ., Previous studies were unable to distinguish which microtubule doublets the FC was facing in the new flagellum , or indeed if this side of the junction is more flexible , interacting with a range of microtubule doublets ., Despite the apparent disorganisation inside the growing new flagellum tip15 , we have seen that the FC was consistently aligned with doublets 3–5 in the new flagellum ( Fig 5A; n = 3 ) ., Thus , the FC follows specific microtubule doublets within both the new and old flagellum ( Fig 5B ) ., To examine if the location of the axoneme in the old flagellum was perturbed by the passage of the FC , several images of flagellar cross-sections from multiple cells were aligned with the microtubule quartet in the sub-pellicular MT array to the bottom left of the flagellum ( e . g . Fig 5C ) ., An ellipsoid was placed where the axoneme was found in each image , which was invariably in the top left corner of the flagellum ( Fig 5D ) ., However , close to the FC , the position of the axoneme shifted ~100 nm away to a more central location ( red ellipsoids ) ., This shift in axonemal position around the FC can also be seen in longitudinal sections of the region ( e . g . Fig 2F ) , and tells us that the FC presence rearranges the internal space of the old flagellum ., For several reasons , discussed later , it is possible that the filamentous network seen in chemically fixed cells between the FC and the axonemes represents a physical link between these three structures ., We measured the distances the filamentous network would span , from the membrane of the flagellum to the closest doublet microtubule at the level of the FC ., The membrane-axoneme distance was 61 ± 1 nm and 55 ± 9 nm ( n = 2 ) respectively in the new and old flagella in chemically fixed cells ., In high pressure frozen cells , the axoneme was found closer to the membrane in new flagella ( 33 ± 11 nm; n = 7 ) , than in old flagella ( 65 ± 16 nm; n = 7; Fig 5C ) ., Surprisingly , we also found a fibrous structure in this space between the FC and the axoneme in the old flagellum ., In both chemically fixed electron tomograms of the FC , a novel , electron dense fibre was observed inside the old flagellum near the old FC layer and the flagellar membrane ( Fig 6A ) ., This fibre appears as a filament , ~20 nm by ~45 nm in cross-section and longer than the field of view in a single tomogram ( Fig 6B ) ., We have named this component the FC fibre ., When tilted to show the flagellum in cross-section , the position of the FC fibre is off-centre towards the cellular side of the axoneme ( Fig 6C ) ., This is also shown in the 3D model , where the FC is consistently found between MT doublets 7 and 8 ( Fig 6D ) ., In tomograms of both FCs , the proximal extremity of the FC fibre initiates ~400 nm prior to the FC ( Fig 6E ) ., The complete length of the FC fibre ( ~870 nm ) could only be measured in one tomogram , and its distal end extends ~200 nm further than the distal extremity of the old flagellum plate ., The FC fibre had connections to the old flagellum FC plate ( Fig 6F ) , but only a few connections to the doublet microtubules ( Fig 6G ) ., In this paper , we have used a combination of fixation and imaging procedures to reveal the three dimensional ultrastructure of the FC of the procyclic form of T . brucei , a mobile cellular junction 14 , 17 , 18 ., A combined detailed analysis of these data and previous publications on this structure shows that the FC behaves like a motorized double-sided vehicle that travels along microtubule doublets 7–9 in the old flagellum and in contact with microtubule doublets 3–5 in the new flagellum ( Fig 7 ) ., Inside the new flagellum , the distance to the axoneme is 33 nm , a distance a kinesin molecule could easily span 24 , 25 ., On the other side of the junction , the protein or protein complexes involved in linking the FC and the old axoneme must span the greater ~65 nm ., This connection must also be very strong as it does not only move the FC , but also displaces the axoneme as it passes by ., We revealed structural variations of the FC and correlated them with the cell’s cycle stage judged by the length of the new flagellum ., When the new flagellum is at , or close to , its stop point ( at which the new flagellum tip stops translocating along the old flagellum18 ) , the interstitial layer of the FC was reduced to about half the original thickness ., The thickening of the old-flagellum layer of the FC in some of these flagella introduces the possibility of rearrangement of the external components ., The maturation of the FC structure over the cell cycle is a novel finding and might hint towards the mechanism for removal of this structure after it has fulfilled its purpose ., A novel component of the FC was described—the FC-fibre ., Because of this fibre’s morphology , length and location that all correlate well with those described of intraflagellar transport trains ( IFT particles delivers flagellum building material to the flagellum tip using molecular motor proteins walking on the axonemal microtubules ) 26–28 , we suggest that it might represent a row of IFT particles ( further discussion in S3 Fig and S1 Text ) ., This study also has methodological interest , as we studied the same structure with an unprecedented combination of electron microscopy methods ., We showed that the high pressure frozen and freeze substituted FC appears more similar to the cryo-sectioned and cryo-visualised FC , with smooth flagellar membranes and unfragmented extracellular material ., The thicknesses of the various FC plates vary depending on sample preparation ( Table 1 ) ., Even though frozen hydrated sections of cells revealed the periodic organisation of the FC at the molecular level , cutting artefacts such as compression 22 added to the difficulty of visualizing the old and new flagellar plates especially when the FC plates are oriented perpendicular to the cutting direction ., Therefore , all fixation methods are valuable for specific purposes , and quantitative results achieved using only one method should be interpreted with caution ., The FC structure provided by the 3D architecture presented here establishes a new level of insight into a junctional apparatus that possesses the capacity for lateral mobility ., This insight into the FC substructure and morphogenesis is a necessary platform for future studies of molecular components and their assignment within the highly organized structure ., In addition the structural definition will be critical for studies designed to reveal where the molecular motor is located and how it operates ., Logarithmically growing procyclic 427 cells in SDM-79 medium were fixed by, a ) adding 2 . 5% glutaraldehyde to the culture or, b ) high pressure frozen using Leica EM Pact II ( Leica Microsystems , Vienna ) as in 15 , 29 ., In brief , chemically fixed cells were postfixed ( 2 . 5% glutaraldehyde , 2% formaldehyde in 100 mM phosphate buffer pH 7–7 . 4 for 2 hours; then 1% osmium tetroxide in 100 mM phosphate buffer for 1–2 hours ) , en-bloc stained ( 2% magnesium uranyl acetate in water for 2 h ) and dehydrated with increasing concentrations of ethanol , immersed in propylene oxide and infiltrated by increasing concentrations of epon ., High pressure frozen samples were freeze substituted ( 2% uranyl acetate from a 20% methanolic stock solution , in dehydrated acetone for 1 h ) ., Infiltration with increasing concentration of HM20 ( 3:1 , 2:1 , 1:1 , 1:3 , 0:1 acetone:HM20 for several hours each ) was performed at -50°C , where polymerization using UV light was initiated ., Polymerization was finished with 48 h UV illumination at room temperature ., Thin sections ( 75 nm ) were cut using an UltraCut microtome ( Leica Microsystems , Vienna ) , and post stained with 3 min lead citrate only ( chemically fixed samples ) , or 8 minutes 2% uranyl acetate followed by 3 minutes Reynold’s lead citrate ( high pressure frozen samples ) 30 ., Sections 250–300 nm thick was cut , post-section stained and 15 nm colloidal gold particles ( BBInternational , Cardiff , UK ) was applied to both surfaces of the grid ., Serial sections incorporating the entire FC were imaged using a Ultrascan 785 4k x 4k camera binned to 2k x 2k ( Gatan , Pleasanton , CA , USA ) every degree , ±60° , at 23000 x magnification on a F30 Tecnai microscope ( FEI Company , Eindhoven , The Netherlands ) , then rotated 90° and a second axis was acquired ., Pixel size was ~1 nm ., Tomograms were reconstructed using the IMOD software 31 , and 3D models were made by outlining objects of interest in the tomograms ., The lengths of new flagella were measured by taking lower magnification images of the thick serial sections containing the cell in which the FC had been imaged ., Serial sections were aligned and 3D models of the new flagellum were made ., Cells were prepared by harvesting with centrifugation and resuspended in 20% dextran and 0 . 2% sucrose in medium ., Within 3–4 minutes of resuspension , cells were high pressure frozen and then treated as in 12 ., In brief , 80–100 nm thick frozen hydrated sections were cut and for tomography imaged every 1 . 5° and tilted ±60°on F20 Tecnai microscope ( pixel size 0 . 76 nm; FEI Company , Eindhoven , The Netherlands ) ., Fourier transform image was made from a subarea of a single 0 . 76 nm slice in IMOD . | Introduction, Results, Discussion, Materials and Methods | Cellular junctions are crucial for the formation of multicellular organisms , where they anchor cells to each other and/or supportive tissue and enable cell-to-cell communication ., Some unicellular organisms , such as the parasitic protist Trypanosoma brucei , also have complex cellular junctions ., The flagella connector ( FC ) is a three-layered transmembrane junction that moves with the growing tip of a new flagellum and attaches it to the side of the old flagellum ., The FC moves via an unknown molecular mechanism , independent of new flagellum growth ., Here we describe the detailed 3D architecture of the FC suggesting explanations for how it functions and its mechanism of motility ., We have used a combination of electron tomography and cryo-electron tomography to reveal the 3D architecture of the FC ., Cryo-electron tomography revealed layers of repetitive filamentous electron densities between the two flagella in the interstitial zone ., Though the FC does not change in length and width during the growth of the new flagellum , the interstitial zone thickness decreases as the FC matures ., This investigation also shows interactions between the FC layers and the axonemes of the new and old flagellum , sufficiently strong to displace the axoneme in the old flagellum ., We describe a novel filament , the flagella connector fibre , found between the FC and the axoneme in the old flagellum ., The FC is similar to other cellular junctions in that filamentous proteins bridge the extracellular space and are anchored to underlying cytoskeletal structures; however , it is built between different portions of the same cell and is unique because of its intrinsic motility ., The detailed description of its structure will be an important tool to use in attributing structure / function relationships as its molecular components are discovered in the future ., The FC is involved in the inheritance of cell shape , which is important for the life cycle of this human parasite . | Trypanosoma brucei is an uni-cellular parasite transmitted to humans and cattle by the bloodsucking tsetse fly ., Once swimming in the mammalian bloodstream , it causes the devastating African sleeping sickness in humans and nagana in cattle ., During its complex life cycle , it undergoes many cell shape changes , which are important for efficient parasite transmission ., Here , we have studied a cell structure intrinsically involved in shape acquisition during division of the T . brucei life cycle form that multiplies in the fly midgut ., Using electron tomography we show the 3D architecture of a motile cellular junction that slides with the tip of the growing new flagellum along the side of the old flagellum ., This enables the new flagellum to zip in to the cell body structure alongside the old flagellum after which the cleavage furrow is established between these two flagella , producing two daughter cells of similar cell shape ., We present here a detailed architectural overview of this junction; we show that it matures with time and pushes the old flagellum’s axoneme sideways as it passes ., This structural map enables insight into the function of this extraordinary mobile cellular junction . | medicine and health sciences, classical mechanics, pathology and laboratory medicine, microtubules, diagnostic radiology, engineering and technology, built structures, pathogens, cell cycle and cell division, cell processes, condensed matter physics, parasitology, developmental biology, membrane structures, pressure, cellular structures and organelles, cytoskeleton, research and analysis methods, membrane technology, imaging techniques, high pressure, life cycles, tomography, pathogen motility, physics, radiology and imaging, parasitic life cycles, diagnostic medicine, cell biology, virulence factors, electron density, biology and life sciences, structural engineering, physical sciences, flagella | null |
journal.pgen.1004799 | 2,014 | Stratification by Smoking Status Reveals an Association of CHRNA5-A3-B4 Genotype with Body Mass Index in Never Smokers | As obesity represents a substantial and growing threat to public health , efforts to identify the determinants of obesity are of considerable scientific and societal importance ., Genome-wide association studies ( GWAS ) have identified numerous variants associated with body mass index ( BMI ) 1 , but a substantial proportion of the estimated heritability remains to be accounted for ., At the same time , a number of modifiable environmental factors have been identified that influence BMI , with cigarette smoking a strong lifestyle influence on BMI 2 ., In a previous Mendelian randomisation analysis , we used a single nucleotide polymorphism in the CHRNA5-A3–B4 gene cluster associated with heaviness of smoking within smokers 3 to confirm the causal effect of smoking in reducing BMI 4 ., We sought to extend these findings in a larger sample drawn from the Causal Analysis Research in Tobacco and Alcohol ( CARTA ) consortium ( http://www . bris . ac . uk/expsych/research/brain/targ/research/collaborations/carta/ ) ., We used the same genetic variant , characterised by two SNPs ( rs16969968 and rs1051730 ) which are in perfect linkage disequilibrium ( LD ) in samples of European ancestry , and therefore reflect the same genetic signal ( hereafter rs16969968-rs1051730 ) ., This variant is associated with approximately 1% phenotypic variance in cigarettes per day and approximately 4% variance in cotinine levels ( the primary metabolite of nicotine , and a more precise measure of exposure ) 5 , 6 ., Mendelian randomisation analyses of the causal effects of smoking heaviness require stratification according to smoking status – any causal effects of the exposure ( i . e . , smoking heaviness ) should be reflected in an association of the instrument ( i . e . , genotype ) among current smokers only , and not never smokers ( former smokers might be expected to be intermediate between current and never smokers ) 7 ., The never smoking group therefore enables a test of the specificity of the instrument ( i . e . , that the variant only affects the outcome through the exposure of interest ) 8 ., Critically , the rs16969968-rs1051730 variant has not been shown to be associated with smoking initiation ( i . e . , it does not influence risk of being an ever versus a never smoker ) in previous GWAS of smoking behaviour 9 , which reduces the risk of introducing collider bias when stratifying on smoking status ., In the course of these analyses , we observed an unexpected finding , which we report here ., Specifically , we observed an association of rs16969968-rs1051730 with higher BMI in never smokers ., This association has not previously been reported in GWAS of BMI published to date ., We therefore focus on the implications of this novel finding , and not the Mendelian randomisation analysis of the causal effects of smoking on BMI ., Our total sample size comprised 148 , 730 never smokers , former smokers and current smokers ., In the 66 , 809 never smokers , we observed positive association of rs16969968-rs1051730 with BMI ( Table 1 ) , indicating an association operating via pathways other than smoking ( percentage change per minor allele +0 . 35 , 95% CI +0 . 18 to +0 . 52 , P\u200a=\u200a6 . 38×10−5 ) ., We also confirmed the expected inverse association of rs16969968-rs1051730 with BMI in the 38 , 913 current smokers ( percentage change −0 . 74 , 95% CI −0 . 97 to −0 . 51 , P\u200a=\u200a2 . 00×10−10 ) , consistent with a causal , weight-reducing effect of cigarette smoking on BMI ., There was no evidence of association in the 43 , 009 former smokers ( percentage change −0 . 14 , 95% CI −0 . 34 to +0 . 07 , P\u200a=\u200a0 . 19 ) ., An interaction test indicated that these estimates differed from each other ( P\u200a=\u200a4 . 95×10−13 ) ., Similar associations were observed for weight ( Table, 1 ) and waist circumference ( data available on request ) , but not height ( Ps ≥0 . 27 for all smoking categories ) ., Between-study heterogeneity was low ( I2 values ≤36% ) , and there was no evidence for effect modification by sex ., Critically , when data were examined without stratification by smoking status no clear evidence of association with BMI was observed ( P\u200a=\u200a0 . 22 ) , indicating that a conventional GWAS would have failed to detect this signal ., The 0 . 35% per minor allele BMI increase in never smokers represents a change of approximately 0 . 09 kg/m2 ., This is smaller than the effect of rs9939609 in FTO ( ∼0 . 4 kg/m2 ) 10 but is comparable in terms of variance explained to the other variants identified by Speliotes and colleagues 1 ., As noted above , the rs16969968-rs1051730 variant has not been shown to be associated with smoking initiation in previous GWAS of smoking behaviour 9 ., This is also true in our data ( ever smoker versus never smoker: OR per minor allele 1 . 01 , 95% CI 0 . 99 to 1 . 03 , P\u200a=\u200a0 . 50 ) , although we observed an association with smoking cessation ( current smoker versus former smoker: OR per minor allele 1 . 08 , 95% CI 1 . 06 to 1 . 10 , P\u200a=\u200a1 . 44×10−12 ) , consistent with previous studies 11 ., Therefore , we do not believe that these findings are due to collider bias , whereby stratifying on the exposure measure can induce associations between instrument and outcome 12 ., Our results indicate that rs16969968-rs1051730 may be associated with BMI in never smokers , via pathways other than smoking , as well as with heaviness of smoking among current smokers ., At this stage we can only speculate as to the mechanism through which rs16969968-rs1051730 may exert a positive effect on BMI in never smokers ., In GWAS , the CHRNA5-A3-B4 gene cluster was confirmed to be associated with heaviness of smoking , and downstream health outcomes including lung cancer and peripheral arterial disease 9 , 13 , 14 ., It has been shown that the rs16969968 variant is functional and leads to an amino acid change ( D398N ) in the α5 nicotinic acetylcholine receptor ( nAChR ) subunit protein 15 ., Animal models indicate that this subunit modulates tolerance to high doses of nicotine 16 ., Candidate gene studies have suggested an association of rs16969968-rs1051730 with other substance use phenotypes , such as cocaine use 17 , while other variants in this region have been reported to be associated with alcohol consumption 18 , although the evidence for these associations is currently weak ., Therefore , one possibility is that nAChRs play a role in central mechanisms mediating responding to rewarding stimuli in general , which could include natural rewards such as food ., It is also notable that rs3743075 , located within the CHRNA3 gene and correlated with rs16969968-rs1051730 ( r2\u200a=\u200a0 . 34 , D′\u200a=\u200a1 . 00 ) , shows association ( N\u200a=\u200a974 , P\u200a=\u200a9 . 06×10−5 ) with BMI ( defined as <30 kg/m2 vs ≥30 kg/m2 ) ( dbGaP Study Accession: pha003015 . 1 ) ., There is evidence from animal models that activation of hypothalamic α3β4 nAChRs leads to activation of pro-opiomelanocortin neurons , and subsequent activation of melanocortin 4 receptors , which have been shown to be critical for nicotine-induced decreases in food intake 19 ., Therefore , another possibility is that nAChR sub-units play a role specifically in mediating food intake , through as yet undescribed mechanisms ., In other words , the effects we have observed operate via other nAChRs , and other genes in this region ( namely CHRNA3 and CHRNB4 ) may contribute to our finding ., Clearly further work is required to explore this possibility ., The use of more detailed body composition measures such as percent body fat and its distribution may also serve to refine the nature of the association ., Our results , if confirmed , have important implications for the design of future GWAS ., The association we observed in never smokers would essentially be undetectable in an unstratified sample , since the effect size observed in the combined sample would require approximately 791 , 000 participants to detect even at an uncorrected P-value of 0 . 05 , and even then would indicate an inaccurate effect size ., This is essentially because the effect of rs16969968-rs1051730 on BMI that operates via pathways other than smoking is countered by the weight-reducing effect of smoking ., Therefore , since there are roughly twice as many never smokers as current smokers on average across our sample , these two effects negate each other ., On the other hand , a sample of approximately 160 , 000 never smokers would be required to detect the effect we observed with genome-wide significance ., Assuming the proportions of never , former and current smokers in our sample , this would imply a total sample size of around 350 , 000 ., While this is larger than published GWAS of BMI 1 , it is achievable ., Therefore , although we cannot say how frequent a scenario such as the one we observed here will be , additional variants may be identified in GWAS stratified by environmental exposures known to have pronounced effects on the phenotype of interest , such as cigarette smoking or physical activity on BMI ., The pleiotropic effect of rs16969968-rs1051730 ( or LD of this variant with another variant causally influencing BMI ) , if shown to be robust via replication , has important implications for Mendelian randomisation studies assessing the causal effects of smoking ., In this case , we can be reasonably confident that the BMI-reducing effect of the variant operates through smoking because the association with BMI in current smokers is in the opposite direction to the association in never smokers ., Furthermore , if the effects on BMI that operate via pathways other than smoking and the effects that operate via the weight-reducing effects of smoking are independent , then the true causal estimate of the magnitude of effect of smoking in reducing BMI is likely to be larger than estimated with this variant ., However , some caution must be exercised in conducting and interpreting the results of other Mendelian randomisation analyses using this variant because rs16969968-rs1051730 may influence outcomes through its effects on BMI , instead of or in addition to smoking heaviness ., One possible solution is to use genetic variants for BMI as a method of reciprocal randomization to determine the direction of causation within inter-correlated networks of mechanistic pathways ( i . e . , network Mendelian randomisation ) 20 ., A limitation to our analysis is that we were only able to control for potential population stratification indirectly in most samples , by restricting analyses to participants of self-reported European ancestry ., We were not able to use other methods , such as adjustment for principal components , given that not all contributing studies hold the necessary genetic data ., However , we note that the minor allele frequency of the rs16969968-rs1051730 differed only slightly across studies ( between 0 . 30 and 0 . 36 ) ., Testing for gene-environment interaction in GWAS is not novel 21 , and examples exist which incorporate smoking status as an environmental factor 22 ., However , this remains relatively uncommon , due to methodological challenges ( e . g . , introducing collider bias ) and sample size constraints ., A key challenge is the identification of suitable environmental variables on which to stratify GWAS analyses , from the multitude available ., We suggest that focusing on environmental factors that are most strongly associated with the phenotype of interest , are likely to have profound biological effects , and which can be characterised in a relatively consistent way across studies , is likely to be the best strategy ., Smoking status meets all of these criteria , and the data presented here demonstrate how stratification on well-characterized environmental factors known to impact on health outcomes ( such as smoking status ) may reveal novel genetic associations with health outcomes ., As our data indicate , these associations may operate through genetic influences on the environmental factors themselves , or through new pathways which are masked by the environmental factors ., We used data on individuals ( ≥16 years ) of European ancestry ( ascertained via self report , or based on the genome-wide genotype data where available ) from 29 studies in the Causal Analysis Research in Tobacco and Alcohol ( CARTA ) consortium ( http://www . bris . ac . uk/expsych/research/brain/targ/research/collaborations/carta/ ) : the 1958 Birth Cohort ( 1958 BC ) , the Avon Longitudinal Study of Parents and Children ( ALSPAC , including both mothers and children ) , the British Regional Heart Study ( BRHS ) , the British Womens Heart and Health Study ( BWHHS ) , the Caerphilly Prospective Study ( CaPS ) , the Christchurch Health and Development Study ( CHDS ) , the Cohorte Lausannoise ( CoLaus ) study , the Exeter Family Study of Child Health ( EFSOCH ) , the English Longitudinal Study of Ageing ( ELSA ) , FINRISK , the Danish GEMINAKAR twin study , Generation Scotland , the Genomics of Overweight Young Adults ( GOYA ) females , GOYA males , the Helsinki Birth Cohort Study ( HBCS ) , Health2006 , Health2008 , the Nord-Trøndelag health study ( HUNT ) , Inter99 , the Northern Finland Birth Cohorts ( NFBC 1966 and NFBC 1986 ) , MIDSPAN , the Danish MONICA study , the National Health and Nutrition Examination Survey ( NHANES ) , the MRC National Survey of Health & Development ( NSHD ) , the Netherlands Twin Registry ( NTR ) , the Prospective Study of Pravastatin in the Elderly at Risk ( PROSPER ) and Whitehall II ., References to these individual studies are available on request ., All studies received ethics approval from local research ethics committees ( see Text S1 for full details ) ., Within each study , individuals were genotyped for one of two single nucleotide polymorphisms ( SNPs ) in the CHRNA5-A3-B4 nicotinic receptor subunit gene cluster , rs16969968 or rs1051730 ., These single nucleotide polymorphisms are in perfect linkage disequilibrium with each other in Europeans ( R2\u200a=\u200a1 . 00 in HapMap 3 , http://hapmap . ncbi . nlm . nih . gov/ ) and therefore represent the same genetic signal ., Where studies had data available for both SNPs , we used the SNP that was genotyped in the largest number of individuals ., Height ( m ) , weight ( kg ) and waist circumference ( cm ) were assessed within each study , directly measured for 99% of participants , and self-reported for GOYA females ( N\u200a=\u200a1 , 015 ) and a sub-set of NTR ( N\u200a=\u200a602 ) ., Body mass index ( BMI ) was calculated as weight/height2 ., Smoking status was self-reported ( either by questionnaire or interview ) ., Individuals were classified as current , former , or never cigarette smokers ., Where information on smoking frequency was available , current smokers were restricted to individuals who smoked regularly ( typically at least one cigarette per day ) ., Where information on pipe and cigar smoking was available , individuals reporting being current or former smokers of pipes or cigars but not cigarettes were excluded from all analyses ., For studies with adolescent populations ( ALSPAC children and NFBC 1986 ) , analyses were restricted to current daily smokers who reported smoking at least one cigarette per day ( current smokers ) and individuals who had never tried smoking ( never smokers ) ., Descriptive characteristics of smoking frequency data are provided in Text S2 ., Analyses were conducted within each contributing study using Stata and R software , following the same analysis plan ., Analyses were restricted to individuals with full data on smoking status and rs16969968-rs1051730 genotype ., Within each study , genotype frequencies were tested for deviation from Hardy Weinberg Equilibrium ( HWE ) using a chi-squared test ., Mendelian randomisation analyses of the association between rs16969968-rs1051730 and BMI were performed using linear regression , stratified by smoking status ( never , former and current ) and sex , and adjusted for age ., BMI was log transformed prior to analysis ., An additive genetic model was assumed on log values , so that each effect size could be exponentiated to represent the percentage increase in BMI per minor ( risk ) allele ., For NHANES , which has a survey design , Taylor series linearization was implemented to estimate variances ., For studies including related family members appropriate methods were used to adjust standard errors: in GEMINAKAR , twin pair identity was included as a cluster variable in the model , in MIDSPAN linear mixed effects regression models fitted using restricted maximum likelihood were used to account for related individuals ., ALSPAC mothers and children were analysed as separate samples; as there are related individuals across these samples , sensitivity analyses were performed excluding each of these studies in turn ., Results from individual studies were meta-analysed in Stata ( version 13 ) using the “metan” command ., As I2 values were all equal to or below 36% ( indicating low to moderate heterogeneity ) , fixed effects meta-analyses were performed ., The “metareg” command was used to examine whether SNP effects varied by sex and estimates were combined as there was no evidence for effect modification by sex ., Evidence for interaction between genotype and smoking status was assessed using the Cochran Q statistic ., Data are available from the Institutional Data Access/Ethics Committees of the individual studies that contributed to this analysis , for researchers who meet the criteria for access to confidential data ., Full details are provided in Text S3 ., Sample size calculations were performed using Quanto software ( http://biostats . usc . edu/Quanto . html ) ., The following parameters were used: 80% power to detect associations , minor allele frequency of 0 . 33 , mean and standard deviation for BMI of 25 kg/m2 and 3 . 8 kg/m2 respectively , alpha values of 0 . 05 and 5×10−8 . | Introduction, Results, Discussion, Methods | We previously used a single nucleotide polymorphism ( SNP ) in the CHRNA5-A3-B4 gene cluster associated with heaviness of smoking within smokers to confirm the causal effect of smoking in reducing body mass index ( BMI ) in a Mendelian randomisation analysis ., While seeking to extend these findings in a larger sample we found that this SNP is associated with 0 . 74% lower body mass index ( BMI ) per minor allele in current smokers ( 95% CI -0 . 97 to -0 . 51 , P\u200a=\u200a2 . 00×10−10 ) , but also unexpectedly found that it was associated with 0 . 35% higher BMI in never smokers ( 95% CI +0 . 18 to +0 . 52 , P\u200a=\u200a6 . 38×10−5 ) ., An interaction test confirmed that these estimates differed from each other ( P\u200a=\u200a4 . 95×10−13 ) ., This difference in effects suggests the variant influences BMI both via pathways unrelated to smoking , and via the weight-reducing effects of smoking ., It would therefore be essentially undetectable in an unstratified genome-wide association study of BMI , given the opposite association with BMI in never and current smokers ., This demonstrates that novel associations may be obscured by hidden population sub-structure ., Stratification on well-characterized environmental factors known to impact on health outcomes may therefore reveal novel genetic associations . | We found that a single nucleotide polymorphism in the CHRNA5-A3-B4 gene cluster , which is known to influence smoking heaviness , is associated with lower body mass index ( BMI ) in current smokers , but higher BMI in never smokers ., This difference in effects suggests that the variant influences BMI both via pathways other than smoking , and via the weight-reducing effects of smoking , in opposite directions ., The overall effect on BMI would therefore be undetectable in an unstratified genome-wide association study , indicating that novel associations may be obscured by hidden population sub-structure . | genetics, biology and life sciences, human genetics | null |
journal.pcbi.1000946 | 2,010 | Quantitative Analysis of Immune Response and Erythropoiesis during Rodent Malarial Infection | Malarial infection of humans is a major cause of morbidity and mortality , continuing to cause around 250 million cases and close to a million deaths annually 1 ., The vast majority of severe cases and deaths are due to Plasmodium falciparum , which is endemic in most of sub-Saharan Africa and other tropical areas 2 ., Although there is no simple relationship between the pathogenic processes and clinical syndromes , disease only begins once the asexual parasite begins to multiply within the hosts red blood cells ( RBCs ) 3 ., The asexual dynamics depend on a complex interaction between the malaria parasite and the hosts immune and erythropoetic responses 4 ., Experimental methods have helped elucidate key aspects of this interaction ., Such factors include the parasites destruction of RBCs due to reproduction 5 , 6 , immune-mediated clearance of merozoites and parasitised RBCs ( pRBC ) 7 , 8 , and immune-mediated clearance of unparasitised RBCs ( uRBC ) ., In particular , there is evidence that loss of uRBCs is responsible for the vast majority of the anaemia 9–11 ., Suppression of RBC production ( dyserythropoiesis ) during the acute phase may also contribute to anaemia 12 , 13 , although recent modelling suggests that , overall , the level of erythropoiesis increases during malaria infection 8 , 9 , 14 ., A full understanding of the infection dynamics requires quantitative analysis of the relative importance of the contributory factors 3 ., Such an assessment is vital to help inform malaria treatment and intervention programmes 8 , 15 , 16 ., In particular , the design of effective vaccines and immunotherapies depends largely on our understanding of the innate and adaptive immune responses 2 , 17 ., In this context , rodent malaria models allow a highly replicable , highly controlled experiment ., Although there are important differences between rodent and human malarias , a quantitative understanding of the rodent system , where we can control both host and parasite genetics , should help our understanding of the human case in which controlled experiments are unethical ., As in other areas of science , mathematical models can be used to make inferences about complex dynamical systems by fitting them to data ., This approach allows us to formally and quantitatively test and compare competing hypotheses , and to make quantitative predictions for future empirical testing ., It is the most powerful and rapid way of culling possible , but incorrect , hypotheses ., In the mathematical modelling literature on malaria , there are a number of studies that quantitatively fit models to data 6 , 8 , 9 , 14 , 15 , 18–22 ., However , the poorly developed statistical , diagnostic and computational methodologies of fitting nonlinear dynamical models to noisy data ( see 23 and Discussion ) meant that these studies had to focus on particular aspects of the host-pathogen system in isolation ., The method used was maximum likelihood ., Its application to nonlinear systems is problematic because the nonlinearities create complex multi-dimensional likelihood surfaces ., Search algorithms easily become trapped in local maxima , leading to false inferences 24 ., Even if one is reasonably sure of having found the global maximum , evaluating parameter confidence intervals and covariances is computationally expensive and laborious , and computing predictive intervals practically impossible 25 ., Recent developments in adaptive , population-based Markov chain Monte Carlo ( McMC ) methods overcome all of the problems associated with maximum likelihood 24 , 26–30 ., The use of a Bayesian framework enables us to incorporate prior knowledge and uncertainty about the parameters ., It allows us to quantify our relative belief in one model predicting the data over another , rather than accepting and rejecting models using conventional , but arbitrary , cut-offs ., In order to use Bayesian statistics , we need to know the structure and variance of the measurement errors ., Fortunately , these are known for our data sets ., In this study we develop a set of models to test competing hypotheses describing the asexual stage of the malaria parasite ., We fit the models to a set of data on Plasmodium chabaudi infections 31 using an adaptive McMC algorithm ., We provide parameter estimates , examine differences between mouse and parasite strains , and make quantitative predictions about the immune and erythropoietic systems dynamics , and their effects on the RBC population ., In modelling the asexual dynamics , there are three general processes we need to consider:, ( i ) the infection of RBCs ,, ( ii ) the immune response , and, ( iii ) the response of the erythropoietic system to malaria-induced anaemia ., The immune systems response to malaria is exceedingly complex and there is still much to learn about it qualitatively , let alone quantitatively 17 ., Mathematical models have generally represented the immune response either as a single variable functionally linked to parasite density , or as separate innate and adaptive components 8 , 21 , 32–35 ., The model of Recker et al . ( 2004 ) further discriminates , on the basis of human serologic data , between short-term , partially cross-reactive immune responses and long-term specific responses 36 ., These models have given valuable insights into the immune dynamics , but it is important to acknowledge that the immune response consists of multiple arms , each targeting different aspects of the parasite 2 ., Here we model the immune system as time-dependent immune-mediated clearance rates of merozoites , pRBCs and uRBCs ., This allows us to bypass the debate about the highly interdependent innate and adaptive arms of the immune response , i . e . , when they are activated , what they target , and how they develop over time , and instead focus on the functional consequences in terms of the infection dynamics ., We also draw attention to a key aspect of malaria asexual reproduction universally ignored in previous modelling studies ., It is established that individual RBCs may be parasitised by more than one merozoite ., Multiply-parasitised RBCs are often observed in experiments , but it is not known whether their subsequent behaviour is the same as that of singly-parasitised RBCs; previous models have generally assumed that their dynamics are identical ., Here we test that assumption ., In particular , we test whether multiply-parasitised RBCs have a greater death rate than other RBCs , and whether they produce a greater number of merozoites than singly-parasitised cells ., We used data obtained from a previous experiment 31 ., Briefly , three different mice phenotypes were infected with either of two genetically distinct clones of Plasmodium chabaudi ( AS or AJ ) ., Both clones were originally isolated from thicket rats ( Thamnomys rutilans ) in the Central African Republic 37 ., The AS clone is associated with a lower peak density relative to AJ; it also has lower virulence , as measured by anaemia and weight loss 38 ., Three different phenotypes of BALB/c mice were used:, ( i ) wildtype mice;, ( ii ) nu/nu mice ( “nude mice”; Harlan UK ) ; and, ( iii ) nude mice reconstituted with T cells taken from wildtype mice ., The mutation nu is a recessive mutation that blocks the development of the thymus ., Nude mice therefore lack mature T cells , whereas heterozygotes ( nu/+ ) have a normal immune system 39 ., Nude and reconstituted mice are smaller than the wildtype and are hairless ., Mice of each phenotype ( wildtype , nude , reconstituted ) were innoculated with pRBCs of either AS or AJ ., This gave six treatment groups ., There were seven mice in both treatment groups for nude mice , and six mice in each treatment group for reconstituted and wildtype mice ., Measurements of RBC and parasite density were taken on days 0 , 2 , 4 , and then daily until day 18 when the experiment was terminated ., Parasite density was measured daily at 08:00 hrs using quantitative PCR , at which point the asexual merozoites have yet to replicate within pRBCs ., Both RBC and parasite densities are expressed in terms of the number per microlitre ( ) of blood ., Full details of the experimental methods are given in 31 ., We removed a single data point from one of the reconstituted AJ-infected replicates ., This mouse had much lower parasite density on day 14 than all the other mice ., The data point was therefore considered to be an outlier ., The averaged dynamics of each treatment are shown in Fig . 1 ., The AJ clone ( solid line ) does not show the normal higher peak density compared to the AS clone ( dotted line ) ; however , the AJ clone exhibits a higher density during the exponential growth phase compared to AS ., Parasite density tends to level off in reconstituted and nude mice from day 12 , but continues declining in the wildtype , presumably because of a stronger immune response in these mice ., In reconstituted and wildtype mice , the AJ clone causes greater anaemia than the AS clone ., All three mouse phenotypes show an earlier drop in RBC density from days 6–8 when infected with AJ compared to AS ., The recovery of RBC density in nude mice is weaker than in reconstituted and wildtype mice ., We discuss these observations below in relation to inferences from the model fitting ., In Plasmodium chabaudi , pRBCs rupture synchronously every 24 hours , releasing on average 6–8 parasites ( merozoites ) into the bloodstream 40 ., These newly released merozoites infect further RBCs and the cycle repeats ., The rupture of pRBCS ( schizogony ) occurs at approximately midnight under normal lighting conditions 41 ., We use a discrete-time formulation to model the dynamics , where each time step corresponds to a single day ., The start of day is defined as the point immediately following rupture of pRBCs , before any infection has occurred ( i . e . , the point at which merozoites are released into the bloodstream ) ., The densities of merozoites and uRBCs at the start of day are denoted and , respectively ., We assume that the processes determining RBC density occur on two non-overlapping timescales ., The first corresponds to the short infection phase during which merozoites infect RBCs , which occurs within a few minutes following schizogony ., The second and subsequent timescale ( the remainder of the day ) corresponds to the RBC turnover phase: here the parasites replicate within pRBCs , new uRBCs are released into the bloodstream and , if active , the ( non-merozoite ) immune responses clear pRBCs and uRBCs ., At the end of the RBC turnover phase , surviving pRBCs rupture and release new merozoites ., Using the statistical algorithm described in the Supporting Information ( Text S1 , Figure S1 and Figure S2 ) , we fit to the observed data on RBC and parasite densities ., The fitted parameters are given in Table 2 , along with their prior distributions ., The prior distributions were based either on values taken from the literature , or approximate estimates obtained before the main model-fitting ( see Text S1 for details ) ., Our aim is to find a set of minimal adequate models which explain the data well and contain as few parameters as possible ., We take as our baseline the model described above ., This is denoted and contains 20 fitted parameters ., We developed a set of nested and non-nested models in which specific assumptions are made about the immune and erythropoietic responses ( outlined in Table 3 ) ., All models were fitted to the data ., Each model fit was then evaluated relative to that of the baseline ., The model fits were compared using Bayes factors , which naturally penalise overfitting 44 ( refer to Text S1 for further details on measurement error , the likelihood function , model fitting , assessment and comparison ) ., We estimate parameters separately for each mouse , rather than across treatments ., Even inbred mice are phenotypically different , and these differences result in variability in parasite and RBC dynamics ., Immune responses are significant sources of variability in vivo; but we might also expect variation between mice in parameters such as infection rate , because of the multifactorial nature of such processes which involve the interaction of many host and parasite proteins ., We therefore make no assumption about which parameters are invariant across mice and instead estimate parameters separately for each mouse ., This method allows us to infer parameter ( and hence process ) variability within and between treatments from the posterior estimates ., We begin by analysing the baseline model , ., The fits to all mice are shown in Figs ., 2–4 ., Note that the posterior predictive interval ( PPI ) of the dynamics widens from around day 15 for some mice because of the lack of data ., The model fits appear to adequately explain the data ., A more rigorous assessment of the model fits is attained by plotting the overlaid standardised residuals for parasite and RBC densities ., Fig . 5 shows the standardised residuals ( the blue crosses ) for all mice across the six treatments ., Poor fits are suggested by outlying and serially correlated residuals ., The fits to parasite density are accurate but not perfect ., There are no outliers , but the model tends to overestimate parasite density on day 4 ., This is unexpected because we expect parasite density to be growing exponentially during this time , and indeed this is how the model behaves ., This discrepancy suggests that parasite density may initially grow at a slower than exponential rate ., Also , the model tends to underestimate parasite density on day 11 ., Currently , we have no explanation for this ., The fits to RBC density are accurate , except on day 8 where RBC density is marginally overestimated ., Therefore , the model may not be correctly capturing the trough in RBC density ., We calculated the Bayes factors of models , for , relative to the baseline model ., We adopt the scale of interpretation for Bayes factors proposed by Jeffreys 45 and reproduced in Table 4 ., For ease of interpretation , the Bayes factors are converted to deciBans; i . e . , ( Bayes factor ) ., Bayes factors were calculated for each mouse , giving a total of 38 values for each model comparison ., The full list of Bayes factors is given in Table S1 and Table S2 ., For conciseness and clarity , we report for each model:, ( i ) the sum of the deciBans for all mice within each treatment , and, ( ii ) the sum of the deciBans for all mice across treatments ( Table 5 ) ., We also report the standard error of the deciBans ., Errors occur because deciBans are estimated from a finite sample of the posterior distribution ., Our inferences are conservative; thus , we interpret a deciBan of as “barely worth mentioning” ( see Table 4 ) ., Statistical comparison of parameter values between treatments was performed using Analysis of Variance ( ANOVA ) in the R statistical package 46 ., The method was as follows ., For a given parameter , we first took the mean of the posterior distribution , , for each individual mouse ., The mean for a given treatment , , was then calculated as the average of the posterior mean values taken across all mice in the treatment ., The aim of this paper was to provide a quantitative assessment of the immune and erythropoietic responses in Plasmodium chabaudi infections ., Hypotheses were drawn from experimental data and the mathematical modelling literature ., These were fit to data on malaria infected mice using a Bayesian statistical framework ., Crucially , by quantifying the experimental error , we were able to provide a rigorous assessment of the model fit ., In particular , we were able to evaluate and compare the accuracy of different models in explaining the data ., Models were compared using Bayes factors , which impose a penalty for additional parameters ., We interpreted our results with reference to the product of Bayes factors ( sum of deciBans ) within and across treatments ., Our results provide very strong evidence that the immune response to P . chabaudi involves clearance of both parasitised and unparasitised RBCs ., Both effects were evident during the initial peak of parasite density , implying that control of the peak is regulated by the immune response ., Previous modelling studies have suggested that innate or early specific immune responses regulate the initial dynamics of parasite density and anaemia 8 , 9 , 21 , 47 ., Our study provides a statistically rigorous analysis in support of this hypothesis ., Parasite-infected erythrocyte surface antigens ( PIESA ) may be an important immune target in both rodent and human malarias ., In the case of P . falciparum , a longitudinal study of Kenyan children found that clinical malaria was caused by parasite isolates expressing PIESA variants that corresponded to gaps in the repertoire of antibodies carried by the children before they became ill 48 ., We have shown that uRBC clearance by the immune system plays a key role in determining the infection dynamics of P . chabaudi ., Experimental studies confirm that in the rodent malaria P . berghei 10 , and also in P . falciparum which infects humans 11 , the vast majority of anaemia is due to uRBC loss ., Our results suggest that the level of anaemia following control of the initial peak ( from about day 12 ) , is mediated by the activity of the uRBC targeting response ., This provides a mechanistic explanation for the variation in RBC dynamics between individual mice ., If uRBC clearance decreases following control of the initial peak , then the increase in erythropoiesis that occurs from approximately day 10 allows the host to recover quickly from anaemia; in contrast , prolonged uRBC clearance is associated with a slow recovery from anaemia ., Due to the lack of data to describe parasite density , the role of the pRBC targeting response during this later stage of infection is less certain ., Our results suggest that the immune responses targeting pRBCs and uRBCs do not show a high degree of synchronisation ., This implies they may be controlled by different effector mechanisms ., Our model does not account for the antigenic variation that occurs in P . chabaudi infections 49 , 50 ., Indeed , our model formulation only permits a single “switching-on” and “switching-off” of each immune component ( merozoite , pRBC , uRBC ) , and therefore does not distinguish between non-specific ( innate ) versus specific antibody responses to antigenically distinct variants ., We modelled the infection up until day 18 , at which point antigenic variation may have a significant effect on the dynamics ., In some mice , the data show a second drop in RBC density following the recovery from initial anaemia ( Fig . 2 ) ., Although we only have data on parasite density up to day 14 , this second anaemia is commensurate with a recrudescent parasite density ., However , it is significant that our model is able to explain the observed dynamics so well without including antigenic variation ( Fig . 5 ) ., Extending the model up to , for example , day 30 post-infection would require explicit modelling of immune responses to the different antigenic variants ., Such modelling would probably need to include both short-lived ( innate ) and long-lasting antibody responses , and may also need to consider cross-reactivity ., The cascade of sequentially dominant antigenic variants seen in P . falciparum infections has recently been explained as the result of short-lived cross-reactive immune responses directed against shared epitopes 36 ., Our results are consistent with the observation that T-cell-deficient ( “nude” ) mice have impaired immune responses , and are unable to resolve malaria infections 2 ., T cells play an important role during the early stages of malarial infection , by amplifying the phagocytic and cell-mediated antiparasite responses; later in the infection , they help B cells to produce antibody , and assist in regulating the innate response 51–53 ., Immune-mediated clearance of uRBCs was necessary to explain the dynamics in all three phenotypes , but nude mice had the higher maximum clearance rate of uRBCs ., The infection rate of RBCs with merozoites , as reflected in the parameter , could also be higher in nude mice ., As a simple proxy for the real biological system , these results indicate that nude mice are less able to limit the replication of the malaria parasite , and that their less specific immune response is associated with greater destruction of uninfected cells ., At the individual mouse level , there was no evidence for immune-mediated clearance of merozoites ., However , the cumulative evidence over all mice suggests that it is required to explain our data ., Our interpretation of this result is that merozoite clearance is weak during the first few weeks of infection , and that pRBC and uRBC clearance are the major determinants of the dynamics during this time ., Previous models have shown that , for a given level of immune activity , merozoite clearance is less effective at controlling parasite density compared to an equivalent response that clears pRBCs 7 , 8 ., One explanation for this is that the duration of the merozoite infection phase ( estimated to be on the order of minutes ) is too short for the immune system to effectively target merozoites 7 ., However , there is no a priori reason that one or several fast-acting , highly effective immune responses could not operate during this phase ., There is considerable empirical evidence that merozoite surface protein one ( MSP1 ) is a target of immune mechanisms in malaria infections 54 ., The presence of high levels of naturally acquired IgG antibodies to merozoite surface protein two ( MSP2 ) is also strongly associated with protection against clinical malaria 55 ., Recent results suggest that this naturally acquired protection is not specific in relation to the major allelic dimorphisms of MSP2 56 ., We have shown that erythropoiesis upregulates during malarial infection , and that wildtype and reconstituted mice have higher upregulation than nude mice ., This may reflect that the erythropoietic response only upregulates to the extent that the host is controlling the parasite ., Recent theoretical results have shown that excessive upregulation of erythropoiesis facilitates the growth of the parasite , and may result in greater anaemia and a higher peak of parasite density 57 ., We also investigated whether there is a time delay before the upregulation of erythropoiesis , and a lag in the feedback between RBC density and the level of erythropoiesis ., The results for the AJ-infected wildtype mice suggest that upregulation of erythropoiesis occurs from day 10 ., Both reconstituted and nude mice show no evidence of a time delay ., Our results also suggest a time lag of 2–3 days in the feedback between RBC density and erythropoiesis in AJ-infected reconstituted mice; however there is no evidence for a time lag in the other treatments ., The reasons for this are unclear ., One possibility is that our putative time lag is compensating for another process not included in the model ., Only analysis of other data sets may reveal what this may be ., In summary , our results show that the immune system plays a key role in determining the RBC and parasite dynamics in malaria-infected mice ., We have shown that immune-mediated clearance of both parasitised and unparasitised RBCs is necessary to explain the RBC and parasite dynamics ., Previous models have examined the implications of RBC age structure for the infection dynamics 14 , 22 ., In particular , recent work by Mideo et al . ( 2008 ) suggests that P . chabaudi may preferentially infect mature RBCs ( normocytes ) , but produce more merozoites in younger cells ( reticulocytes ) 14 ., Future models may need to consider how RBC age structure and immune system dynamics can be combined to obtain a more complete picture of the asexual stage of malaria . | Introduction, Materials and Methods, Results, Discussion | Malarial infection is associated with complex immune and erythropoietic responses in the host ., A quantitative understanding of these processes is essential to help inform malaria therapy and for the design of effective vaccines ., In this study , we use a statistical model-fitting approach to investigate the immune and erythropoietic responses in Plasmodium chabaudi infections of mice ., Three mouse phenotypes ( wildtype , T-cell-deficient nude mice , and nude mice reconstituted with T-cells taken from wildtype mice ) were infected with one of two parasite clones ( AS or AJ ) ., Under a Bayesian framework , we use an adaptive population-based Markov chain Monte Carlo method and fit a set of dynamical models to observed data on parasite and red blood cell ( RBC ) densities ., Model fits are compared using Bayes factors and parameter estimates obtained ., We consider three independent immune mechanisms: clearance of parasitised RBCs ( pRBC ) , clearance of unparasitised RBCs ( uRBC ) , and clearance of parasites that burst from RBCs ( merozoites ) ., Our results suggest that the immune response of wildtype mice is associated with less destruction of uRBCs , compared to the immune response of nude mice ., There is a greater degree of synchronisation between pRBC and uRBC clearance than between either mechanism and merozoite clearance ., In all three mouse phenotypes , control of the peak of parasite density is associated with pRBC clearance ., In wildtype mice and AS-infected nude mice , control of the peak is also associated with uRBC clearance ., Our results suggest that uRBC clearance , rather than RBC infection , is the major determinant of RBC dynamics from approximately day 12 post-innoculation ., During the first 2–3 weeks of blood-stage infection , immune-mediated clearance of pRBCs and uRBCs appears to have a much stronger effect than immune-mediated merozoite clearance ., Upregulation of erythropoiesis is dependent on mouse phenotype and is greater in wildtype and reconstitited mice ., Our study highlights the informative power of statistically rigorous model-fitting techniques in elucidating biological systems . | Malaria is a disease caused by a protozoan parasite of the genus Plasmodium ., Every year there are around 250 million human cases of malaria , resulting in around a million deaths ., Most of the severe cases and deaths are due to Plasmodium falciparum , which is endemic in much of sub-Saharan Africa and other tropical areas ., The pathology of malaria is related to the asexual stage of the parasite ., Understanding the infection dynamics during this stage is therefore essential to inform malaria treatment and vaccine design ., Experimental infections of rodents represent an important first step towards understanding the more complicated human infections ., We developed a series of models representing different hypotheses about the main processes regulating the infection dynamics during the asexual stage ., Models were fit to data on Plasmodium chabaudi infections of mice , using a Bayesian statistical framework ., The accuracy of different models in explaining the RBC and parasite densities was quantified ., We identify the role of different types of immune-mediated mechanism , and show that RBC production ( erythropoiesis ) increases during infection ., Differences between mouse phenotypes are explained ., Our study highlights the informative power of model-fitting techniques in explaining biological systems . | mathematics/statistics, public health and epidemiology/infectious diseases, immunology/immune response, computational biology | null |
journal.pgen.1002970 | 2,012 | Cofilin-1: A Modulator of Anxiety in Mice | Exploiting naturally occurring genetic variation to identify mechanisms that give rise to behavioural phenotypes in mammals has proved to be extremely difficult 1 ., The abundance and small size of loci that contribute to behavioural variation frustrate gene identification and make it difficult to know which among them are central to the responsible biological mechanisms 2 ., A major challenge is to devise methods that move quickly from locus to mechanism 3 ., Using heterogeneous stock ( HS ) mice descended through more than 50 generations from eight inbred progenitor strains 4 we have previously identified 205 quantitative trait loci ( QTLs ) that contribute to variation in one or more of four anxiety tests: the elevated plus maze , open-field arena , freezing to the context and reluctance to try a novel food ., Performance levels on these tests reflect , at least in part , activity in the ventral hippocampus 5 , 6 , 7 , 8 , 9 and these tests were chosen in order to interrogate an underlying psychological construct of anxiety from different perspectives ( a single measure , such as variation in locomotor activity in the open-field arena , will include traits irrelevant to anxiety 10 ) ., We set out to determine causal genes for anxiety in the HS ., Recombinants that have accumulated since the founding of the HS means that QTLs are mapped to intervals of approximately 3 Mb 4 , much higher resolution than obtained by mapping in backcrosses or intercrosses 3 ., Nevertheless , mapping in the HS rarely identifies single genes so additional approaches are necessary to identify candidate genes ., Here we considered an alternative approach based on two assumptions ., The first is that the mosaic structure of the genomes of inbred laboratory mice could be used to reduce the regions containing candidate genes ., Because HS mice are descended from a small number of founders , any pair of mice will share a fraction of their genome 11 so that all genomic regions can be classified as either identical or non-identical by descent ., Since a QTL must lie in a region where sequence differences distinguish strains in the same way as the QTL alleles , in a cross between two strains the QTL must lie in a region that is not identical by descent ., In multiple crosses , or in animals descended from multiple strains such as the HS , this relationship , though more complex , still holds and can be used to fine-map QTLs 12 , 13 ., The second assumption is that genes that influence the same , or related , traits have similar functions which are captured by existing functional annotations 14 ., The gene ontology ( GO ) database , for example , assigns biological descriptors ( GO terms ) to genes 15 ., Genes assigned the same GO term can be regarded as members of a category of genes that are more closely related in terms of some aspect ( s ) of their biology than are randomly-chosen genes ., Therefore the presence of a highly non-random pattern of functional annotations is an indication that we have correctly identified genes influencing a trait ., Importantly , we do not make any assumptions about which annotations are relevant to a trait prior to performing our analysis ., We used 205 QTLs that contribute to variation in four different anxiety tests: the elevated plus maze , open-field arena , freezing to the context and reluctance to try a novel food ., The identification of these QTLs in HS mice is described in 4 ., Our first aim was to determine regions within QTLs that are most likely to contain genes involved in the phenotype ., To do so , we began by dividing up the genomes of the HS progenitors according to the pattern of ancestral allelic similarities and differences at a locus ., Our intention was to identify regions of the genome descended from a common progenitor ., Just as each HS mouse is descended from eight inbred strains , the progenitors in turn have ancestors in common ., Using a dynamic programming algorithm we partitioned the genome into regions in which all sequence variants detected in the near-complete genome sequence 16 are consistent with a single phylogenetic tree 17 ., We next used the phylogenetically determined strain distribution patterns to find regions likely to contain genes with variants that could be causally related to phenotypes using a merge analysis 13 ., On most phylogenetic trees some founder strains are indistinguishable and so share the same leaf: in other words , the tree merges the eight strains into groups that share alleles ., Causal variants lie in intervals where the tree partitions strains consistent with the allelic effects of the QTL ., We refer to these intervals as consistent QTL intervals ., The 205 anxiety QTLs include 5 , 932 genes ( 29 genes per QTL ) , while the consistent QTLs contain 458 genes ( 2 . 4 genes per QTL ) ., Figure 1 presents an example of a merge analysis for a QTL on chromosome 19 ., Reasoning that causal genes within QTLs for a specific trait are likely to share functions we looked for enrichment of functional annotations ( gene ontology ( GO ) annotations ) in the 458 genes within the consistent QTLs ., Many tests of functional enrichment assume that the functions of neighboring genes in consistent QTLs are uncorrelated ., However neighbouring genes may have similar functions , as tandem duplications , which occur throughout the genome , often give rise to functionally related genes 18 ., We addressed this problem using a permutation test that accounts for gene order within genomic intervals ., The test assigns P-values to GO annotations , representing the strength of the evidence against the null hypothesis that GO annotations are randomly distributed amongst consistent QTLs ., We summarised the functional coherence of GO annotations associated with the set of genes within consistent QTLs ., We define the ‘information score’ as the sum of the negative base-10 logarithms of the P-values ( logP ) for all GO terms associated with genes within the intervals ., The information score can be considered to be a measure of the degree of coherence within the set of genes compared to that in a random sample of genes , and has similarities to the self-information measure of information theory ., For the enrichment analysis we combined genes from all phenotypes , since our aim is to identify biological features that reflect the underlying psychological construct of anxiety , rather than to identify test specific features ., The location of QTLs for different measures of anxiety sometimes coincides , for example when we have multiple measures from a single test , such as the open-field arena and elevated plus maze ., For overlapping QTLs , we included the QTL with the smallest 95% confidence interval ., Figure 2A shows a significant enrichment in information score for the genes in the consistent QTL intervals compared to values calculated from 10 , 000 sets of randomly sampled genes for each of the trait sets ., Figure 2B shows that no significant enrichment was found when an identical analysis was performed using all genes at each QTL , ignoring the results of the merge analysis ., We tested for over-representation of GO terms , since it is not clear how to validate genes associated with under-represented GO terms ( an under-represented gene would be one that is not involved in the phenotype ) ., At a 10% false discovery rate ( FDR ) we identified 16 GO terms that are over represented in 167 genes at 57 QTLs ( Table S1 ) ., More than 90% of the genes were identified by domain-level terms ( biological and cellular process ) or high-level terms ( anatomical structure development , system development , developmental process , multicellular organismal process , cellular metabolic process ) ., Only three GO terms yielded information about specific mechanisms: two genes ( cofilin-1 ( Cfl1 ) and destrin ( Dstn ) ) were associated with “positive regulation of actin filament depolymerization’ , and a single gene was associated with both “eye pigment granule organization and biogenesis” and “lens morphogenesis in camera-type eye” ., However this gene , Shroom2 , is also associated with the GO term “negative regulation of actin filament depolymerization” , suggesting that actin filament depolymerization might be an important mechanism involved in anxiety ., We examined the effect of disrupting polymerisation/depolymerisation of actin filaments in the hippocampus of mice by using a conditional mutant , n-Cofflx/flx , CaMKII-cre , in which Cfl1 is deleted in the principal neurons of the developed forebrain ( which includes the hippocampal formation ) 19 , 20 , 21 ., Previous work has demonstrated that CamKIIα-cre mice are indistinguishable to wild-type littermates ( e . g . 22–23 ) so we employed littermate mice as controls for the experiments described below ., Anxiety occurs when there is a conflict between competing goals or response options 24 , 25 ., For example , most unconditioned laboratory tests of anxiety rely on the conflict between whether the animal should approach and explore the relatively more open and exposed sections of the apparatus , or avoid these potentially more dangerous areas ., Changes in approach/avoidance behaviour in novel , mildly aversive environments were used as a measure of anxiety , and are dependent , in part , on the ventral hippocampus 5 , 6 , 7 , 8 ., An anxious rodent will be slower to enter , and will spend less time in , the more open and exposed sections of the apparatus ( e . g . open field arena ( OFA ) and elevated plus maze ( EPM ) ) ., They will also defecate when placed in a brightly lit OFA 6 ., Numerous studies have used anxiolytic drugs to show a correspondence between the behaviour of rodents in the OFA and EPM and human anxiety 26 , 27 ., In both the OFA ( Figure, 3 ) and EPM assays ( Figure, 4 ) we found that the Cfl1 mutants were significantly less anxious than controls ., Mutants showed significantly increased total activity ( Figure 3A ) and decreased latency to enter the central region in the OFA ( Figure 3C ) ., They also defecated less during the OFA test ( Figure 3B ) ., The Cfl1 knockouts also spent significantly more time in the open arms of the EPM ( Figure 4D ) ., They had longer path lengths within the open arms ( Figure 4C ) , had a reduced latency to enter an open arm for the first time ( Figure 4E ) , and made more entries/visits into the open arms ( Figure 4A ) ., Importantly , however , the number of entries into the closed arms of the EPM did not differ between the groups , suggesting that these changes in behaviour do not simply reflect a generalized locomotor hyperactivity in the Cfl1 knockouts ( Figure 4B ) ., To explore this further , we examined locomotion of single-housed mutant mice under stress-free conditions using infrared sensors to detect spatial displacement over time in standard mouse cages ., In a 24 hour period , neither total activity , nor activity during the light or dark phases , were significantly different between the two genotypes ( Figure 5 ) ., In this paper we show how the near-complete sequence from the progenitors of the HS can be use in conjunction with gene annotations to identify genes influencing anxiety at QTLs in HS mice ., The method we applied involves partitioning QTLs into intervals that can be summarized by a single phylogenetic tree among the HS founders , testing whether that partitioning was consistent with alleles influencing anxiety at each QTL , and then searching for common functions in candidate genes positioned within those intervals ., Crucially , we were able to show there was no enrichment for function when we included all genes under each QTL , thus confirming the value of phylogenetic filtering ., Our method is a development of two analytical techniques , probabilistic ancestral haplotype reconstruction ( HAPPY ) 28 and merge analysis 13 , but it is not a replacement for either; rather , it depends on both ., HAPPY is a tool for mapping in populations whose progenitors are known ( or can be inferred ) , while merge analysis identifies which variants might be functional , based on a comparison between the HAPPY derived allelic effects and those of the variant ., Incorporating phylogenetic filtering into merge analysis allows us to determine which regions ( rather than which variants ) are putatively functional and hence to prioritize genes that lie in these intervals for functional studies ., Phylogenetic filtering is the methodological advance described here ., Our approach has some obvious limitations ., Above all , the relevant gene at most QTLs still remains unknown ., At best , we identified genes at 57 QTLs out of 205 ., Even allowing that the same QTL influences multiple measures , genes at more than half of the QTLs are not identified ., This may in part reflect our reliance on an imperfect set of annotations ., As the quality and density of annotations increases , it may be possible to detect more functional patterns among genes at consistent QTLs ., However failure to find enrichment may also reflect a problem inherent in all sequence based approaches: finding functionally relevant sequence does not immediately translate into finding functionally relevant genes ., Typically , as here , genes are identified because they either contain , or lie close to the functionally relevant sequence , but proximity does not unequivocally indentify the correct genes ., We were also unable to find many terms that pointed to a potential mechanism ., Again this likely reflects the relative poverty of annotations ., Despite these limitations , we think our method has important advantages ., Notably it addresses an emerging problem in mouse complex trait , namely the need to prioritize large numbers of candidate genes ., Until recently there were relatively few loci mapped at sufficiently high resolution to suggest high quality candidate genes for functional studies ., The use of resources that can deliver near gene-level mapping resolution ( HS mice , commercial outbreds 29 or the Collaborative Cross 30 , 31 ) , together with the realization that hundreds , if not thousands , of individual genetic variants are involved 32 , is about to transform that situation ., A critical problem for mouse complex trait analysis problem now is how to validate the large number of candidate genes the new mapping resources identify ., The many genes we identified by searching for enrichment of domain-level or high-level GO terms likely provide a useful starting point for functional studies ., It should be noted that they include a number of ion channels and neurotransmitter receptors ( see Table S1 ) ., Two further observations are worth making about the use of sequence for the identification of Cfl1 as a quantitative trait gene ., First , the sequence variants contributing to the QTL likely lie in a regulatory region ., From the available progenitor strain sequence we know that no sequence variants segregate in the HS within the Cfl1 gene itself ., The nearest 5′ variant is a SNP at 5 , 489 , 197 ( the transcriptional start site of Cfl1 is at 5 , 490 , 455 ) and the nearest 3′ is a SNP at 5 , 494 , 237 ( the end of the gene is annotated as 5 , 494 , 031 ) ., Second , previous mapping of transcript abundance in the HS identified a ci-acting expression QTL for Cfl1 ( in the hippocampus ) with a logP of 26 . 4 and a peak at approximately 5 . 8 Mb ( 33 see http://gscan . well . ox . ac . uk/gsBleadingEdge/wwwqtl . cgi ) ., It is possible that the variants contributing to the expression QTL are also those that contribute to the behavioural phenotype ( unfortunately we cannot determine whether the alleles in the HS act in the same direction as in the knockout experiment , due to the correlated nature of allelic effects in the HS 4 ) ., A second issue that warrants discussion relates to the importance of what we have found , namely a relationship between actin filament depolymerisation and genetic differences in anxiety behaviour in the mouse ., Since the method depends on gene annotations , we face the objection that we are limited to the discovery of what is already known ., Does our work represent an advance in understanding the biology of anxiety ?, Rust and co-workers have previously shown that Cfl1 plays an important role in controlling dendritic spine morphology and that the n-Cofflx/flx , CaMKII-cre mice are deficient in long-lasting forms of synaptic plasticity when assessed using hippocampal slices 21 ., n-Cofflx/flx , CaMKII-cre mice display behavioural impairments in long-term associative spatial memory 21 , as shown by impairment on the standard , spatial reference memory version of the Morris water maze task , in which mice were required to form a long-term association between a particular spatial location and the presence of the escape platform ., This raises the possibility that differences in spatial memory abilities and in spatial exploration could have contributed to the observed differences between n-Cofflx/flx and n-Cofflx/flx , CaMKII-cre mice in both the OFA and EPM in the present study ., Against this it is important to point out two things ., First , despite their inability to form long-term associative spatial memories , the n-Cofflx/flx , CaMKII-cre mice displayed normal performance on tests of short-term spatial memory 21 ., This suggests that the Cfl1 knockout mice are able to discriminate between spatial locations perfectly well , and to acquire this information rapidly ., It also shows that these mice do not have a general problem with all aspects of spatial information processing ., Second , lesions of the ventral hippocampus have no effect on spatial learning and memory performance ., In contrast , lesions of the dorsal hippocampus impair spatial learning and memory but have no effect on tests of anxiety 34 ., This double dissociation between the effects of dorsal and ventral hippocampal lesions suggests that the hippocampus may have multiple , dissociable functions associated with different sub-regions of the hippocampus , and that changes in anxiety levels in the Cfl1 knockout mice are unlikely to be due to differences in spatial memory abilities or spatial exploration ., We have assumed here that the effects we observe in the transgenic animals are due to genetic ablation restricted to the hippocampus , but we cannot exclude the involvement of the amygdala ., While expression of the Cre recombinase occurs predominantly in the pyramidal neurons of the hippocampus , it also occurs in the striatum , and amygdala 23 ., The latter structure is also involved in mediating emotional behaviours , although there appears to be some division of labour between hippocampus and amygdala 35 The anxiety tasks associated with ventral hippocampal lesions are the approach/avoidance tests used here 8 , 34 , 36 which it is worth noting are generally unaffected by lesions of the amygdala ., Thus we argue that dysfunctional cytoskeletal remodeling and the consequent alterations in synaptic plasticity in the hippocampus , and particularly the ventral hippocampus , are the most likely mechanism that contributes to the altered anxiety levels in Cfl1 knockout mice ., Cytoskeletal remodelling is linked to synaptic plasticity and synaptic plasticity is a key neural substrate for emotional behaviours , including anxiety ., Indeed , NMDA receptor-mediated synaptic plasticity in the hippocampus is a key determinant of anxiety levels 37 ., Anxiety-like states in rodents 38 , 39 and humans 40 alter hippocampal dendrites , presumably reflecting synaptic changes ., In vertebrates , excitatory synapses are found predominantly on dendritic spines where actin is highly enriched and provides the structural foundation for changes associated with postsynaptic specialization 41 ., Electrophysiological measures of synaptic plasticity , long-term potentiation and depression have been associated with growth and shrinkage of dendritic spines respectively 42 , 43 , 44 ., Disruption of genes involved in spine formation can also cause deficits in anxiety behaviour 45 and it has been shown that Lipocalin-2 ( Lcn2 ) regulates stress-induced anxiety in mice via changes in spine morphology and density 46 ., Our findings add to this growing literature on the relationship between anxiety-like behaviour and alterations in dendrites ., Cfl1 is likely to affect anxiety via the hippocampus , and more specifically the ventral hippocampus ., To our knowledge this is the first time that Cfl1 has been implicated as a gene influencing anxiety-like behavior ., To take into account the differing degrees of relatedness in the HS we use a mixed models approach where a covariance matrix of the genetic random effects quantifies relatedness in the HS ., Variance components were estimated using the R package EMMA 47 ., To compare the fit of the strain distribution pattern to the genetic action of the QTL we applied a statistical test , called merge analysis 13 ., Merge analysis is related to imputation methods used in human GWAS ., It tests whether the strain distribution pattern sequence variants across the HS founders is consistent with the estimated trait values for the founders , by comparing the fit of a QTL linear model in which each founder strain can take a different trait value to one in which those founders sharing the same DNA variant allele are merged and constrained to take the same value ., The merge statistic is the negative logarithm of the P-value ( logP ) of the ANOVA of the merged model ., When this value equals , or exceeds the logP of the unmerged model ( the unconstrained 8-way haplotype test 28 ) the DNA variant could be a QTL allele ., We applied merge analysis with one modification ., Our aim was to determine regions likely to contain genes involved in the phenotype , rather than identify the causal variant ., So , within the 95% confidence interval for each QTL , we segmented the locus into intervals between the SNPs in the HS mice , based on the ancestral recombination graph among the eight HS progenitors ., Within each interval all sequence variants detected in the Sanger mouse genomes database 16 are consistent with the same ancestral tree ( i . e . every pair of variants obeys the 4-gamete test ) ., This method is described in 17 ., On most trees some founder strains are indistinguishable so share the same leaf ., Therefore we used the tree to represent each interval in the merge analysis , by generating a pseudo multi-allelic marker whose alleles correspond to the leaves , and comparing the fit of the tree to the 8-way haplotype test ., We designate a merge interval as one whose logP value equals or exceeds the logP of the haplotype test ., Thus the merge intervals act as an importance filter on the QTL intervals , subdividing each QTL into regions that could contain causal variants , and therefore are more likely to contain the causal genes ., The test was coded in R as an extension to the R HAPPY package ( http://www . well . ox . ac . uk/rmott/happy ) ., If the merge intervals were more likely to contain causal genes than the QTL intervals as a whole , we would expect them to be enriched for certain classes of genes ., We tested for over-representation of gene function annotations within the merge intervals ., Our null hypothesis is that the merge analysis places intervals randomly within QTL intervals , rather than correctly identifying causal variants for the trait being investigated ., However we have to take into account a number of potential biases ., First , there may be a bias due to chromosomal location or G+C content of the QTLs ., Our sampling procedure therefore draws sampled intervals matched both for chromosome and G+C content ., Second , enrichment in GO descriptors could simply be due to larger numbers of genes found within the intervals ., We used a procedure that matched the gene numbers within the random intervals to that of the QTL intervals to avoid this type of bias ., Finally , tests that assume independence , such as the hypergeometric test , may not provide robust estimates because neighbouring genes within a QTL may have similar functions ( for example , functionally similar genes arising from tandem duplications ) ., We employed a Monte Carlo simulation method to test for over-representation of gene function annotations within genomic intervals ., This method does not assume that the function of each gene sampled is independent ., The Monte Carlo simulation first identifies all genes that overlap QTLs ., For each biological process GO annotation term j , we counted the number of QTLs ( nQj ) that overlap any of the genes associated with that annotation ., To identify GO terms that are significantly enriched among genes within consistent QTLs , we created a null distribution from 5 , 000 sets of randomly sampled genomic intervals , each with a length distribution identical to that of the test set of QTLs ., Each set was drawn from regions of similar nucleotide ( G+C ) content ., Chromosomes were divided into 1 Mb-sized windows and each window assigned to one of 10 equally populated %G+C bins ., For each of the test QTLs we picked a random genomic location from the same chromosome that was located in a genomic window from the corresponding %G+C bin ., Each randomly sampled interval was overlaid with a set of simulated consistent QTL intervals identical to that of the test QTL ., For each GO term j , the number of randomly sampled regions nrj that overlap genes associated with j was calculated , ignoring genes outside the simulated consistent QTLs ., The fraction of these 5 , 000 sets for which nrj≥nQj is pj , which represents an estimate of the probability that annotation j is observed in nQj QTLs simply by chance ., A further 5 , 000 sets of randomly sampled regions ( defined as above ) were used to determine the experimental false discovery rate ( FDR ) ., For each set , the number of significantly over-represented annotation terms was recorded ., The P-value threshold giving the desired FDR value was then applied to the results for downstream analysis ., Gene targeting of the Cfl1 gene was performed in 129 Sv embryonic stem cells 19 , 20 ., The conditional Cfl1 allelle was backcrossed onto a C5BL6/J background for more than 20 generations ., Inactivation of Cfl1 in the principle neurons of the adult forebrain was achieved by crossing a CamKIIα-cre transgene onto the conditional Cfl1 strain 20 , 21 , 23 ., Behavioral analyses were performed on male mice using age-matched littermates ( n-cofflx/flx ) as controls ., A first cohort of 6–7 week old mice was tested first in the open-field and next in the elevated plus maze ., A second cohort of 8–10 week old mice was used for activity recording in a home cage ., Mice were housed in an animal facility with 12-hour light-dark cycle and water and food access ad libitum ., Animal treatment and care were provided in accordance with institutional guidelines ., Home cage locomotion of single-housed mice was assessed in standard mouse cages ( Type II ) using TSE InfraMot infrared sensors ( TSE Systems , Bad Homburg , Germany ) ., Mice were transferred to new cages 12 hours before starting the recordings ., Anxiety was assessed in mice using two different , ethological , unconditioned tests of anxiety ., These were the open field arena ( OFA ) and the elevated plus maze ( EPM ) ., A standard rectangular OFA with 0 . 5 m side length ( TSE Systems , Bad Homburg , Germany ) was used ., At the beginning of the experiment , mice were placed in one of the corners facing away from the center region ., Locomotor activity and latency of entering the center region were assessed using the VideoMot2 video tracking system ( TSE Systems , Bad Homburg , Germany ) ., Fecal boli were counted manually at the end of each experiment ., A T test was used to compare locomotor activity; the Mann-Whitney test was used to compare fecal boli counts and the Mantel-Haenszel test implemented in the ‘survival’ package was used to compare latency of entering the central area ., n\u200a=\u200a8 for Cfl1 mutants; n\u200a=\u200a13 for controls ., A pale grey polyvinyl chloride EPM with the following dimensions was used: arm ( either open or closed ) length: 300 mm , width 50 mm , and height of the closed arms 150 mm ., At the beginning of the experiment , mice were placed in a closed arm facing away from the center region ., Time spent in open arms , visits to open and closed arms and distance travelled in open arms were assessed using the VideoMot2 video tracking system ( TSE Systems , Bad Homburg , Germany ) ., Latency to enter the open arm was recorded manually ., Statistical tests were performed using the R statistical package; the Mann-Whitney test was used to compare the percentage time spent in open arms and the number of visits to open arms ., n\u200a=\u200a8 for Cfl1 mutants; n\u200a=\u200a11 for controls ., All animal work was conducted according to UK guidelines and approved by the UK Home Office . | Introduction, Results, Discussion, Materials and Methods | The genes involved in conferring susceptibility to anxiety remain obscure ., We developed a new method to identify genes at quantitative trait loci ( QTLs ) in a population of heterogeneous stock mice descended from known progenitor strains ., QTLs were partitioned into intervals that can be summarized by a single phylogenetic tree among progenitors and intervals tested for consistency with alleles influencing anxiety at each QTL ., By searching for common Gene Ontology functions in candidate genes positioned within those intervals , we identified actin depolymerizing factors ( ADFs ) , including cofilin-1 ( Cfl1 ) , as genes involved in regulating anxiety in mice ., There was no enrichment for function in the totality of genes under each QTL , indicating the importance of phylogenetic filtering ., We confirmed experimentally that forebrain-specific inactivation of Cfl1 decreased anxiety in knockout mice ., Our results indicate that similarity of function of mammalian genes can be used to recognize key genetic regulators of anxiety and potentially of other emotional behaviours . | Thousands of small effect loci are believed to contribute to behavioural variation in mammals ., Their abundance and small size frustrate gene identification and make it difficult to know which among them are central to the responsible biological mechanisms ., Using imputed genome sequences from 2 , 000 outbred mice and by testing for an enrichment of functional annotations , we identify 167 candidate genes involved in anxiety ., Unexpectedly , annotations implicate actin depolymerizing factors ( ADFs ) , including cofilin-1 ( Cfl1 ) , as being involved with the expression of anxiety phenotypes in mice ., We confirmed that forebrain-specific inactivation of Cfl1 decreased anxiety in knockout mice . | animal genetics, functional genomics, genetic screens, genetics, molecular genetics, biology, genomics, genetics and genomics, gene function | null |
journal.pgen.1008042 | 2,019 | Whole genome sequencing of experimental hybrids supports meiosis-like sexual recombination in Leishmania | Protozoan parasites of the genus Leishmania present a remarkable epidemiologic and clinical diversity , producing a spectrum of human and veterinary diseases ranging from localized , self-limiting cutaneous lesions , to more chronic and destructive mucocutaneous involvement , to disseminating , visceral infection that is fatal in the absence of treatment ., Leishmania have a dimorphic life cycle consisting of extracellular promastigotes that multiply within the alimentary tract of the sand fly vector , and intracellular amastigotes that multiply within host mononuclear cells ., The diversity of clinical outcomes , as well as reservoir host range and vector species compatibilities , have distinct parasite species associations , with over 20 species associated with human infections ., The origins of this diversity , whether by gradual accumulation of mutations through mitotic cell division , and/or by sexual recombination producing admixtures of divergent genomes , remain a matter of considerable debate 1 ., Hybridization , defined as reproduction between members of genetically distinct populations and producing offspring of mixed ancestry 2 , is common in nature and has wide-ranging effects on speciation and the evolution of populations ., The isolation of Leishmania strains that have been characterized as hybrids is by now well described ., Multi locus genotyping using a variety of techniques identified hybrids between closely related New World species 3–7 , between closely related Old World species 8–10 , and between two very divergent species , L . infantum and L . major 11 ., Using more discriminatory genotyping approaches , mainly whole genome sequencing , natural hybridization has also been reported at the intraspecific level for L . infantum , L . donovani , and L . tropica 12–14 ., Some L . tropica strains in particular show high levels of allelic diversity and heterozygosity consistent with full genome-hybridization due to natural outcrossing ., Experimentally , we and others have demonstrated that inter- and intraspecific hybrids can be generated in the sand fly vector , formally demonstrating that promastigote stages of Leishmania possess the machinery for genetic exchange 15–18 ., Using pairwise combinations of parental lines expressing distinct drug resistant markers , double drug resistant lines could be recovered from sand flies co-infected with different strains of L . major , or with L . major and L . infantum , that in every case appeared to be full genomic hybrids based on their bi-parental inheritance of a limited number of allelic markers distributed across the nuclear genome ., The majority of the experimental hybrids were close to diploid , though triploid and tetraploid offspring were also observed ., Mating competency was confined to promastigote stages developing in the fly , and both Old and New World vector species could support hybrid formation ., Based on the experimental outcrosses performed so far in which only a low frequency of co-infected flies yielded hybrids ( 2–20% ) , mating must be considered a non-obligatory part of the parasite life-cycle ., Overall , the current debate regarding Leishmania reproductive strategies reflects mainly the mode of genetic exchange , its frequency and impact on population structure , not whether or not it occurs 19 , 20 ., Among kinetoplastid protists , the most well studied mating system is that of Trypanosoma brucei , for which a meiotic process is well supported based on the identification of a haploid parasite stage in the vector 21 , and on the patterns of allele inheritance and recombination observed in experimental hybrids 22 , 23 ., While genome hybridization is one of the signatures of meiosis , it can also be explained by a parasexual process , as observed in some fungi 24 and proposed for Trypanosoma cruzi 25 and Leishmania 26 , involving fusion of cells from both parents with generation of a transient polyploid state , followed by chromosome shuffling and random loss ., True sex , incorporating meiosis with generation of haploid gametes or gamete-like cells , cell fusion or syngamy , and fusion of haploid nuclei , has not been directly observed in Leishmania , although in vitro cell fusion events were recorded in 1990 for two species , L . infantum and L . tropica 27 ., Each of these processes , if they occur at all , may be difficult to detect because sex does not appear to be an obligatory stage of the life cycle , there is no obvious sexual dimorphism , and the mating competent forms are so far confined to promastigote stages developing in vivo , i . e . the sand fly midgut ., While sex might be inferred from the presence and expression of meiotic gene orthologues in Leishmania 28 , these orthologues can have other functions and are known to be maintained even in asexual species 29 ., We have therefore turned to a genetic analysis of experimental hybrids , for which chromosome inheritance patterns expected under meiosis might be revealed , including balanced parental contributions , and recombination between homologous chromosomes ., Importantly , in so far as variation in chromosome copy number is thought to be a constitutive , well tolerated , and potentially adaptive mechanism in Leishmania 30–32 , then analysis of the genome structures of experimental hybrids can also reveal somy inheritance patterns and the extent to which genome hybridization is a source of aneuploid variation ., In the current studies of 3 different species of Leishmania , including L . major , L . infantum , and L . tropica , we have used whole genome sequencing to reveal the genome structures , chromosome inheritance patterns , and recombination events present in experimental intra- and interspecies hybrids ., The highly predictable somy and allele inheritance patterns , and especially the genome wide recombinations observed in backcrosses involving experimental and natural hybrids , provide strong evidence for a meiotic-like sexual cycle in Leishmania ., We have previously described the recovery of hybrids from P . duboscqi and Lu ., longipalpis sand flies co-infected with different pairwise combinations of L . major strains originating from across the geographic range of this species 15 , 16 ., For the whole genome sequencing analysis , we selected for comparison all of the hybrids previously described , plus two additional hybrids ( fl6b and fl5b in Table 1A ) , that were generated between LmFV1/SAT , originating in Israel , and LmLV39/HYG , originating from southern Russia ., We further confined the initial analysis to the hybrids that were generated in P . duboscqi , a natural vector of L . major transmission , and that had an approximate 2n DNA content , for which assessing parental inheritance is more tractable compared to the polyploid hybrids ., The parental and 16 progeny clone sequences were aligned to L . major Friedlin FV1 genome Version 6 ( LmjFV1 . 06 ) ( http://tritrypdb . org ) using Novoalign ( http://www . novocraft . com/ ) , and yielded an average 60x coverage per sample ., The mapped reads were processed to obtain total read depth , reference , and alternate allele frequencies using AGELESS software ( http://ageless . sourceforge . net/ ) ., The analysis identified 76103 homozygous SNPs in the LmLV39/HYG parent in comparison to LmFV1/SAT , or 0 . 11 SNPs/Kb , and only a relatively low number ( 3573 ) of heterozygous SNPs ( Table 1A ) ., By contrast , each hybrid possessed a high number of heterozygous SNPs ( 72 , 449–77 , 069; Table 1A ) , reflecting their bi-allelic inheritance of the approximate 76 , 000 SNPs that were homozygous and different between the parents ., The somy values of the parents and progeny clones were rounded off to the closest 0 . 5 value and depicted using a heatmap ( Fig 1A ) ., The actual somy values are not absolute integers ( S1 Table ) , which may be attributed to the tendency of Leishmania chromosomes to show somy differences amongst cells in culture , referred to as mosaic aneuploidy 33 ., Overlaid on the heatmap are the proportionate values for each parental contribution , rounded off to the closest 0 . 1 ., The profiles indicate that both parents were mostly disomic , with the exception of chromosome 31 , which was pentasomic in both the parents , and chromosomes 5 and 23 , which were trisomic in LmFV1/SAT ., Of the chromosomes that were disomic in both parents ( 16x33 = 528 chromosome copies ) , 98% were disomic in the hybrids , and of these , 99% showed a variant allele frequency of roughly 0 . 5 , having inherited an equal contribution from both parents , consistent with a meiosis-like process ., For the two chromosomes that were trisomic in the LmFV1/SAT parent ( 16x2 = 32 chromosomes ) , we found disomic and trisomic hybrid chromosomes 41% and 53% of the time , respectively , close to expected frequencies for a meiotic process in which gametes have a roughly 50% chance of receiving either one or two copies of each trisomic chromosome ., Of the trisomic chromosomes , all but 2 inherited their extra copy from the LmFV1/SAT parent , as expected ., For chromosome 31 , the hybrid somies ranged from 4–5 , with each parent contributing two copies in most cases ., The main exceptions to the expected chromosome inheritance patterns were the 2% of chromosomes in which a new trisomic chromosome was contributed from a disomic parent ( 12/576 = 2% ) , and the 0 . 7% of the chromosomes for which a partial or total loss of heterozygosity ( LOH ) was inferred ( highlighted by blue boxes ) ., The LOH events were visualized using bottle brush plots in which the allele count at each SNP position is displayed ., Bottle brush plots of representative chromosomes from hybrids showing either balanced contribution of parental alleles , gain of somy , or complete or partial LOH are shown in Fig 1B ., Each partial LOH is thought to have arisen from a single crossover event that likely occurred following meiosis ., L . tropica is the causative agent of zoonotic and anthroponotic cutaneous leishmaniasis ( ACL ) , which is endemic throughout the Middle East and in some areas of Africa and the Indian sub-continent ., Prior population genetic studies have identified discreet geographic regions where L . tropica isolates possess only low levels of heterozygous SNPs , and other regions where they display extensive heterozygosity , consistent with genetic exchange 13 , 34 , 35 ., Experimentally , the mating competency of L . tropica strains has not been demonstrated , so far as we are aware ., For the generation of experimental hybrids in L . tropica , we introduced stable drug resistance markers into two different strains , LtMA37/NEO originating from Jordan , and LtL747/HYG from Israel ., In comparison to one another , these strains possess 157085 homozygous SNPs , or 4 . 9 SNPs/Kb , and only 5756 heterozygous SNPs ( Table 1C ) ., These strains were then used to co-infect Lu ., longipalpis , a non-natural vector that is permissive to L . tropica development in our laboratory colonized flies ., Out of a total of 143 co-infected flies , double drug resistant hybrids were recovered from 48 flies ( 34% ) , formally demonstrating the mating competency of members of this species ., Ten of the 48 double drug resistant lines recovered from 10 different flies were cloned and tested by PCR to confirm inheritance of both parental antibiotic resistance markers ( labeled as LtHLMA in S1 Fig ) ., Two diploid clones ( a and b ) generated from each of five different hybrid lines were selected for whole-genome sequencing ., In each case the two clones presented nearly identical genotypes , and are likely derived from the same hybridization event ., All of the hybrids were equivalent to F1 progeny , heterozygous at positions where each of the parents was homozygous for a different nucleotide , resulting in each of the hybrids possessing a high density of 155428–166612 heterozygous SNPs ( 4 . 9–5 . 2 SNPs/Kb ) ( Table 1C ) ., In the somy analysis of the parents and each of the hybrid clones ( Fig 2B ) , all appeared to be near-diploid ., Of the chromosomes that were disomic in both parents ( 5x33 = 165 ) , 99% were disomic in the hybrids , and all showed variant allele frequencies of close to 0 . 5 ., Chromosome 23 was trisomic in the LtMA37/NEO parent and all but one of the hybrids , with LtMA37/NEO contributing two copies to the trisomic chromosome , as expected ., Chromosome 31 was tetrasomic in all samples , and showed variant allele frequencies of 0 . 5 in all of the hybrids ., Thus , the parental somy contributions were non-random and 99% of the time met expectations of chromosomal segregation during a meiosis-like process ., The exceptions were the hybrid clones LtHLM4a/b that were trisomic at chromosome 4 despite being disomic in both parents , possibly a result of chromosomal non-disjunction ., No LOH was observed in any of the hybrids ., Altogether , the whole genome sequencing of the experimental hybrids generated within and between three different Old World Leishmania species demonstrate that the progeny clones are near full genomic hybrids , with highly predictable somy and allele inheritance patterns that are strongly consistent with a meiotic-like process ., Rare instances of chromosomes showing a gain of somy or loss of heterozygosity were also observed ., To generate the first experimental backcross hybrids in Leishmania we chose a L . major hybrid , 1 . 16 . A1 , generated between LmFV1/SAT and LmLV39/HYG with ploidy close to 2n , and that demonstrated robust growth in culture and in flies ., The mating studies involved co-infection of P . duboscqi flies with 1 . 16 . A1 and L . major lines stably transfected with a third antibiotic resistance marker , blasticidin-S deaminase ( BSD ) , and selection for midgut promastigotes that were doubly drug resistant to either SAT + BSD or to HYG + BSD ., A series of 4 independent backcross experiments involving 1 . 16 . A1 and LmFV1/BSD resulted in a low frequency of flies yielding hybrids ( Table 2 , expts 1–4 ) ., Of a total of 316 midguts from co-infected flies that could be evaluated for hybrid recovery , double drug resistant lines could be recovered from 2 flies ( 0 . 6% ) ., PCR tests confirmed that both progeny clones had in fact inherited all three drug resistance markers ( S2A Fig ) ., In two experiments there were sufficient flies to include for comparison co-infections involving the parental lines , which in prior studies have produced an average of 11 . 3% hybrid recovery 15 , 16 ., In this case , co-infections with LmFV1/BSD and LmLV39/HYG produced recoverable hybrids in a total of 10 of 75 flies ( 13 . 3% ) ., PCR tests confirmed their inheritance of both selectable markers ( S2B Fig ) ., Since intraspecies F1 hybrids have so far been generated by outcrossing L . major strains of discrete geographic origin , we considered the possibility that the mating efficiency of the F1 hybrid might be improved if outcrossed with a third L . major strain unrelated to the original parents ., We have previously confirmed the mating competency of a L . major strain originating in Senegal , West Africa , LmSd/BSD 16 ., In the two outcross experiments involving flies co-infected with 1 . 16 . A1 and LmSd/BSD , hybrids were recovered from 3 of 201 flies ( 1 . 5% ) ( Table 2 , expts 5&6 ) , all of which were PCR positive for SAT and BSD ( S2C Fig ) ., Co-infections with LmSd/BSD and LmLV39/HYG yielded a higher frequency of hybrid recovery from 7 of 31 flies ( 22 . 6% ) ( Table 2 , expts 6 , S2D Fig ) ., Together , the backcross and outcross mating attempts indicate that while not sterile , the intraspecies F1 hybrid had reduced fertility compared to the parents used for their generation ., The availability of 2 backcross and 3 outcross progeny allowed us for the first time to test for the presence of recombination events characteristic of meiosis in other organisms ., To extract recombination patterns in outcrosses , we considered a total of 37368 markers that were common to the LmFV1/SAT and LmSd/BSD parental lines , but homozygous different from the LmLV39/HYG line , effectively treating the outcross progeny as backcrosses ., The parental inheritance patterns were depicted as bottle brush plots and the recombination loci were determined visually ., The schematic in S5A Fig shows the possible inheritance profiles for the backcrosses and outcrosses , assuming a maximum of 2 recombinations based on random assortment , crossovers and selection ., The actual profiles of the backcrosses and outcrosses indicate that each of the expected profiles was observed ( representative examples shown in S5B Fig ) ., We incorporated the bottle brush plots into circos plots 36 depicting the allelic contributions genome wide , revealing regions of homozygosity and heterozygosity that allowed us to visualize the recombination patterns ( Fig 3A ) ., Based on these plots , we compiled the recombination loci on each chromosome to generate a physical map ( S2 Table , Fig 3B ) ., Each of the backcrosses and outcrosses had between 17 and 25 recombination events across 36 chromosomes , 21 recombinations per genome on average , or 1 recombination / 1 . 54 Mb ., Backcrosses 1 and 2 each had 17 chromosomes with a single cross-over , and 1 or 3 chromosomes , respectively , that had two cross-overs ., Outcrosses 1 , 2 , and 3 had 17 single and 1 double , 15 single and 1 double , and 23 single and 1 double recombinations , respectively ., We found a total map size of 1840 centimorgan ( cM ) across the 32 Mb genome which translates to 1 cM per 17 , 391 bp ., Physical length correlated with genetic length using a linear regression model ( p = 0 . 003 ) ., Too few backcrosses and outcrosses were available for sequencing to draw meaningful conclusions regarding recombination hotspots or coldspots ., To test the mating competency of natural hybrids in L . tropica , we introduced stable drug resistance markers into two strains , LtKub/SAT from Syria and LtRup/HYG from Afghanistan , that in our prior studies were each found to possess high levels of heterozygous SNPs ( approximately 100 , 000 SNPs in comparison to the L . tropica L590 reference genome ) in patterns consistent with these isolates being naturally occurring full genome hybrids 13 ., Both strains showed robust growth in culture and in Lu ., longipalpis flies ( S6 Fig ) ., Hybridization of these lines with each other or with LtMA37/NEO or LtL747/HYG , was tested in 3 independent experiments ( Table 3 , expts 1–3 ) ., Of the 251 flies that were co-infected with LtRup/HYG and either LtKub/SAT , LtL747/HYG , or LtMA37/NEO , no hybrids were recovered ( Table 3 , expt 1 ) ., By contrast , co-infection of the same population of flies with LtMA-37/NEO and LtL747/HYG , again yielded a high rate of hybrid recovery in 29 of 69 flies ( 42% ) ., Ten of these hybrids were cloned and genotyped by PCR to confirm their hybrid genotypes ( labeled as LtHLMB in S1 Fig ) ., Mating attempts involving the other natural hybrid , LtKub/SAT , in two experiments yielded a total of 56 hybrid lines when paired with either LtL747/HYG or LtMA37/NEO ( Table 3 , expts 2&3 ) , all of which were cloned and their hybrid genotypes confirmed ( labeled respectively as LtHKLA/B hybrids in S3B Fig , and LtHKMA/B hybrids in S3A Fig ) ., A high rate of hybrid recovery was again obtained from the same population of flies co-infected with LtMA37/NEO and LtL747/HYG ( 65% ) , 10 of which were cloned and their hybrid genotypes confirmed ( labeled as LtHLMC in S1 Fig ) ., Thus , the exceptional outcrossing efficiency in reciprocal matings of two predominantly homozygous L . tropica strains was reinforced , while the ability of two natural hybrids to mate was strain dependent , with one strain essentially sterile and the other showing good mating compatibility when tested in outcrosses with the homozygous strains ., Eight progeny clones generated from the crosses between Lt/Kub/SAT and LtL747/HYG , and 9 clones generated between LtKub/SAT and LtMA37/NEO , designated LtHKM or LtHKL , respectively , were submitted for whole genome sequencing ., This allowed us to study genome wide patterns of chromosome segregation and recombination involving a natural hybrid ., The sequencing identified a high number of both heterozygous and homozygous SNPs in LtKub/SAT that were different in comparison to either Lt747/HYG or LtMA37/NEO and that were passed on to the progeny clones ( Table 1D & 1E ) ., When we enumerated the somies and the parental contributions of the chromosomes in the 567 hybrid chromosomes where each of the three parents were approximately disomic ( estimated somy between 1 . 6 and 2 . 5 ) , we found that 97% were disomic with an equal contribution from both parents , as expected under meiosis ( S1 Table , S7 Fig ) ., However , we also found a few trisomic chromosomes ( 14 or 3% ) where an extra copy was contributed by one of the disomic parental lines , and 4 chromosomes where only the homozygous parent contributed to the hybrid ( blue boxes ) ., These 4 chromosomes were monosomic in each case , and we speculate that the chromosome contribution from the LtKub/SAT parent was lost as a consequence of a non-disjunction event during meiosis ., Aneuploidy was observed in the chromosomes where one or both the parents had somies greater than 2 , although of the 36 chromosomes for which the LtKub/SAT parent was trisomic ( chromosomes 12 & 5 ) and should have had an equal opportunity to contribute a double copy , a single copy contribution was observed 92% of the time ., The skewed single copy contribution of the trisomic chromosomes might have occurred by chance , or perhaps by haplotype selection during adaptation to clonal growth in culture or in the fly 32 ., We next constructed the zygosity profiles of the hybrids between LtKub/SAT and LtL747/HYG or LtMA-37/NEO ., We identified the SNPs against the L . tropica L590 reference genome using SAMtools utility 37 and filtered out all the markers with coverage less than 10 ., We tagged the SNPs as homozygous if the major allele frequency was greater than 90% and as heterozygous if both the major and minor allele frequencies were between 15% and 85% ., We divided the genome into blocks of 5kb and enumerated the heterozygous and homozygous SNP counts within each window ., We colored the block red if the heterozygous proportion within the block was greater than 90% , blue if the homozygous proportion was greater than 90% and yellow otherwise ., LtKub/SAT was mostly heterozygous while the other parental strains , LtMA37/NEO and LtL747/HYG were homozygous , as expected ( Fig 4 ) ., By contrast , the outcrosses contained blocks of long runs of homozygosity , heterozygosity , or sequences that were neither homozygous nor heterozygous , similar to the patterns that are observed in the backcross progeny ( see Fig 3 ) ., This suggested that the LtKub hybrid shares haplotypes with those present in LtMA37 and LtL747 , and therefore might be a natural cross between strains containing genotypes similar to these strains ., To test this possibility , we used a new population genetics software ( https://popsicle-admixture . sourceforge . io ) and performed the analysis as follows: we leveraged 159900 homozygous SNPs in LtMA37/NEO against the reference strain ( TritrypDB L590 V . 33 ) and removed 134846 homozygous SNPs that were common with LtL747/HYG ., The SNPs at the remaining 23775 markers were homozygous and different between LtMA37/NEO and LtL747/HYG ., We created separate Circos plots for the two groups of outcross progeny , and redrew the plots by coloring the SNPs as green if they matched Lt747/HYG , red if they matched LtMA37/NEO , and yellow if they were heterozygous ( Fig 5 ) ., As expected , Lt747/HYG and LtMA37/NEO were both homozygous and contained alternate genotypes ., LtKub/SAT was mostly heterozygous due to contributions from both Lt747- and LtMA37- like genotypes ., The outcrosses between Lt747/HYG and LtKub/SAT contained longs runs of heterozygous and homozygous SNPs similar to backcrosses , and the homozygous regions matched Lt747/HYG ., Similarly , the outcrosses between LtMA37/NEO and LtKub/SAT contained long runs of heterozygous and homozygous SNPs for which the homozygous regions matched LtMA37/NEO ., These results strongly support the hypothesis that LtKub is a hybrid between strains that contained alternate genotypes matching Lt747 and LtMA37 ., The transitions between the long runs of heterozygous and homozygous regions are the recombination breakpoints ( S2 Table ) , as highlighted on the Circos plots in which both single , double , and triple crossovers were observed ., The hybrids between LtKub/SAT and LtL747/HYG , and between LtKub/SAT and LtMA37/NEO recorded an average of 22 recombinations each ( S2 Table ) , which translates to 1 recombination every 1 . 45Mb ., Double and triple crossovers were routinely observed in the hybrids between LtKub/SAT and LtMA37/NEO in comparison to the hybrids between LtKub/SAT and LtL747/HYG ( S2 Table; p-value of 0 . 0082 using t-test ) ., The recombinations observed across the 17 hybrids plotted by chromosome indicated that although the crossover points were distributed throughout the genome , certain hotspots ( more than 2 recombinations in a 50kb window ) were observed in 18 chromosomes ( Fig 6A ) ., The larger chromosomes on an average contained more recombinations in comparison to the smaller chromosomes as evaluated by linear regression ( p-value of 0 . 009 ) ., We translated the observed recombinations into cM distances by calculating probability of finding recombinations across the genome in sliding non-overlapping blocks of 20Kb ( see methods ) and drew a genetic map based on the blocks that contained recombinations ( Fig 6B ) ., We found a total map size of 2091 . 4 cM across the 32 Mb genome , which translates to 1 cM per 15 , 300 bp , very close to the recombination frequency that we recorded for L . major ., Lastly , a series of backcross and outcross mating attempts involving the interspecies hybrids were undertaken in Lu ., longipalpis flies ., In 8 independent experiments ( Table 4 ) , the flies were co-infected with a pool of the 4 near diploid F1 hybrids ( H2 , H4 , H7 , H6 ) , or with each of the hybrids individually , and paired with either LmFV1/BSD or LmSd/BSD ., The midgut promastigotes were selected for growth in either SAT + BSD or HYG + BSD ., From a total of 420 co-infected flies that could be evaluated for hybrid recovery , no backcross or outcross progeny were obtained ( Table 4 ) ., Thus , the interspecies hybrids appear to be sterile under the mating conditions employed ., By comparison , when the flies were co-infected with the parental lines LiL/HYG and LmSd/BSD , a total of 10 of 140 flies ( 7 . 1% ) yielded hybrids , with recovery rates in the different experiment ranging from 3% to 23% ., PCR tests confirmed that the progeny clones generated from these 10 lines contained both parental selectable markers ( hybrids labeled as LimHLS , S4 Fig ) ., We present here the first comprehensive analysis of experimental intra- and interspecies hybrids in Leishmania by analyzing high-resolution whole genome sequencing data ., We determined the chromosomal somy and studied the parental inheritance of 44 hybrids generated within and between different Old World species of Leishmania , including L . major , L . infantum , and L . tropica , and compared them against the inheritance patterns expected under meiosis ., The somies and parental chromosomal contributions matched the expected inheritance patterns 97%-99% of the time , supporting a predominant meiotic-like process in Leishmania , which we believe is the most parsimonious interpretation of the genome-wide inheritance patterns presented in this report ., The hybrids appeared equivalent to F1 progeny , heterozygous throughout most of the genome for the homozygous alleles that were different between the parents ., The majority of the hybrid clones that we have generated and analyzed in this report were near diploid , showing balanced segregation of the chromosomes , the majority of which were disomic in the parents ., Trisomic chromosomes in the parents were passed on to the progeny in single or double copy , never in their original trisomic state , and in frequencies expected by Mendelian segregation ., Tetrasomic chromosomes were passed on in double copy and not in quadruple copy ., Such predictable , balanced allotments of parental chromosomes during hybridization seem highly unlikely to have arisen by a random , parasexual process ., While it is true that the meiotic intermediates in aneuploid lines of Leishmania would not be strictly haploid , aneuploidy is not incompatible with meiosis ., Meiotic chromosome segregation is well described in triploid strains of S . cerevisiae which accurately produce viable tetrads containing 2 spores with 2 copies and 2 spores with 1 copy of each homolog 38 ., Furthermore , all 3 copies of a trisomic chromosome in a close to diploid strain of S . cerevisiae were shown to undergo recombination in a single meiosis 39 ., In Drosophila , although the fertility of triploid females is reduced , viable offspring between diploids and triploids can be readily obtained 40 ., Finally , it is worth noting that some women with non-mosaic trisomy 21 are fertile and pass on the extra chromosome to a high proportion of their offspring 41 ., Polyploid , predominantly triploid hybrids , have been routinely recovered from experimental matings in Leishmania 15–17 , and are observed across organisms capable of sexual reproduction , such as amphibians 42 , plants 43 , and fruit flies 40 ., The genomic analysis of 4 interspecies hybrids with close to 3n DNA content showed parental contributions consistent with syngamy between a parental ‘2n’ cell that failed to undergo meiosis and a ‘1n’ cell from the other parent , similar to what has been suggested for T . brucei 44 for which triploid progeny are also common ., Their inheritance profiles again argue against a parasexual process involving random chromosome loss from a tetraploid intermediate that would seem highly unlikely to produce progeny for which almost all of the chromosomes that were disomic in each parent were allotted one extra copy , with the extra copies within each clone always contributed by the same parent ., Our recovery of a few hybrids with close to 4n DNA content , one of which was analyzed here , is interesting because they suggest that fusion of diploid cells producing a tetraploid hybrid can indeed occur in Leishmania ., The progeny clone analyzed remained close to tetrasomic for the majority of chromosomes ., While it might be argued that this hybrid represents a parasexual intermediate that has not yet experienced chromosome loss , a stronger case might be made for a tetraploid meiotic cycle , originally described for Saccharomyces 45 , for which diploid cells of two different mating types fuse and then undergo meiosis followed by fusion of haploid nuclei to produce diploid progeny ., The triploid and tetraploid hybrids in Leishmania might be the result of one or both of the parental nuclei failing to undergo meiosis following fusion of the diploid cells ., While the tetraploid meiotic cycle does not involve the generation of gametes , it is still referred to in the context of a sexual reproductive cycle in yeast 46 ., Sexual reproduction can be generalized to mean all forms of meiotic reproduction in protists , which frequently retain the ability to reproduce asexually via mitosis ., The instances of unbalanced chromosome inheritance that might support a parasexual process in Leishmania were the exceptions , not the rule ., Specifically , they were manifested as a gain of somy in comparison to either parent , observed approximately 2% of the time , and uniparental inheritance , observed approximately 1% of the time , that we interpret as LOH subsequent to the original hybridization event ., Non-disjunction of one parental chromosome at meiosis or during subsequent mitotic generations in the fly or in culture seem more likely to explain the instances of trisomy than a parasexual process | Introduction, Results, Discussion, Materials and methods | Hybrid genotypes have been repeatedly described among natural isolates of Leishmania , and the recovery of experimental hybrids from sand flies co-infected with different strains or species of Leishmania has formally demonstrated that members of the genus possess the machinery for genetic exchange ., As neither gamete stages nor cell fusion events have been directly observed during parasite development in the vector , we have relied on a classical genetic analysis to determine if Leishmania has a true sexual cycle ., Here , we used whole genome sequencing to follow the chromosomal inheritance patterns of experimental hybrids generated within and between different strains of L . major and L . infantum ., We also generated and sequenced the first experimental hybrids in L . tropica ., We found that in each case the parental somy and allele contributions matched the inheritance patterns expected under meiosis 97–99% of the time ., The hybrids were equivalent to F1 progeny , heterozygous throughout most of the genome for the markers that were homozygous and different between the parents ., Rare , non-Mendelian patterns of chromosomal inheritance were observed , including a gain or loss of somy , and loss of heterozygosity , that likely arose during meiosis or during mitotic divisions of the progeny clones in the fly or culture ., While the interspecies hybrids appeared to be sterile , the intraspecies hybrids were able to produce backcross and outcross progeny ., Analysis of 5 backcross and outcross progeny clones generated from an L . major F1 hybrid , as well as 17 progeny clones generated from backcrosses involving a natural hybrid of L . tropica , revealed genome wide patterns of recombination , demonstrating that classical crossing over occurs at meiosis , and allowed us to construct the first physical and genetic maps in Leishmania ., Altogether , the findings provide strong evidence for meiosis-like sexual recombination in Leishmania , presenting clear opportunities for forward genetic analysis and positional cloning of important genes . | Leishmania promastigotes are able to undergo genetic exchange during their growth and development in the sand fly vector , however , it is still not known if they have a true sexual cycle involving meiosis ., Here , we used whole genome sequencing to follow the chromosomal inheritance patterns of 44 experimental hybrids generated between different strains of L . major , L . infantum , and L . tropica ., In almost every case the number of chromosomes and the allele contributions from each parent matched the inheritance patterns expected under meiosis ., Rare instances of hybrid chromosomes that did not fit Mendelian expectations were observed , including gain or loss of somy , and loss of heterozygosity ., Strong evidence for a meiotic-like process was also obtained from the genome wide patterns of recombination observed in the offspring generated from backcrosses involving an experimental or natural hybrid , consistent with crossing over occurring between homologous chromosomes during meiosis ., The frequency and position of the recombination breakpoints observed on each chromosome allowed us to construct the first physical and genetic maps in Leishmania ., The results demonstrate that forward genetic approaches are possible for positional cloning of important genes . | sequencing techniques, meiosis, trisomics, cell cycle and cell division, cell processes, cloning, departures from diploidy, parasitic protozoans, organisms, genome sequencing, protozoans, leishmania, dna, molecular biology techniques, research and analysis methods, aneuploidy, artificial gene amplification and extension, chromosome biology, molecular biology, biochemistry, leishmania infantum, eukaryota, cell biology, nucleic acids, genetics, biology and life sciences, dna recombination, polymerase chain reaction | null |
journal.pbio.1001134 | 2,011 | A Dual Binding Mode for RhoGTPases in Plexin Signalling | Plexins constitute a large family of semaphorin receptors that mediate the repulsive chemotactic response necessary for axon guidance in the developing nervous system ., They also play an important role in regulating cell migration , angiogenesis , and immune responses 1 , 2 ., Mutations in plexin receptors have been found in cancers from a variety of tissues 3 , 4 ., There are four classes of Plexins ( A , B , C , and D ) 1 ., Their architecture is conserved across the family with a large extracellular region including the ligand binding sema domain , a single transmembrane spanning helix , and an intracellular region that transduces signals to a number of downstream pathways 1 , 2 , 5 ., Recently , truncated ectodomain structures of plexins from different classes in complex with their cognate semaphorin ligands have been solved 6–8 ., They revealed a common architecture in which two plexin monomers bind one semaphorin dimer ., This bivalency has been shown to be crucial for the function of the plexin-semaphorin complex 6 ., Plexins are transmembrane receptors distinguished by their ability to interact directly with small GTPases of the Ras and Rho family through their intracellular region 9 , 10 ., They consist of two domains , the GTPase activating protein ( GAP ) domain , first identified by sequence similarity to RasGAP , and the RhoGTPase binding domain ( RBD ) 11–13 ., Recent structural studies of the intracellular region of human Plexin-B1 and mouse Plexin-A3 revealed that the GAP domain is an integral structural unit , with the RBD forming a domain insertion into one of the exposed GAP domain loops 14 , ., Importantly , the catalytic machinery remained identical , with catalytic arginines found in the same positions in RasGAP and both Plexin-B1 and Plexin-A3 14–17 ., Within the plexin family , the human Plexin-B1 signalling pathway is the most extensively characterized to date; two members of the Ras superfamily have been identified as targets of the Plexin GAP activity so far , R-Ras and M-Ras 9 , 18 ., Inactivation of R-Ras by Plexin-B1 GAP leads to suppression of integrin activation and cell migration , ultimately leading to repulsive axonal guidance 19 , 20 ., Downregulation of M-Ras leads to reduced dendritic outgrowth and branching 18 ., The Plexin-B1 RBD has been shown to bind to the Rho GTPases Rnd1 , Rac1 , and RhoD exclusively in their active , GTP-bound form 21–23 ., Small GTPases of the Rho family are key players in remodelling of the actin cytoskeleton and are involved in a plethora of processes initiated by extracellular stimuli 24 , 25 ., Both Rac1 and Rnd1 are important for the ligand-induced activation of the plexin GAP activity and Rac1 has been found to increase semaphorin binding to Plexin-B1 19 , 26–28 ., Simultaneous binding of semaphorin on the extracellular side and a RhoGTPase on the intracellular side is a prerequisite for plexin GAP activity 27 , 29 ., Bivalent semaphorin binding can be mimicked by extracellular , antibody-induced , clustering of the intracellular domain and activation is observed in the presence but not in the absence of Rnd1 9 , 29 ., This suggests that semaphorins have a crucial role in bringing together plexin receptors as a step towards activation ., Despite a number of structural studies on the plexin RBD and its complex with Rnd1 15 , 30 , 31 it remains unclear how RhoGTPases modulate plexins and how the concomitant binding of ligands on the extracellular and the intracellular side of the receptor is integrated into a single signalling output , inactivation of Ras ., To address this question we characterized the complex between the intracellular region of Plexin-B1 and a constitutively active form of the RhoGTPase Rac1 both structurally and functionally ., Several constructs of the intracellular domain of human Plexin-B1 were designed , of which three , Plexin-B1cyto , Plexin-B1Δ1 , and Plexin-B1Δ2 , could be solubly expressed in insect cells ( Figure 1a ) ., Rac1 was rendered constitutively active by introducing a Gln61Leu mutation 32 in addition to loading with the non-hydrolyzable GTP analogue GppNHp ., This Rac1 mutant , expressed in E . coli , was used in all subsequent experiments and is named Rac1* hereafter ., We have determined the crystal structure of Plexin-B1Δ1 in complex with Rac1* to a resolution of 3 . 2 Å and refined it to a crystallographic R-factor of 20 . 7% ( Rfree\u200a=\u200a23 . 8% , Figure 1b , Table 1 , Figure S1 ) ., The overall structures of Plexin-B1Δ1 and Rac1* in the complex are very similar to their apo-structures 15 , 33 with rmsd values of 1 . 5 Å and 0 . 6 Å , respectively ., However , there is some flexibility between the Plexin-B1 GAP and the RBD with the RBD being rotated by ∼6° compared to the apo-structure ( Figure S2 ) ., Rac1* binds exclusively to the RBD and does not form any contacts with the GAP domain ., The interface between Rac1* and the Plexin-B1 RBD covers a buried surface area of 707 Å2 and is dominated by hydrophobic interactions ., Plexin-B1 residues Trp1807Plex , Leu1815Plex , Thr1823Plex , and Tyr1839Plex form a continuous hydrophobic patch that is complemented by Rac1 residues Phe37Rac , Val36Rac , Leu67Rac , and Leu70Rac ( Figure 1b ) ., All of these residues are almost completely buried within the interface ( at least 80% of the solvent accessible surface area ) with the exception of Val36Rac ( 38% ) ., Thr1823Plex and Tyr1839Plex are part of a potential hydrogen bonding network involving Asp1821Plex , Ser1824Plex , Asn1834Plex , and His1838Plex that is likely to be crucial for the structural integrity of the domain ., The hydrophobic interaction between Plexin-B1Δ1 and Rac1* is extended by two potential hydrogen bonds formed between the sidechain of Asp38Rac and the backbone amides of Val1811Plex and Ala1812Plex ., Remarkably , all of the Plexin-B1 residues described above are conserved across A- and B-class plexins ( Figure S3 ) , therefore most likely preserving this mode of recognition ., On Rac1* , all residues mentioned above map onto the switch I or switch II region 11 ( Figure S4 ) whose conformation resembles that of active Rac1 in other Rac1-effector complexes 34 ., Since these regions undergo large conformational changes upon GTP binding , this explains why Plexin-B1 is highly specific for active , GTP-bound Rac1 21 ., Recently , the structure of the RBD fragment of Plexin-B1 in complex with the constitutively active RhoGTPase , Rnd1 , has been reported 15 ., Structural superposition of the RBD-RhoGTPase complexes gives an rmsd of 0 . 96 Å ( Figure S5 ) ., Despite a sequence identity of only 32% between Rac1 and Rnd1 , the Plexin-B1 RBD-Rnd1 complex interface is very similar to the one described here ., All hydrophobic interactions as well as the two potential hydrogen bonds are conserved in both structures ., To corroborate our structural findings we studied the affinity between Plexin-B1cyto and Rac1* , as well as Rnd1 , using surface plasmon resonance ( SPR ) ., Rnd1 is constitutively active due to its lack of GTPase activity 35 ., Plexin-B1cyto binds to Rac1 and Rnd1 with an affinity of 18 . 9 µM and 22 . 9 µM , respectively ( Figure 1c–e , Figure S6 ) , which is in agreement with recently published affinities determined by isothermal titration calorimetry 15 ., We found that a series of Plexin-B1 mutations in the hydrophobic interface , Trp1807GluPlex , Leu1815ProPlex ( previously linked to prostate cancer 4 ) , and Leu1815GluPlex , completely abolished its interactions with Rac1* and Rnd1 ( Figure 1d–e , Figure S6 ) ., To validate these effects on binding in a functional context , we performed COS cell-based collapse assays with the full-length transmembrane receptor , testing for Plexin-B1 activity in vivo 36 ., Surprisingly , none of the mutants shown to abolish Rac1* or Rnd1 binding had an effect on the collapse response of the cells ( Figure 1f–h ) ., We explored this finding further in an independent experimental assay to monitor directly Ras GTPase activity in vivo ., In agreement with our results from the collapse assay , none of the interface mutants had an effect on the GAP activity of Plexin-B1 towards R-Ras in this COS cell-based pull-down ( Figure S7 ) ., Since the necessity of RhoGTPase binding for plexin function is well established 9 , 27 , it was unclear how to correlate the biophysical and cellular results ., The relative position of Rac1* in regard to the putative Ras binding site revealed no mechanism for the direct regulation of the catalytic activity of Plexin-B1 by the small RhoGTPase ., To address whether the N-terminal residues missing in the Plexin-B1Δ1 construct might harbour an important site for RhoGTPase mediated plexin activity , we solved the crystal structure of the entire cytoplasmic domain of Plexin-B1 ( Plexin-B1cyto ) in complex with Rac1* ( Figure 2a ) ., The 4 . 4 Å model is of high-quality for this resolution range , reflected by the crystallographic R-factor of 23 . 4% ( Rfree\u200a=\u200a26 . 4% , Table 1 , Figure S8 ) ., The asymmetric unit contains a trimeric arrangement comprising three copies of the Plexin-B1cyto-Rac1* unit , with each Rac1* molecule contacting two Plexin-B1cyto molecules ( Figure 2a–b ) ., This arrangement is not the result of crystallographic symmetry but does show near perfect 3-fold geometry ( 120° between pairs of Rac1* molecules and 117° , 119° , and 124° between the copies of Plexin-B1cyto ) ., Moreover , the interfaces between Plexin-B1cyto and Rac1* are essentially identical across the three copies in the asymmetric unit and are not found in any of the crystallographic symmetry generated interfaces ., These observations strongly suggest that this 3-fold complex is not purely a product of crystal lattice formation ., We were not able to show a 3-fold complex with the soluble constructs in analytical ultracentrifugation at a plexin concentration of 250 µM ( unpublished data ) ., This suggests that high local concentrations in the crystal or indeed at the plasma membrane are necessary for this arrangement and that the 3-fold interaction might be too weak to be detected in solution 37 ., There are no major conformational changes within the three Plexin-B1cyto-Rac1* units when compared to the one-to-one complex ., This 3-fold arrangement is mediated by a previously unidentified second binding site for the Rho-GTPase on Plexin-B1cyto ( site B , the previously observed Plexin-B1 RBD-RhoGTPase interface hereafter called site A ) ., Site B involves the N-terminal region of α-helix 11 and the loop preceding it ( residues 1913Plex–1923Plex ) plus α-helix 16 ( residues 2036Plex–2039Plex ) and is in close proximity to the putative Ras binding site ( Figure 2a , c , Figure S3 ) ., It covers a total buried surface area of ∼570 Å2 , therefore significantly extending the interface for Rac1 binding , and is dominated by hydrophobic interactions ., On Plexin-B1cyto the majority of contacts ( 60% of buried surface area ) are made by the loop residues 1913Plex–1918Plex ( Figure 2c ) ., Interestingly , these residues are disordered in the apo-structures of Plexin-B1 and Plexin-A3 ., The site B interface on Rac1* is predominantly formed by residues that precede the switch I region ( residues 24–33 , Figure 2c ) ., The conformation of these residues is known not to depend on the activation state of the RhoGTPase 11; thus , the specificity of Plexin-B1 for active RhoGTPases appears to result exclusively from interactions with site A . The 3-fold complex is further stabilized by contacts between two adjacent Plexin-B1cyto molecules , on the one side mainly involving a loop comprising residues 1808Plex–1813Plex , and on the other side a surface directly adjacent to site B ( residues 1919Plex–1938Plex and residues 2036Plex–2044Plex , Figure 2c ) ., However , this plexin-plexin interaction is unlikely to be stable without the addition of the bridging Rac1* since it only contributes a total buried surface area of ∼310 Å2 ., In order to assess the potential functional significance of site B , we designed three Plexin-B1 mutants , Thr1920GluPlex , Arg1921AlaPlex , and Leu2036ArgPlex ., We first studied the binding affinity of these mutants to Rac1* and Rnd1 using SPR ., None of the site B mutations had a significant effect on the affinity towards either of the RhoGTPases , suggesting that site A alone is sufficient for Rac1* and Rnd1 binding ( Figure 3a , Figure S9 ) ., However , these mutations as well as additional ones at these and other site B residues ( Ile1917Plex , Leu1923Plex , and Ala2039Plex ) completely abolished the typical collapse response in the COS-cell assay ( Figure 3b ) ., Every site B mutation tested was detrimental to Plexin-B1 activity and led to a complete loss of function ., All mutant proteins showed a similar expression level , as judged by immunofluorescence , and were present in the plasma membrane , indicating their structural integrity ( unpublished data ) ., In the same background , the Plexin-B1 site B mutants also did not show any GAP activity towards R-Ras , thus further validating the findings of the collapse assay ( Figure S7 ) ., These results indicate that although site B is not essential for binding of the RhoGTPase , it is crucial for Plexin-B1 activity , suggesting that the 3-fold complex seen in the crystal has functional significance ., In accordance with this putative functional role , all of the residues in site B are conserved across all species and classes of plexins with the exception of Ala1913Plex and Pro1915Plex , whose sidechains do not participate in the Rac1-site B interaction ( Figure 3c ) ., The 3-fold complex revealed by the crystal structure and the cell collapse assays suggest that GTPase binding at site B contributes to plexin function ., However , the SPR experiments reveal no direct evidence for GTPase binding at this site ., We therefore sought an explanation for this lack of binding ., In both the one-to-one and 3-fold complex structures , we were unable to trace the N-terminal helix ( residues 1511Plex–1562Plex , Figure S3 ) due to a lack of well-ordered electron density ., Interestingly , there is a similar absence of electron density in this region for the high resolution apo-structure of Plexin-B1 15 ., This suggests that the N-terminal helix of Plexin-B1 has some internal flexibility , likely around the hinge region adjacent to Ile1563 ., In agreement with this , the three residues preceding Ile1563 are Gly1562 , Ser1561 , and Gly1560 , which may allow large conformational freedom of the N-terminal helix even in a trimeric arrangement ., In contrast , in the apo-structure of mouse Plexin-A3 14 , this region was well-defined ., Superposition of the Plexin-A3 structure with the 3-fold complex reveals that this helix would block site B , therefore preventing its interaction with Rac1* ( Figure 4a ) ., This steric hindrance model predicts that shortening of the N-terminal helix will remove this block and allow Rac1* and Rnd1 to bind to site B . To test this model we generated mutant constructs lacking the N-terminal helix ( Plexin-B1Δ2 ) and assayed for RhoGTPase binding in SPR ., Indeed , RhoGTPase binding to the site A Plexin-B1Δ2 mutant Leu1815GluPlex was now observed , suggesting that truncation of the N-terminal helix has exposed site B ( Figure 4b , Figure S10 ) ., However , binding can only be observed with high coupling densities of the Plexin-B1Δ2 on the SPR chip ( Figure 4b ) ., This is consistent with a bivalency effect in which two adjacent plexin molecules bind the same RhoGTPase molecule , implying that the mutated site A is still competent to contribute to an avidity effect 38 ., At low coupling densities the Plexin-B1Δ2 molecules are too far apart from each other to allow a bivalent interaction with Rac1* or Rnd1 ., We did not observe an increase in affinity for wild-type N-terminal truncated Plexin-B1Δ2 compared to the full-length Plexin-B1cyto even at high coupling densities ( unpublished data ) ., Binding studies on the isolated Plexin-B1 RBD show similar affinities 21 to those we determined for the full-length cytoplasmic region ., Thus these observations suggest that , if intact , site A dominates binding to the RhoGTPase ., Interestingly , sequence analysis of the N-terminal cytoplasmic segment of Plexin-B1 ( residues 1511–1539 ) predicts a trimeric coiled-coil ( Figure S11 ) and similar regions in other plexins from all classes are also predicted to adopt a coiled-coil conformation ., In accordance with this , Ile1563Plex in Plexin-B1cyto , the first residue visible in the electron density map , points towards the inside of the 3-fold complex locating the three N-terminal segments in close proximity to each other ( Figure 2a , right panel ) ., This proximity suggests an explanation for the observation of the higher oligomeric state 3-fold complex in the crystal ., Although the N-terminal helix is not well-ordered in the structure , it could form a trimeric coiled-coil , albeit containing significant flexibility ., Plexin-semaphorin signalling is dependent on signals from both the extra- and intracellular side ., Several studies have shown that both semaphorin binding on the outside and RhoGTPase binding on the inside of the cell are required for plexin activity to occur 9 , 27 ., The nature of these signals and how they are integrated into a single output , namely RasGAP activity , has been a critical question in this field and several models have been proposed 6–8 , 14 , 15 , 39 ., Recently , several structures of truncated plexin ectodomains in complex with their cognate semaphorins have been reported 6–8 ., Despite ranging across three different classes , all of these ectodomain complexes share the same overall architecture with one semaphorin dimer bringing together two plexin monomers ., In combination with a detailed biophysical and cellular characterization , these structural data have led to the proposal that the bivalency effect is a prerequisite for plexin signalling 6 , 8 ., For the cytoplasmic region , our structures of Plexin-B1 in complex with Rac1* do not show major structural rearrangements when compared to the apo-structure of Plexin-B1 15 ., For the one-to-one complex , Rac1* is positioned distant from the Ras binding site on the Plexin-B1Δ1 molecule ., This excludes the possibility of a direct interaction or regulation of RasGAP activity by the RhoGTPase ., Instead , the 3-fold complex reveals an additional binding site on a neighbouring Plexin-B1cyto molecule that is in close proximity to the predicted Ras binding site ., This interaction may result in a small conformational change in the Ras binding region , although a detailed analysis of these changes cannot be made due to the low resolution of our data ., It is , however , noteworthy that allosteric regulation of Ras binding by RhoGTPase binding has been proposed by He et al . based on a homology model of the Plexin-A3-Rnd1 complex 14 ., We cannot exclude the possibility that within the protein crystal the trimeric arrangement is favoured over other site B mediated oligomeric states due to the lattice contacts ., Indeed , the 3-fold arrangement constitutes the asymmetric unit in the crystal and therefore accommodates slight variations between the three Plexin-B1-Rac1 units ( see Results section ) ., The occurrence of a 3-fold arrangement in crystals of Plexin-B1-Rac1 complexes appears to be dependent on the juxtamembrane , N-terminal helix ., Physiologically , this region connects the intracellular domain with the transmembrane and extracellular region ., This suggests a mechanism by which both semaphorin binding on the outside and RhoGTPase binding on the inside are connected to result in RasGAP activity ( Figure 5 ) ., The first step in this model is binding of the RhoGTPase to binding site A of the intracellular domain ., Although RhoGTPase binding has been shown to be a prerequisite for Ras binding , it is not sufficient to trigger signalling 9 ., Semaphorin binding on the outside of the cell may result in clustering of the receptors 6 either from an autoinhibited , monomeric , or dimeric state 14 , 15 , 39 ., Such extracellular rearrangement could be transmitted to the intracellular N-terminal helix ., The rearrangement of this juxtamembrane helix would free up binding site B , allowing the RhoGTPase to bridge two plexin molecules and stabilize the 3-fold arrangement ., Formation of a trimeric cluster could result in the proper positioning of the catalytic machinery allowing RasGAP activity to occur , since it has been shown that clustering of the intracellular domain is crucial for this activity 9 ., In summary , we propose that receptor clusters nucleated by the dimeric complex on the extracellular side and the trimeric complex on the intracellular side will integrate both RhoGTPase and Semaphorin binding into a single signalling output ., A series of constructs of the intracellular domain of human Plexin-B1 ( GenBank ID: NP_001123554 ) lacking both C- and N-terminal regions as well as the RBD were designed and cloned into pBacPAK9 with a C-terminal His6-Tag for purification ., Of these constructs three could be solubly expressed via baculovirus infection in Sf9 cells ( Plexin-B1Δ1 , residues 1533–2135; Plexin-B1Δ2 , residues 1543–2135; and Plexin-B1cyto , residues 1511–2135 ) ., Cells were harvested at 2 , 000×g for 15 min , resuspended in binding buffer ( 20 mM phosphate , pH , 7 . 4 , 500 mM NaCl , 0 . 5 mM β-mercaptoethanol ) , sonicated , and then centrifuged at 46 , 000×g for 1 h at 4°C ., The supernatant was collected and the protein was purified by ion metal affinity chromatography followed by size exclusion chromatography in 10 mM Hepes , pH 7 . 5 , 150 mM NaCl , 2 mM TCEP 40 ., Mutations were generated by a two-step overlapping PCR using Pyrobest Polymerase ( Takara ) ., Mutant plexin constructs used for SPR studies were expressed in human HEK 293T cells essentially as described 41 ., Three days after transfection the cells were harvested and purified following the protocol used for the wild-type proteins ., All mutant proteins had similar expression level compared to Plexin-B1cyto as determined by SDS-PAGE ., Rac1 Gln61Leu ( residues 1–176 , GenBank ID: CAB53579 ) and Rnd1 ( residues 5–200 , GenBank ID: BAB17851 ) were cloned into the expression vector pET22b , expressed in E . coli BL21 Star ( Invitrogen ) , and purified following an established protocol described elsewhere 40 ., After purification Rac1 was incubated with 10 mM EDTA , pH 8 . 0 , and calf intestine alkaline phosphatase ( NEB ) to degrade any bound nucleotide ., Subsequently the protein was loaded with the non-hydrolyzable GTP analogue GppNHp and purified by size exclusion chromatography in 10 mM Hepes , pH 7 . 5 , 150 mM NaCl , 2 mM MgCl2 , 2 mM TCEP ., SEMA4Decto ( residues 22–677 ) was expressed in CHO lecR cells as previously described 6 ., The Ras binding domain of Raf-1 ( residues 51–131 ) was fused to GST ( GST-RBD ) , expressed in E . coli BL21 Star ( Invitrogen ) , and purified following an established protocol described elsewhere 40 ., Prior to crystallization all proteins were concentrated by ultrafiltration to 10 mg/ml and complexes were formed by mixing Plexin-B1 and RhoGTPase in a 1∶1 . 2 molar ratio ., Nano-litre crystallization trials were set-up using a Cartesian Technologies robot ( 100 nl protein solution plus 100 nl reservoir solution ) in 96-well Greiner plates 42 , placed in a TAP Homebase storage vault maintained at 295 K , and imaged via a Veeco visualization system 43 ., The PlexinB1cyto-Rac1* complex crystallized in 1 M Li2SO4 , 0 . 5 M ammonium sulphate , 0 . 1 M citrate , pH 5 . 6 , and Plexin-B1Δ1-Rac1* complex crystallized in 20% PEG 3350 , 0 . 2 M KSCN , 0 . 1 M Bis-Tris Propane , pH 6 . 5 ., Diffraction data were collected at 100 K with the crystals being flash-cooled in a cryo N2 gas stream ., Prior to flash-freezing , crystals were treated with a cryo protectant solution consisting of 25% ( v/v ) glycerol in mother liquor ., The Plexin-B1Δ1-Rac1* crystals crystallized as thin needles and data were collected at the microfocus beamline ID23-2 at the European Synchrotron Radiation Facility , France , following a helical data collection strategy ., Plexin-B1cyto-Rac1* crystals crystallized as thin squares and data were collected at beamline I03 at Diamond Light Source , UK ., X-ray data were processed and scaled with the HKL suite 44 ., Data collection statistics are shown in Table 1 ., Both structures were solved by molecular replacement using PHASER 45 with the structure of human Plexin-B1 ( PDB ID: 3HM6 15 ) and active Rac1 ( PDB ID: 1MH1 33 ) as search model ., The solution was manually adjusted using COOT 46 and refined using autoBUSTER 47 ., Refinement statistics are given in Table 1; all data within the indicated resolution range were included ., The 4 . 2 Å structure was refined using 3-fold NCS as implemented in autoBUSTER 47 and tight geometric restraints to minimize the introduction of any model bias ., Stereochemical properties were assessed by MOLPROBITY 48 ., Ramachandran statistics are as follows ( favoured/disallowed ( % ) ) : Plexin-B1cyto-Rac1* 91 . 7/0 . 2 , Plexin-B1Δ1-Rac1* 95 . 5/0 . 2 ( pre-proline residue Leu1981 is in a disallowed region in both structures ) ., Superpositions were calculated using SHP 49 ., Buried surface areas of protein-protein interactions were calculated using the PISA webserver ( http://www . ebi . ac . uk/msd-srv/prot_int/pistart . html ) ., SPR experiments were performed using a Biacore T100 machine ( GE Healthcare ) at 25°C in standard buffer supplemented with 0 . 05% ( v/v ) Tween 20 ., Protein concentrations were determined from the absorbance at 280 nm using calculated molar extinction coefficients ., All plexin constructs for surface attachment were enzymatically biotinylated within an engineered C-terminal tag ., These proteins were then attached to surfaces on which 5 , 000 RU of streptavidin were coupled via primary amines 50 yielding a density of 500–5 , 000 response units ( RU ) of biotinylated protein ., All experiments were done in duplicates with independently purified proteins ., The signal from experimental flow cells was corrected by subtraction of a blank and reference signal from a mock or irrelevant protein coupled flow cell ., In all experiments analyzed , the experimental trace returned to baseline after each injection and the data fitted to a simple 1∶1 Langmuir model of binding ., Kd values were obtained by nonlinear curve fitting of the Langmuir binding isotherm ( bound\u200a=\u200aC*max/ ( Kd+C ) , where C is analyte concentration and max is the maximum analyte binding ) evaluated using the Biacore Evaluation software ( GE Healthcare ) ., Cellular collapse assays were performed essentially as described 36 ., Briefly , COS-7 cells were seeded on glass coverslips and transfected with full-length human Plexin B1 carrying an N-terminal Flag-tag essentially as described 42 ., Two days after transfection , cells were treated with medium containing secreted SEMA4Decto and incubated for 30 min at 37°C ., Finally , the cells were fixed and stained with anti-Flag primary antibody ( Sigma ) and Alexa 488-labelled secondary antibody ( Invitrogen ) ., Cell nuclei were counterstained with DAPI ( Invitrogen ) and cells were visualized with a TE2000U fluorescence microscope ( Nikon ) equipped with an Orca CCD camera ( Hamamatsu ) ., Plexin B1-expressing cells were classified as collapsed or non-collapsed on the basis of reduced surface area ., Each experiment was repeated twice and 2×200 cells were counted each time ., Results are shown as mean with error bars representing standard error of the mean ., Pull-down assays were performed essentially as described 51 ., COS-7 cells were seeded in 6-well dishes and transfected with full-length human Plexin-B1 and its mutants , respectively , and R-Ras ., Two days after transfection , cells were treated with medium containing secreted SEMA4Decto and incubated for 10 min at 37°C ., Cells were washed twice with ice-cold phosphate-buffered saline and then lysed with lysis buffer ( 50 mM Tris-HCl , pH 7 . 5 , 200 mM NaCl , 5 mM MgCl2 , 10% glycerol , 1% Non-ident P-40 substitute , 2 mM β-mercaptoethanol ) ., Cell lysates were incubated with GST-RBD pre-coupled to glutathione-agarose beads ( GE Healthcare ) for 45 min at 4°C ., After three wash steps with lysis buffer the beads were collected in Laemmli sample buffer and analyzed by SDS-PAGE and immunoblotting with R-Ras- and GST-specific antibodies , respectively . | Introduction, Results, Discussion, Materials and Methods | Plexins are cell surface receptors for the semaphorin family of cell guidance cues ., The cytoplasmic region comprises a Ras GTPase-activating protein ( GAP ) domain and a RhoGTPase binding domain ., Concomitant binding of extracellular semaphorin and intracellular RhoGTPase triggers GAP activity and signal transduction ., The mechanism of this intricate regulation remains elusive ., We present two crystal structures of the human Plexin-B1 cytoplasmic region in complex with a constitutively active RhoGTPase , Rac1 ., The structure of truncated Plexin-B1-Rac1 complex provides no mechanism for coupling RhoGTPase and Ras binding sites ., On inclusion of the juxtamembrane helix , a trimeric structure of Plexin-B1-Rac1 complexes is stabilised by a second , novel , RhoGTPase binding site adjacent to the Ras site ., Site-directed mutagenesis combined with cellular and biophysical assays demonstrate that this new binding site is essential for signalling ., Our findings are consistent with a model in which extracellular and intracellular plexin clustering events combine into a single signalling output . | Axon guidance is fundamental to the development of the central nervous system ., The growing axon is guided to its correct location by a plethora of extracellular signals ., One of the most important extracellular signals is semaphorin , which binds to plexin receptors on the axon ., Usually , this kind of extracellular ligand binding is sufficient to transmit the extracellular signal to the intracellular space to trigger changes in the cell , like axon growth ., However , activation of plexin receptors requires a “dual” ligand binding: semaphorin on the extracellular side , and a RhoGTPase on the intracellular side ., Signal transduction can only occur if both ligands are present ., How this intricate regulation mechanism is organized and how concomitant ligand binding can be integrated into a single signalling output within the cell has remained largely unclear ., Here , we present crystal structures of one plexin receptor , Plexin-B1 , in complex with an intracellular RhoGTPase ligand ( Rac1 ) and show that binding of Rac1 brings together three Plexin-B1 molecules ., In this trimeric arrangement each plexin molecule interacts with two Rac1 ligand molecules ., This leads to a previously unidentified plexin-Rac1 ligand interface that is crucial for its function ., Further biophysical and cellular analysis in combination with previous findings on the extracellular plexin-semaphorin complex allow us to propose a model for how ligand-induced clustering events on the extra- as well as intracellular side are combined to trigger signal transduction . | molecular neuroscience, protein interactions, mechanisms of signal transduction, signaling in selected disciplines, neuroscience, gtpase signaling, developmental signaling, cell movement signaling, protein structure, signaling pathways, signaling in cellular processes, proteins, ras signaling, signal initiation, biology, recombinant proteins, molecular biology, biochemistry, signal transduction, molecular cell biology | A novel binding site for RhoGTPases on the intracellular region of plexins induces a trimeric ligand—receptor arrangement that appears crucial for plexin function. 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journal.pgen.1005004 | 2,015 | Recent Selective Sweeps in North American Drosophila melanogaster Show Signatures of Soft Sweeps | The ability to identify genomic loci subject to recent positive selection is essential for our efforts to uncover the genetic basis of phenotypic evolution and to understand the overall role of adaptation in molecular evolution ., The fruit fly Drosophila melanogaster is one of the classic model organisms for studying the molecular bases and signatures of adaptation ., Recent studies have provided evidence for pervasive molecular adaptation in this species , suggesting that approximately 50% of the amino acid changing substitutions , and similarly large proportions of non-coding substitutions , were adaptive 1 , 2 , 3 , 4 , 5 , 6 , 7 , 8 , 9 ., There is also evidence that at least some of these adaptive events were driven by strong positive selection ( ~1% or larger ) , depleting levels of genetic variation on scales of tens of thousands of base pairs in length 10 , 11 ., If adaptation in D . melanogaster is indeed common and often driven by strong selection , it should be possible to detect genomic signatures of recent and strong adaptation 12 , 13 , 14 ., Three cases of recent and strong adaptation in D . melanogaster are well documented and can inform our intuitions about the expected genomic signatures of such adaptive events ., First , resistance to the most commonly used pesticides , carbamates and organophosphates , is known to be largely due to three point mutations at highly conserved sites in the gene Ace , which encodes the neuronal enzyme Acetylcholinesterase 15 , 16 , 17 ., Second , resistance to DDT evolved via a series of adaptive events that included insertion of an Accord transposon in the 5’ regulatory region of the gene Cyp6g1 , duplication of the locus , and additional transposable element insertions into the locus 18 , 19 ., Finally , increased resistance to infection by the sigma virus , as well as resistance to certain organophosphates , has been associated with a transposable element insertion in the protein-coding region of the gene CHKov1 20 , 21 ., In-depth population genetic studies 17 , 19 , 21 of adaptation at these loci revealed that in all three cases adaptation failed to produce classic hard selective sweeps , but instead generated patterns compatible with soft sweeps ., In a hard selective sweep , a single adaptive haplotype rises in frequency and removes genetic diversity in the vicinity of the adaptive locus 22 , 23 , 24 ., In contrast , in a soft sweep multiple adaptive alleles present in the population as standing genetic variation ( SGV ) or entering as multiple de novo adaptive mutations increase in frequency virtually simultaneously bringing multiple haplotypes to high frequency 25 , 26 , 27 , 28 , 29 ., In the cases of Ace and Cyp6g1 , soft sweeps involved multiple de novo mutations 17 , 19 , 21 that arose after the introduction of pesticides , whereas in the case of CHKov1 , a soft sweep arose in out-of-African populations from standing genetic variation ( SGV ) 17 , 19 , 21 present at low frequencies in the ancestral African population 20 , 21 ., Unfortunately , most scans for selective sweeps in population genomic data have been designed to detect hard selective sweeps ( although see 30 ) and focus on such signatures as a dip in neutral diversity around the selected site 22 , 24 , 31 , an excess of low or high-frequency alleles in the frequency spectrum of polymorphisms surrounding the selected site ( i . e . Tajima’s D , Fay and Wu’s H , and Sweepfinder ) 32 , 33 , 34 , 35 , 36 , the presence of a single common haplotype 37 , or the observation of a long and unusually frequent haplotype ( iHS ) 36 , 38 , 39 , 40 ., In a soft sweep , however , multiple haplotypes linked to the selected locus can rise to high frequency and levels of diversity and allele frequency spectra should therefore be perturbed to a lesser extent than in a hard sweep ., As a result , methods based on the levels and frequency distributions of neutral diversity have low power to detect soft sweeps 13 , 28 , 41 , 42 ., Some genomic signatures do have power to detect both hard and soft sweeps ., In particular , linkage disequilibrium ( LD ) measured between pairs of sites or as haplotype homozygosity should be elevated in both hard and soft sweeps ., This expectation holds for hard sweeps and for soft sweeps that are not too soft , that is soft sweeps that have such a large number of independent haplotypes bearing adaptive alleles that linkage disequilibrium is no longer elevated beyond neutral expectations 41 , 43 ., Given that none of the described cases of adaptation at Ace , Cyp6g1 , and CHKov1 produced hard sweeps , it is possible that additional cases of recent selective sweeps in D . melanogaster remain to be discovered ., Here we develop a statistical test based on modified haplotype homozygosity for detecting both hard and soft selective sweeps in population genomic data ., We apply this test in a genome-wide scan in a North American population of D . melanogaster using the Drosophila Genetic Reference Panel ( DGRP ) data set 44 , consisting of 162 fully sequenced isogenic strains from a North Carolina population ., Our scan recovers the three known soft sweeps at Ace , Cyp6g1 , and CHKov1 , and identifies a large number of additional recent and strong selective sweeps ., We develop an additional haplotype homozygosity statistic that can distinguish hard from soft sweeps and argue that the haplotype frequency spectra at the top 50 candidate sweeps are best explained by soft selective sweeps ., In this paper , we develop a set of new statistics for the detection and characterization of positive selection based on measurements of haplotype homozygosity in a predefined window ., Our reasoning in developing these statistics is that haplotype homozygosity , defined as a sum of squares of the frequencies of identical haplotypes in a window , should be a sensitive statistic for the detection of both hard and soft sweeps , as long as the window is large enough that neutral demographic processes are unlikely to elevate haplotype homozygosity by chance 41 , 43 ., At the same time , the window must not be so large that even strong sweeps can no longer generate frequent haplotypes spanning the whole window ., In order to determine an appropriate window length for the measurement of haplotype homozygosity in the DGRP data set , we first assessed the length scale of linkage disequilibrium decay expected in the DGRP data under a range of neutral demographic models for North American D . melanogaster ., This length scale should roughly correspond to the window size over which we are unlikely to observe substantial haplotype structure by chance ., We considered six demographic models ( Fig . 1 ) ., The first demographic model is an admixture model of the North American D . melanogaster population proposed by Duchen et al . 45 ., In this model , the North American population was co-founded by flies from Africa and Europe 3 . 05×10–4 Ne generations ago ( where Ne ≈ 5x106 ) ., The second model is a modified admixture model , also proposed by Duchen et al . 45 , in which the founding European population underwent a bottleneck before the admixture event ( see S1 Table for complete parameterizations of both admixture models ) ., The third model has a constant effective population size of Ne = 106 46 , which we considered for its simplicity , computational feasibility and , as we will argue below , its conservativeness for the purposes of detecting selective sweeps using our approach in the DGRP data ., The fourth model is a constant Ne = 2 . 7x106 demographic model fit to Watterson’s θW estimated from short intron autosomal polymorphism data from the DGRP dataset ( Methods ) ., Finally , we fit a family of out-of-Africa bottleneck models to short intron regions in the DGRP data set using DaDi 47 ( S2 Table ) ( Methods ) ., The two bottleneck models we ultimately used are a severe but short bottleneck model ( NB = 0 . 002 , TB = 0 . 0002 ) and a shallow but long bottleneck model ( NB = 0 . 4 , TB = 0 . 0560 ) , both of which fit the data equally well among a range of other inferred bottleneck models ( see S1 Fig . for parameterization ) ., All models except for the constant Ne = 106 model fit the DGRP short intron data in terms of the number of segregating sites ( S ) and pairwise nucleotide diversity ( π ) ( S3 Table ) ., We compared the decay in pair-wise LD in the DGRP data at distances from a few base pairs to 10 kb with the expectations under each of the six demographic models using parameters relevant for our subsequent analysis of the DGRP data ( Fig . 2 ) ., Specifically , we matched the sample depth of the DGRP data set ( 145 strains after quality control ) and assumed a mutation rate ( μ ) of 10–9 events/bp per generation 48 and a recombination rate ( ρ ) of 5×10–7 centimorgans/bp ( cM/bp ) 49 ., In the DGRP data analysis below , we exclude regions with a low recombination rate ( ρ < 5x10–7 cM/bp ) ., The use of ρ = 5x10–7 cM/bp should therefore generate higher LD in simulations than in the DGRP data and thus should be conservative for the purposes of defining the expected length scale of LD decay ., Fig . 2 shows that LD in the DGRP data is elevated beyond neutral expectations at all length scales ( consistent with the observations in 50 ) , and dramatically so at the 10 kb length scale ., The elevation in LD observed in the data is indicative of either linked positive selection driving haplotypes to high frequency , a lack of fit of current demographic models to the data , or both ., Simulations under the most realistic demographic model , admixture 45 , have the fastest decay in LD ( S2 Fig . ) ., This is likely because admixture models with two bottlenecks that are fit to diversity statistics generate more haplotypes compared to single bottleneck models , since the same haplotype is unlikely to be sampled independently in both bottlenecked ancestral populations ., In contrast , LD under the constant Ne = 106 demographic scenario decays slower than in any other demographic scenario , as expected given that this model has the smallest effective population size ., Fig . 2 suggests that windows of 10 kb are large enough that neutral demography is unlikely to generate high values of LD and elevate haplotype homozygosity by chance , and should thus prevent a high rate of false positives ., At the same time , the use of 10 kb windows for the measurement of haplotype homozygosity should still allow us to detect many reasonably strong sweeps , including the known cases of recent adaptation ., The footprint of a hard selective sweep extends over approximately s/log ( Nes ) ρ basepairs , where s is the selection strength , Ne the population size , and ρ the recombination rate 22 , 23 , 51 ., Sweeps with a selection coefficient of s = 0 . 05% or greater are thus likely to generate sweeps that span 10 kb windows in areas with recombination rate of 5×10–7 cM/bp ., As the recombination rate increases , only selective sweeps with s > 0 . 05% should be observed in the 10 kb windows ., Genomic analyses have suggested that adaptation in Drosophila is likely associated with a range of selection strengths , including values of ~1% 7 , 8 , 10 or greater as observed at Ace , Cyp6g1 , and CHKov1 ., Our use of 10 kb windows in the rest of the analysis should thus bias the analysis toward detecting the cases of strongest adaptation in Drosophila ., We investigated haplotype spectra in simulations of neutral demography and both hard and soft selective sweeps arising from de novo mutations as well as SGV ., For all haplotype spectra and homozygosity analyses in this paper we use windows of 400 SNPs , corresponding roughly to 10 kb in the DGRP data ( Fig . 2 ) ., Haplotypes within a 400 SNP window are grouped together if they are identical at all SNPs in the window ., We fixed the number of SNPs in a window to eliminate variability in the haplotype spectra due to varying numbers of SNPs ., The lower SNP density of the constant Ne = 106 model ( S3 Table ) effectively increases the size of the analysis window in terms of the number of base pairs when defining the windows in terms of the number of SNPs ., Thus , the constant Ne = 106 model should reduce the rate of false positives because the recombination rate under this model is artificially increased ., We therefore use the constant Ne = 106 model for the subsequent simulations of neutrality and selective sweeps ., To visualize sample haplotype frequency spectra , we simulated incomplete and complete sweeps with frequencies of the adaptive mutation ( PF ) at 0 . 5 or 1 at the time when selection ceased ., ( Note that below we will investigate a large number of scenarios , focusing on the effects of varying selection strength and the decay of sweep signatures with time ) ., The number of independent haplotypes that rise in frequency simultaneously in soft sweeps—we call this “softness” of a sweep—should increase either, ( i ) when the rate of mutation to de novo adaptive alleles at a locus becomes higher and multiple alleles arise and establish after the onset of selection at a higher rate , or, ( ii ) when adaptation uses SGV with previously neutral or deleterious alleles that are present at higher frequency at the onset of selection 27 , 29 ., More specifically , for sweeps arising from multiple de novo mutations , Pennings and Hermisson 29 showed that the key population genetic parameter that determines the softness of the sweep is θA = 4NeμA , proportional to the product of Ne , the variance effective population size estimated over the period relevant for adaptation 14 , 52 , and μA , the mutation rate toward adaptive alleles at a locus per individual per generation 14 ., The mutation-limited regime with hard sweeps corresponds to θA << 1 , whereas θA > 1 specifies the non-mutation-limited regime with primarily soft sweeps ., As θA becomes larger , the sweeps become softer as more haplotypes increase in frequency simultaneously 29 ., In the case of sweeps arising from SGV , the softness of a sweep is governed by the starting partial frequency of the adaptive allele in the population prior to the onset of selection ., For any given rate of recombination , adaptive alleles starting at a higher frequency at the onset of selection should be older and should thus be present on more distinct haplotypes and give rise to softer sweeps 27 ., As can be seen in Fig . 3 , most haplotypes in neutral demographic scenarios are unique in our 400 SNP windows , whereas selective sweeps can generate multiple haplotypes at substantial frequencies ., Our plot of the haplotype frequency spectra and the expected numbers of adaptive haplotypes show that sweeps arising from de novo mutations become soft with multiple frequent haplotypes in the sample when θA, ≥ 1 . Sweeps from SGV become soft when the starting partial frequency of the adaptive allele prior to the onset of selection is ≥ 10–4 ( 100 alleles in the population ) ., In both cases , sweeps become monotonically softer as θA increases or , respectively , the starting partial frequency of the adaptive allele becomes higher ., These results conform to the expectations derived in 29 ., The increase of haplotype population frequencies in both hard and soft sweeps can be captured using haplotype homozygosity 30 , 39 , 41 ., If pi is the frequency of the ith most common haplotype in a sample , and n is the number of observed haplotypes , then haplotype homozygosity is defined as H1 = Σi = 1 , …n pi2 ., We can expect H1 to be particularly high for hard sweeps , with only one adaptive haplotype at high frequency in the sample ( Fig . 4A ) ., Thus , H1 is an intuitive candidate for a test of neutrality versus hard sweeps , where the test rejects neutrality for high values of H1 ., A test based on H1 may also have acceptable power to detect soft sweeps in which only a few haplotypes in the population are present at high frequency ., However , as sweeps become softer and the number of sweeping haplotypes increases , the relative contribution of individual haplotypes towards the overall H1 value decreases , and the power of a test based on H1 is expected to decrease ., To have a better ability to detect hard and soft sweeps using homozygosity statistics , we developed a modified homozygosity statistic , H12 = ( p1 + p2 ) 2 + Σi>2 pi2 = H1 + 2p1p2 , in which the frequencies of the first and the second most common haplotype are combined into a single frequency ( Fig . 4B ) ., A statistical test based on H12 is expected to be more powerful in detecting soft sweeps than H1 because it combines frequencies of two similarly abundant haplotypes into a single frequency , whereas for hard sweeps the combination of the frequencies of the first and second most abundant haplotypes should not change haplotype homozygosity substantially 53 ., We also considered a third test statistic , H123 , which combines frequencies of the three most prevalent haplotypes in a sample into a single haplotype and then computes homozygosity ., We will primarily employ H12 in subsequent analyses but will consider the effects of using H1 and H123 briefly as well ., To assess the ability of H12 to detect sweeps of varying softness and to distinguish positive selection from neutrality , we measured H12 in simulated sweeps arising from both de novo mutations and SGV while varying s , PF , and the time since the end of the sweep , TE , measured in units of 4Ne generations in order to model the decay of a sweep through recombination and mutation events over time ., We first investigate the behavior of H12 under different selective regimes and then investigate its power in comparison with the popular haplotype statistic iHS ., Fig . 5A shows that for complete and incomplete sweeps with s = 0 . 01 and TE = 0 , H12 monotonically decreases as a function of θA over the interval from 10–2 to 102 ., When θA ≤ 0 . 5 , many sweeps are hard and H12 values are high ., When θA ≈ 1 , and practically all sweeps are soft , but not yet extremely soft , H12 retains much of its power ., However , for θA > 10 , where sweeps are extremely soft , H12 decreases substantially ., Similarly , H12 is maximized when the starting frequency of the allele is 10–6 ( one copy of the allele in the population generating hard sweeps from SGV ) and becomes very small as the frequency of the adaptive allele increases beyond >10-3 ( >1000 copies of the allele in the population ) ( Fig . 5B ) ., Therefore , H12 has reasonable power to detect soft sweeps in samples of hundreds of haplotypes , as long as they are not extremely soft , but remains somewhat biased in favor of detecting hard sweeps ., H12 also increases as the ending partial frequency of the adaptive allele after selection ceased ( PF ) increases from 0 . 5 to 1 ( Fig . 5A and 5B ) and as the selection strength increases from 0 . 001 to 0 . 1 ( Fig . 5C and 5D ) ., We observe that sweeps arising from SGV with low selection coefficients have lower H12 values ( Fig . 5D ) ., This is most likely because such weak sweeps are effectively harder: as more of the haplotypes fail to establish , fewer haplotypes end up sweeping in the population leading to higher values of haplotype homozygosity ., Fig . 5E and 5F further show that incomplete and complete sweeps decay with time due to recombination and mutation events , resulting in monotonically decreasing values of H12 with time ., Overall this analysis demonstrates that H12 has most power to detect recent sweeps driven by strong selection ., We also assessed the ability of H12 to detect selective sweeps as compared to H1 and H123 by calculating the values of H1 , H12 , and H123 for sweeps generated under the parameters s = 0 . 01 , TE = 0 and PF = 0 . 5 ., H12 consistently , albeit modestly , increases the homozygosity for younger soft sweeps as compared to H1 ( S3 Fig . ) ., The increase in homozygosity using H123 is marginal relative to homozygosity levels achieved by H12 , so we chose not to use this statistic in our study ., Finally , we compared the abilities of H12 and iHS ( integrated haplotype score ) , a haplotype-based statistic designed to detect incomplete hard sweeps 39 , 40 , to detect both hard and soft sweeps ., We created receiving operator characteristic ( ROC ) curves 54 , which plot the true positive rate ( TPR ) of correctly rejecting neutrality in favor of a sweep ( hard or soft ) given that a sweep has occurred versus the false positive rate ( FPR ) of inferring a selective sweep , when in fact a sweep has not occurred ., In our simulations of selective sweeps we used θA = 0 . 01 as a proxy for scenarios generating almost exclusively hard sweeps , and θA = 10 as a proxy for scenarios generating almost exclusively soft sweeps ., We chose θA = 10 for soft sweeps because this is the highest θA value with which H12 can still detect sweeps before substantially losing power given our window size of 400 SNPs and sample size of 145 ., Note that for soft sweeps with a lower value of θA the power of H12 should be higher ., We modeled incomplete sweeps with PF = 0 . 1 , 0 . 5 , and 0 . 9 , with varying times since selection had ceased of TE = 0 , 0 . 001 , and 0 . 01 in units of 4Ne generations ., We simulated sweeps under three selection coefficients , s = 0 . 001 , 0 . 01 , and 0 . 1 ., Fig . 6 and S4 Fig . show that the tests based on H12 and iHS have similar power for the detection of hard sweeps , although in the case of old and strong hard sweeps ( TE = 0 . 01 , s ≥ 0 . 01 ) iHS performs slightly better than H12 ., On the other hand , H12 substantially outperforms iHS in detecting soft sweeps and has high power when selection is sufficiently strong and the sweeps are sufficiently young ., As sweeps become very old , neither statistic can detect them well , as expected ., We applied the H12 statistic to DGRP data in sliding windows of 400 SNPs with the centers of each window iterated by 50 SNPs ., To classify haplotypes within each analysis window , we assigned the 400 SNP haplotypes into groups according to exact sequence identity ., If a haplotype with missing data matched multiple haplotypes at all genotyped sites in the analysis window , then the haplotype was randomly assigned to one of these groups ( Methods ) ., To assess whether the observed H12 values in the DGRP data along the four autosomal arms are unusually high as compared to neutral expectations , we estimated the expected distribution of H12 values under each of the six neutral demographic models ., Fig . 7 shows that genome-wide H12 values in DGRP data are substantially elevated as compared to expectations under any of the six neutral demographic models ., In addition , there is a long tail of outlier H12 values in the DGRP data suggestive of recent strong selective sweeps ., To identify regions of the genome with H12 values significantly higher than expected under neutrality , we calculated critical values ( H12o ) under each of the six neutral models based on a 1-per-genome false discovery rate ( FDR ) criterion ., Our test rejects neutrality in favor of a selective sweep when H12 > H12o ( Methods and S1 Text ) ., The critical H12o values under all neutral demographic models are similar to the median H12 value observed in the DGRP data ( Table 1 ) , consistent with the observations of elevated genome-wide haplotype homozygosity and much slower decay in LD at the scale of 10 kb in the DGRP data compared to all neutral expectations ( Fig . 2 ) ., We focused on the constant Ne = 106 model because it yields a relatively conservative H12o value ( Table 1 ) and preserves the most long-range , pair-wise LD in simulations ( Fig . 2 ) ., For our genomic scan we chose to use the 1-per-genome FDR value calculated under the constant Ne = 106 model with a recombination rate of 5×10–7 cM/bp ., Note that most H12o values are similar to the genome-wide median H12 value of 0 . 0155 ., In order to call individual sweeps , we first identified all windows with H12 > H12o in the DGRP data set under the constant Ne = 106 model ., We then grouped together consecutive windows as belonging to the same ‘peak’ if the H12 values in all of the grouped windows were above H12o for a given model and recombination rate ( Methods ) ., We then chose the window with the highest H12 value among all windows in a peak and used this H12 value to represent the entire peak ., We focused on the top 50 peaks with empirically most extreme H12 values , hypothesized to correspond to the strongest and/or most recent selective events ( Fig . 8A ) ., The windows with the highest H12 values for each of the top 50 peaks are highlighted in Fig . 8A ., The highest H12 values for the top 50 peaks are in the tail of the distribution of H12 values in the DGRP data ( Fig . 7 ) and thus are outliers both compared to the neutral expectations under all six demographic models and the empirical genomic distribution of H12 values ., We observed peaks that have H12 values higher than H12o on all chromosomes , but found that there are significantly fewer peaks on 3L ( 2 peaks ) than the approximately 13 out of 50 top peaks expected when assuming a uniform distribution of the top 50 peaks genome-wide ( p = 0 . 00016 , two-sided binomial test , Bonferroni corrected ) ., The three peaks with the highest observed H12 values correspond to the three known cases of positive selection in D . melanogaster at the genes Ace , Cyp6g1 , and CHKov1 17 , 19 , 21 , confirming that the H12 scan is capable of identifying previously known cases of adaptation ., In S4 Table , we list all genes that overlap with any of the top 50 peaks ., Fig . 9A and S5 Fig . show the haplotype frequency spectra observed at the top 50 peaks ., In contrast , Fig . 9B shows the frequency spectra observed under the six demographic models with the corresponding critical H12o values ., We performed several tests to ensure the robustness of the H12 peaks to potential artifacts ( S1 Text ) ., We first tested for associations of H12 peaks with inversions in the sample , but did not find any ( S1 Text , S5 Table ) ., In addition , we reran the scan in three different data sets of the same population and confirmed that unaccounted population substructure and variability in sequencing quality do not confound our results ( S1 Text , S7 Fig . ) ., We also sub-sampled the DGRP data set to 40 strains ten times and plotted the resulting distributions of H12 values ., We found that in all subsamples there is an elevation in haplotype homozygosity relative to neutral demographic scenarios , suggesting that the elevation in haplotype homozygosity values is driven by the whole sample and not a particular subset of individuals ( S8 Fig . ) ., Finally , to ensure that haplotype homozygosity is not elevated by family structure , we excluded all related individuals and reran the scan , again recovering the majority of our top peaks ( S1 Text , S7 Fig . ) ., We scanned chromosome 3R using H1 and H123 as our test statistics in order to determine the impact of our choice of grouping the two most frequent haplotypes together in our H12 test statistic on the location of the identified peaks ( S9 Fig . ) ., We found that the locations of the identified peaks are similar with all three statistics , but that some smaller peaks that cannot be easily identified with H1 are clearly identified with H12 and H123 , as expected ., We applied the iHS statistic as described in Voight et al . 2006 40 to all SNPs in the DGRP data to determine the concordance in the sweep candidates identified by iHS and H12 ( Methods ) ., Briefly , we searched for 100 kb windows that have an unusually large number of SNPs with standardized iHS values ( |iHS| ), > 2 . The positive controls Ace , Cyp6g1 , and CHKov1 are located within the 95 top 10% iHS 100 kb windows ( Fig . 8B ) , validating this approach ., To determine how often a candidate region identified in the H12 scan is identified in the iHS scan and vice versa , we overlapped the top 50 H12 peaks with the 95 top 10% iHS 100Kb windows ., We defined an overlap as the non-empty intersection of the two genomic regions defining the boundaries of a peak in the H12 scan and the non-overlapping 100Kb windows used to calculate enrichment of |iHS| values ., We found that 18 H12 peaks overlap 28 |iHS| 100Kb enrichment windows ., In contrast , fewer than 5 H12 peaks are expected to overlap approximately 7 iHS 100Kb windows by chance ( Methods ) ., The concordance between the two scans confirms that many of the peaks identified in the two scans are likely true selective sweeps and also suggests that the two approaches are not entirely redundant ., Our analysis of H12 haplotype homozygosity and the decay in long range LD in DGRP data suggests that extreme outliers in the H12 DGRP scan are in locations of the genome that may have experienced recent and strong selective sweeps ., The visual inspection of the haplotype spectra of the top 10 peaks in Fig . 9A and the remaining 40 peaks in S5 Fig . reveals that they contain many haplotypes at substantial frequency ., These spectra do not appear similar to those generated by hard sweeps in Fig . 3 or extreme outliers under neutrality in Fig . 9B , but instead visually resemble incomplete soft sweeps with s = 0 . 01 and PF = 0 . 5 either from de novo mutations with θA between 1 and 20 or from SGV starting at partial frequencies of 5x10–5 to 5x10–4 prior to the onset of selection ( Fig . 3 ) ., The sweeps also appear to become softer as H12 decreases , consistent with our expectation that H12 should lose power for softer sweeps ., In order to gain intuition about whether the haplotype spectra for the top 50 peaks can be more easily generated either by hard or soft sweeps under various evolutionary scenarios , we developed a new haplotype homozygosity statistic , H2/H1 , where H2 = Σi>1 pi2 = H1—p12 is haplotype homozygosity calculated using all but the most frequent haplotype ( Fig . 4C ) ., We expect H2 to be lower for hard sweeps than for soft sweeps because in a hard sweep only one adaptive haplotype is expected to be at very high frequency 53 ., The exclusion of the most common haplotype should therefore reduce haplotype homozygosity precipitously ., As sweeps get softer , however , multiple haplotypes start appearing at high frequency in the population and the exclusion of the most frequent haplotype should not decrease the haplotype homozygosity to the same extent ., Conversely H1 , the homozygosity calculated using all haplotypes , is expected to be higher for a hard sweep than for a soft sweep as we described above ., The ratio H2/H1 between the two measures should thus increase monotonically as a sweep becomes softer , thereby offering a summary statistic that , in combination with H12 , can be used to test whether the observed haplotype patterns are more likely to be generated by hard or soft sweeps ., Note that we intend H2/H1 to be measured near the center of the sweep where H12 is the highest ., Otherwise , when H2/H1 is estimated further away from the sweep center , mutation and recombination events will decay the haplotype signature and hard and soft sweep signatures can become indistinguishable ., To assess the behavior of H2/H1 as a function of the softness of a sweep , we measured H2/H1 in simulated sweeps of varying softness arising from de novo mutations and SGV with various s , PF , and TE values ., Fig . 10 shows that H2/H1 has low values for sweeps with θA ≤ 0 . 5 or when the starting partial frequency of the adaptive allele prior to the onset of selection is <10–5 , i . e . , when sweeps are mainly hard ., As a sweep becomes softer , H2/H1 values approach one because no single haplotype dominates the haplotype spectrum ., In the case of sweeps arising from de novo mutations , H2/H1 values are similar for partial ( PF = 0 . 5 ) and complete sweeps ( PF = 1 ) and for sweeps of varying strengths ( s = 0 . 001 , 0 . 01 , 0 . 1 ) ., However , in the case of sweeps arising from SGV , sweeps with higher selection strengths do have higher H2/H1 values , reflecting the hardening of sweeps for smaller s values as we discussed previously ( Fig . 5D ) ., Both sweeps from de novo mutations and SG | Introduction, Results, Discussion, Methods | Adaptation from standing genetic variation or recurrent de novo mutation in large populations should commonly generate soft rather than hard selective sweeps ., In contrast to a hard selective sweep , in which a single adaptive haplotype rises to high population frequency , in a soft selective sweep multiple adaptive haplotypes sweep through the population simultaneously , producing distinct patterns of genetic variation in the vicinity of the adaptive site ., Current statistical methods were expressly designed to detect hard sweeps and most lack power to detect soft sweeps ., This is particularly unfortunate for the study of adaptation in species such as Drosophila melanogaster , where all three confirmed cases of recent adaptation resulted in soft selective sweeps and where there is evidence that the effective population size relevant for recent and strong adaptation is large enough to generate soft sweeps even when adaptation requires mutation at a specific single site at a locus ., Here , we develop a statistical test based on a measure of haplotype homozygosity ( H12 ) that is capable of detecting both hard and soft sweeps with similar power ., We use H12 to identify multiple genomic regions that have undergone recent and strong adaptation in a large population sample of fully sequenced Drosophila melanogaster strains from the Drosophila Genetic Reference Panel ( DGRP ) ., Visual inspection of the top 50 candidates reveals that in all cases multiple haplotypes are present at high frequencies , consistent with signatures of soft sweeps ., We further develop a second haplotype homozygosity statistic ( H2/H1 ) that , in combination with H12 , is capable of differentiating hard from soft sweeps ., Surprisingly , we find that the H12 and H2/H1 values for all top 50 peaks are much more easily generated by soft rather than hard sweeps ., We discuss the implications of these results for the study of adaptation in Drosophila and in species with large census population sizes . | Evolutionary adaptation is a process in which beneficial mutations increase in frequency in response to selective pressures ., If these mutations were previously rare or absent from the population , adaptation should generate a characteristic signature in the genetic diversity around the adaptive locus , known as a selective sweep ., Such selective sweeps can be distinguished into hard selective sweeps , where only a single adaptive mutation rises in frequency , or soft selective sweeps , where multiple adaptive mutations at the same locus sweep through the population simultaneously ., Here we design a new statistical method that can identify both hard and soft sweeps in population genomic data and apply this method to a Drosophila melanogaster population genomic dataset consisting of 145 sequenced strains collected in North Carolina ., We find that selective sweeps were abundant in the recent history of this population ., Interestingly , we also find that practically all of the strongest and most recent sweeps show patterns that are more consistent with soft rather than hard sweeps ., We discuss the implications of these findings for the discovery and quantification of adaptation from population genomic data in Drosophila and other species with large population sizes . | null | null |
journal.pcbi.1005637 | 2,017 | An optimal strategy for epilepsy surgery: Disruption of the rich-club? | Epilepsy is a chronic neurological disorder that affects about 1% of people worldwide 1 ., Antiepileptic drugs are the preferred treatment , but in around one third of cases , drugs do not stop seizures , and patients for whom this is the case are potential candidates for surgery 2 ., Surgeons use an array of data modalities , including intra-cranial electroencephalogram ( iEEG ) , in an attempt to map regions of the brain thought to be crucial for the generation of seizures 3 ., If these regions of the brain are amenable to surgery ( e . g . they do not overlie eloquent cortex ) , then they are removed with the hope that the individual is rendered seizure free as a consequence ., However , long-term success rates from surgery may be as low as 15% , presumably in part due to failures of the assumptions used in the decision making process 4 , 5 ., It is therefore crucial to advance our understanding of the mechanisms that generate seizures and the reasons why removing regions of brain tissue may or may not lead to seizure freedom ., In this regard , seizures are increasingly recognised as arising in large-scale brain networks 6–9 ., Emerging from such networks , both healthy and pathological dynamics are observed , for example through EEG , MEG or fMRI ., These dynamics emerge due to the interplay between intrinsic properties of brain areas , structural connectivity , and modulating influences across multiple temporal and spatial scales 10–12 ., This networks paradigm has led to imaging or electrographic data being used to inform network representations of the brain ( for example structural or functional brain networks ) , and graph theoretical measures are used to characterise the topology of these networks 13–19 ., Studies analysing graph theoretical properties of networks have reported differences between functional and structural networks derived from healthy individuals versus people with epilepsy 20–26 ., The emerging field of dynamics on networks is complementary to these traditional , “static” network analyses 27 , 28 , and moves beyond the study of the topology of networks ., In this approach , mathematical models are used to link networks and the intrinsic properties of individual nodes to dynamic data 29 , which provides an avenue to understand the relationship between structure and function 30 ., In particular , mathematical models that can recreate elements of pathological dynamics , for example the occurrence of seizures , have been used to understand the network mechanisms of disorders such as epilepsy 9 , 31–37 ., Such approaches are also being used in translational applications , for example providing additional information to complement clinical interpretation , namely within the diagnosis of epilepsy 35 , 38 ., Crucially , a dynamics on networks approach can be extended to study perturbations to networks ., On one hand , lesions and traumatic brain injury can lead to the emergence of pathological brain activity , on the other hand , perturbations such as pharmacological treatment , single pulse electrical stimulation ( and other electrical stimulations ) , transcranial magnetic stimulation , thermocoagulation , among others , can transform brain dynamics from pathological to healthy states 36 , 39–41 , therefore revealing potential avenues for therapy ., In the case of epilepsy surgery , we have demonstrated that a network model derived from iEEG data could provide relevant predictions for the outcome of epilepsy surgery 42 ., Our findings have been recently replicated in an independent cohort of 16 people with pharmacoresistant epilepsy 43 offering further support to a dynamics on network approach ., However , the ways in which networks with different topologies respond to perturbations is at present unknown ., For example , in analogy to epilepsy surgery , it is unclear whether particular networks are amenable to a reduction in pathological dynamics upon removing nodes and if so which nodes would be best to target ., Here , we use a dynamics on networks approach to study the generation of pathological activity in networks and how the removal of nodes can restore healthy dynamics ., Our starting point is a neural mass model that has previously been shown to generate epileptiform rhythms in focal seizures 32 , 37 , 44 , and that we have successfully used to quantify and predict the outcome of epilepsy surgery 42 ., It has been shown that the model , when placed close to a saddle-node on invariant circle ( SNIC ) bifurcation , can generate spontaneous , recurrent transitions to epileptiform dynamics ( both inter-ictal spikes as well as seizures ) when driven by noise 32 , 37 , 42 ., In our framework , the neural mass model describes the dynamics of a single node within a wider network ., The systematic exploration of node removal in brain networks is computationally demanding , and hence we seek a computationally efficient version of this model that preserves the quantification of the effect of removing nodes ., We show that a modification of the theta-neuron model 45 is appropriate for this purpose since it is the canonical form of the bifurcation under consideration ., This model is capable of generating spiking dynamics , which here represents seizure-like activity ., The computational benefits of the theta-neuron model allow us to study the emergence of spiking dynamics in different types of networks and also to systematically quantify the effect of removing different nodes ., Here , we study small-world , random , rich-club and scale-free and find that rich-club and scale-free networks more readily generate spiking dynamics , since they require a lower strength of coupling between connected nodes to do so ., In terms of the contribution of nodes , we find that rich-club , random and scale-free networks possess a small number of nodes that drive spiking dynamics , whereas the propensity of generate spiking dynamics is more evenly distributed across nodes in small-world networks ., Collectively , this suggests that patients whose brain networks display rich-club properties should be particularly amenable to current surgery paradigms ., In order to test the relevance of these findings , we analyse data from patients who underwent surgery and for whom postoperative outcome is known ., We demonstrate that functional networks inferred from iEEG during seizures display a rich-club connectivity structure and that the proportion of rich-club nodes removed correlates with the success of surgery ., This study was approved by the Internal Review Board of the Inselspital ( approval No . 159399 , dated 26th of November , 2013 ) ., All patients gave written informed consent that imaging and EEG data may be used for research purposes ., In order to model epilepsy surgery , we consider large-scale brain networks , where each network node is capable of generating epileptiform activity but will do so depending on the connectivity structure of the network ., In this framework , a node putatively represents a portion of brain tissue potentially responsible for the emergence of seizure activity across the network ., We assume that the dynamics of each node can be described by a neural mass model , such as the Wendling model 37 , 42 ., The model depicts the dynamics of a macroscopic circuit in which a population of excitatory pyramidal neurons interacts with three populations of interneurons ( representing one excitatory and two inhibitory populations ) ., The two inhibitory populations are classed as slow and fast , representing dendritic-projecting GABAA and somatic-projecting GABAA interneurons , respectively ., The dynamics is described by the following 10 first-order ordinary differential equations ( ODEs ) :, z˙1 ( t ) =z6 ,, z˙2 ( t ) =z7 ,, z˙3 ( t ) =z8 ,, z˙4 ( t ) =z9 ,, z˙5 ( t ) =z10 ,, z˙6 ( t ) =AaS{z2 ( t ) −z3 ( t ) −z4 ( t ) }−2az6 ( t ) −a2z1 ( t ) ,, z˙7 ( t ) =Aa ( p+C2S{C1z1 ( t ) } ) −2az7 ( t ) −a2z2 ( t ) ,, z˙8 ( t ) =BbC4S{C3z1 ( t ) }−2bz8 ( t ) −b2z3 ( t ) ,, z˙9 ( t ) =GgC7S{C5z1 ( t ) −z5 ( t ) }−2gz9 ( t ) −g2z4 ( t ) ,, z˙10 ( t ) =BbC6S{C3z1 ( t ) }−2bz10 ( t ) −b2z5 ( t ) ,, where z1-z5 are the output potentials in mV of the neuronal populations , namely z1 , z2 , z3 , and z4 are the outputs of the pyramidal cells , excitatory population , slow inhibitory population , and fast inhibitory population , respectively ., z5 is the output of the slow inhibitory population that interacts with the fast inhibitory population ., z6-z10 are auxiliary variables , S is a sigmoid function ,, S ( ν ) =2e01+er ( ν0−ν ) ,, and A , a , B , b , G , g , C1-C7 , p , e0 , r , and ν0 are parameters ( see Table 1 for their biophysical interpretation and values ) ., The output of the model z2 ( t ) –z3 ( t ) –z4 ( t ) corresponds to the aggregated membrane potential of the excitatory cell population and its bifurcations have been extensively characterized 47 ., In particular , a SNIC bifurcation has been identified as one mechanism for the generation of epileptiform rhythms observed in typical focal epilepsies 32 ., This model and bifurcation were also previously employed to estimate brain network ictogenicity to predict the outcome of epilepsy surgery 42 ., Therefore the parameters A , B and C were chosen so that the neural mass is in a steady state close to the SNIC bifurcation that gives rise to spiking dynamics which we consider a proxy for the patho-phenotype of the epileptic brain ( see the third figure , left panel , in 32 ) ., p is an extrinsic input parameter that represents stimuli from other areas of the cortex ., Although the neural mass model described above represents the dynamics of four interacting neuronal populations , at the scale we are interested in , it describes the dynamics of a single node in a wider network consisting of other interacting neural masses ., Following previous studies 33 , 48 , we account for the coupling between neural masses ( nodes ) using the extrinsic input parameter p ., We make the input of the j-th node both time and node dependent as follows ,, pj ( t ) =p0, ( j ) +ξ, ( j ) ( t ) +1N∑i≠jλijaijS{z2, ( i ) ( t ) −z3, ( i ) ( t ) −z4, ( i ) ( t ) } ., Here the index j denotes node j ( j = 1 , 2 , … , N , where N is the number of nodes ) ., p0, ( j ) is used to control the distance to the SNIC bifurcation; ξ, ( j ) ( t ) represents noisy inputs from other areas of the cortex outside of the network under consideration; λij is the coupling strength from node i to node j; and aij is the i , jth entry of the adjacency matrix ( the node receives the outputs of all his in-neighbours ) 33 , 48 ., We consider Gaussian noise with mean p0, ( j ) and, 〈ξ, ( i ) ( t ) ξ, ( j ) ( t′ ) 〉=σp2δi , jδ ( t−t′ ) ,, where σp2 is the variance ., A node is in a resting state if pj ( t ) < pc , where pc is the critical point at which the SNIC bifurcation takes place ., Since the Wendling Model ( WM ) becomes computationally expensive for studying large networks , we look for a parsimonious representation for spiking dynamics in brain networks ., Taking into account that nodes of WM are operating in the vicinity of a SNIC bifurcation , we substitute networks of neural masses with networks in which each node is represented by the normal form of the SNIC , i . e . the theta-neuron model 45 ., It is important to stress that although this model is traditionally used to describe the dynamics of a neuron , here we use it ( as effectively the canonical form of the SNIC bifurcation ) to represent the dynamics of a neural mass in an epileptic spiking regime ., The canonical model ( CM ) is an alternative formulation of a quadratic integrate and fire neuron ., It comprises the following ODE:, θ˙j= ( 1−cos\u2061θj ) + ( 1+cos\u2061θj ) Ij ( t ) ,, where θj is the phase of node j , and Ij ( t ) is its input current ., The SNIC bifurcation occurs at Ic = 0 ., At Ij < Ic , the phase oscillator is resting , whereas at Ij > Ic it is oscillating ., We define the coupling between the “canonical neural masses” analogous to the coupling defined within the WM ,, Ij ( t ) =I0, ( j ) +ξ, ( j ) ( t ) +1N∑i≠jwijaij1−cos\u2061 ( θi−θi, ( s ) ) ,, where Ij is the input current of node j , I0, ( j ) +ξ, ( j ) ( t ) represents noisy inputs coming from other areas , wij is the coupling strength from node i to node j , and aij is the i , jth entry of the adjacency matrix ., As in the WM , we consider Gaussian noise ( mean I0, ( j ) , and variance σI2 ) ., We define the output of the in-neighbour i as 1−cos\u2061 ( θi−θi, ( s ) ) , where θi, ( s ) is its steady state , so that if the node is resting its output is zero , and if it reaches θi, ( s ) +π , its output is maximum ., This uncoupled steady state θi, ( s ) is obtained from setting θ˙i=0 ,, θi, ( s ) =−Re{cos−1\u2061 ( 1+I0, ( i ) 1−I0, ( i ) ) } ., We take the real part so that θi, ( s ) =0 at I0, ( i ) >0 ., At I0, ( i ) <0 , there are two fixed points: θi, ( s ) is a stable fixed point , and −θi, ( s ) is an unstable fixed point ., A similar coupling in networks of theta-neurons was recently studied in 49 ., Other authors have considered delta-like interactions 50 , or rapid rises in the synaptic gating variable 51 , which are a reasonable approximation for neurons , but inappropriate for neural masses ., Note that the output of a neural mass is an average over the activity of a population of neurons , and so it displays properties of a low-pass filter 52 ., For simplicity , we consider homogeneous nodes in both models , i . e . , all nodes in a network are at the same distance to the SNIC bifurcation ( p0, ( j ) =p0 and I0, ( j ) =I0 ) , and have the same coupling strength ( λij = λ and wij = w ) ., This is a strong assumption that enables us to focus explicitly on the contribution of the network structure to the network ictogenicity ., Thus , there are three free parameters in each model: ( p0 , σp , λ ) in WM , and ( I0 , σI , w ) in the CM ., Since our aim is to consider whether the two network models display similar changes in dynamics upon the removal of nodes , it is important that these parameters are comparable between models ., Taking into account that we require that the node dynamics switch between the resting state and the spiking dynamics , the three parameters are interdependent ., For example , as parameter values of the nodes move closer to the SNIC the required noise variance to elicit spikes becomes smaller ., Note , however , that the variance of the noise should not be too large as we wish to ensure that network interactions play a role in the emergent dynamics ., Thus , we define σp*=σp/ ( pc−p0 ) and σI*=σI/ ( Ic−I0 ) to scale the effect of noise by the distance to the SNIC bifurcation so that the effect of the noise on the dynamics of both models is comparable ., In order to establish a relation between the coupling strength and the noise , we also define λ* = 2e0cλ/ ( Nσp ) and w* = 2cw/ ( NσI ) , where c is the mean degree of the network ., These relations compare the noise to the average maximum input that a node can receive , 2e0cλ/N and 2cw/N for WM and the CM , respectively ., It provides a scale that compares noise perturbations to inputs received from in-neighbours ., Note that , with respect to the input parameter , the dynamics of a node j change from resting to spiking in WM if pj ( t ) > pc , and likewise , in the CM a node j transitions to spiking if Ij ( t ) > Ic ., Thus , in both models we have the following condition for a node j to be in the parameter region corresponding to a spiking regime at time t ,, x0, ( j ) +ξ, ( j ) ( t ) +CN∑i≠jaijYi ( t ) >T ,, where x0, ( j ) +ξ, ( j ) ( t ) is the noise , C the homogeneous coupling strength , Yi ( t ) the output of node i , and T the bifurcation point ., If we assume that the network is in the resting state with an average node output of 〈Y〉 , then we can estimate the critical coupling Cc at which on average a certain node starts to spike ,, Cc=N ( T−x0, ( j ) +〈ξ, ( j ) ( t ) 〉 ) 〈Y〉kj, ( i ) ,, where kj, ( i ) is the in-degree of node j ( 〈ξ, ( j ) ( t ) 〉 = 0 in the case of Gaussian noise ) ., Therefore , for a given network of size N , the larger the in-degree , the smaller is Cc , meaning that nodes with higher in-degree are more likely to transition to spiking ., This is valid in both models ., Similarly , one can find the critical distance to the SNIC bifurcation ,, x0c, ( j ) =T−C〈Y〉kj, ( i ) N ,, which is smaller than T due to the inputs from the network ( 〈Y〉 > 0 ) ., The adjacency matrix encodes the network structure on top of which the nodes interact ., We consider random , scale-free , small-world and rich-club networks , both directed and undirected ( we discarded networks with disconnected components ) 53 , 54 ., In order to quantify the “importance” of each node , we analyze the following traditional measures: degree , average neighbour degree , eigenvector centrality , betweenness centrality , closeness centrality , clustering coefficient , and local efficiency 55 , 56 ., Additionally , we also consider eigencentrality based on Jaccard dissimilarity 57 and dynamical importance 58 ., In the case of directed networks , we also consider in-degree , out-degree , as well as the sum and product of these measures ., We focus our analysis upon two measurements that are relevant for our purposes of studying epileptic dynamics and surgery in silico , namely Brain Network Ictogenicity ( BNI ) 23 , 42 , 59 , and Node Ictogenicity ( NI ) 42 ., BNI is a practical approach for quantifying the tendency of a network to generate spiking dynamics ., It measures the average fraction of time spent in spiking dynamics by each node 23 , 42 , 59:, BNI=1N∑iTimespentinspikingdynamicsbynodeiTotaltime ., Specifically , in the WM , first we extract the spikes generated by a node by applying a threshold to the average absolute amplitude of the model output over a sliding window of 0 . 05 s ., Then , contiguous epochs of spiking dynamics are identified by evaluating the overlap of 1 s time windows centred in each spike ., Finally , the time spent in spiking dynamics corresponds to the total time of these spiking epochs 42 ., In the CM we use the same method , with similar time scales ( we use as conversion time scale the ratio of the full widths at half maximum of the spikes in each model ) ., NI quantifies the contribution of each node to the ictogenicity of the network by measuring the relative difference in BNI upon removing node i from the network:, NIi=BNIpre−BNIpostiBNIpre ,, where BNIpre corresponds to the BNI over the network prior to node resection and BNIposti is the BNI after the removal of node i ., Note that NIi = 1 means that the removal of node i renders the network free of spiking dynamics , whereas NIi = 0 means that the resection of node i made no difference to the BNI ., In practice , this quantity measures the success of a given surgery resection in silico , and it may have the potential to guide the search for an optimal surgical strategy ., In general , this quantity may also be useful to quantify the result of temporary ablation , assuming that the ablation takes place in a much slower time scale than the network dynamics ., In this paper we set BNIpre = 0 . 5 ( we have confirmed that the results are qualitatively the same for other reference values of BNIpre ) ., To evaluate if the CM can be used as a proxy of the WM in this framework , we compare the NI ordering of the two models for a number of networks ., Note that NI is essentially a vector with N entries quantifying the result of removing each node individually , being of particular interest the relative impact of each node removal compared to the others , rather than the absolute value of each one ( which is parameter dependent ) ., We use a weighted Kendalls rank correlation measure 60 , 61 , which is defined as follows ., Given two rankings ( NI ) of the same items ( nodes of the network ) , we calculate, τ=P−QP+Q ,, where P is the number of items in the same order in the two rankings , and Q counts the number of items in reverse order ., When τ = 1 the two rankings predict the same ordering , whereas τ = −1 means a reverse order of all items ., Here we consider a weighted measure to take into account the relative values of NI: each NI comparison between two nodes i and j is weighted by the product of the distances in NI predicted by the two models , |NIWMi−NIWMj|×|NICMi−NICMj| , ( where NIWMi and NICMi are the NIs of node i calculated using WM and the CM , respectively ) ., We assume that there are no ties ., We focus on patients with pharmacoresistant epilepsy , since such patients are candidates for surgery ., Data were collected from 16 patients ( 11 female , mean age 31 , and median post-surgical follow up 3 years ) who underwent pre-surgical monitoring at Inselspital Bern 42 , 62 ., Following epilepsy surgery , six patients fell into Engel class I ( free of disabling seizures ) , five into Engel class II ( rare disabling seizures ) and five into Engel class IV ( no worthwhile improvement ) ., All patients gave written informed consent that imaging and iEEG data may be used for research purposes ., Other details about the data can be found elsewhere 42 , 62 ., Before analysis , the signals were down-sampled to a sampling rate of 512 Hz and re-referenced against the median of all the channels free of permanent artefacts as judged by visual inspection by an experienced epileptologist ( K . S . ) ., For each patient , two peri-ictal epochs were considered , which included three minutes before seizure onset , the seizure itself and three minutes after seizure termination ( seizure onset and offset were identified by visual inspection ( K . S . ) ) ., Following the methods described in 62 , 63 , first we applied a band-pass filter between 0 . 5 and 120 Hz and a notch filter ( 48 to 52 Hz ) using a Butterworth filter ., Each epoch was divided in a set of 8 seconds segments ( the segments were chosen 1 second apart from each other ) ., For each segment we obtained 10 univariate iterated amplitude adjusted Fourier transform ( IAAFT ) surrogates independently ., Next , the segments were divided in 10 subsegments of 1024 sampling points ( 2 seconds ) distributed with minimal overlap ., Thus , we generated an ensemble of 10 subsegments for the original time series , and 100 subsegments for the surrogates ( 10 for each surrogate ) ., To estimate the correlations between the time series of each iEEG channel , we used the Pearsons equal-time ( zero-lag ) cross-correlation coefficient ρ , and a non-parametric Mann-Whitney-Wilcoxon U-test was performed to assess the significance of different medians of ρ between the original time series ( ρo ) and the surrogates ( ρsurr ) ., We further applied Bonferroni-Holm corrections to account for multiple comparisons ., Finally , we obtained a surrogate-corrected correlation matrix using the heuristic formula 63 , 64, C=ρo−ρsurr1−ρsurrs ,, where s = 1 if the null hypothesis of the statistical test is rejected , or s = 0 otherwise ., Using this method , we derived 102 ± 18 functional networks based on cross-correlation for each patient , depending on the duration of each seizure epoch ., The organization of functionally derived networks into rich-clubs 65–67 was studied using a weighted rich-club parameter ϕ ( k ) 66 ., The richness parameter is the degree k , and the procedure consists in finding groups ( clubs ) of nodes whose richness is larger than k ., For a given degree k , we counted the number of connections E>k of the club , and summed their weights W>k ., We then calculated the fraction of weights shared by the club out of the maximum edge weights that the club could have if they were linked by the strongest connections of the network , i . e . ,, ϕw ( k ) =W>k∑l=1E>kwlrank ,, where wlrank are the ranked weights of the network ., This fraction is not enough to verify the existence of a rich-club , since even random networks can have an increasing function ϕw ( k ) as a result of chance alone ( nodes with higher degree are more likely to be connected ) ., Therefore , ϕw ( k ) is normalized relative to ϕrand ( k ) obtained from a set of comparable random networks ,, ϕ ( k ) =ϕw ( k ) ϕrand ( k ) ., Thus , a network exhibits rich-club organization if there is a range of degree k for which ϕ ( k ) > 1 65 , 66 ., We generated 100 random networks by applying a reshuffle procedure to the weights while keeping the topology of the original network intact , followed by a link and weight reshuffle procedure that preserves the original degree distribution 56 , 67 ., ϕrand ( k ) was calculated as the average rich-club coefficient for each level of k ., Finally , we evaluate the statistical significance of rich-club organization using a permutation test 67 , by testing whether ϕ ( k ) was statistically significantly larger than ϕrand ( k ) ( a one-sided p value was calculated as the percentage of the distribution of ϕrand ( k ) that exceeds ϕ ( k ) ) ., We measured the rich-clubs of the average functional networks of the pre-seizure , seizure , post-seizure , and whole peri-ictal epochs for each patient separately ., We compared the dynamics of the CM to the WM in terms of the effect that model parameters have on BNI and the profile of NI for a suite of networks ., Fig 1 demonstrates typical dynamics of each model applied to the same network ( a directed random network with N = 10 , and mean degree c = 1 . 6 ) ., Both models display spiking dynamics , with a heterogeneous distribution of activity across nodes ., For each model , nodes 2 , 5 , 7 , 9 and 10 show a greater extent of spiking dynamics than other nodes; thus the distribution of activity across the network is preserved in the canonical model ., On the other hand , it is clear that the resting state is noisier in the CM ., A predominant feature accounting for this is that the ratio of amplitude of the spiking trajectory to noise is larger in the WM ., Moreover , one should also realize that whereas in the WM only positive inputs can move the system towards the SNIC bifurcation , in the CM both positive and negative inputs displace the phase , θ , from the resting state ., Furthermore , the output of the resting state in the CM is zero , but non-zero in the WM ., As described in the Methods , although we have identified three free parameters , the network dynamics are in fact affected by only two competing factors: the distance to the SNIC bifurcation and the coupling strength ., The strength of noise required to elicit spikes is correlated with the distance to the SNIC bifurcation ( that is smaller noise variance is required to elicit spikes the closer the system is to the bifurcation ) ., Thus , we can fix the noise variance and consider BNI as a function of the distance to the SNIC bifurcation and coupling strength ., Fig 2 provides an evaluation of this function for an ensemble of 10 random networks with 15 nodes and demonstrates that the smaller the distance to the bifurcation , the easier it is to generate spiking dynamics , and consequently BNI is larger ., In addition , BNI grows with increases in coupling strength ., Fig 2 demonstrates that the shape of the BNI surface is similar for the two models , which provides evidence that the normal form of the SNIC is appropriate for the study of the propensity of a network to generate spiking dynamics ., Similar results were obtained for both smaller and larger networks ., Our results thus far indicate that despite some expected quantitative differences , network dynamics , and in particular the way that BNI changes with respect to system parameters , are qualitatively similar across the WM and the CM ., However , our primary focus is to determine whether the CM would provide the same prediction for the effect on BNI of node removals ( i . e . NI ) ., With the application of surgical resections in mind , we are predominantly interested in how comparable the ordering of NI is between the two models ., In order to investigate this , we calculated the distribution of NI for a suite of random networks of size N = 15 , 30 and 50 and calculated the similarity in ordering of NI using Kendalls τ ( see Methods ) ., Fig 3 shows that within models , the NI distribution is robust across different parameter sets for which BNIpre = 0 . 5 , which is our starting point for the calculation of NI ( see Methods ) and defines a line in the surface of Fig 2 ., Across different choices of parameters within the WM , we find τ > 0 . 97 for all networks considered , indicating a strong preservation of the ordering of NI when different parameters are used ., Within the CM , τ > 0 . 89 and thus there is slightly more variation across NI orderings for this model ., Fig 3C and 3D show τ for comparisons of NI orderings between the two models ., Fig 3C demonstrates that when model parameters are chosen randomly , the ordering of NI is preserved between the two models for small networks , but differences in predictions between the two models arise in larger networks ( for example with 50 nodes ) ., However , Fig 4D demonstrates that a parameter set for each model can be found such that NI distributions are preserved across models in the larger networks studied ( 50 nodes , τ = 0 . 85 ± 0 . 09 ) ., We note that as N increases , nodes become topologically similar in a random network , and therefore one can expect a homogeneous distribution of NI ., However , we are primarily interested in networks for which nodes exist that should be resected to reduce the presence of spiking dynamics ., We therefore study networks for which we might expect the distribution of NI to be heterogeneous ( as we will show below ) ., A natural choice is a scale-free network characterized by a power law degree distribution P ( k ) ∼k−γ with a small exponent ( γ < 3 ) 54 ., Fig 3 demonstrates that for scale-free networks arbitrary choices of parameters yield a strong similarity in ordering of NI ( τ = 0 . 87 ± 0 . 16 ) and that model parameters exist for which the ordering is essentially identical ( τ = 0 . 996 ± 0 . 003 ) ., Fig 3E demonstrates the computational advantage gained by using the CM over the WM ., We find that the ratio of computational time of the WM to the CM when estimating BNI is 4 . 6 for networks of size N = 15 , 4 . 9 for N = 30 , and 6 . 2 for N = 50 ., Note that this gain does not correspond to the ratio of floating point operations needed by each model to simulate a time step because the time scales are different between these two models ., Crucially , such gain will be very useful when applying this framework in the clinical setting , as it represents a speed-up in the computational time from days to hours ., Having demonstrated similarity in the ordering of NI across the WM and CM , we proceed in the following sections to use the CM to study how NI varies across different types of network ., We fix the number of nodes that we consider to be 64 , in line with a typical number of iEEG and depth electrodes used in pre-surgical planning applications 42 , 62 , 68 ., Fig 4 shows how BNI varies as a function of the coupling strength under different choices of network topology ., Scale-free networks are the most prone to transit to spiking dynamics since BNI becomes non-zero for smaller coupling strengths relative to the other topologies ., The effect is more noticeable in networks with smaller exponent γ , which have a greater degree variance ( i . e . are more heterogeneous ) ., However , the maximal value of BNI is less than one for scale-free networks , in particular in directed networks ., Fig 4 demonstrates that rich-club networks exhibit a similar profile of increases in BNI with increases coupling strength features to scale-free networks , which is presumably a consequence of similarities in the degree distributions of these networks ., Small-world and random networks have similar profiles , implying that the high clustering coefficient of small-world networks has little impact on a network’s ic | Introduction, Methods, Results, Discussion | Surgery is a therapeutic option for people with epilepsy whose seizures are not controlled by anti-epilepsy drugs ., In pre-surgical planning , an array of data modalities , often including intra-cranial EEG , is used in an attempt to map regions of the brain thought to be crucial for the generation of seizures ., These regions are then resected with the hope that the individual is rendered seizure free as a consequence ., However , post-operative seizure freedom is currently sub-optimal , suggesting that the pre-surgical assessment may be improved by taking advantage of a mechanistic understanding of seizure generation in large brain networks ., Herein we use mathematical models to uncover the relative contribution of regions of the brain to seizure generation and consequently which brain regions should be considered for resection ., A critical advantage of this modeling approach is that the effect of different surgical strategies can be predicted and quantitatively compared in advance of surgery ., Herein we seek to understand seizure generation in networks with different topologies and study how the removal of different nodes in these networks reduces the occurrence of seizures ., Since this a computationally demanding problem , a first step for this aim is to facilitate tractability of this approach for large networks ., To do this , we demonstrate that predictions arising from a neural mass model are preserved in a lower dimensional , canonical model that is quicker to simulate ., We then use this simpler model to study the emergence of seizures in artificial networks with different topologies , and calculate which nodes should be removed to render the network seizure free ., We find that for scale-free and rich-club networks there exist specific nodes that are critical for seizure generation and should therefore be removed , whereas for small-world networks the strategy should instead focus on removing sufficient brain tissue ., We demonstrate the validity of our approach by analysing intra-cranial EEG recordings from a database comprising 16 patients who have undergone epilepsy surgery , revealing rich-club structures within the obtained functional networks ., We show that the postsurgical outcome for these patients was better when a greater proportion of the rich club was removed , in agreement with our theoretical predictions . | Epilepsy is a chronic neurological disorder that affects about 1% of people worldwide ., The administration of antiepileptic drugs is the preferable treatment , but in around one third of cases , drugs do not stop seizures , and these patients are potential candidates for surgery ., Epilepsy surgery however is too often unsuccessful , with around one half of patients continuing to experience seizures ., In this work we use mathematical models to study epilepsy surgery so to inform surgeons concerning the brain tissue that should be considered for surgery resection ., We show that functional networks derived from data of epileptic patients considered for surgery present rich-club organization ., For this kind of network structure , we propose an optimal surgery strategy that consists of disrupting the rich-club . | medicine and health sciences, neural networks, neuroscience, surgical and invasive medical procedures, mathematics, scale-free networks, algebra, network analysis, directed graphs, epilepsy, computer and information sciences, graph theory, centrality, surgical resection, eigenvectors, linear algebra, neurology, biology and life sciences, physical sciences | null |
journal.pcbi.1003716 | 2,014 | On the Use of Human Mobility Proxies for Modeling Epidemics | One of the biggest challenges that modelers have to face when aiming to understand and reproduce the spatial spread of an infectious disease epidemic is to accurately capture population movements between different locations or regions ., In developed countries this task is generally facilitated by the existence of data or statistics at the national or regional level tracking individuals movements and travels , by purpose , mode , and other indicators if available ( see e . g . transport statistics in Europe 1 , commuting , migration data or other types of mobility at country level 2–6 ) ., Access to highly detailed and updated data may however still be hindered by national privacy regulations , commercial limitations , or publication delays ., The situation becomes increasingly complicated in less-developed regions of the world , where routine data collection may not be envisioned at similar levels of details 7 , but which , most importantly , may be characterized by a high risk of emergence and importation of infectious disease epidemics or may suffer of endemic diseases ., Depending on the infectious disease under study , different mobility processes may play a relevant role in the spatial propagation of the epidemic while others appear to be negligible , as determined by the typical timescales and mode of transmission of the disease , and the geographic scale of interest ., For rapid directly transmitted infections , daily movements of individuals represent the main mean of spatial transmission ., At the worldwide scale , air travel appears to be the most relevant factor for dissemination , as observed during the SARS epidemic 8 , 9 and the 2009 H1N1 pandemic 10 , 11 ., On smaller regional scales , instead , daily commuting is significantly linked to the spread of seasonal influenza 12 , 13 , affecting the epidemic behavior at the periphery of the airline transportation infrastructure 14 ., To overcome issues in accessing commuting data when simulating spatial influenza spread , epidemic models have traditionally relied on mobility models to synthetically build patterns of movements at the desired scale 14–16 ., The gravity model 17 and the recently proposed radiation model 18 have been shown to fit well the commuting patterns observed in reality on different spatial scales 12 , 14–16 , 18–20 ., Next to mobility modeling approaches , alternative tools for understanding daily human movements have more recently flourished thanks to the availability of individual data obtained from different sources , namely mobile phone call records carrying temporal and spatial information on the position of the cell phone user at the level of tower signal cells 21–23 ., Such direction of research has gained great popularity , leading to the discovery of universal characteristics of individual mobility patterns , and the possibility to study mobility in space and at timescales that were unreachable before 21–26 ., Such increasing volumes of finely resolved human mobility data , thanks to the near ubiquity of mobile phones , also offered an opportunity to contrast the huge deficit of quantitative data on individual mobility from underdeveloped regions ., They were indeed used to shed light on malaria diffusion and identify hotspot areas 24 , 26 , 27 , to monitor human displacements in case of natural disasters 25 , 28 and to study disease containment strategies in Ivory Coast 29 ., Despite the variety of modeling approaches and data sources , the impact of using different proxies for human commuting in epidemic models for rapidly disseminated infections is still poorly understood ., Each approach or source of data clearly has its own intrinsic strengths and weaknesses , related to accuracy and availability of the dataset ., More specifically , mobility models require some assumptions or input data for calibration and fit to the real commuting behavior ., The gravity model requires full knowledge of mobility data for its parameter fitting and can be extended to other regions where data is not available in case of empirical evidence pointing to “universal” commuting behavior at a given resolution scale , i . e . well described by the same set of parameter values 14 , or by making assumptions on generalizability ., The radiation model requires population distribution values and the total commuter flows out of a given region , a quantity that may not be easily accessible at the desired level of resolution or with sufficient coverage ., While mobile phone data can provide mobility information at a high granularity level , they are also characterized by a number of issues that may hinder their use ., Phone data are inevitably affected by biases related to the population sampling: coverage is usually not homogenous across space and it depends on the market share of the operator providing the data ., Phone ownership and usage may differ across social groups , gender or age classes depending on the country under study 30 , 31 , and access to users metadata to evaluate the representativeness of the sample is limited by privacy concerns 32 ., Given the recent availability of these data , the impact of such biases on mobility estimates is still poorly understood ., Recent studies have assessed the effects of using gravity models in mathematical epidemic models 12 , 33 , however similar works on the use of data-saving options like the radiation models or of alternative strategies like mobile phone activity data for epidemic applications are still missing ., The aim of this paper is therefore to assess the adequacy of two specific proxies – mobile phone data and the radiation model – to reproduce commuter movement data for the modeling of the spatial spread of influenza-like-illness ( ILI ) epidemics in a set of European countries ., We first compare the commuting networks extracted from the official census surveys of three European countries ( Portugal , Spain and France ) to the corresponding proxy networks extracted from three high-resolution datasets tracking the daily movements of millions of mobile phone users in each country ., More specifically , we examine through a detailed statistical analysis the ability of mobile phone data to match the empirical commuting patterns reported by census surveys at different geographic scales ., We then examine whether the observed discrepancies between the datasets affect the results of epidemic simulations ., To this aim , we compare the outcomes of stochastic SIR epidemics simulated on a metapopulation model for recurrent mobility that is based either on the mobile phone commuting networks or the radiation model commuting networks , with respect to the epidemics simulated by integrating the census data ., We evaluate how the simulated epidemic behavior depends on the underlying mobility source and on the spatial resolution scale considered , by investigating the time to first infection in each location and the invasion epidemic paths from the seed ., The study relied on billing datasets that were previously recorded by a mobile provider as required by law and billing purposes , and not for the purposes of this project ., To safeguard personal privacy , individual phone numbers were anonymized by the operator before leaving storage facilities , in agreement to national regulations on data treatment and privacy issues , and they were identified with a security ID ( hash code ) ., The research was reviewed and approved by the MITs Institutional Review Board ( IRB ) ., As part of the IRB review , authors , who handled the data , and the PI participated in ethics training sessions at the outset of the study ., We use a metapopulation modeling approach 40 , 41 to perform numerical simulations of epidemic scenarios ., We assume the national population of every country to be spatially structured in subpopulations defined by the administrative subdivisions described in the previous subsection ., We focus on rapid directly transmitted infections , such as influenza-like-illnesses , for which daily regular movements of individuals for commuting purposes were found to correlate well with the observed regional spread 12 , 13 ., We consider a simple SIR compartmental model 41 , where individuals can be either susceptible ( S ) , infectious ( I ) or recovered ( R ) from the infection , assuming a life-long immunity for recovered individuals ., The dynamics is discrete and stochastic and individuals are assumed to be homogeneously mixed within each subpopulation ., No additional substructure of the population is considered ( e . g . schools or workplaces ) , as our aim is to introduce a rather simple epidemic model to test the adequacy of different commuting sources for the simulation of ILI dissemination within a country ., We therefore neglect unnecessary details that may hinder the interpretation of results ., Subpopulations are coupled by directed weighted links representing the commuting fluxes between two locations , thus defining the metapopulation structure of the model 40 , 41 ., No other type of movement is considered ., Human mobility is described in terms of recurrent daily movements between place of residence and workplace so that the infection dynamics can be separated into two components , each of them occurring at each location 42 ., The number of newly infected individuals during the working time in location is randomly extracted from a binomial distribution considering trials ( susceptible individuals living and working in location , , and susceptible individuals living in and working in , ) and a probability equal to the force of infection being the transmissibility of the disease , and the total population and the total number of infectious individuals living in location and working in , respectively ., Similarly , the infection events taking place at the resident location during the remaining part of the day are randomly extracted from a binomial distribution considering susceptible individuals and probability equal to the force of infection ., We model an influenza-like-illness transmission characterized by an exponentially distributed infectious period with average days 43 , 44 , and explore three epidemic scenarios by varying the transmissibility β and corresponding to the following values of the basic reproductive number ( average number of secondary cases per primary case in a fully susceptible population 41 ) : , , , representing a mild , moderate , and severe epidemic , respectively ., Simulations are fully stochastic , individuals are considered as integer units and each process is modeled through binomial and multinomial extractions ( more details on the simulation algorithm are reported in Section 4 in Text S1 ) ., Each day of the simulation is modeled with commuting movements informed by the three sources considered for a typical working day; therefore no weekends or holidays are envisioned in the model ., Simulations are initialized with 10 individuals localized in a given seed ., As seeds we consider the countrys capital ( Lisbon , Madrid and Paris ) , a peripheral location with a small population ( Barrancos , Lleida and Barcelonnette ) , and a medium size location , characterized by an average population and an average number of connections through commuting links ( Braga , Jaen and Rennes ) ., Although the countries under study are geographically contiguous , they are considered as independent entities since the investigated datasets do not include refined data about cross-border commuters ., A sensitivity analysis on the role of cross-border commuting in the spread of ILI is reported in Section 3 in Text S1 ., Once a set of initial conditions is defined ( mobility network , , and seeding location ) , we simulate 1 , 000 stochastic realizations for each epidemic scenario , for a total duration of 8 months ., Such timeframe is chosen as a reference estimate of the expected time comprising the interval from the initial seeding of a pandemic event to the international alert ( approximately two months in the case of the 2009 H1N1 pandemic 45 ) and the average time period needed to develop a vaccine against the circulating virus ( approximately six months ) 46 ., During this timeframe the value of the basic reproductive number is kept constant , and no change in behavior that could be self-initiated in response to the epidemic 47 , 48 , or imposed by public health interventions is considered , for the sake of clarity in the comparison of the results ., The census commuting networks for Portugal include, ( i ) 1 , 643 , 938 commuters traveling between the 278 municipalities through 25 , 634 weighted directed connections , and, ( ii ) 469 , 089 commuters traveling between the 18 districts on a fully connected network ., In Spain we consider the provinces geographical scale only , as constrained by the information available in the census survey ., The commuting network is formed by 47 nodes and 722 weighted directed edges , representing the daily travel flows of 537 , 331 commuters ., The commuting networks for France are defined at the district scale ( 8 , 019 , 636 commuters moving along 38 , 077 weighted directed edges connecting 329 nodes ) , and at the department level ( 4 , 957 , 193 commuters for 7 , 994 weighted directed links among 96 nodes ) ., For all countries , at all scales considered , all administrative units are included in the datasets ( i . e . they have at least one incoming or outgoing commuting flux to another administrative unit in the country ) ., A summary of the basic statistics of the networks extracted from census data is reported in Table 1 ., Commuting patterns from mobile phone records are extracted from a sample of 1 , 058 , 197 anonymous users in Portugal , 1 , 034 , 430 in Spain , and 5 , 695 , 974 in France ., Records referred to 2 , 068 towers in Portugal , 9 , 788 towers in Spain , and 18 , 461 in France ., Once mapped onto the administrative units , we find 452 , 113 , 460 , 211 and 1 , 676 , 103 total commuters in the mobile data samples in Portugal , Spain , and France , respectively , corresponding to the lowest administrative hierarchy ., Population tracked by the operators samples is distributed nationwide and approximately equal to 9% of the census population in Portugal and France , and 2% of the census population in Spain ., By taking into account these scaling factors , cell phone population correlates well with the census population at the highest geographical resolution considered , with a Pearson correlation coefficient between the two quantities equal to for Spanish provinces , Portuguese municipalities and French districts ., Population coverage is rather uniform in France with more than half of the districts in the interval of the national coverage value ( grey colored units in Figure 1 ) , while larger discrepancies are observed in the geographic distribution of the tracked population in Spain and Portugal ., In Spain we observe a significant undersampling of the population in Galicia and Basque regions ., In Portugal , we observe larger regional fluctuations around the national coverage value: most of the municipalities report an undersampled population , whereas the region close to the capital , Lisbon , shows an oversampling as large as 3 times the national coverage ., Commuting networks obtained from census data and mobile phone activity data share the same number of nodes at all hierarchies considered in all countries , given that all administrative units were covered by both datasets , however variations are observed in the number of commuting links ( Table 1 ) ., The set of links common in both datasets in the Portugal case at the municipality level account for about 60% of the total links of each network and include more than 96% of the total travel flux of both networks ., Aggregating the datasets at the level of Portuguese districts , both networks become very close to fully connected , almost achieving a perfect overlap ( more than 99% of links falling in the intersection ) ., Similar figures are obtained for French districts , though the common 95% of traffic is distributed over 82% of the census links and only 52% of the mobile phone links ., Spain displays a different situation , with the census commuting network topology being completely included into the mobile phone one ., Census commuting links represent only 37% of connections of the mobile phone dataset , however accounting for 87% of its total traffic ., We compare the probability density distributions of the travel fluxes in both networks ( Figure 2 ) , after considering the basic normalization scaling to the population of each administrative unit ( see Methods ) ., All distributions display a broad tail and very similar shapes in each country , and differences are observed in particular for small traffic values ., In Portugal and France , the very weak commuting flows are not captured by the mobile phone dataset , clearly as an outcome of the smaller users sample size in the mobile phones case with respect to census ., Such discrepancy disappears when we move to larger spatial scales , as in the case of Spain ., Restricting our analysis on the topological intersection , a side-by-side weight comparison on each link shows a high correlation between the two datasets ( Spearmans rank correlation coefficient >0 . 7 for the largest administrative units , Table 2 ) , however commuting fluxes in the mobile phone network are found to be larger than the census ones across almost the entire interval of values ( panels d-f of Figure 2 ) ., Deviations appear larger for smaller fluxes ( commuters ) in Portugal and France , with a good agreement for the largest values , whereas they are uniform in the case of Spain ., Similar results are obtained when we analyze the total number of commuters leaving a given administrative unit , as well as the total number of incoming commuters in a given unit ., A strong correlation between the two datasets is found for both quantities , generally independent of the level of aggregation considered ( Spearmans coefficient >0 . 88 for Portugal and France ) , whereas small values of the Lins coefficient indicate the presence of strong differences in the absolute values for the two datasets ( <0 . 53 across all countries and for all administrative levels , for both quantities , Table 2 ) ., Spain has a rather low Spearmans coefficient for the incoming fluxes of commuters with respect to the other countries ( 0 . 54 vs . values >0 . 88 ) , showing a poor capacity of the mobile phone data to properly account for the attraction of commuters of a given location ., The correlations found along the various indicators do not ensure the statistical equivalence of the two datasets ( a Wilcoxon-test for matched pairs would reject the null hypothesis of zero median differences between paired values of the same quantities ) ., We further analyze whether the observed discrepancies between the weights in the mobile phone networks and the census networks show any dependency on the variables that characterize the underlying spatial and social structure , namely the Euclidean distance between the connected nodes ( calculated from the coordinates of the administrative units centroid ) , the population of the origin node and the population of the destination node ( Figure 3 ) ., The overestimation of the magnitude of commuting fluxes in the mobile phone dataset does not show a significant dependence on the population sizes ., Fluxes are instead found to be more similar when they connect units at shorter distances with respect to longer distances across the countries ., Such variation disappears if we consider the topological distance defined by a neighbor joining approach ( see Section 3 . 4 in Text S1 ) ., Spatial aggregation into larger administrative units does not alter this overall picture but weakens the effect observed on distance ( see details in Section 2 . 1 in Text S1 ) ., If we refine the normalization of the mobile phone networks by taking into account the total number of commuters in each administrative unit , the agreement with the census dataset improves in the side-by-side weight comparison on every link ( see Section 3 in Text S1 ) ., This approach allows us to explicitly discount the systematic overestimation found with the basic normalization , resulting in higher Lin concordance coefficients ( Table S1 in Text S1 ) ; discrepancies between mobile phone and census data are however still observed for very small and very large commuting flows ., We examine whether the observed non-negligible discrepancies in the commuting fluxes of the two datasets are also significant from an epidemic modeling perspective , altering substantially the outcome of disease spreading scenarios ., We compare scenarios obtained from stochastic metapopulation models equally defined and initialized , except for the mobility data they integrate ( see Methods ) ., In addition to the census commuting network and the mobile phone commuting network , we also consider the synthetic commuting network generated with the radiation model ., Epidemics starting from different seeds in the three countries , and characterized by different values of the basic reproductive number , yield large variations of the Jaccard index value measuring the similarity in the epidemic invasion paths produced by the use of mobile phone data and of the radiation model with respect to the census benchmark ( in , see Figure 4 ) ., Epidemic invasion trees obtained from proxies for mobility are more similar to the ones obtained from the model integrating census data when the seed is located in the capital city of the country ., In addition , increases with larger values of ., If the seed is instead located in a peripheral node , values of the Jaccard similarity index fall always below 0 . 4 in the three countries , and decrease with larger values of the transmissibility ., Mobile phone data performs similarly to the radiation model once the corresponding epidemic models are seeded in a central location , except for the case of Lisbon , and performs better or similar when they are seeded in a peripheral location ., If the epidemic starts from a mid-size populated region , the relative performance of the radiation model against mobile phone data in the epidemic outcomes depends on , with improvements observed as increases ., To test for the role of overestimation of flows , we also performed the same analysis by considering the refined normalization of the mobile phone commuting data that keeps the same total number of commuters per administrative region as in the census dataset and explicitly discounts overestimation biases ., The refined normalization allows the mobile phone data to better reproduce the invasion paths obtained from census commuting flows for central and medium-type locations for all , and to perform slightly worse in case the seed is located in a peripheral location ( Figure 5 for the case of France ) ., When focusing on the time of arrival in a given location , we find a systematic difference between models based on proxy networks and the benchmark model integrating census data ., Mobile phone data , overestimating the census commuting fluxes if a basic normalization is considered , leads to a positive difference corresponding to a faster spreading ( Figure 4 ) ., On the other hand , epidemics on the radiation model tend to unfold slower than simulations on the census network , with later arrival times as indicated by negative values of ( except in the case of France where the median of is approximately equal to zero in all cases ) ., For small values of , the arrival times of simulations running on a proxy network may be substantially different from the ones obtained with census data , with of the order of months ., While the transmission potential of the disease drives the magnitude of the impact of the discrepancies , the role of the seed location appears to be less relevant here than what previously observed in the study of the invasion paths ., A slightly decreasing trend in the positive median values of is observed in the mobile phone vs . census results , going from peripheral to medium to central location , the effect being more pronounced in Spain and in France ., By discounting a posteriori the average anticipation of the model built on mobile phone data , which is trivially due to the overestimation of the census commuting fluxes , we find a very good correlation between arrival times for the models built on the census network and on the mobile phone network , with most of the points lying close to the identity line ( Lin concordance correlation coefficient ranging from 0 . 77 to 0 . 88 , panels c , f and i of Figure 4 ) ., If we consider the refined normalization , anticipation effects produced with the mobile phone data are preserved but reduced in magnitude ( Figure 5 ) ., Epidemic peak times are also affected by the different distributions of commuting flows in the two networks ( see Section 2 . 2 in Text S1 ) ., As soon as the disease reaches most of the nodes , the epidemic model integrating the mobile phone network displays a more homogeneous behavior , with epidemic peaks that follow very shortly after each other in all the subpopulations , while peak times in the census networks span a wider time frame ., On coarser spatial scales ( Portuguese districts , French departments ) , we obtain a higher similarity between simulated results with proxies vs . census ( see Section 2 . 1 in Text S1 ) , closer to the results observed for Spanish provinces ., The performance of the epidemic model built on the radiation is noticeably poorer than the mobile phone network if we consider the coarse-grained scale , for all seeds but the capital ., The differences between arrival times are generally reduced by the coarse-graining , but remain significant when the reproduction number is small ( ranging between 0 and 120 days ) ., Next to traditional census sources or transportation statistics , several novel approaches to quantifying human movements have become recently available that increase our understanding of mobility patterns 21–28 , 49–52 ., Adequately capturing human movements is particularly important for improving our ability to simulate the spatiotemporal spread of an emerging disease and enabling advancements in our predictive capacity 53 , 54 ., Previous work has focused on testing mobility models performance in reproducing the movements of individuals 18 , 19 , and its impact on epidemic simulation modeling results when fully supported by data 19 ., The full knowledge of mobility data from national statistics is however largely limited to few regions of the world 14 , whereas in many others it may not be routinely collected nor accessible ., If mobility models often require aggregated input data from national statistics on movement habits 18 or the full mobility census database 19 for the fitting procedure , mobile phone data may be thought as an ideal alternative candidate for a proxy of human movements in absence of ( complete and/or high-resolution ) mobility data from official sources 24 , 26 , 27 ., To systematically test this hypothesis exploiting the full resolution of both the proxy data and the official census data for commuting , we have compared these two datasets in three European countries and performed a rigorous assessment of the adequacy of proxy commuting patterns – extracted from mobile phone data or synthetically modeled – to reproduce the spatiotemporal spread of an emerging ILI infection ., Mobility data from mobile phones is able to capture well the fluxes of the commuting patterns of the countries under study , reproducing the large fluctuations in the travel flows observed in the census networks ., In all countries the intersection between the two networks includes the vast majority of the commuting flows and the correlation measured on links traffic and nodes total fluxes of incoming or outgoing commuters is high ( though not statistically equivalent ) ., This suggests that mobile phone data can be used as a surrogate tracking the commuting patterns of a given country , identifying the relative importance of its mobility connections in terms of flows magnitude , with a resolution that is equivalent to the one adopted by official census surveys or higher ., This is a particularly relevant result for data-poor situations , where census data may not be available and official statistics may not be enough to correctly inform a mobility model ., Discrepancies are however found , especially in the overestimation of commuting flows per link and in the larger variations observed for weaker flows and longer distances , that appear to be responsible for the differences observed in the simulated epidemics ., Epidemics run on mobile phone commuting networks reproduce well the invasion pattern simulated on the census commuting when the seed is located in a central location and is large ., The capital city is indeed strongly connected to the rest of the country; therefore it behaves as a potential seeder of the direct transmission to the majority of the other cities , leading to very similar star-shaped infection trees from the seed ., These rather similar sets of infected locations at the first generation of the invasion path provide a twofold contribution to the increase of : on the one side , they correspond to a large fraction of the total number of infected subpopulations , so they contribute a large relative weight in the computation of ; on the other , common infected locations are likely to maintain the similarity of the invasion paths at the second generation too , repeating the process in an avalanche fashion ., Such behavior becomes increasingly stronger as grows larger ., The opposite situation is instead found when seeds are located in peripheral nodes , reporting low values of the Jaccard index ., The analysis of the commuting networks has indeed shown that larger discrepancies exist for small weights ., Once considered in the framework of an epidemic propagation , such discrepancies are expected to lead to strong differences in the invasion already at the first generation of infected locations ., If these locations directly infected by the seed strongly differ , their contribution to the decrease of the similarity of the invasion paths will become increasingly stronger for further generations: different nodes are infected and likely different neighbors of those nodes will be affected by the disease , so that deviations cumulate at each successive step of the invasion ( Figure 6 ) ., Diseases with a higher transmission potential would enhance this behavior , as with a large value of the peripheral seed can more quickly infect a large fraction of the system in the mobile phone network , than in the census dataset ., Such effect is also present in the radiation model that is not able to describe the epidemic behavior better than the mobile phone data when the seeding location is characterized by a small population or degree ., Not being able to capture well the mobility coupling between peripheral regions and the rest of the country , the radiation model misses most of the seeding events on long distances even when is large ( Figure 6 ) ., Using a synthetic proxy is therefore not always preferable to data alternatives , and mobile phones appear to be more reliable in matching the spatial epidemic spread starting from peripheral locations ., A clear bias , which is observed consistently across all countries and for all resolution scales considered , is the faster rate of spread of the simulation based on the mobile phone commuting network with respect to the census one ., This is clearly induced by the larger commuting flows obtained following the extraction of commuting patterns from mobile phone data using a basic normalization ., The effect is stronger for as it is enhanced by the intrinsic large fluctuations characterizing epidemics close to the threshold ., In such scenarios , even relatively small differences between networks topologies can strongly alter the invasion path of the disease , consistently with the results of p | Introduction, Materials and Methods, Results, Discussion | Human mobility is a key component of large-scale spatial-transmission models of infectious diseases ., Correctly modeling and quantifying human mobility is critical for improving epidemic control , but may be hindered by data incompleteness or unavailability ., Here we explore the opportunity of using proxies for individual mobility to describe commuting flows and predict the diffusion of an influenza-like-illness epidemic ., We consider three European countries and the corresponding commuting networks at different resolution scales , obtained from, ( i ) official census surveys ,, ( ii ) proxy mobility data extracted from mobile phone call records , and, ( iii ) the radiation model calibrated with census data ., Metapopulation models defined on these countries and integrating the different mobility layers are compared in terms of epidemic observables ., We show that commuting networks from mobile phone data capture the empirical commuting patterns well , accounting for more than 87% of the total fluxes ., The distributions of commuting fluxes per link from mobile phones and census sources are similar and highly correlated , however a systematic overestimation of commuting traffic in the mobile phone data is observed ., This leads to epidemics that spread faster than on census commuting networks , once the mobile phone commuting network is considered in the epidemic model , however preserving to a high degree the order of infection of newly affected locations ., Proxies calibration affects the arrival times agreement across different models , and the observed topological and traffic discrepancies among mobility sources alter the resulting epidemic invasion patterns ., Results also suggest that proxies perform differently in approximating commuting patterns for disease spread at different resolution scales , with the radiation model showing higher accuracy than mobile phone data when the seed is central in the network , the opposite being observed for peripheral locations ., Proxies should therefore be chosen in light of the desired accuracy for the epidemic situation under study . | The spatial dissemination of a directly transmitted infectious disease in a population is driven by population movements from one region to another allowing mixing and importation ., Public health policy and planning may thus be more accurate if reliable descriptions of population movements can be considered in the epidemic evaluations ., Next to census data , generally available in developed countries , alternative solutions can be found to describe population movements where official data is missing ., These include mobility models , such as the radiation model , and the analysis of mobile phone activity records providing individual geo-temporal information ., Here we explore to what extent mobility proxies , such as mobile phone data or mobility models , can effectively be used in epidemic models for influenza-like-illnesses and how they compare to official census data ., By focusing on three European countries , we find that phone data matches the commuting patterns reported by census well but tends to overestimate the number of commuters , leading to a faster diffusion of simulated epidemics ., The order of infection of newly infected locations is however well preserved , whereas the pattern of epidemic invasion is captured with higher accuracy by the radiation model for centrally seeded epidemics and by phone proxy for peripherally seeded epidemics . | biology and life sciences, infectious disease modeling, population modeling, computational biology | null |
journal.pgen.1001174 | 2,010 | Continuous Requirement for the Clr4 Complex But Not RNAi for Centromeric Heterochromatin Assembly in Fission Yeast Harboring a Disrupted RITS Complex | Eukaryotic genomes are characterized by domains of transcriptionally permissive euchromatin and relatively transcriptionally inert heterochromatin ., In addition to its important role in transcriptional regulation , heterochromatin plays a critical role in the regulation of genomic stability ., In fission yeast constitutive heterochromatin assembles at the centromeres , telomeres and the mating type locus ., This heterochromatin is required for high fidelity chromosome transmission , protecting chromosome ends from fusion to form dicentric chromosomes , and preventing co-expression of both sets of mating type information which could lead to haploid meiosis and cell death ., A major hallmark of heterochromatin in most eukaryotes is the presence of methyl groups on lysine 9 of histone H3 ., In fission yeast ( Schizosaccharomyces pombe ) , methylation of H3 K9 is carried out by a single enzyme , Clr4 ( the homolog of Suvar39 enzymes in higher eukaryotes ) , which is responsible for mono , di and tri-methylation of H3K9 1 ., This mark is in turn bound by proteins bearing a chromodomain , including the HP1 homologs Swi6 and Chp2 , and importantly , Clr4 itself , leading to models for perpetuation and spreading of heterochromatin 2–5 ., A fourth chromodomain protein , Chp1 , has high affinity for binding the methyl mark 6 ., Chp1 is a component of the RITS complex ( RNA-induced initiation of transcriptional silencing complex ) , which is critical for the accumulation of heterochromatin at centromeres 7 , 8 ., Heterochromatin assembly in several organisms also depends upon the cellular RNA interference ( RNAi ) pathway 9 ., RNAi is triggered by double-stranded RNA ( dsRNA ) , which is processed by the RNAseIII-like activity of Dicer into short interfering ( si ) RNAs ., siRNAs are loaded into RNA –induced silencing complexes ( RISC ) , where they associate with Argonaute proteins , and base-pair with and promote the sequence-dependent destruction of RNA via cleavage mediated by Argonaute ., In fission yeast , the RNAi effector complex is called RITS , and consists of the sole argonaute protein , Ago1 , in complex with Tas3 which physically links Ago1 to Chp1 8 , 10 ., Association of the RITS complex with centromeres is co-dependent on the RNA dependent RNA polymerase complex , RDRC 11 ., RITS and RDRC physically associate with the Clr4-containing Clr-C complex 5 , 12 , and trigger an RNAi-mediated positive feedback loop to enhance H3K9me2 accumulation and heterochromatin assembly 13 ., Accumulation of H3K9me2 allows recruitment of heterochromatin- binding proteins such as Swi6 ( the fission yeast homolog of HP1 ) and cohesin to centromeric repeats , and is required for efficient chromosome segregation ( reviewed in 14 ) ., Clearly , the mechanism by which RITS and RDRC are initially recruited to centromeres to promote RNAi-dependent accumulation of H3K9me2 is critical to our understanding of heterochromatin assembly ., Somewhat paradoxically , the outer repeats of the centromere are transcribed by RNA polymerase II , and this transcription correlates with heterochromatin assembly 15–18 ., Recently , two models have been proposed for how centromeric transcripts may initiate recruitment of RITS/RDRC ., The first proposes that single stranded centromeric transcripts fold into hairpin structures to provide dsRNA template for the activity of dicer ( Dcr1 ) to generate centromeric siRNAs to target RITS to homologous centromeric sequences 19 ., Alternatively , RNA degradation products ( priRNAs ) associate with Ago1 , and if derived from centromeric transcripts , target Ago1 to centromeres 20 ., Ago1 slices transcripts that are homologous to priRNAs , recruiting RDRC which promotes dsRNA synthesis ., dsRNAs are cleaved by Dcr1 to form centromeric siRNAs that recruit RITS to centromeres 8 ., In both models , centromeric RITS/RDRC then promotes Clr-C association , and H3K9 methylation facilitating binding of Chp1 to chromatin ., These models infer that small RNAs and the RNAi pathway act as the priming signal for heterochromatin assembly , with Dcr1 or Ago1 playing the initiating role respectively ., However , RITS possesses two potential centromeric targeting motifs: Ago1 which binds siRNAs and can target centromeric transcripts and Chp1 which has high affinity for binding H3K9me2 6 , 7 ., We questioned whether indeed RNAi is the upstream event for heterochromatin assembly , or whether Clr4 functions upstream of RNAi to generate H3K9me2 to recruit RITS ., Dissection of the requirements for the initial assembly of centromeric heterochromatin is greatly hampered by the inter-relatedness of the RNAi and chromatin modifying pathways and the positive feedback loops involved in full heterochromatin assembly ., Deletion of genes required for assembly of heterochromatin ablates heterochromatin , complicating analysis of whether the gene contributes to the establishment or maintenance of heterochromatin ., To define the contribution of siRNAs and H3K9me to targeting RITS to centromeres , we have generated mutants that separate Ago1 from the RITS complex 10 , that remove Chp1 from the RITS complex 21 , and mutations within the chromodomain of Chp1 6 , 22 that weaken the high affinity of Chp1s chromodomain for binding H3K9me2/3 ., Data accumulated from analysis of these mutants strongly supports that centromeric targeting of RITS critically depends on Chp1 and in particular , Chp1 chromodomains high affinity for binding H3K9me2/3 ., The tas3WG mutant separates Ago1 from Tas3-Chp1 10 ., This mutant bears a two amino acid alanine substitution of residues W265 and G266 within the conserved WG/GW Ago “hook” ( or interaction domain ) of Tas3 10 , 23 , that renders the Chp1-Tas3 subcomplex incapable of associating with Ago1 ., Surprisingly , tas3WG cells can maintain preassembled heterochromatin , most likely via retention of the subcomplexes of RITS at centromeres because of Chp1-Tas3 association with H3K9me2 and Ago1s association with centromeric siRNAs 10 ., Interestingly , following removal of all H3K9me2 and loss of heterochromatin –dependent siRNAs ( in clr4Δ backgrounds ) , tas3WG cells fail to generate de-novo heterochromatin on reintegration of clr4+ 10 ., We reasoned that genes that are particularly important for the establishment of heterochromatin would likewise be defective for heterochromatin assembly if transiently depleted in the tas3WG background ., In contrast , genes with a less critical role for heterochromatin assembly might be expected to assemble heterochromatin efficiently if transiently depleted in tas3WG cells ., Here , we directly test the contribution of proteins involved in the RNAi pathway or in methylation of H3K9 to the initiation of centromeric heterochromatin ., We find that transient depletion of any Clr-C component perturbs the establishment of heterochromatin in tas3WG cells ., In contrast , transient depletion of genes involved in the RNAi pathway does not block heterochromatin assembly in tas3WG cells ., Thus there appears to be a continuous requirement for the Clr-C complex , but not RNAi , during heterochromatin assembly in cells bearing a disrupted RITS complex ., Consistent with this , RNAi-defective cells retain low levels of the heterochromatin mark , H3K9me2 , whereas this mark is completely absent from Clr-C mutant cells ., Because RNAi-defective cells maintain residual H3K9me2 , it is not heterochromatin initiation which is being monitored following reintroduction of RNAi components , but more downstream aspects of heterochromatin assembly ., To determine if RNAi is required for the initial step in heterochromatin assembly , we additionally removed H3K9me2 from RNAi defective cells by making compound mutants with clr4Δ , and interrogated whether on re-expression of clr4+ , Clr-C could target centromeric sequences ., We found that Clr4 can promote de novo methylation of centromeric repeats when overexpressed in cells otherwise lacking Clr4 and either of the RNAi components Ago1 or Dcr1 ., Thus , Clr-C can target H3K9me2 to centromeric repeats independently of the RNAi pathway ., This data , plus our finding that ago1-deficient cells retain significant levels of H3K9me2 on centromeric repeats shows that Ago1 and Ago1 bound priRNAs are not necessary for the initiation of assembly of centromeric heterochromatin ., Instead , our data strongly indicates that RNAi-independent mechanisms function together with RNAi in the cooperative assembly of centromeric heterochromatin ., To precisely define the point of action of regulators of heterochromatin assembly , we have employed the tas3WG allele which disrupts the RITS complex ., Transient gene depletion experiments in tas3WG cells previously showed that the H3K9 methyltransferase , Clr4 , is required to establish centromeric heterochromatin when Ago1 is separated from Chp1-Tas3 10 ., Clr4 is a component of a large complex of proteins called Clr-C ., Cells lacking Clr-C components lose centromeric heterochromatin 24–29 , but the role of individual Clr-C components in heterochromatin assembly is poorly understood ., We therefore assessed the point of action of Clr-C components in heterochromatin establishment , using transient gene depletion experiments in the tas3WG background ., Rik1 was the first protein identified in complex with Clr4 30 , and it resembles the UV DNA damage binding protein , UV-DDB1 , including homology to the CPSF-A factor involved in RNA processing 31 ., Rik1 is thought to act upstream of Clr4 , and to help recruit Clr-C to chromatin , since Rik1 remains localized at centromeres in mutants that mislocalize Clr4 5 , 28 ., We tested whether transient depletion of rik1+ in tas3WG cells would prevent assembly of heterochromatin ., We introduced the tas3WG-TAP and tas3-TAP alleles into a rik1Δ strain that carries a ura4+ transgene within the outer repeats of centromere 1 ( cen::ura4+ ) ., Wild type cells efficiently assemble heterochromatin on cen::ura4+ , silencing its expression , and allowing growth on media containing the drug 5-FOA , which is toxic to cells that express ura4+ ., Cells lacking rik1+ fail to silence the centromeric transgene ., The re-establishment of centromeric heterochromatin was monitored following reintroduction of rik1+ into its normal genomic locus ., Addition of rik1+ to rik1Δ tas3-TAP cells allowed efficient establishment of heterochromatin and silencing of the cen::ura4+ transgene ( Figure 1B ) ., In contrast , on reintroduction of rik1+ into tas3WG-TAP cells , heterochromatin did not reassemble to silence the cen::ura4+ reporter ., Transcription of endogenous dg and dh centromeric repeats was measured by real time PCR in cDNA prepared from these strains ., In wild type cells , centromeric transcripts are processed by siRNA-dependent Ago1-mediated processing and by RNAi-independent turnover 32–34 ., In addition , heterochromatin that assembles on repeat sequences can reduce access of RNA polymerase , thus preventing transcript accumulation 35 ., Centromeric transcripts from dh ( Figure 1C ) and dg ( Figure 1D ) accumulate in cells lacking rik1+ , similar to cells lacking clr4+ ., On reintegration of rik1+ into tas3-TAP cells , centromeric transcripts become normally processed , resulting in no net gain in transcript levels in rik1Δ to rik1+ tas3-TAP cells relative to tas3-TAP cells ., Strikingly , both dg and dh transcript levels remain high in tas3WG cells following reintegration of rik1+ , consistent with the observed silencing defect of the cen::ura4+ reporter in these strains ., This accumulation of transcripts is at least in part due to defective processing of centromeric transcripts into siRNAs by the RNAi machinery since siRNAs were not detectable by Northern blotting in tas3WG-TAP rik1Δ to rik1+ cells whereas rik1+ reconstituted tas3-TAP cells synthesized centromeric siRNAs as efficiently as tas3-TAP cells ( Figure 1E ) ., Raf1 ( Cmc1 , Dos1 , Clr8 ) and Raf2 ( Cmc2 , Dos2 , Clr7 ) have also been identified as components of Clr-C 27 , 29 ., They are required for localization of Swi6 25 , and are important for silencing the mating type locus 26 ., raf1+ encodes a WD repeat protein which can bind Rik1 25 , and raf2+ encodes a putative Zn finger protein which binds to Pcu4 26 ., raf1Δ and raf2Δ were crossed into tas3-TAP and tas3WG -TAP backgrounds , and wild type genomic copies of raf1+ or raf2+ were reintegrated into the corresponding deletion mutants and assessed for heterochromatin assembly ., As seen for transient depletion experiments with rik1 , tas3-TAP cells efficiently re-assembled centromeric heterochromatin on reintroduction of raf1+ or raf2+ , whereas silencing of the cen::ura4+ reporter was not apparent in tas3WG-TAP backgrounds ( Figure 2A and 2B ) ., Centromeric transcripts accumulate to high levels in raf1 and raf2 deleted cells , and although transcript levels drop following reintegration of raf1+ into raf1Δ tas3-TAP cells or of raf2+ into raf2Δ tas3-TAP cells , high levels of dg and dh transcripts are maintained in both raf1+ and raf2+ reconstituted tas3WG-TAP cells ( Figure 2C and 2E , Figure S1A and S1B ) ., Consistent with this failure to suppress high levels of centromeric transcription in tas3WG-TAP cells transiently depleted for raf1+ or raf2+ , these cells fail to engage the RNAi pathway to promote destruction of centromeric transcripts into siRNAs ( Figure 2D and 2F ) ., Pcu4 is the fission yeast cullin4 , and it has been identified in complex with the UV-DDB1 E3 ubiquitin ligase 35 , 36 , and with the related Rik1 protein in the Clr-C complex 27–29 ., To define the role of Pcu4 in heterochromatin establishment , we monitored heterochromatin assembly following reintroduction of the pcu4+ gene into pcu4Δ tas3-TAP and tas3WG-TAP strains ., Clearly centromeric transcripts accumulate in pcu4Δ cells , and processing of transcripts is efficiently resumed following re-introduction of the wild type gene into tas3-TAP cells ( Figure 3A and 3B ) ., However , both dh and dg transcript levels are maintained at high levels following pcu4+ reintroduction into pcu4Δ tas3WG -TAP cells ., This failure to silence centromeric transcripts was reflected in the failure to produce abundant centromeric siRNAs in these tas3WG reconstituted cells ( Figure 3C ) ., In summary , all components of Clr-C are defective for silencing of endogenous centromeric or centromeric reporter transcripts following their transient depletion in tas3WG cells , suggesting that their continuous presence is required for the initiation of heterochromatin in RITS-defective cells ., Next we analyzed H3K9 methylation on centromeric sequences following transient depletion of components of the Clr-C complex ., In wild type cells , H3K9me accumulates to high levels on centromeric repeats through both RNAi-dependent and RNAi-independent mechanisms 15 , 30 ., In cells lacking pcu4 , H3K9me2 levels on dh sequences are not above the background seen for clr4Δ cells which lack H3K9me2 ( Figure 3D , upper panel ) ., Following pcu4+ reintroduction into pcu4Δ tas3-TAP cells , H3K9me2 levels rise to that seen in tas3-TAP cells , whereas no significant accumulation of H3K9me2 is observed in pcu4+ reconstituted tas3WG-TAP cells ., Thus Clr4 mediated H3K9 methylation is abrogated in tas3WG-TAP cells transiently depleted for pcu4 ., This methylation defect is not likely due to defective reassembly of the Clr-C complex following transient depletion of pcu4 , since H3K9 methylation resumes effectively in pcu4+ reconstituted tas3-TAP cells ., Chp1 binds H3K9me2/3 and Chp1 recruitment to centromeres is a hallmark of heterochromatin ., ChIP experiments performed with anti-Chp1 antibodies demonstrated that pcu4Δ cells are also defective for Chp1 association with centromeres , and pcu4+ reconstitution of pcu4Δ tas3-TAP but not of pcu4Δ tas3WG-TAP cells promotes Chp1 association with centromeres ( Figure 3D , lower panel ) ., We also assessed H3K9me2 levels at centromeres in other strain backgrounds ., In all Clr-C mutants ( raf2 , rik1 , raf1 ) , centromeric H3K9me2 levels were no higher than seen in clr4Δ cells ( Figure 4A and 4C ) ., Following reconstitution with raf2+ , clr4+ , rik1+ , or raf1+ , tas3-TAP cells accumulated “wild type” levels of H3K9me2 , but tas3WG-TAP cells failed to accumulate H3K9me2 at centromeres ., Very similar results were obtained for Chp1 association with centromeres ( Figure 4B and 4D ) , consistent with tas3WG-TAP cells being dependent on constitutive expression of all components of the Clr-C complex to provide H3K9me2 at centromeric sites for recruitment of Chp1 ., In sum , these experiments demonstrate that in cells where the association of Ago1 with Chp1-Tas3 has been abrogated , that reintroduction of Clr-C components is not sufficient to direct H3K9me2 accumulation on centromeric repeats ., Clr-C defective cells should still express heterochromatin independent siRNAs , and Ago1 in these cells would be expected to maintain association with primal RNAs ., Thus targeting of Ago1 by priRNAs to centromeric repeats is not sufficient to drive Clr-C recruitment to centromeres when Ago1 is physically separated from Tas3-Chp1 ., Next we examined whether RNAi components contribute to the initial steps in heterochromatin assembly ., RNAi defective cells , such as dcr1Δ , are expected to retain priRNAs but lose most of their siRNAs ., In contrast to Clr-C defective cells , dcr1Δ cells maintain low levels of H3K9me2 at centromeres ( Figure S2D ) ., Following overexpression of dcr1+ , both dcr1Δ tas3-TAP and dcr1Δ tas3WG -TAP cells efficiently assembled heterochromatin 10 ., This suggested that H3K9me2 , and not siRNA , acts at an early stage of heterochromatin initiation ., However , in these experiments it was unclear whether overexpression of dcr1+ suppressed an establishment defect in tas3WG dcr1+ reconstituted cells 10 , 14 ., We directly tested whether integration of dcr1+ into the genomic dcr1Δ locus of tas3-TAP and tas3WG-TAP cells could support reassembly of centromeric heterochromatin ., Cells lacking dcr1+ fail to silence the cen::ura4+ centromeric transgene ., Following reintegration of dcr1+ , silencing of the cen::ura4+ reporter resumed in both dcr1Δ to dcr1+ tas3-TAP and dcr1Δ to dcr1+ tas3WG-TAP cells ( Figure S2A ) ., Cells lacking dcr1+ accumulate high levels of centromeric transcripts , but following dcr1+ reintegration , centromeric transcript levels were reduced in both the dcr1+ reconstituted tas3-TAP and tas3WG-TAP cells , confirming that reintegration of dcr1+ promoted efficient assembly of centromeric heterochromatin ( Figure S2B , S2C ) ., In addition , dcr1Δ cells cannot generate siRNAs from centromeric transcripts , but on reintegration of dcr1+ , siRNA production resumed efficiently in both tas3-TAP and tas3WG-TAP backgrounds ( Figure S2D ) ., Together , these results confirmed and extended our data obtained with overexpressed dcr1+ 10 ., dcr1+ and siRNAs are not critical for Clr-C activity at centromeres , but are important for amplification of the H3K9me2 signal during later stages of heterochromatin assembly ., We next asked whether transient depletion of genes that act upstream of Dcr1 in the RNAi pathway would cause defective heterochromatin establishment ., RDRC acts upstream of Dcr1 , generating dsRNA for siRNA production ., RDRC consists of the RNA-dependent RNA polymerase ( Rdp1 ) , the RNA helicase Hrr1 , and a non-canonical poly ( A ) polymerase , Cid12 11 ., Cells lacking any component of RDRC show reduced RITS association and reduced H3K9me2 at centromeres , and have reduced siRNA production 11 , 19 , 20 ., We introduced the tas3-TAP and tas3WG-TAP alleles into deletion mutants of all components of RDRC , and then tested whether silenced chromatin assembled on the cen::ura4+ reporter following reintegration of genomic clones encoding these genes ., For cells lacking cid12+ , hrr1+ , or rdp1+ , reintegration of these genes into knockout tas3-TAP cells allowed efficient assembly of heterochromatin ., Interestingly , as seen for Dcr1 , reintroduction of the genes into the mutant tas3WG-TAP strains also supported silencing of the cen::ura4+ reporter ( Figure 5A and 5B , Figure 6A ) ., Transcript analyses performed on the RDRC reconstituted strains revealed that cells lacking RDRC components accumulate both centromeric dg and dh transcripts , but that on reconstitution with the wild type gene , dg and dh transcript levels dropped to levels close to those normally found in tas3-TAP or tas3WG-TAP cells , which is considerably less than seen in RDRC mutant cells ( Figure 5C and 5E , Figure 6C , and Figures S3A , S3B , and S4A ) ., Thus processing of centromeric transcripts is efficiently resumed following reintroduction of RDRC components into RDRC deficient tas3WG cells , and this conclusion is further supported by detection of siRNAs in RDRC reconstituted cells ( Figure 5D , 5F and Figure 6B ) ., In summary , in contrast to cells transiently depleted for Clr-C components , centromeric heterochromatin assembly can occur efficiently following the transient depletion of RDRC components or of dcr1+ in tas3WG cells ., Cells lacking dcr1+ accumulate H3K9me2 on centromeric sequences , whereas Clr-C deficient cells completely lack H3K9me2 ., We therefore asked whether cells lacking RDRC components accumulate centromeric H3K9me2 , and whether H3K9me2 levels could signal the difference in outcome , promoting heterochromatin assembly in tas3WG cells following transient depletion of RDRC , but not Clr-C components ., We assessed centromeric H3K9me2 levels in RDRC deficient cells and following reintegration of RDRC components ., In these experiments , we note that in all RDRC mutants , the level of H3K9me2 at centromeres is considerably higher than seen in cells lacking clr4+ , although at least 2 fold reduced compared with wild type cells ., Reintegration of RDRC components into the corresponding RDRC null cells supported centromeric accumulation of H3K9me2 of both tas3-TAP and tas3WG-TAP cells to levels found normally ( Figure 6D and 6E ) ., In addition , although Chp1 association with centromeres is diminished in hrr1Δ cells , reintroduction of hrr1+ into tas3WG-TAP cells promoted Chp1 association ( Figure S4B ) ., Together then this data shows that heterochromatin assembly occurs efficiently following transient depletion of genes required for siRNA synthesis , including RDRC components that act upstream of Dcr1 ., In addition , the ability of heterochromatin to reform efficiently , following transient depletion of RNAi components in tas3WG cells , correlates with the persistence of low levels of H3K9me2 on centromeric repeats in the mutant backgrounds ., Very recently it has been proposed that Ago1 is the most upstream factor in heterochromatin assembly ., It is thought to act as an acceptor for RNA degradation products , termed pri-RNAs , which , based on frequency of occurrence , preferentially target antisense transcripts resulting from bidirectional transcription of DNA repeats ., Cleavage of nascent centromeric transcripts by priRNA-directed activity of Ago1 is proposed to recruit the RDRC complex and eventually promote siRNA-dependent recruitment of RITS , and subsequent robust assembly of heterochromatin via recruitment of Clr-C 20 ., This model therefore places Ago1 as an initiator , upstream of RDRC and Dcr1 and of the Clr-C complex and H3 K9 methylation ., This model is supported by the detection of small RNAs ( priRNAs ) in dcr1Δ strains , and of siRNAs in cells lacking clr4+ , or in which heterochromatin assembly is blocked because of mutation of H3K9 , supporting that heterochromatin is not essential for small RNA generation 19 , 20 ., Finally , the model would suggest that siRNAs and priRNAs act upstream of heterochromatin assembly , and that Ago1 is the most upstream component of the RNAi pathway ., Indeed , Halic and Moazed argue that Ago1 activity is required for the initial deposition of H3K9me , since in their publication strains lacking Ago1 exhibit lower levels of centromeric H3K9me accumulation than strains deficient in other components of the RNAi pathway 20 ., To test the role of Ago1 in heterochromatin assembly , we first performed transient depletion experiments for Ago1 in the tas3WG-TAP background ( Figure 7 ) ., We integrated a genomic clone of ago1+ into ago1 null tas3-TAP and tas3WG-TAP cells , and monitored heterochromatin assembly ., In contrast to ago1 null cells , where centromeric transcripts are highly elevated ( above the levels seen in clr4Δ cells ) , reintroduction of ago1+ into either tas3-TAP or tas3WG-TAP cells reduced centromeric dh and dg transcripts to levels seen normally in tas3-TAP and tas3WG-TAP cells ( Figure 7A and Figure S5A ) ., Consistent with this suppression , we found that unlike ago1 null cells , where siRNA production is severely reduced , centromeric siRNAs are synthesized at normal levels following reintroduction of ago1+ ( Figure 7B ) ., Next , we performed ChIP experiments to monitor H3K9me2 levels in ago1Δ cells ., ago1 deletion reduces H3K9me2 accumulation at centromeres below that of wild type cells , but above that of clr4Δ cells ., On reintroduction of ago1+ , centromeric H3K9me2 levels accumulate to normal levels ( Figure 7C ) , similar to the results seen on reintegration of other RNAi components into tas3WG cells ., These data suggest that cells lacking ago1 behave similarly to other RNAi defective strains , and functionally that there is sufficient H3K9me2 in ago1Δ tas3WG cells to drive heterochromatin assembly following reintroduction of ago1+ ., We further analyzed centromeric H3K9me2 levels in RNAi defective strains ., At the 2 sites tested within centromeric dg and dh repeats , H3K9me2 levels were significantly elevated in ago1Δ cells above the background levels in clr4Δ or ago1Δ clr4Δ cells , and H3K9me2 accumulation at centromeres was similar in all RNAi deficient backgrounds tested ., This data would suggest that Ago1 , like other RNAi components , is not acting upstream of Clr-C for heterochromatin assembly ., Our demonstration that heterochromatin can assemble following transient depletion of RNAi components in tas3WG cells is suggestive that the RNAi pathway is acting downstream of Clr-C ., However , given that low levels of centromeric H3K9me2 are maintained in RNAi-defective cells , it is difficult to assess whether RNAi is required for the initial step in heterochromatin initiation ., To address this question , we removed residual H3K9me2 from RNAi defective cells by introduction of the clr4Δ allele ., We then tested whether H3K9me2 could be deposited at centromeres following expression of Clr4 in these cells that lack both Clr4 and Ago1 or Clr4 and Dcr1 ( Figure 8A ) ., Following overexpression of clr4+ in ago1Δclr4Δ cells , H3K9me2 could be detected on centromeric repeats above the background observed in clr4 null cells , and similar to levels found normally in ago1Δ cells ., Similar results were obtained following overexpression of clr4+ in dcr1Δclr4Δ cells ( Figure 8B ) ., Thus , when overexpressed , Clr4 can target centromeric repeats to initiate H3K9me2 deposition in the absence of a functional RNAi pathway ., We note , however , that reintroduction of clr4+ into its normal locus in these cells is not sufficient , in the absence of the RNAi pathway , for accumulation of detectable centromeric H3K9me2 ( data not shown ) ., Together , these experiments strongly indicate that Clr-C can initiate H3K9me deposition at centromeres via RNAi-independent mechanisms , but that cooperation between RNAi-dependent and RNAi-independent factors normally results in full heterochromatin assembly ( summarized in Figure 9 ) ., We have utilized our novel mutant , tas3WG , to identify genetic requirements for heterochromatin initiation as opposed to those required for the maintenance of pre-existing heterochromatin ., We used an approach in which genes required for heterochromatin formation are deleted and reintroduced and then heterochromatin assembly is examined ., In wild-type cells , heterochromatin can be established regardless of the factor removed , indicating that establishment mechanisms are robust to perturbations of the system ., However , in cells harboring a disrupted RITS complex , we found that the establishment of silencing becomes sensitive to the prior presence of particular silencing factors ., Our data demonstrate that the ability to assemble heterochromatin in such gene removal-restoration experiments in tas3WG cells correlates with the prior presence of H3K9me2 on centromeric repeats , but does not require the prior presence of small RNA species ., These data strongly suggest that RNAi-independent mechanisms of recruitment of Clr-C play a key role in the assembly of centromeric heterochromatin ., We also tested whether RNAi is required in an obligate manner to initiate de novo heterochromatin assembly in cells that lack any prior H3K9me ., To accomplish this , we generated clr4Δ dcr1Δ cells and found that some deposition of H3K9me2 at centromeric sequences occurred upon overexpression of clr4+ ( Figure 8C ) ., These experiments clearly demonstrate that Clr-C can function to initiate de novo centromeric heterochromatin assembly independently of the RNAi pathway ., Recently , Ago1 and its associated priRNAs have been proposed to trigger heterochromatin formation ., This notion is partly based on an observation that an ago1Δ strain had little or no H3K9me2 at centromeres , suggesting an upstream role for Ago1 20 ., If this hypothesis were correct , we anticipated that in our tas3WG system , that transient depletion of Ago1 might block heterochromatin assembly similar to what we observed for Clr4 ., In contrast , we found that tas3WG cells formed robust centromeric heterochromatin on reintroduction of ago1+ into ago1Δ cells ( Figure 7A–7C ) ., Consequently , we re-examined the reported critical Dicer-independent role for Ago1 in driving H3K9Me ., In contrast to a recent study 20 , we found that H3K9me2 levels in ago1Δ cells were no lower than in other RNAi-defective backgrounds ( Figure 7D and 7E ) ., To further probe for a potential role of priRNAs in heterochromatin initiation , we generated cells that lack both Ago1 ( which binds priRNAs ) and Clr4 , and tested whether reintroduction of Clr4 could promote de novo centromeric H3K9me2 in the absence of Ago1-priRNA targeting activities ., This experiment revealed that indeed Clr4 , when overexpressed , can initiate H3K9me2 deposition at centromeres independent of Ago1 ( Figure 8C ) ., Taken together , our data demonstrate ( 1 ) that Clr-C components can act independently of members of the RNAi pathway to initiate heterochromatin assembly and ( 2 ) the ability to promote heterochromatin assembly in tas3WG cells correlates with the prior levels of centromeric H3K9me2 in mutant backgrounds , and not the initial small RNA abundance and ( 3 ) , that Ago1-bound priRNAs are unlikely to be the key initiator of heterochromatin assembly ., The functional data that we present therefore counters the model for heterochromatin initiation proposed recently 20 , and supports that RNAi-independent factors , together with the RNAi pathway , are necessary for full heterochromatin assembly ., The conclusions that derived from our observations contrast with the widely held belief that RNAi initiates heterochromatin assembly at fission yeast centromeres ., Although it has been shown by several labs that small RNAs derived from exogenous hairpin RNAs can induce silencing of genomic loci 37 , 38 , these effects tend to be very weak and very locus specific ., In these experiments , silencing efficiency correlates with proximity to sites of heterochromatin , or is enhanced by overexpression of heterochromatin proteins ., The production of the majority of centromeric small RNAs depends on the presence of heterochromatin ., However , low levels of small RNAs are found in Clr-C deletion backgrounds 12 , 20 or in histone H3K9R mutant cells 19 ., Interestingly , Clr-C mutants that completely lack H3K9me are deficient | Introduction, Results, Discussion, Materials and Methods | Formation of centromeric heterochromatin in fission yeast requires the combined action of chromatin modifying enzymes and small RNAs derived from centromeric transcripts ., Positive feedback mechanisms that link the RNAi pathway and the Clr4/Suv39h1 histone H3K9 methyltransferase complex ( Clr-C ) result in requirements for H3K9 methylation for full siRNA production and for siRNA production to achieve full histone methylation ., Nonetheless , it has been proposed that the Argonaute protein , Ago1 , is the key initial trigger for heterochromatin assembly via its association with Dicer-independent “priRNAs . ”, The RITS complex physically links Ago1 and the H3-K9me binding protein Chp1 ., Here we exploit an assay for heterochromatin assembly in which loss of silencing by deletion of RNAi or Clr-C components can be reversed by re-introduction of the deleted gene ., We showed previously that a mutant version of the RITS complex ( Tas3WG ) that biochemically separates Ago1 from Chp1 and Tas3 proteins permits maintenance of heterochromatin , but prevents its formation when Clr4 is removed and re-introduced ., Here we show that the block occurs with mutants in Clr-C , but not mutants in the RNAi pathway ., Thus , Clr-C components , but not RNAi factors , play a more critical role in assembly when the integrity of RITS is disrupted ., Consistent with previous reports , cells lacking Clr-C components completely lack H3K9me2 on centromeric DNA repeats , whereas RNAi pathway mutants accumulate low levels of H3K9me2 ., Further supporting the existence of RNAi–independent mechanisms for establishment of centromeric heterochromatin , overexpression of clr4+ in clr4Δago1Δ cells results in some de novo H3K9me2 accumulation at centromeres ., These findings and our observation that ago1Δ and dcr1Δ mutants display indistinguishable low levels of H3K9me2 ( in contrast to a previous report ) challenge the model that priRNAs trigger heterochromatin formation ., Instead , our results indicate that RNAi cooperates with RNAi–independent factors in the assembly of heterochromatin . | Centromeres are the chromosomal regions that promote chromosome movement during cell division ., They consist of repetitive DNA sequences that are packaged into heterochromatin ., Disruption of centromeric heterochromatin leads to chromosome loss that can result in miscarriages and genetic disorders ., We have sought to define the precise steps leading to heterochromatin assembly using fission yeast as the model system ., To accomplish this we employed our novel Tas3WG mutant strain that can propagate preassembled heterochromatin but cannot support its de novo establishment ., Current models suggest that small RNAs initiate heterochromatin assembly by targeting the RNAi machinery and subsequently the Clr-C chromatin-modifying complex to the centromere ., Here , we demonstrate that transient depletion of components of the RNAi pathway that generate or bind small RNAs does not perturb heterochromatin assembly in our Tas3WG strain ., Instead , transient depletion of the Clr-C complex blocks heterochromatin assembly , suggesting a critical role for continuous Clr-C activity during heterochromatin assembly in Tas3WG cells ., We have directly tested whether Clr-C can target centromeres when expressed in cells deficient for RNAi and Clr-C ., We find that RNAi–independent recruitment of Clr-C can occur and likely contributes to the critical initiating mechanisms of heterochromatin assembly . | molecular biology/histone modification, molecular biology/rna-protein interactions, molecular biology/centromeres, molecular biology/chromosome structure, genetics and genomics/chromosome biology, genetics and genomics/epigenetics, molecular biology/chromatin structure | null |
journal.pgen.1004731 | 2,014 | Multiple Regulatory Systems Coordinate DNA Replication with Cell Growth in Bacillus subtilis | DNA replication must be coordinated with the cell cycle to ensure proper genome inheritance ., For many bacteria cellular physiology dictates the rate of growth and division ., In nutrient-rich media that support rapid growth rates , bacteria synthesize DNA more rapidly by increasing the frequency of DNA replication initiation 1–3 ., This control system is termed nutrient-mediated growth rate regulation and although it has been appreciated for decades , the molecular mechanisms that connect cell physiology with DNA replication initiation remain debatable ., Historically it has been thought that there is a constant cell mass or cell size at the time of bacterial DNA replication initiation and it has been proposed that a positive regulator would accumulate in a growth-dependent manner to trigger DNA replication initiation when cells attained a critical size 4 ., However , modern quantitative analysis of single bacterial cells within steady-state populations has shown that the relationship between DNA replication initiation and cell mass is variable , indicating that the control for timing of DNA replication initiation is not governed by a direct connection with mass accumulation 5 ., DnaA is the master bacterial DNA replication initiator protein and is a candidate factor to connect cell physiology with DNA synthesis ., DnaA is a member of the AAA+ family of ATPases and shares homology with archaeal and eukaryotic initiator proteins ., DnaA directly stimulates DNA replication initiation from a single defined origin of replication ( oriC ) once per cell cycle ., Multiple ATP-bound DnaA molecules bind to an array of recognition sequences ( DnaA-box 5′-TTATCCACA-3′ ) within oriC where they assemble into a helical filament that promotes duplex DNA unwinding 6 , 7 ., Studies in Escherichia coli have suggested that the amount of ATP-bound DnaA dictates the rate of DNA replication initiation ., Artificial overexpression of DnaA increases the frequency of DNA replication initiation 8 , 9 ., Conversely , decreasing the amount of DnaA per cell by synthetically promoting early cell division delays DNA replication initiation and modest increases in DnaA levels alleviate this delay , supporting the view that growth-dependent accumulation of DnaA is the trigger for replication initiation in E . coli 10 ., However , it remains uncertain whether the amount of ATP-bound DnaA is the primary regulator that coordinates DNA replication initiation with cell growth in wild-type E . coli cells 11 ., In contrast to E . coli , studies in Bacillus subtilis have suggested that the amount of DnaA may not dictate the rate of DNA replication initiation ., Artificially decreasing cell size by stimulating cell division ( thereby lowering the amount DnaA per cell to ∼70% of wild-type ) did not affect DNA replication initiation 10 ., Moreover , results from overexpression of DnaA in B . subtilis are not clear ., Increased expression of DnaA alone causes cell elongation , cell growth inhibition , and induction of the SOS DNA damage response due to depletion of DnaN because of autoregulation of the dnaA-dnaN operon by DnaA 12 ., To circumvent this problem DnaA was co-overexpressed with DnaN , and under this condition DNA replication initiation does increase 12 ., However , subsequent experiments demonstrated that overexpression of DnaN alone increases DNA replication initiation by repressing the activity of the regulatory protein YabA ( an inhibitor of DnaA ) 13 , suggesting that this could account for the effect on DNA replication initiation when DnaA and DnaN were co-overexpressed ., In this study we have investigated nutrient-mediated growth rate control of DNA replication initiation in B . subtilis ., We find that changes in DnaA protein level are not sufficient to account for nutrient-mediated growth rate regulation of DNA replication initiation , although this regulation does require both DnaA and oriC ., We then present evidence suggesting that multiple regulatory systems are involved in coordinating DNA synthesis with cell physiology , and that depending on the nature of the growth limitation , control of DNA replication acts through either oriC-dependent or oriC-independent mechanisms ., Steady-state bacterial growth rates can be manipulated by culturing cells in media that contain differing amounts of nutrients , with rich media supporting faster growth because resources are not required to synthesize cellular building blocks de novo ., In response to different nutrient-mediated steady-state growth rates , bacteria control DNA synthesis by varying the frequency of DNA replication initiation while maintaining a constant speed of elongation 1–3 , 14 ., The rate of DNA replication initiation can be determined by marker frequency analysis ( i . e . - measuring the ratio of DNA at the replication origin ( ori ) versus the replication terminus ( ter ) using quantitative PCR ) , and Figure 1A shows the positive correlation between DNA replication initiation and nutrient-mediated growth rates ( cell doublings per hour measured using spectrophotometry ) ., It is important to state that experimental approaches which change bacterial growth rates without altering the chemical composition of the cell ( e . g . – varying temperature ) do not influence the rate of DNA replication initiation ( Figures 1B , S1; 15 , 16 ) ., Thus , varying nutrient availability modulates bacterial physiology , in turn affecting cell growth and DNA replication initiation 3 ., It has been reported that DnaA protein level determines the frequency of DNA replication initiation in E . coli 8 , 9 , therefore we wondered whether the amount of DnaA could account for nutrient-mediated growth rate regulation of DNA replication initiation in B . subtilis 14 ., Western blot analysis shows that DnaA concentration increases with faster steady-state growth rates ( Figure 1C; the tubulin homolog FtsZ was used as a loading control because its concentration is growth-rate independent 17 , 18 ) ., Since B . subtilis cell size increases as a function of growth rate , the number of DnaA molecules would also be greater in larger cells formed during fast growth ( Figure S2 ) 14 ., This conclusion is in agreement with absolute quantification of DnaA proteins per cell determined at different growth rates using mass spectrometry ( 163-337 molecules at 0 . 5 doublings/hr; 875-1791 molecules at 1 . 0 doublings/hr ) 18 ., These results indicate that the amount of DnaA protein could account for nutrient-mediated growth rate regulation of DNA replication initiation in B . subtilis ., To directly test whether the amount of DnaA protein determines the rate of DNA replication initiation , the endogenous dnaA gene was placed under the control of an IPTG-inducible promoter ( this also alleviated autoregulation of the dnaA-dnaN operon 12 ) ., At near wild-type DnaA levels growth rates were normal , ori:ter ratios were unchanged , and the distribution of origin regions per cell visualized using a TetR-YFP/tetO reporter system was equivalent to wild-type ( Figures 2A-C , S4A ) ., In contrast , when the amount of DnaA fell significantly ( between ∼50–30% of wild-type , depending upon the media ) , growth rates slowed , ori:ter ratios dropped , DNA replication was inhibited as judged by origin region localization , and cells became elongated ( Figures 2A–C , S4A ) ., As noted above dnaA is located in an operon upstream of dnaN ( encoding the sliding clamp component of the replisome ) in B . subtilis , and Western blot analysis confirmed that the level of DnaN correlated with the level of DnaA ( Figure S3B ) ., Depletion of DnaN can cause replication fork stalling and induction of the SOS DNA damage response , which likely contributes to the slow growth and cell elongation phenotypes observed at low IPTG concentrations 12 ., However , replication fork stalling would also be expected to cause an increase in the ori:ter ratio , suggesting that the observed decreases may be an overestimate of the true initiation frequency ., We conclude that wild-type DnaA levels are necessary to achieve the proper frequency of DNA replication initiation at both slow and fast steady-state growth rates ., Only modest overexpression of DnaA could be achieved using the IPTG-inducible promoter ( Figure 2A ) , much lower than the changes in DnaA concentration observed at different nutrient-mediated growth rates ( Figure 1C ) ., Therefore , to further increase DnaA protein levels a second copy of the dnaA gene was integrated at an ectopic locus under the control of a xylose-inducible promoter ( again the endogenous dnaA-dnaN operon was expressed using an IPTG-inducible promoter to avoid autorepression ) ., This strain was grown in media that supported a slow growth rate and varying amounts of xylose were added to induce DnaA ( >10 fold overexpression was achieved , which was in the range observed for different nutrient-mediated growth rates; Figures 2E , 4D ) ., When DnaA levels were elevated ∼2–4 fold a modest increase in the ori:ter ratios was observed , although critically the resulting initiation frequencies remained well below the rate generated in rich media ( Figures 2D , S4B ) ., These results indicate that changes in DnaA protein levels are not sufficient to account for nutrient-mediated growth rate regulation of DNA replication initiation in B . subtilis ., Surprisingly , further overexpression of DnaA lead to a dramatic decrease in the ori:ter ratios ., To determine whether this inhibition was specific , DnaA was overexpressed in a strain where oriC was inactivated by partial deletion ( ΔoriC ) , the endogenous dnaA-dnaN operon was expressed using a constitutive promoter to avoid autorepression , and genome replication was driven by a plasmid-derived replication origin ( oriN; integrated ∼1 kb to the left of oriC ) that is recognized and activated by its cognate initiator protein ( RepN ) ., It is important to note that while initiation at oriN does not require either oriC or DnaA , the downstream B . subtilis initiation proteins DnaD , DnaB and DnaC ( helicase ) are necessary for oriN activity 19 ., Therefore , if overexpression of DnaA was either inhibiting the expression of genes required for DNA replication ( e . g . – nucleotide biosynthesis 20 ) or sequestering essential replication factors , then DNA replication initiation from oriN would be expected to decrease ., However , overexpression of DnaA in the ΔoriC oriN+ background did not alter ori:ter ratios , showing that high overexpression of DnaA specifically inhibits DNA replication initiation at oriC ( Figure S4C ) ., We hypothesized that nutrient-mediated growth rate control of DNA replication initiation could act via regulation of DnaA activity rather than protein abundance ., There are two known trans-acting regulators of B . subtilis DnaA during steady-state growth , Soj and YabA ., Soj is a dynamic protein that can act as either a negative or a positive regulator of DnaA , depending upon its quaternary state 21–23 ., YabA is a negative regulator of DnaA that forms a protein bridge between the initiator DnaA and the DNA polymerase sliding clamp processivity factor , DnaN , and is thought to inhibit DNA replication by spatially sequestering DnaA away from the replication origin and by inhibiting DnaA oligomerization 24–27 ., Interestingly , the number of both proteins per cell was found to positively correlate with growth rate 18 ., To determine whether either of these regulatory proteins is required for nutrient-mediated growth rate regulation of DNA replication initiation , single knockout mutants were cultured in a range of media and analyzed using marker frequency analysis ., It was found that both of the mutant strains retained the ability to coordinate DNA replication initiation with nutrient-mediated changes in growth rate ( Figures 3A , S5A ) ., To test whether Soj and YabA acted redundantly to control the nutrient-mediated activity of DnaA , the double mutant was constructed and analysed by marker frequency analysis ., Again proper regulation of DNA replication initiation was maintained in the Δsoj ΔyabA mutant , indicating that neither regulatory protein is required ( Figures 3A , S5B ) ., Interestingly , both the single and double mutants displayed a reduced growth rate in rich media , suggesting that the burden of overactive DNA replication initiation may be exacerbated during multifork replication ., In the case of the soj mutant it is also possible that the slow growth phenotype is related to its role in chromosome origin segregation 28–30 ., We also determined whether the alarmone ( p ) ppGpp , a small molecule induced during nutrient limitation , is required for nutrient-mediated growth rate regulation of DNA replication initiation in B . subtilis ., Marker frequency analysis was performed on a strain lacking the three known ( p ) ppGpp synthases ( RelA , YwaC , YjbM ) ., It was found that regulation of DNA replication initiation was unaffected by the absence of ( p ) ppGpp , suggesting that ( p ) ppGpp is not involved in the regulatory mechanisms coordinating DNA replication with nutrient availability during steady-state cell growth ( Figures 3B , S5C ) ., Since both overexpression of DnaA and deletion of DnaA regulatory proteins did not alter nutrient-mediated growth rate regulation of DNA replication initiation , it was unclear whether DnaA was actually a component of this system ., To determine whether DnaA activity at oriC is required , marker frequency analysis was performed in a strain where oriC was inactivated ( ΔoriC ) and DNA replication initiates from the plasmid-derived oriN ., It was found that ori:ter ratios remain constant over a wide range of growth rates in the ΔoriC mutant , indicating that nutrient-mediated growth rate regulation of DNA replication was lost ( Figures 4A , S6A ) ., Critically , DNA replication initiation from oriC is unaffected by the addition of oriN ( Figures 4B , S6B ) ., This shows that it is the absence of DnaA activity at oriC , rather than the presence of oriN , which accounts for the loss of nutrient-mediated growth rate regulation in the ΔoriC mutant ., However , it could not be concluded whether the ΔoriC mutation acted by removing replication origin function or by deleting a site that is required for the nutrient-mediated growth rate regulation ., Therefore , a mutation was introduced into dnaA that alters the critical “arginine finger” residue ( Arg264→Ala ) , thereby disabling DnaA filament assembly and initiation activity ( note that a DnaA arginine finger mutant remains competent for DNA binding and ATP binding ) 22 , 31 ., Again the dnaAR264A mutant strain contains oriN in order to maintain viability ., Like the ΔoriC mutant the DnaAR264A variant also lost growth rate regulation in response to nutrient availability , indicating that DnaA activity at oriC is necessary for growth rate regulation in B . subtilis ( Figures 4C , S6C ) ., Moreover , since the ori:ter ratios of the ΔoriC and dnaAR264A mutants remains constant during nutrient-mediated growth rate changes and since DNA replication elongation speed is independent of the nutrient-mediated growth rate 2 , 14 , the results suggest that within a population of cells the average frequency of DNA replication initiation at oriN is independent of the nutrient-mediated growth rate ., Western blot analysis showed that in rich media there was less DnaA and DnaN in the ΔoriC strain , whereas conversely there was more DnaA and DnaN in the dnaAR264A mutant ( Figure S3C ) ., The latter result suggests that autoregulation of the dnaA promoter requires ATP-dependent filament formation by DnaA , however , the former result was more puzzling ., To investigate this further the amount of DnaA in the ΔoriC strain was determined over a range of nutrient-mediated growth rates ., While the concentration of DnaA was observed to increase as a function of growth rate in the wild-type strain , DnaA levels did not display the same correlation with growth rate in the ΔoriC mutant ( Figure 4D ) ., This suggests that in the ΔoriC mutant nutrient-mediated growth rate-dependent expression of DnaA is lost either because DNA replication initiation from oriN is constitutive or because the deletion within oriC affects dnaA expression ( although this region is downstream of the dnaA gene ) ., The apparent decrease in growth rates observed for strains initiating DNA replication solely through oriN , particularly in rich media ( Figures 4A , 4C , S6A , S6C ) , is likely due to the formation of cells lacking DNA as a direct consequence of decoupling DNA replication initiation from growth rate ( Figure 4E ) ., This result underscores the importance of growth rate regulation of DNA replication initiation to ensure bacterial fitness ., Since nutrient-mediated growth rate regulation of DNA replication initiation did not appear to act through either DnaA protein accumulation or known DnaA regulators , we considered alternative possibilities for how DNA replication could be connected to cell physiology ., One hypothesis was that this regulation could be linked to metabolism , either through the amount of a metabolic intermediate or the activity of a critical enzyme ., Genetic evidence has suggested a relationship between several glycolytic enzymes and DNA replication in B . subtilis and E . coli 32 , 33 , but it has not been established whether these connections act directly at the level of DnaA-dependent initiation ., ATP would be another rational candidate since DnaA is an ATP-dependent protein , but it has been found that the concentration of ATP in B . subtilis ( as well as in E . coli ) is invariant over a wide range of growth rates ( L . Krasny and R . Gourse , personal communication; 34 , 35 ) ., Another hypothesis was that this regulation could be linked to the synthesis of an essential cellular complex , such as the ribosome or the cell membrane 36 , 37 ., In this way a bacterial cell would integrate nutritional information based on the availability of multiple substrates required to construct such macromolecules ., In order to identify possible routes through which nutrient availability could impact DNA replication we analyzed a range of genetically altered strains , targeting respiration , central carbon metabolism , protein synthesis , fatty acid synthesis , and phospholipid synthesis ( Table 1 ) , that all manifest decreased steady-state growth rates in rich complex media ., Genes were either disrupted by antibiotic cassettes or depleted using regulated expression systems; importantly , depletion of essential genes was not lethal under the experimental conditions used ., Knock-out strains were compared to wild-type while depletion strains were analyzed without and with inducer ( indicated in Figures 5 , 6 , S7 , S8 with “-” and “+” , respectively ) ., DNA replication was measured using marker frequency analysis ., Strikingly , in all of the strains examined the ori:ter ratio decreased to match the slower growth rates caused by gene disruption/depletion ( Figures 5–6 , S7–S8; black symbols ) ., The uniform response of DNA replication in slow growing mutants suggested that a single mechanism might account for this regulation , in accord with nutrient-mediated regulation of DNA replication initiation ( Figures 4 , S6 ) ., To examine this hypothesis the deletion and depletion strains were crossed into the ΔoriC strain that initiates DNA replication using oriN ., For several mutants ( ndh , gapA , pdhB , fabHA , plsC and ltaS ) the ori:ter ratio was not significantly affected ( ≤1/10 of the percentage decrease observed for oriC+ ) , suggesting that the regulatory signal specifically targeted DNA replication initiation at oriC ( Figures 5 , S7; red symbols ) ., However , there were a number of mutants ( pykA , pgsA , and multiple ribosomal protein genes ) that produced a marked decrease in the ori:ter ratio of the ΔoriC strain ( ≥1/2 of the percentage decrease observed for oriC+ ) , suggesting that in these cases DNA replication was being regulated through an oriC-independent mechanism ( Figures 6A–C , S8A-C; red symbols ) ., Interestingly , in some cases manipulation of different genes within a single biological pathway ( e . g . – carbon metabolism or phospholipid synthesis ) resulted in the regulation of DNA replication through the different regulatory systems ., Since the ΔoriC oriN+ strain does not require DnaA activity to initiate DNA replication , it suggested that the observed oriC-independent growth rate regulation might be DnaA-independent ., To address this possibility the oriC-independent mutants ( Figures 6A–C , S8A–C ) were crossed into a ΔdnaA strain that initiates DNA replication using oriN ( Figure S3C ) ., When PykA was depleted in the ΔdnaA mutant the ori:ter ratio no longer decreased , indicating that DnaA was required for this response , although apparently not for its role in origin recognition and DNA unwinding ( Figures 6D , S8D; green symbols ) ., In contrast , when either ribosomal genes were deleted or PgsA was depleted in the ΔdnaA mutant the ori:ter ratios did decrease , suggesting that DnaA-independent mechanisms act under these conditions ( Figures 6E–F , S8E–F; green symbols ) ., Taken together , the genetic analysis reveals that in B . subtilis there is likely more than one regulatory system linking DNA replication with cell growth ., The importance of growth rate regulation of DNA replication in response to nutrient availability is self-evident , but the biological relevance of growth rate regulation of DNA replication in response to genetic manipulations is less clear ., To address this issue we evaluated the response of DNA replication to sublethal concentrations of antibiotics that produced slow steady-state growth rates ., We chose small molecules that inhibit either fatty acid synthesis ( cerulenin ) or protein synthesis ( chloramphenicol ) because our genetic analyses indicated that the former regulated DNA replication through oriC while the latter acted independently of both oriC and DnaA ., Incubation with either antibiotic caused a decrease in the ori:ter ratios in the wild-type strain , showing that growth rate regulation of DNA replication in response to genetic perturbations of essential cellular activities is physiologically relevant ( Figures 7A–B , S9A–B; black symbols ) ., To further assess whether changes in DNA replication caused by these small molecules reflected the results using genetic approaches , the ΔoriC oriN+ strain was analyzed ., Only chloramphenicol elicited a significant decrease in the ori:ter ratios in the ΔoriC strain ( Figures 7A–B , S9A–B; red symbols ) ., Finally , the ΔdnaA oriN+ strain was analyzed in the presence of chloramphenicol and again the ori:ter ratio decreased ( Figures 7B , S9B ) ., This result is fully consistent with the data from ribosomal gene deletions and indicates that regulation of DNA replication in response to perturbed ribosome activity is DnaA-independent ., We have found that nutrient-mediated growth rate regulation of DNA replication initiation in B . subtilis requires both DnaA and oriC ., To our knowledge this is the first time that a specific DNA replication initiation protein has been shown to play an essential role in this regulatory system , and because DnaA is the earliest acting initiation factor we propose that DnaA is the target for the nutrient-mediated growth rate regulatory system ., Critically however , we show that changes in DnaA protein level are not sufficient to account for nutrient-mediated growth rate regulation of DNA replication initiation in B . subtilis ., This is in contrast to the generally accepted mechanism for control of bacterial DNA replication initiation based on work using E . coli 8 , 9 ., B . subtilis contains a bipartite origin that flanks the dnaA gene ( incA and incB regions containing the dnaA promoter lie 1 . 3 kb upstream of the incC region which contains the DNA unwinding element ) 38 ., When the expression of the dnaA-dnaN operon was placed under the control of the inducible Pspac promoter in order to test the effect of DnaA overexpression on DNA replication initiation , a ∼9kb plasmid was recombined upstream of dnaA by single cross-over ., Therefore , integration of this vector resulted in the considerable displacement of the two origin regions from one another without any significant consequence ., It will be extremely interesting to determine the maximum and minimum distances that the inc regions can be moved , as well as ascertaining the role of the upstream region in DNA replication initiation ., We have shown that neither of the known DnaA regulatory proteins present during vegetative growth , Soj and YabA , are required for nutrient-mediated growth rate regulation of DNA replication initiation ., We have also determined that the alarmone ( p ) ppGpp is not required for this regulation , consistent with a previous report that induction of the stringent response inhibits DNA replication elongation but not initiation in B . subtilis 39 , 40 ., This result marks an apparent distinction between the role of ( p ) ppGpp in B . subtilis and in proteobacteria such as E . coli and Caulobacter crescentus where ( p ) ppGpp has been shown to regulate DNA replication initiation 41 , 42 ., The use of genetic manipulations and small molecule inhibitors presented here reinforce and extend previously observed connections for bacterial DNA replication with central carbon metabolism 32 , 33 and with phospholipid synthesis 43 , 44 ., In addition our work identifies new links for B . subtilis DNA replication with respiration , fatty acid synthesis , lipoteichoic acid synthesis , and ribosome biosynthesis ., The results indicate that growth rate regulation of DNA replication in B . subtilis can be controlled through either oriC-dependent , oriC-independent/DnaA-dependent , or oriC-independent/DnaA-independent mechanisms ( summarized in Figure 7C ) ., Based on these novel findings we propose that multiple systems coordinate DNA replication with bacterial cell growth , with distinct regulators responding to diverse physiological and chemical changes ., This model deviates from the long-standing concept of a single universal cellular property utilized to link bacterial DNA replication with cell growth 4 ., Since nutrient-mediated growth rate regulation of DNA replication initiation requires DnaA activity at oriC , this suggests that factors affecting DNA synthesis through an oriC-independent mechanism ( PykA , PgsA , and ribosomal proteins ) are unlikely to be responsible for the nutrient sensing system ., We suspect that nutrient-mediated regulation may be influenced by more than one control system , thereby forming a robust network capable of integrating information from multiple metabolic and cellular sources ., We note that for deletion/depletion mutants regulating DNA replication through oriC-dependent and oriC-independent/DnaA-dependent mechanisms , ori:ter ratios either remained constant or slightly increased in the ΔoriC and ΔdnaA strains , respectively ( Figures 5 , 6D , S7 , S8D ) ., Because the average initiation frequency of oriN appears to be growth rate independent ( Figures 4A , 4C , S6A , S6C ) , the measured ori:ter ratios of these strains indicates that replication elongation speeds are either not being affected or are slightly decreasing ., Therefore , for both oriC-dependent and oriC-independent/DnaA-dependent regulatory mechanisms , the observed decrease in ori:ter ratios in the oriC+ strain most likely reflects inhibition of DNA replication initiation ., In contrast , for the oriC-independent/DnaA-independent mutants where the ori:ter ratio was decreased when DNA replication initiated from oriN , this difference could be due to a change in DNA replication elongation ( although this would mean that the elongation speed was increased ) ., Our current aim is to determine the molecular mechanisms underlying each system that coordinates DNA replication with cell growth ., We hypothesize that the oriC-dependent regulatory system targets DnaA activity at oriC ., We speculate that the oriC-independent/DnaA-dependent regulatory system could influence DNA replication initiation by affecting the abundance or activity of a downstream replication initiation factor ., For example , DnaA is also a transcription factor that is thought to directly regulate the expression of>50 genes , including dnaB 20 , 45 ., Alternatively , DnaA could act by titrating initiation factors away from oriN ., Lastly , in the case of the oriC-independent/DnaA-independent regulatory system it needs to be established whether DNA replication is affected at the step of initiation or elongation , after which the role of appropriate candidate proteins can be investigated ., Strains are listed in Table S1 ., Plasmids are listed in Table S2 and Table S3 ., Nutrient agar ( NA; Oxoid ) was used for routine selection and maintenance of both B . subtilis and E . coli strains ., For experiments in B . subtilis cells were grown using a range of media ( using the following concentrations unless otherwise noted ) : Luria-Bertani ( LB ) medium , Antibiotic 3 ( PAB ) medium , Brain-Heart Infusion ( Bacto ) , or defined minimal medium base ( Spizizen minimal salts supplemented with Fe-NH4-citrate ( 1 µg/ml ) , MgSO4 ( 6 mM ) , CaCl2 ( 100 µM ) , MnSO4 ( 130 µM ) , ZnCl2 ( 1 µM ) , thiamine ( 2 µM ) ) supplemented with casein hydrolysate ( 200 µg/ml ) and/or various carbon sources ( succinate ( 1% ) , glycerol ( 0 . 5% ) , glucose ( 0 . 5% ) ) ., Supplements were added as required: tryptophan ( 20 µg/ml ) , phenylalanine ( 40 µg/ml ) , chloramphenicol ( 5 µg/ml ) , erythromycin ( 1 µg/ml ) , kanamycin ( 2 µg/ml ) , spectinomycin ( 50 µg/ml ) , tetracycline ( 10 µg/ml ) , zeocin ( 10 µg/ml ) ., Unless otherwise stated all chemicals and reagents were obtained from Sigma-Aldrich ., Sodium azide ( 0 . 5%; Sigma ) was added to exponentially growing cells to prevent further metabolism ., Chromosomal DNA was isolated using a DNeasy Blood and Tissue Kit ( Qiagen ) ., The DNA replication origin ( oriC ) region was amplified using primers 5′-GAATTCCTTCAGGCCATTGA-3′ and 5′-GATTTCTGGCGAATTGGAAG-3′; the DNA replication terminus ( ter ) region was amplified using primers 5′-TCCATATCCTCGCTCCTACG-3′ and 5′-ATTCTGCTGATGTGCAATGG-3′ ., Either Rotor-Gene SYBR Green ( Qiagen ) or GoTaq ( Promega ) qPCR mix was used for PCR reactions ., Q-PCR was performed in a Rotor-Gene Q Instrument ( Qiagen ) ., By use of crossing points ( CT ) and PCR efficiency a relative quantification analysis ( ΔΔCT ) was performed using Rotor-Gene Software version 2 . 0 . 2 ( Qiagen ) to determine the origin:terminus ( ori:ter ) ratio of each sample ., These results were normalized to the ori:ter ratio of a DNA sample from B . subtilis spores which only contain one chromosome and thus have an ori/ter ratio of 1 ., To visualize cells during exponential growth starter cultures were grown overnight and then diluted 1∶100 into fresh medium and allowed to achieve at least three doublings before observation ., Cells were mounted on ∼1 . 2% agar pads ( 0 . 25× minimal medium base ) and a 0 . 13–0 . 17 mm glass coverslip ( VWR ) was placed on top ., To visualize individual cells the cell membrane was stained with either 2 µg/ml Nile Red ( Sigma ) or 0 . 4 µg/ml FM5-95 ( Molecular Probes ) ., To visualize nucleoids DNA was stained with 2 µg/ml 4′-6-diamidino-2-phenylindole ( DAPI ) ( Sigma ) ., Microscopy was performed on an inverted epifluorescence microscope ( Nikon Ti ) fitted with a Plan-Apochromat objective ( Nikon DM 100x/1 . 40 Oil Ph3 ) ., Light was transmitted from a 300 Watt xenon arc-lamp through a liquid light guide ( Sutter Instruments ) and images were collected using a CoolSnap HQ2 cooled CCD camera ( Photometrics ) ., All filters were Modified Magnetron ET Sets from Chroma and details are available upon request ., Digital images were acquired and analysed using METAMORPH software ( version V . 6 . 2r6 ) ., Analysis was performed using ImageJ software: foci counting utilized the particle analysis plugin; cell lengths | Introduction, Results, Discussion, Materials and Methods | In many bacteria the rate of DNA replication is linked with cellular physiology to ensure that genome duplication is coordinated with growth ., Nutrient-mediated growth rate control of DNA replication initiation has been appreciated for decades , however the mechanism ( s ) that connects these cell cycle activities has eluded understanding ., In order to help address this fundamental question we have investigated regulation of DNA replication in the model organism Bacillus subtilis ., Contrary to the prevailing view we find that changes in DnaA protein level are not sufficient to account for nutrient-mediated growth rate control of DNA replication initiation , although this regulation does require both DnaA and the endogenous replication origin ., We go on to report connections between DNA replication and several essential cellular activities required for rapid bacterial growth , including respiration , central carbon metabolism , fatty acid synthesis , phospholipid synthesis , and protein synthesis ., Unexpectedly , the results indicate that multiple regulatory systems are involved in coordinating DNA replication with cell physiology , with some of the regulatory systems targeting oriC while others act in a oriC-independent manner ., We propose that distinct regulatory systems are utilized to control DNA replication in response to diverse physiological and chemical changes . | DNA replication must be coordinated with cellular physiology to ensure proper genome inheritance ., Model bacteria such as the soil-dwelling Bacillus subtilis can achieve a wide range of growth rates in response to nutritional and chemical signals ., In order to match the rate of DNA synthesis to the rate of nutrient-mediated cell growth , bacteria regulate the initiation frequency of DNA replication ., This control of bacterial DNA replication initiation was first observed over forty years ago , however the molecular basis for this regulation has remained hotly debated ., In this paper we test one of the leading models for nutrient-mediated growth rate regulation in bacteria , namely that the abundance of the master DNA replication initiation protein DnaA dictates the frequency of DNA replication events ., Critically , our results show that changes in DnaA protein level are not sufficient to account for nutrient-mediated growth rate regulation of DNA replication initiation in B . subtilis ., We then go on to show that there are strong connections between DNA replication and several essential cellular activities , which unexpectedly indicates that there is likely more than one single regulatory pathway involved in coordinating DNA replication with cell physiology ., We believe that our work changes thinking regarding this long-standing biological question and reinvigorates the search for the molecular basis of these critical regulatory systems . | bacteriology, gram positive bacteria, biochemistry, developmental biology, dna replication, bacterial genes, genetics, biology and life sciences, dna, microbial growth and development, molecular genetics, microbiology, bacterial growth | null |
journal.pcbi.1005222 | 2,016 | Modeling Cellular Noise Underlying Heterogeneous Cell Responses in the Epidermal Growth Factor Signaling Pathway | Intracellular signaling pathways must respond appropriately to various signals from the external environment ., However , a variety of noise inside and outside of the cells can evoke heterogeneous responses in individual cells even when exposed to the same stimuli 1 , 2 ., Although such heterogeneity interferes with a precise signaling response , it often plays essential roles in biological functions ., Examples include diverse responses between amoeba cells that can undergo collective chemotaxis , and enhancement of signal entrainment in NF-κB response over a wider range of dynamic inputs by cellular noise 3 , 4 ., Cellular noise is categorized into intrinsic and extrinsic noise 5–7 ., Intrinsic noise is generally evoked by small numbers of molecules , representing fluctuations in biochemical reactions , transcriptional noise , molecular diffusion , etc ., One of the best-known examples is the stochastic gene expression in Escherichia coli 5 ., On the other hand , extrinsic noise is defined by the differences in amounts of proteins in individual cells ( protein variability ) and external physical environments , such as cell-to-cell contact , cell cycle phase and cell shape ., The relationships between heterogeneous cellular responses and extrinsic noise in various signaling pathways have been reported 4 , 8–10 ., However , the mechanistic effects of these types of noise to determine heterogeneity of signal responses are unclear ., In this study , using mathematical modeling and simulations , we determined how cellular noise regulates heterogeneous cell responses , focusing on the epidermal growth factor ( EGF ) signaling pathway as an example ., The EGF signaling pathway regulates cell growth , proliferation , differentiation , and apoptosis 11 , 12 ., EGF ligands bind to EGF receptors ( EGFR ) , and the signal is transmitted to an intracellular biochemical reaction network ., This signal transduction eventually phosphorylates extracellular signal-regulated kinase ( ERK ) and causes transient accumulation of ERK at the nucleus 13 , 14 ., The EGF dose response of phosphorylated ERK shows a graded response 10 , 15–17 ., However , it was recently reported that the dose response of nuclear ERK activity is in fact switch-like 14; a threshold mechanism regulated by ERK may be involved in cell fate decision ., The switch-like behavior is more sensitive to perturbations than the graded response 18 , and hence the effect of biological noise is considered to be critical to determine nuclear ERK activity ., In fact , heterogeneous cell responses in nuclear ERK have been observed 13 , 14 ., To determine how such heterogeneity in nuclear ERK response is evoked , we performed mathematical modeling and simulation analysis of the EGF signaling pathway ., We developed a mathematical model of the EGF signaling pathway integrating feedback regulation between ERK and nuclear pore complex ( NPC ) , which is essential for switch-like activation of nuclear ERK , and previously developed mathematical schemes 19–21 ., We also developed a new method to compare simulation results with experimental data by estimating Apparent Measurement Error ( AME ) ., Finally , we elucidated how intrinsic and extrinsic noise regulate heterogeneity in nuclear ERK responses ., As shown in Fig 1 , we developed a novel mathematical model based on the biological reaction networks of the EGF signaling pathway integrated with autoregulatory control of ERK translocation ., The details of our model are as follows ., The reaction scheme of EGF signaling pathway is based on several published mathematical models 19–21 ., EGF signaling is initiated by binding between EGF ligands and EGFR on the cell membrane , and EGF–EGFR complexes are subsequently dimerized and autophosphorylated 22–24 ., Phosphorylated EGFR dimer ( pEGFR ) transmits the signal through two pathways , i . e . , the src homology and collagen protein ( Shc ) -independent/dependent pathways ., Shc bound to pEGFR associates with growth factor receptor-bound protein 2 ( Grb2 ) , while Grb2 can directly associate with pEGFR 25 , 26 ., Grb2 in both pathways recruits Son of Sevenless ( Sos ) from the cytoplasm to the membrane , which binds to the membrane-anchored protein Ras 27 , 28 ., This association leads to exchange of guanosine diphosphate of Ras ( RasGDP ) for guanosine triphosphate ( RasGTP ) ., The inactivation of RasGTP is mediated by GTPase activating protein ( GAP ) 19 , 20 , 29 ., The details of this reaction scheme are shown in S1A Fig . Although little is known about the detailed reaction processes involved in Raf activation , a model of Raf activation was recently proposed based on single-molecule observations 30 , 31 ., In this model , both RasGDP and RasGTP can associate with Raf ., However , the association rate between Raf and RasGTP was higher than that of RasGDP ., Only the RasGTP–Raf complex is able to activate Raf through an intermediate state ., Kinetic parameters in the reactions were estimated from experimental data 31 ., This activation scheme of Raf is included in our model ( S1B Fig ) ., Activated Raf doubly phosphorylates cytoplasmic MEK ( ppMEK ) , and subsequently ppMEK also doubly phosphorylates ERK ( ppERK ) ., In addition , ppERK inhibits Sos through phosphorylation , which acts as negative feedback in EGF signaling 32 , 33 ., We assumed that Raf , MEK , and ERK are dephosphorylated by different phosphatases 20 ., All biochemical reactions related to the EGF signaling pathway in our model are shown in S1A and S1B Fig . ERK transiently translocates into the nucleus through binding with NPC after EGF stimulation 13 ., Several regulatory mechanisms of ERK translocation have also been proposed 14 , 34 , 35 ., ERK-mediated phosphorylation of NPC reduces the nuclear accumulation of importin-beta that transports several proteins , including ERK , from the cytoplasm to the nucleus 34 , 35 , suggesting that activated ERK may potentially regulate its own translocation ., In addition , we recently demonstrated that ERK-mediated phosphorylation of NPC is involved in the switch-like behavior of nuclear ERK translocation 14 ., Based on these biological findings , we developed a new mathematical model to describe ERK translocation ( S1C Fig ) ., In our model , cytoplasmic and nuclear ERK bind to NPC and translocate between the cytoplasm and the nucleus ., An NPC has multiple phosphorylation sites for ERK in FG nucleoporins , which regulate the permeability barrier properties of the NPC 34 , 36 ., The dynamic behaviors of such multiple phosphorylation systems have been reproduced using two-step reaction models ., For example , retinoblastoma tumor suppressor protein regulated by multiple phosphorylation was modeled using two-step phosphorylation , i . e . , considering non- , hypo- , and hyperphosphorylated forms in several models 37–39 ., Therefore , we introduced two phosphorylation states of NPC that are mediated by nuclear ppERK into our model ( pNPC and ppNPC in S1C Fig ) ., Further , it was reported that the translocation rate of ERK was dependent on phosphorylation states of both ERK and NPC 14 , 34 , 40 ., To clarify the effect of NPC phosphorylation on the ERK translocation , we assumed the following: the translocation rates of non-phosphorylated and phosphorylated ERK are different among the phosphorylation states of NPC , both NPC and pNPC mediate the translocation of phosphorylated ERK from cytoplasm to nucleus , and ppNPC allows unidirectional translocation of ERK , from nucleus to cytoplasm ( S4 Table ) ., Overall , in our model , cytoplasmic ERK is phosphorylated and translocates into the nucleus transiently after EGF stimulation ., Thereafter , nuclear ppERK phosphorylates NPC in a two-step process , which finally induces the export of phosphorylated ERK from the nucleus to the cytoplasm ( Fig 1 ) ., Our model consists of 78 chemical species and 150 biochemical reactions ., The initial conditions , i . e . , the number of molecules , of each species are shown in S1 Table ., ERK and its phosphatases were considered to be distributed in both the cytoplasm and the nucleus ( Fig 1 ) ., The biochemical reaction processes , association , dissociation , phosphorylation , dephosphorylation , and degradation , were described by mass-action law ( S2 Table ) ., Details of the simulation method and how to determine the kinetic parameters are described in the Materials and Methods ., To confirm the biological validity of our mathematical model , we first implemented deterministic simulations ., Here , pERK represents the total amount of singly/doubly phosphorylated ERK , including their complexes , and nERK represents the fold change in nuclear ERK , defined as the ratio of the total amount of nuclear ERK to the initial value ., Simulated time courses of both pERK and nERK showed transient dynamics , and peak levels increased with elevated EGF concentration ( Fig 2A and 2B ) ., These dynamics were consistent with the typical dynamics after EGF stimulation observed in cell lines of various types 13 , 14 , 19 , 40 ., Next , we calculated the EGF dose response of peak levels of pERK and nERK ( Fig 2C and 2D ) , which showed good agreement with the experimental data 14 ., The dose response of pERK showed a graded pattern ( Hill coefficient = 1 . 46 ) , while that of nERK showed switch-like behavior ( Hill coefficient = 2 . 99 ) ., To confirm that this difference was caused by ERK-mediated regulation of NPC , we performed simulation without the regulation from ERK to NPC ., To remove this regulation , the kinetic parameters related to ERK-mediated phosphorylation of NPC were set to zero ( reaction number 137–144 in S1C Fig , bottom right ) ., As shown in Fig 2E and 2F , the dose response of nERK changed from switch-like to graded , and the Hill coefficient of nERK corresponded to that of pERK ( Hill coefficient = 1 . 46 ) ., This result indicated that ERK-mediated phosphorylation of NPC is responsible for the switch-like response of nERK ., The ERK-mediated phosphorylation of NPC accelerates a nuclear export of ERK in our model , establishing a negative autoregulation of nuclear ERK ( Fig 1 ) ., To investigate the mechanism by which the negative autoregulation changed dynamics of nERK , the dose response of nERK at fold change level was shown in S2 Fig . The negative autoregulation of nuclear ERK drastically reduced the maximum fold change level of nuclear ERK , resulting in decreasing the range of effective concentration ( EC ) 10 and EC90 ( S2 Fig ) ., While the level of EC90 was decreased from 1 . 00 to 0 . 12 by the negative autoregulation , the level of EC10 did not change ., This is because ERK-mediated phosphorylation of NPC regulates the nuclear export but not the nuclear import ( Fig 1 ) ., As the range of EC10 and EC90 becomes narrow , Hill coefficient is increased ., Therefore , a reduction in EC90 level caused by ERK-mediated regulation of NPC enabled the dynamics of ERK translocation to be changed from graded to switch-like ., Indeed , knockdown of nucleoporin 153 , one of the relevant components of NPC that is most effectively phosphorylated by ERK , altered the dose response of nuclear ERK from switch-like to graded 14 ., Thus , our model could recapitulate the essential dynamics of the EGF signaling pathways , suggesting that our model can be used for further simulation analysis ., To investigate the effects of intrinsic and extrinsic noise on heterogeneity in nuclear ERK activity , we implemented simulations with either intrinsic or extrinsic noise ( see Materials and Methods for details ) ., In this study , the intrinsic and extrinsic noise were defined as fluctuation in reactions and protein variability , respectively ., Here , fluctuation in the reactions means that biochemical reaction occurs stochastically , which can be simulated using the Gillespie algorithm 41 ., On the other hand , protein variability means that there are differences in the levels of proteins between individual cells , and the noise level is represented by the coefficient of variation ( CV ) ., In these simulations , we used a typical CV value of protein variability , 30% , as a representative value of extrinsic noise 8 , 42 ., The distributions of peak levels of nuclear ERK obtained from simulations with intrinsic or extrinsic noise are shown in Fig 3 ., The distribution with extrinsic noise was clearly broader than that with intrinsic noise at high concentrations of EGF ( Fig 3A ) ., For statistical comparison , the CV of nuclear ERK was calculated from simulated distributions ., The CV of nuclear ERK with extrinsic noise was higher than that with intrinsic noise ( Fig 3B ) , suggesting that extrinsic but not intrinsic noise contributed to the heterogeneity in nuclear ERK activity ., The variability of proteins between individual cells can be measured by various experimental methods ., However , it is still difficult to measure variability of all protein species present in a mammalian cell ., To estimate all protein variability in the EGF signaling pathway , we directly compared simulations with experiments ., In addition to biological noise , the observed data included several measurement errors derived from measurement principles and setups ., Here , such measurement errors were defined as the Apparent Measurement Error ( AME ) , which was determined by our newly developed method ( details are described in Supporting Information ) ., As shown in S3 Fig , by applying AME , the distribution of nuclear ERK in simulations corresponded to the observed data 14 ., Using the identified AME , we estimated the variability of all proteins in the EGF signaling pathway ., First , simulations with both types of noise were implemented under different concentrations of EGF when the CV of protein variability changed from 0% to 50% ., The resulting distributions of fold changes in nuclear ERK without and with AME are shown in S4 and S5 Figs , respectively ., The CV of nuclear ERK response was calculated for statistical comparison of these simulation results with experimental data 14 ., As shown in Fig 4A , AME strongly affected the distributions at low EGF concentration ( < 0 . 01 ng/mL ) but had little effect at high concentrations , and by applying AME the CV of nuclear ERK corresponded to the pattern of experimental data ., Simulation results at 25% CV of protein variability showed excellent agreement with experimental data ( Fig 4A and 4B ) ., For further quantitative comparison , we calculated mutual information , which has been proposed as a good metric to characterize fidelity for a biological system 43 ., As shown in Fig 4C , the mutual information between EGF and nuclear ERK also showed that 25% CV of protein variability was the best fit to the experimental value 14 ., The distributions of nuclear ERK in simulation results at 25% CV of protein variability were also consistent with experimental data ( Fig 4D ) ., Thus , our new method using AME made it possible to predict variability of signaling proteins from only signal output data ., In our simulations , extrinsic noise was reproduced by sampling initial proteins randomly from a log-normal distribution ., However , it has been reported that individual cells have different expression capacities , leading to variations in the levels of proteins in a correlated manner 6 ., To investigate the effects of such covariation among proteins on heterogeneity , we implemented simulations in which all protein levels under the initial conditions were correlated ( Fig 5A ) ., The covariation among proteins did not influence the distribution of nuclear ERK at the steady-state level without EGF stimulation , as shown in Fig 5B ., However , at high concentrations of EGF , CV of nuclear ERK with covariation was lower than that with covariation ( Fig 5C ) ., This tendency for covariation to suppress the heterogeneity in nuclear ERK was found regardless of the application of AME ( Fig 5C ) ., Thus , our simulation results suggested that covariation among proteins is involved in heterogeneous cellular responses in the EGF signaling pathway ., To investigate the contribution of each molecular species to heterogeneity in nuclear ERK , we implemented simulations by changing the variability of each protein ., The effects of the variability of each protein on heterogeneity in nuclear ERK at low ( 0 . 05 ng/mL ) and high ( 50 ng/mL ) concentrations of EGF are shown in Fig 6A and 6B , respectively ., At lower EGF , variability of EGFR , Ras , Raf , and MEK generated marked heterogeneity in nuclear ERK , whereas variability of ERK and Sos generated large degrees of heterogeneity at higher EGF concentrations ., These results suggest that different proteins contributed to cellular heterogeneity in nuclear ERK at different EGF concentrations ., Therefore , we investigated the contributions of the proteins in the presence of various concentrations of EGF ( Fig 6C ) ., The contributions to heterogeneity in nuclear ERK were divided into the following three types: ( 1 ) EGFR , Ras , Raf , and MEK; ( 2 ) ERK and Sos; ( 3 ) GAP , Grb2 , and Shc ., Species in the first type evoked large heterogeneity between effective concentration ( EC ) 10 and EC90 ( Fig 6C , top ) , where cells that did and did not respond to EGF stimulus were mixed ( Fig 4D ) ., Accordingly , heterogeneity generated by variability of EGFR , Ras , Raf , and MEK would be closely related to the response to EGF stimulus ., On the other hand , species in the second type stably generated large degrees of heterogeneity over EC90 ( Fig 6C , middle ) ., In this region , heterogeneous responses occurred at high levels of nuclear ERK , indicating that all cells would respond to the stimulus ( Fig 4D ) ., Therefore , variability of ERK and Sos had little effect on the response to EGF ., Species in the third type showed little heterogeneity at any concentration of EGF ( Fig 6C , bottom ) , and therefore these species had no contribution to the response to EGF in addition to those in type two ., This result indicated that only particular species involved in the EGF signaling pathway , i . e . , EGFR , Ras , Raf , and MEK , regulate heterogeneity of nuclear ERK in relation to EGF signaling response , i . e . , these proteins function as sensitive nodes in the signaling response ., In the apoptotic pathway , whether apoptosis was induced or not was not correlated with variability of any single species included in the pathway 8 , suggesting that heterogeneity was regulated by variability of at least more than two species ., Our simulation results indicated the possibility of predicting EGF signaling response at the single-cell level by knowing the initial concentrations or variability of four species , i . e . , EGFR , Ras , Raf , and MEK ., In this study , we developed a novel mathematical model of the EGF signaling pathway integrated with the mechanisms regulating the nuclear translocation of ERK ., Although the nuclear translocation of ERK is critical for cell fate decision 44 , the dynamics and the regulation mechanism have not been taken into consideration in conventional mathematical models 19–21 ., Our model included ERK-mediated regulation of NPC explicitly , which could realize the observed dynamics of nuclear ERK , i . e . , switch-like behavior ., Our model assumed that nuclear ppERK phosphorylates the NPC in a two-step process , and then ppNPC but not NPC and pNPC mediated the translocation of nuclear phosphorylated ERK to the cytoplasm ., The nuclear ppERK positively regulates its own nuclear export through NPC , and therefore activated ERK inhibits its own accumulation in the nucleus , which generated a negative autoregulation of nuclear ERK ., Without this negative autoregulation , as phosphorylated ERK was simply distributed in both the cytoplasm and the nucleus through NPC , nERK and pERK showed the same dynamics , i . e . , graded response ., Thus , ERK-mediated regulation of NPC was responsible for the switch-like response , which may play a crucial role in cell fate decision ., Although the details of the molecular basis underlying ERK nuclear translocation are still controversial and our model includes several assumptions regarding the regulatory mechanism between ERK and NPC , we emphasize that our model captures the essential behaviors of ERK in response to EGF stimulation , including time course and dose response ., Next , we investigated the effects of intrinsic and extrinsic noise , i . e . , fluctuations in reactions and protein variability , on heterogeneity in nuclear ERK and found that extrinsic rather than intrinsic noise contributed to cellular heterogeneity ., Our model assumed the EGF signaling pathway in a mammalian cell in which the volume is typically > 10−12 L , which is much larger than yeast or bacteria ( 10−16–10−14 L ) 45 , 46 ., Therefore , a mammalian cell has a huge number of molecules even if the same concentrations of proteins are present in yeast and bacterial cells ., For example , the EGF signaling pathway consists of 103–107 molecules 40 and the HGF signaling pathway consists of 104–107 molecules 9 ., On the other hand , E . coli and yeast cells possess 10−1–103 and 102–106 protein molecules , respectively 47 , 48 ., In general , fluctuations due to intrinsic noise are strongly dependent on the number of molecules , i . e . , fluctuations are larger with smaller numbers of molecules ., Therefore , intrinsic fluctuation is considered to be negligibly small , and extrinsic noise evokes larger fluctuations in our model ., In addition , it was reported that the abundance of transcripts in mammalian cells was regulated by extrinsic noise , i . e . , cellular state , population context , and microenvironment 49 , indicating that extrinsic noise plays a key role in the transcriptional program ., Our simulations demonstrated the importance of extrinsic noise in the signaling response ., These results suggest that extrinsic noise plays significant roles in mammalian cellular responses ., Using a newly developed method to estimate AME , we could predict that CV of protein variability in the EGF signaling pathway was 25% ., In other signaling pathways , ranges of measured CV of signaling proteins were 8%– 75% in the hepatocyte growth factor ( HGF ) signaling pathway 9 , 21%– 28% in the apoptotic intrinsic pathway 8 , and 15%– 30% in normally cycling human cells 42 ., Thus , estimated CV was included in the range of observed protein variability , indicating that our method is useful for estimating or predicting the variability of all signaling proteins ., The AME estimated by our method agreed completely with observed heterogeneity in nuclear ERK without EGF stimulation , suggesting that at the basal level , variability arising from cellular noise was too small to contribute to cellular heterogeneity ., Moreover , in our simulations , covariation among proteins under initial conditions suppressed variation in nuclear ERK at high concentrations of EGF ., This suggests that heterogeneous signaling responses can be regulated by such covariation in the cell ., Further analysis using estimated protein variability showed that distinct species in EGF signaling pathway have different effects on heterogeneity in nuclear ERK , i . e . , EGFR , Ras , Raf , and MEK generated heterogeneity related to the signaling response ., These particular proteins function as sensitive nodes causing heterogeneous cell responses in the EGF signaling pathway ., On the other hand , proteins other than sensitive nodes , i . e . , GAP , Grb2 , and Shc , could not influence the heterogeneity at any concentrations of EGF ., Such differential contribution to heterogeneity may be due to the mechanism of reactions involving each protein in the pathway ., In our model , sensitive nodes are involved in enzymatic reactions such as phosphorylation , while insensitive nodes are related to binding–unbinding reaction ., This suggests that heterogeneity in signaling responses may be regulated by the type of network edges , i . e . , reaction mode in the signaling pathway ., In terms of cellular functions , the expression level or the activity of sensitive nodes would be tightly regulated in the cell for appropriate responses in the signaling pathway , as they are strongly involved in the cellular heterogeneous responses ., In fact , mutations of EGFR , Ras , and Raf are related to epithelial mesenchymal transition , migration , and tumor invasion of breast cancers , and furthermore MEK mutation was observed in malignant melanoma 50–52 ., Thus , sensitive nodes seem to be committed to maintain normal cellular homeostasis ., As the expression levels of these sensitive nodes are closely related to the signaling response , knowing these concentrations may make it possible to predict the signaling response at the single-cell level before stimulation ., Deterministic and stochastic simulations were implemented in this study ., Two types of cellular noise , i . e . , intrinsic and extrinsic noise , were simulated ., The intrinsic noise was defined by fluctuations in reactions , which were realized by a stochastic simulation method ( Gillespie algorithm ) 41 , 53 ., The extrinsic noise was defined by protein variability between individual cells , which was represented by independently sampling initial values of each protein from log-normal distributed random variables at various CV 8 , 9 , 18 ., We implemented four types of simulation: 1 ) without either type of noise; 2 ) with only intrinsic noise; 3 ) with only extrinsic noise; 4 ) with both types of noise ., All kinetic parameters were constant among all simulations except the CV of protein variability ( S1 , S2 and S3 Tables ) ., In the case of stochastic simulations , we performed 5000 simulations per condition to obtain statistically stable results ., The initial conditions of five species , EGFR , Ras , Raf , MEK and ERK , were estimated from experimental data 22 , 40 ., Similarly , six kinetic parameters related to Raf activation were experimentally determined values ( S2 Table ) ., Other initial conditions and kinetic parameters were determined by manual parameter tuning as follows ., First , kinetic parameters in published models were used as the initial estimates 20 , 40 ., Then , we performed deterministic simulations changing each parameter one by one , and compared the simulation results with experimental data , i . e . , time course and dose response of both phosphorylated and nuclear ERK 14 ., Repeating this process , we finally determined the parameter set that reproduced the experimental data ., The mutual information between EGF stimulus and nuclear ERK response was calculated from simulation results and experimental data ., Here , the concentration of EGF and nuclear ERK level were used as input signal ( S ) and output response ( R ) , respectively ., Mutual information , I ( R; S ) , was defined by the following equation:, I ( R;S ) =ΣsΣRP ( R , S ) log2 ( P ( R , S ) P ( R ) P ( S ) ), ( 1 ), where P ( R , S ) , P ( R ) , and P ( S ) represent the joint probability distribution functions of S and R , and the marginal probability distribution functions of S and R , respectively ., As the distributions of simulated S and R were discretized , direct estimates of mutual information using ( Eq 1 ) were biased ., To obtain the unbiased solution , we calculated the mutual information by optimizing the discretized size and jackknife sampling , as described previously 54 ., The experimental data used in this research were originally reported in our previous paper 14 . | Introduction, Model, Results, Discussion, Materials and Methods | Cellular heterogeneity , which plays an essential role in biological phenomena , such as drug resistance and migration , is considered to arise from intrinsic ( i . e . , reaction kinetics ) and extrinsic ( i . e . , protein variability ) noise in the cell ., However , the mechanistic effects of these types of noise to determine the heterogeneity of signal responses have not been elucidated ., Here , we report that the output of epidermal growth factor ( EGF ) signaling activity is modulated by cellular noise , particularly by extrinsic noise of particular signaling components in the pathway ., We developed a mathematical model of the EGF signaling pathway incorporating regulation between extracellular signal-regulated kinase ( ERK ) and nuclear pore complex ( NPC ) , which is necessary for switch-like activation of the nuclear ERK response ., As the threshold of switch-like behavior is more sensitive to perturbations than the graded response , the effect of biological noise is potentially critical for cell fate decision ., Our simulation analysis indicated that extrinsic noise , but not intrinsic noise , contributes to cell-to-cell heterogeneity of nuclear ERK ., In addition , we accurately estimated variations in abundance of the signal proteins between individual cells by direct comparison of experimental data with simulation results using Apparent Measurement Error ( AME ) ., AME was constant regardless of whether the protein levels varied in a correlated manner , while covariation among proteins influenced cell-to-cell heterogeneity of nuclear ERK , suppressing the variation ., Simulations using the estimated protein abundances showed that each protein species has different effects on cell-to-cell variation in the nuclear ERK response ., In particular , variability of EGF receptor , Ras , Raf , and MEK strongly influenced cellular heterogeneity , while others did not ., Overall , our results indicated that cellular heterogeneity in response to EGF is strongly driven by extrinsic noise , and that such heterogeneity results from variability of particular protein species that function as sensitive nodes , which may contribute to the pathogenesis of human diseases . | Individual cell behaviors are controlled by a variety of noise , such as fluctuations in biochemical reactions , protein variability , molecular diffusion , transcriptional noise , cell-to-cell contact , temperature , and pH . Such cellular noise often interferes with signal responses from external stimuli , and such heterogeneity functions in induction of drug resistance , survival , and migration of cells ., Thus , heterogeneous cellular responses have positive and negative roles ., However , the regulatory mechanisms that produce cellular heterogeneity are unclear ., By mathematical modeling and simulations , we investigated how heterogeneous signaling responses are evoked in the EGF signaling pathway and influence the switch-like activation of nuclear ERK ., This study demonstrated that cellular heterogeneity of the EGF signaling response is evoked by cell-to-cell variation of particular signaling proteins , such as EGFR , Ras , Raf , and MEK , which act as sensitive nodes in the pathway ., These results suggest that signaling responses in individual cells can be predicted from the levels of proteins of sensitive nodes ., This study also suggested that proteins of sensitive nodes may serve as cell survival mechanisms . | phosphorylation, egfr signaling, mathematical models, simulation and modeling, cellular structures and organelles, research and analysis methods, erk signaling cascade, proteins, mathematical and statistical techniques, ras signaling, cytoplasm, biochemistry, biochemical simulations, signal transduction, cell biology, post-translational modification, biology and life sciences, computational biology, cell signaling, signaling cascades | null |
journal.pcbi.1006095 | 2,018 | Principles that govern competition or co-existence in Rho-GTPase driven polarization | Complex cell morphologies arise , in part , through the specialization of cortical domains ( e . g . , the apical and basal domains of epithelial cells , or the front and back of migratory cells ) ., Elaboration of such domains involves the local accumulation of active Rho-family GTPases , which regulate cytoskeletal elements to promote specific downstream events , such as vesicle trafficking , membrane deformation , or directed growth 1–3 ., For some cells , it is vital to establish a single specialized domain ( e . g . the front of a migrating cell ) , whereas others require the establishment of multiple domains simultaneously ( e . g . the dendrites of a neuron ) 4 , 5 ., The mechanistic basis for specifying uni- or multi-polar outcomes remains elusive ., Rho-family GTPases switch between GTP-bound active and GDP-bound inactive forms ( Fig 1A ) ., Active GTPases are tethered to the inner surface of the plasma membrane , where diffusion is slow ., In contrast , inactive GTPases are preferentially bound by guanine nucleotide dissociation inhibitors ( GDIs ) , which extract the bound GTPase to the cytoplasm , where their diffusion is comparatively fast ., Activated GTPases can promote local activation of cytosolic GTPases via positive feedback ., This generates a membrane domain with concentrated active GTPase , concomitantly depleting the cytosolic GTPase pool ( Fig 1B ) ., Synthesis and degradation of GTPases occurs on a slow timescale compared to activation and inactivation ( for example , in budding yeast the Rho-GTPase Cdc42 polarizes within 2 minutes but has a half-life of more than 20 hours ) 6–8 ., Thus , the general dynamics of the system can be captured by mass-conserved activator-substrate ( MCAS ) models , with a slowly-diffusing activator and a rapidly-diffusing substrate ( Fig 1C ) 9–11 ., Such models can generate local peaks of activator , reflecting the establishment of a polarized concentration profile of active GTPase ( Fig 1D ) ., Proposed MCAS models differ primarily in the formulation of the positive feedback mechanism ., One set of models yields Turing instability 9 , 11 , where positive feedback is sufficient to amplify molecular-level fluctuations leading to peak formation ., Classically , Turing systems can generate single or multiple peaks 12 , 13 , depending on whether the size of the modeled domain is larger than a characteristic wavelength dependent on the reaction and diffusion parameters ., This has been shown by Linear Stability Analysis ( LSA ) of the homogeneous steady state ( HSS ) 14–16 ., However , even when multiple peaks emerge from the homogeneous state , most of the peaks in Turing-type MCAS models eventually disappear through a process called “competition” , leaving a single large peak as the winner 11 , 17 , 18 ., Otsuji et al . 11 reasoned that competition arose due to mass-conservation , and further suggested that this might be a general behavior of Turing-type MCAS models ., In biological systems , competition-like behavior was observed during polarity establishment in yeast cells , where it was suggested to underlie the growth of only one bud per cell cycle 7 , 17 , 18 ., Another set of models relies on bistable reaction kinetics to produce “wave-pinning” behavior 10 , 19–21 ., Such models can generate membrane domains with separate phases of uniform high or low activator concentrations connected by a sharp “wavefront” ., The wave front spreads laterally but eventually stops ( gets pinned ) due to depletion of the cytoplasmic substrate , forming stable wide “mesa”-like concentration profiles ., In the absence of spatial cues , wave-pinning models can generate multiple mesas when initiated by random fluctuations 10 ., Studies done on 1-dimensional wave-pinning model show that multiple mesas appear to be meta-stable 20 , 22 and do not readily exhibit competition ., An attractive hypothesis for why some cells are uni-polar and others multi-polar would be that these behaviors arise from differences in the biochemical mechanisms of positive feedback , yielding competition in Turing-type or meta-stability in wave-pinning models ., However , some Turing-type MCAS models appear to switch to multi-polarity when domain size 11 , 22 or protein amount 17 is increased ., Thus , it could be that parameter values ( protein concentration , catalytic activity , cell size , etc . ) rather than regulatory feedback mechanisms dictate whether uni- and multi- polar outcomes are observed ., Here , we investigate the transient multi-peak scenario , and show that the different models discussed are all capable of generating unipolar or multipolar outcomes ., The switch between these outcomes is primarily dictated by a “saturation rule” that is general to MCAS models: Every biologically relevant model in this category has an innate saturation point that sets the maximum local Rho-GTPase concentration ., When peaks form such that peak concentrations are well below this saturation point , competition is effective and multi-polar conditions resolve rapidly to a uni-polar steady state ., However , if the GTPase concentration in two or more peaks approaches the saturation point , then competition becomes ineffective , and the peaks become meta-stable ., Because the saturation rule does not depend on the specifics of the biochemical reactions , our results yield general and testable predictions ., Two-species MCAS systems consist of two partial differential equations ( PDEs ) , governing the dynamics of a slowly diffusing activator ( GTP-bound GTPase at the membrane ) u , and a rapidly diffusing substrate ( GDP-bound GTPase in the cytoplasm ) v . In one spatial dimension , these systems take the general form:, ∂ u ∂ t = D u ∂ 2 u ∂ x 2 + F ( u , v ) ( 1a ), ∂ v ∂ t = D v ∂ 2 v ∂ x 2 - F ( u , v ) ( 1b ), where the dynamics of u and v are governed by a diffusion term and a reaction term , F ( u ,, v ) ( For the dimensionless version , see Supporting Information section 1 ) ., To reflect the different compartments ( membrane and cytoplasm ) populated by the different species , the diffusion constant of u , Du , is typically two orders of magnitude smaller than Dv , so that u spreads much more slowly than v . F ( u ,, v ) describes the biochemical interconversions between u and v . For GTPases , the inactive form of the GTPase v is converted to the active form u through the action of guanine nucleotide exchange factors ( GEFs ) f, ( u ) , while u is converted to v through the action of GTPase activating proteins ( GAPs ) g, ( u ) ., The functions f, ( u ) and g, ( u ) take into account potential positive feedback mediated by the active GTPase ., Because the inactive GTPase is not thought to participate in biochemical reactions other than as a substrate to produce active GTPase , under the assumption of mass action kinetics v appears only in the activation term ., As the model assumes only the exchange between u and v , but not synthesis or degradation of either , the system is mass-conserved , so that the total abundance of the GTPase M = ∫ ( u +, v ) dx is a constant over time ., Generation of a GTPase-enriched domain in MCAS models occurs through positive feedback leading to local accumulation of the activator , u , and concomitant depletion of the substrate , v . Locally depleted v is quickly resupplied from the whole cytoplasm due to its high mobility , resulting in a global depletion of v . This reduces the net rate , F ( u ,, v ) , at which fresh u is generated ( Eq 2 ) , impeding further growth of the u-enriched domain , and the system reaches a steady state ., At steady state , reaction and diffusion must be balanced for u and v:, 0 = D u ∂ 2 u ∂ x 2 + F ( u , v ) ( 3a ), 0 = D v ∂ 2 v ∂ x 2 - F ( u , v ) ( 3b ), Given a total protein content M , these equations govern the steady state peak shape u ( x ) and substrate level v ( x ) for a single peak in an MCAS model ( Further discussed in Supporting Information section 2 ) ., Positive feedback can occur through f, ( u ) ( i . e . active GTPase locally stimulates GEF activity ) or g, ( u ) ( i . e . active GTPase locally inhibits GAP activity ) ., Examples of feedback via GEF activation include the simple Turing-type model f, ( u ) = au2 , g, ( u ) = b , Goryachev’s simplified model f, ( u ) = au2 + cu , g, ( u ) = b 9 , and Mori’s wave-pinning model f ( u ) = a u 2 1 + k u 2 , g ( u ) = b 10 ., Examples of feedback via GAP inhibition include f ( u ) , = 1 , g ( u ) = b ( 1 + u ) 2 , which resembles model I in 11 ., To illustrate the behaviors of different MCAS models , we simulated examples of Turing-type and wave-pinning MCAS models:, F ( u , v ) = a u 2 v - b u ( 4 ), F ( u , v ) = a u 2 1 + k u 2 v - b u ( 5 ) With the appropriate choice of parameters , the Turing-type model ( Eq 4 ) yields a peak given any spatial perturbation of the homogeneous steady state , while the wave-pinning model ( Eq 5 ) requires a supra-threshold perturbation to destabilize the homogeneous state ., The Turing-type model typically yields a narrow peak at steady state , while the wave-pinning model typically yields a wide mesa ( Fig 1E ) ., Simulations with greater total amounts of GTPase M yield higher peaks in the Turing-type model , but broader mesas ( with the same peak height ) in the wave-pinning model ( Fig 1E ) , and simulations initiated with two unequal peaks yield rapid competition in the Turing-type model but apparent co-existence in the wave-pinning model ( Fig 1F ) ., These behaviors are all consistent with previous reports 10 , 11 , 20 , 21; to understand why they yield different outcomes we first revisit the basis for competition ., When two unequal peaks are present in the same domain , each peak recruits GTPase from the cytoplasm , thereby globally depleting cytoplasmic GTPase until cytoplasmic concentration reaches a quasi-steady state ., As exchange of GTPase between each peak and the cytoplasm is dynamic , the two peaks are now effectively recruiting GTPase from one another ., If the larger peak ( the one that contains more GTPase ) recruits GTPase more effectively , it will grow at the expense of the smaller peak , eventually yielding a uni-polar outcome ( Fig 2A , scenario 1 ) ., If instead , the smaller peak recruits GTPase more effectively , then it will grow while the larger peak shrinks , eventually yielding two equal peaks , as observed in some more complex models 17 ( Fig 2A , scenario 2 ) ., If two unequal peaks recruit GTPase equally , then the two unequal peaks would simply coexist ( Fig 2A , scenario 3 ) ., To understand how these considerations play out for different peaks , we need to know whether the larger peak recruits more GTPase ., To assess how much GTPase would be recruited to a specific peak , consider first the Turing-type model ( Eq 4 ) in the limit Dv → ∞ ., This model combines a quadratic ( in, u ) activation term with a linear inactivation term ( Fig 2B ) ., Thus , for a fixed value of v , there are two values of u at which activation and inactivation balance each other precisely ( i . e . fixed points of the net reaction curve F ( u ,, v ) in Fig 2C , denoted as umin and umid ) ., Given the concentration profile of a peak ( Fig 2D , upper panel ) , F ( u ,, v ) determines whether any given location on the membrane will gain GTPase from the cytoplasm or lose GTPase to the cytoplasm ( Fig 2D , lower panel ) ., At the trough in Fig 2D ( umin ) , u approaches the lower fixed point of F ( u ,, v ) , yielding no net gain or loss of GTPase ., On the lower flanks of the peak , u values lie between umin and umid , and inactivation outpaces activation , so there is a net loss of u ( Fig 2B , 2C and 2D ) ., When u rises above umid , up until the top of the peak ( umax ) , there is net recruitment of GTPase from the cytoplasm ( Fig 2B , 2C and 2D ) ., At steady state , diffusion from the center of the peak to the flanks balances these flows of GTPase , requiring a narrow peak ( where negative ∂ 2 u ∂ x 2 counteracts net recruitment at the center: Eq 3 ) ( Fig 2D ) ., At steady state , the net loss from the region between umin and umid ( blue area in Fig 2B and 2C ) must be balanced by the net recruitment from the region between umid and umax ( red area in Fig 2B and 2C ) ( For analytical support of this argument , see Supporting Information section 2 . This is also referred to as the wave-pinning condition in 10 ) ., If we started from a steady state peak and increased M , umax would increase and the red area would become larger ., To reach a steady state , v would have to decrease , weakening the influence of the activation term in F ( u ,, v ) ( Fig 2E ) , and equalizing the red and the blue areas at steady state ( though each area would end up larger than for the initial peak ) ., Another way to understand this is the following argument: If we added more inactive GTPase to the depleted cytoplasm beneath an existing steady state peak , the activation rate ( linear in, v ) would increase , causing the peak to grow and depleting inactive GTPase ., When v gets back down to its starting steady state level , the peak will be larger , so both the activation and inactivation rates will be larger ., However , the activation rate will dominate due to the non-linear positive feedback , causing further depletion of v until the net rates balance ., Thus , for any F ( u ,, v ) that encodes a non-linear positive feedback , a larger peak will recruit cytoplasmic GTPase more strongly and cause a more severe cytoplasmic depletion ., Now consider a scenario in which two unequal peaks are present in the same domain ., Both peaks would grow until cytoplasmic v becomes sufficiently depleted ., In the limit where Dv → ∞ , v is spatially homogeneous , so the same net reaction applies to both peaks , but the peaks will have a different umax ( Fig 2F ) ., The overall recruitment or loss of GTPase for each peak u ( x ) is given by:, ∫ F ( u , v ) d x ( 6 ), The more GTPase there is in the larger peak compared with the smaller one , the larger the difference in “recruitment power” between them ( Fig 2F ) ., Thus , in a scenario with unequal peaks in the same domain , the larger peak experiences a net gain of GTPase , while the smaller peak experiences a net loss , further exacerbating the inequality between the two peaks until the smaller peak is eliminated ., The Turing model ( Eq 4 ) with Dv → ∞ always competes to yield a uni-polar endpoint ( scenario 1 in Fig 2A ) ., The argument above requires only mass-conservation and non-linear positive feedback , which is a core requirement for polarization in general 12 ., Therefore , it would seem that all MCAS models should compete , regardless of the specific F ( u ,, v ) ., To verify this , we generated steady states with two symmetric peaks in a domain , and performed linear stability analysis to show that such steady states are unstable ( Supporting Information section 3 ) ., Perturbations that destabilize the steady state yield either competition between the peaks or merging of the peaks ., Here we focus on competition ., Our analysis in the limit of Dv → ∞ indicates that given sufficient time , two peaks will always compete to produce a single peak ., This result does not depend on the form of F ( u ,, v ) ., If competition ( scenario 1 in Fig 2A ) applies to all MCAS models , then why did we not observe competition in simulations of the Wave-pinning model ( Fig 1G ) ?, In contrast to the Turing-type model ( Eq 4 ) , the reaction term of the Wave-pinning model ( Eq 5 ) has saturable positive feedback , introducing a third fixed point in F ( u ,, v ) ( Fig 3A ) ., When the total protein content in the system is small , umax does not approach this fixed point ( Fig 3B ) ., Under these conditions , narrow peaks compete with each other to yield a uni-polar outcome , as with the Turing-type model ( Fig 3C ) ., But when protein content of the peak is increased , umax approaches the third fixed point , and the reaction rate F ( u ,, v ) at the top of the peak approaches zero ( Fig 3B ) ., To satisfy the steady-state condition ( Eq 3a ) , ∂ 2 u ∂ x 2 must also approach zero ., In other words , the top of the peak must broaden to become a wide mesa ., Once this occurs , increasing M only negligibly increases umax , and instead of developing higher peaks the model develops broader mesas with comparable umax ( Fig 3B ) ., As umax saturates in these peaks , we shall call this maximum value the “saturation point” ( usat ) of the model ., When umax approached the saturation point usat , simulations with two saturated mesas did not show obvious competition ( Fig 3D ) ., Applying a drastic perturbation in which 50% of the GTPase in one mesa was transferred to the other led to a rapid adjustment with both mesas returning to an almost identical umax but with different widths , after which the unequal mesas co-existed for prolonged simulation times ( Fig 3E ) ( Note that the two peaks did not “equalize”: they retained unequal total GTPase content . ) Thus , the same model can yield rapid competition or competition so slow as to yield prolonged co-existence , simply as a result of varying the total amount of GTPase in the system ., To investigate more broadly how model parameters might influence the timescale of competition between peaks , we simulated competition between two unequal peaks in the Wave-pinning model , in the limit with Dv → ∞ ., If we start with a two-peak steady state and noise , the two peaks will eventually resolve to one , given sufficient time ., As a measure of competition time that should be insensitive to the precise degree of the noise , we tracked the time it took for unequal peaks with active GTPase content ratio 3:2 to evolve to a content ratio of 99:1 ., Parameter changes caused dramatic changes in competition times , color coded on a log scale in Fig 4A ., Notably , increasing M always led to slower competition ( Fig 4A , left panel ) ., As discussed above , increasing GTPase content causes umax to approach the saturation point ., Defining a saturation index in terms of how closely umax at the two-peak steady state approached the saturation point ( ( usat − umax ) /usat ) , we found that the effects of varying parameters on the saturation index closely paralleled the parameter effects on the timescale of competition ( Fig 4A , right panel ) ., A similar congruence was observed using peak width as a different measure of how closely the system approaches saturation ( Fig S5B in S1 Text ) ., These findings suggest that a large majority of the variation in competition times can be explained simply by the degree to which peaks in the model approach the saturation point ., If we plot competition time against umax normalized to the saturation point , all of the simulations with different parameter values display one of two clearly distinct behaviors ( Fig 4B ) ., Parameter changes can alter GTPase content in the peaks ( Fig 4A and 4B , point 1 vs 2 ) , the saturation point ( point 3 vs 4 ) , or the shapes of the peaks ( point 5 vs 6 ) ., In all cases , whenever umax is not close to saturation , competition occurs rapidly ., Conversely , as umax approaches the saturation point , competition slows sharply and the two-peak situation becomes meta-stable , resembling the co-existence scenario 3 in Fig 2A ., The basis for the drastically slowed competition in simulations with peaks close to saturation can be intuitively understood in terms of each peak’s “recruitment power” ( Eq 6 ) ., When peaks approach saturation , unequal peaks differ in width but have almost identical umax and hence only a negligible difference in recruitment power ( Fig 4C ) ., In the saturated regions of peaks , F = 0 , so these areas do not directly contribute to overall recruitment ., For that reason , the extra GTPase in a broader peak does not give it a significant advantage over the narrower peak , and the driving force for competition is negligible ., Analysis of the eigenvalues from linear stability analysis of this system shows that the timescale of competition slows exponentially as the peaks increase in width by saturation ., This conclusion , again , is general to all MCAS models and can be applied to all formulations F ( u ,, v ) that allow a third fixed point ( Supporting Information section 4 , Fig . S5A ) ., When cytoplasmic diffusion is finite ( Dv < ∞ ) , a saturation point emerges even if there is no explicit saturation in the reaction term ., With finite Dv , increasing M in the Turing-type model ( Eq 4 ) yields saturated mesas that become broader as M increases ( Fig 5A ) , similar to that seen with the wave-pinning model ( Eq 5 ) ., To understand this behavior , recall that at steady state , ( Eq 3 ) must hold ., Adding ( Eq 3a ) and ( Eq 3b ) , integrating and enforcing the periodic boundary condition yields a linear relationship between u and v , regardless of the reaction term:, v s s = - D u D v u s s + q ( 7 ), where q is a constant over space that is depleted globally over time analogous to v in the Dv → ∞ limit ., This reflects the fact that in addition to global substrate depletion , activation due to positive feedback depletes v locally under a peak of u , creating a “dip” in the concentration of the cytoplasmic GTPase v that corresponds to the peak of u in a linear manner ( Fig 5B ) ., Local depletion results in an emergent saturation effect , because substituting Eq 7 into the reaction term of the Turing type model ( Eq 4 ) gives:, F ( u s s , q ) = a u s s 2 ( - D u D v u s s + q ) - b u s s ( 8 ), This new reaction term F ( u , q ) is a cubic in u , and can have three fixed points ( Fig 5C ) ., The upper fixed point reflects the u concentration at which local depletion of v precisely balances the net recruitment of u , yielding an emergent saturation point ., Thus , even when there is no saturation inherent in the reaction term of the model , local depletion of v under the peak acts to limit local production of u , introducing a saturation effect ., Given sufficient total mass M , umax approaches this saturation point , resulting in a saturated mesa for reasons described above with the wave-pinning model ( Fig 5D ) ., In this case , it is possible to derive a simple expression for the saturation point ( For details , see Supporting Information section 2 ) :, u sat = 2 b D v a D u ( 9 ), As with saturation due to the wave-pinning reaction term , saturation by local depletion also slowed competition dramatically , leading to meta-stable peaks ( Fig 5E ) ., Exploration of a wide parameter range indicated that as with saturation via the reaction term , saturation due to local depletion of substrate is also a dominant contributor to the timescale of competition ( Fig 5F ) ., When Dv < ∞ , two unequal peaks no longer “see” the same level of substrate , v . Instead , the local v rapidly reaches a quasi steady-state with each peak ( Fig 5G ) ., When two unsaturated peaks coexist , the higher peak has a stronger recruitment power for reasons discussed in Fig 2F ., This drives a greater depletion and hence lower baseline of v under the higher peak , generating a cytoplasmic v gradient that drives a flow of GTPase towards the higher peak , and hence competition ( Fig 5G ) ., In contrast , when two unequal but saturated peaks coexist , they have similar recruitment power , so there is a negligible cytoplasmic gradient , and competition occurs on a dramatically slower timescale ., During competition , GTPase is transferred from the “losing” peak to the “winning” peak through the cytoplasm ., Thus , increased distance between the peaks or a decreased diffusion constant in the cytoplasm would be expected to slow the transfer and hence slow competition ( an effect not seen when Dv → ∞ ) ., To assess how effective increased distance could be in slowing competition , we initially considered the effect of increasing cell size while keeping overall GTPase concentration constant ( Fig 6 , gray line ) ., Competition slowed dramatically as domain size L was increased , but this does not distinguish whether increasing distance between peaks or increasing total GTPase content M ( moving the peaks closer to saturation ) is responsible for the slowing of competition ., Increasing L without changing M resulted in GTPase dilution and hence smaller peaks that competed more rapidly despite the increased distance between peaks ( Fig 6 , blue line ) ., To maintain equivalent peaks , we increased L while adding the exact amount of GTPase required to fill the cytoplasm in the extended domain so that the amount of GTPase in the peaks remained constant ., This scenario allowed us to quantify the effect of increasing distance between peaks without confounding changes in peak size ., The result was that competition became slower in a sub-linear manner with distance ( Fig 6 , red line ) ., Thus , distance between peaks can slow competition , but does so in a much more gradual manner than the approach to saturation ., Our analysis has focused on specific illustrative models , but many other forms of F ( u ,, v ) in Eq 2 can also support polarization ., For example , positive feedback strength may vary , yielding different exponents for the activation term ( e . g . f, ( u ) = u1 . 2 with weak feedback , or f, ( u ) = u3 with strong feedback ) ., Or , positive feedback may operate by reducing inactivation rather than by increasing activation ( e . g . f, ( u ) = 1 , g, ( u ) = u/ ( 1 + u2 ) ) ., Or , positive feedback may be accompanied by negative feedback , as proposed for the yeast polarity circuit 17 , 23 ( e . g . f, ( u ) = u2 − cu3 ) ., As local cytoplasmic depletion is a universal mechanism of saturation , we would expect that competition time slows down as the system approaches saturation in all of these models ., Indeed , all of these variations displayed a saturation point , leading to a transition from unsaturated to saturated peaks as M was increased ., And in each case , the change in peak shape was accompanied by a dramatic slowing of competition ( Fig 7A–7E ) ., This suggests that our findings are broadly applicable to MCAS models ., The only counterexample we have encountered so far is model II from 11 , where, F ( u , v ) = a 1 ( u + v ) ( D u D v u + v ) ( u + v ) - a 2 ( 10 ), Unlike other reaction terms based on mass action kinetics ( Eq 2 ) , this reaction term is not dependent on v , but rather on the combined concentration of u and v . Thus , activation in this model is no longer restricted by v depletion as in the other models mentioned above , and v can assume negative values when u is high , avoiding saturation ( Fig 7F ) ., This eliminates the effect of local depletion: When v is substituted with - D u D v u + q , F ( u , q ) is a curve lacking a third fixed point ( and hence lacking saturation ) ., However , as concentrations of u or v cannot be negative in cells , this model is not physiologically relevant ., To simplify the analysis , the discussion above focused on competition between peaks in 1D ., The conclusions that differences in recruitment power drive competition and that a peak’s recruitment power saturates as peaks become larger both hold in 2D as well as 1D ( Supporting Information section 6 ) ., However , simulations show that saturated mesas compete on faster timescales in 2D than in 1D ( Fig 8A ) ., As discussed below , this is due to a second driving force for competition that depends on the 2D curvature of the peaks ., In wave-pinning models , the edges of growing 1D mesas resemble traveling wave-fronts ( 10 ) ., The speed of the traveling wave , c0 , depends on the abundance of cytoplasmic substrate , v ( Fig 8B ) ., As more v is converted to u , cytoplasmic v is depleted until c0 drops to zero , at which point the wave is pinned , forming a steady state peak ., However , in 2D , a circular wave spreading as a peak grows will have a speed less than c0 , because diffusive spreading of u from the front to activate neighboring membrane is diluted by the geometry of the wave front ( Fig 8B ) ., Previously developed theory ( 24–26 ) indicates that in this context the wave speed c is dependent on the curvature of the wavefront , κ:, c = c 0 ( v ) - κ D u ( 11 ), For a circular peak of radius R , κ = 1 R . Thus , smaller peaks with high curvature spread more slowly than otherwise similar larger peaks ., For a situation in which unequal circular mesas coexist in 2D , these considerations show that even if c0 and v are the same for both peaks , the difference in peak curvature suffices to give the larger peak an advantage over the smaller one ( Supporting Information section 7 ) ., Each peak grows or shrinks depending on whether c0 is larger or smaller than D u R . As peaks initially develop , v is high enough that both peaks can grow , but as v becomes depleted , c0 decreases until the smaller peak transitions to shrinking ( Fig 8C ) ., This liberates more v so that the larger peak can continue to grow until it is the only peak present ., Thus , in 2D there are two drivers of competition between unequal peaks: a difference in recruitment power between peaks of different height , and a difference in curvature between peaks of different radii ., Unlike in 1D , the latter can drive competition even for saturated peaks with negligible difference in peak height ., Although saturated mesas are able to compete in 2D , simulation results suggest that such competition is slow relative to that between unsaturated peaks ( Fig 8A ) ., To systematically compare competition rates for different peaks , we calculated the net flux of GTPase from the losing peak to the winning peak , expressed as mole GTPase per second ., As the flux changes over the course of competition , we chose the point at which the winning peak had 60% and the losing peak had 40% of the total GTPase in the peaks ., We first consider the limit where Dv is infinite , and use the wave-pinning model to generate saturated or unsaturated peaks ., We kept all parameters including total GTPase M constant and varied the parameter k ( Eq 5 ) to generate peaks of different shapes but similar GTPase content ( Fig 9A ) ., This revealed that competition fluxes were much larger for peaks that were far from saturation than for saturated peaks ( Fig 9B ) ., For saturated peaks , the fluxes matched those predicted for curvature-driven competition ( see Supporting Information section 7 ) ( Fig 9B , red line ) ., However , when peaks were no longer in the saturated regime , the fluxes diverged from the prediction for curvature-driven competition , and were approximately proportional to the differences in peak height ( Fig 9B , green line ) ., These results indicate that when peaks are saturated , competition is driven by curvature , whereas when peaks are not saturated , competition fluxes become significantly larger and competition is primarily driven by difference in peak height , as in 1D ., Similar results were obtained simulating Turing-type models with finite cytoplasmic diffusion , where saturation emerges as a consequence of local v depletion ., As in 1D , competing peaks far from saturation generated a significant cytoplasmic gradient of v driving large fluxes of GTPase , while saturated mesas did not ( Fig 9C ) ., These observations suggest that although peak curvature contributes to competition in 2D , it provides a relatively weak driving force ., The dominant factor for competition timescale is still the difference in recruitment power , which decreases rapidly as peaks approach saturation ., Since Turing’s landmark 1952 paper 13 , the power of two-component reaction-diffusion models to generate a variety of spatial patterns has fascinated mathematical biologists ., Early models with slowly-diffusing activators and rapidly-diffusing substrates formed activator peaks with a spacing dictated by a characteristic wavelength 12 ., However , addition of a constraint specifying that the total mass of activator and substrate in the system be conserved led to the finding that some such MCAS systems evolved over time from multipolar to unipolar outcomes with a single pe | Introduction, Results, Discussion, Methods | Rho-GTPases are master regulators of polarity establishment and cell morphology ., Positive feedback enables concentration of Rho-GTPases into clusters at the cell cortex , from where they regulate the cytoskeleton ., Different cell types reproducibly generate either one ( e . g . the front of a migrating cell ) or several clusters ( e . g . the multiple dendrites of a neuron ) , but the mechanistic basis for unipolar or multipolar outcomes is unclear ., The design principles of Rho-GTPase circuits are captured by two-component reaction-diffusion models based on conserved aspects of Rho-GTPase biochemistry ., Some such models display rapid winner-takes-all competition between clusters , yielding a unipolar outcome ., Other models allow prolonged co-existence of clusters ., We investigate the behavior of a simple class of models and show that while the timescale of competition varies enormously depending on model parameters , a single factor explains a large majority of this variation ., The dominant factor concerns the degree to which the maximal active GTPase concentration in a cluster approaches a “saturation point” determined by model parameters ., We suggest that both saturation and the effect of saturation on competition reflect fundamental properties of the Rho-GTPase polarity machinery , regardless of the specific feedback mechanism , which predict whether the system will generate unipolar or multipolar outcomes . | Cell morphology is a critical determinant of cell function , and the conserved Rho-family GTPases ( Cdc42 , Rac , Rho , or Rop in plants ) are key regulators of cell morphology ., Rho-GTPases self-organize by concentrating into clusters at the cortex , and several mathematical models have been proposed that capture the essential features of such pattern formation ., However , it has been unclear how such systems reliably generate either a single cluster ( unipolar outcome ) or multiple clusters ( multipolar outcome ) ., In this paper , we show that a broad class of models for Rho-GTPase polarization all exhibit the ability to switch between a regime in which rapid winner-takes-all competition between clusters yields unipolar outcomes and a regime in which competition is so slow that multipolar outcomes occur at biologically relevant timescales ., We find that the switch in model behavior follows a surprisingly simple rule , and elucidate the fundamental principles that underpin that rule ., Our theoretical study explains how the same biochemical system can robustly yield unipolar or multipolar outcomes , and makes experimentally testable predictions . | cell physiology, enzymes, enzymology, geometry, simulation and modeling, cell polarity, fungi, model organisms, mathematics, experimental organism systems, cellular structures and organelles, research and analysis methods, saccharomyces, proteins, guanosine triphosphatase, cell membranes, yeast, cytoplasm, biochemistry, biochemical simulations, hydrolases, eukaryota, cell biology, curvature, biology and life sciences, yeast and fungal models, physical sciences, computational biology, saccharomyces cerevisiae, organisms | null |
journal.ppat.1006557 | 2,017 | Reversible unfolding of infectious prion assemblies reveals the existence of an oligomeric elementary brick | Transmissible spongiform encephalopathies ( TSEs ) , or prion diseases , constitute a group of rare , fatal neurodegenerative diseases affecting humans and animals ., Creutzfeldt-Jakob disease ( CJD ) , Gerstmann-Sträussler-Scheinker syndrome ( GSS ) and fatal familial insomnia ( FFI ) are the most common forms of human TSEs ., The prion theory , initially proposed to describe TSE pathogenesis 1 , has recently been extended to a larger panel of neurodegenerative disorders resulting from protein misfolding and aggregation 2 , 3 ., While the aetiology of TSEs is associated with a template-assisted conformational change in the normal prion protein ( PrPC ) into an abnormal conformer ( PrPSc ) , the molecular mechanism of the templating process and its dynamics remain obscure ., The most accepted theoretical mechanisms describing prion conversion remain Griffith’s autocatalysis hypothesis , proposed in 1967 , and the seeding-elongation theory of Caughey-Lansbury ( CL ) , proposed in 1995 4 , 5 ., Although other models have been proposed , all these models include either Griffith or CL kernels 6–8 and do not describe the templating process in terms of molecular and structural events ., The field of protein folding has benefitted from more than five decades of conceptual and methodological development , whereas the exploration of amyloid assemblies and their mode of packing has only recently emerged but is of growing interest ., For non-mammalian prions requiring poly N/Q repetition for segment assembly , the templating process occurs via induced-fit incorporation of the monomer at one extremity of the growing amyloid fibril 6 ., This mechanism fits well with the structural model reported by Sawaya and collaborators 9 ., Concerning mammalian prions , the formation of PrPSc assemblies is far from being elucidated ., Many attempts to explore the architecture of prion assemblies in detail have been reported ., While low-resolution structural approaches such as analyses of small angle X-ray scattering , hydrogen-exchanges and molecular dynamics based on experimental constraints have generated several models of PrP packing 10–18 , these strategies have failed to describe the dynamics of the templating process ., Indirect approaches , such as polymerization kinetic modelling and partial unfolding using ionic detergent , chaotropic treatment and temperature , have also failed to describe the templating process 19–25 ., Therefore , the best mechanism that might be intuitively considered is the fitted-induced adjustment of PrPC at both extremities of the PrPSc assembly 10 and the propagation of an allosteric state from PrPSc to PrPC through an interface of interaction between these two conformers 26 ., Although the structural models of PrPSc are divergent , they provide potentially useful information with regard to the architecture of the assemblies , such as the existence of a periodical repetition of multimeric PrP as an elementary base ., For example , electronic diffraction performed on a 2D quasi-crystal revealed that the architecture of prion assemblies was based on the periodical repetition of a PrP trimer 15 ., Another model based on a simulation of molecular dynamics performed on a H2H3 segment of PrP suggests the stacking of tetrameric PrP as the basis of amyloid fibre formation by the helix H2H3 segment 13 ., Recently , Wadsworth and colleagues used electron tomography to show the existence of periodic elements constituting the extractive PrPSc tropo-filament 16 ., The existence of such periodical repetition suggests the existence of a mesoscale organisation of PrP protomer in prion assemblies and immediately raises the question of the sequence of events during template-assisted conversion and of the potential structural polymorphisms that should exist among PrPSc conformational variants or strains ., To investigate the intimate architecture of infectious prion assemblies , we used sedimentation velocity and size exclusion chromatography methods to examine the quaternary structural transition during the partial unfolding of PrPSc assemblies at equilibrium using a chaotropic agent ., During this partial unfolding process , we revealed that PrPSc assemblies are composed of oligomeric elementary bricks , referred to as suPrP , which are innocuous once isolated ., The condensation of these molecules into larger assemblies results in the reacquisition of infectious properties through a conformational change at the protomer level , in concert with their condensation ., Moreover , we demonstrated that PrPSc assemblies are in steady-state equilibrium with suPrP and could play a role in the spreading of the prion replication centre ., Unfolding of prions was performed by incubation with increasing concentrations of urea ( from 0 to 6 M ) for 60 min ., After a solubilisation step involving dodecyl maltoside and sarkosyl , PrP size distribution was analysed by sedimentation velocity ( SV ) in an iodixanol gradient , as previously described 27 , 28 ., The SV buffer and brain homogenate contained identical concentrations of urea to prevent PrP refolding during the centrifugation step ( Fig 1B ) ., When the 263K-brain homogenate was treated with increasing concentrations of urea , a transition in the PrP quaternary structure was observed ., This transition corresponded to a disassembly process , leading to the formation of small PrP conformers with a sedimentation peak centred on fraction 2 ( Fig 1B ) ., These conformers were designated as suPrP ., At the resolution of the SV , suPrP co-sedimented with the unfolded state of PrPC ( PrPU ) obtained after the treatment of uninfected hamster brain homogenate with 6 M urea ( black curve in Fig 1B ) ., The analysis of PrP sedimentogram evolution as a function of the urea concentration revealed the existence of an isobestic point , suggesting a two-state transition process within the range of 1 to 6M urea , without the accumulation of disassembling intermediates ( for details see S1 Appendix ) ., Moreover , the sedimentogram centroid ( see Materials and Methods ) as a function of the urea concentration presented a sigmoidal shape , indicating a cooperative disassembly process ( Fig 1D , in black ) ., After urea removal by dialysis prior SV , PrP spontaneously refolded into two sharp populations peaking in fractions 2 and 9 , and designated rf2PrP and rf1PrP , respectively ( Fig 1C ) ., Furthermore , the formation of rfPrP assemblies was concerted with the disappearance of the small conformers generated during the unfolding step , as shown in Fig 1B ., As for urea-induced disassembly , the evolution of the sedimentogram centroid as a function of urea presented a sharp sigmoidal shape , suggesting a highly cooperative refolding process ( Fig 1D , in red ) ., Comparatively , the refolding of PrPU ( i . e . PrPC treated with 6M urea ) by dialysis did not generate larger-sized PrP aggregates , indicating that rfPrPs are specific to PrPSc disassembling and reassembling ., The velocity sedimentograms suggest that suPrP is either monomeric as PrPU , or formed from either small-sized oligomers or low-density PrP assemblies ., To estimate more precisely suPrP size , urea-treated and detergent-solubilized ( as for SV ) 263K brain homogenates were analysed by size exclusion chromatography ( SEC ) ., The SEC column was equilibrated with a running buffer containing 6 M urea ( but no detergents ) to avoid refolding during the separation ., Of note , the presence of 6M urea in the running buffer disintegrate 29 , 30 all lipoid micellar structuration potentially interfering with the intrinsic hydrodynamic size of suPrP ., After SEC fractionation , the amount of PrP was estimated by western blot ., While the urea-unfolded , purified PrPC from uninfected hamster brains ( see S2 Appendix ) eluted at 11 . 3 ml , the elution volume of urea-treated suPrP from 263K-infected brain was approximately 8 . 2 ml , suggesting an oligomeric state ( Fig 1E ) ., According to our column calibration , suPrP would be in the range of a PrP trimer ( with a standard error of ±1 protomer ) ., Urea-treatment of PrPSc purified in the absence of detergents and associated lipids ( S3 Appendix and 31 ) led to a quasi-identical chromatogram ( green curve in Fig 1E ) ., Therefore , we can exclude a size overestimation of suPrP due to the contribution of a noncovalent interaction with lipids or detergent trace ., Collectively , these data indicate that PrPSc could disassemble into smaller oligomeric conformers , designated suPrP , showing a sedimentation peak centred on fraction 2 ., The formation of suPrP followed a two-state disassembly and cooperative process: PrPSc ⟶ suPrP ., The refolding step leads suPrP to condense and generate rf1PrP and rf2PrP assemblies ( Fig 1F ) ., We next sought to determine whether the isolation of such suPrP conformers is a generic prion characteristic or restricted to the 263K prion strain ., Therefore , two different prion strains , T1Ov-21K and T2Ov-19K , obtained after adaptation of human CJD prions ( cortical MM2 subtype ) to tg338 mice 32 , 33 , were similarly treated with 6 M urea , followed by dialysis ( Fig 1A and Fig 2A ) ., As shown in Fig 2B and 2C , both T1Ov-21K and T2Ov-19K PrPSc unfolded into small objects that sedimented in the upper fractions of the gradient , as observed for suPrP263K , and refolded into larger-sized assemblies upon the removal of urea by dialysis ., These observations joined those obtained using 263K PrPSc ( Fig 1B and Fig 2A ) and suggest the existence of a generic process leading to the formation of suPrP conformer endowed with the necessary and sufficient structural information to reassemble into rfPrP during the dialysis/refolding step ., Due to biohazard restrictions , SEC of the suPrP21K and suPrP19K samples was not possible ., However , the quasi-similar SV patterns of the suPrP21K and suPrP19K strains compared with suPrP263K enabled the retention of a similar oligomer size as a first approximation ., To further assess the relationship between suPrP , rfPrP and the parental PrPSc assemblies , we compared their proteinase K resistance and templating activities in vitro or in vivo ., All the fractions from the 263K-brain homogenate treated with 6 M urea before SV were PK-sensitive ( Fig 3A ) , as compared to their non-urea treated counterparts ., Similarly , the top fractions from the T1Ov-21K and T2Ov-19K brain homogenates treated under the same conditions were PK sensitive ( Fig 3C ) , indicating that suPrP is PK sensitive ., In contrast , the rfPrP species were truly PK resistant ( Fig 3B and 3C ) , indicating a structural rearrangement of PrP protomers during the refolding and polymerization of suPrP into rfPrP ., In the particular case of T1Ov-21K and T2Ov-19K , rfPrP presented the parental 21K and 19K proteolytic signatures 32 , respectively , in reference to unglycosylated PrPres ( Fig 3C ) ., This observation suggests that during suPrP refolding into rfPrP , the tertiary structure of the PrP protomer refolds to acquire a proteolytic pattern similar to the native T1Ov-21K and T2Ov-19K strains ., Thus , the structural determinant responsible for this differential proteolysis is maintained in the structure of suPrP ., To compare the specific templating activity of both suPrP and rfPrP with that of untreated PrPSc assemblies , we used protein misfolding cyclic amplification ( PMCA ) ., Serial ten-fold dilutions of suPrP enriched fractions ( see also S4 Appendix ) , rfPrP-enriched SV fractions and fractions from the SV fractionation of untreated infected brain material ( see Fig 2A–2C ) were mixed with uninfected brain homogenate from hamster PrP transgenic ( tg7 line ) or ovine PrP mice ( tg338 line ) 28 and run for one round of PMCA for 48 hours 27 ., The PMCA products were subsequently treated with proteinase K and analysed for PrPres content by immunoblotting ., All samples were normalized according to the total amount of PrP prior to PMCA to compare the specific templating activity per PrP unit ., As shown in Fig 3D–3F , no PrPres signal was detected after PMCA amplification of suPrP-enriched fractions from the three strains ., In sharp contrast , a positive signal was observed after PMCA amplification of the rfPrP fractions up to a 105−106-fold dilution ., To exclude the contribution of cofactors or putative , remnant PrPSc assemblies to the refolding of inactive suPrP into active rfPrP after urea removal , size exclusion chromatography fractions collected after suPrP size-separation ( Fig 1E , green curve ) were dialyzed to remove urea and titrated for their templating activity by PMCA ., As shown in Fig 3H , the templating activity ( relative to that observed for 263K brain homogenate ) perfectly correlates with suPrP size exclusion profile ., According to PMCA titration , the templating activity of the suPrP peak was almost similar to that found in whole brain homogenate ., Altogether , these observations suggest a quasi-total restoration of the templating activity after refolding of suPrP263K into rfPrP ., Moreover , the correlation between suPrP SEC profile with templating activity of these fractions after urea removal makes highly improbable the contribution of cofactors or remnant PrPSc seed to suPrP ⟶ rfPrP process ., We next compared the infectivity of suPrP263K and rfPrP263K conformers using an incubation time bioassay in hamster PrP transgenic mice 28 ., A second objective of this experiment was to determine whether the 263K strain structural determinant ( SSD ) was fully conserved after suPrP263K refolding and condensation into rfPrP263K ., Pools of reporter tg7 mice were inoculated intracerebrally with 1:10-diluted , freshly prepared aliquots from the aforementioned fractions ., As rfPrP formation occurs according to a cooperative process ( as shown in Fig 1 ) , the dilution of suPrP should significantly reduce the rate of rfPrP formation and enable trapping of the suPrP oligomer , even after the dilution of urea prior to inoculation ., The tg7 individual survival time values are reported in Fig 3I ., Only one out of five mice inoculated with suPrP-enriched fractions ( fractions 1 to 3 ) developed a clinical prion disease and accumulated detectable PrPres in the brain ., The remaining mice , as the mice inoculated with intermediate fractions from the same gradient ( fractions 9 to 11 ) remained free of symptoms and of PrPres in the brain up to 350 days post-inoculation ., Previous dose/survival time curves for 263K prions showed that at the limiting dilution ( 10−6 ) , the mean survival time was established as approximately 100 days 28 ., These observations indicate that the suPrP fractions exhibited extremely low levels of infectivity 34 that can occasionally trigger disease in reporter mice ., In marked contrast , mice inoculated with the upper ( fraction 1 to 3 ) and intermediate fractions ( fraction 9 to 11 ) from SV-fractionated , urea-treated and dialysed 263K-brain material ( i . e . , enriched in rfPrP fractions ) succumbed to disease after 56±1 and 58±1 days , respectively ., In terminally sick mice , the brain PrPres electrophoretic profile and neuroanatomical distribution of PrPres were consistent and were similar to those observed in association with 263K prions ( Fig 2J and 2K ) ., A healthy mouse euthanized at 200 days post-inoculation with a suPrP fraction was negative for PrPres accumulation ( Fig 2J and 2K ) ., Collectively , these observations suggest that the suPrP conformer has generic properties , at least for the three prion strains examined here ., The suPrP conformer is PK sensitive and exhibits low , if any , templating activity ., However , once polymerized into rfPrP during the refolding step , the complex reacquires its templating capacity and the PrPSc strain structural determinant , as shown via bioassay experiments for rfPrP263K and the electrophoretic typing for rfPrPT1-Ov-21K and rfPrPT2-Ov-19K ., The occurrence of a quaternary structural transition from PrPSc to suPrP and rfPrP at a low urea concentration ( as low as 1 M urea , Fig 1B and 1C ) strongly suggests the existence of equilibrium between rfPrP / PrPSc and suPrP under physiological conditions ( i . e . , without any need for urea treatment ) ., This equilibrium state , schematized in Fig 4A , suggests that the PrPSc depolymerization rate , as first-order kinetics , is independent of the PrPSc concentration , while the condensation rate is suPrP concentration dependent ., To document the existence of such equilibrium , in the absence of urea treatment , a high-speed dilution method was used to displace the equilibrium towards the formation of suPrP ( S5 and S7 Appendixs ) ., Purified 263K PrPSc assemblies ( S3 Appendix ) were rapidly diluted , and the relaxation of the mean average molecular size of PrP assemblies ( <N> ) was monitored as a function of time by static light scattering ( Fig 4B ) ., A decrease in the mean average size of PrP assemblies was observed , indicating equilibrium displacement of PrPSc assemblies towards smaller-sized objects ., Three mutually exclusive hypotheses can explain the formation of small objects immediately after dilution ., i ) PrPSc may depolymerize into monomers ., However , it is highly unlikely that in the absence of a chaotropic context , PrPSc assemblies generate monomers similar to PrPC by simple dilution , while a 6 M urea treatment of PrPSc leads to the formation of the suPrP conformer ., ii ) PrPSc may spontaneously fragment through simple dilution and soft agitation ., This event is also highly improbable ., Indeed , comparison between the amplification titre via PMCA at t0 and at the plateau ( t1 ) revealed a 3-log loss in PrPSc templating activity induced by dilution ( Fig 4C ) ., Fragmentation may generate more de novo templating seeds and should therefore lead to increased PMCA activity 35 ., iii ) The third hypothesis links PrPSc depolymerization to suPrP by equilibrium displacement during dilution ., This hypothesis would be fully consistent with the reduction of PMCA templating activity ., Total equilibrium displacement at a high dilution ( 1 μM to 20 nM ) should provide an <N> value corresponding to the size of suPrP263K ( <N> = 3 , dots red line in Fig 4B ) ., The higher <N> value ( <N>≈35 ) observed at the relaxation plateau suggested that the dilution factor was not sufficiently significant to achieve quasi-total equilibrium displacement , and larger assemblies still remained ., Accordingly , complete loss of PMCA activity , as observed with suPrP263K , did not occur ( for higher dilution factor see S1 Appendix , Fig S7 ) ., To characterize the reverse condensation of suPrP263K into rfPrP263K assemblies ( Fig 4A ) , we used two complementary approaches based on suPrP ⇌ rfPrP equilibrium displacement in favour of rfPrP ., The first approach involved a isopycnic concentration method favouring an increase in PrP local concentration 36 in concert with urea removal ( S6 Appendix ) ., After isopycnic sedimentation of a 6M urea-treated 263K-brain homogenate , the majority of PrP was detected in fractions 6–16 , with a peak being observed in fraction 9 , corresponding to a low-density object ( Fig 4D ) ., Fraction 9 was collected and immediately subjected to SV ., The resulting sedimentograms showed that isopycnically concentrated PrP was composed of one major population of larger-sized assemblies peaking in fraction 8 ( Fig 4E ) ., These assemblies exhibited similar specific PMCA templating activity per PrP unit compared to the 263K assemblies , suggesting that the isopycnic concentration of suPrP enabled the regeneration of rfPrP assemblies ( Fig 4F ) ., The second approach was based on equilibrium displacement towards the formation of rfPrP after a decrease in the urea concentration by fast dilution ., First , semi-purified suPrP263K ( S4 Appendix and Materials and Methods ) in 6 M urea was concentrated six-fold by ultrafiltration ( suPrP263K6X ) ., Subsequently , suPrP263K 6X was 10-fold diluted in PMCA reactional medium to dilute the urea concentration below 0 . 6 M . As shown in Fig 4G , dilution of urea restored the templating activity up to a 106-fold serial dilution , when the non-concentrated suPrP263K remained inactive ., To analyse the effect of urea dilution on the PrP sedimentation profile , 6X suPrP263K in 6 M urea was diluted 10-fold ( for a final urea concentration of 0 . 6 M ) , and the quaternary structure was analysed by SV ., As shown in Fig 4H , the sedimentogram revealed the formation of large PrP assemblies that were initially absent in non-concentrated suPrP263K ., Furthermore , analysing the templating activity of fractions 1 to 3 and 15 to 18 by PMCA ( Fig 4I ) revealed an activity similar to 263K prions ., Thus , this second approach based on dilution of the urea concentration below 1 M revealed the spontaneous condensation of semi-purified suPrP263K into rfPrP ., Collectively , the equilibrium displacement experiments demonstrated that rfPrP / PrPSc assemblies are in dynamic equilibrium with suPrP ., Equilibrium displacement by dilution decreases both the size of PrPSc assemblies and the templating propensity after complete relaxation ., However , the equilibrium could be shifted towards the formation of rfPrP / PrPSc assemblies with templating activity by urea dilution or suPrP concentration ., Urea-induced PrPSc disassembling process generates a PK-sensitive and non-infectious conformer ( suPrP ) , which upon refolding , exhibits the main structural determinants of extractive PrPSc ., The isobestic point deduced from the superposition of the PrP SV sedimentograms at increasing urea concentrations ( within range of 1M to 6M urea , Fig 1B ) and the sigmoidal shape of the sedimentogram centroid ( Fig 1D ) indicate that the formation of suPrP263K occurs according to a cooperative two-state unfolding process ( PrPSc ⟶ suPrP ) ., This two-state mechanism implies that urea-induced depolymerization leads to the accumulation of a unique conformer ( suPrP ) , without any significant accumulation of other reaction intermediates ., The use of isopycnic concentrations and refolding by urea dilution demonstrated the kinetic entanglement between suPrP263K and rfPrP263K ., As is the case for condensation/polymerization reactions , the rate of this process depends non-linearly on the concentration of the reactant ( i . e . , suPrP ) ., This dependency of the rate of rfPrP formation on the suPrP concentration is also at the origin of the hysteresis and shift observed between the two-centroid sedimentogram sigmoids reported in Fig 1D ., Indeed , the existence of hysteresis between the two curves implicates that the pathway of suPrP formation ( PrPSc ⟶ suPrP ) differs from the rfPrP one ( suPrP ⟶ rfPrP ) ., The comparison between urea1/2 of unfolding ( U1/2PrPSc→suPrP ) and refolding ( U1/2suPrP→rfPrP ) shows a difference of 1 . 1 M urea ., This difference indicates that the cooperativity of suPrP ⟶ rfPrP process is higher than the PrPSc ⟶ suPrP ., According to standard phenotypic criteria ( PrPres electrophoretic signature , incubation duration , PrPres neuroanatomical distribution ) , the strain structural determinant ( SSD ) of 263K prion assemblies is fully conserved throughout the process of disassembling into suPrP and refolding into rfPrP ., Similarly , the conserved , specific proteolytic signatures of rfPrPT1-ov-21K and rfPrPT2-ov-19K strains after the refolding of their respective suPrPs also indicates that the structural determinant responsible for differential proteolysis is contained within suPrPT1-Ov-21K and suPrPT2-Ov-19K ( Fig 5B ) ., Taken together , these observations suggest that suPrP is an elementary brick containing all the folding and structural information that is necessary and sufficient for reassembly into bona fide infectious rfPrPs with strain properties of the parental prion ., Encoding of the SSD within the suPrP oligomeric structure suggests that this molecule can adopt different conformations ., For practical reasons , we were unable to estimate the precise size of suPrPT1-Ov-21K and suPrPT2-Ov-19K ., Thus , it remains to be determined whether this structural polymorphism resides not only in the tertiary structure of the PrP protomer but also in the number of protomers forming the suPrP oligomers ( i . e . , quaternary structure of suPrP ) ., Notably , while rfPrP and extractive PrPSc exhibit a similar SSD , their respective size distribution patterns differ ., The variability in the size distribution of rfPrP and its size homogeneity compared with extractive PrPSc assemblies have been well described based on the theory of condensation/polymerization ., This last being highly stochastic and sensitive to local fluctuation 37 ., Thus , the broader size distribution of extractive PrPSc assemblies should be considered with regard to biological environmental fluctuations in which PrPC is converted into PrPSc , whereas the in vitro refolding of suPrP into rfPrP occurs in a physically controlled environment ., Using SEC , we demonstrated that suPrP263K is oligomeric and might correspond to a PrP trimer ( we attributed +/-1 monomer due to SEC technic and calibration method ) ., This last observation contrasts with previous experiments which suggest the existence of monomeric β-enriched conformer during guanidine-hydrochloride treatment 19 ., Strikingly , suPrPs from 263K , T1Ov-21K and T2Ov-19K resist up to 8 M urea , whereas the overall PrPSc quaternary structure start to disassemble at ~1 M urea ., These observations suggest that 263K , T1Ov-21K and T2Ov-19K assemblies have at least two levels of organization involving protomer interactions of different strength ., One is highly sensitive to urea , suggesting ‘weak’ intermolecular interactions ., Disruption of these interactions results in the formation of suPrP species ., The second type of arrangement is highly stable , resistant to chaotropic unfolding , and maintains the oligomeric cohesion of suPrP ( Fig 5C ) ., The existence of these two modes of packing therefore involves at least two distinct PrP domains , one domain involved in the formation of the oligomeric elementary subunit and a second domain involved in its condensation ., By using protease finger-print coupled to epitope-mapping , Kocisko and collaborators revealed that a 16kDa fragment , including the region of 143–154 , remained the ultimate domain of PrP resisting to guanidine treatment 38 ., This domain may constitute a region involved in the formation of suPrP ., Is such multiscale organization architecture ( or at least a signature thereof ) observed in the current PrPSc structural models that have recently been elaborated ?, The structural interpretation of our observations do not plead for PrPSc assemblies formed by the unique juxtaposition of parallel element in-registered β , as the interactions between each protomer are energetically equivalent 12 , 14 ., Two other models show different types of interactions to form the fibril core and to stack the subunits 15 , 39 , 40 ., Daggett and co-workers proposed a β-spiral model based on the repetition of a trimeric core 39 ., Even if this hypothesis is consistent with the data obtained in the present study , the high content of α-helices in this model is challenged by studies showing that PrPSc does not include an α-helix secondary structure 14 ., The β-helix model presents an interesting organization based on a trimer of β-helices , despite the presence of α-helix contents 40 ., A recent cryo-electron microscopy structural characterization 17 of GPI-anchorless PrPSc ( PrPSc GPI- ) led to refinement of the β-solenoid model , which now resembles the β-helix model without any remnant α-helix ., This model strongly supports the existence of dimers resulting from head-to-head and tail-to-tail contacts ., These dimers can be interpreted as the elementary bricks of the fibrils ., Moreover , PrPSc GPI- assemblies comprise two fibrils , implying lateral interactions between each fibril ., Thus , it is reasonable to suggest that the elementary brick of these fibres is a dimer-dimer or a tetramer , depending on the strength of the interactions ., In the present study , we did not obtain any information on whether suPrP is the elementary brick of a protofibril or fibre , even if an axis with 3-fold symmetry would favour a fibre brick over a protofibril brick 41 ., The estimated size of suPrP , based on the limited precision of size exclusion chromatography , is at least within the range of what is observed in these structural models ., Reflecting the resolution of the size determination experiments , we cannot exclude the idea that size estimation corresponds to a mixture of dimers and tetramers , as suggested by the recent structural model 17 ., These data are consistent with these last two models ., Moreover , the possibility that different prion strains differ in the size and structure of their elementary bricks is not excluded ., As demonstrated either by PMCA or bioassay , suPrP oligomers have very low templating activity and infectivity , if any ., The immediate consequence of these observations is the absence of remnant seeds catalysing suPrP ⟶ rfPrP process ., Indeed , these two techniques are highly sensitive , detecting seeds present in a 263K-infected brain homogenate diluted up to 107- and 106-fold , respectively 27 , 28 ., One important question that could arise is why suPrP does not completely refold into rfPrP after dilution at the time of mouse inoculation or PMCA titration ?, The reason of the absence of significant refolding could be the concentration dependency of the formation rate of rfPrP as the suPrP ⟶ rfPrP is a multimolecular process and therefore highly concentration dependent ., This concentration dependency of suPrP ⟶ rfPrP refolding has been demonstrated as well by the isopycnic concentration method as by PMCA using 6-fold concentrated suPrP263K ( Fig 4G–4I ) ., We show that suPrP and infectious assemblies ( rfPrP and PrPSc ) exhibit clearly different biochemical and biological properties , such as resistance to PK and templating activity/infectivity , suggesting a profound structural rearrangement of suPrP during polymerization into infectious assemblies ( Fig 5A ) ., Therefore , unique condensation of suPrP into rfPrP , without significant structural modification is not supported by the data obtained in the present study ., Gain of PK resistance is attributed to a polypeptide backbone rearrangement , or formation of quaternary structure restricting protease accessibility ., Indeed , PK sensitivity is not a question of the size of the assemblies if we consider a strict periodic repetition of an element ., Moreover , the acquisition of infectivity and templating activity implies the creation of a templating interface in concert with the polymerization process of suPrP into rfPrP ., These two observations strongly suggest that during polymerization , protomers constituting suPrP undergo at least a tertiary structural rearrangement leading to the formation of PK-resistant and infectious assemblies ., It is not surprising that condensation/polymerization occurs in concert with a major structural modification ., The best example is the formation of amyloid assemblies by disordered domains of amyloidogenic proteins or even PrPC to PrPSc conversion ., From a physical point of view , this deep structural rearrangement is related to non-linearity of energy as function of size 26 , 41 ., The lack of infectivity of suPrP oligomer , should be put in perspective of the observations made by Silveira and collaborators who determined that 263K PrPres oligomers smaller than a pentamer were virtually devoid of infectivity/templating activity 42 ., Based on considering suPrP as a trimer ( +/- 1 monomer ) and the requirement of a condensation process to generate infectious assemblies , it can be expected that the necessary and sufficient structural rearrangements could occur with a suPrP dimer ( i . e . , a hexamer +/- 2 protomers of PrP ) ., The suPrP dimer would therefore constitute the minimal size of PrP assemblies with replicative properties and could correspond to th | Introduction, Results, Discussion, Materials and methods | Mammalian prions , the pathogens that cause transmissible spongiform encephalopathies , propagate by self-perpetuating the structural information stored in the abnormally folded , aggregated conformer ( PrPSc ) of the host-encoded prion protein ( PrPC ) ., To date , no structural model related to prion assembly organization satisfactorily describes how strain-specified structural information is encoded and by which mechanism this information is transferred to PrPC ., To achieve progress on this issue , we correlated the PrPSc quaternary structural transition from three distinct prion strains during unfolding and refolding with their templating activity ., We reveal the existence of a mesoscopic organization in PrPSc through the packing of a highly stable oligomeric elementary subunit ( suPrP ) , in which the strain structural determinant ( SSD ) is encoded ., Once kinetically trapped , this elementary subunit reversibly loses all replicative information ., We demonstrate that acquisition of the templating interface and infectivity requires structural rearrangement of suPrP , in concert with its condensation ., The existence of such an elementary brick scales down the SSD support to a small oligomer and provide a basis of reflexion for prion templating process and propagation . | Prions are self-propagating assemblies with all necessary and sufficient replicative information stored in the 3D structure of the misfolded form of PrP called PrPSc ., Since the emergence of the prion theory in the 80s , many attempts have been done to identify prion replicative information at molecular scale ., Different models have been constructed based on a broad panel of experimental observations and some of them predict the existence of periodic elements constituting prion assemblies ., Here , by using partial unfolding approaches , we trapped an oligomeric conformer that we called suPrP , which could constitute the elementary brick of prion assemblies ., Once isolated , this elementary brick is devoid of infectivity ., However , it becomes infectious once condensated into larger assemblies ., The identification of the elementary PrP building block provides a new structural basis for understanding prion replicative information storage and spreading . | urea, medicine and health sciences, condensation, chemical compounds, condensed matter physics, vertebrates, organic compounds, animals, mammals, materials science, materials physics, sedimentation, oligomers, materials by structure, hamsters, infectious diseases, polymer chemistry, zoonoses, chemistry, phase transitions, polymerization, physics, rodents, organic chemistry, biology and life sciences, chemical reactions, physical sciences, depolymerization, amniotes, organisms, prion diseases | null |
journal.pcbi.1004603 | 2,015 | Regulated CRISPR Modules Exploit a Dual Defense Strategy of Restriction and Abortive Infection in a Model of Prokaryote-Phage Coevolution | Prokaryotes have evolved diverse molecular defense systems over billions of years of co-evolution with phages 1 , 2 ., Clustered Regularly Interspersed Palindromic Repeats ( CRISPRs ) , found in roughly 40% of sequenced bacteria and 90% of archaea , are peculiar in that they confer adaptive immunity against invading phages 3–6 ., CRISPR , as a defense mechanism , works via targeted acquisition of 26–72bp fragments ( called protospacers ) from the target DNA , and subsequently use of acquired fragments ( spacers ) for target restriction through an RNAi-like mechanism 7 , 8 ., Acquisition events appear to concentrate around short 2–5bp motifs ( protospacer adjacent motifs , or PAMs ) in the target DNA 4 , 9 , 10 ., CRISPR loci are organized as cassettes in which short repeats interleave spacers , and are located adjacent to highly diverse genes that code for the CRISPR associated protein machinery 4 , 11 ., Intriguingly , in addition to acquiring phage fragments , CRISPR systems can also acquire spacers from the host genome ., This has been experimentally demonstrated in two model systems: first , selective induction of the acquisition machinery ( in the absence of interference ) in laboratory strains of Escherichia coli resulted in the accumulation of a large number of self-targeting spacers 12; second , abolition of interference activity ( and not the acquisition machinery ) in wild type Streptococcus thermophilus resulted in unbiased acquisitions of self-targeting spacers alongside phage-targeting spacers 13 ., However , a large-scale survey of CRISPR cassettes in microbial genomes identified that only about 0 . 4% of the spacers are self-targeting , which , considering the relative size of prokaryotic genomes over phages , suggests some mechanism of selection against self-targeting spacers , perhaps to avoid autoimmunity 14–16 ., Indeed , directed experiments have conclusively shown that self-targeting can result in severe lethality 9 , 17–21 ., We therefore face a conundrum: how do prokaryotes maintain functional CRISPR systems 22 ?, Despite the conceptual similarities with restriction-modification systems that avoids autoimmunity by methylating the host genomes’ target restriction sites 23 , no analogous genome wide self- vs . non-self-discrimination ( SND ) mechanism is known for CRISPR systems ., In fact , as noted above , the evidence thus far suggests that an efficient SND may not exist ( The SND mechanism described by Marrafini and Sontheimer explains the evasion of self-destruction of CRISPR locus only and does not confer genome wide protection 24 ) ., But there are other routes to avoiding autoimmunity ., Toxin/anti-toxin or abortive infection systems restrict the scope of autoimmunity to infected populations via infection-induced activation 25 ., Indeed , upregulation of CRISPRs upon phage infection has been demonstrated experimentally 26–28 ., This makes it possible that the accumulated self-targeting spacers may function as “toxins” , which can be activated upon infection ., We therefore address the following two questions in this study: Clearly , the answers to these questions depend on key ecological and CRISPR kinetic parameters ., For instance , while CRISPRs are highly active against phages in wild type S . thermophilus ( a lactic acid bacteria widely used in industrial production of cheese ) 9 , artificial induction is essential to activate the system in E . coli 29 ., To this end , we develop and analyze a dynamical model that integrates prokaryote-phage coevolutionary dynamics , with regulated , infection-induced CRISPR acquisition and interference activity ., Several models of CRISPR-mediated prokaryote-phage coevolutionary dynamics have been previously reported 20 , 30–35 ., While refs ., 33–35 account for an abstract CRISPR-associated cost , they do not include the specifics of autoimmunity kinetics/the regulatory aspect of CRISPRs ., The model we develop here is detailed enough to incorporate the adaptive aspects of CRISPR , and general enough to allow intuitive ( analytic ) interpretations of the resulting qualitatively distinct steady states ., We interrogate the model using simulations and bifurcation analyses , and we find that as a function of key host , ecological , and CRISPR evolutionary parameters , the operational behavior of CRISPRs ( and the resulting host densities ) decomposes into four qualitatively distinct regimes ., In those regimes where CRISPR is advantageous to the host , both restriction and abortive infection operate; the latter dominates restriction in SND absence ., Crucially , CRISPR maintenance is determined by an upper bound on the activation level of CRISPRs in uninfected populations ., This critical limit of activation—beyond which host extinction is inevitable—is determined by a simple dimensionless combination of parameters ., We compare the current experimental data on CRISPR kinetics with these qualitative observations , which helps to explain the spacer deletion mechanism and absence of CRISPR activity in highly virulent and multi-drug resistant clinical isolates ., Before proceeding to model the complexity of CRISPR dynamics in general , we start by considering the case of a simple prokaryotic immune system with regulated autoimmunity ., The goal here is to analyze the influences of the regulation , immunity and autoimmunity on the resulting coevolutionary dynamics ., Fig 1 illustrates a simple coevolutionary model in which the immune system , apart from conferring immunity , also induces autoimmunity that is regulated in a cell state ( infected / uninfected ) specific manner ., Dynamic variables are denoted with Roman letters , and parameters are denoted with Greek symbols ., Any parameter associated with production of an item i is denoted as αi and that with its degradation is denoted by γi ., Free cells ( p ) , grow exponentially at a rate of αp , under a carrying capacity constraint of Φp ., Phages ( v ) infect free cells to produce infected cells at a rate of αq ., Infected cells can lyse to release phages at a rate of γq→v or undergo immunity to become a free cell at a rate of γq→p , or undergo autoimmunity at a rate of γq→ϕ ., Free cells undergo autoimmunity at a suppressed rate of δγp→ϕ , ( 0 ≤ δ ≤ 1 ) ., Note γp→ϕ need not necessarily equal γq→ϕ , for reasons that will become clear later when we discuss the detailed CRISPR model ., The condition δ = 0 implies complete repression of autoimmunity in free cells , whereas δ = 1 indicates no difference in repression across the two cell states ., The burst size of phages is αv ., Phages also die at a rate of γv ., Table 1 describes the variables and model parameters ., The dynamical equations for this model can be written as:, p˙=αpp ( 1−p+qΦp ) −δγp→ϕp−αqpv+γq→pqq˙=αqpv− ( γq→ϕ+γq→p+γq→v ) qv˙=αvγq→vq−γvv−αqpv, ( 1 ), Measuring all the state variables in units of Φp , and time in units of τ = αqΦp−1t , and denoting all the transformed variables and parameters with their corresponding Roman alphabets , we obtain:, P˙=APP ( 1−P−Q ) −δGP→ϕP+GQ→PQ−PVQ˙=PV− ( GQ→ϕ+GQ→P+GQ→V ) QV˙=αvGQ→VQ−GVV−PV, ( 2 ), We can study the influence of regulation ( determined by the parameter δ ) , immunity and autoimmunity rates ( GQ→V , GQ→ϕ and GP→ϕ ) on the above dynamical system using a bifurcation analysis ., These results are summarized in Fig 1 ., Fig 1A shows that , as a function of δ , two fixed points collide at a critical value of δ ( which we denote by δ1 ) , beyond which one of them ceases to exist ., Fig 1B shows that in the ( δ , GP→Φ ) space , beyond a critical curve that falls roughly as GP→Φ−1 , hosts go extinct ., Fig 1C reveals in the ( δ , GQ→Φ ) space , beyond a line of critical points , phages go extinct ., Behavior in the ( δ , GQ→P ) space is similar ., We provide an analytical treatment below ., Bifurcations occur when the number of fixed points or their stability properties change in response to a dynamical parameter ., Our system can approach three qualitatively distinct steady states: the first corresponds to host extinction , which we denote by E*= ( Pe* , Qe* , Ve* ) = ( 0 , 0 , 0 ) ., The second corresponds to a phage free system , which occurs with pure cultures where phages have not been introduced , or when hosts completely evade phage infection , which we denote by F*= ( Pf* , Qf* , Vf* ) = ( Pf* , 0 , 0 ) ., The third corresponds to the case of prokaryote-phage coexistence , which we denote by C*= ( Pc* , Qc* , Vc* ) ., In the phage free situation , the system evolves along the curve P˙=APP ( 1−P ) −δGP→ϕP , towards the fixed point Pf*=1−δGP→ϕAP ., Non-extinction/positivity condition on this expression reveals a criticality condition on δ for maintenance of hosts carrying our simple immune system in the phage free case: δ<APGP→ϕ=δ1 ., This is precisely the curve mapped out in Fig 1B beyond which the hosts go extinct; when δ = δ1 , F* = E* , and when δ > δ1 , F* is infeasible ., Hence , as long as the immune system ( with an autoimmunity side effect ) is suppressed below a critical nondimensional ratio of the free cell reproduction rate to that of its autoimmunity potential , the phage free steady state is feasible ., The non-trivial fixed point for the case of coexistence , C* , is given by:, Pc*=GVαv ( 1+GQ→ϕ+GQ→PGQ→V︸immune\xa0advantage ) −1Vc*=AP ( 1−Pc* ) −δGP→ϕAPPc*+GQ→ϕ+GQ→PGQ→VQc*=Pc*Vc*GQ, ( 3 ), Here GQ = ( GQ→ϕ + GQ→P + GQ→V ) denotes the overall removal rate of infected cells ., In this coexistence regime , the steady state expression for Pc* decomposes into the two parts: steady-state value when the dynamics is phage limiting and the advantage offered by the immune system in overcoming phage lysis ., This advantage is given by the ratio of the sum of immunity and autoimmunity rates conferred by the immune system in infected cells to that of the phage specific lysis rates ., Thus inducing autoimmunity , alongside immunity , in infected cells ( abortive infection ) is beneficial to the prokaryotic population when coevolving with phages ., As is the case with predator-prey models , Pc* is independent of the cell’s own growth rate 36 , and is completely determined by the immunity and autoimmunity parameters , along with the phage specific parameters ., Furthermore , positivity conditions on the steady state values yields the feasibility conditions for the existence of this steady state: ( 0<Pc*<1 ) , and ( 0 ≤ δ < δ2 ) with δ2=AP ( 1−Pc* ) GP→Φ ( as Vc*≤0 otherwise ) , giving us a tighter constraint on δ for coexistence ., Notice that δ2 < δ1 ., So regardless of the presence or absence of phages , a free cell autoimmunity suppression level of δ < δ1 is required for the population to avoid losing the immune system altogether ., When free cells completely repress the immune system ( δ = 0 ) , or when there is no autoimmunity ( GQ→ϕ = 0 ) , Vc* and Qc* achieve their maximum values ., As δ→δ2 , the values of Vc* and Qc* are reduced progressively ., The form of these equillibria implies that by increasing the net autoimmunity rate in free cells , lower net viral abundance is achieved ., However , by doing so the range of δ that supports coexistence is narrowed ., When δ > δ2 , the coexistence steady state C* is infeasible , and the system operates in the phage free regime , at which point , the condition δ < δ1 has to be satisfied to avoid host extinction ., The bifurcation analysis in Fig 1A maps this behavior: C* continues to be stable until δ < δ2 , whereas beyond δ2 the otherwise unstable F* becomes stable ( stability of the steady states ascertained by the Routh-Horwitz criteria 36 ) ., To analyze the influence of abortive infection on coevolution , we produced a two-parameter bifurcation diagram for the ( δ , GQ→ϕ ) space ( Fig 1C ) ., Two distinct regimes are clear: a coexistence regime , and a regime where hosts evade phages ., A third regime corresponding to host extinction also occurs for autoimmunity suppression exceeding the value δ1 ( for the parameters in this figure , it occurs along the line δ = 1 ) ., The bifurcation diagrams are similar for a variety of other parameter combinations tested ., Coexistence occurs for low values of GQ→ϕ , and are progressively lost as δ is increased ., We can trace the line of critical points analytically as follows ., Recall that the switch from coexistence to phage evasion is principally determined by the equality δ=δ2=AP ( 1−Pc* ) GP→ϕ ., If we let GQ = ( GQ→V + GQ→P + GQ→Φ ) and substituting for Pc* , we obtain 1−δGP→ΦAP=GVαvGQ→VGQ−1 ., When αvGQ→VGQ>>1 , as a function of GQ→ϕ and δ , this condition spans the line:, δK1+GQ→ϕK2=1, ( 4 ), where the intercepts are given by K1=APGP→ϕ1−GVαv ( 1+GQ→PGQ→V ) and K2=GQ→VαvGV− ( 1+GQ→PGQ→V ) ., For the parameters in Fig 1C , Routh-Horwitz criteria 36 reveals that the achieved C* values are stable ., Beyond this boundary , coexistence is infeasible , and cells assume a density determined completely by δ , and independent of GQ→ϕ: Pf*=1−δAPGP→ϕ ., Clearly , both K1 and K2 are reduced with increasing values of GQ→P ( immune rate ) , the net effect being reduction of the area under the line resulting in loss of coexistence ., To map the influence of immunity , one can similarly establish the critical line determining the boundary of coexistence explicitly as a function of ( δ , GQ→P ) ., In summary , our bifurcation analysis of this simple model, ( i ) reveals the precise regimes for the three possible fates of a prokaryotic immune system with regulated autoimmunity ( complete evasion of phages , coexistence with phages , or extinction ), ( ii ) shows that infected cell autoimmunity ( alongside restriction ) is beneficial to the prokaryotic population , and, ( iii ) reveals a strict limit on the free cell autoimmunity levels above which host extinction occurs ., Perhaps the most characteristic feature of CRISPRs is their adaptive ability for continued novelty resulting from spacer acquisitions and deletions ., The model above does not incorporate spacer turnover kinetics or its regulation ., Neither does it allow us to explicitly determine the influence of host protospacer levels on the interval of autoimmunity regulation 0 ≤ δ < δ1; the larger this window , the higher the cellular tolerance for CRISPRs ., We will therefore proceed to incorporate CRISPR specific reactions into the simple model described above ., We will show that, ( i ) the simple model arises as a particular limit of a more general model , and, ( ii ) by thwarting the accumulation of self-targeting spacers through an SND ( whose existence/absence is hard to ascertain from existing data ) , and/or through a highly active spacer deletion mechanism , the range of free-cell CRISPR activity levels , δ , is widened ., Furthermore , the general model will reveal other idiosyncratic features of CRISPR and its maintenance in populations over ecological time scales ., In this section we develop a more detailed model of CRISPR dynamics , which generalizes the simple model discussed above ., Our modeling strategy in this section ( see Fig 2 ) is intermediate to models that fix a constant rate of immunity ( as in 30 ) and agent-based models that describe strain-specific immunity ( as in 32 ) ., Briefly , we track spacer accumulations over time and use linear mass action kinetics to model the CRISPR reactions and the resulting ecological dynamics due to immunity and autoimmunity ., Such an approach offers the computational advantage to model growing populations while simultaneously accounting for the underlying regulatory dynamics of CRISPR and its kinetics ., While this model cannot capture strain-specific behavior , we can nonetheless make qualitative and even quantitative predictions for the average spacer accumulation kinetics resulting from the adaptive nature of CRISPR dynamics ., The key variables in this detailed model are described in Table 2 and discussed below ., We let πv denote the total number of phage protospacers per phage genome ., The amount of self-targeting spacers per prokaryotic genome is defined relative to the phage protospacer amount as βπv ., Thus β = 0 implies no self-targeting protospacers per prokaryotic genome , which can also be interpreted as the absence of self-targeting protospacers due to the presence of an SND ., At any time , both the free and infected cell populations ( denoted as p and q respectively ) have an associated CRISPR spacer content , the “per-cell” quotas which are completely specified by {ypA , ypI , ypS} and {yqA , yqI , yqS} respectively ( Table 2 ) ., Here y⋅A denotes the active spacer quota per cell ( i . e . , phage reactive ) , y⋅I denotes the inactive spacer quota per cell ( i . e . , phage inactive , due to mutations in the corresponding PAMs in phages ) and y⋅S denotes the self-targeting spacer quota per cell ., The average phage protospacer quota per infected cell available for its new spacer acquisitions is denoted by xA ., The per capita quotas of the various types of CRISPR spacer content are used to model the rates of acquisition and interference reactions in each subpopulation ., Let γq→p be the rate of immunity conferred per active spacer; then at any given time the immunity rate per infected cell is assumed to be γq→pyqA ., Similarly , if γq→ϕ denotes the rate of autoimmunity conferred per self-targeting spacer , the autoimmunity rate per infected cell is then γq→ϕyqS ., To obtain the corresponding term for the free cell population we will first need to model infection-mediated CRISPR activation ., As the operonic structure of CRISPR/Cas genes lends itself to regulation based on free/infected cell states ( 28 , 29 , 37–40 ) , we simply scale the rates of all the CRISPR reactions ( acquisition , deletion and interference ) by δ ( 0 ≤ δ ≤ 1 ) , in the free cell population relative to that of the infected population ., So δ = 0 implies that all CRISPR reactions in free cells are switched off whereas δ = 1 implies that there is no differential CRISPR expression between the free and infected cell populations ., Note that , only infected cells can acquire novel phage protospacers , while both infected and free cell populations can acquire self-targeting protospacers ., The latter events occur when δ > 0 ., Under these modeling assumptions , the corresponding autoimmunity rate per self-targeting spacer is given by δγq→ϕ; this is scaled by the per capita free cell quota of self-targeting spacers to calculate the autoimmunity rate per free cell , δγq→ϕypS ., In the absence of SND , given the large host genome size relative to that of phage ( e . g . E . coli genome is roughly 100× the length of phage λ ) and short PAM demarcating protospacers , we expect an abundant host protospacer pool ., In our model , this would imply a large host to phage protospacer ratio ( β > 1 ) ., On the other hand , if SND is present , then its efficiency determines the β value , with higher efficiencies implying lower β values and vice versa ., Similarly , the parameter δ determines the activation level of CRISPRs in free cells relative to that of infected cells; thus δ = 0 represents complete repression , and δ = 1 signifies no difference in CRISPR activation between free and infected cell populations ., To study the influence of host protospacers levels and regulation on prokaryotic densities , we vary δ and β across a large range of biologically feasible values ( Fig 4 ) ., Remarkably , as we observed in the case of our simple model , the steady state prokaryotic densities show a sharp , threshold-like behavior as a function of the degree of CRISPR regulation δ: hosts switch from maximal densities to complete extinction as the degree of free-cell CRISPR activity , δ , increases ( Fig 4A ) ., Even in the case of comparable levels of host and phage protospacer ( β = 1 ) , greatly reduced levels of activation in free versus infected cells ( δ < 0 . 01 ) are required to guarantee host existence ., While this tight window of prokaryotic existence is relaxed slightly at lower host protospacer levels , these results indicate that tight regulatory control is necessary for a wide range of host protospacer levels ., It is therefore clear that the presence or absence of an SND is a crucial determinant of CRISPR maintenance in populations ., Fig 4B shows the time course of several typical simulations for various ( β , δ ) combinations , to illustrate the effects of these two key parameters on intracellular spacer contents ., For a wide range of parameters and initial conditions we find that the system approaches a steady state ., We now work to derive an analytical understanding of the critical limit on δ ( denoted by δ1 ) that permits population survival ., As in the simplified model , exact conditions for the threshold-like behavior of the system in the δ and β space can be obtained by considering the phage free system , in which case , the full system reduces to:, P˙=APP ( 1−P ) −δGQ→ϕYPSPY˙PA=MVYPI−YPA−δGCYPAY˙PI=MVYPA−YPI−δGCYPIY˙PS=δβ−δGCYPS, These give rise to the following fixed points: {P*=1−δGQ→ϕYPSAP , YPA*=0 , YPI*=0 , YPS*=βGC\xa0} ., In the absence of any feedback from infections , and in the presence of an active spacer deletion mechanism , the active and inactive spacer contents are progressively lost from the population ., The influence of CRISPR induced autoimmunity on free cell density is manifest in the steady state expression for free cells ., For a population to not completely lose their CRISPR activity , the condition P* > 0 must be satisfied ., This leads us to the condition required for sufficient suppression of CRISPR in free cells:, δ<APGQ→ϕYPS*=APGCGQ→ϕβ ,, ( 6 ), For values of δ exceeding this upper bound , the system goes extinct ., The same constraint holds for a system with phage , as non-negativity of the net cellular growth rate is essential to avoid the only steady state of extinction ., Note that , in the presence of a perfect SND , β = 1 and so the constraint on δ is effectively removed altogether ., But in the absence of such a mechanism ( β > 0 ) , the internal steady state level of self-targeting spacers determines an upper limit on the free-cell CRISPR activity , δ ., The role of another crucial parameter is also apparent from this analysis: the spacer deletion rate ., High spacer deletions can effectively remove self-targeting spacer accumulations , thus suppressing autoimmunity ., So in addition to CRISPR regulation , the spacer deletion rate can also be increased to maintain CRISPR+ hosts in a population with larger host protospacer levels ., ( We will use simulations below to determine how large this rate should be relative to the spacer acquisition rate . ) ., For a wide variety of parameters and initial conditions tested , we found that the system converged to steady states ( see Fig 4B for an example ) ., Let ( YQA* , YQS* , YPS* ) denote the resulting steady state levels of intracellular spacer contents over CRISPR evolutionary time scales ., These can then determine fixed rates of immunity ( GQ→PYQA* ) and autoimmunity ( GQ→ϕYQS* , GP→ϕYPS* ) ., To do so , we use the simplified model shown in Fig 1 , which replaces all immunity and autoimmunity rates ( which were originally functions of the spacer variables ) by fixed rate constants ., In such a limit , a thorough analysis of the coevolutionary dynamics is feasible ., These results indicate that as long as the constraint on δ is met and the steady state intracellular levels of self-targeting spacers in infected cells is non-zero , CRISPRs can exploit the abortive infection strategy alongside restriction ., In the absence of SND , by contrast , the levels of self-targeting spacers will be much higher than phage reactive spacers ., Under these conditions , the model predicts that CRISPRs will function principally as an abortive infection system ., We stress that we are not considering the situation that individual spacer sequences themselves are fixed in the population , but rather , the total number of them ., Given the importance of the dimensionless parameters {δ , β , GC} in determining the evolutionary maintenance of CRISPR+ hosts , we now focus on understanding the influence of these parameters on the general model ., Free cell densities in the {β , GC} space for a given value of δ reveal a characteristic four-regime pattern ., Fig 5 shows the free cell densities achieved ( first column ) and phage densities ( second column ) for various values of ( β , GC ) values under two cases of δ: δ = 0 . 01 and δ = 0 . 0001 ., Regime I occurs at low β and very high GC values ., Here both free cells and phages coexist; while the former assume significantly low levels ( but never extinct ) , the latter achieve their highest densities ., Regime II occurs at low β and low GC values ., Here hosts achieve their highest densities driving phage densities to very low values , if not extinction ., In regime III , which occurs at high but still plausible β values , host extinction occurs ., Regime IV is an extension of regime II’s behavior , but at high GC and high β values ., Hints to explain the existence of these four qualitative regimes , and their boundaries , are provided by the corresponding intracellular steady state spacer levels and the constraint on δ we derived in the previous section ., As we proceed to higher β values , the active spacer levels decrease and self-targeting spacer levels increase ( see for example Fig 4B ) ., Higher β values lead to larger steady state levels of self-targeting spacers , effectively increasing the autoimmunity rate of infected cells ., This inhibits immune mediated feedback of active spacers to the free cell population ( through inheritance ) and causes a reduction in the overall active spacer levels ., Self-targeting spacers , on the other hand , can be independently acquired in free cells at a rate determined by δ ., According to this basic intuition , we can now derive rough conditions for falling in each of the four qualitative regimes ., ( Regime I ), At high GC values ( GC → ∞ ) CRISPR cassettes are empty and the immunity and autoimmunity reactions are overwhelmed by phage lysis ., Under these conditions , both the steady state spacer levels and their derivatives become zero , making the factor GQ→PYQA*+GQ→ϕYQS*GQ→V=0 , resulting in no net growth advantage to CRISPR hosts ( compare to PC* steady state of the simple model ) ., In this regime , the coevolutionary dynamics is phage limiting , resulting in steady state free cell levels of GVαv−1 in terms of the simple model ., ( Regime II ), At lower GC values , and when the existence condition on δ is satisfied , both immunity and autoimmunity operate , allowing prokaryotes to evade phage lysis at significant rates ., In this regime , phages are driven to very low densities or extinction ., ( Regime III ), At lower GC values , progressing to higher β values increases steady-state levels of self-targeting spacers , thereby increasing the risk of not satisfying the constraint on δ ., In such cases , regime III operates for all higher values of β , and extinction is inevitable ., ( Regime IV ), This regime operates in the region where high levels of β are matched by corresponding high GC values that are sufficient to reduce self-targeting spacer levels so as to satisfy the δ constraint ., In this regime , host extinction occurs ., Here no active spacer mediated immunity occurs , but CRISPRs transform to a full-fledged abortive infection system ., When δ = 0 , regime III does not occur , and regime IV extends into regime III ., Thus the boundaries between regimes I and {II , IV} can be mapped by GQ→PYQA*+GQ→ϕYQS*GQ→V=0 , and that between {II , IV} and III can be mapped by the critical condition on δ ., To study how ABI influences the coevolutionary dynamics in the general model , we remove the autoimmunity term from the model and compare the resulting prokaryotic and phage densities across several host protospacer and CRISPR activation levels ( Fig 6 ) ., We find that while removing ABI in infected cells increases the size of the coexistence regime and allows for improved phage densities ., Indeed , this is the same effect predicted by our bifurcation analysis of the simplified model , where lower abortive infection rates lead to increased coexistence owing to higher phage turnover ., A handful of prokaryote-phage experimental systems for studying CRISPR dynamics have been established ., However , the extreme diversity of CRISPRs 11 makes it difficult to draw broad conclusions from any one biological model system ., Computational models , which allow exploration over a wide range of feasible parameters , provide an attractive alternative ., In this work , we analyzed the influence of infection-induced activation of CRISPRs and their autoimmunity side effect on prokaryote-phage coevolutionary dynamics ., Our model integrates the classical ingredients of the prokaryotic CRISPR immune system , along with aspects of regulation and autoimmunity ., Our analysis suggests that CRISPRs exploit both restriction and abortive infection ., Moreover , we identified a key constraint that determines the growth advantage associated with CRISPRs as a prokaryotic immune system ., As summarized in Fig 7 , our model reveals a characteristic four-regime pattern determined principally by three effective parameters: the activation level of CRISPRs in uninfected population , the host to phage protospacer ratio , and spacer deletion to acquisition rate ratio in CRISPRs ., In the presence of SND , the host to phage protospacer ratio is close to zero , and CRISPRs operate exclusively by exploiting restriction , while in the absence of SND , they tend to principally exploit the abortive infection route ., Several previous models have also studied CRISPR associated fitness costs , although as abstract functions ., Nevertheless , these models reproduce and help to explain some of the key experimental and comparative genomics findings on CRISPRs ., Levin and colleagues exploited classical density dependent ecological models to numerically analyze the invasion of costly CRISPR genotypes in the presence of innate ( envelope ) resistance and conjugative plasmids 20 , 30 , 42 , and showed that selection due to continuous phage exposure and absence of less costly resistance mechanisms improve CRISPR maintenance in the population ., Similar in spirit , Gandon and Vale make general discussions based on their analysis of general epidemiological models on the evolution of a CRISPR-like resistance mechanism , when the side effect associated is that of beneficial horizontal gene transfer impedance 35 ., Childs et al . , established a multiscale agent-based simulation model to characterize CRISPR spacer and viral diversity during coevolution , and conclude that population dynamics is more sensitive to spacer acquisition rates than interference rates 32 ., Weinberger et al . , derive a critical threshold on CRISPR associated cost as a function of coevolving viral diversity , innate resistance and spacer acquisition rate and conclude that high viral diversity selects against CRISPRs 34 ., Iranzo et al . , used numerical simulations of a general agent based simulation model that additionally accounted for CRISPR loss and horizontal transfer , to exhaustively study CRISPR maintenance as a function of various kinetic parameters in their model 33 ., They also concluded that CRISPR loss is encouraged at high prokaryote/phage population sizes ., Our analyses complement these studies summarized above , and they advance our understanding of CRISPR mechanisms in general ., We have delineated the precise conditions under which CRISPRs can be lost even at low viral diversities ., The level of complexity in our model , intermediate to previous simulations of agent-based models and models requiring radical simplifications and that do not account for the adaptive nature of CRISPR kinetics , provides an opportunity for mathematical analysis and intuitive understanding of the results ., We have presented an analytical treatment of a particular limit of our model ( which empirically hold for wide parameter | Introduction, Results, Discussion | CRISPRs offer adaptive immunity in prokaryotes by acquiring genomic fragments from infecting phage and subsequently exploiting them for phage restriction via an RNAi-like mechanism ., Here , we develop and analyze a dynamical model of CRISPR-mediated prokaryote-phage coevolution that incorporates classical CRISPR kinetics along with the recently discovered infection-induced activation and autoimmunity side effects ., Our analyses reveal two striking characteristics of the CRISPR defense strategy: that both restriction and abortive infections operate during coevolution with phages , driving phages to much lower densities than possible with restriction alone , and that CRISPR maintenance is determined by a key dimensionless combination of parameters , which upper bounds the activation level of CRISPRs in uninfected populations ., We contrast these qualitative observations with experimental data on CRISPR kinetics , which offer insight into the spacer deletion mechanism and the observed low CRISPR prevalence in clinical isolates ., More generally , we exploit numerical simulations to delineate four regimes of CRISPR dynamics in terms of its host , kinetic , and regulatory parameters . | To counteract viral infections , bacteria and archaea have evolved a variety of defense systems ., These can broadly be classified into either restriction or suicide mechanisms ., The former enforces nicks in the invading DNA making it unusable for production of further infectious particles; the latter , by contrast , induces cell death whereby an infected cell activates specific host suicidal pathways that are otherwise strongly repressed , thus inhibiting further infection ., Examples of the former class include restriction-modification ( R-M ) and the recently discovered CRISPR systems , while the latter class includes a variety of toxin/anti-toxin systems ., CRISPRs , in contrast to R-Ms , adapt to target viral genomes by updating the database of target sites they recognize ., The adverse side effect of such a mechanism , however , is that CRISPRs can target the host genome itself resulting in undesirable cell death ( autoimmunity ) ., The recent discovery of infection-induced activation of CRISPR systems suggests that these negative side effects may be limited to periods of infection ., This led us to hypothesize that such regulatory control—similar to abortive infection mechanisms—can be advantageous by limiting viral spread through suicide of infected cells ., To test this hypothesis , we mathematically model CRISPR induced prokaryote-phage coevolutionary dynamics in the presence of infection-regulated CRISPR activity ., Our results indicate that , except in limited growth rates , regulated CRISPRs exploit both autoimmunity and target restriction and can therefore be considered a hybrid class that leverages both restriction and suicide mechanisms to limit phage infection . | null | null |
journal.pntd.0001775 | 2,012 | Toxocariasis and Epilepsy: Systematic Review and Meta-Analysis | Human toxocariasis is a parasitic zoonosis caused by the larval stages of the ascarids Toxocara canis ( T . canis ) , the common roundworm of dogs , and by the roundworm of cats , Toxocara cati ( T . cati ) 1 ., The reported prevalence of soil contamination with Toxocara spp ., eggs is variable between studies , going from a percentage of 6 . 6 to 87 . 1% 2–9 ., Therefore toxocariasis is one of the most prevalent zoonotic helminth infections , occurring whenever the man–soil–dog relationship is particularly close ., High seroprevalence rates of Toxocara spp ., ( presence of sera anti-Toxocara spp . antibodies ) have been found in tropical countries , where the humid climate favours the survival of parasite eggs in the soil , and in rural settings , where the poor hygiene and the rare administration of anthelmintic treatments to dogs increases the probability of human infection 10–12 ., Nevertheless , the reported seroprevalence in apparently healthy adults from urban areas of Western countries is of 2–5% 13 , whit a wider range ( 2 . 4%–31 . 0% 14 , 15 ) when considering all the studies carried out in Europe , independently from age of participants and type of setting ., Despite being the most prevalent human helminthic infection in some industrialized countries 16 , toxocariasis remains relatively unknown to the public 17 and the true magnitude of the global burden of Toxocara spp ., -associated human disease has still to be evaluated 18 ., Humans are infected by the accidental ingestion of embryonated Toxocara spp ., eggs present in contaminated soil or food , or by the ingestion of encapsulated larvae contained in the raw tissues of paratenic hosts , such as cows , sheep or chickens 1 , 19 ., The clinical manifestations of human toxocariasis vary from asymptomatic infection to severe organ injury , depending on the parasite load , the sites of larval migration and the hosts inflammatory response 20 ., Two severe clinical syndromes are classically recognised: visceral larva migrans ( VLM ) , systemic disease caused by larval migration through major organs , and ocular larva migrans ( OLM ) , in which the disease is limited to the eyes and the optic nerves ., Two less severe syndromes have also been described: ‘covert toxocariasis’ , seen mainly in children and characterized by fever , headache , behavioural and sleep disturbances , cough , anorexia , abdominal pain , hepatomegaly , nausea and vomiting , and ‘common toxocariasis’ , seen predominantly in adults with weakness , pruritus , rash , difficult breathing and abdominal pain 20 ., Clinical involvement of the central nervous system ( CNS ) in visceral larva migrans is thought to be rare , although in experimental animals the larvae frequently migrate to the brain 21–23 ., The CNS migration may lead to a variety of neurological disorders such as meningo-encephalitis , myelitis , cerebral vasculitis , optic neuritis 23 , 24 and probably cognitive 25 and behavioural 26 disorders ., Concerning epilepsy , early reports have suggested a high exposure rate to Toxocara spp ., among people with epilepsy ( PWE ) 27 , 28 ., In particular , in 1966 Woodruff et al . 27 found that 7 . 5% of PWE had a positive skin reaction to an antigen prepared from adult T . canis , in contrast to 2 . 1% of apparently healthy persons ., In addition , they noted a statistically significant association between contact with dogs and positive skin test to toxocaral antigen in PWE ., Following these preliminary observations and prompted by the development of serodiagnostic tests with improved sensitivity and specificity , further studies have been carried out in different populations to investigate the possible association between Toxocara spp ., seropositivity and epilepsy , suggesting that toxocariasis could play a role in the incidence of epilepsy in endemic areas 29–31 ., Considering that toxocariasis is one of the most common helminthiasis worldwide and that it is a potentially preventable disease , a correct estimate of the association between toxocariasis and epilepsy is necessary ., We carried out a systematic literature revision and a meta-analysis to evaluate the possible association between human toxocariasis and epilepsy and to highlight some methodological points to be taken into account for the elaboration of future surveys ., A systematic search without past time or language restriction was conducted to identify published and unpublished articles dealing with the association between toxocariasis and epilepsy ., The following online databases were independently examined by two researchers ( GQ and BM ) : MEDLINE , IngentaConnect , ScienceDirect ( Elsevier ) , Refdoc ( ex ArticleScience ) , Scopus , Highwire ., In addition , the database from the Institute of Neuroepidemiology and Tropical Neurology of the University of Limoges ( IENT ) : Virtual Library on African Neurology , BVNA ( http://www-ient . unilim . fr/ ) , which contains more than 9000 references of medical dissertations , theses and articles dealing with tropical neurology and parasitology , was examined ., In MEDLINE combined text words and Medical Subject Headings ( MeSH ) terminology were used ., The following search key words and Boolean operators were entered: “toxocariasis” AND “epilepsy” AND “epidemiology” ., The term “toxocarosis” as an alternative to “toxocariasis” was also considered ., The literature search was adapted for the other databases ., Titles and available abstracts were scanned for relevance , identifying papers requiring further consideration ., Reference lists of all available reviews , primary studies and books found were screened manually ., When necessary , corresponding authors of relevant studies were contacted ., Experts in the field were also contacted to find out other eventual non-published studies ., The systematic search was realized up to October 2011 ., Considering epilepsy as the outcome and toxocariasis as the exposure , all the studies meeting the following eligibility criteria were included: Studies including only acute symptomatic seizures or specific seizure patterns or epileptic syndromes were excluded ., Full copies of all reports identified by the electronic or hand searching were obtained and two reviewers ( GQ and BM ) independently assessed their eligibility and extracted data ., The following data were independently recorded in an ad hoc created collecting form: author , country , study design , study population ( number , age group , gender , setting ) and recruitment methods ., For toxocariasis , specific information was recorded on methods used for diagnosis ., Considering epilepsy , details on definition and assessment were extracted ., Discrepancies between reviewers were rechecked and consensus was achieved by discussion ., For each survey , the crude odds ratio ( OR ) on the association between toxocariasis and epilepsy and the relative 95% confidence interval ( CI ) were recalculated ., Furthermore , statistical power was calculated as a priori and a posteriori ., A priori statistical powers were calculated following the hypothesis that the objective of the survey was to identify a minimum OR of 2 ( i . e . , Toxocara spp . exposure leads to twice more epilepsy ) with one control per case , based on the number of PWE and the percentage of Toxocara spp ., seropositivity in PWOE ., The a posteriori statistical powers were calculated upon the results of the surveys ., In both cases a 5% alpha risk was considered ., Powers were calculated using Epi-Info 6 . 04 32 ., To estimate the association between toxocariasis and epilepsy we performed a meta-analysis applying a random effects model , assuming that the true effect size of exposure varies from one study to the other , and that the studies in our analysis represent a random sample of effects that could have been observed 33 ., A common risk was estimated as a common OR from all the studies ., The homogeneity was tested by the Cochran Q test of heterogeneity ., In order to account for the different age groups considered , the analysis was then separately applied to the studies including an exclusively juvenile population 30 , 31 ., Furthermore , considering that Western Blot ( WB ) is as sensitive but more specific than enzyme-linked immunosorbent assay ( ELISA ) 34 , we also conducted an analysis restricted to the studies using WB as diagnostic or confirmatory test 35–38 ., The meta-analysis was performed using EasyMA , 2001 version 39 ., The PRISMA ( Preferred Reporting Items for Systematic reviews and Meta-Analyses ) statement 40 was used as a guide in the reporting of this study ., A flowchart summary of the literature search is shown in Figure, 1 . A PRISMA flowchart is also shown ( Figure S1 ) ., Electronic search produced 131 publications , among which 25 dealt with epilepsy and toxocariasis ., The removal of duplicate citations and the screening of abstracts permitted to isolate 8 documents 27 , 31 , 35–38 , 41 , 42 ., Two additional publications 28 , 30 were found by hand searching ( reference lists check ) ., Full text review of the 10 documents permitted to exclude 3 of them for not fulfilling the inclusion criteria: one 27 was excluded because methods to assess epilepsy were not reported and toxocariasis infection was detected through a skin test; furthermore the included cases consisted of a highly selected group of severe patients with epilepsy ., Another study 28 was excluded because toxocariasis infection was exclusively assessed in a sample of PWE without control group ., The last study 42 was excluded because of the lack of reporting of aggregated data for each group ., Considering the 7 articles meeting the inclusion criteria , the materials and methods of the study reported by Nicoletti et al . ( 2007 ) 36 had been previously detailed in Nsengiyumva et al . 43 , while the study population of Winkler et al . ( 2008 ) 38 has been better described in Winkler et al . ( 2009 ) 44 ., The methodological aspects of these articles have been therefore assessed considering both the publications ., Seven case-control studies 30 , 31 , 35–38 , 41 were included , providing a total subjects number of 1867 ( 850 PWE and 1017 PWOE ) ., Two of them 30 , 31 considered a population aged 1–17 years while one excluded children aged 10 years or younger 38 ., The studies were carried out in 6 different countries ( USA , Italy , Bolivia , Turkey , Burundi and Tanzania ) , both in rural 35 , 36 , 38 and urban 30 , 31 , 37 , 41 settings ., In the study by Akyol et al . 41 10% of participants were from rural areas , but no significant relationship was found between residency and seropositivity rate ., The general characteristics of the included studies are shown in table, 1 . Three surveys had a matched case-control design 35–37 among them age was the only common matching criteria ., Only one study was a population-based survey 35 ., The epilepsy definition proposed by the International League Against Epilepsy ( ILAE ) in 1993 45 was applied in 3 studies 35–37 while Glickman et al . 30 considered the definition proposed in 1972 by Alter 46 , and Winkler et al . 38 defined epilepsy according to the World Health Organization ( WHO ) Neurosciences Research Protocol proposal 47 ., In the work by Arpino et al . 31 a general definition of “positive seizure history” was considered as cases entry criteria ., Considering seizures , 4 studies 35–37 , 41 applied the classification of epilepsies and epileptic syndromes proposed by the ILAE in 1981 48 , while one 38 used an adjusted classification for rural African hospitals suggested in 2007 49 ., All PWE were prevalent cases and none of the studies clearly specified if active or lifetime epilepsy was considered , the second being more probable ., Controls were out- or in-patients attending the same hospital of cases 30 or people going to hospital for vaccination or haematological check 36 , 37 or volunteers 41 ., A negative history for seizures 31 , 36–38 , 41 and for both seizures and other neurological diseases 31 , 36 , 37 was considered for controls definition ., In the population-based survey controls were selected from the same community , but different households , of cases 35 , whereas another study selected controls from the same province of PWE excluding blood relationship 36 ., In an attempt to determine the accuracy of the seizures classification EEG recordings were examined in some studies 31 , 35–37 ., A neurologist confirmed both cases and controls through anamnesis and complete neurological examination in 4 studies 35–38 ., In order to obtain demographic data and information concerning factors possibly associated with Toxocara spp ., exposure a questionnaire was administered to cases and control subjects in 5 studies 30 , 31 , 36 , 37 , 41 ., Data were usually obtained by the patients mother when the study population was infantile 30 , 31 ., The questionnaire version used was specified only in one study 36 and interviewers qualifications were stated only in 2 surveys 36 , 37 ., Presence of anti-Toxocara spp ., antibodies in sera was assessed using antibodies-ELISA ( Ab-ELISA , commercial or in-house kits ) 30 , 31 , 41 , or immunoblot 36 , 37 or Ab-ELISA followed by WB confirmation 35 , 38 ., Laboratories performing the analysis were blind to the case-control status of sera samples in 3 studies 35–37 ., The results of the included studies are shown in Table, 2 . Toxocara spp ., seropositivity ranged from 6 . 5% to 50 . 8% in the control group and from 12 . 0% to 59 . 7% in PWE ., Seroprevalence rate was higher among PWE than control subjects in all the 7 included surveys , even if the association between Toxocara spp ., seropositivity and epilepsy was statistically significant in 4 of them 30 , 31 , 35 , 37 ., In one study the crude OR bordered on statistical significance , anyway , after adjustments on other variables according to a multivariate model using the conditional logistic regression , a stronger and significant association was found 36 ., Significant crude ORs ranged between 2 . 04 and 2 . 85 ., A priori statistical power ranged 32 . 8–90 . 9% and a posteriori statistical power 8 . 0–89 . 6% ., A meta-analysis was at first performed on all the 7 studies included ., Results are presented in figure, 2 . A significant ( p<0 . 001 ) common OR of 1 . 92 ( 95%CI 1 . 50–2 . 44 ) was estimated ., The test of heterogeneity was not significant ( p\u200a=\u200a0 . 545 ) , indicating homogeneity of the studies included ., When analysis was restricted to the 2 studies considering only a juvenile population 30 , 31 , as shown in figure 3 , a common OR of 2 . 23 ( 95% CI 1 . 35–3 . 69; p\u200a=\u200a0 . 002 ) was found ., The test for heterogeneity was also not significant ( p\u200a=\u200a0 . 655 ) ., The meta-analysis was at last restricted to the 4 studies using WB test 35–38 , as shown in figure 4 , leading to an OR of 1 . 91 ( 95% CI 1 . 33–2 . 75 , p<0 . 001 ) and a non significant test for heterogeneity ( p\u200a=\u200a0 . 430 ) ., We performed a systematic literature revision and a meta-analysis of available data to evaluate the association between epilepsy and toxocariasis ., To our knowledge this is the first meta-analysis on this argument ., Based on our literature search , we analyzed data from 7 case-control studies carried on in rural or urban settings and in various countries worldwide ., We are confident that our literature search is exhaustive as conducted on several electronic databases and also on a specific database containing literature on tropical neurology and parasitology including theses and memos unpublished in international or electronic databases ., Seroprevalence rate of anti-Toxocara spp ., antibodies was higher among PWE than control subjects in all the 7 studies analysed 30 , 31 , 35–38 , 41 even if only 4 showed a significant positive association between Toxocara spp ., seropositivity and epilepsy 30 , 31 , 35 , 37 and a fifth reached statistical significance only after adjustment for other variables 36 ., In our meta-analysis we found evidence of positive association , with a common OR of 1 . 92 ( 95%CI 1 . 50–2 . 44 ) and a lack of heterogeneity between the studies ., Our result is noteworthy for coming from studies across different populations in disparate geographic locations and socioeconomic climates ., This consistency of observations among different populations is in favor for a causal relationship between toxocariasis and epilepsy 50 ., Anyway , various important points should be taken into account when interpreting our data ., First of all , the studies evaluated were retrospective case-controls ascertaining both Toxocara spp ., seropositivity and epilepsy in a cross-sectional manner; thus , the inclusion of “prevalent” rather than “incident” cases does not permit to demonstrate a temporal relationship between the exposure ( Toxocara spp . ) and the outcome ( epilepsy ) and doesnt allow to exclude a possible “reverse causality” ., It has been in fact hypothesized that the abnormal behavior patterns ( e . g . pica and hyperactivity ) and the elevated number of falls to the ground of PWE ( especially children or mentally retarded ) could predispose them to Toxocara spp ., exposure rather than the contrary 30 ., In particular , evidence of association has been reported between Toxocara spp ., seropositivity and mental retardation 51 , 52 ., We underline anyway that the study by Nicoletti et al . ( 2002 ) 35 found no statistical difference in seroprevalence between PWE with or without mental retardation ., On the other hand , a significant difference in the frequency of mental retardation between seropositive and seronegative subjects was found by Nicoletti et al . ( 2008 ) 37 , but it lead to only a slight reduction of association after restriction of the analysis to the PWE without mental retardation ., Selection of cases and controls represents one of the most important pitfalls in case-control studies ., In the studies evaluated , with the exception of the only population based survey 35 , PWE and PWOE were generally enrolled from a hospital setting , and their source population was often not clearly defined ., This constitutes a possible recruitment bias , especially in rural settings , where people receiving care are not representative of the general population ., In particular , concerning controls , hospital controls might resemble cases more than population controls , biasing OR towards the null 53 ., Furthermore , a volunteer bias could have affected the study by Akyol et al . 41 , where the control group was composed by volunteers coming from an undefined source ., However , the population based survey showed a positive association , similar to the results found by the positive hospital based studies , suggesting that the selection bias effect could be limited ., Considering cases and controls characteristics , PWE and PWOE should be comparable at least for age , because the prevalence of both epilepsy and anti-Toxocara spp ., antibodies vary with age , and for geographical provenience and education , which are likely related to the level of exposure to Toxocara spp ., On this point , the studies examined are often lacking of detailed descriptive data ., We report as an example , the comment by Quet et al . 54 on the study by Akyol et al . 41 , which noticed how the greater number of students observed in the control group could suggest a higher education in controls than cases ., The educational level was in fact expressed as a binary variable ( less or more than primary school ) in this study , which could give an unreliable estimation of education; in such cases the number of school years might be a more precise measure ., In order to account for the different age groups included , and considering that young age seems to contribute to Toxocara spp ., exposure 1 , we restricted our meta-analysis to the studies with an exclusively juvenile population and we obtained also in this case a significant positive association ( OR 2 . 23 , 95% CI 1 . 35–3 . 69 , p\u200a=\u200a0 . 002 ) ., In particular , in the study by Arpino et al . 31 the relationship was more remarkable in children under 5 years old ., Based on these findings , it has been suggested that the parasite may act as a cofactor in determining the occurrence of seizures in children with a predisposing background 31 ., Only a prospective cohort follow-up study could avoid these biases ., However such a design , leaving subjects exposed to toxocariasis and without intervention , is ethically not acceptable ., A potential weakness of our study is the use of different and not always clears epilepsy definitions in the included articles ., On this point , considering that the lag time between Toxocara spp ., infection and epilepsy occurrence is not yet defined , we underline the importance of including lifetime and not only active epilepsy , as likely properly done in the studies examined ., On the other hand , the lack of exhaustive descriptive data on the age of onset , on the probable etiology and on seizures classification didnt permit us to differentiate our analysis for these factors ., The significant positive association found in some studies between Toxocara spp ., seropositivity and partial epilepsy could in fact be biologically plausible , given the higher prevalence of idiopathic epilepsy among the generalized forms 35 , 37; while the lack of association between partial epilepsy and toxocariasis found by Nicoletti et al . ( 2007 ) 36 has been related by the authors to a lack of power ., In the study by Akyol et al . 41 , besides the lack of a precise definition of epilepsy , the authors reported a higher frequency of generalized epilepsies , as expected , because of the inclusion of only cryptogenic ( in the abstract ) or idiopathic ( in the methods ) epilepsies 54 ., This could have affected the results , showing no statistically significant association between toxocariasis and epilepsy ., A correct classification of seizures , possibly with the help of EEG recordings , is therefore an important element that should be taken into account in future studies to permit a correct interpretation of the results ., Regarding the diagnosis of toxocariasis , the major limitation in confronting different studies consists in the heterogeneity of techniques ( Ab-ELISA or WB or both ) used to detect sera anti-Toxocara spp ., antibodies , mostly due to different cost and availability ., Also when considering ELISA , different kits ( commercial or in house ) with different sera dilutions were utilized and a serum pre-adsorption with larval Ascaris extracts was carried on only in some studies ., It would have been interesting evaluating and reporting the sensitivity and specificity of the used assays , which has never been done in the studies examined ., Considering that the WB confirmation of positive results from the ELISA ( especially where pre-absorption is not carried out ) has been recommended 55 , and given the higher specificity of WB 34 , we restricted our meta-analysis to the studies applying WB , obtaining results similar to the global analysis ( OR 1 . 91 , 95%CI 1 . 33–2 . 75 , p<0 . 001 ) ., When interpreting these data , we are of course aware that other factors , such as Toxocara spp ., excretory-secretory ( TES ) antigen preparations , parasite strains , and WB technical procedures , could have influenced the results obtained by different investigators ., It should also be kept in mind that a single seropositivity has limited pathological significance and could probably represent past rather than recent infection ., Furthermore , the presence of sera antibodies against Toxocara spp ., does not provide evidence of either an active systemic infection or a CNS involvement ., Diagnosis of neurotoxocariasis is in fact based on the history; blood tests , including differential blood cell count and determination of serum total IgE; CSF investigation , including the detection of anti-Toxocara spp ., antibodies and neuroimaging 13 ., The absence of significant results was associated with a lower power ( type II error ) , making not really surprising the lack of statistical confirmation of the difference found ., The statistical power of a study can be improved performing surveys in areas with high level of exposure assessed with the most sensitive assay or , when the number of cases is small , increasing the ratio of controls to cases up to 4/1 53 ., The low a posteriori power of the studies by Winkler et al . 38 ( 8 . 0% ) and Akyol et al . 41 ( 11 . 3% ) could be mostly accounted to the small sample size and in particular the lower number of controls than cases , highlighting one more time the central role of the elaboration of the control group ., In our paper we referred to toxocariasis etiological agent as Toxocara spp ., and not only T . canis ., TES in fact are not species-specific and the differentiation between T . canis and T . cati remains challenging ., Considering the prominence historically given to T . canis , the role of T . cati in human toxocariasis could have been underestimated ., Further work should be encouraged to differentiate the two parasites and to better address future prevention strategies 56 ., The most frequent infectious agent involved in the differential diagnosis of subjects with late-onset epilepsy or inflammatory brain nodules is the larval stage of Taenia solium ( T . solium ) , aetiological agent of neurocysticercosis ( NCC ) ., Concomitant T . solium and Toxocara spp ., seropositivity is a possible event in areas endemic for both helminthes ., Anyway , albeit there is yet no evidence on the mechanisms underlying toxocariasis-induced epilepsy , according to us toxocariasis should not be ruled out as an accidental association ., First of all , the presence of anti-T ., solium antibodies , as in the case of toxocariasis , could represent only a previous exposure without established infection ., Furthermore , considering the studies included in our analysis , in the study by Nicoletti et al . ( 2002 ) 35 only 7 PWE over a total of 113 were positive to both T . solium and Toxocara spp ., and in the study by Nicoletti et al . ( 2007 ) 36 , finding a positive association between Toxocara spp ., seropositivity and epilepsy , seropositivity for cysticercosis was considered as a variable in the multivarate analysis ., Of course , the interpretation of serological results should always take into account the background seroprevalence of both Toxocara spp ., and T . solium in the studied population and cysticercosis seropositivity should always be evaluated as a possible confounder when carrying on surveys on infectious agents and epilepsy ., In conclusion , a positive association between Toxocara spp ., -seropositivity and epilepsy could be hypothesized; nevertheless , even the modestly strong association demonstrated in our meta-analysis does not necessarily prove causality ( i . e . , Toxocara spp . infestation caused the epilepsy ) ., Further studies , considering incident rather than prevalent cases and with a population-based design , should be performed ., An internationally accepted epilepsy definition and seizures classification should be applied and cases and controls should be comparable at least for age , sex , geographic provenience , education and socio-economic background ., Pica , pet owning , mental retardation and other possible toxocariasis risk factors should be assessed trough a validated questionnaire administered by trained investigators and assessors and laboratory personnel should be blind to the status of participants ., The improvement of techniques permitting to distinguish recent from past infections , such as antigen-ELISA ( Ag-ELISA ) , should be encouraged in order to better investigate the time relationship between Toxocara spp ., infection and epilepsy occurrence ., Assessing the link between toxocariasis and epilepsy is of interest as toxocariasis is a potentially preventable disease ., Nowadays , despite the implementation of regular and intensive de-worming programs in western countries , the parasite still prevails , indicating that prevention is not easy in practice ., Good hygiene practices should be encouraged and further strategies to prevent Toxocara spp ., transmission should be identified and applied , permitting to experimentally investigate the causation hypothesis 50 ., The existence of a causal relationship and the estimation of the impact of toxocariasis on the global burden of epilepsy may strongly contribute in encouraging further programs on toxocariasis prevention worldwide , in order to control both the Toxocara spp ., transmission and the related epilepsy burden . | Introduction, Methods, Results, Discussion | Human toxocariasis is a zoonotic infection caused by the larval stages of Toxocara canis ( T . canis ) and less frequently Toxocara cati ( T . cati ) ., A relationship between toxocariasis and epilepsy has been hypothesized ., We conducted a systematic review and a meta-analysis of available data to evaluate the strength of association between epilepsy and Toxocara spp ., seropositivity and to propose some guidelines for future surveys ., Electronic databases , the database from the Institute of Neuroepidemiology and Tropical Neurology of the University of Limoges ( http://www-ient . unilim . fr/ ) and the reference lists of all relevant papers and books were screened up to October 2011 ., We performed a systematic review of literature on toxocariasis ( the exposure ) and epilepsy ( the outcome ) ., Two authors independently assessed eligibility and study quality and extracted data ., A common odds ratio ( OR ) was estimated using a random-effects meta-analysis model of aggregated published data ., Seven case-control studies met the inclusion criteria , for a total of 1867 participants ( 850 cases and 1017 controls ) ., The percentage of seropositivity ( presence of anti-Toxocara spp . antibodies ) was higher among people with epilepsy ( PWE ) in all the included studies even if the association between epilepsy and Toxocara spp ., seropositivity was statistically significant in only 4 studies , with crude ORs ranging 2 . 04–2 . 85 ., Another study bordered statistical significance , while in 2 of the included studies no significant association was found ., A significant ( p<0 . 001 ) common OR of 1 . 92 95% confidence interval ( CI ) 1 . 50–2 . 44 was estimated ., Similar results were found when meta-analysis was restricted to the studies considering an exclusively juvenile population and to surveys using Western Blot as confirmatory or diagnostic serological assay ., Our results support the existence of a positive association between Toxocara spp ., seropositivity and epilepsy ., Further studies , possibly including incident cases , should be performed to better investigate the relationship between toxocariasis and epilepsy . | Human toxocariasis is an infection caused by the larval stage of the worms Toxocara canis and less frequently Toxocara cati , common parasites of domestic and peridomestic dogs and cats ., It is a cosmopolitan infection , occurring whenever the man-soil-dog relationship is particularly close , especially in tropical countries , where the humid climate favours the survival of parasite eggs in the soil , and in rural settings , where the poor hygiene increases the probability of human infection ., Epilepsy affects nowadays at least 65 million of people worldwide and is particularly common in tropical areas , probably because of the presence of cases caused by infectious diseases largely absent in industrialized countries ., For several decades , researchers have investigated the possible association between toxocariasis and epilepsy ., In this study we conducted a statistical analysis of all the data available on the relationship between these two conditions ., The combined results of the 7 studies included indicate an association between the two diseases ., Further studies are necessary to demonstrate a causal relationship ( i . e . toxocariasis causes epilepsy ) ., Considering that toxocariasis is a preventable and common disease , a better understanding of the relationship between toxocariasis and epilepsy may contribute to improving prevention of epilepsy worldwide . | medicine, infectious diseases, public health and epidemiology, epidemiology, global health, neurology, neurological disorders, public health, veterinary science | null |
journal.pgen.1006082 | 2,016 | Molecular Basis and Therapeutic Strategies to Rescue Factor IX Variants That Affect Splicing and Protein Function | Exonic mutations represent by far the most frequent cause of human genetic disorders , 1 and their pathogenic effect is usually attributed to alterations of the amino acid code ., However , the exons contain also an intricate series of splicing regulatory elements ( “splicing code” ) that are essential for their recognition and overlap with the aminoacid code ., In fact , the correct selection of canonical splice sites that define the exon boundaries ( 3’ss and 5’ss , respectively , which include the polypyrimidine tract and the branch site at the 3’ss ) requires a series of auxiliary regulatory elements ., According to their location and activity , the auxiliary elements are known to function as exon splicing enhancer and silencers ( ESEs and ESEs , respectively ) or intron splicing silencers ( ISSs and ISSs , respectively ) ., Typically , in the exon , Serine/arginine-rich ( SR ) proteins2 recognize ESE whereas heterogenous nuclear RiboNucleoProteins ( hnRNP ) interact with ESS , 3 inducing exon inclusion or skipping , respectively ., Due to the presence of these regulatory elements , exonic mutations are strong candidates to affect splicing and the most frequent defect they produce is exon skipping , as shown in several disease genes and model systems ., 4–8 However , in affected genes , the relative contribution of splicing and protein function in the disease pathogenesis is largely unknown ., Intriguingly , while correction strategies for missense mutations are far from being proved , there are several tools enabling the rescue of splicing and proposing them as innovative therapeutic strategy , which include antisense oligonucleotides , chemical compounds and modified U1 small nuclear RNAs ( U1 snRNA ) ., 9–12 Moreover , these strategies can be exploited in combination . 13, In exon skipping mutations , modification of the U1 snRNA , which is the key component of the spliceosomal small nuclear ribonucleoprotein ( U1 snRNP ) , is able to rescue exon skipping ., The engineered U1 snRNPs defined as Exon Specific U1s ( ExSpeU1 ) bind in the intron downstream of the 5’ splice site and rescue exon skipping variants in Spinal Muscular Athrophy14 , Netherton syndrome15 Cystic fibrosis and Hemophilia B . 9, The activity of these molecules in primary cells derived from patients14 , 15 and in vivo in mouse models through AAV delivery14 suggests that ExSpeU1s have a strong therapeutic potential ., In factor IX ( FIX ) exon 5 , ExSpeU1s rescued exon-skipping mutations at the 5ss and at the polypyrimidine tract with a complete recover of the functional factor IX activity . 9, Mutations is FIX exon 5 are associated to Hemophilia B , a rare X-linked hemorrhagic disorder ( 1/35000 males ) with reduced levels of factor FIX , a key coagulation protein of liver origin . 16, The level of FIX antigen or clotting activity in the plasma determines the variability of the disease severity ., 17 Hemophilia B represents a paradigmatic example of human disease with a heterogeneous mutational pattern 18 and even a small increase of FIX levels ( >2% ) would significantly ameliorate the clinical phenotype ., The FIX gene contains eight exons and seven introns and transcribe 2 . 8 kb long mRNA 19 ., Exonic mutations ( missense , nonsense and synonymous ) represent the first cause of coagulation factor deficiencies , thus providing ideal models to address the relationship between splicing and protein function ., As a matter of fact , in several cases the results from the expression of the missense coagulation factor variants did not recapitulate the residual expression levels in the affected patients and disease causing mechanism of synonymous mutations is frequently unclear ., In this study , we focused on exonic mutations in FIX gene that have been found in Hemophilia B patients , and particularly on exon 5 that encodes for EGF2 domain , which is crucial for the coagulant activity in the intrinsic coagulation pathway . 20, This exon contains several missense , nonsense and synonymous mutations , 21 whose potential effect on pre mRNA splicing has not been studied so far ., Here , we show for the first time that FIX exon 5 contains dense splicing regulatory information that overlap with the aminoacid code and that several FIX exonic mutations result in exon skipping ., Furthermore , a unique ExSpeU1 , through an SRSF2-mediated mechanism , rescues all FIX splicing defects ., Most importantly , through complementary expression studies with minigene splicing assays and full-length protein constructs we dissected for each mutation the relative contribution of splicing alteration , defective protein secretion or abnormal coagulant activity ., Lastly , this relationship allows us to select those exonic mutations that most likely will have a therapeutic benefit from splicing correction ., To map exonic splicing regulatory elements in FIX exon 5 we initially performed multiple deletions analysis ., We created eleven 10 bp-long deletions distributed throughout the entire exon ( from Δ1 to Δ11 ) ( Fig 1A ) ., These deletions were tested in the previously validated FIX exon 5 minigene system9 , where the WT exon is not completely defined , showing ~80% of exon inclusion as reported in human liver 9 ., Consistent with the presence in the exon of dense splicing regulatory information , most deletions affected splicing ., Based on the splicing changes , we identified two ( Δ4 and Δ5 ) and eight ( Δ2 , Δ3 , Δ6 , Δ7 , Δ8 , Δ9 and Δ10 ) regions with silencer and enhancer properties , as their deletion resulted in exon inclusion or skipping , respectively ( Fig 1B ) ., As the most striking and deleterious effect on splicing was observed with the Δ9 deletion and neighboring Δ10 deletion , we evaluated more in detail these regions by creating 3 bp long deletions ., Splicing assays showed that Δ9 . 2 , Δ10 . 1 and Δ10 . 3 induced significant exon skipping ( <10% ) ( Fig 1C ) , which suggests the presence of multiple and overlapping exonic regulatory sequences in this region ., To further clarify the role of exonic elements , we mapped binding sites of SR-proteins according to ESE finder , 22 hnRNPA1 motifs23 and predicted ESE and ESS 24 , 25 ., In silico analysis showed the presence in the exon of multiple and frequently overlapping silencers and enhancers ( Fig 1A ) ., Interestingly , the Δ9 and Δ10 regions , associated to severe exon skipping , contain several potential ESEs ., In addition , among the SR proteins and according to ESE finder , SRSF2 is the most represented factor with four potential binding sites ., Moreover , the exon contains also 5 recently identified consensus SRSF2 binding motif SSNG26 ( S = C/G , N = any ) ( Fig 1A ) ., Thus , both in silico and experimental data indicate that FIX exon 5 contains several splicing regulatory elements with both enhancer and silencer function ., The presence of these dense splicing regulatory elements suggests that FIX exon 5 may be extremely susceptible to mutation-induced splicing derangement ., The FIX mutation database reports several missense , nonsense or even synonymous mutations in exon 5 that are associated with Hemophilia B . 21, To understand the contribution of exonic mutations on splicing , we focused on changes with unclear disease-causing mechanism ( synonymous or conservative amino acid substitutions ) and/or on variants located in strongest splicing regulatory elements ( i . e . in the Δ9 and Δ10 elements ) ., Overall , we evaluated 16 disease-causing mutations: 2 synonymous ( V107V and R116R ) , 11 missense and 3 nonsense ( Table 1 ) ., We also include in the analysis the artificial R116G variant ., Strikingly , in splicing assay , 9 mutations induced significant exon 5 skipping ( Fig 1D and Table 1 ) ., In this experimental system , R116R and Q97Stop showed complete or nearly complete exon skipping ( below 5% of exon inclusion ) suggesting that in these cases the primary disease-causing mechanism is the severe splicing defect ., Interestingly , 6 mutations maintained some residual levels of normal splicing: V170V , R116Stop and Q121H showed between 5–10% of exon inclusion whereas R116G , L117F , A118V and Q121Stop showed ~ 25% of exon inclusion ., The presence of some degree of leaky splicing indicates that in these cases the splicing defect contributes partially to the disease pathogenesis ., These results clearly indicate that a significant proportion of exon 5 mutations negatively affect the splicing process ., To identify splicing factors potentially involved in regulating exon 5 splicing , we performed overexpression experiments ., Wild-type ( wt ) and two mutated minigenes ( V107V and R116R ) were co-transfected with different splicing factors followed by analysis of the splicing pattern ( Fig 2A ) ., Several factors including most of the Serine/arginine-rich proteins ( SRSF3 , SRSF4 , SRSF7 and SRSF1 ) along with ESRP1 and hnRNP A1 had a negative effect on splicing , reducing the percentage of exon inclusion in the wild-type minigene ., In contrast , Polypyrimidine tract binding protein 1 ( PTBP1 ) , Cytotoxic granule-associated RNA binding protein ( TIA1 ) , and Serine/arginine-rich splicing factor 2 ( SRSF2 ) induced exon inclusion ., This enhancing effect was also evident for the two synonymous exonic variants ., Since SRSF2 was the most active factor , and the only serine/arginine-rich splicing factor with enhancing activity on exon 5 , we evaluated more in detail its role through silencing experiments ., Indeed , in the wt context , SRSF2 silencing reduced the percentage of exon 5 inclusion , strongly suggesting that this factor is crucial for its definition ( Fig 2B and 2C ) ., In parallel , to understand the potential regulatory sequences that mediate the SRSF2-dependent splicing enhancement , we tested the effect of SRSF2 overexpression on the deletion mutants ( S1 Fig ) ., The transfection experiments showed that SRSF2 significantly improves exon skipping in almost all deletion mutants , suggesting that this factor promotes exon 5 definition through its binding throughout the entire exon ., We previously reported that modified U1 snRNA binding in FIX intron 5 rescued exon skipping caused by mutations located at the polypyrimidine tract or at the 5ss . 9, To evaluate the potential therapeutic effect of ExSpeU1s on the exonic mutations , we initially tested a panel of ExSpeU1s that bind at different intronic positions ( Fig 3A ) ., These molecules were evaluated on R116R that , by remarkably affecting exon definition ( Fig 1D ) , is one of most severe exonic mutation identified ., Out of 9 ExSpeU1 FIX tested , 6 rescued exon 5 inclusion at least to the level of the wild-type condition ( Fig 3B ) ., The lower or absent rescue activity in FIX13 , FIX16 and FIX 22 ExSpeU1s could be due either to their lack of interaction with the corresponding intronic sequences or to their reduced expression levels ., One of the active ExSpeU1s was then evaluated on the natural splicing mutations ( Fig 3C ) and on the artificial deletion mutants ( S2 Fig ) ., The splicing assays showed that cotransfection of ExSpeU1 completely rescued all exon-skipping events ., Therefore , loading of modified U1 snRNA on FIX intron 5 represents a therapeutic strategy for splicing correction not only for polypyrimidine and 5ss mutations9 but also for mutations that affect splicing regulatory elements located in the exon ., To address whether the ExSpeU1-mediated splicing enhancement requires SRSF2 , we performed silencing experiments ., The SRSF2-silenced cells were cotransfected with ExSpeU1 and R116R variant ., As expected , ExSpeU1 completely restored exon inclusion , but the concomitant silencing of SRSF2 remarkably reduced its activity ( Fig 3D , compare lanes 3 with lane 4 ) ., In a second set of experiments , by exploiting U1 decoy molecules ( Fig 3E ) , we evaluated whether SRSF2-mediated splicing improvement requires the endogenous U1 ., The U1 decoy D1 is an antisense RNAs that when trasfected in cells it functionally inhibits the normal U1 snRNP activity by complementarity27 ., Cotransfection of D1 , but not the D3 control , was previously shown to induce exon skipping in several gene systems 14 27 ., As the ExSpeU1-mediated splicing enhancement on R116R is not appreciably affected by the U1 decoy ( D1 treatment , Fig 3D , compare lanes 8 and 9 ) , ExSpeU1 can functionally overcome the absence of the endogenous U1 snRNP ., This result is consistent with previous data obtained in other gene systems . 14, When we tested the effect of the U1 decoy on the SRSF2-mediated splicing improvement , we observed that functional inhibition of the endogenous U1 with the D1 treatment has a minimal effect on the splicing pattern ( Fig 3E , compare lanes 11 and 12 ) ., Similar result was obtained in the wild-type context where D1 treatment reduces splicing efficiency in WT minigene ( Fig 3E , compare lanes 1 with, 2 ) but has no effect in the presence of SRSF2 ( Fig 3E , compare lanes 4 with 5 ) ., All together these results suggest that ExSpeU1 promotes splicing facilitating loading of SRSF2 on the defective FIX exon 5 sequences ., To understand how the exonic mutations negatively affect splicing we focused on the two splicing severe synonymous V107V and R116R variants , on which we performed protein pull-down experiments followed by mass-spectroscopy ., This analysis identified three major splicing factors with splicing inhibitory activity that bind to the mutated sequences: DAZ associated protein 1 ( DAZAP1 ) , Heterogeneous nuclear ribonucleoprotein H1 ( hnRNP H1 ) and Heterogeneous nuclear ribonucleoprotein A1 ( hnRNP A1 ) ( S3 Fig ) ., Upon western blotting we confirmed increased hnRNPA1 and DAZAP1 binding on V107V and increased binding of DAZAP1 on R116R ( Fig 4A ) ., In contrast , hnRNP H1 did not show any differential binding between wt and mutant sequences ., As hnRNP A1 is a well known splicing inhibitor 28–31 , and DAZAP1 contributes to hnRNPA1 to exon skipping in another disease-causing mutation , 28 we evaluated their contribution with silencing experiments ( Fig 4B ) ., Silencing of HNRNPA1/2 slightly increased the percentage of exon inclusion in V107V ( from 8 . 1±1 . 5 to 15 . 0±2 . 5 , t-test p≤0 . 05 ) whereas R116R was not appreciably affected ., In contrast , DAZAP1 did not promote exon inclusion and unexpectedly it reduced splicing in the V107V mutant ( from 8 . 1±1 . 5 to 1 . 0±0 . 2 , t-test p≤0 . 01 ) ( Fig 4C ) ., These data suggest that the formation of the novel binding sites for hnRNPA1 and DAZAP1 in the mutants partially contributes to exon skipping ., To explore those exon 5 mutations that could benefit from ExSpeU1- mediated splicing correction , we measured the effect of 2 synonymous and 6 missense mutations associated with exon skipping on the secreted FIX protein and coagulant activity levels ( Table 1 ) ., Expression studies with the exonic variants in the FIX cDNA context , which is not influenced by splicing , showed that the synonymous V107V and R116R substitutions did not affect the protein biology nor influence or pause the ribosomal translation due to codon preferences32 ( Table 1 , lanes cDNA FIX:Ag and cDNA FIX:C ) ( Fig 5 ) ., The R116G , A118V and Q121H missense mutations resulted in reduced secretion but strikingly , the lower amounts of the secreted proteins have a normal specific coagulant activity ( Table 1 , lanes cDNA FIX:Ag and cDNA FIX:C ) ( Fig 5 ) ., In contrast , L117F variant strongly impaired the FIX secretion , as indicated by the barely detectable FIX antigen in medium ( Table 1 ) ( Fig 5 ) ., Lastly , we included in the analysis as controls two mutations that do not affect splicing , Q97K and Q97E ., These variants were efficiently secreted but displayed an impaired coagulant activity ( Table 1 ) ., Exons are known to accommodate two complementary and overlapping information: splicing signals and amino acids code ., Exonic mutations are first checked by the spliceosome and then any residual amount of normally spliced transcript is evaluated for protein functionality ., The final effect of a mutation on gene expression is the result of the severity of aberrant splicing along with the functional consequence of the amino acid substitution ., In the paradigmatic model of FIX exon 5 we demonstrate that the final outcome , as well as the possible therapeutic rescue , depends on the impact of each mutation on mRNA splicing and/or protein biology ., Our results clearly indicate that understanding the combined effect of exonic mutations on splicing and protein function is fundamental for correct diagnosis at the molecular level and for establishing the therapeutic feasibility of a splicing rescue strategy ., In normal conditions FIX exon 5 is correctly recognized by the spliceosome and mostly included in the final transcript ., This leads to the production of a normal FIX protein that folds correctly in the ER , it is efficiently secreted and , when present in the blood , activates properly coagulation ( Fig 5 , WT ) ., Based on the effect on splicing , secretion and coagulant activity , exonic splicing mutations ( ESMs ) can be divided into three major groups which define their molecular basis and potential therapeutic splicing intervention ., In the first case , the disease-causing mechanism is entirely due to aberrant splicing and therefore splicing correction is therapeutic ( Fig 5 , class I ) ., The two synonymous V107V and R116R variants belong to this class: they affect binding of splicing factors and induce severe exon skipping with the production of a non-functional mRNA ., Their ExSpeU1-mediated splicing improvement result in the production of the correct mRNA and normal protein ., Affected patients have mild/ moderate phenotype and the residual splicing levels well correlate with the reported FIX:ag and FIX:C activities ( Table 1 ) ., In the second type of mutations ( Fig 5 , ESM class II ) , the presence of two defects , one on splicing and the other on secretion determines at the end the lack or very reduced amounts of the protein ., In this case , the partial splicing defect produces low amounts of normally spliced transcript that , when translated , result in a defective FIX protein with a significantly reduced , but not completely abolished , secretion ., However , as the protein once secreted maintains a normal coagulant activity , an efficient ExSpeU1-mediated correction is expected to partially rescue its function ., Natural mutations A118V and Q121H , and the artificial R116G belong to this class ( Fig 5 , ESM class II ) ; A118V and Q121H are associated to a mild phenotype and to residual FIX:C levels that well correlates with the presence of partial defects in splicing and secretion ( Table 1 ) ., Lastly , mutations like L117F ( Fig 5 , ESM class III ) show a splicing defect but , as the resulting amino acid change severely affects secretion , splicing improvement would not recover FIX function ., In this case , splicing correction should be complemented by additional strategies to bypass the secretion defect and possibly improve misfolding ., Interestingly , some nonsense mutations partially ( Q121Stop ) or severely ( Q97Stop and R116Stop ) affect splicing ( Fig 1D ) ( Table 1 ) ., These nonsense ESMs might also indirectly benefit from ExSpeU1 splicing rescue ., In the experimental system we have used , which is NMD-independent 33 , the consequence of nonsense mutation on pre mRNA processing can be exclusively attributed to their effect on splicing ., In these cases , the splicing correction will not have any direct positive effect by itself but become mandatory for subsequent read-through therapies ., In principle , exonic mutations can create a splicing silencer or disrupt a splicing enhancer ., However , in several cases , due to the promiscuous composition of the regulatory elements and the intrinsic combinatorial control of splicing , 25 , 34 both mechanisms are involved . 35 , 36, Consistent with a major role of silencers in FIX exon 5 , we show , using protein pull-down analysis coupled by mass-spectroscopy , that two synonymous mutations ( V107V and R116R ) increase binding to a negative splicing regulator hnRNP A128 and also to DAZAP1 ( Fig 4A ) ., DAZAP1 associate with hnRNPA1 and was previously implicated in exon skipping . 28, However , their silencing have a small effect on splicing ( Fig 4C ) , suggesting that additional factors are involved ., In any case , notwithstanding the exon skipping mechanism , ExSpeU1 rescued all nine exonic mutations ( Fig 3B ) ., Overall , including the previously reported five variants at the polypyrimidine tract and at the 5ss , 9 a unique ExSpeU1 can rescue 14 different splicing mutations , increasing the number of affected individuals that would benefit from this therapeutic strategy ., This effect on different types of mutations is probably due to the presence in the exon of several SRSF2 binding sites ., SRSF2 is the most active factor promoting exon 5 inclusion ( Fig 2 ) and its silencing inhibits the ExSpeU1-mediated splicing rescue ( Fig 2C ) ., Thus , binding of ExSpeU1 in the intron downstream the 5ss facilitates recruitment of SRSF2 on the exon , compensating the negative effect of the exonic and intronic mutations ., This mechanism is consistent with the fact that SRSF2 is known to facilitate interaction between U1 and U2 snRNP . 37, The ExSpeU1 interaction with intronic sequences is also expected to reduce possible off targets with the advantage , in common with splicing correction strategies and in contrast to classical gene therapy approaches , of maintaining expression of the gene in the normal chromosomal context ., In the perspective of a therapeutic intervention , AAV vector represents a reliable system to deliver ExSpeU1s , as we have recently demonstrated 14 , and liver is a well established target tissue for this vector . 38, In conclusion , our result establishes that mutations in FIX exon 5 can contribute to the disease combining splicing and protein dysfunctions and identifies those variants eligible for splicing-switching therapeutic molecules ., In exons , dissection of the relative contribution of splicing versus amino acid dysfunction is critical for making a correct diagnosis at the molecular level and for establishing the therapeutic feasibility of a splicing rescue strategy ., The splicing correction approach based on precise engineering of the U1 core spliceosomal RNP can be easily applied to different type of defective exons and diseases increasing the potential therapeutic spectrum of these novel class of molecules ., For the reporter minigene splicing assay , we have used the previously described pTBFIX exon 5 minigene . 9, Overlapping PCR approach was used for introducing disease-causing point mutations and oligonucleotides are listed in S1 Table ., The minigenes of FIX exon 5 deletions ( Δ1 to Δ11 and Δ9 . 1 to Δ10 . 4 ) were commercially synthesized ( GenScript , NJ , USA ) ., Expression vectors for the recombinant FIX variants were produced by site-directed mutagenesis using the QuickChange II Site-Directed Mutagenesis Kit ( Stratagene , La Jolla , CA , USA ) ., The mutations were introduced into the human FIX cDNA cloned into the pCMV5 vector39 , using primers listed in S1 Table ., Exon-specific U1 snRNAs were created by replacing the sequence between the sites BclI and BglII with oligonucleotides as done previously . 9, Oligonucleotides are listed in S1 Table ., All the clones were verified by sequencing ., Splicing Assays: HeLa cell line was grown in Dulbeccos modified Eagles medium with Glutamax I ( Gibco ) ( DMEM with glutamine , sodium pyruvate , pyridoxine and 4 . 5 g/l glucose ) supplemented with 10% fetal calf serum ( Euro Clone ) and Antibiotic Antimycotic ( Sigma ) ., HeLa cells grown on six well plates were transfected with Effectene reagents ( Qiagen ) according to the manufacturers instructions ., 500 ng of FIX exon 5 minigenes were transfected either alone or with 500 ng of ExSpeU1-encoding plasmids and the same was performed for co-transfection of splicing factors as previously reported 28 , 40 , 41 ., GFP expression was routinely assessed in cotransfection experiments and showed more than 80% of transfection efficiency ., Cells were incubated for 24 hours and then collected for RNA analysis ., Total RNA extraction was performed using TRIreagent ( Invitrogen ) and cDNA was generated using 2 μg of total RNA and M-MuLV Reverse Transcriptase ( NEB , UK ) ., Alpha2 , 3 and Bra2 oligonucleotides were used for PCR amplification of pTBFIX exon 5 minigenes , as described previously . 9, PCR products were resolved on 1 . 5% agarose gel electrophoresis ., Quantification of exon inclusion was performed using the ImageJ software ., Protein assays: Expression vectors for FIX exonic variants were transiently transfected in HEK293 cells , and secreted FIX antigen ( ELISA ) and coagulant activity ( aPTT-based assays ) were evaluated as previously described . 39, For the pull-down analysis , the RNA templates were short RNA oligonucleotides , listed in S2 Table and the protocol was previously described . 29, Briefly , 10 μg of RNA oligo treated with m-periodate were mixed with dehydrazide agarose beads ( Sigma ) equilibrated with NaOAc and incubated on a rotator at 4°C overnight ., After washing with Solution D ( 20 mM Hepes pH = 7 . 9 , 100 mM KCl , 0 . 2 mM EDTA pH = 8 . 0 , 100 mM DTT , 6% v/v Glycerol ) , the RNA-beads complex was incubated with HeLa cell nuclear extract ( C4 , Biotech ) and 6 mg/mL of heparin ., The beads were then washed six times with Solution D and the samples were loaded on 12% SDS-polyacrylamide gels ., Gels were stained with Coomassie brilliant blue R250 ., The protein bands were excised and analyzed with mass spectrometer ( LCQ DECA XP-ThermoFinnigam ) and proteins were identified by analysis of the peptide MS/MS data with Turbo SEQUEST ( Thermo Finnigam , CA , USA ) and MASCOT ( Matrix Science , UK ) ., For the validation , protein samples were separated by NuPAGE 4%–12% Bis-Tris precast gels ( Life Technologies , CA , USA ) and electroblotted onto nitrocellulose membranes ., The primary antibodies that were applied in western blotting analysis are: rabbit polyclonal anti-hnRNPA1 antibody ( 1:1000 , Santa Cruz ) and rabbit polyclonal anti-DAZAP1 antibody ( 1:1000 ) ., Silencing for HNRNPA1/2 , DAZAP1 and SRSF2 was performed twice after 24 and 48 hours using Oligofectamine ( Invitrogen , CA , USA ) , according to the manufacturers instructions ., The sense strands of RNAi oligos ( Dharmacon , CO , USA and Sigma Aldrich , MO , USA ) , which were used to silence the target genes , are listed in the S3 Table ., 24 hours after the second treatment with siRNA the cells were transfected with the minigenes , as described above ., After additional 24 hours , cells were collected and divided in two equal fractions for RNA and protein depletion analysis ., Confirmation of HNRNPA1/2 and DAZAP1 silencing was done using Western blot ( antibodies listed above ) and for the SRSF2 using Sybr Green qPCR and ΔΔCt relative quantification with GAPDH as an endogenous control ., The primers are listed in S4 Table ., U1 snRNA 5′ functional inhibition was achieved by co-transfection of D1 plasmid as previously described . 27, The in silico splicing analyses were performed using Human Splicing Finder ( HSF ) 23 with implementation of ESS Hexamers25 , predicted sites of SR proteins binding with ESE finder 22 and SRSF2 consensus 26 . | Introduction, Results, Discussion, Materials and Methods | Mutations that result in amino acid changes can affect both pre-mRNA splicing and protein function ., Understanding the combined effect is essential for correct diagnosis and for establishing the most appropriate therapeutic strategy at the molecular level ., We have identified a series of disease-causing splicing mutations in coagulation factor IX ( FIX ) exon 5 that are completely recovered by a modified U1snRNP particle , through an SRSF2-dependent enhancement mechanism ., We discovered that synonymous mutations and missense substitutions associated to a partial FIX secretion defect represent targets for this therapy as the resulting spliced-corrected proteins maintains normal FIX coagulant specific activity ., Thus , splicing and protein alterations contribute to define at the molecular level the disease-causing effect of a number of exonic mutations in coagulation FIX exon 5 ., In addition , our results have a significant impact in the development of splicing-switching therapies in particular for mutations that affect both splicing and protein function where increasing the amount of a correctly spliced protein can circumvent the basic functional defects . | Clarification of if an exonic variant has an effect on splicing and/or on protein function is an important aspect in clinical genetics and in development of appropriate therapeutic strategies , and most of published evidence consider splicing and protein function separately ., In exons , the presence of dense splicing regulatory and amino acidic coding information implies that mutations may have a double pathogenic effect acting on splicing and/or on protein function ., To address this issue we focused on coagulation factor IX ( FIX ) exon 5 , where we identified natural mutations that induce different degree of exon skipping ., All exon skipping mutations were completely corrected by a novel splicing-switching therapeutic approach based on modified U1 snRNP ., To detect the substitutions that might benefit from this correction , we investigated splicing recovered mutations for FIX protein secretion and specific activity ., This analysis identified synonymous mutations causing remarkable exon skipping and missense mutations with a partial effects on both splicing and secretion , but compatible with normal FIX coagulant properties , as target variants for the splicing-switching therapy . | medicine and health sciences, gene regulation, physiological processes, substitution mutation, mutation, nonsense mutation, forms of dna, dna, small interfering rnas, gene expression, rna splicing, complementary dna, biochemistry, rna, rna processing, nucleic acids, physiology, genetics, secretion, biology and life sciences, non-coding rna, silencer elements | null |
journal.pbio.0050209 | 2,007 | Structure of the Chloroplast Ribosome: Novel Domains for Translation Regulation | The chloroplast of plants and algae is believed to have originated from the endosymbiosis of an ancient photosynthetic bacteria into a eukaryotic host ., Remnants of that ancient bacteria remain in the modern chloroplast , as it maintains a circular genome and transcription and translation machinery similar to that of prokaryotes 1 , 2 ., Chloroplasts are responsible for photosynthetic energy production in plants and algae , and have recently been targeted as a platform for production of recombinant therapeutic proteins , making the understanding of translation in this organelle essential 3 ., Approximately 60 proteins are translated in the plastid , a small fraction of the total proteins functioning in this organelle ., The majority of chloroplast proteins are encoded by the nuclear genome and post-translationally imported into the plastid 4 ., Coordinate expression from the nuclear and plastid genomes is required for development in photosynthetic organisms , and is achieved in chloroplasts primarily through regulation of translation 5 , 6 ., Translation of many chloroplast genes is also regulated in response to light , and to maintain stoichiometric accumulation of multiprotein-complex subunits 7 , 8 ., All of this regulation involves a host of protein translation factors , and the formation of RNA–protein complexes on chloroplast mRNA 5′ untranslated regions ( 5′ UTRs ) 9–13 ., Some of these protein factors are specific to individual mRNAs , whereas others serve classes of messages ., Due to the bacterial ancestry of the organelle , translation in the chloroplast has been considered bacterial-type translation , and many of the requisite bacterial-type translation factors can be identified in chloroplasts , although not all of these are exact homologs of the bacterial proteins 14 ., Translation regulation in the chloroplast is more complex than in bacteria , and this complexity requires additional RNA and protein components not found in prokaryotic systems ( reviewed in 5 , 15 ) ., A number of protein factors have been identified as essential components of chloroplast translation , although how these factors interact with an mRNA to facilitate chloroplast translation is not known ., Chloroplast messages also experience pausing during their translation , which has been implicated in maintaining the proper stoichiometry of gene expression from polycistronic mRNAs , as well as in cotranslational membrane insertion or cofactor association 16 , 17 ., mRNA secondary structures or rare codon usage are often suggested as the cause of pausing during elongation; however , for mRNAs studied in chloroplasts ( particularly psbA and atpA ) , these alone are insufficient to account for the pause sites ., RNA elements identified as regulatory components in the translation of chloroplast messages are primarily located in the 5′ UTR ., These elements include Shine-Dalgarno ( S-D ) sequences , stem-loop structures , and A/U rich elements 10 , 18–20 ., Nearly all bacterial mRNAs use base pairing between a S-D sequence located in the 5′ UTR of the mRNA and a complementary sequence located near the 3′ end of the 16S rRNA 21 ., Base pairing between these sequences is essential for bacterial translation initiation , and bacterial S-D elements are located 7 ± 2 nucleotides ( nt ) upstream of the initiator AUG to allow for a simple physical positioning of the initiator AUG in the P-site of the ribosome 21 , 22 ., In plastids , only some mRNAs have recognizable S-D sequences , and these are found over a large range of the 5′ UTR , some up to 100 nt upstream of the start site AUG 23 , 24 ., This diverse positioning of S-D elements precludes a simple physical positioning of plastid mRNAs on the ribosome , and indicates that chloroplasts have a fundamentally different mechanism than bacteria for translation initiation ., The complete proteome of chloroplast ribosomes from both green algae 25 , 26 and higher plants 27 , 28 has been elucidated ., A majority of the protein components of chloroplast ribosomes have clear homologs in bacterial 70S ribosomes ., However , a significant number of chloroplast-unique proteins and domains were also identified ( Tables S1 and S2 ) ., Five plastid-specific ribosomal proteins ( PSRPs ) have been identified in Chlamydomonas reinhardtii , four of which are located on the small subunit of the ribosome ., Three other ribosomal proteins , S2 , S3 , and S5 , have large chloroplast-unique domains on otherwise homologous bacterial ribosomal proteins 26 ., Together , these protein additions increase the mass of the small subunit of the chloroplast ribosome by 25% compared to a bacterial 30S subunit ( Table S1 ) ., Based on overall conservation of protein components and rRNAs , and the locations of proteins with chloroplast-unique domains ( Figure 1 ) , it has been hypothesized that novel structures have been added to the small subunit of the ribosome to accommodate the specific demands on chloroplast translation regulation 26 ., In light of the accumulating evidence that translation regulation in the chloroplast is far more complex than in bacteria , and that chloroplast ribosomes contain unique protein components compared to 70S-type ribosomes , it is important to elucidate the structure of a chloroplast ribosome from the model organism most used to study chloroplast gene expression , C . reinhardtii ., Using single-particle reconstruction from cryo-electron micrographs we have determined the structure of the C . reinhardtii chloroplast ribosome to a resolution of 15 . 5 Å ., This structure shows that the chloroplast ribosome expands upon a core 70S-type bacterial ribosome structure with multiple chloroplast-unique domains ., These chloroplast-unique structures are found on the small subunit of the ribosome near the mRNA entrance and exit channels ., The potential role of these structures in translation regulation in the chloroplast is discussed , including their involvement in translation initiation via positioning of initiation mRNA–protein complexes ( mRNPs ) , and the potential involvement of these unique domains in the processivity of chloroplast translation ., Single-particle reconstruction was used to calculate a three-dimensional map of the chloroplast ribosome to a resolution of 15 . 5 Å ( as determined by 0 . 5 cutoff Fourier Shell Correlation criteria; Figure S2 ) ., The chloroplast ribosome has clearly defined large and small subunits , and a distinct intersubunit space ( Figure 2 and Video S1 ) ., Common features defined from bacterial 70S ribosome structures can also be identified on the chloroplast ribosome; the large subunit of the chloroplast ribosome has an easily distinguished central protuberance ( CP in Figure 2A ) , L1 arm ( L1 ) , and stalk ( ST ) ., The small subunit has clear head ( h ) , body ( b ) , platform ( pt ) , shoulder ( sh ) , and spur ( sp ) domains ., The small subunit also has clearly distinguishable additional structures on its solvent-exposed face that are not present on bacterial ribosomes; these include a large multilobed structure emerging in the vicinity of the mRNA exit channel and extending down across the body of the ribosome ( cuα , Figure 2A ) , additional connection of the head and beak regions ( cuβ ) , and a thickening of the shoulder region ( cuγ ) ., The platform is also lifted slightly away from the body and towards cuα ., On the large subunit , chloroplast-unique density extends from the base of the L1 arm back towards the center of the large subunit ( CUλ ) ., There is also extensive connection between the L1 arm and nearby features on the body of the large subunit , but this is also seen to some degree in the Escherichia coli cryo-electron microscopy ( cryoEM ) maps ( Figure 2B; 29 , 30 ) and represents flexibility inherent to this part of the large subunit ., Overall , interface surfaces and the tRNA and essential translation factor-binding regions located between the large and small subunits of the chloroplast ribosome appear highly similar with these same features in bacterial ribosomes , whereas solvent-exposed surfaces of the chloroplast ribosome show some striking differences from those of bacteria ., It is difficult to distinguish individual bridges ( as defined in 29 and 31 ) in the central region of the intersubunit space ( i . e . , bridges B3 and B5 ) , but the bridging patterns appear conserved between E . coli and the chloroplast ribosome , with only a few exceptions ., Bridges 1b and 1c are seen as a single bridge off the upper central protuberance , and bridge 4 is expanded in the chloroplast ribosome and makes contact with the lower body region of the small subunit ( unpublished data ) ., The identification of these conserved bridges supports the idea that interactions between the large and small subunits are largely unchanged between chloroplast and bacterial ribosomes ., Normal mode flexible fitting was used to fit bacterial ribosome crystal structure data to our chloroplast ribosome map ( see Materials and Methods ) ., A difference map between this fitting and our chloroplast map reveals densities both unique to ( cu structures ) and lacking from ( mesh/ribbon in Figure 3 ) the chloroplast ribosome ., A majority of the densities lacking from the chloroplast ribosome can be understood in light of proteomic and genomic data on the chloroplast ribosomal proteins 25 and comparison of predicted rRNA secondary structures ( Comparative RNA Web Site , http://www . rna . ccbb . utexas . edu; Figures S3 and S4 ) ., For example , large subunit proteins L25 and L30 are clearly identified in a difference map as lacking from the chloroplast ribosome ( Figure 3A ) ., These proteins were not identified in proteomic analysis of the C . reinhardtii chloroplast ribosome , nor were genes encoding these proteins identified in the completed C . reinhardtii nuclear genome sequence ( http://genome . jgi-psf . org/Chlre3/Chlre3 . home . html ) ., There is no known function for either of these proteins on the ribosome 32 ., The L29 protein was not identified via proteomics 25 , and an L29 homolog has yet to be found in the C . reinhardtii genome database , but density in the area where this protein is found on the bacterial ribosome is clearly present in the chloroplast ribosome structure ., L29 is involved in interactions with trigger factor and SRP , both of which have homologs in the C . reinhardtii chloroplast 33 , 34 ., It is likely that the small size of L29 precluded its identification via proteomics , and that remaining gaps in the genome sequence are harboring the gene for chloroplast L29 ., Small rRNA helices are also lacking from the chloroplast ribosome in a number of places on both the small and large subunits ( Figure 3 ) ., In each case , the absence of rRNA density corresponds to a small region of the rRNA that is not conserved between chloroplasts and bacteria ( Figures S3 and S4 ) ., The regions of rRNA that differ between chloroplast and bacteria are not involved in any known function of the ribosome; they do not interact with antibiotics , are not associated with any aspect of translation initiation , and do not participate in intersubunit bridges 35–38 ., Comparison with predicted secondary structure diagrams from both mitochondrial and 80S ribosomes indicates that all of these helices are found in regions of variability off the conserved rRNA core shared by all ribosomes 39 ., The identification of structural differences that correspond exactly with our previous proteomic analysis and with predicted rRNA secondary structure differences gives us a very high degree of confidence that the map of the chloroplast ribosome that we have calculated is correct , and validates the chloroplast-unique densities that we identify as real and significant ., Comparison of the chloroplast ribosome with cryoEM reconstructions of the E . coli ribosome reveals that the head of the chloroplast ribosome is rotated and tilted away from the large subunit by approximately 5° , which results in a slight lift of the beak ( Figure S5 ) ., Connectivity between the beak and the shoulder in this area originates from chloroplast-unique density ( cuβ ) , whereas connections are only seen between the beak helix and the shoulder in E . coli ribosomes ., This is similar to movements seen in eukaryotic ribosomes upon internal ribosome entry site ( IRES ) binding ( see Discussion ) ., Similarity to the mitochondrial ribosome is also observed in the differences between chloroplast and bacterial large subunits ., Like the chloroplast ribosome , mitoribosomes do not have L25 , and there is a crevasse between the central protuberance and the stalk side of the large subunit where this protein sits in bacteria 40 ., This effect is smaller but similar on the chloroplast ribosome since the central protuberance is much expanded on the mitoribosome ., In bacteria , the ribosomal E-site is commonly occupied by tRNA after purification , but we do not see this for our chloroplast ribosome ., There is some evidence of partial occupancy at the factor-binding site of the chloroplast ribosome ., The discontinuous density in the factor-binding site is not shown here , but may contribute to regions of large subunit density in the stalk base area and small subunit density on the back of the shoulder that appear extended into the intersubunit space , and also to cuε on the PSRP-7 antibody-bound map ( see below ) ., Further computational separation of a larger dataset may allow us to calculate a map representing full occupancy at this site , and further proteomic analysis could verify the identity of the bound factor ., Chloroplast-unique structures and changes near the mRNA exit and entrance channels dominate a comparison of E . coli and chloroplast ribosomes ( Figures 3 and 4 ) ., Connectivity with rRNA or proteins that have bacterial homologs allows prediction of the identity of some of the novel structures found on the chloroplast ribosome ., The largest region of chloroplast-unique density on the small subunit emerges from the neck region of the ribosome , adjacent to the mRNA exit channel , and extends down along the platform ( cuα; Figure 4A ) ., This multilobed structure of approximately 90 kDa makes contact with the head and neck of the small subunit , and partially overlaps the positions of S1 and S2 on bacterial ribosomes ( compare Figure 4A and 4C ) ., The upper lobe of cuα limits access to the mRNA exit channel to about 25 Å from both side and top ., The mRNA exit channel is the site of initial interactions between mRNAs and the ribosome; and in bacteria , this is the site of the S-D interaction that positions the start site AUG of the mRNA at the P-site of the ribosome 21 , 22 , 41 , 42 ., Below cuα at the mRNA exit channel and following down the underside of the chloroplast-unique density , there is an extended trough on the chloroplast ribosome , accentuated by the lifting of the platform domain ( Figure 4B ) ., Proteins S21 , near the mRNA exit channel , and S1 , S2 , and S5 are partially displaced from their positions on the bacterial ribosome by the chloroplast ribosome trough ., These displacements may indicate movement of these proteins into cuα ., Connectivity of cuα with the main body of the small subunit of the ribosome suggests that S1 and the chloroplast-unique domain of S2 comprise the majority of cuα ., Chloroplast S1 is the only small subunit protein that is significantly smaller than its bacterial homolog ( Table S1 ) , but like bacterial S1 , binds mRNA 43 ., Chloroplast S2 has a large chloroplast-unique amino-terminal extension 26 , more than doubling its size compared to bacterial S2 ( 63 kDa vs . 27 kDa , Table S1 ) ; two TRAM domains in this addition give chloroplast S2 the potential to bind RNA 44 ., In ultraviolet ( UV ) cross-linking experiments , both of these proteins are strongly labeled by a radiolabeled mRNA 5′ UTR ( Figure 5 ) ., Also labeled were L1 and an incompletely denatured protein complex containing at least S5 and PSRP-7 , the other two large chloroplast-unique proteins on the small ribosomal subunit ., In the same experiment using E . coli ribosomes , only S1 is strongly labeled ( Figure 5 ) ., The mass of cuα is estimated at 90 kDa , which adds over 10% greater mass to the small subunit of the chloroplast ribosome compared with the E . coli 30S subunit , and allows for S1 ( 44 kDa ) and S2 to be contained within this structure ., Given proximity to the mRNA exit channel and the RNA-binding properties of S1 and S2 , cuα is perfectly situated to act as a landing pad for chloroplast initiation complex mRNPs ., Interaction between these mRNPs and the ribosome could be utilized to position mRNAs , both with and without S-D sequences , for translation initiation ., Another large region of chloroplast-unique density on the small subunit is found in the beak and head region of the ribosome , adjacent to the mRNA entrance channel ( cuβ; Figure 6A ) ., This is the first surface of the ribosome that coding regions of mRNA encounter during translation , and proteins in this region are important for helicase activity of the bacterial ribosome 45 ., cuβ connects the beak helix ( h33 ) with S3 and S10 ( see Figure 3C ) , and approaches the mRNA entrance channel at the front underside of the beak ., Chloroplast S3 has a large internal chloroplast-unique domain , as well as good homology with bacterial S3 at its N- and C-termini , which together predict that the S3 chloroplast-unique domain comprises cuβ ( compare predicted location of S3 cu domain in Figure 1 with cuβ in Figure 6 ) ., In an attempt to localize the largest plastid-specific ribosomal protein , PSRP-7 , chloroplast ribosomes were incubated with PSRP-7 antibody prior to freezing and imaging ., A separate reconstruction from this data yielded a map at 19 . 4 Å , and revealed additional structure on the solvent-exposed face of the small subunit ( Figure 6B and 6C ) ., Most of this additional density stems from chloroplast-unique structures already defined on the unliganded chloroplast ribosome , which are expanded in this antibody-bound map ., A few regions of density that do not correspond to densities on the unliganded structure may represent the bound antibody ( Figure 6C ) ., These densities are found both emerging from the expanded shoulder of the small subunit ( cuδ ) , and from the head of the small subunit and towards the factor-binding site at the subunit interface ( cuε ) ., cuε may also be related to the protein occupancy at the factor-binding site , because visualization at lowered thresholds reveals that cuε is contiguous with density in the factor-binding site ., Antibody-binding appears to stabilize chloroplast-unique structures on the small subunit , particularly cuα , and at very low thresholds connects the mid-region of cuα with the tip of cuδ ( asterisk in Figure 6B ) ., The tip region of cuδ is visualized at very low thresholds on the unliganded chloroplast ribosome map , which further suggests that antibody binding is stabilizing part of the chloroplast-unique density on the surface of the small subunit ., cuδ extends toward the head parallel to cuα , and lies across the line of direct access to the mRNA entrance channel ( Figure 6C ) ., Chloroplast-unique structures near the mRNA entrance channel—via S3 , which is involved in helicase activity in bacterial ribosomes 45 , or PSRP-7 , which binds mRNA ( Figure 5 and 46 ) —are likely involved in recognizing structured elements in coding regions of chloroplast mRNAs and may act to alter the processivity of translation ., These structures may also be involved in mRNA positioning for translation initiation , in analogy to the mRNA gate structure on the mitochondrial ribosome 40 ., Mammalian mitochondrial mRNAs do not have 5′ UTRs 47 , 48 , and the mRNA gate is hypothesized to function in the proper positioning of these leaderless messages for translation initiation 40 ., Antibody-binding stabilization of at least cuα indicates that there is flexibility in these chloroplast-unique structures and that , because of this flexibility , the full extent of these features is not yet resolved in our structure ., Lowering the threshold visualization levels of either the bound or the unbound maps reveals additional density near the mRNA exit channel , extending up along the head and occluding access to the channel ( unpublished data ) , suggesting that alterations in this area must occur to provide mRNAs access to the small subunit of the ribosome ., Chloroplast ribosomes imaged in complex with mRNA , tRNA , and protein factors may be needed to fully resolve structures in this area ., This is the first report of the structure of a chloroplast ribosome , and comes from the organism from which the majority of information on plastid translation has been derived ( C . reinhardtii ) ., The translation machinery in chloroplasts is clearly based on a prokaryotic-like core , though translation in eukaryotic plastids is more complex than in bacteria from both regulatory and physical perspectives ., A large body of research has shown that translation is the key regulated step in chloroplast gene expression 49 ., These studies have identified many key events in chloroplast gene expression: interactions of individual photosynthetic proteins with their own and partner protein mRNAs , the formation of mRNPs between nuclear-encoded translation factors and the 5′ UTRs of mRNAs , and the effects of light-induced signals on mRNP formation and translation initiation 7 , 9 , 10 , 12 , 13 , 50–52 ., Structural analysis of the chloroplast ribosome and identification of chloroplast-unique structures on the ribosome provide an important understanding of the physical components utilized for translation regulation in this organelle ., Chloroplast-unique structures dominate the solvent-exposed face of the small subunit , and approach both the mRNA entrance and exit channels ., These structures are ideally situated to regulate translation initiation , and genetic and biochemical data suggest that these structures accompany and complement the use of modified S-D sequences and translation initiation mRNP formation ., Proteomic studies identified chloroplast-unique ribosomal proteins , primarily on the small subunit of the ribosome 26 ( Tables S1 and S2 ) ., The structure presented here allows us to visualize these chloroplast-unique proteins as novel structural domains on the chloroplast ribosome ., The large subunit of the chloroplast ribosome differs from the bacterial 50S subunit by only a few proteins , and we see only one significant chloroplast-unique region on this subunit ( CUλ; Figures 2A and 3B ) ., The primary function of the large subunit of the ribosome is peptide bond formation , and this most basic function of the ribosome has been conserved between eukaryotic , bacterial , and organellar ribosomes ., Here , we confirm this expected structural conservation in the core of chloroplast ribosomes ., The small ribosomal subunit is responsible for interactions with mRNAs and initiation factors that position messages for translation initiation 21 , and it also has the duty of quality control in codon decoding during translation ., Regions of the small subunit that are responsible for decoding and quality control are structurally conserved with bacterial ribosomes , whereas the chloroplast-unique additions are seen on the small subunit of the ribosome in areas that intersect the path of mRNA during translation initiation , the key regulated step of chloroplast translation ., The large chloroplast-unique structure found near the mRNA exit channel and extending down along the platform of the small subunit ( cuα; Figures 2–4 ) is located near the site of binding for S1 in bacteria ., S1 is the only ribosomal protein to bind mRNAs in bacteria , and it binds to mRNAs and the ribosome through six repeats of an RNA-binding motif; the S1 protein in chloroplasts has only three RNA-binding motifs ., The additional domains on S2 and the chloroplast-unique protein PSRP-7 both contain RNA-binding domains that may complement the smaller chloroplast S1 protein in mRNA binding ( Figure 5 ) ., The S-D interaction between bacterial mRNAs and the 16S rRNA also occurs in the mRNA exit channel area , and functions to position mRNAs for translation initiation ., As mentioned previously , S-D sequences in chloroplast mRNAs do not share the bacterial consensus spacing from the start site AUG ., This difference in spacing requires a fundamentally different mechanism for bacteria and chloroplasts to position mRNAs for translation initiation , and suggests that the additional chloroplast-unique structure located at the mRNA exit channel may function as adapters that positions chloroplast mRNAs properly for initiation ., cuα is perfectly situated to function as this adapter for interactions between chloroplast initiation complex mRNPs and the chloroplast ribosome ., Programmed pausing has been observed in the translation of several chloroplast mRNAs , and in the case of D1 protein , this pausing is intimately associated with assembly of the nascent polypeptide with cofactors and partner subunits into the thylakoid membrane 16 ., In bacteria , side chains from S3 form part of the lining of the mRNA entrance channel 22 , and mutation to S3 affects the ability of the bacterial ribosome to unwind downstream coding-region secondary structure for ribosome translocation along an mRNA 45 ., Interactions between coding regions and 5′ UTRs of chloroplast mRNAs also impact translation efficiency , and these interactions can be modified by proteins binding to the 5′ UTR of the mRNA 53 , 54 ., The locations of cuβ and cuδ near the mRNA entrance channel ( see Figure 6 ) , combined with the mRNA-binding properties of PSRP-7 , allow for interactions between these ribosomal proteins and coding regions of mRNAs that may assist positioning during translation initiation , or that recognize structured elements in coding regions of mRNAs and modify processivity during translation elongation ., That cuε reaches from the beak into the factor-binding site , and that elongation factor Ts is covalently linked to the ribosome through PSRP-7 in many chloroplasts ( as the PETs polyprotein 46 ) , suggest possible involvement in programmed pausing through modification of ribosome function in this important region ., Modified ribosome structures that are thought to impact translation initiation have also been identified in other organisms ., A large structural element was found adjacent to the platform on the small subunit of the 80S-type ribosome from trypanosome 55 ., The rRNA responsible for this structure is found in expansion segments of the small subunit rRNA that are found only in trypanosomes ., Trypanosome mRNAs are also unique in that they all receive the same 5′ UTR through transsplicing , and interactions between the 5′ UTR and the ribosome are required for translation 56 ., The novel structure on the trypanosome ribosome is implicated in translation initiation by virtue of its proximity to the mRNA exit channel , and also its potential to interact through base pairing with conserved regions in the trypanosome mRNA 5′ UTR 55 ., The structure of the hepatitis C virus ( HCV ) IRES element complexed to the human 40S ribosomal subunit also revealed a structure similarly situated to cuα 57 ., This IRES is used for positioning viral mRNAs with their start site AUG at the P-site of the ribosome in the absence of cellular translation factors 58 ., The collective movements of the 40S subunit head upon IRES binding are quite similar to those seen between chloroplast and bacterial ribosomes ., It has been suggested that these movements promote formation of preinitiation complexes in the absence of canonical initiation factors 57 , 59 ., Each of these systems has highly regulated translation initiation , which suggests that structural adaptation to the ribosome for specialized translation regulation may be quite ubiquitous in nature ., The small subunit of the ribosome has evolved and adapted as a means to regulate translation initiation , whereas the large subunit of the ribosome is more evolutionarily stable , maintaining the basic function and integrity of the core reactions of peptide bond formation and nascent peptide delivery ., The chloroplast ribosome structure and mRNA cross-linking presented here have allowed us to propose a mechanistic model for chloroplast translation initiation ( Figure 7 ) ., Such a model for bacterial translation is quite simple ( as in Figure 1 ) : mRNAs , even as they are being transcribed , are positioned via S1 binding and the S-D interaction with their start site AUG in the P-site of the ribosome ready for initiation ., For this reason , bacterial initiation is dominated by accessibility of the S-D element and its physical spacing from the start site AUG ., Plastid mRNAs are normally unable to access the ribosome on their own ( Figure 7 , panel 1 ) , and require binding of nuclear-encoded proteins to activate translation ( panel 2 ) ., We propose that cuα acts as a landing pad for initiation complex mRNPs as a first stage of mRNA interaction with the chloroplast ribosome ( panel 3 ) ., Via this interaction , mRNAs with divergently spaced S-D sequences , or without S-D sequences , can be placed such that their start site AUG is correctly positioned in the ribosomal P-site for translation initiation ., Interactions between chloroplast mRNA coding regions and 5′ UTRs may be sensed or accommodated by cuβ and cuδ near the mRNA entrance channel , and these domains may also assist in positioning of the start site AUG for initiation ., Additionally , during translation , these chloroplast-unique structures may recognize sequence-specific or secondary-structured mRNA elements ( Figure 7 , panel 4 ) and communicate this to the ribosome in the form of modification of the processivity of translation or translocation ., Such interactions would explain programmed pausing during translation that allow for proper membrane or cofactor association of nascent polypeptide chains ., The chloroplast ribosome structure presented here will allow for focused experimental design to examine interactions of ribosomal proteins and plastid mRNAs , as well as the role that these interactions play in translation initiation in the chloroplast ., A clearer picture of the physical interactions involved in translation initiation—between mRNAs , their associated proteins , and the chloroplast ribosome—will also assist in designing more appropriate transgenes for increased recombinant protein expression in chloroplasts ., This structure will also serve as a complement to studies on the basic mechanisms of translation that have traditionally used bacteria as a model ., C . reinhardtii cultures , strain cc3395 , were grown to mid-log phase , harvested , and disrupted using a nitrogen bomb at 600 psi in buffer containing 25 mM Tris-HCl ( pH 8 . 0 ) , 25 mM KCl , 25 mM MgCl2 , 5 mM DTT , 0 . 05 mM spermine , 2 mM spermidine , 1% Triton X-100 , 2% polyoxyethylene 10 tridecyl ether ., Lysates were cleared at 40 , 000×g prior to sucrose gradient centrifugation ., Gradients were made in the above buffer ( minus detergents ) at 25%–45% sucrose with a 10% sucrose step on top ., Sucrose gradients were overlaid with cleared cell lysate and centrifuged at 100 , 000 × g for 18 h ., Fractions were collected down the gradients and monitored by SDS-PAGE and RNA gel staining ., Chloroplast ribosomes and 80S cytosolic ribosomes partially copurify , so gradient fractions containing detectable cytosolic ribosomes were omitted from further processing ., Fractions containing chloroplast ribosomes were then diluted in sucrose-free buffer ( as above , minus detergents ) , and collected by ultracentrifugation at 250 , 000×g ., Ribosome pellets were resuspended ( buffer as above , minus detergents ) , snap frozen in liquid nitrogen , and stored at −80 °C until use ., Ribosomes , or ribosomes that had been incubated with PSRP-7 antibody , were applied to 300-mesh copper grid covered with a continuous carbon substrate that had been plasma cleaned | Introduction, Results, Discussion, Materials and Methods, Supporting Information | Gene expression in chloroplasts is controlled primarily through the regulation of translation ., This regulation allows coordinate expression between the plastid and nuclear genomes , and is responsive to environmental conditions ., Despite common ancestry with bacterial translation , chloroplast translation is more complex and involves positive regulatory mRNA elements and a host of requisite protein translation factors that do not have counterparts in bacteria ., Previous proteomic analyses of the chloroplast ribosome identified a significant number of chloroplast-unique ribosomal proteins that expand upon a basic bacterial 70S-like composition ., In this study , cryo-electron microscopy and single-particle reconstruction were used to calculate the structure of the chloroplast ribosome to a resolution of 15 . 5 Å ., Chloroplast-unique proteins are visualized as novel structural additions to a basic bacterial ribosome core ., These structures are located at optimal positions on the chloroplast ribosome for interaction with mRNAs during translation initiation ., Visualization of these chloroplast-unique structures on the ribosome , combined with mRNA cross-linking , allows us to propose a model for translation initiation in chloroplasts in which chloroplast-unique ribosomal proteins interact with plastid-specific translation factors and RNA elements to facilitate regulated translation of chloroplast mRNAs . | Translation of mRNA into protein is the main step for the regulation of gene expression in the chloroplast , the photosynthetic organelle of plant cells ., Translation is conducted by the ribosome , a large macromolecular machine composed of RNA and protein ., Studies have shown that the composition of the chloroplast ribosome is similar to that of bacterial ribosomes , but also that chloroplast ribosomes contain a number of unique proteins ., We present the three-dimensional structure of the chloroplast ribosome , as calculated using cryo-electron microscopy and single-particle reconstruction ., Chloroplast-unique structures are clearly visible on our ribosome map , and expand upon a basic bacterial ribosome-like core ., The role of these chloroplast-unique ribosomal proteins in regulating translation of chloroplast mRNAs , including light-regulated translation , is suggested by the location of these structures on the ribosome ., Biochemical data confirm a predicted function in chloroplast translation for some of the unique proteins ., Our model for translation in the chloroplast incorporates decades of biochemical and genetic studies with the structure presented here , and should help guide future studies to understand the molecular mechanisms of translation regulation in the chloroplast . | biochemistry, cell biology, in vitro, molecular biology | Cryo-electron microscopy and single-particle reconstruction were used to calculate the structure of the chloroplast ribosome. Chloroplast-unique proteins are visualized as novel structural additions to a basic bacterial ribosome core. |
journal.pcbi.1002606 | 2,012 | Microbial Co-occurrence Relationships in the Human Microbiome | In nature , organisms rarely live in isolation , but instead coexist in complex ecologies with various symbiotic relationships 1 ., As defined in macroecology , observed relationships between organisms span a wide range including win-win ( mutualism ) , win-zero ( commensalism ) , win-lose ( parasitism , predation ) , zero-lose ( amensalism ) , and lose-lose ( competition ) situations 2 , 3 , 4 ., These interactions are also widespread in microbial communities , where microbes can exchange or compete for nutrients , signaling molecules , or immune evasion mechanisms 4 , 5 , 6 ., While such ecological interactions have been recently studied in environmental microbial communities 7 , 8 , 9 , 10 , it is not yet clear what the range of normal interactions among human-associated microbes might be , nor how their occurrence throughout a microbial population may influence host health or disease 11 ., Several previous studies have identified individual microbial interactions that are essential for community stability in the healthy commensal microbiota 12 , 13 , 14 , 15 , and many are further implicated in dysbioses and overgrowth of pathogens linked to disease 16 ., Each human body site represents a unique microbial landscape or niche 17 , 18 , and relationships analogous to macroecological “checkerboard patterns” 3 of organismal co-occurrence have been observed due to competition and cooperation 5 , 9 , 19 , 20 ., For example , dental biofilm development is known to involve complex bacterial interactions with specific colonization patterns 21 , 22 , 23 ., Likewise , disruption of relationships among the normal intestinal microbiota by overgrowth of competitive pathogenic species can lead to diseases , e . g . colonization of Clostridium difficile in the gut 24 ., However , no complete catalog of normally occurring interactions in the human microbiome exists , and characterizing these co-occurrence and co-exclusion patterns across body sites would elucidate both their contributions to health and the basic biology of their ecological relationships ., Thus , characterizing key microbial interactions of any ecological type within the human body would serve as an important first step for studying and understanding transitions among various healthy microbial states or into disease-linked imbalances ., As has been also been pointed out in macroecology , however , the analytical methodology needed to comprehensively detect such co-occurrence relationships is surprisingly complex 25 ., Most existing studies employ simple measures such as Pearsons or Spearmans correlation to identify significant abundance relationships 13 , 15 , 26 ., These methods are suboptimal when applied without modification to organismal relative abundances 27 ., Since absolute microbial counts are not known and measurements depend on sampling and sequencing depth , an increase in one relative abundance must be accompanied by a compositional decrease in another , leading to spurious correlations among non-independent measurements 28 ., In addition , sparse sequence counts can cause artefactual associations for low-abundance organisms with very few non-zero observations 27 ., Conversely , association methods such as log-ratio based distances 28 that have been developed specifically for such compositional data are difficult to assign statistical significance , a vital consideration in high-dimensional microbial communities containing hundreds or thousands of taxa ., Here , we have addressed these issues to catalog a baseline of normal microbial interactions in the healthy human microbiome ., The Human Microbiome Project ( HMP ) 29 sampled a disease-free adult population of 239 individuals , including 18 body habitats in five areas ( oral , nasal , skin , gut , and urogenital ) , providing 5 , 026 microbial community compositions assessed using 16S rRNA gene taxonomic marker sequencing 29 ., We have developed a suite of methods to characterize microbial co-occurrence and co-exclusion patterns throughout the healthy human microbiome while suppressing spurious correlations ., Specifically , these were, 1 ) an ensemble approach including multiple similarity and dissimilarity measures , and, 2 ) a compendium of generalized boosted linear models ( GBLMs ) describing predictive relationships , both assessed nonparametrically for statistical significance while mitigating the effects of compositionality ., Together , these methods provide a microbiome-wide network of associations both among individual microbes and between entire microbial clades ., Among the 726 taxa and 884 clades in the HMP data , we examined both intra-body site and inter-body site relationships as a single integrated microbial co-occurrence network ., Each relationship represents co-occurrence/co-exclusion pattern between a pair of microbes within or between body sites among all subjects in the HMP ( in contrast to studies within single subjects of microbial co-occurrences across biogeography , e . g . 30 , 31 ) ., This ecological network proved to contain few highly connected ( hub ) organisms and was , like most biological networks , scale-free ., Co-occurrence patterns of the human microbiome were for the most part highly localized , with most relationships occurring within a body site or area , and there were proportionally few strong correspondences spanning even closely related body sites ., Each pair of organisms was assessed for positive ( e . g . cooperative ) or negative ( e . g . competitive ) associations , and in many cases these patterns could be explained by comparing the organisms phylogenetic versus functional similarities ., In particular , taxa with close evolutionary relationships tended to positively associate at a few proximal body sites , while distantly related taxa with functional similarities tended to compete ., The resulting network of microbial associations thus provides a starting point for further investigations of the ecological mechanisms underlying the establishment and maintenance of human microbiome structure ., Global properties of the microbiome-wide network of microbial associations are summarized in Figures 2 and, 3 . A dominant characteristic of the network was its habitat-specific modularity ., After grouping the 18 body sites into five broad areas ( oral , skin , nasal , urogenital , and gut ) , the large majority of edges were found clustered within body areas ( 98 . 54% ) , and these clusters were sparsely connected through a minority of edges ( 1 . 46% ) ., This is confirmed by the networks high modularity coefficient of 0 . 28 ( as defined by 32 ) and Markov clustering of the network ( see Methods and Figure S2 ) ., It has long been observed that sites within the human microbiome are distinct in terms of microbial composition 33 , and this proved to be true of microbial interactions as well: microbial relationships within each body areas community were largely unique ( Table 2 ) ., The microstructure of interaction patterns - and thus in the underlying ecology - was different for different areas , however ., For example , all vaginal sites within the urogenital area were interrelated in a single homogeneous community , whereas interactions within the oral cavity suggested microbial cross-talk among three distinct habitats 34 ., This can be observed quantitatively based on the proportions of microbial interactions spanning body sites within each area , e . g . 69 . 57% among the vaginal sites and 53 . 19% among the oral sites , both exceeding the microbiome-wide baseline ., The skin was further unique in that the large amount ( 57 . 65% ) of its associations related microbes in corresponding left and right body sites ( left and right antecubital fossae and retroauricular creases ) , reflecting consistent maintenance of bilateral symmetry in the skin microbiome ., We began decomposing the network by categorizing microbial associations within each body area into body-site-specific relationships of two types: cross-site and within-site interactions ., On average , these two classes make up 53 . 11 and 46 . 89 percent of the total edges , respectively ( Table 2 ) ., First focusing on cross-site associations , a majority ( 66 . 10% ) of such relationships were co-occurrences between the same or taxonomically related clades in proximal or bilateral body sites ., This reflects coordinated community structure among ecologically related niches , such as similar dental plaques , vaginal sites , and bilateral skin sites ., Body sites specifically connected by many positive associations were either in direct contact ( e . g . tongue and saliva ) , proximal ( e . g . sub- and supragingival plaques ) , or similar in terms of environmental exposure ( e . g . bilateral skin sites ) , thus providing mechanisms to support comparable microbiota and exhibiting high levels of microbial co-occurrence ., This pattern held true for the minority ( 33 . 90% ) co-exclusions as well , with many occurring between bilateral skin sites or within subgroups of the oral cavity 34 ., This suggested that the first level of hierarchical co-occurrence structure in this network corresponded with groups of body sites representing distinct microbial habitats ., Conversely , within-site relationships showed a much more balanced ratio of microbial co-occurrence ( 48 . 26% ) vs co-exclusion ( 51 . 74% ) interactions ., Many of the negative within-site relationships were associated with the abundant signature organisms characteristic of each body site 35 , for example Streptococcus in the oral cavity and Bacteroides in the gut ., The relative abundances of these signature taxa varied greatly among individuals , in some cases ( e . g . Bacteroides ) spanning from 1% to 97% within a body site across the HMP population ., It is generally very difficult to determine from relative abundance measurements alone whether these negative associations represent true anti-correlation ( e . g . one organism out-competing another ) or overgrowth of one organism while the rest of the population remains unchanged ( resulting in a negative correlation due to compositionality of these data ) ., This problem has a long history in quantitative ecology 27 , 28 ., Our methods generally determine these relationships in the human microbiome to be stronger than what would be expected from compositionality alone ( see Methods and Text S1 ) , and the negative interactions detected here are thus likely biologically informative ., This is supported by the fact that they are strongest in cases where distinct alternative dominant community members occurred among different individuals ( e . g . Prevotellaceae vs . Lactobacillaceae in the vaginal area 36 or Propionibacterium vs . Staphylococcus on the skin 35 , 37 ) ., The increase in negative interactions within habitats is also in line with the fact that most competitive mechanisms require proximity or physical contact 38 , whereas positive interactions are likely to also occur from microbiome-wide shared environmental exposures ., We further assessed several other measures of network community structure ., Globally speaking , the network followed a scale-free degree distribution typical of biological systems , meaning that most clades possessed few interactions but a few clades possessed many ( Figure 3A 39 ) , The network had a low average path length of three ( contrasted with six in randomized networks ) , meaning that short paths existed between most clades 40 , and it possessed a low average per-node cluster coefficient ( 0 . 1 ) measuring the local density of connections ., Together , these values indicate that the microbial association network is structured to be scale-free and thus robust to random disruption 39 , with only sparse local multi-organism clusters ., Since these data only describe phylotypes at approximately the genus level , it remains to be seen whether a greater degree of locally clustered functional associations emerges among Operational Taxonomic Units ( OTUs ) , species , or strains within these phylotypes ., As the cluster coefficient distribution was not well described by the inverse node degree distribution 41 , the network possesses no strong hierarchical modularity despite its scale-freeness , in contrast to the strong habitat-centric modularity ., The diversity of microbial interactors ( i . e . number of unique phylotypes ) within each body site also proved to directly dictate its interaction density ( Figure 3B ) ., That is , communities with a greater number of different organisms had a proportionally greater number of positive and negative associations ., Within these sites , the number of relationships scaled directly with the number of unique phylotypes ( adjusted R2 of 0 . 75 ) , the only body site with more interactions than expected for its diversity being the tongue dorsum ( see also Table S2 ) ., This site also harbored the top-ranking hub phylotype ( Firmicutes , see Figure 3A ) ., In combination with the behavior of specific microbial hubs as discussed below , this might argue that most microbial taxa form strong metabolic or functional associations with adjacent taxa inhabiting the same body site habitat , allowing consortia to specialize within highly localized microbial niches 33 ., When randomizing between rather than within body sites , no body site pairs possessed more cross-site associations than expected ( with the slight exception of tongue dorsum ) , whereas most body sites were significantly enriched for within-site relationships ( the only exceptions being posterior fornix , mid-vagina , and antecubital fossae , which tended toward too few phylotypes to reach significance; see Figure 3D and Table S2 ) , again confirming the microbiomes habitat-driven modularity ., When calculating network properties in a body-area-specific manner , we found that the overall average path length between nodes in the oral cavity , which contributes most of the samples , was much larger ( ∼3 . 4 ) than those of the other body areas ( ranging from ∼1 . 1 to ∼2 . 0 ) ., In addition to supporting the aforementioned degree of inter-site habitat formation in the oral cavity , this intriguingly suggests that other body sites in which fewer samples are currently available ( see Table 1 ) have not yet exhausted the detection of microbial relationships in the human microbiome ., More samples and greater sequencing depth may further improve detection power ., We next examined the associations of individual clades with respect to interaction degree , observing highly connected “hub” clades to be found within each body area ., Two classes of hubs appeared in the association network: clades highly connected within one body site , and clades acting as “connectors” between multiple body sites ., Hubs included both specific taxa ( e . g . Porphyromonas , see Figure 3A , Table S3 ) and larger taxonomic groupings ( e . g . the phylum Firmicutes ) ., Within-site hubs were often , although not always , abundant signature taxa ( detailed below ) , high-degree exceptions including Atopobium on the tongue ( 28 total associations , 16 within-site ) and Selenomonas on both tooth plaques ( 20 total/19 within and 7 total/3 within for supra- and subgingival , respectively ) ., The latter provides a striking example of the niche-specificity of these low-abundance within-site interactors , as Selenomonas averages only 1 . 1% and 1 . 2% of the sub- and supragingival plaque communities , respectively , but associates preferentially ( 20 of 27 , 74% ) with members of the greater oxygen availability supragingival community ., The clades detection as a within-site hub thus corresponds with the ecology that might be expected of an organism known to be oxygen-sensitive , fastidious , and grown best in co-culture 42 ., Between-site hubs typically operated among body sites within the same area as described above , with two of the five most connected hub clades in the network falling into this connector category linking multiple body sites , Firmicutes and Proteobacteria on the tongue ( see Figure 3A ) ., The Firmicutes and Porphyromonas ( phylum Bacteroidetes ) hubs in the tongue also had the largest numbers of negative connections among all phylotypes , and all of these highly interactive clades centered on the tongue and spanned multiple related oral habitats ., Signature clades such as the Firmicutes are of course highly functionally diverse , and this network suggests that the few abundant members in any one habitat 35 might instead serve as “information processors” throughout a body area ., In contrast to the low-abundance within-site hubs , this would allow them to provide baseline functionality complemented by distinct , less abundant clades with which they co-occur within differing body site habitats ., Correspondingly , Firmicutes and other inter-site hub nodes showed a higher connectivity than the clades with highest intra-site degree ( e . g . Bacteroidales in the subgingival plaque ) ., Such clades with unusually frequent inter-site associations are thus outliers relative to the networks overall habitat-specific trend and suggest that inter-site hubs are particularly critical for associating similar sites within the same body area ., In the oropharynx , for example , Streptococcus spp ., with a modest degree of functional variation might be present throughout the habitat , interacting with distinct , more specialized clades within each body site 13 ., Almost all such high-connectivity hubs occurred among oral sites ( e . g . Porphyromonas , Streptococcus , Veillonella , and others ) , the first notable exception being the Propionibacterium hub on skin sites ( left and right retroauricular crease ) ., All of these follow the same pattern , however , in which abundant phylotypes likely possessing within-clade functional diversity are distributed among related habitats within each individual ., We additionally examined the phylogenetic rather than biogeographical distribution of these associations , testing whether clades tended to support more phylogenetically related ( within-clade ) or diverse ( between-clade ) interactions ., We first investigated purely quantitative degree distributions by summarizing clades at the class level ., Associations were summarized as the fraction of all possible interactions that were observed to occur , separated into positive and negative bins ( Figure 3E ) ., In addition , clade-specific over-representation of these bins was tested for significance by randomization ( see Methods and Table S2 ) ., The only classes that showed significantly more negative ( and , simultaneously , cross-clade ) associations than expected were the Bacteroidia , Bacilli , and Fusobacteria ., Most of the common classes in the human microbiome had more intra-clade edges than expected by chance ( Actinobacteria , Bacilli , Bacteroidia , Betaproteobacteria , Clostridia , Epsilonproteobacteria , Fusobacteria , Gammaproteobacteria , Mollicutes , and Spirochaetes ) , most of which also have high cluster coefficients ( Figure S3 ) ., Taken together with the biogeographical interactions assessed above , the enrichment for within-class associations likely indicates a phylogenetic aspect of the same behavior ., Specifically , if one member of such a class is abundant in one body site within an individual , it ( or closely related class members ) also tends to be enriched in related body sites ., We next considered relationships between class-level clades throughout the microbiome , summarized in Figure, 4 . Surprisingly , the Actinobacteria and Bacilli form only co-exclusion relationships with other classes , most strongly with Bacteroidia and Fusobacteria , and primarily within the oral cavity ., These clades ( which include the extremely abundant streptococci ) might thus be largely self-sufficient in the functional diversity needed to maintain an oral community , excluding other clades when appropriately supported by e . g . environmental factors ., Although a few classes were linked by positive as well as negative interactions ( e . g . Clostridia and Bacteroidia ) , none of these reached significance on randomization ., Classes connected by both positive and negative links might suggest either that the clades exhibit co-occurrence only in some environments or that some members of the two classes co-occur while others co-exclude ., As the oral communities are both the most data-rich and the most alpha-diverse in the human microbiome 35 , it is not surprising that most relationships are observed within and among them ., For instance , 97% of the specific mutual exclusions between Bacilli and Bacteroidia members occur in oral sites , as do 81% of the members of the Clostridia and Bacteroidia ., The second largest contribution to the latter exclusion ( ∼18% ) comes from the gut , reflecting the frequently discussed Bacteroides/Firmicutes ratio observed in Western populations 15 , 43 , and similar tradeoffs ( with few positive associations ) were observed in other habitats such as the skin ( e . g . Staphylococcus in the Bacilli and Propionibacterium in the Actinobacteria 37 ) ., Co-exclusions such as these have previously been observed in the human microbiota to induce distinct alternative community configurations , which may differ across persons 15 , 36 as well as time points ( e . g . early and late colonizers in community establishment or repopulation after disturbance ) ., Although our methodology does not explicitly describe alternative community configurations , co-occurrence networks can in some cases capture them as extreme exclusion relationships between key microbial taxa ., For instance , Ravel et ., al reported five different vaginal communities in an independent cohort of healthy women , four dominated by Lactobacilli and the fifth diverse and featuring members of the Actinobacteria , Clostridia , Bacteroidia , and other classes ., These alternative configurations occur as mutual exclusions in our genus-level phylotypes between Lactobacillus and members of this fifth diverse community ( particularly anaerobes such as Anaerococcus and the Prevotellaceae ) ., Furthermore , we see a strong negative correlation in stool samples between Bacteroides and members of the gut community , including the Ruminococcaceae and other Firmicutes ., In other body sites , the clade relationship network ( Figure 4 ) features a negative interaction between Bacilli and Bacteroidia classes that mostly occurs in the oral cavity , and oral Porphyromonas ( a member of the Bacteroidia ) is among the most highly connected negative hubs ., Porphyromonas is abundant ( avg . 3 . 3% s . d . 3 . 9% ) in oral habitats but not in most cases the dominant clade; the clade also includes potential oral pathogens 44 , and this may be one of the more striking examples of functional competition and co-exclusion occurring with a specific clade among several oral communities ., The digestive tract is home to one of the most diverse and densely populated microbial communities in the human body 11 ., Oral sites made up half of the body sites surveyed here , as well as exhibiting the greatest within-subject microbial diversity 35 ., Correspondingly , associations between microbes within and among oral sites likewise comprised the majority ( 86 . 46% ) of all edges in our co-occurrence network , also forming its largest connected component ., This consisted of two clusters of organisms from the mouth soft tissues ( gingiva , mucosa , and palate ) and distal areas ( tongue , throat , tonsils , and saliva ) ; the oral hard surfaces ( sub- and supra-gingival plaques ) formed an additional isolated habitat that showed significantly fewer microbial associations with the remainder of the oral cavity ( Figure 5 ) ., A complementary analysis of the HMP microbiomes has revealed evidence of three sub-habitats within the oral cavity based on overall similarity of their microbial communities 34 , and these results demonstrate that the shared community structures of these habitats were to a lesser degree recapitulated in terms of specific microbial associations ( see Figure 5 below ) ., Although the current study is associative and does not by itself establish causative mechanisms of interaction for these microbial associations , many that we detect in the oral cavity in particular are supportd by known metabolic or biochemical interactions ., For instance , in the context of cell to cell interaction , Fusobacterium species are known to be bridging organisms in the development of oral biofilms by co-aggregation through physical contact 45 ., This bridging occurs during biofilm maturation , allowing a more complex use of resources including sugars ( the predominant carbon source for early colonizers ) and proteins ( used by late colonizers ) ., In the hard palate , for example , positive associations were found between Fusobacterium and Capnocytophaga , Peptosptreptococcus , and Porphyromonas , which are in agreement with previously published cell-to-cell interactions 46 , 47 , and these predictions additionally implicate Leptotrichia and Parvimonas ., Dental plaque associations included Parvimonas , Prevotella , and Treponema , also in agreement with existing evidence 48 ., However , those previously published aggregations are strain specific and , this study may be observing broader effects than the direct cell-cell contact preferences in previously described associations ., Conversely , metabolic shifts may explain negative associations detected between other co-habiting microbes , e . g . Tannerella and Streptococcus in the subgingival plaque ., The anaerobic Tannerella requires a much lower pO2 than Streptococcus and is proteolytic , while Streptococcus is a saccharolytic colonizer of the tooth surface that uses sugars as its primary source of carbon and is oxygen tolerant 49 , 50 ., This continuous nutritional , metabolite ( e . g . hydrogen peroxide ) , and oxygen gradient between the supragingval and the subgingival biofilms , along with differential exposure to host factors in saliva , is reflected through the gradual drop of the abundance of Tannerella as the streptococci increase ( Figure S4 ) ., A similar example can be found in the Prevotella and species from the Flavobacteriaceae ( represented here by Capnocytophaga; mean abundance 1 . 68±2 . 76% ) in the tonsils ., Less exposed surfaces of tonsillar crypts offer an anaerobic micro-environment favoring species like Prevotella , while other areas support the growth of carbon dioxide-dependant Capnocytophaga , a tradeoff that we detect here as a specific negative association ., Phyla such as the TM7 and Synergistetes have only recently been characterized at the genetic level in the oral cavity 51 , 52 , and little is yet known about their roles in this microbial ecosystem ., We identified a number of novel co-occurrences between members of these under-characterized phyla , including a positive association between members of the TM7 phylum ( mean abundance 0 . 62±1 . 14% ) and Moryella genus members ( mean abundance 0 . 29±0 . 47% ) in the tongue dorsum and a positive relationship between members of the Synergistetes phylum and Treponema in the subgingival biofilm ., Since limited data on metabolic byproducts or requirements for these clades in the oral community are available , these newly identified putative interactors provide specific hypothesis for follow-up studies ( e . g . by co-culture experiments ) ., The degree to which microbial shedding from the oral cavity along the digestive tract might seed the distal commensal gut microbiota is as yet unclear 53 ., We found few ( 7 ) relationships between organisms in the two areas meeting our significance criteria , none of which were consistently supported by a majority of available data ( Figure S5 ) , suggesting no such direct microbial seeding within our level of detection in the healthy adult microbiome ., Interactions detected within the gut itself consisted primarily of negative associations between Bacteriodes and Clostridia , especially members of the Ruminococcaceae family ., These negative relationships reflect the tradeoff between Bacteroides ( mean abundance 48 . 79±22 . 94% , range 1 . 47–97 . 14% ) and Firmicutes ( mean abundance 27 . 04±16 . 52% , range 1 . 49–91 . 78% ) , the two dominant gastrointestinal taxa and the subject of previous close study 15 , 54 ., While oral microbial transit is clearly important during founding of the microbiome in infancy and in extreme cases such as illness 55 , 56 , these data suggest that it occurs at low levels in the normal adult microbiome ., In such hosts , the naturally dense microflora of the lower gut may serve to further exclude the few bacteria that survive gastrointestinal transit 53 ., It is common practice to group microbial communities by ecological similarity 33 , 35 , and we extended this analysis method by summarizing relationships among similar habitats based on microbial cross-talk ( Figure 5 ) ., Specifically , we organized pairs of body sites by the frequency with which they demonstrated co-occurring ( or excluding ) microbes ( see Methods ) ., Overall , this network recapitulates similarities in community structure among these microbial habitats as assessed by beta-diversity 35 , with the added information of which microbes might drive these similarities ., Conversely , co-exclusions spanning multiple habitats might represent cases in which competitive relationships or differing responses to host environment might bridge multiple habitats ., Stool microbes ( representing the gut microbiota ) , as above , did not demonstrate any detectable associations with inhabitants of the mouth; the airways microbiota ( nares ) likewise associated minimally with other body sites , although they were detectably structurally similar to the skin communities ., The sub- and supra-gingival plaques were distinct from other mouth sites , and the vaginal communities and skin were again all highly similar ., The sparsity of this body site network again illustrates that phylotypes rarely participate in detectable ecological relationships spanning distal body site habitats ., We hypothesized based on previous findings in environmental communities 19 that patterns of microbial co-occurrence and exclusion might be explained by their evolutionary relatedness and functional similarity ., For example , closely related microbes might compete for limited resources , while functionally complementary bacteria would exhibit mutualism ., To test this hypothesis , we compared two genomic properties of all microbial clades appearing in our network , their phylogenetic similarity ( i . e . evolutionary relatedness ) and a “functional” similarity score based on counting shared orthologous gene families ( i . e . a measure of shared pathways and metabolic capacity ) ., Phylogenetic distances were calculated as evolutionary divergence based directly on 16S sequence dissimilarity between all pairs of microbes ., We compared this with a “functional” distance calculated as the Jaccard index of non-shared COG families between all pairs of microbial genomes ( see Methods ) ., For most pairs of microbes , these measures were highly correlated ( Figure 6 ) , not necessarily surprising in that both are influenced by gradual sequence change driven by molecular evolution ., However , several exceptions to this pattern were apparent among the interacting organisms of our study ., First , a dramatic separation of phylogenetic and functional distances occurred between positively and negatively associated clades ( Figure 6 , green lower left vs . red upper right ) : positive associations were enriched for both phylogenetic and functional similarity , while negative associations showed the inverse pattern ., This was partially explained by the basic observation that similar organisms occupy similar niches , as most relationships among similar organisms occurred between clades at different body sites and often between the same clade at two proximal ( e . g . oral ) or bilateral sites ( e . g . left and right retroauricular creases | Introduction, Results/Discussion, Methods | The healthy microbiota show remarkable variability within and among individuals ., In addition to external exposures , ecological relationships ( both oppositional and symbiotic ) between microbial inhabitants are important contributors to this variation ., It is thus of interest to assess what relationships might exist among microbes and determine their underlying reasons ., The initial Human Microbiome Project ( HMP ) cohort , comprising 239 individuals and 18 different microbial habitats , provides an unprecedented resource to detect , catalog , and analyze such relationships ., Here , we applied an ensemble method based on multiple similarity measures in combination with generalized boosted linear models ( GBLMs ) to taxonomic marker ( 16S rRNA gene ) profiles of this cohort , resulting in a global network of 3 , 005 significant co-occurrence and co-exclusion relationships between 197 clades occurring throughout the human microbiome ., This network revealed strong niche specialization , with most microbial associations occurring within body sites and a number of accompanying inter-body site relationships ., Microbial communities within the oropharynx grouped into three distinct habitats , which themselves showed no direct influence on the composition of the gut microbiota ., Conversely , niches such as the vagina demonstrated little to no decomposition into region-specific interactions ., Diverse mechanisms underlay individual interactions , with some such as the co-exclusion of Porphyromonaceae family members and Streptococcus in the subgingival plaque supported by known biochemical dependencies ., These differences varied among broad phylogenetic groups as well , with the Bacilli and Fusobacteria , for example , both enriched for exclusion of taxa from other clades ., Comparing phylogenetic versus functional similarities among bacteria , we show that dominant commensal taxa ( such as Prevotellaceae and Bacteroides in the gut ) often compete , while potential pathogens ( e . g . Treponema and Prevotella in the dental plaque ) are more likely to co-occur in complementary niches ., This approach thus serves to open new opportunities for future targeted mechanistic studies of the microbial ecology of the human microbiome . | The human body is a complex ecosystem where microbes compete , and cooperate ., These interactions can support health or promote disease , e . g . in dental plaque formation ., The Human Microbiome Project collected and sequenced ca ., 5 , 000 samples from 18 different body sites , including the airways , gut , skin , oral cavity and vagina ., These data allowed the first assessment of significant patterns of co-presence and exclusion among human-associated bacteria ., We combined sparse regression with an ensemble of similarity measures to predict microbial relationships within and between body sites ., This captured known relationships in the dental plaque , vagina , and gut , and also predicted novel interactions involving members of under-characterized phyla such as TM7 ., We detected relationships necessary for plaque formation and differences in community composition among dominant members of the gut and vaginal microbiomes ., Most relationships were strongly niche-specific , with only a few hub microorganisms forming links across multiple body areas ., We also found that phylogenetic distance had a strong impact on the interaction type: closely related microorganisms co-occurred within the same niche , whereas most exclusive relationships occurred between more distantly related microorganisms ., This establishes both the specific organisms and general principles by which microbial communities associated with healthy humans are assembled and maintained . | systems biology, medicine, genomics, metagenomics, mathematics, global health, statistics, biology, computational biology, microbiology, statistical methods, microbial ecology | null |
journal.pcbi.1000892 | 2,010 | Frequency-Dependent Selection Predicts Patterns of Radiations and Biodiversity | Speciation is one of the most complex phenomena in nature , yet the effects of its tempo and mode for biodiversity patterns are still controversial 1 , 2 ., Pre-existing niches is considered the dominant mechanism explaining the initial explosion of diversity observed in radiations 3–7 ., In contrast , non-adaptive radiations 8 , 9 driven by niche-independent mechanisms such as sexual selection , rapid range expansion across multiple barriers or the simultaneous formation of multiple geographical barriers , dispersal limitation or isolation by distance without physical barriers due to genetic incompatibilities do not predict such a temporal trend of declining speciation rates during a radiation 10–13 ., Although ecological opportunity ( the availability of an unoccupied adaptive zone ) or rapid range expansion across multiple barriers can explain rates of diversification in some radiating lineages , this is not sufficient for a radiation to occur 14–17 ., Instead of attributing the propensity to have a radiation with decaying through time speciation rates to external influences like niche availability or rapid range expansion an alternative hypothesis can be based in the genome properties evolved during the evolutionary history of organisms ., We explore this hypothesis using two models , one with frequency-dependent selection and one without it ., Both models involve DNA sequence-based evolution of populations via a process of sexual reproduction , assortative mating , mutation , and genetic-distance-based speciation ., The models we have analyzed in the present study are similar in spirit to previous speciation models 12 , 13 , 18 , 19 but different in two key details: ( 1 ) no approximations of the tempo and mode of speciation incorporating sexual reproduction and frequency-dependent selection have previously been shown to explain observed patterns of decay through time of the speciation rate during a radiation without invoking pre-existing niches ., Furthermore , we show that the decay through time of the speciation rate during a radiation without invoking pre-existing niches has dramatic consequences to species richness and diversity , and ( 2 ) we contrast the models with two small radiations for a broad range of parameter values: the Tilapia cichlid genus 11 and the Darwins finches 20 , two groups where assortative mating has been previously documented 21–24 ., We note that larger radiations cannot be handled computationally ., This represents a current limitation to explore broad patterns of speciation and diversity that requires further research ., We simulated the evolution of a population whose members , at the beginning , have identical genomes ., The population evolves under the combined influences of sexual reproduction and mutation ( Text S1 ) ., During reproduction , potential mates are identified from those whose genomes are sufficiently similar to that of the reproducing individual ( ) ., This parameter implicitly captures the effects of the accumulation of genetic incompatibilities by prezygotic or postzygotic reproductive isolation 18 , 25–27 ., A mate is chosen from this set at random ., An offspring is then dispersed in the environment ., This minimal form of mating called assortative mating 13 , 28 , 29 is sufficient for speciation at least when there is no genetic linkage 18 , 19 ., Genomic similarity between two individuals is defined as the proportion of identical nucleotides along the genome ., The genomic similarity among individuals can be represented by an evolutionary graph in which nodes are individuals and edges connect reproductively compatible individuals 30 ( Figs . 1 and 2 ) ., We identify a species as a group of organisms reproductively separated from all the others by genetic restriction on mating , but connected among themselves by the same condition ., Thus , two individuals connected at least by one pathway through the evolutionary graph are considered conspecific , even if the two individuals themselves are reproductively incompatible ., We consider three main assumptions that allow us to approximate the tempo of speciation and also to identify the conditions for each of two alternative modes of speciation in the evolutionary graph: ( 1 ) Our density of individuals is one per site , and these numbers are kept constant by assuming zero-sum dynamics ., Birth-death zero-sum stochastic models are equivalent to their non zero-sum counterparts at stationarity 31; ( 2 ) Factors influencing speciation may differ between regions of the genome , and regions of the genome involved in reproductive isolation may differ between taxa and the temporal stages of the speciation process 32 ., In our model , the genome of each individual is considered effectively infinite ( i . e . , a very large string of nucleotides , Text S1 ) , and ( 3 ) The mate choice function explaining the viability of the offspring is given by a step-shaped function ., This is the simplest representation of Dobzhansky-Muller reproductive incompatibility 33–35 ., Functions with equal viability in a range , ( see Material and Methods ) , declining linearly and exponentially 12 give qualitatively the same results as the results presented here using the step-shaped function ., At the beginning of the simulation , all individuals are reproductively compatible , corresponding to a completely connected graph ., Because of mutations that can eventually reduce genetic similarity below the threshold required for mating , the graph will lose connections as generations pass ( Fig . 1 ) ., The rate at which connections are lost in the evolutionary graph , and thus the tempo of speciation , depends on the mechanisms driving genome diversification ., To explore the tempo of speciation and its implications for biodiversity patterns , we generated a second model with negative frequency-dependent selection ., In this model there are not external factors creating pre-existing niches , which can be populated only by individuals of a specific genotype and can be filled up to a carrying capacity ., In contrast , any rare genotype has higher fitness than common types ., The reason may be natural selection driven by the ecology in which the organism is embedded ( e . g . , bacteria or pathogens attacking reproductive proteins of common types without altering the probability to die among individuals ) 36 , 37 or some form of sexual selection that lead to rare-type advantage ( e . g . , sexual conflict , molecular incompatibility or heterozygote advantage in sexually selected genes ) 38–40 and have more success at mating , whereas common types are likely–but not guaranteed–to become rare ., Despite potential costs of the rare types ( i . e . , Allee effects , mating costs , etc ) , experimental and theoretical studies have shown that the selective value of a given genotype is often a function of its frequency in the population 41–46 ., In summary , frequency-dependent selection in this context is a type of sexual selection with niches not imposed from outside the system but created by rare types with greater mating success that can spread their alleles more quickly through the population ., Apart from the asymmetry introduced by the different reproductive probabilities at the individual level , these two models are identical ( Text S1 ) ., With appropriate parameter values satisfying the mathematical condition required for speciation ( where is the genetic similarity matrix at equilibrium ) both models can produce speciation events ( i . e . , sexual isolation of subpopulations in the genome space , Equation A-30 and Box 1 in Text S1 ) ., We identified two distinct modes of speciation that can , under the right conditions , occur in the evolving graph: mutation-induced speciation and fission ( Fig . 1 ) ., Mutation-induced speciation happens when a newly produced offspring is disconnected from its parents ., This form of speciation requires the mutation rate to exceed some minimum value ( ) necessary to satisfy the inequality , where is the offspring and are the parents of ( Fig . 2 ) ., Because the minimum number of steps equals 1 , the minimum mutation rate to have mutation-induced speciation is given by: ( 1 ) For example , if offspring become inviable once genetic divergence exceeds ( i . e . , ) , then the minimum mutation rate needed to achieve mutation-induced speciation is ., There is a second mode of speciation which is also a consequence of mutations in the evolutionary graph ., We call this mode “fission” because it takes place when the death of an individual breaks a link in what was the sole genetic pathway connecting some members of a species; this gives rise to one or more new species ., Because of the strict condition for mutation-induced speciation to happen , fission is the only mode of speciation in the biologically relevant portion of model parameter space ( Section A3 in Text S1 ) ., The speciation rate ( ) in the genetic similarity matrix ( ) has two different dynamics according to the initial minimum genetic similarity value to have fertile offspring ( ) : ( 2 ) where is the expected mean genetic similarity in the matrix at equilibrium 18 , with the population size ., If , then is the rate of dropping links in the evolutionary graph that is proportional to the speciation rate for the model without frequency-dependent selection ( Fig . 2 and Text S1 ) ., Fitting to the speciation rates obtained via simulation yielded least-squares regression coefficient estimates of and the slope ( ) : ( 3 ) This approximation suggests that the long term rate of speciation is independent of population size ( Section A3 and Figs . 1 and 3 in Text S1 ) ., The models generate changes over time in the tempo of speciation , the distribution of incipient species abundance , and both the number and diversity of contemporary species ., In Figs ., 3 and 4 , we summarize the following two key predictions for the species number through time and species richness consistent with Darwins finches and cichlid fish ., First , we predict that whether the rate of speciation will remain constant or decline over time depends on the addition of frequency-dependent selection ., Fig . 3a shows how the number of extinct and extant species varies over time ., After a transient period , during which mutation introduces genetic variability into the initially identical population , the number of species increases rapidly ., The two models then diverge dramatically ., In the model without frequency-dependent selection , speciation rate remains constant ., This pattern is consistent with the literature on whole-tree cladistic analysis 47 , the record of marine invertebrate fossils from the Phanerozoic eon 48 , and ( over shorter time frames ) observed genetic differences among North American songbirds 49 ., The number of contemporary species ( Fig . 3b ) , diversity ( Inset Fig . 3b ) , and the abundance of the new species ( Fig . 3c ) are lower than in the frequency-dependent model ., In the frequency-dependent case , rapid speciation is followed by a plateau with few speciation events , consistent with molecular data for several groups showing declining speciation rates through time 16 , 50–53 ., This model predicts a greater number of contemporary species , higher diversity , and a more symmetric abundance distribution of incipient species; these are all attributes of rapid radiations ., Second , frequency-dependent selection reproduces cichlid radiations in absence of pre-existing niches and the absence of frequency-dependent selection generates the Darwins finches radiation ., Fig . 4a and 4b show the best fit to the data for the number of species and speciation events through time ., We predict decline over time and constant speciation rate in the cichlids and Darwins finches with and without frequency-dependent selection , respectively ( data not shown ) ., The expected distributions of species abundance derived from those predictions depart dramatically ., For the genus , the model predicts high diversity , with most species having similar abundances ( inset Fig . 4a ) ; for the Darwins finches , the model predicts much lower species diversity , with most species being rare ( insets Fig . 4b ) ., Most speciation studies have concluded that sympatric speciation only occurs if a stringent set of conditions is met 4 , 7 ., Likewise , for the models we have explored , sympatric speciation can be highly unlikely or even impossible in biologically relevant areas of parameter space ( i . e . , , where , Text S1 ) ., Note , however , that even though geographical barriers and dispersal limitation , and/or range expansion have played an important role in radiations , those factors do not generate decay through time in speciation rate in the absence of niche filling 10–13 ( see also Fig . 5 in Text S1 ) ., Interestingly , the absence of frequency-dependent selection does not capture the exponential growth in number of species in the last stage of the Darwins finches radiation ., Time lag for extinctions 50 , taxonomic splitting but also the increase in heterogeneity with time in the Galápagos archipelago ( i . e . , more islands , habitat diversity and food types ) 20 are some of the factors that may hamper model predictions in this case ., Nevertheless , the balance of results for both the cichlids and the Darwins finches suggest that neutral and frequency-dependent selection mechanisms have played a role in radiating lineages ., Current biodiversity theory , from population genetics 13 to island biogeography and its extensions 54 , explain species abundance patterns for many groups , but cannot predict different trends in the tempo of speciation nor their implications for radiations and diversity patterns ., The models we have explored generate alternative tempo of speciation and these models can be compared with the patterns of diversity underlying classic radiations ., In the context of these models , we have also determined the conditions necessary for the mutation-induced mode of speciation; if these are not met , then fission must be the only speciation mode ., Finally , we have shown that frequency-dependent selection generates more symmetric and larger incipient species abundances , resulting in lower extinction rates ., These results reinforce the notion that the incipient species abundance can have a dramatic impact on contemporary diversity patterns 54 , and suggest that both the tempo and mode of speciation themselves have a large effect on current community dynamics ., Alternative models of speciation that incorporate additional molecular or ecological components exist ( i . e . , spatial heterogeneity and dispersal limitation 19 , 55 , recombination rate , insertions and deletions 56 and the explicit mechanisms that cause genetic incompatibilities 57 , 58 ) ; however , it is not yet possible to evaluate those models with speciation rates and diversity data ., Fitting models with a large number of parameters remains a challenge for the future - we have shown that a quasi-likelihood method offer a powerful approach ., In summary , the particular mechanisms underlying the dynamics of the evolutionary graph affect the tempo of speciation and diversity , but we nevertheless find theoretical distributions in agreement with the observed patterns of radiations and biodiversity for diverse taxa ., Underlying the result are two simple models of a sexually reproducing population with and without frequency-dependent selection and with mating restrictions that depend on genetic distance ., By examining these models under different parameter combinations and confronting them with data , we conclude that the properties of genomes during lineage diversification may influence patterns of radiations and biodiversity and the pre-existing environmental niches are not necessary for radiations to occur ., Our simulation is a stochastic , individual-based , zero-sum birth and death model of a sexual population with overlapping generations and age-independent birth and death rates ., For the simulations reported in the paper , we considered haploid and hermaphroditic individuals where only one individual can exist in each site ., Genomes consist of an infinite string of nucleotides and the genomic similarity between two individuals is defined as the proportion of identical nucleotides along the genome ., Reproduction starts with a randomly selected individual looking for a mate among all the sufficiently similar individuals ., To qualify , an individual must have a genetic similarity greater than the minimum value required for fertile offspring ., From all such potential mates , we select the second parent at random ., In the frequency-dependent selection model , individuals with few connections , and therefore with more rare alleles , have more success at mating and their alleles spread quickly through the population ., Mating produces a haploid offspring that differs from both parents following free recombination and mutation ( Text S1 ) ., Each nucleotide is inherited from one of the parents with the same probability ., The results reported here are for asynchronous mating ., Synchronous mating gave similar results , although speciation times were typically longer ., According to tests of multiple model variants in the model without frequency-dependent selection , including parameter variation , self-incompatibility ( i . e . , by adding a to limit the reproduction of excessively similar individuals , Fig . 5a in Text S1 ) , and mating and dispersal limited to adjacent patches ( i . e . , 8-patch Moore neighborhood ) with and without a wrapped torus ( Fig . 5b in Text S1 ) , our results apply quite generally , with the key required properties to generate declining through time speciation rates being the limitations on genetic distance associated with mating and the frequency-dependent selection mechanism ., Results for Fig . 3 are obtained by time-averaging over replicates lasting generations each ., Given individuals in the initial population , a generation is an update of time steps ., Parameter variation does not affect the overall behavior ., Results for Fig . 4 are obtained after replicates for each parameter combination lasting generations each ., We sampled the transients ( each generation ) and the steady state at the end of each replicate for the species through time and species abundance ., We have explored parameter combinations in the range , , and community size , that satisfy the mathematical condition required for speciation ( , equation A-30 and Box 1 in Text S1 ) ., Our results apply quite generally in a broad range of community size ( Fig . 3 in Text S1 ) and speciation rates ( Fig . 4 in Text S1 ) ., The fit to the number of species and speciation events through time was done following these steps:, 1 ) Normalize time for observed data and each simulation from the first speciation event to present time within the range 0 , 1 ,, 2 ) From each possible interval , starting with the size of the data until the size of the output in each simulation ( generations with increments of 1 generation at each time ) , we generated the sequence of speciation times that minimizes the difference with the observed speciation times , and, 3 ) Identify the best fit as the one that minimizes the sum of the absolute values of the misfits: ( 4 ) where is defined as , i . e . , in terms of the misfit between observed and simulated species richness , , and the misfit in the timing of speciation events ., and are our model parameters ., Our search is performed for a broad range of plausible empirical values for and constant and satisfying ( Text S1 ) ., If our errors per data point are a random variable following the exponential distribution , , and , assuming error independence , our measure of misfit is the model negative log-likelihood 59 ., Confidence intervals have been calculated by taking the percentiles and from the distributions of values of different model replicates ., Model replicates were generated with the best parameter estimates for and along with a family of pairs within 2 log-likelihood units away from the minimum 60 ( Fig . 4 in Text S1 ) . | Introduction, Results, Discussion, Materials and Methods | Most empirical studies support a decline in speciation rates through time , although evidence for constant speciation rates also exists ., Declining rates have been explained by invoking pre-existing niches , whereas constant rates have been attributed to non-adaptive processes such as sexual selection and mutation ., Trends in speciation rate and the processes underlying it remain unclear , representing a critical information gap in understanding patterns of global diversity ., Here we show that the temporal trend in the speciation rate can also be explained by frequency-dependent selection ., We construct a frequency-dependent and DNA sequence-based model of speciation ., We compare our model to empirical diversity patterns observed for cichlid fish and Darwins finches , two classic systems for which speciation rates and richness data exist ., Negative frequency-dependent selection predicts well both the declining speciation rate found in cichlid fish and explains their species richness ., For groups like the Darwins finches , in which speciation rates are constant and diversity is lower , speciation rate is better explained by a model without frequency-dependent selection ., Our analysis shows that differences in diversity may be driven by incipient species abundance with frequency-dependent selection ., Our results demonstrate that genetic-distance-based speciation and frequency-dependent selection are sufficient to explain the high diversity observed in natural systems and , importantly , predict decay through time in speciation rate in the absence of pre-existing niches . | Ecological opportunity , or filling a pre-existing unoccupied adaptive zone , is considered the dominant mechanism explaining the initial explosion of diversity ., Although this type of niche filling can explain rates of diversification in some lineages , it is not sufficient for a radiation to occur ., Instead of attributing the propensity to have an explosion of new species to external influences like niche availability , an alternative hypothesis can be based in frequency-dependent selection driven by the ecology in which organisms are embedded or endogenous sources mediated by gametes during fertilization ., We show that genome diversification driven by higher reproductive probability of rare genotypes generates rapid initial speciation followed by a plateau with very low speciation rates , as shown by most empirical data ., The absence of advantage of rare genotypes generates speciation events at constant rates ., We predict decline over time and constant speciation rate in the cichlids and Darwins finches , respectively , thus providing an alternative hypothesis for the origin of radiations and biodiversity in the absence of pre-existing niche filling ., In addition to predicting observed temporal trends in diversification , our analysis also highlights new mechanistic models of evolutionary biodiversity dynamics that may become suitable to generate neutral models for testing observed patterns in speciation rates and species diversity . | computational biology/evolutionary modeling, ecology/community ecology and biodiversity, evolutionary biology, ecology/theoretical ecology | null |
journal.pgen.1007104 | 2,017 | A case-control collapsing analysis identifies epilepsy genes implicated in trio sequencing studies focused on de novo mutations | One of the most important recent developments in human genomics is the use of a trio sequencing paradigm to implicate new disease genes in sporadic disease by evaluating patterns of de novo mutations ( DNMs ) ., This framework compares the observed pattern of DNMs in probands to the expected based on the size of the protein-coding sequence and the estimated tri-nucleotide mutation rate1 , and has implicated scores of genes conferring risk of epilepsy2 , 3 , intellectual disability4–6 , autism7–10 , and other neurodevelopmental conditions4 ., This approach is costly because of the need to sequence complete trios and often is not practical or possible for conditions that present after childhood where parents may not be available for sequencing ., Moreover , a precise estimate of mutation rate is not available for small insertion/deletions ( indels ) 1 , limiting the ability to assess the significance of genes harboring de novo indels ., In parallel to these developments , collapsing analyses , which typically compare the burden of rare , presumably deleterious variants gene by gene in cases versus controls , have proven increasingly successful in implicating diseases genes , for example in amyotrophic lateral sclerosis11 , 12 , idiopathic pulmonary fibrosis13 , 14 , and monogenic disorders15 ., Surprisingly , however , it has not yet been assessed whether the collapsing framework can identify the genes implicated by analysis of trio sequencing data ., We addressed this question by implementing a genome-wide gene-based collapsing analysis using whole exome sequencing ( WES ) data generated from 488 epileptic encephalopathy ( EE ) patients , including those previously analyzed using the trio-based DNM analysis framework , and a large cohort of unrelated control individuals to assess the efficacy of case-control analysis to identify disease genes implicated by DNM analysis for EE ., Strikingly , despite a modest sample size , we identified three known EE genes achieving genome-wide significance ( p<2 . 68×10−6 ) , and found that the majority of the known EE genes ( 17 out of 25 ) originally implicated in trio sequencing are nominally significant ( p<0 . 05 ) ., While not all known EE genes reached genome-wide significance , the significant enrichment of known genes among nominally significant p-values genome-wide suggests that with larger samples sizes many of these genes will reach p-values that will exceed that threshold ., Collectively , our results show that collapsing analysis can effectively implicate genes carrying causal DNMs , and trio sequencing is not the only effective strategy for gene discovery even in genes that confer risk largely due to DNMs ., We argue that the fundamental reason for this is that existing filtering strategies are increasingly accurate in identifying very young mutations including those that are de novo in the proband ., The collapsing analysis compared a total of 488 cases with 12 , 151 controls ( S1 Fig ) ., Three genes ( Fig 1 , Table 1 , S1 Table , and S2 Fig ) , KCNT1 , SCN2A and STXBP1 , showed enrichment of qualifying variants in EE patients and achieved genome-wide significance ( p<2 . 68×10−6 ) ., No other genes were found to be genome-wide significant by both Fisher’s exact test and logistic regression p-values , but 17 of the 25 genes ( 68% , including the three above ) known to be associated with dominant EE ( https://www . omim . org/phenotypicSeries/PS308350 ) were nominally significant ( logistic regression p<0 . 05 ) in this dataset , all showing enrichment of qualifying variants in EE patients ( Table 1 ) ., This is in contrast to the total of 885 nominally significant ( logistic regression p<0 . 05 ) genes out of all the 18 , 503 genes tested ( Fisher’s exact p = 2 . 33×10−17 ) ., We used a hypergeometric test to assess whether these 25 known dominant EE genes tend to have lower p-values in our case-control gene-based collapsing analysis compared with the rest of the genome ., Specifically , at each observed ranking of the 25 epilepsy genes ( based on logistic regression p-values ) , we performed a hypergeometric test to assess whether there were more epilepsy genes at this ranking , or lower , than one would expect if the ranks were randomly assigned to all 18 , 503 genes tested ( Table 1 ) ., There was a consistent pattern that known dominant EE genes tended to have smaller p-values in our dataset ( Table 1 ) ., In the 25 genes known to cause dominant forms of EE , 74 of the 488 cases ( 15 . 16% ) had at least one qualifying variant , compared to 302 of the 12 , 151 controls ( 2 . 49% , Fisher’s exact p = 1 . 95×10−32 ) ., Among the 64 of the 74 cases with trio WES data , a total of 73 qualifying variants were found in these 25 EE genes , and 47 of these qualifying variants ( 64 . 4% ) were confirmed to be de novo in our previous DNM analyses ( Table 1 and S2 Table ) , including all the qualifying variants in STXBP1 ( n = 6 ) , DNM1 ( n = 5 ) , KCNQ2 ( n = 3 ) , GNAO1 ( n = 2 ) , CDKL5 ( n = 3 ) , ALG13 ( n = 1 ) and SLC35A2 ( n = 1 ) identified in the 488 cases ( no inherited qualifying variant was observed in these genes in all cases; Table 1 ) ., Comparing 488 EE cases and 12 , 151 controls using a gene-based collapsing analysis of “qualifying variants” , we successfully identified three known EE genes at genome-wide significance level ., In addition , known EE genes were found to have smaller than expected association p-values compared with the rest of the genome ., We showed that DNMs contributed to the majority of qualifying variants in the 25 known dominant EE genes identified in cases , and in several genes they accounted for all of them ., As most of these 25 EE genes are originally implicated by sequencing trios and analyzing DNMs , our results clearly demonstrate the efficacy of case-control gene-based collapsing analysis to identify genes without spending effort specifically ascertaining DNMs by sequencing trios ., Several factors affect the power of case-control gene-based collapsing analysis , including locus heterogeneity , penetrance , and how “qualifying variants” are defined as a class to represent the properties of bona fide pathogenic mutations ., Because most if not all known EE-causing mutations are not observed in ExAC , we required the qualifying variants to be absent in ExAC ., Remarkably , because of the large sample size of ExAC , most standing variation is essentially filtered out ( except mutations arising in recent generations , including DNMs ) , and indeed 64 . 4% of the qualifying variants in the 25 known EE genes are confirmed to be de novo in 64 cases , thus recovering many of the EE genes originally implicated by DNM analysis ., Notably , all the six STXBP1 and five DNM1 qualifying variants in cases are de novo , highlighting the power of using ExAC to filter out standing variation ., However , even at the sample size of ExAC , where widespread mutational recurrence is observed16 , background variation in controls may still prevent a gene that is securely implicated in DNM analysis from reaching genome-wide significance in case-control analysis ., For example , in DNM1 , even with five qualifying variants ( all DNMs ) in unrelated cases , there are 18 qualifying variants in controls unfiltered by ExAC ., These 18 qualifying variants may be private but not DNMs , and may be further filtered out by a larger and more genetically diverse control datasets ., Indeed , although most genes known to cause EE ( and other neurodevelopmental disorders ) are intolerant to standing functional variation17 , implying a lower rate of background variation than the genomic average , our empirical data shows considerable variability in the frequency of qualifying controls across the 25 EE genes ( Table 1 ) ., Versions of collapsing that focus on subregions of genes will likely allow finer discriminations amongst pathogenic variants and background variation ., As a class , disease-causing DNMs clearly represent the extreme of rare variation by typically not being able to pass even one generation due to extremely strong negative selection ., However , this does not mean every DNM identified in an individual is pathogenic , and there are DNMs presenting as standing variation in human population datasets like ExAC and these DNMs are unlikely to be pathogenic18 ., By focusing on qualifying variants absent in ExAC , such presumably benign DNMs can be excluded from collapsing analysis ., Conversely , if a pathogenic variant is inherited and the parent is not known to be affected ( e . g . , due to incomplete penetrance or variable phenotype ) , it would not be identified in trio-based analyses focused on DNMs but may be captured in case-control analyses ., The DNM analysis framework typically compares observed rate of DNMs in cases with expectation relying on estimates of the mutability of genes since very large populations of control trios are not available for direct comparisons ., Precisely estimating mutation rate across the human genome is difficult and the current DNM analysis framework cannot effectively accommodate indels well due to lack of accurate estimations of mutation rate for this class of variants ., However , case-control analysis directly compares the pattern of qualifying variants empirically observed in both cases and controls and is not affected by mutation rate estimates ., When a disease gene is securely implicated using a case-control framework , caution is needed to interpret the causality of qualifying variants identified in that gene ., Importantly , an excess of qualifying variants in cases versus controls does not imply all qualifying variants in cases are pathogenic or all qualifying variants in controls are benign ., Instead , interpretation should be performed per variant per individual after the case-control association testing is performed ., Certainly , for an individual case , knowledge of whether a variant is de novo or not remains an important consideration in diagnostic interpretation19 ., However , our work clearly shows that a collapsing analysis using only probands can also discover genes that cause disease due to DNMs ., This not only makes discovery easier and more economical in early onset disorders , but opens up the possibility of identifying genes that carry causal DNMs in diseases that present later in life when parents are not readily available ., These results have clear implications for discovery strategies in a range of different genetic diseases ., We started with WES or whole genome sequencing ( WGS ) data generated from 496 cases selected from several genetic studies of EE and 12 , 916 controls selected from other studies and not known to have neurodevelopmental , neuropsychiatric , or severe pediatric diseases ., The cases were originally recruited and studied by groups including the Epi4K Consortium , the Epilepsy Phenome Genome Project ( EPGP ) , the Epilepsy Genetics Initiative ( EGI ) —a signature program of Citizens United for Research in Epilepsy ( CURE ) , and EuroEPINOMICS-RES Consortium ., Written informed consent was collected at the time of recruitment at each of the clinical sites ., Patient collection and sharing of anonymized specimens for research was approved by site-specific Institutional Review Boards and ethic committees ., Details of the IRB and approval numbers are available from S3 Table ., To maximize sample size , both cases and controls included individuals with diverse ancestries including African , Caucasian , East Asian , Hispanic , Middle Eastern , and South Asian ., After relatedness check and principal component analysis , a total of 488 cases and 12 , 151 controls remained for association analysis , and 75 . 6% of cases ( n = 369 , S4 Table ) had been analyzed previously in trio or single-patient interpretation analyses ., Sequencing was performed at multiple sites ( S2 Table ) ., All data starting from either FASTQ or BAM files were processed through the alignment and annotation pipeline at the Institute for Genomic Medicine at Columbia University Medical Center ( formerly Center for Human Genome Variation at Duke University ) ., Case ( S2 Table ) and control samples were sequenced after exome capture using a variety of technologies ( Agilent Clinical Research Exome , IDT xGen Exome Research Panel V1 . 0 , Illumina Nextera Rapid Capture—Expanded Exome 62MB , SeqCap EZ Exome v2 , SeqCap EZ Exome v3 , SeqCap EZ MedExome , SureSelect Human All Exon - 50MB , SureSelect Human All Exon - 65MB , SureSelect Human All Exon V4 , SureSelect Human All Exon V4 - 50MB , SureSelect Human All Exon V4 + UTR , SureSelect Human All Exon V5 , SureSelect Human All Exon V5 + UTR , and VCRome2_1 ) or whole genome sequenced according to standard protocols ., After quality filtering the raw sequence data using CASAVA ( Illumina , Inc . , San Diego , CA ) , the Illumina lane-level FASTQ files were aligned to the Human Reference Genome ( NCBI Build37/hg19 ) using the Burrows-Wheeler Alignment Tool ( BWA ) . 20, Picard ( http://picard . sourceforge . net ) was used to remove duplicate reads and process these lane-level SAM files , resulting in a sample-level BAM file that was used for variant calling ., Variant and genotype calling was performed using the GATK software with local re-alignment around insertion/deletion variants and base quality recalibration for variants21 ., Variants for analysis were restricted to the consensus coding sequence public transcripts ( CCDS release 14 ) plus 2 base pair intronic extensions22 ., Variants were further required to have:, i ) at least 10-fold coverage ,, ii ) quality score ( QUAL ) of at least 30 ,, iii ) genotype quality ( GQ ) score of at least 20 ,, iv ) quality by depth ( QD ) score of at least 2 ,, v ) mapping quality ( MQ ) score of at least 40 ,, vi ) read position rank sum ( RPRS ) score greater than -3 ,, vii ) mapping quality rank sum ( MQRS ) score greater than -6 ,, viii ) indels were required to have a maximum Fisher’s strand bias ( FS ) of 200 ,, ix ) variants were screened according to VQSR tranche calculated using the known SNV sites from HapMap v3 . 3 , dbSNP , and the Omni chip array from the 1000 Genomes Project to “PASS” SNVs were required to achieve a tranche of 99 . 9% for SNVs in genomes and exomes and 99% for indels in genomes ,, x ) for heterozygous genotypes , the alternate allele ratio was required to be ≥25% ., Finally , variants were excluded if they were among a predefined list of known sequencing artifacts or if they were marked by EVS ( http://evs . gs . washington . edu/EVS/ ) 23 or ExAC ( http://exac . broadinstitute . org/about ) 16 as being problematic variants ., Variants were annotated to Ensembl 7324 using SnpEff25 ., Any exomes with gender discordance between clinically-reported and X:Y coverage ratios were removed , as were contaminated samples according to VerifyBamID26 ., Before running gene-based collapsing analysis , we implemented both sample- and site-level pruning procedures to minimize the systemic bias in data that might lead to spurious association or reduced power to detect real association ., The site-pruning procedure ( coverage harmonization ) is described in the section below ., Here , we described the sample-level pruning procedure including removing related individuals and population outliers identified in principal component analysis ( PCA ) ., To identify related individuals , we generated genotype data in PLINK format27 and then used KING28 to calculate pairwise kinship coefficients for all case and control subjects ., We used the kinship coefficient 0 . 1 as a cutoff and removed samples introducing relatedness while preferentially retaining cases; we retained samples with a higher overall coverage in the CCDS regions to break ties if applicable ., After this step , 492 of the 496 cases and 12 , 248 of the 12 , 916 controls were kept for further analysis ., Next we ran PCA using EIGENSTRAT29 on the 492 cases and 12 , 248 controls with a LD-pruned ( r2 threshold 0 . 1 ) list of single-nucleotide polymorphisms ( SNPs ) extracted from exomic sequencing data ., After removing outliers given a sigma threshold ( 6 . 0 along the top10 principal components ) for 5 iterations , a total of 488 cases and 12 , 151 controls entered gene-based collapsing analysis ( S1 Fig ) ., For the 488 cases and 12 , 151 controls entering association analysis , at least 10-fold coverage was achieved for an average of 93 . 20% in cases and 95 . 19% in controls of the 33 . 27 MB of the consensus coding sequence ( CCDS release 14 ) plus 2 base pair ( bp ) intronic extensions ( to accommodate canonical splice site variants ) ., To address the confounding effect introduced by imbalance of coverage between cases and controls , we pruned out sites with uneven coverage in cases and controls using our previously described site-pruning procedure30 ., Specifically , for each site in CCDS plus 2 bp extensions , we determined the percentages of cases and controls that had at least 10-fold coverage , and that site was excluded from further analysis if the percentages differed by >11 . 97% between cases and controls ., This site-pruning procedure removed 8 . 58% of the CCDS ( +2bp intronic extensions ) bases from the analysis ., After site pruning , at least 10-fold coverage was achieved for an average of 88 . 27% in cases and 88 . 12% in controls of the 33 . 27 MB CCDS ( +2bp intronic extensions ) bases ., These sites entered the association analysis where case and control populations had a comparable coverage to accurately compare patterns of variation gene by gene ., To identify genes associated with EE under the case-control association analysis framework , we performed a genome-wide search for an enrichment of “qualifying variants” in protein-coding genes in cases compared to controls looking for risk alleles ., A “qualifying variant” was determined by a set of criteria , based on allele frequency and functional predictions , designed to capture the characteristics of pathogenic variants associated with EE ., Specifically , in this study , we focused on “ultra-rare” , highly impactful variants , and a variant was determined to be qualifying if it: 1 ) was absent in the Exome Variant Server ( EVS ) and Exome Aggregate Consortium ( ExAC release 0 . 3 ) ; 2 ) had ≤4 copies of variant allele in the 488 cases plus 12 , 151 controls; and 3 ) was predicted to be loss-of-function ( stop_gained , frame_shift , splice_site_acceptor , splice_site_donor , start_lost , or exon_deleted ) or missense “probably damaging” by PolyPhen-2 ( HumDiv ) ., We focused on this subset in an effort to try to capture the de novo variant signal that has been previously reported to play a role in a range of epilepsies and in particular EE subtypes 2 , 31 , 32 ., For each gene , an indicator variable ( 1/0 states ) was assigned to each individual based on the presence of at least one qualifying variant in the gene ( state 1 ) or no qualifying variant in that gene ( state 0 ) ; this was equivalent to a dominant genetic model ., Accordingly , for a given gene , a qualifying case ( or control ) was defined to be a case ( or control ) subject carrying at least one qualifying variant in that gene ., We used two-tailed Fisher’s exact test to evaluate statistical significance of genic association ., To address the potential confounding effect of background rate of “qualifying variants , ” we further constructed a logistic regression model including the total number of “ultra-rare” ( absent in EVS and ExAC and having ≤4 copies of variant allele in the 488 cases plus 12 , 151 controls ) synonymous variants per individual as covariate ., To account for bias due to small counts of qualifying variant , we employed a Firth correction with profile likelihood based tests 33 , 34 ., With 18 , 668 CCDS genes we aimed to test , we adopted the genome-wide significance level of p = 2 . 68×10−6 using Bonferroni correction ( 0 . 05/18 , 668 ) ., Quantile-quantile plots were generated using a permutation-based expected probabilities distribution ., To achieve this , for each model ( matrix ) we randomly permuted the case and control labels of the original configuration: 488 cases and 12 , 151 controls and then recomputed the Fisher’s Exact test for all genes ., This was repeated 1 , 000 times ., For each of the 1 , 000 permutations we ordered the p-values and then took the mean of each rank-ordered estimate across the 1 , 000 permutations , i . e . , the average 1st order statistic , the average 2nd order statistic , etc ., These then represent the empirical estimates of the expected ordered p-values ( expected -log10 ( p-values ) ) ., This empirical-based expected p-value distribution no longer depends on an assumption that the p-values are uniformly distributed under the null ., For comparison we have provide QQ plots for the actual p-values ( S2 Fig ) and empirically-based expected p-value distribution ( S3 Fig ) ., To compute the permutation-based expected p-value distribution for Firth logistic regression , due to the presence of the covariate ( the total number of “ultra-rare” synonymous variants per individual ) , we implemented permutation using the R package “BiasedUrn” ( https://cran . r-project . org/web/packages/BiasedUrn/ ) to maintain the confounding role of covariate in each permuted data set while the association between genotype and disease was broken35 ., Permutation was performed 1 , 000 times and the empirical-based expected p-value distribution was calculated in the same way as described above ., For comparison to the BiasedUrn permuted p-values , we have provided QQ plots for the actual p-values generated from the Firth logistic regression ( S4 Fig ) . | Introduction, Results and discussion, Materials and methods | Trio exome sequencing has been successful in identifying genes with de novo mutations ( DNMs ) causing epileptic encephalopathy ( EE ) and other neurodevelopmental disorders ., Here , we evaluate how well a case-control collapsing analysis recovers genes causing dominant forms of EE originally implicated by DNM analysis ., We performed a genome-wide search for an enrichment of qualifying variants in protein-coding genes in 488 unrelated cases compared to 12 , 151 unrelated controls ., These qualifying variants were selected to be extremely rare variants predicted to functionally impact the protein to enrich for likely pathogenic variants ., Despite modest sample size , three known EE genes ( KCNT1 , SCN2A , and STXBP1 ) achieved genome-wide significance ( p<2 . 68×10−6 ) ., In addition , six of the 10 most significantly associated genes are known EE genes , and the majority of the known EE genes ( 17 out of 25 ) originally implicated in trio sequencing are nominally significant ( p<0 . 05 ) , a proportion significantly higher than the expected ( Fisher’s exact p = 2 . 33×10−17 ) ., Our results indicate that a case-control collapsing analysis can identify several of the EE genes originally implicated in trio sequencing studies , and clearly show that additional genes would be implicated with larger sample sizes ., The case-control analysis not only makes discovery easier and more economical in early onset disorders , particularly when large cohorts are available , but also supports the use of this approach to identify genes in diseases that present later in life when parents are not readily available . | Trio exome sequencing and de novo mutation ( DNM ) analysis has been the main approach to discovering genes responsible for severe sporadic disorders , including a range of neurodevelopmental disorders ., This approach requires sequencing parents , identifying DNMs from trio sequence data , and comparing the observed rate of DNMs to the expected ., In this study , we adopted a case-control design , performed a gene-based collapsing analysis , and rediscovered several of the epileptic encephalopathy ( EE ) genes originally implicated by DNM analysis of EE trios ., Our collapsing analysis focused on ultra-rare , highly impactful variants ( “qualifying variants” ) by filtering against large-scale population datasets , and this approach revealed that most of the standing variation can be filtered out and DNMs are enriched in “qualifying variants” ., Our study suggests that a case-control analysis approach can be used to identify disease genes with causal mutations that are predominantly de novo in place of trio-based analysis methods ., This offers an efficient and cost effective alternative approach when large-scale trio sequencing is not possible . | sequencing techniques, medicine and health sciences, alleles, multivariate analysis, genome sequencing, gene sequencing, mathematics, statistics (mathematics), genome analysis, molecular biology techniques, discrete mathematics, combinatorics, research and analysis methods, epilepsy, mathematical and statistical techniques, principal component analysis, statistical methods, molecular biology, genetic loci, permutation, neurology, genetics, biology and life sciences, physical sciences, genomics, dna sequencing, computational biology, genomic medicine | null |
journal.pcbi.1003774 | 2,014 | Mechanical Cell-Matrix Feedback Explains Pairwise and Collective Endothelial Cell Behavior In Vitro | How the behavior of cells in a multicellular organism is coordinated to form structured tissues , organs and whole organisms , is a central question in developmental biology ., Keys to answering this question are chemical and mechanical cell-cell communication and the biophysics of self-organization ., Cells exchange information by means of diffusing molecular signals , and by membrane-bound molecular signals for which direct cell-cell contact is required ., In general , these developmental signals are short-lived and move over short distances ., The extracellular matrix ( ECM ) , the jelly or hard materials that cells secrete , provides the micro-environment the cells live in ., Apart from its supportive function , the ECM mediates molecular 1 and biomechanical 2 signals between cells ., Mechanical signals , in the form of tissue strains and stresses to which cells respond 3 , can act over long distances and integrate mechanical information over the whole tissue 4 , and also mediate short-range , mechanical cell-cell communication 2 ., How such mechanical cell-cell communication via the ECM can coordinate the self-organization of cells into tissues is still poorly understood ., Here we propose a cell-based model of endothelial cell motility on compliant matrices to address this problem ., A widely used approach to study the role of cell-ECM interactions in coordinating collective cell behavior is to isolate cells ( e . g . , endothelial cells isolate from bovine aortae or from human umbilical cords or foreskins ) and culture them on top of or inside an artificial or natural ECM ( e . g . , Matrigel ) ., This makes it possible to study the intrinsic ability of cells to form tissues in absence of potential organizing signals or pre-patterns from adjacent tissues ., A problem particularly well-studied in cell cultures is the ability of endothelial cells to form blood vessel-like structures , including the formation of vascular-like networks from dispersed cells and the sprouting of spheroids ., To this end , cell cultures can be initialized with a dispersion of endothelial cells on top of an ECM material ( e . g . , Matrigel , collagen , or fibrin ) 5 , 6 , with endothelial spheroids embedded within the ECM 7 , 8 , or with confluent endothelial monolayers 9–11 ., Although the conditions required for vascular-like development in these in vitro culture systems are well established , the mechanisms driving pattern formation of endothelial cells are heavily debated , and a wide range of plausible mechanisms has been proposed in the form of mathematical and computational models reproducing aspects of angiogenesis ( reviewed in 12–14 ) ., Typical ingredients of network formation models are, ( a ) an attractive force between endothelial cells , which is, ( b ) proportional to the cell density , and, ( c ) inhibited or attenuated at higher cellular densities ., The attractive force can be due to mechanical traction or due to chemotaxis ., Manoussaki , Murray , and coworkers 15 , 16 proposed a mechanical model of angiogenic network formation , based on the Oster and Murray 17 , 18 continuum mechanics theory of morphogenesis ., In their model , endothelial cells exert a uniform traction force on the ECM , dragging the ECM and the associated endothelial cells towards them ., The traction forces saturated at a maximum cell density ., Namy and coworkers19 replaced the endothelial cells passive motion along with the ECM for active cell motility via haptotaxis , in which cells move actively towards higher concentrations of the ECM ., Both models also included a strain-biased random walk term for the endothelial cells , but they found that it had little effect on network formation; the mechanism was dominated by cell aggregation ., In their model based on chemotaxis , Preziosi and coworkers 20 , 21 assumed that cells attract one another via the secreted chemoattractant VEGF ., Due to diffusion and first-order degradation , the chemoattractant forms exponential gradients around cells leading to cell aggregation in much the same way as that assumed in the Manoussaki and Namy models ., These chemotaxis-based hypotheses formed the basis for a series of cell-based models based on the cellular Potts model ( CPM ) ., Assuming chemotactic cell-cell attraction , and a biologically-plausible overdamped cell motility , the cells in these CPM models form round aggregates , in accordance with the Keller-Segel model of cell aggregation 22 ., Additional assumptions , including an elongated cell shape 23 or contact inhibition of chemotaxis 24 are needed to transform these circular aggregates into vascular-like network patterns ., Related network formation models studied the role of ECM-bound growth factors 25–27 and a range of additional secreted and exogenous growth factors 27 , and studied the ability of the contact-inhibition mechanism to produce three-dimensional blood-vessel-like structures 28 ., Szabó and coworkers found that in culture , astroglia-related rat C6 cells and muscle-related mouse C2C12 cells organize into network-like structures on rigid culture substrates 29 , such that ECM-density or chemoattractant gradients are excluded ., They proposed a model where cells were preferentially attracted to or preferentially adhered to locally elongated structures ., As an alternative mechanism for “gel-free” network formation it was found that elongated cells can also produce networks in absence of chemoattractant gradients 30 ., Paradoxically , despite the diverse assumptions underlying the mathematical models proposed for vascular network formation , many are at least partly supported by experimental evidence ., This suggests that a combination of chemotaxis , and chemical and mechanical cell-ECM interactions drives network formation , or that each alternative mechanism operates in a different tissue , developmental stage , or culture condition ., A problem is that one mathematical representation may represent a range of equivalent alternative underlying mechanisms ., For example , a model representing cell-cell attraction cannot distinguish between chemotaxis-based cellular attraction 20 , 21 , 23 , 24 , attraction via haptotaxis 19 , direct mechanical attraction 15 , 31 or cell shape dependent adhesion 29 , 32 , because the basic principles underlying these models are equivalent 12 , 24 ., As a solution to this problem , a sufficiently correct complete description of endothelial cell behavior should suffice for the emergence of the subsequent levels of organization of the system , an approach that requires that the system has been experimentally characterized at all levels of organization ., The role of cell traction and ECM mechanics during in vitro angiogenesis have been characterized experimentally particularly well , making it a good starting point for such a multiscale approach ., Endothelial cells apply traction forces on the extracellular matrix , as demonstrated by a variety of techniques , e . g . , wrinkle formation on elastic substrates 9 , force-generation on micropillar substrates 33 , and traction force microscopy 6 , 34 ., Using scanning electron microscopy , Vernon and Sage 9 found that ECM ribbons radiate from endothelial cells cultured in Matrigel , suggesting that the traction forces locally reorient the extracellular matrix ., The cellular traction forces produce local strains in the matrix , which can affect the motility of nearby cells 2 ., Thus endothelial cells can both generate , and respond to local strains in the extracellular matrix , suggesting a feedback loop that may act as a means for mechanical cell-cell communication 2 and hence coordinate collective cell behavior ., Here , we use a hybrid cellular Potts and finite element model to show that a set of assumptions mimicking mechanical cell-cell communication via the ECM suffices to reproduce observed single cell behavior 35 , 36 , pairwise cell interactions 2 , and collective cell behavior: network formation and sprouting ., First we set out to capture , at a phenomenological level , the response of endothelial cells to static strains in the ECM in absence of cellular traction forces ., When grown on statically , uniaxially stretched collagen-enriched scaffolds , murine embryonic heart endothelial ( H5V ) cells orient in the direction of strain , whereas cells grown on unstrained scaffolds orient in random directions 37 ., Because the collagen fibers make the scaffold stiffen in the direction of strain , we hypothesized that the observed alignment of cells is due to durotaxis , the propensity of cells to migrate up gradients of substrate rigidity 38 and to spread on stiff substrates 39 , 40 ., In our model we assumed, ( a ) strain stiffening: a strained ECM is stiffer along the strain orientation than perpendicular to it , such that, ( b ) due to durotaxis the endothelial cells preferentially extend pseudopods along the strain orientation , along which the ECM is stiffest , giving cells the most grip ., To keep the ECM mechanics simulations computationally tractable , we assumed an isotropic and linearly elastic ECM ., With these assumptions it is not possible to model strain stiffening explicitly ., We therefore mimicked strain stiffening by assuming that stiffness is an increasing , linear function of the local strain ., Durotaxis was modelled as follows , to reflect the observation that focal adhesion maturation occurs under the influence of local tension 41: At low local stiffness , we applied standard cellular Potts dynamics to mimic the iterative formation and breakdown of ECM adhesions , producing “fluctuating” pseudopods ., However , if the stiffness was enhanced locally , we assumed that the resulting tension in the pseudopod led to maturation of the focal adhesion 41 , 42 , stabilizing the pseudopod as long as the tension persists ., To mimic such focal adhesion maturation in the cellular Potts model , we increased the probability of extension along the local strain orientation , and reduced the probability of retraction ( see Methods for detail ) ., Figure 1 A shows the response of the simulated cells to uniaxial stretch along the vertical axis ., With increasing values of the durotaxis parameter ( see Eq . 8 ) , the endothelial cells elongate more ., To test the sensitivity of the durotaxis model for lattice effects , we varied the orientation of the applied strain over a range and measured the resulting orientation of the cells ., Figure 1 shows that the average orientation of the cells follows the orientation of the stretch isotropically ., Thus the durotaxis component of our model phenomenologically reproduces published responses of endothelial cells to uniaxial stretch 37 ., We next attempted to mimic the forces applied by cells onto the extracellular matrix , in absence of durotaxis ., Traction-force microscopy experiments 34 , 39 show that endothelial cells contract and exert tensional forces on the ECM ., The forces are typically directed inward , towards the center of the cell , and forces concentrate at the tips of pseudopods ., A recent modeling study by Lemmon and Romer 43 found that an accurate prediction of the direction and relative magnitudes of these traction forces within the cell can be obtained by assuming that each lattice node i covered by the cell pulls on every other node the cell covers , j , with a force proportional to their distance , di , j ., Because this model gives experimentally plausible predictions for fibroblasts , endothelial cells , and keratocytes 43 , we adopted it to mimic the cell-shape dependent contractile forces that endothelial cells exert onto the ECM ., Figure 2 shows the contractile forces ( black ) and resulting ECM strains ( blue ) generated in our model by two adjacent cells ., The traction forces and ECM strains become largest at the cellular “pseudopods” , qualitatively agreeing with traction force fields reported for endothelial cells 34 ., The two previous sections discussed how the simulated cells can respond to and induce strain in the ECM in an experimentally plausible way ., To test how the simulated cells respond to the strains they generate themselves , we studied the behavior of simulated , single cells in presence of both the cell traction mechanisms and the durotaxis mechanisms ., During each time step , we used the Lemmon and Romer 43 model to calculate traction forces corresponding to current cell positions ., Next , we started the finite element analysis from an undeformed matrix , calculating steady-state strains for the current traction forces ., To simulate cell movement , which was biased by the local matrix strains using the durotaxis mechanism , we then applied one cell motility simulation time step , or Monte Carlo step ( MCS; the MCS is the unit of time of our simulation; see Methods for detail and Discussion for an estimate of the real time corresponding to an MCS ) ., After running the CPM for one MCS we again relaxed the matrix such that the next step started with an undeformed matrix ., Thus we currently did not consider cell memory of substrate strains ., As Figure 3 and Video S1 demonstrate , in this model matrix stiffness affects both the morphology and motility of the simulated cells ., On the most compliant substrate tested ( 0 . 5 kPa ) the simulated cells contract and round up , whereas cells spread isotropically on the stiffest substrate tested ( 32 kPa ) ., Overall , the cellular area increases with substrate stiffness ( Figure 3 B ) ., On matrices of intermediate stiffnesses ( around 12 kPa ) the cells elongate , as reflected by measurements of the cell length ( Figure 3 C ) and eccentricity ( Figure 3 D ) that both have maximum values at around 12 kPa ., Such a biphasic dependence of cellular morphology on the stiffness of the ECM mimics the behavior of endothelial cells 39 and cardiac myocytes 36 in matrices of varying stiffness ., The dependence of cell shapes on substrate stiffnesses is due to the transition from fluctuating to adherent pseudopods with increasing stiffness ., Focal adhesions of cells on soft substrates all remain in the “fluctuating” state , irrespective of the local strains ., On intermediate substrates , some pseudopods , due to increased traction , move to an extended state ( mimicking a mature focal adhesion ) , generating more traction in this direction ., Hence an initial stochastic elongation self-enhances in a feedback loop of increasing traction and strain stiffening ., Such a self-enhancing cell-elongation starting from an initial anisotropy in cell spreading has previously been suggested by Winer et al 44 ., Extensions perpendicular to the long axis of an elongated cell do not occur since there is insufficient traction and the volume constraint is limiting ., At matrices of high stiffness all pseudopods attempt to extend , mimicking the formation of static focal adhesion , until the volume constraint becomes limiting ., This makes the cells spread more on stiff substrates than on soft substrates , with weaker volume constraints ( lower values of ) producing a stronger effect of substrate stiffness on cell shape and cell area ( Figure S1 ) ., We also measured the random motility of the cells by characterizing their dispersion coefficients , which we derived from the mean square displacements of the cells ( Figure S2; see section Morphometry for detail ) ., The dispersion coefficients show biphasic behavior , with the highest motilities occurring at around 12 kPa ( Figure 3 E ) ., The biphasic dependence of the dispersion to substrate stiffness is in accordance with in vitro behavior of neutrophils 45 , and smooth muscle cells 46 ., Here it is typically thought to be due to a balance of adhesion and actin polymerization , or due to the interplay between focal adhesion dynamics and myosin-based contractility 45 ., In our model , the effect is more likely due to the appearance of eccentric cell shapes at intermediate stiffnesses; as a result , only the tips of the cell generate sufficient strain in the matrix to extend pseudopods , producing more persistent motion than the round cells at stiff or soft substrates ., It will be interesting to see if a similar relationship between cell shape and cell motility holds in vitro ., Thus the model rules for cell traction and stretch guidance based on durotaxis and strain stiffening suffice to reproduce an experimentally plausible cellular response to matrix stiffness ., Strains induced by endothelial cells on a compliant substrate with low concentrations of arginine-glycine-aspartic acid ( RGD ) -containing nonapeptides can affect the behavior of adjacent cells 2 ., On soft substrates ( 5 . 5 kPa or below ) the cells reduced the motility of adjacent cells , whereas on stiff substrates ( 33 kPa ) such an effect was not found ., On substrates of intermediate stiffness ( 5 . 5 kPa ) , adjacent endothelial cells repeatedly attached and detached from one another , and cells moved more slowly in close vicinity of other cells , than when they were on their own ., Because the extent to which cells could affect the motility of nearby cells depended on matrix compliancy , mechanical traction forces could act as a means for cell-cell communication 2 ., To test if the simple strain-based mechanism represented in our model suffices for reproducing such mechanical cell-cell communication , we initiated the simulations with pairs of cells placed adjacent to one another at a distance of fourteen lattice sites corresponding to a distance of 35 µm , and ran a series of simulations on substrates of varying stiffness ( Figure 4 A and Video S2 ) ., The cells behaved similar to the single cell simulations ( Figure 3 ) , with little cell-cell interactions at the lower and higher stiffness ranges ., Consistent with previous observations 2 , cell pairs on substrates of intermediate stiffness ( 12 kPa ) dispersed more slowly than individual cells ( paired two-sample t-test at 5000 MCS , p<0 . 05 for 12 kPa ) , whereas individual cells and cell pairs dispersed at indistinguishable ( p<0 . 05 ) rates on stiff ( 14 kPa or more ) or soft ( 10 kPa or below ) substrates ( Figure 4 , B-D ) and Figure S3 ) ., Also in agreement with the previous , experimental observations 2 , on a simulated substrate of intermediate stiffness ( 12 kPa ) the cells responded to the matrix strains induced by the adjacent cell by repeatedly touching each other , and separating again ( Figure 4 E ) ., The contact duration of cells on soft and stiff substrates , when they get close enough to each other , are typically longer than for intermediate substrates ., This behavior is also similar to observations in vitro2 ., As one might expect that strongly adherent cells will not repeatedly touch and retract , but rather stay connected upon first contact , we investigated the effect of cell adhesion on these parameters ( Figure S4 ) ., Consistent with this intuition , for stronger adhesion , the contact count tends to be reduced and the contact durations tend to increase , but the overall trend holds: at intermediate matrix stiffnesses we continue to observe more frequent cell contacts than for more soft or more stiff matrices ., Thus the observed pairwise cell behavior is primarily driven by durotaxis ., Mechanical strain can also coordinate the relative orientation of cells ., Fibroblasts seeded on a compliant gel tend to align in a head-to-tail fashion along the orientation of mechanical strain 47 ., Bischofs and Schwarz 48 proposed a computational model to explain this observation ., Their model assumes that cells prefer the direction of maximal effective stiffness , where the cell has to do the least work to build up a force ., This work is minimal between two aligned cells , because maximum strain stiffening occurs along the axis of contraction ., Interestingly , visualization of our model results ( Figure 1 C ) suggested similar head-to-tail alignment of our model cells at around 12 kPa ., To quantify cell alignment in our simulations , we measured the angle α between the lines and , defining the long axes of the cells ( Figure 4 F ) ., We classified the angles as acute ( ; i . e . no alignment ) or obtuse ( ; alignment ) ., At matrix stiffnesses up to around 10 kPa , about one fourth of the angles α were obtuse , corresponding to the expected value for uncorrelated cell orientations ., However , at 12 kPa and 14 kPa significantly more than a fourth of the angles α between the cell axes were obtuse ( 55/100 for 12 kPa , p<1×10−8 and 52/100 for 14 kPa , p<1×10−8 , binomial test ) , and for substrate compliancies of 8 to 16 kPa significantly more of the angles α were obtuse than for 4 kPa ( p<0 . 01 for 8 kPa , and p<1×10−12 for 10 kPa to 16 kPa; two-tailed Welchs t-test ) , suggesting that the mechanical coupling represented in our model causes cells to align in a head-to-tail fashion ., After observing that the local , mechanical cell-ECM interactions assumed in our model sufficed for correctly reproducing many aspects of the behavior of individual endothelial cells on compliant matrices and of the mechanical communication of pairs of endothelial cells on compliant matrices , we asked what collective cell behavior the mechanical cell-cell coordination produced ., When seeded subconfluently onto a compliant matrix ( e . g . , Matrigel ) , endothelial cells tend to organize into polygonal , vascular-like networks 5 , 6 , 49 , 50 ., To mimic such endothelial cell cultures , we initialized our simulations with ( approximately ) 450 cells uniformly distributed over a lattice of 300×300 pixels ( 0 . 75×0 . 75mm2 ) , corresponding to a cell density of 800 endothelial cells per mm2 ., In accordance with experimental observations on gels with low concentrations of collagen 6 or RGD-peptides 2 , after 3000 MCS networks had not formed on soft matrices ( 0 . 5-4 kPa ) or on stiff matrices ( 16-32 kPa ) ( Figure 5 A ) : The cells tended to form small clusters ( Figure 5 A ) ., Interestingly , on matrices of intermediate stiffness after around 300 MCS the cells organized into chains ( 8 kPa ) or network-like structures ( 10 kPa and 12 kPa ) similar to vascular network-like structures observed in endothelial cell cultures 5 , 6 , 49 , 50 ., The optimal stiffness ( ≈10kPa ) for network formation is slightly lower than the stiffness of the substrate ( ≈12kPa ) on which single cells elongate the most ( Figure 3 A ) ., In comparison with a single cell , the collective pulling of a cell colony creates larger strains in the substrate ., Consequently , the strain threshold inducing cell elongation is crossed at smaller substrate stiffness ., Figure 5 B and Video S3 show a time-lapse of the development of a network configuration on a substrate of 10kPa ., The cells organized into a network structure within a few hundred MCS ., The network was dynamically stable , with minor remodeling events taking place , including closure and bridging of lacunae ., Figure 5 C shows such a bridging event in detail ., In an existing lacuna ( 1800 MCS ) stretch lines bridged the lacuna , and connected two groups of cells penetrating the lacuna ( 1980 MCS ) ., The cells preferentially followed the path formed by these stretch lines ( 2150 MCS ) and reached the other side of the lacuna by 2400 MCS ., Such bridging events visually resemble sprouting in bovine endothelial cell cultures on compliant matrices ( Figure 5 D , Video S4 , and 6 ) ., To stay close to the experimental conditions used for the observations of pairwise endothelial cell-cell interaction on compliant substrates 2 that we compared the simulations of pairwise interactions with , in this experiment we used a 2 . 5 kPa gel functionalized with 5 µg/ml RGD peptide - a stiffness at which no network-formation is found in our simulations ., Although we thus do not yet reach full quantitative agreement between model and experiment , note that network formation occurs at substrate stiffness of 10kPa on polyacrylamide matrices enriched with a low ( 1 µg/ml ) concentration of collagen 6 ., We next asked if the mechanical model could also reproduce sprouting from endothelial spheroids 7 , 8 ., Video S5 and Figure 6 shows the results of simulations initiated with a two-dimensional spheroid of cells after 3000 MCS ., On soft ( 0 . 5–8 kPa ) and on stiff ( 32 kPa ) matrices the spheroids stayed intact over the time course of the simulation ., On matrices of intermediary stiffness ( 10–12 kPa ) the spheroids formed distinct sprouts , visually resembling the formation of sprouts in in vitro endothelial spheroids 7 , 8 ., On the 14 kPa and 16 kPa matrices the cells migrated away from the spheroid , with some cell alignment still visible for the 14 kPa matrices ., Observation of a sprout protruding from a spheroid at 10 kPa suggests that a new sprout starts when one of the cells at the edge of the cluster protrudes and increases the strain in front of it ., In a positive feedback loop via an increase in perceived stiffness the strain guides the protruding cell forward ., The strain in its wake then guides the other cells along ( Figure 6 C ) ., In this paper we introduced a computational model of the in vitro collective behavior of endothelial cells seeded on compliant substrates ., The model is based on the experimentally supported assumptions that, ( a ) endothelial cells generate mechanical strains in the substrate 34 , 43 ,, ( b ) they perceive a stiffening of the substate along the strain orientation , and, ( c ) they extend preferentially on stiffer substrate 37 ., Thus , in short , the assumptions are: cell traction , strain stiffening , and durotaxis ., The model simulations showed that these assumptions suffice to reproduce , in silico , experimentally observed behavior of endothelial cells at three higher level spatial scales: the single cell level , cell pairs , and the collective behavior of endothelial cells ., In accordance with experimental observation 36 , 39 , the simulated cells spread out on stiff matrices , they contracted on soft matrices , and elongated on matrices of intermediate stiffness ( Figure 3 ) ., The same assumptions also sufficed to reproduce experimentally observed pairwise cell-cell coordination ., On matrices of intermediate stiffness , endothelial cells slowed down each other ( Figure 4 B ) and repeatedly touched and retracted from each other ( Figure 4 E and Video S2 ) , in agreement with in vitro observations of bovine aortic endothelial cells on acrylamide gels 2 ., Also , in agreement with experimental observations of fibroblasts on compliant substrates 47 and previous model studies 48 the cells repositioned into an aligned , head-to-tail orientation ( Figure 4 F ) ., The model simulations further suggest that these pairwise cell-cell interactions suffice for vascular-like network formation in vitro ( Figure 5 ) and sprouting of endothelial spheroids ( Figure 6 ) ., The correlation between pairwise cell-cell interactions and collective cell behavior observed in our computational model parallels observations in vitro ., Cells elongate due to positive feedback between stretch-guided extension and cell traction , as previously suggested by Winer et al . 44 ., Elongated and spindle-shaped cells are considered indicative of future cell network assembly 6 ., Our model suggests that the elongated cell shapes produce oriented strains in the matrix , via which cells sense one another at a distance ., In this way new connections are continuously formed over “strain bridges” ( see , e . g . , Figure 5 C , D and Video S4 ) , while other cellular connections break , producing dynamically stable networks as illustrated in Video S3 ., Such dynamic network restructuring was also observed during early embryonic development of the quail embryo 51 and in bovine aortic endothelial cell cultures ( Figure 5 D and 6 ) , but not in human umbilical vein endothelial cell cultures 23 , 50 ., Also in agreement with experimental results , the collective behavior predicted by our model strongly depends on substrate stiffness ., The strongest interaction between cell pairs is found on substrates of intermediate stiffness , enabling network formation 2 , whereas network assembly does not occur on stiffer or on softer substrates6 ., These agreements with experimental results are encouraging , but our model also lacks a number of properties of in vitro angiogenesis that pinpoint key components still missing from our description ., We compared the simulation of pairwise cell-cell interactions with previous experiments conducted on polyacrylamide gels , functionalized with RGD ligands 2 , which have linear elastic behavior for small deformations 52–54 ., Strain-stiffening of polyacrylamide gels has been reported for deformations over 2 µm 55 ., Thus with pixels in our model measuring 2 . 5 µm×2 . 5 µm , strain-stiffening seems a reasonable assumption ., Nevertheless , a possible alternative interpretation of the cell pair simulations is that the increased tension generated in pseudopods pulling on the matrix leads to a higher probability of focal adhesion maturation41 , 42 ., A further issue is that in our simulations , single cells dispersed somewhat more quickly on soft gels than on stiff gels ( Figure 3 E and Figure S2 ) ., This model behavior contradicts experimental observations that endothelial cells move fastest on stiff substrates 2 ., Another open issue concerns the time scales of our simulations ., In the present paper time we use the Monte Carlo step as a ( computational ) unit of time ., To estimate the actual time corresponding to 1 MCS , we scale the single cell dispersion coefficients shown in Figure 3 E to experimental dispersion coefficients of bovine endothelial cells on compliant substrates in vitro 2 ., Reported dispersion coefficients of endothelial cells range from around ( on substrates of ) to around ( on substrates of ) ( as derived from the MSDs in Figure 3a , c in 2 and based on ; cf . Eq . 13 ) ., The dispersion coefficients of single cells in our simulations are in the range of ( Figure 3 ) , assuming pixels of ., Thus , based on fitting of single cell dispersion rates , the estimated length of 1 MCS is 0 . 5 to 3 seconds ., The typical time scale of a vascular network formation simulation is around 3000 MCS ( Figure 5 ) , i . e . , to for these time scale estimates ., In experiments , network formation takes longer , around 24 hr ., Thus in our current model the time scales of cell dispersion and network formation do not match exactly ., A possible reason of this discrepancy is the short persistent length of cell motility in standard cellular Potts models ., To better match the time scales of single cells and collective cell behavior in our model , in our future work we will increase the persistence length of the endothelial cells by using the available cellular Potts methodology 56–58 , or model the subcellular mechanisms of cell motility in more detail , e . g . by including mean-field models of actin polymerization 59 , 60 ., A further open issue is the interaction between substrate mechanics and cell-substrate adhesivity ., Although the model correctly predicts the absence of network formation on stiff substrates , it cannot yet explain the observation that reducing the substrate adhesivity of the endothelial cells rescues network formation on stiff substrates 6 ., On compliant gels endothelial cells must secrete fibronectin to form stable networks , whereas fibronectin polymerization inhibitors elicit spindle-like cellular phenotypes associated with network formation on stiff matrices , under conditions where networks do not normally form 6 ., To explain these observations , straightforward future extensions of the model will include a more detailed description of cell-substrate adhesion , combined with models of ECM secretion and proteolysis 13 , 25 , 27 , 61 ., The current model also assumes a uniform density ( i . e . , the infinitesimal strain assumption ) and thickness of the extrace | Introduction, Results, Discussion, Methods | In vitro cultures of endothelial cells are a widely used model system of the collective behavior of endothelial cells during vasculogenesis and angiogenesis ., When seeded in an extracellular matrix , endothelial cells can form blood vessel-like structures , including vascular networks and sprouts ., Endothelial morphogenesis depends on a large number of chemical and mechanical factors , including the compliancy of the extracellular matrix , the available growth factors , the adhesion of cells to the extracellular matrix , cell-cell signaling , etc ., Although various computational models have been proposed to explain the role of each of these biochemical and biomechanical effects , the understanding of the mechanisms underlying in vitro angiogenesis is still incomplete ., Most explanations focus on predicting the whole vascular network or sprout from the underlying cell behavior , and do not check if the same model also correctly captures the intermediate scale: the pairwise cell-cell interactions or single cell responses to ECM mechanics ., Here we show , using a hybrid cellular Potts and finite element computational model , that a single set of biologically plausible rules describing, ( a ) the contractile forces that endothelial cells exert on the ECM ,, ( b ) the resulting strains in the extracellular matrix , and, ( c ) the cellular response to the strains , suffices for reproducing the behavior of individual endothelial cells and the interactions of endothelial cell pairs in compliant matrices ., With the same set of rules , the model also reproduces network formation from scattered cells , and sprouting from endothelial spheroids ., Combining the present mechanical model with aspects of previously proposed mechanical and chemical models may lead to a more complete understanding of in vitro angiogenesis . | During the embryonic development of multicellular organisms , millions of cells cooperatively build structured tissues , organs and whole organisms , a process called morphogenesis ., How the behavior of so many cells is coordinated to produce complex structures is still incompletely understood ., Most biomedical research focuses on the molecular signals that cells exchange with one another ., It has now become clear that cells also communicate biomechanically during morphogenesis ., In cell cultures , endothelial cells—the building blocks of blood vessels—can organize into structures resembling networks of capillaries ., Experimental work has shown that the endothelial cells pull onto the protein gel that they live in , called the extracellular matrix ., On sufficiently compliant matrices , the strains resulting from these cellular pulling forces slow down and reorient adjacent cells ., Here we propose a new computational model to show that this simple form of mechanical cell-cell communication suffices for reproducing the formation of blood vessel-like structures in cell cultures ., These findings advance our understanding of biomechanical signaling during morphogenesis , and introduce a new set of computational tools for modeling mechanical interactions between cells and the extracellular matrix . | biotechnology, bioengineering, biomedical engineering, physics, cell motility, developmental biology, cell biology, engineering and technology, cell migration, biology and life sciences, physical sciences, computational biology, morphogenesis, biophysics, pattern formation, biophysical simulations | null |
journal.pcbi.1005386 | 2,017 | Testing the limits of gradient sensing | The ability to detect the direction of a chemical gradient is fundamental to many biological processes ., To survive or carryout their proper function , individual cells must be able to undergo directed growth ( chemotropism ) or movement ( chemotaxis ) toward chemical signals , such as nutrients or hormones ., An ideal system for studying gradient sensing is chemotropism during the mating response of S . Cerevisiae ( yeast ) ., Yeast cells can exist as one of two haploid types: MATa or MATα ., MATa cells seek a mating partner by sensing a gradient of the pheromone α-factor secreted by MATα cells ( Fig 1A ) ., Gradient sensing strategies fall into two major categories: temporal and spatial ., Temporal sensing mechanisms , in which an organism moving through its environment compares the concentration between its current and previous locations , are commonly utilized by small cells such as E . Coli ( ~1μm ) ., Spatial sensing mechanisms , in which the organism compares the concentration difference across the cell body , are commonly used by large cells including most eukaryotes , such as D . Discoideum ( ~15μm ) ., The fact that yeast cells are not motile suggest they use a spatial sensing mechanism , despite being smaller ( ~4μm in diameter ) than most eukaryotic cells ., Experimental studies have reported that yeast cells are capable of sensing linear gradients as shallow as 0 . 1 nM/μm 1 , 2 ., All information on the extracellular pheromone gradient comes from receptors on the cell’s surface ., Therefore , these receptors set the ultimate limits on gradient sensing ., To quantify the challenges faced by a cell in detecting shallow gradients , we can estimate the average number of ligand-bound or active receptors ( receptor occupancy ) in the front half of the cell ( pointing up the gradient ) versus the back half of the cell ( pointing down the gradient ) ., We begin with estimating the size of the fluctuations about the mean receptor occupancy ., The average receptor occupancy is roughly given by n=NcKD+c , where N is the total number of receptors; KD is the dissociation constant , and c is the average concentration ., The average occupancy in each half can be estimated using the average concentration in the front ( or back ) of the cell ., This expression is an approximation because it does not correctly take into account the spatial dependence of the gradient across the cell ., For a cell with 10000 receptors in a linear pheromone gradient of 0 . 1 nM/μm centered at the KD ( about 7 nM ) of the receptor , the difference in receptor occupancy is Δn = nfront−nback ≈ 45 ., This calculation estimates a less than 1% difference in receptor occupancy between the front & back of the cell ., Following the work of Lauffenburger 3 , the magnitude of the fluctuations in receptor occupancy is σn=NKD∙c ( KD+c ) 2≈50 ., Hence for a gradient of 0 . 1 nM/μm , the signal is masked by the noise: Δn ≈ 45 ± 50 , and a cell cannot predict the direction of the gradient based on an instantaneous measurement of receptor occupancy ., To explain how cells overcome these fluctuations , various noise-reduction mechanisms have been proposed: including time-averaging , gradient sharpening via extracellular degradation of pheromone by the protease Bar1 4 , 5 and removal of active receptors via endocytosis to avoid resampling 6 , 7 ., The limits of these mechanisms have been estimated using mathematical models ., Time-averaging requires sufficient time for the cell to sample multiple binding and unbinding events ., In the yeast mating system , the KD of Ste2 binding to α-factor is known to be around 7nM 8–12 ., Reported values for the unbinding rate are extremely slow: on the order of 10−4–10−3 s-1 12 , 13 ., These values imply a binding rate on the order of 104–105 ( M∙s ) -1 , which is many orders of magnitude slower than the diffusion limit of approximately 109 ( M·s ) -1 ., With a dissociation rate of 0 . 0011 s-1 12 , changes in receptor occupancy occur on the order of 10’s of minutes ., Given that yeast cells begin chemotropic growth within 30 minutes of exposure to pheromone , there seems to be insufficient time for the cell to accurately sense a shallow gradient using the time-averaging mechanism alone ., MATa cells secrete Bar1: a protease that degrades extracellular α-factor ., This process is known to locally sharpen the pheromone gradients between neighboring cells and has been suggested as a mechanism for sensing shallow gradients and ensuring that two or more cells avoid competing for the same mate 4 , 5 ., It is not known if this sharpening effect sufficiently reduces the noise to enable the cells to gradient sense ., Active receptors are removed from the membrane through endocytosis and newly synthesized receptors are brought to the membrane on vesicles ., This receptor cycling has been suggested as a mechanism to improve gradient sensing 6 , 7 by removing pheromone from the environment and preventing re-sampling of the same ligand molecule ., Additionally , the endocytosis rate of active receptors , 0 . 0021 s-1 14 , 15 , is faster than reported unbinding rates ., Therefore , receptor endocytosis may improve the sampling frequency ., The results discussed above are based on mathematical models in which simplifying assumptions are made to allow analytic tractability ., To go beyond these models , we built a simulation platform based on fundamental biophysical processes that allows us to evaluate noise-reduction mechanisms and study gradient sensing with minimal assumptions ., In particular , we develop a Particle-Based Stochastic Reaction-Diffusion Model to study receptor dynamics in a gradient ( Fig 1B ) ., We choose this approach , because non-particle-based methods using Reaction Diffusion Master Equations ( RDME ) , although computationally fast , discretize space ., This discretization implicitly assumes each computational voxel is well-mixed , which reduces spatial accuracy 16 ., Additionally , these methods are difficult to implement when the computational domain has complex boundary conditions such as the partially absorbing boundaries we use to create a gradient ., Currently available particle-based simulation packages , such as Smoldyn or MCell , treat 2nd order reactions by assuming that once two reactants are within a specified capture radius , the reactions occurs with certainty 4 , 17 ., However , for our system , the slow association rate would make the capture radius unphysically small ., Thus , we choose to create our own Particle-Based Stochastic Reaction-Diffusion Model and use the methods developed by Erban and co-workers 18 to treat 2nd order reactions ., Their method allows for customization of the binding radius ., We also develop novel methods for simulating the development of chemical gradients that do not exist in current publically available software packages ., Our simulations reveal:, 1 ) time-averaging and receptor cycling , wherein Ste2 exocytosis is isotropic ( unpolarized ) , are insufficient for yeast cells to confidently detect the direction of shallow gradients before initiating polarized growth , and, 2 ) isotropic exocytosis of Bar1 may improve gradient sensing for cells with fast reaction rates ., Additionally , our simulations reveal that, 1 ) the physical barrier of the cell membrane sharpens the gradient , and, 2 ) diffusion of the receptor reduces the cell’s ability to detect the direction of the gradient ., Our approach bridges the theoretical mathematical models and in vivo experimental approaches ., One potential mechanism for detecting chemical gradients is for cells to use the spatial distribution of active receptors ., However , fluctuations in binding and release of ligand and receptor diffusion introduce significant uncertainty in receptor occupancy , making this task more difficult ., For a cell attempting to sense a shallow gradient , this uncertainty can mask the signal ., For example , a cell with 10000 receptors in a shallow gradient of 0 . 1 nM/μm centered at the KD of the receptor , has a difference in occupancy between the front and back of the cell of Δn ≈ 45 ± 50 3 ., This estimate does not include the effect of receptor diffusion , which further reduces the difference in receptor occupancy ., Our results , which best match the theory of Berezhkovskii and Szabo 20 , indicate that the effects from stochastic binding and unbinding are the largest source of variability in receptor occupancy ., Because the cell receives all information on the extracellular pheromone concentration from the Ste2 receptor , fluctuations in receptor occupancy represent the first major source of noise during gradient sensing ., Downstream signaling events could generate additional sources of noise and/or act to amplify gradients in receptor occupancy ., However , these effects are beyond the scope of our current investigation ., Instead , we evaluated potential cellular mechanisms cells might employ to reduce noise from fluctuations in receptor occupancy ., Recent work derives the relationship between the length of time-averaging and the amount of noise-reduction 20 ., Simulations of our particle-based stochastic reaction-diffusion model corroborate these predictions ( Fig 3 ) ., For example , using the measured rates , kon = 1 . 6×105 ( M·s ) -1 and koff = 0 . 0011 s-1 8 , 12 , we find a yeast cell must wait 40min or longer to appreciably reduce fluctuations in receptor occupancy ., Since yeast cells typically polarize and initiate growth within 20 – 30min of exposure to pheromone , it is unlikely a cell can sense a gradient as shallow as 0 . 1 nM/μm ., Consistent with this observation , it has recently been shown that during chemotropic growth the polarity site is mobile and reorients toward the gradient when initial polarization is in the wrong direction 26 , 27 ., Recent estimates of the reaction rates are an order of magnitude smaller than the rates above 13 , which further reduces the likelihood that a yeast cell can sense shallow gradients before polarizing their growth ., Receptor endocytosis has also been suggested as a noise-reduction mechanism , because it removes ligand from the environment thereby eliminating noise from re-binding the same ligand molecule 6 ., It may also serve to reduce noise due to receptor diffusion ., To investigate the effects of endocytosis on gradient sensing , we adapted our model to absorb rather than release pheromone molecules ., Contrary to expectations , we do not find any advantage to removing the ligand ., That is , ligand absorption does not reduce the noise as compared to releasing ligand ( Fig 3C and 3D ) ., The pheromone protease , Bar1 , has also been suggested as a possible mechanism to improve gradient sensing ., We implicitly modeled Bar1 as a concentration field , which radially decays away from the cell’s surface ., Our results indicate that Bar1 does improve the cell’s ability to sense an emerging gradient ( Fig 10B ) ., Specifically , during the first 20 min that a shallow gradient ( 0 . 1 nM/μm ) forms around the cell , the presence of Bar1 increases the cell’s confidence in estimating the direction of the gradient ., Curiously , our results predict this advantage only occurs if the reaction rates for Ste2 and pheromone are fast ( kon = 1 . 6×106 ( M·s ) -1 and koff = 0 . 011 s-1 ) ., This advantage may also be dependent upon other parameters , e . g . the concentration of Bar1 near the cell and the catalytic rate of Bar1 on pheromone ., Future work will be dedicated to studying how these parameters affect gradient sensing ., Because we directly simulate diffusion , our method captures subtle effects in the distribution of pheromone , e . g . boundary effects from the cell membrane ., Similar effects have been shown to play a significant role in other systems , for example in the arrangement of actin bundles in filopodia 28 ., Here , we found that because the cell is impermeable to pheromone , it acts to sharpen the gradient ( Figs 4A and 4B and 8 ) ., Importantly , we find this sharpening is reflected in the active receptor distribution ( Figs 4C and 4D and 9 ) ., Our simulation methods are analogous to some yeast gradient sensing experiments , in which a pheromone gradient is established by flowing two different concentrations of pheromone into a microfluidics chamber 5 , 26 , 29–31 ., In those experiments , it is not uncommon for multiple cells to be adjacent , e . g . as mother-daughter pairs or as multi-cell clusters ., Our results suggest these adjacent cells experience a sharper than expected gradient ., Additionally , we performed simulations using small ( 1 . 75 μm radius ) cells and found less gradient sharpening than with 2 . 5 μm radius cells ., Consequently , these cells had less of a difference in receptor occupancy ( S2 Text , Section E ) ., Our results complement other work which corroborates this relationship between cell size and the cell’s ability to gradient sense 32 ., Gradient sharpening is strongest if pheromone is injected into the computational domain from only one side ., This arrangement is common in microfluidic gradient chambers 30 , 33 ., Additional steric effects may also be present due to the microfluidic chambers themselves ., For example , many microfluidic chambers have a height similar to a yeast cell ( ~5μm ) ., Therefore , the presence of a cell severely impedes the flow of pheromone in the chamber and further sharpens the gradient ., Hence experiments that aim to test the limit of gradient sensing ( i . e . the shallowest gradient a cell can detect ) must carefully consider steric effects from the cell itself , any adjacent neighbors and the experimental tools ., These effects alter the gradient , such that the true gradient experienced by the cells is sharper than expected ., While we are motivated by understanding the mechanisms used by yeast to detect gradients of pheromone , our simulation methods should find applications in other contexts ., We derive two methods for generating a linear particle gradient in 3-dimensional particle-based stochastic diffusion simulations ., These methods explain how many and how far to inject particles into the simulation space per time step ., We also capitalize on hardware acceleration via the use of GPGPUs to achieve massive parallelization ., As a result we were able to simulate tens of thousands of molecules over billions of time steps ., In most particle-based reaction-diffusion simulations , a binding event is executed as follows ., When a ligand molecule is nearby ( within the binding radius of ) an unbound receptor molecule , the ligand molecule is removed from the system , and the receptor molecule is switched to the ‘bound’ state ., To match the macroscopic binding kinetics , the binding radius is calculated from the binding rate and the diffusion constants of the two molecular species:, rbind=kon4π ( Dα+DSte2 ), ( 10 ), This expression can be derived from Fick’s Law of diffusion and works well when the reaction is diffusion limited ., Because α-factor binding to Ste2 is not diffusion limited , to match the rates reported in the literature using this method requires a binding radius on the order of Angstroms , which is much smaller than the size of Ste2 ( GPCRs protrude about 4nm outside the cell membrane 35 ) ., As discussed by Erban and Chapman the unrealistic binding radius results from the assumption that the binding probability is 100% 18 ., That is , a ligand molecule within the binding radius of an unbound receptor binds with certainty ., The model put forward by Erban and Chapman , removes this assumption and establishes a mathematical framework , in which the binding probability is a function of the binding radius 18 , 36 ., That is , a ligand molecule within a specified binding radius binds with a probability that produces an average binding rate consistent with the macroscopic rate constant kon ., We choose the binding radius to be 4nm and calculate the binding probability by numerically solving the following system of equations derived by Erban and Chapman 36:, konΔtrbind3=Pbind∫014πz2g ( z ) dz, ( 11 ), where ,, g ( r^ ) = ( 1−Pbind ) ∫01K ( r^ , r^′ , γ ) g ( r^′ ) dr^′+∫1∞K ( r^ , r^′ , γ ) g ( r^′ ) dr^′+PbindK ( r^ , α , γ ) α2∫01g ( z ) z2dz, K ( z , z′ , γ ) =z′zγ2π ( exp\u2061− ( z−z′ ) 22γ2−exp\u2061− ( z+z′ ) 22γ2 ), α=runbindrbind, γ=2 ( Dα+DSte2 ) Δtrbind, Using the values for Δt , kon , Dα , DSte2 , rbind and runbind given in Table 1 , a pheromone molecule has a 0 . 2% chance of binding ., We provide a detailed description of how we calculate the probability in the Supplemental Methods ( S1 Text , Section A ) ., Importantly , we note that to apply the method of Erban and Chapman to our system , we must double the binding probability ., The probability calculated from their method is appropriate when the ligand molecule can approach the receptor from any direction ., However , in our system , the pheromone molecules can only approach the receptor from the outside of the cell ., Therefore to achieve macroscopic rate constants consistent with experimental measurements , we double the binding probability ., This adjustment was also used in recent work 37 ., See the Supplemental Methods ( S1 Text , Section A ) ., Given a dissociation rate koff , we can calculate the probability , Punbind , that a ligand molecule dissociates from a ‘bound’ receptor in the time interval Δt as follows:, Punbind=1−exp−koffΔt, ( 12 ), A new pheromone molecule is created a fixed distance , runbind , and in a random direction from the receptor ( Fig 1B . W ) ., As with the binding radius , we take runbind = 4nm ., We do not allow a pheromone molecule to be released inside the cell ., Lastly , the receptor molecule is switched to the ‘unbound’ state ., Let ( x ( t ) , y ( t ) , z ( t ) ) be the current position at time t , then to diffuse a pheromone molecule in 3D , the new position ( x ( t + Δt ) , y ( t + Δt ) , z ( t + Δt ) ) is found from the following equations:, x ( t+Δt ) =x ( t ) +Z12DΔt, ( 13a ), y ( t+Δt ) =y ( t ) +Z22DΔt, ( 13b ), z ( t+Δt ) =z ( t ) +Z32DΔt, ( 13c ), The Zis are independent random numbers drawn from a Gaussian distribution with a mean of 0 and a variance of 1 ., The new position is modified if it is located outside the simulation volume or inside the cell ., Reflecting boundary conditions are imposed at the four boundaries: y = ±5μm and z = ±5μm ( Fig 1B . X ) ., Additionally , pheromone molecules reflect off the surface of the cell , because the cell membrane is impermeable to pheromone ( Fig 1B . Y ) ., Details for calculating the reflection off the cell surface are provided in the Supplemental Methods ( S1 Text , Section B ) ., The last two boundaries , x = ±5μm , are constructed to establish a linear pheromone gradient along the x-axis ., We describe two different methods for treating the x = ±5μm boundaries ., In method 1 , each boundary has a fixed concentration ., In method 2 , one boundary has a fixed concentration while the other is partially absorbing ., The next two sections describe the physical interpretation and algorithmic implementation for each method ., In this method , we model a fixed concentration at each boundary ., A gradient is formed when we set the concentration at one end of the computational domain higher than at the other ., This method is consistent with the design of many microfluidic chambers used to study gradient sensing 5 , 26 , 29–31 ., To maintain a fixed concentration at each boundary , pheromone molecules are added to and removed from the simulation volume in processes called ‘injection’ and ‘ejection’ , respectfully ( Fig 1B . Z ) ., For ejection , we remove all pheromone molecules located outside the boundaries ( x < –5μm , or x > 5μm ) ( Fig 1B . Z ) ., For injection , we create a number of new pheromone molecules and position them near either the x = 5μm or x = –5μm boundary ., On average , for a concentration c at the boundary , the number to inject at each time step is calculated using the equation:, ninj=0 . 6022nM∙μm3∙c∙aDαΔτπ, ( 14 ), where a is the area of the boundary ( 100μm2 for most of our simulations ) ; Dα is the diffusion constant for pheromone molecules , and Δτ is the elapsed time between two injection processes ., The derivation of Eq 14 is found in the Supplemental Methods ( S1 Text , Section C ) ., Although Eq 14 provides the average number of molecules to be injected , due to the stochastic nature of diffusion , the actual number injected can vary for a given time step ., During an injection step , the number to inject is a random number drawn from a Poisson distribution with a mean of ninj ., The position of a newly injected molecule is also determined randomly ., The y and z positions are determined from a uniform probability distribution across their respective domains ( e . g between –5μm and 5μm , inclusively ) ., The x position is calculated as a random distance , called the ‘injection distance’ , into the simulation volume from the boundary ( Fig 1B . Z ) ., The probability distribution function for the injection distance , dinj , is given by:, P ( dinj ) =121−erf ( dinj4DαΔτ ) , ( 15 ), The derivation of Eq 15 is provided in the Supplemental Material ( S1 Text , Section C ) ., Because it is computationally expensive to generate a random number from the distribution given by Eq 15 , we select a random value from a pre-calculated list ., This list has more than 12 million random values whose distribution matches Eq 15 ., Further discussion for implementing Eq 15 is provided in the Supplemental Methods ( S1 Text , Section C ) ., For computational efficiency , these two processes ( injection and ejection ) are implemented on a slightly coarser time scale , Δτ , than the time scale for diffusion Δt ., It is important to note that ejection and injection must be calculated on the same time scale ., Details and justification for the two time scales are discussed below in “Algorithm Overview” ., In this method , we model a fixed concentration at one boundary ( x = 5μm ) , while the other boundary ( x = –5μm ) is partially absorbing ., We use this method to simulate the formation of a gradient from a source located at the positive x boundary ( see Results Section III ) ., At the x = 5μm boundary , the average number of molecules to inject at each time step , ninj , is given by:, ninj=0 . 6022nM∙μm3∙ ( cx=5∙aDαΔτπ+12a∙g∙DαΔτ ), ( 16 ), The derivation of Eq 16 is found in the Supplemental Methods ( S1 Text , Section C ) ., Note that in addition to defining the desired concentration at the boundary , cx = 5 , we also define the desired gradient at the boundary: g ., Eq 14 is a special case of Eq 16 , in which there is no gradient ( g = 0 nM/μm ) outside our volume ( x > 5μm ) ., At the x = –5μm boundary , no new molecules are injected , and during ejection , not all molecules located outside the boundary ( x < –5μm ) are removed ., Instead , each pheromone molecule has a probability of being reflected back inside the volume; otherwise , the molecule is removed ., To achieve a steady state gradient of g , the probability of reflection is given by:, PRef=1−gcx=−51πDαΔτ+12g, ( 17 ), if and only if ,, cx=−5g≫4DαΔτ, The derivation of Eq 17 is found in the Supplemental Methods ( S1 Text , Section C ) ., The concentration , cx = –5 , and gradient , g , are the steady state concentration and gradient at the x = –5μm boundary when no cell is present in the computational domain ., In the presence of a cell the pheromone molecules coming from the opposite boundary must diffuse around the cell ., Therefore in this situation the resulting concentration will be less than cx = –5 , and the gradient will be steeper than g ., As in method 1 , the injection and ejection processes are implemented on a slightly coarser time scale , Δτ , than the primary time scale: Δt ., Because these processes are calculated less frequently , our program is more computationally efficient ., Each receptor molecule , ‘bound’ or ‘unbound’ , diffuses on the cell membrane ( Fig 1B . Y ) ., We approximate diffusion on this surface by first diffusing the receptor in 3-dimensions and then projecting the receptor back onto the surface of the cell ., This approach is computationally efficient and accurate for small time steps ., The details of these two steps for diffusing a receptor molecule are as follows ., First , a new position is calculated using Eq 13 and a diffusion constant appropriate for proteins in the plasma membrane ( D = 0 . 0025 μm2/s ) 34 ., Let this new position be ( x˜ , y˜ , z˜ ) ., Second , we project ( x˜ , y˜ , z˜ ) onto the surface of the cell , which is modeled as a sphere of radius R and centered at the origin ( 0 , 0 , 0 ) using the equations:, r˜=x˜2+y˜2+z˜2, ( 18a ), x ( t+Δτ ) =x˜Rr˜, ( 18b ), y ( t+Δτ ) =y˜Rr˜, ( 18c ), z ( t+Δτ ) =z˜Rr˜, ( 18d ), The derivation of Eq 18 is provided in the Supplemental Methods ( S1 Text , Section D ) ., For computational efficiency , we diffuse the receptors on a slightly coarser time scale , Δτ , than the primary time scale: Δt ., We do not sacrifice much accuracy , because the diffusion constant for membrane-bound receptors is small compared to that of extracellular pheromone molecules ., Our simulation algorithm and order of operations closely follows the general algorithm described by 36 ., We modify their algorithm , because of the spatial domains ( outside or on the cell ) and non-uniform distribution of molecules ( the ligands have a linear concentration gradient and the receptors are restricted to the surface of the sphere ) ., Our simulation algorithm is broken into the six processes described above: binding reactions , unbinding reactions , diffusion of pheromone molecules , ejection of pheromone molecules , injection of pheromone molecules and diffusion of receptors ., The last three processes are simulated on a coarser time scale ( time step = Δτ ) than the first three processes ( time step = Δt ) ., Below , we provide pseudo-code of our simulation algorithm ., At each time step I . Binding Reactions–Each ‘unbound’ receptor has a chance to bind a single , nearby pheromone molecules ., II ., Unbinding Reactions–Each ‘bound’ receptor , including those from step 1 ) , has a chance to release its pheromone molecule ., III ., Pheromone Diffusion–Each pheromone molecule diffuses ., They reflect off the cell’s surface , and the y = ±5μm and z = ±5μm boundaries ., After every ( Δτ/Δt ) steps IV ., Receptor Diffusion–Each receptor molecule diffuses on the surface of the cell ., V . Ejection–Remove all pheromone molecules outside the x = ±5μm boundaries ., VI ., Injection–Add new pheromone molecules near the x = ±5μm boundaries ., We choose the coarse time scale , Δτ , based on two criteria ., One , receptor diffusion should not be many times larger than the binding radius ., And two , the average injection distance should be much less than 2 . 5μm , which is the distance from the x = ±5μm boundaries to the cell ., For Δτ = 50μs , receptors diffuse about 0 . 5nm , which is much smaller than 4nm ., Also for Δτ = 50μs , the average injection distance is about 0 . 05μm , which is much smaller than 2 . 5μm ., While our model is derived from physical processes , it can be thought of as an Agent-Based Model; wherein , we simulate 10 , 000–20 , 000 molecules ( the “agents” ) , each of which follows a set of rules ., Because many of these rules are independent of other molecules , we can parallelize the algorithm at each process ., For example , during pheromone diffusion , process III , a new position is calculated for each pheromone molecule ., This calculation is independent from all other molecules ., Hence , the diffusion of many pheromone molecules can be calculated simultaneously ., Ideally , we would calculate the new position of every pheromone molecule in parallel ., To achieve massive parallelization with minimal coding effort , we turn to Hardware Acceleration using NVIDIA GPUs ., We write the program in CUDA C , which is an extension of the C Programming Language , developed by NVIDIA to facilitate High Performance Computing on their GPGPUs ., Each of the six processes is executed on the GPU , one at a time , as arranged above , in order to ensure all reactions are complete before the molecules diffuse ., That is to say , there is a global synchronization between processes ., All simulations were run on UNC’s KillDevil cluster , which has 2 . 67GHz Intel processors connected to NVIDIA M2070 GPUs ., In a typical simulation , we simulate roughly 17 , 000 particles for 3 . 6 billion time steps ( 1 hour at 1μs time steps ) , which takes about 34 hours to complete ., The parallelization offers scalability in complexity but not a corresponding increase in computation time ., For example , a simulation similar to the one above , but with double the ligand concentration , takes about 45 hours: a 32% increase ., Using the CUDA nomenclature , the binding , unbinding and receptor diffusion processes launch 53 Blocks per Grid and 192 Threads per Block ., The pheromone diffusion and ejection processes launch 18 Blocks per Grid and 256 Threads per Block; the injection process launches 2 Blocks per Grid ( one each for x = ±5μm boundary ) and 256 Threads per Block ., To determine if receptor cycling can reduce noise , we developed a simplified model of receptor cycling ., We compare simulations of the basic model described above , in which receptors bind and unbind pheromone , to simulations of the simplified receptor cycling model , in which receptors bind pheromone and are endocytosed ., Specifically , an active ( pheromone-bound ) receptor , Ste2* , can be endocytosed and replaced with an unbound receptor , Ste2 ., The new Ste2 molecule is added to a random position on the cell surface ., This method of endocytosis with immediate replacement keeps the total number of receptors constant , and allows us to directly compare results from this model to results from the basic model ., Although the endocytosis rate for Ste2* is 0 . 0021 s-1 14 , 15 , we use a rate of 0 . 0011 s-1 , which is the unbinding reaction rate from the basic model ( See Table 1 ) ., In our algorithm , process II ( Unbinding Reactions ) is replaced with the following ., II ., Endocytosis Reactions–Each ‘bound’ receptor , including those from step 1 ) , has a chance to be endocytosed ., To determine if the pheromone protease Bar1 can reduce noise and improve gradient sensing , we have also developed a simplified model for a cell releasing Bar1 ., In addition to the basic model described above , in which receptors bind and unbind pheromone , we include the reaction of Bar1 degrading pheromone ., In principle this catalytic reaction can be modeled much like the binding reaction; that is , individual Bar1 molecules could be simulated and have a probability of degrading nearby pheromone molecules ., However we choose to avoid the computational cost of this method ., Instead , we model Bar1 concentration as a static , radial field extending from the surface of the cell:, Bar1 ( r ) =Bar10Rr, ( 19 ), where r is the distance from the center of the cell; R is the radius of the cell , and Bar10 is the Bar1 concentration at the surface of the cell ., Similar to previous modeling work , we set Bar10 = 0 . 85nM 5 ., Based on their distance from the cell surface , pheromone molecules have a probability of being degraded as given by:, Pcat ( r ) ≈kcat∙Bar1 ( r ) ∙Δt, ( 20 ), As modeled previously , we set the catalytic reaction rate , kcat , to be 2 . 5e8 ( M·s ) -1 5 ., In our algorithm , the process for Bar1-mediated catalytic reaction is inserted between process II ( Unbinding Reactions ) and III ( Pheromone Diffusion ) ., Here , we label the Bar1 process as IIB ., IIB ., Catalytic Reactions–Each pheromone molecule has a chance , based on its current position , of being degraded . | Introduction, Discussion, Methods | The ability to detect a chemical gradient is fundamental to many cellular processes ., In multicellular organisms gradient sensing plays an important role in many physiological processes such as wound healing and development ., Unicellular organisms use gradient sensing to move ( chemotaxis ) or grow ( chemotropism ) towards a favorable environment ., Some cells are capable of detecting extremely shallow gradients , even in the presence of significant molecular-level noise ., For example , yeast have been reported to detect pheromone gradients as shallow as 0 . 1 nM/μm ., Noise reduction mechanisms , such as time-averaging and the internalization of pheromone molecules , have been proposed to explain how yeast cells filter fluctuations and detect shallow gradients ., Here , we use a Particle-Based Reaction-Diffusion model of ligand-receptor dynamics to test the effectiveness of these mechanisms and to determine the limits of gradient sensing ., In particular , we develop novel simulation methods for establishing chemical gradients that not only allow us to study gradient sensing under steady-state conditions , but also take into account transient effects as the gradient forms ., Based on reported measurements of reaction rates , our results indicate neither time-averaging nor receptor endocytosis significantly improves the cell’s accuracy in detecting gradients over time scales associated with the initiation of polarized growth ., Additionally , our results demonstrate the physical barrier of the cell membrane sharpens chemical gradients across the cell ., While our studies are motivated by the mating response of yeast , we believe our results and simulation methods will find applications in many different contexts . | In order to survive , many organisms must not only be able to detect the presence of a chemical compound , but also in which direction that compound increases or decreases in concentration ., For example , bacteria cells prefer to move towards areas with high sugar concentrations ., The process by which cells determine the direction of a chemical gradient is called “Gradient Sensing” ., Of particular interest is the gradient sensing capability of yeast cells ., These cells have been observed detecting the direction of extremely shallow gradients , which produce only a 2% difference in the number of molecules across the cell ., Because the molecular-level noise is much larger than this signal , it is unclear what noise-reduction mechanism the cell employs to reduce the noise and detect the signal ., We developed a 3D computational simulation platform to calculate and study the exact positions of molecules during this process ., Our platform utilizes High Performance Computing clusters and GPGPUs ., We find that , of the two prevailing models in the literature , neither time-averaging nor receptor endocytosis sufficiently reduces molecular noise for yeast cells to reliably detect chemical gradients before they initiate polarized growth ., This finding implies yeast must possess a mechanism for reorienting the direction of growth after cell polarization has occurred ., We also find the cell membrane and similarly , any other physical barrier nearby the cell can improve the cell’s likelihood of detecting the gradient ., Our simulation methods and results will be applicable in other areas of research . | engineering and technology, signal processing, cell processes, noise reduction, simulation and modeling, cellular structures and organelles, research and analysis methods, pheromone receptors, biophysics, cell membranes, physics, endocytosis, biochemistry, signal transduction, biochemical simulations, cell biology, secretory pathway, biology and life sciences, pheromones, physical sciences, computational biology, biophysical simulations | null |
journal.pcbi.1005236 | 2,016 | Numerical Approach to Spatial Deterministic-Stochastic Models Arising in Cell Biology | It is not uncommon for a cell-biological model to include some components that might be stochastic in nature ( small copy numbers , rare events ) , whereas others , if uncoupled , would behave deterministically ( large copy numbers , fast reactions ) ., Through their interaction , fluctuations in a stochastic subsystem may induce significant random perturbations in the ‘deterministic’ one , thus rendering the entire system stochastic ., Calcium sparks in cardiomyocytes and other cells 1 is one such example , where calcium released from intracellular stores through calcium channels may , in turn , influence probabilities of opening and closing of those channels ., Here , calcium concentration can often be regarded as a ‘deterministic’ component of the system , whereas the dynamics of calcium channels is inherently stochastic ., Similarly , stochastic openings of voltage-sensitive ion channels depend on the ‘deterministic’ membrane potential , which , in turn , is affected by stochastic electric currents passing through the channels 2 ., Stochasticity in otherwise deterministic cellular subsystems may also be brought about by their coupling to dynamics of cytoskeletal filaments , translation events , and other processes involving macromolecules and small organelles present in small numbers ., Simulating such systems as fully stochastic can be prohibitively slow ., Indeed , simulating calcium sparks stochastically with an account of every single calcium ion would be computationally expensive because their number is typically large ., But in the limit of large copy numbers , the intrinsic fluctuations due to discreteness of molecules are insignificant , and one can design faster hybrid algorithms , in which deterministic and stochastic approaches are appropriately combined ., While these efficient methods are approximate , the larger the copy numbers in the ‘deterministic’ subsystem , the more accurate their outcome ., Numerical approaches to interacting systems with disparate levels of stochasticity are an area of active interdisciplinary research ., In the context of cell-biological applications , various hybrid approaches were proposed for ‘well-mixed’ models of biochemical networks with fast and slow components 3 , 4 ., In these models , a fast component , whose copy numbers are only moderately large , is often modeled as a Wiener stochastic process , rather than deterministically ., The corresponding numerical techniques are a combination of methods of solving stochastic ordinary differential equations ( SDEs ) 5 , also termed Langevin equations in the physics literature , and Gillespie-type algorithms 6 , 7 that simulate stochastic reaction events in the slow component ., Unlike stochastic hybrids , the deterministic-stochastic models are mathematically defined as piecewise deterministic Markov processes 8 , 9 , in which the system develops deterministically between consecutive stochastic events ., Numerical approaches to such systems are based on a formulation that couples differential equations , which describe continuous variables , with equations that govern probability distributions of the stochastic components ., The coupling occurs through ‘deterministic’ rates dependent on discrete stochastic variables and transition probability rates that are functions of continuous variables ., Efficient numerical methods for solving deterministic-stochastic models rely on generating Monte Carlo realizations of a hybrid system ., For this , a kinetic Monte Carlo algorithm advancing a stochastic subsystem in time must work in conjunction with a deterministic integrator that updates continuous variables by solving the corresponding differential equations ., A variety of algorithms were proposed for spatially uniform , or well-mixed , deterministic-stochastic models ., Fixed time step methods , applied to hybrid models of membrane potential 2 and calcium dynamics 10 , are conceptually straightforward but incur time-discretization errors in stochastic variables ., In adaptive methods , which were first proposed for solving deterministic-stochastic models of biochemical networks 11 , the treatment of a stochastic subsystem is essentially free of time-discretization errors ., In these algorithms , accurate sampling of stochastic reaction events coupled to continuous variables is achieved by adapting Gillespie’s methods for systems whose transition rates explicitly depend on time ., A similar approach was used in a hybrid stochastic algorithm for well-mixed systems with fast and slow components 12 ., It should be noted that in adaptive methods , special care is required for ensuring synchronous treatment of the ‘deterministic’ and stochastic subsystems ., A rigorous convergence analysis of the hybrid adaptive methods was given in 13 ., Numerical methods for spatially resolved deterministic-stochastic models are less common ., A method described in 14 approximates a stochastic subsystem by a reaction-diffusion master equation 15–18 ., In this approximation , a spatial domain is partitioned into subvolumes which are assumed to be well-mixed at any time , and a state of the stochastic subsystem is described in terms of copy numbers per subvolume ., The master equation is then solved by an optimized variant of the Next Subvolume method 19 ., Designed for models with relatively slow deterministic dynamics , the method of 14 is applicable only if the stochastic subsystems involve sufficiently large copy numbers per subvolume 20 , 21 ., Stochastic subsystems with relatively low copy numbers can be described in terms of states and spatial locations of individual molecules ., The particle-based approach was used to simulate a simple model of assembly of RNA granules in which RNA molecules bind to core complexes 22 ., In the model , spatial distributions of RNA molecules were modeled deterministically by partial differential equations ., The core complexes and RNA granules were treated stochastically as individual particles interacting with the deterministic subsystem while undergoing random walks ., A similar approach was adopted in modeling actin bundles and asters 23 , 24 , where the stochastic subsystem was comprised of tips of actin filaments while ‘deterministic’ actin monomers were modeled as well-mixed because of their relatively fast diffusion ., States and positions of individual channels were also used to define stochastic subsystems in spatial versions of the deterministic-stochastic models of membrane potential 25 and calcium release from inositol 1 , 4 , 5-trisphosphate ( InsP3 ) -receptor channels 26–28 , 2 ., Algorithmically , the methods in these studies combined deterministic descriptions in terms of partial differential equations and the event-driven time stepping schemes 11 ., Calcium-induced calcium release in cardiac muscle cells 29 was already mentioned above as a mechanism that naturally lends itself to a hybrid numerical treatment ., Playing a key role in ensuring robustness of heart contractions in response to action potentials , it has been studied extensively by various methods 30 , including mathematical modeling 31 ., The calcium release in cardiomyocytes occurs by way of clustered ryanodine receptor channels ( RyR ) and , in a healthy heart , takes the form of an avalanche of calcium ‘sparks’ , the localized spikes of calcium concentration 32 ., Recent advances in experimental technologies have generated renewed interest in detailed predictive computational modeling of calcium dynamics in heart muscle cells for normal and pathological conditions 33 , 34 ., Similar to calcium release from the ( InsP3 ) -receptor channels , the problem entails coupling of a spatial deterministic description of calcium and stochastic kinetics of RyR channels and can be solved efficiently by a hybrid numerical method ., All of the above approaches were largely specific solutions to a specific modeling problem or a restricted domain of problems ., In this article , we describe a general-purpose spatial deterministic-stochastic algorithm and discuss techniques used for its validation ., The work was motivated by the need of providing tools for simulating spatial hybrid models to a wide range of cell scientists ., The method is designed to be applicable to a broad spectrum of models , including those where continuous and discrete variables are defined both in volume and in the encompassing membranes ., The current implementation of the method appropriately combines capabilities of one of the Virtual Cell ( VCell ) 35–39 spatial deterministic solvers and an efficient particle-based simulator called Smoldyn 40 , 41 ., ( Note that Smoldyn has been recently adapted to accommodate a different type of hybrid stochastic models 42 , in which the subsystems with disparate levels of stochasticity are segregated in space but can interact in a ‘handshaking’ region 43–46 . ), The development of the VCell hybrid solver benefited from recent integration of Smoldyn into VCell as a method of solving spatial stochastic models 47 ., A distinct feature of our hybrid solver is that the simulations of widespread fluctuations originating from point sources can be carried out in realistic geometries taken from experimental images , as both VCell and Smoldyn provide tools for simulating reaction-diffusion systems in arbitrary geometries 48 , 49 , 40 ., This article is focused on physical underpinnings of the method and its algorithmic details , with special emphasis on rigorous validation of its key elements ., Hybrid algorithms , often proposed heuristically , may appear intuitive , but their rigorous analysis and validation constitute a challenging task 12 , 25 , 7 ., This is particularly true in the context of spatially resolved models ., Tests against deterministic limits , while necessary , are insufficient because convergence to a correct deterministic limit does not yet guarantee correct behavior in the stochastic regime ., Analytical solutions of stochastic models , required for convergence studies in the stochastic regime , are rare , particularly for spatial hybrid systems ., In addition to truncation errors due to the time-space discretization , common to deterministic integrators , probability distributions and correlation functions obtained by Monte Carlo techniques include statistical errors due to finite numbers of realizations ., Thus , the validation of a spatial hybrid solver entails analysis of multidimensional datasets representing multiple realizations of a hybrid system obtained with varying discretization parameters ., The paper is organized as follows ., The algorithm , along with its mathematical fundamentals , is described in Section Mathematical problem and algorithm using a simple model of calcium sparks as an example ., It is then applied to two very different cell-biological phenomena ., The calcium spark model introduced in Section Mathematical problem and algorithm is used in Section Validation of the method for validation of the method against analytical results and numerical solutions obtained by alternative methods ., In Section Application to a hybrid model of spontaneous cell polarization , the method is applied to a hybrid model of spontaneous cell polarization; the actual VCell MathModel script for this application is included in S3 Text as an illustration of the software implementation ., A summary of results and discussion of possible improvements conclude the paper ., Mathematically , the algorithm is based on a formulation of a deterministic-stochastic system , which is somewhat similar to how Wiener processes are described in terms of Langevin equations ., To illustrate the approach and explain the workings of the algorithm , we employ a simple model of calcium sparks , whose ‘deterministic’ subsystem consists of a single variable , the calcium concentration U ( r , t ) , and its stochastic subsystem is comprised of calcium channels , through which calcium flows into the cell from intracellular stores ., In muscle cells , calcium channels form small regularly distributed clusters ., For simplicity , we will treat the calcium sources as single channels having two states , open and closed ., The corresponding discrete stochastic variables are Ξi ( r , t ) ≡ δ ( r − ri ) ξi ( t ) , where the Dirac deltas δ ( r − ri ) define channel locations and the stochastic variables ξi ( t ) accept two values: 1 ( open state ) and 0 ( closed state ) ., The index i enumerates the channels , and r , ri ∈ Ωcell , where Ωcell denotes the space of a cell ., Dynamics of the continuous variable U ( r , t ) are affected by the following mechanisms: calcium release through channels , calcium diffusion , and removal of calcium from the cytosol via calcium pumps ., The variable is therefore governed by a partial differential equation ( PDE ) with stochastic source terms ,, ∂tU=∇⋅ ( D∇U ) +J ( ∑i=1NchΞi ( r , t ) ) −Vp ( U−U0 ) ,, ( 1 ), where D is the calcium diffusion constant , J is the calcium flux through an open channel , Nch is the total number of channels in the cell , Vp is the calcium pump rate constant , and U0 is the steady-state calcium concentration in the absence of open channels ., Eq ( 1 ) is subject to boundary conditions imposed at the cell membrane ., For example , if calcium fluxes at the plasma membrane can be ignored , the corresponding no-flux boundary condition can be written as, −D ( n⋅∇U ) |∂Ωcell=0 ,, ( 2 ), where n is an outward normal to the cell membrane ∂Ωcell ., Dynamics of the stochastic subsystem are described by a two-component probability distribution function , {P0i ( t ) , P1i ( t ) } , given that in our simple model a channel has only two states ., The differential Chapman-Kolmogorov equation that governs Markov processes 5 reduces in this case to, P0i ( t+dt ) = ( 1−konidt ) P0i ( t ) +koffiP1i ( t ) dtP1i ( t+dt ) = ( 1−koffidt ) P1i ( t ) +koniP0i ( t ) dt ( i=1 , 2 , … , Nch ) ,, ( 3 ), where koni and koffi are the rate constants for channel openings and closings , respectively ., ( Because P0i ( t ) +P1i ( t ) ≡1 , it is sufficient to solve only for one of the components , say , for P1i ( t ) ., ) Importantly , parameters koni and koffi may depend on U ( r , t ) ; this would couple Eq ( 3 ) with Eqs ( 1 and 2 ) and also make the equations with different i , which otherwise would be independent , indirectly affect each other ., Note that because of coupling with Ξi ( r , t ) , U ( r , t ) also becomes a stochastic variable ., Eqs ( 1–3 ) fully determine the time-dependent behavior of the deterministic-stochastic system for given initial conditions {U ( r , 0 ) ;{P1i ( 0 ) }} ., Their generalization to multivariate ( multistate ) models is straightforward , yielding descriptions that retain the structure and features of Eqs ( 1–3 ) ., Specifically , a multivariate spatial piecewise-deterministic Markov process is defined in terms of random variables of two types 16 , 28 , the continuous ‘U-type’ and discrete ‘Ξ-type’ variables ., Using vector notation for sets of these variables , all possible outcomes of the process , {U ( r ) , Ξ ( r ) } , form an infinite-dimensional function space 28 ., The only practical approach to solving numerically for a time-dependent probability density functional p ( {U ( r ) , Ξ ( r ) } , t ) is by Monte Carlo simulations of individual realizations of a system based on generation of pseudorandom numbers ., The description in the mold of Eqs ( 1–3 ) provides an intuitive script for an algorithm of this type ., ( Alternatively , one can seek a direct numerical solution of a functional equation governing p ( {U ( r ) , Ξ ( r ) } , t ) 5 , which , however , quickly runs into memory constraints ., Still , this approach can be used for testing purposes , see subsection Fully coupled systems with finite diffusion: validation against direct solutions of Fokker-Planck equation ) ., Our spatial hybrid algorithm employs fixed time step integration due to its conceptual and logistical simplicity ., The downside is that the stability constraints imposed on the time step , which should be sufficiently small to resolve fast ‘deterministic’ reactions , may result in slow performance ., The inefficiency can be partially alleviated by applying an automatic pseudo-steady-state treatment 50 ., A key element of a hybrid method is how the numerical treatments of the ‘deterministic’ and stochastic subsystems are merged ., In our algorithm , the PDEs are discretized in space using a finite-volume scheme 51 , in which a computational domain Ω is partitioned into Nω subvolumes: Ω = {ωj} , j = 1 , … , Nω ., The U-type variables are discretized respectively as U ( r ) → {Uj ≡ U ( rj ) } , where rj is the center of ωj and Uj has a meaning of a subvolume average: Uj=|ωj|−1∫ωjU ( r ) d3r , where |ωj| stands for the volume of ωj ., Spatial histograms of stochastic variables that use the same subvolumes {ωj} as bins would have the similar meaning ., Indeed , let NΞ be the number of particles of a given type Ξ; then the histogram Ξjs=|ωj|−1∫ωj∑i=1NΞΞi ( r ) |ξi=sd3r describes the density of particles of the molecular type Ξ in state s in the vicinity of rj or , more precisely , the number of particles Ξi|ξ = s in ωj divided by the volume of ωj ( s = 1 , … , Nst ( Ξ ) ; here , Nst ( Ξ ) is the number of states of a particle of type Ξ ) ., For example , the spatial binning of the stochastic source term of Eq ( 1 ) yields |ωj|−1∫ωj∑i=1NchΞi ( r ) |ξi=1d3r=nj/|ωj| , where nj is the number of open calcium channels inside ωj ., Then , as expected , Jnj/|ωj| is the rate of change of calcium concentration due to the influx through open channels located in the vicinity of rj ., As a result , both the deterministic and stochastic rates can now be expressed in terms of sets {Uj , Ξjs} with components defined for the same spatial grid , which makes advancing the hybrid system in time conceptually straightforward ., A realization of a piecewise deterministic Markov process at time t + Δt is generated on the basis of a known state at time t as follows ., For sufficiently small time steps Δt , such that the sum of the O ( Δt ) terms in the expansion of the total transition probability for a particle is less than 1 , a particle may undergo at most one stochastic transition per Δt from its current state to a new one ., ( For the example described by Eq ( 3 ) , this requirement yields a condition Δt<<1/max ( koni , koffi ) ) ., Thus , without loss of generality , occurrences of the stochastic transitions can be assigned to the end points of the interval Δt ., Therefore during the interval , the variables Ξ ( r , t ) remain unchanged and , upon the binning described above , the equations for U ( r , t ) become regular deterministic PDEs ( see , e . g . , Eq ( 1 ) of the simple calcium sparks model ) ., The updated values U ( r , t + Δt ) are then found by integrating the PDEs over Δt with the corresponding boundary conditions ( exemplified by Eq ( 2 ) ) ., In our method , this is done by employing a fixed time step PDE solver of VCell ., The update of variables Ξ ( r , t ) is carried out by employing Smoldyn , a particle-based fixed time step Monte Carlo package 40 , 41 ., Using again the simple calcium spark model as an example , the transitions of a channel between open and closed states can be interpreted as ‘unimolecular’ reactions , which are simulated by Smoldyn through acceptance-rejection sampling ., First , those of the rate parameters koni and koffi in Eq ( 3 ) that depend on U ( r , t ) are updated accordingly ., Next , given known states of the channels ξi ( t ) at time t , the probability of a transition to occur by the end of the time interval is computed ., If , for example ξi ( t ) = 0 , i . e . the ith channel is in a closed state at time t , then P0i ( t ) =1 and P1i ( t ) =0 ., As a result , the first of Eq ( 3 ) becomes dP0i/dt=−koniP0i , and because the rate constants stay fixed during the time interval , P0i ( t+Δt ) =exp ( −koniΔt ) and P1i ( t+Δt ) =1−exp ( −koniΔt ) ., Finally , a random number r is generated and compared with P1i ( t+Δt ) ., If r<P1i ( t+Δt ) , the transition to the open state with ξi ( t + Δt ) = 1 is accepted , otherwise it is rejected ., The similar logic applies to channels that are open at time t ., Note that bimolecular reactions , in which one of the participants is described by a U-type variable and the other is represented by a Ξ-variable , can be approximated in deterministic-stochastic models as unimolecular ., Indeed , the copy numbers described by variables of U-type are assumed to be deterministically large even within ωj , so the changes due to binding to , or unbinding from , discrete particles can be ignored ., In other words , the molecules described in terms of concentrations could be treated as ‘catalysts’ in this type of interactions ., In summary , the algorithm includes the following steps: Initializing the system: Accuracy of our spatial deterministic-stochastic solver is affected by truncation errors , arising from discretization of space and time , and statistical errors due to finite numbers of Monte Carlo realizations ., The algorithm was validated against analytical results and through comparison with alternative methods ., The calcium spark model introduced in the previous section was used as a testbed for the tests described below ., In this section , we formulate a deterministic-stochastic model of spontaneous emergence of cell polarity and simulate it with our method ., The model is a hybrid version of a fully stochastic mechanism originally proposed by Altschuler et al . 54 ., Division , differentiation , and proliferation of living cells rely on mechanisms of symmetry breaking ., A key element of these mechanisms is emergence of asymmetric ( polar ) distributions of signaling molecules , often in form of molecular clusters ., While clustering may be spurred by external cues , many cell types can polarize spontaneously ( see 54 , 55 and references therein ) ., Positive feedback in cell signaling is thought to play a crucial role in establishing cell polarity ., The model by Altschuler et al . demonstrates that the positive feedback combined with stochasticity is sufficient for the emergence of a unipolar distribution of membrane-bound molecules ., In the model , molecules from a cytoplasmic pool randomly associate with , and dissociate from , the membrane ., While in the membrane , they diffuse but also recruit more molecules from the pool ., The positive feedback reinforces the clustering ., Remarkably , stochasticity of the system is critical for self-polarization: the effect disappears if the copy number of molecules in the membrane exceeds a certain threshold , so that there are no asymmetric solutions in the deterministic limit ., However , it is not uncommon for the membrane molecular clusters to involve large numbers of molecules ., One such example is focal adhesions whose formation is initiated by membrane proteins called integrins ., Activated by their binding to extracellular matrix , the integrins recruit many other molecules from the cytosol , which together form a focal adhesion ., In our deterministic-stochastic model , the membrane receptor proteins that initiate clustering are distinguished from the cytosolic proteins recruited to the membrane ., We assume that numbers of receptor proteins are sufficiently small to be represented by discrete variables , whereas copy numbers of cytosolic proteins , both recruited to the membrane and remaining in the cytoplasm , can be modeled continuously in terms of surface densities and volumetric concentrations ., We then solve this hybrid model numerically using our method to determine if it retains the property of spontaneous polarization ., The corresponding ‘Langevin-like’ formulation of the problem is as follows ., Consider a cell Ω with the plasma membrane ∂Ω ., Let U ( r , t ) ( r ∈ Ω ) be the volume density of the proteins in the cytoplasm and S ( r , t ) ( r ∈ ∂Ω ) be the surface density of the proteins recruited to the membrane ., To describe receptor proteins residing in the membrane , we introduce discrete variables Γi ( r , t ) = δ ( r − ri ( t ) ) γi ( t ) with r ∈ ∂Ω and i = 1 , … , Nr , where Nr is the total number of receptors in the membrane ., The discrete random variables γi ( t ) accept two values: 0 ( inactive receptor ) and 1 ( active receptor ) , whereas ri ( t ) are continuous random variables in ∂Ω ( see discussion in subsection capabilities and limitations of the method ) ., Variables U ( r , t ) and S ( r , t ) form the ‘deterministic’ subsystem of the model and are governed by the following equations:, ∂tU=DUΔU∂tS=DSΔsS+k1U∑i=1NrΓi−k2S ,, ( 8 ), where Δ is the Laplacian in Ω , whereas Δs is the Laplace-Beltrami operator describing diffusion in ∂Ω ( see , e . g . , 49 ) ; DU and DS are the corresponding diffusion constants ., The two other terms in the equation for S are the rates with which the cytosolic proteins are recruited to , and dissociated from , the membrane; k1 , k2 are the corresponding on- and off- rate constants ., The boundary condition for the equation describing U reflects the local mass conservation ,, −DU ( n∇U ) |∂Ωcell=−k1U∑i=1NrΓi+k2S ,, ( 9 ), where n is the outward normal ., Realizations of γi ( t ) are governed by Poisson processes with the following transition probabilities:, P ( Γi ( r , t+dt ) γi=1|Γi ( r , t ) γi=0 ) =k3S ( r , t ) dtP ( Γi ( r , t+dt ) γi=1|Γi ( r , t ) γi=1 ) =1−k4dtP ( Γi ( r , t+dt ) γi=0|Γi ( r , t ) γi=1 ) =k4dtP ( Γi ( r , t+dt ) γi=0|Γi ( r , t ) γi=0 ) =1−k3S ( r , t ) dt ,, ( 10 ), where k3 , k4 are the on- and off- rate constants for receptor activation ., Stochastic variables ri ( t ) are modeled on an assumption that inactive receptors diffuse in the membrane , while active receptors are immobile ., Accordingly ,, ri ( t+dt ) ={ri ( t ) +dr ( ri ( t ) , dt ) , ifγi ( t ) =0ri ( t ) , ifγi ( t ) =1 ,, ( 11 ), where dr ( ri ( t ) , dt ) is a realization of a Wiener-type stochastic process described by Green’s function for the diffusion operator ∂t − DΓΔs on ∂Ω; the function is centered at ri ( t ) ., The initial positions of the receptors ri ( 0 ) are uniformly distributed in ∂Ω ., Other initial conditions are discussed below ., The model includes a positive feedback between Γi ( r , t ) and S ( r , t ) , given that the rate of recruitment of cytosolic proteins to the membrane depends on Γi ( r , t ) , while the receptor activation rate depends on S ( r , t ) ., It is easy to see that the system described by Eqs ( 8–11 ) has an inactive steady state: γi ( t ) = 0 for all i , S ( r , t ) = 0 , and U ( r , t ) = U0 ( U0 is the initial uniform concentration of the cytosolic protein ) ., For some parameter sets , however , the inactive steady state can become unstable or the model may exhibit multi-stability ., These possibilities can be explored by solving the model with varying initial conditions ., Alternatively , one can transiently perturb the inactive steady state used as an initial condition ., The latter approach was implemented in the example below by adding a pre-activation pulse to the intrinsic activation rate P ( Γi ( r , t+dt ) γi=1|Γi ( r , t ) γi=0 ) = ( k0e−t/τ+k3S ( r , t ) ) dt and , correspondingly , P ( Γi ( r , t+dt ) γi=0|Γi ( r , t ) γi=0 ) =1− ( k0e−t/τ+k3S ( r , t ) ) dt; k0 and τ are the rate and time constants of the pulse ., The model has been solved by the spatial hybrid method in a spherical cell with radius R = 4 μm for the following model parameters: U0 = 1 μM , Nr = 1000 , DU = 10 μm2/s , DS = DΓ = 0 . 1 μm2/s , k1 = 0 . 01 μM-1s-1 , k2 = 0 . 01 s-1 , k3 = 0 . 01 μm-2s-1 , k4 = 0 . 1 s-1 ., For this parameter set , the inactive state is unstable: activation of a single receptor drives the system to its active state with an average of about 800 active receptors ., Interestingly , spatial averages of all variables have reached their active steady-state regimes relatively quickly ( by t = 10 s , for the robust pre-activation characterized by k0 = 10 s-1 and τ = 1 s , and by t ≈ 350 s , when just ten receptors were initially activated ) , whereas the cluster structure evolves on a much longer time scale , see results in Fig 8 obtained for k0 = 10 s-1 and τ = 1 s ., As in the original stochastic model 54 , the hybrid mechanism yields a spatially heterogeneous steady state with a single cluster of activated receptors and recruited proteins ., But unlike the original model , the total number of proteins in clusters can be large , because the condition of small copy numbers applies in the hybrid model only to the receptors initiating the clustering ., Note the increase of local densities in the surviving clusters ( see color scales in Fig 8 ) , which is consistent with the early stabilization of spatial averages ., While the ‘attractive’ spatial correlations of active receptors originate from the positive feedback , a corresponding deterministic formulation does not yield a spatially heterogeneous steady state ( as was the case with the original model 54 ) , indicating that the discreteness and stochasticity of the receptors also play an essential role in establishing the polar distributions of membrane-bound molecules ., Interestingly , the kinetics of cell polarization predicted by the model is reminiscent of glassy behavior , in which a system approaches a stable steady state by going through a long sequence of metastable states 56 ., The deterministic-stochastic algorithm described in this article integrates a spatial particle-based fixed time step Monte Carlo method ( Smoldyn ) and a conventional PDE solver with compatible time-stepping ( one of the VCell solvers ) ., The PDE solver utilizes finite-volume spatial discretization of PDEs 48 , 49 , which ensures local mass conservation , and a semi-implicit time discretization scheme , in which the diffusion/ advection operator applies to variables at time t + Δt while the reaction and membrane flux terms are evaluated at time t 50 , 51 ., To ensure consistency in handling geometry by the two methods , triangulation of surfaces is performed by applying Taubin smoothing 57 to watertight pixilated surfaces emerging from segmentation of space ., The approach is applicable both to geometries defined analytically and to irregular realistic geometries based on experimental images ., Implementation in VCell Math workspace of the hybrid model of spontaneous cell polarization described in Section Application to a hybrid model of spontaneous cell polarization is detailed in S3 Text ., The corresponding VCell MathModel , ‘Hybrid_cell_polarity_public’ , along with simulation results , can be found by logging to VCell , http://vcell . org , and searching the database of public MathModels under username ‘boris’ ., Stochastic processes are ubiquitous in cellular systems ., A deterministic-stochastic description of interacting components with disparate degrees of stochasticity provides an efficient alternative to a full stochastic treatment of the problem ., In a hybrid numerical approach , an appropriate integration of deterministic and stochastic methods yields significant computational savings ., In this paper , we describe a general-purpose hybrid method for solving spatial deterministic-stochastic models in realistic cell geometries ., The emphasis is placed on the physical fundamentals of the method and its testing ., The method is based on a formulation in terms of stochastic variables of two types: continuous variables , described by partial differential equations with stochastic source terms , and discrete variables governed by stochastic jump processes ., Numerically , the algorithm is a Monte Carlo fixed time step integrator generating realizations of the hybrid system ., The current implementation utilizes a VCell fixed time step PDE solver coupled with a particle-based stochastic simulator Smoldyn ., Validating a hybrid deterministic-stochastic numerical scheme is conceptually nontrivial and logistically challenging ., We tested our method against analytical results and numerical solutions obtained by alternative methods ., The expected convergence of s | Introduction, Results, Methods, Discussion | Hybrid deterministic-stochastic methods provide an efficient alternative to a fully stochastic treatment of models which include components with disparate levels of stochasticity ., However , general-purpose hybrid solvers for spatially resolved simulations of reaction-diffusion systems are not widely available ., Here we describe fundamentals of a general-purpose spatial hybrid method ., The method generates realizations of a spatially inhomogeneous hybrid system by appropriately integrating capabilities of a deterministic partial differential equation solver with a popular particle-based stochastic simulator , Smoldyn ., Rigorous validation of the algorithm is detailed , using a simple model of calcium ‘sparks’ as a testbed ., The solver is then applied to a deterministic-stochastic model of spontaneous emergence of cell polarity ., The approach is general enough to be implemented within biologist-friendly software frameworks such as Virtual Cell . | Mechanisms of some cellular phenomena involve interactions of molecular systems of which one can be described deterministically , while the other is inherently stochastic ., Calcium ‘sparks’ in cardiomyocytes is one such example , in which dynamics of calcium ions , which are usually present in large numbers , can be described deterministically , whereas the channels open and close stochastically ., The calcium influx through the channels renders the entire system stochastic , but a fully stochastic treatment accounting for each calcium ion is computationally expensive ., Fortunately , such systems can be efficiently solved by hybrid methods in which deterministic and stochastic algorithms are appropriately integrated ., Here we describe fundamentals of a general-purpose deterministic-stochastic method for simulating spatially resolved systems ., The internal workings of the method are explained and illustrated by applications to very different phenomena such as calcium ‘sparks’ , stochastically gated reactions , and spontaneous cell polarization . | medicine and health sciences, applied mathematics, electrophysiology, neuroscience, simulation and modeling, algorithms, membrane proteins, ion channels, mathematics, statistics (mathematics), membrane receptor signaling, cellular structures and organelles, macromolecules, research and analysis methods, polymer chemistry, probability density, proteins, mathematical and statistical techniques, statistical methods, chemistry, calcium channels, monte carlo method, biophysics, cell membranes, probability theory, physics, biochemistry, signal transduction, cell biology, physiology, biology and life sciences, physical sciences, cell signaling, neurophysiology | null |
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